diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..dd62bc050f921f9a27d1bf81b7f8802c0f61c88e
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,203 @@
+Copyright @2023-2024 Moore Threads Technology Co., Ltd("Moore Threads"). All rights reserved.
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diff --git a/NOTICE b/NOTICE
new file mode 100644
index 0000000000000000000000000000000000000000..19fe5cbb5a773704a04fe83eb36c5383fd24a2ef
--- /dev/null
+++ b/NOTICE
@@ -0,0 +1,20 @@
+==============================================================
+This repo also contains various third-party components and some code modified from other repos under other open source licenses. The following sections contain licensing infromation for such third-party libraries.
+
+-----------------------------
+majic-animate
+BSD 3-Clause License
+Copyright (c) Bytedance Inc.
+
+-----------------------------
+animatediff
+Apache License, Version 2.0
+
+-----------------------------
+Dwpose
+Apache License, Version 2.0
+
+-----------------------------
+inference pipeline for animatediff-cli-prompt-travel
+animatediff-cli-prompt-travel
+Apache License, Version 2.0
\ No newline at end of file
diff --git a/README.md b/README.md
index d9d04375b482fca13560cac5b519666fb005bc4f..165958bffabd1e4a06c9f641b634e2e135e44a3a 100644
--- a/README.md
+++ b/README.md
@@ -1,13 +1,272 @@
----
-title: AnimateAnyone
-emoji: ๐
-colorFrom: green
-colorTo: green
-sdk: gradio
-sdk_version: 4.37.2
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# ๐ค Introduction
+**update** ๐ฅ๐ฅ๐ฅ We propose a face reenactment method, based on our AnimateAnyone pipeline: Using the facial landmark of driving video to control the pose of given source image, and keeping the identity of source image. Specially, we disentangle head attitude (including eyes blink) and mouth motion from the landmark of driving video, and it can control the expression and movements of source face precisely. We release our inference codes and pretrained models of face reenactment!!
+
+
+**update** ๐๏ธ๐๏ธ๐๏ธ We release our training codes!! Now you can train your own AnimateAnyone models. See [here](#train) for more details. Have fun!
+
+**update**๏ผ๐ฅ๐ฅ๐ฅ We launch a HuggingFace Spaces demo of Moore-AnimateAnyone at [here](https://huggingface.co/spaces/xunsong/Moore-AnimateAnyone)!!
+
+This repository reproduces [AnimateAnyone](https://github.com/HumanAIGC/AnimateAnyone). To align the results demonstrated by the original paper, we adopt various approaches and tricks, which may differ somewhat from the paper and another [implementation](https://github.com/guoqincode/Open-AnimateAnyone).
+
+It's worth noting that this is a very preliminary version, aiming for approximating the performance (roughly 80% under our test) showed in [AnimateAnyone](https://github.com/HumanAIGC/AnimateAnyone).
+
+We will continue to develop it, and also welcome feedbacks and ideas from the community. The enhanced version will also be launched on our [MoBi MaLiang](https://maliang.mthreads.com/) AIGC platform, running on our own full-featured GPU S4000 cloud computing platform.
+
+# ๐ Release Plans
+
+- [x] Inference codes and pretrained weights of AnimateAnyone
+- [x] Training scripts of AnimateAnyone
+- [x] Inference codes and pretrained weights of face reenactment
+- [ ] Training scripts of face reenactment
+- [ ] Inference scripts of audio driven portrait video generation
+- [ ] Training scripts of audio driven portrait video generation
+# ๐๏ธ Examples
+
+## AnimateAnyone
+
+Here are some AnimateAnyone results we generated, with the resolution of 512x768.
+
+https://github.com/MooreThreads/Moore-AnimateAnyone/assets/138439222/f0454f30-6726-4ad4-80a7-5b7a15619057
+
+https://github.com/MooreThreads/Moore-AnimateAnyone/assets/138439222/337ff231-68a3-4760-a9f9-5113654acf48
+
+
+
+
+ |
+
+ |
+
+
+ |
+
+
+
+ |
+
+ |
+
+
+ |
+
+
+
+**Limitation**: We observe following shortcomings in current version:
+1. The background may occur some artifacts, when the reference image has a clean background
+2. Suboptimal results may arise when there is a scale mismatch between the reference image and keypoints. We have yet to implement preprocessing techniques as mentioned in the [paper](https://arxiv.org/pdf/2311.17117.pdf).
+3. Some flickering and jittering may occur when the motion sequence is subtle or the scene is static.
+
+
+
+These issues will be addressed and improved in the near future. We appreciate your anticipation!
+
+## Face Reenactment
+
+Here are some results we generated, with the resolution of 512x512.
+
+
+
+
+ |
+
+ |
+
+
+ |
+
+
+
+ |
+
+ |
+
+
+ |
+
+
+
+
+# โ๏ธ Installation
+
+## Build Environtment
+
+We Recommend a python version `>=3.10` and cuda version `=11.7`. Then build environment as follows:
+
+```shell
+# [Optional] Create a virtual env
+python -m venv .venv
+source .venv/bin/activate
+# Install with pip:
+pip install -r requirements.txt
+# For face landmark extraction
+git clone https://github.com/emilianavt/OpenSeeFace.git
+```
+
+## Download weights
+
+**Automatically downloading**: You can run the following command to download weights automatically:
+
+```shell
+python tools/download_weights.py
+```
+
+Weights will be placed under the `./pretrained_weights` direcotry. The whole downloading process may take a long time.
+
+**Manually downloading**: You can also download weights manually, which has some steps:
+
+1. Download our AnimateAnyone trained [weights](https://huggingface.co/patrolli/AnimateAnyone/tree/main), which include four parts: `denoising_unet.pth`, `reference_unet.pth`, `pose_guider.pth` and `motion_module.pth`.
+
+2. Download our trained [weights](https://pan.baidu.com/s/1lS5CynyNfYlDbjowKKfG8g?pwd=crci) of face reenactment, and place these weights under `pretrained_weights`.
+
+3. Download pretrained weight of based models and other components:
+ - [StableDiffusion V1.5](https://huggingface.co/runwayml/stable-diffusion-v1-5)
+ - [sd-vae-ft-mse](https://huggingface.co/stabilityai/sd-vae-ft-mse)
+ - [image_encoder](https://huggingface.co/lambdalabs/sd-image-variations-diffusers/tree/main/image_encoder)
+
+4. Download dwpose weights (`dw-ll_ucoco_384.onnx`, `yolox_l.onnx`) following [this](https://github.com/IDEA-Research/DWPose?tab=readme-ov-file#-dwpose-for-controlnet).
+
+Finally, these weights should be orgnized as follows:
+
+```text
+./pretrained_weights/
+|-- DWPose
+| |-- dw-ll_ucoco_384.onnx
+| `-- yolox_l.onnx
+|-- image_encoder
+| |-- config.json
+| `-- pytorch_model.bin
+|-- denoising_unet.pth
+|-- motion_module.pth
+|-- pose_guider.pth
+|-- reference_unet.pth
+|-- sd-vae-ft-mse
+| |-- config.json
+| |-- diffusion_pytorch_model.bin
+| `-- diffusion_pytorch_model.safetensors
+|-- reenact
+| |-- denoising_unet.pth
+| |-- reference_unet.pth
+| |-- pose_guider1.pth
+| |-- pose_guider2.pth
+`-- stable-diffusion-v1-5
+ |-- feature_extractor
+ | `-- preprocessor_config.json
+ |-- model_index.json
+ |-- unet
+ | |-- config.json
+ | `-- diffusion_pytorch_model.bin
+ `-- v1-inference.yaml
+```
+
+Note: If you have installed some of the pretrained models, such as `StableDiffusion V1.5`, you can specify their paths in the config file (e.g. `./config/prompts/animation.yaml`).
+
+# ๐ Training and Inference
+
+## Inference of AnimateAnyone
+
+Here is the cli command for running inference scripts:
+
+```shell
+python -m scripts.pose2vid --config ./configs/prompts/animation.yaml -W 512 -H 784 -L 64
+```
+
+You can refer the format of `animation.yaml` to add your own reference images or pose videos. To convert the raw video into a pose video (keypoint sequence), you can run with the following command:
+
+```shell
+python tools/vid2pose.py --video_path /path/to/your/video.mp4
+```
+
+## Inference of Face Reenactment
+Here is the cli command for running inference scripts:
+
+```shell
+python -m scripts.lmks2vid --config ./configs/prompts/inference_reenact.yaml --driving_video_path YOUR_OWN_DRIVING_VIDEO_PATH --source_image_path YOUR_OWN_SOURCE_IMAGE_PATH
+```
+We provide some face images in `./config/inference/talkinghead_images`, and some face videos in `./config/inference/talkinghead_videos` for inference.
+
+## Training of AnimateAnyone
+
+Note: package dependencies have been updated, you may upgrade your environment via `pip install -r requirements.txt` before training.
+
+### Data Preparation
+
+Extract keypoints from raw videos:
+
+```shell
+python tools/extract_dwpose_from_vid.py --video_root /path/to/your/video_dir
+```
+
+Extract the meta info of dataset:
+
+```shell
+python tools/extract_meta_info.py --root_path /path/to/your/video_dir --dataset_name anyone
+```
+
+Update lines in the training config file:
+
+```yaml
+data:
+ meta_paths:
+ - "./data/anyone_meta.json"
+```
+
+### Stage1
+
+Put [openpose controlnet weights](https://huggingface.co/lllyasviel/control_v11p_sd15_openpose/tree/main) under `./pretrained_weights`, which is used to initialize the pose_guider.
+
+Put [sd-image-variation](https://huggingface.co/lambdalabs/sd-image-variations-diffusers/tree/main) under `./pretrained_weights`, which is used to initialize unet weights.
+
+Run command:
+
+```shell
+accelerate launch train_stage_1.py --config configs/train/stage1.yaml
+```
+
+### Stage2
+
+Put the pretrained motion module weights `mm_sd_v15_v2.ckpt` ([download link](https://huggingface.co/guoyww/animatediff/blob/main/mm_sd_v15_v2.ckpt)) under `./pretrained_weights`.
+
+Specify the stage1 training weights in the config file `stage2.yaml`, for example:
+
+```yaml
+stage1_ckpt_dir: './exp_output/stage1'
+stage1_ckpt_step: 30000
+```
+
+Run command:
+
+```shell
+accelerate launch train_stage_2.py --config configs/train/stage2.yaml
+```
+
+# ๐จ Gradio Demo
+
+**HuggingFace Demo**: We launch a quick preview demo of Moore-AnimateAnyone at [HuggingFace Spaces](https://huggingface.co/spaces/xunsong/Moore-AnimateAnyone)!!
+We appreciate the assistance provided by the HuggingFace team in setting up this demo.
+
+To reduce waiting time, we limit the size (width, height, and length) and inference steps when generating videos.
+
+If you have your own GPU resource (>= 16GB vram), you can run a local gradio app via following commands:
+
+`python app.py`
+
+# Community Contributions
+
+- Installation for Windows users: [Moore-AnimateAnyone-for-windows](https://github.com/sdbds/Moore-AnimateAnyone-for-windows)
+
+# ๐๏ธ Try on Mobi MaLiang
+
+We will launched this model on our [MoBi MaLiang](https://maliang.mthreads.com/) AIGC platform, running on our own full-featured GPU S4000 cloud computing platform. Mobi MaLiang has now integrated various AIGC applications and functionalities (e.g. text-to-image, controllable generation...). You can experience it by [clicking this link](https://maliang.mthreads.com/) or scanning the QR code bellow via WeChat!
+
+
+ 
+
+
+# โ๏ธ Disclaimer
+
+This project is intended for academic research, and we explicitly disclaim any responsibility for user-generated content. Users are solely liable for their actions while using the generative model. The project contributors have no legal affiliation with, nor accountability for, users' behaviors. It is imperative to use the generative model responsibly, adhering to both ethical and legal standards.
+
+# ๐๐ป Acknowledgements
+
+We first thank the authors of [AnimateAnyone](). Additionally, we would like to thank the contributors to the [majic-animate](https://github.com/magic-research/magic-animate), [animatediff](https://github.com/guoyww/AnimateDiff) and [Open-AnimateAnyone](https://github.com/guoqincode/Open-AnimateAnyone) repositories, for their open research and exploration. Furthermore, our repo incorporates some codes from [dwpose](https://github.com/IDEA-Research/DWPose) and [animatediff-cli-prompt-travel](https://github.com/s9roll7/animatediff-cli-prompt-travel/), and we extend our thanks to them as well.
diff --git a/app.py b/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..670be8e2af5a18ff8a0ee9409f47befab7838cdd
--- /dev/null
+++ b/app.py
@@ -0,0 +1,263 @@
+import os
+import random
+from datetime import datetime
+
+import gradio as gr
+import numpy as np
+import torch
+from diffusers import AutoencoderKL, DDIMScheduler
+from einops import repeat
+from omegaconf import OmegaConf
+from PIL import Image
+from torchvision import transforms
+from transformers import CLIPVisionModelWithProjection
+
+from src.models.pose_guider import PoseGuider
+from src.models.unet_2d_condition import UNet2DConditionModel
+from src.models.unet_3d import UNet3DConditionModel
+from src.pipelines.pipeline_pose2vid_long import Pose2VideoPipeline
+from src.utils.util import get_fps, read_frames, save_videos_grid
+
+
+class AnimateController:
+ def __init__(
+ self,
+ config_path="./configs/prompts/animation.yaml",
+ weight_dtype=torch.float16,
+ ):
+ # Read pretrained weights path from config
+ self.config = OmegaConf.load(config_path)
+ self.pipeline = None
+ self.weight_dtype = weight_dtype
+
+ def animate(
+ self,
+ ref_image,
+ pose_video_path,
+ width=512,
+ height=768,
+ length=24,
+ num_inference_steps=25,
+ cfg=3.5,
+ seed=123,
+ ):
+ generator = torch.manual_seed(seed)
+ if isinstance(ref_image, np.ndarray):
+ ref_image = Image.fromarray(ref_image)
+ if self.pipeline is None:
+ vae = AutoencoderKL.from_pretrained(
+ self.config.pretrained_vae_path,
+ ).to("cuda", dtype=self.weight_dtype)
+
+ reference_unet = UNet2DConditionModel.from_pretrained(
+ self.config.pretrained_base_model_path,
+ subfolder="unet",
+ ).to(dtype=self.weight_dtype, device="cuda")
+
+ inference_config_path = self.config.inference_config
+ infer_config = OmegaConf.load(inference_config_path)
+ denoising_unet = UNet3DConditionModel.from_pretrained_2d(
+ self.config.pretrained_base_model_path,
+ self.config.motion_module_path,
+ subfolder="unet",
+ unet_additional_kwargs=infer_config.unet_additional_kwargs,
+ ).to(dtype=self.weight_dtype, device="cuda")
+
+ pose_guider = PoseGuider(320, block_out_channels=(16, 32, 96, 256)).to(
+ dtype=self.weight_dtype, device="cuda"
+ )
+
+ image_enc = CLIPVisionModelWithProjection.from_pretrained(
+ self.config.image_encoder_path
+ ).to(dtype=self.weight_dtype, device="cuda")
+ sched_kwargs = OmegaConf.to_container(infer_config.noise_scheduler_kwargs)
+ scheduler = DDIMScheduler(**sched_kwargs)
+
+ # load pretrained weights
+ denoising_unet.load_state_dict(
+ torch.load(self.config.denoising_unet_path, map_location="cpu"),
+ strict=False,
+ )
+ reference_unet.load_state_dict(
+ torch.load(self.config.reference_unet_path, map_location="cpu"),
+ )
+ pose_guider.load_state_dict(
+ torch.load(self.config.pose_guider_path, map_location="cpu"),
+ )
+
+ pipe = Pose2VideoPipeline(
+ vae=vae,
+ image_encoder=image_enc,
+ reference_unet=reference_unet,
+ denoising_unet=denoising_unet,
+ pose_guider=pose_guider,
+ scheduler=scheduler,
+ )
+ pipe = pipe.to("cuda", dtype=self.weight_dtype)
+ self.pipeline = pipe
+
+ pose_images = read_frames(pose_video_path)
+ src_fps = get_fps(pose_video_path)
+
+ pose_list = []
+ pose_tensor_list = []
+ pose_transform = transforms.Compose(
+ [transforms.Resize((height, width)), transforms.ToTensor()]
+ )
+ for pose_image_pil in pose_images[:length]:
+ pose_list.append(pose_image_pil)
+ pose_tensor_list.append(pose_transform(pose_image_pil))
+
+ video = self.pipeline(
+ ref_image,
+ pose_list,
+ width=width,
+ height=height,
+ video_length=length,
+ num_inference_steps=num_inference_steps,
+ guidance_scale=cfg,
+ generator=generator,
+ ).videos
+
+ ref_image_tensor = pose_transform(ref_image) # (c, h, w)
+ ref_image_tensor = ref_image_tensor.unsqueeze(1).unsqueeze(0) # (1, c, 1, h, w)
+ ref_image_tensor = repeat(
+ ref_image_tensor, "b c f h w -> b c (repeat f) h w", repeat=length
+ )
+ pose_tensor = torch.stack(pose_tensor_list, dim=0) # (f, c, h, w)
+ pose_tensor = pose_tensor.transpose(0, 1)
+ pose_tensor = pose_tensor.unsqueeze(0)
+ video = torch.cat([ref_image_tensor, pose_tensor, video], dim=0)
+
+ save_dir = f"./output/gradio"
+ if not os.path.exists(save_dir):
+ os.makedirs(save_dir, exist_ok=True)
+ date_str = datetime.now().strftime("%Y%m%d")
+ time_str = datetime.now().strftime("%H%M")
+ out_path = os.path.join(save_dir, f"{date_str}T{time_str}.mp4")
+ save_videos_grid(
+ video,
+ out_path,
+ n_rows=3,
+ fps=src_fps,
+ )
+
+ torch.cuda.empty_cache()
+
+ return out_path
+
+
+controller = AnimateController()
+
+
+def ui():
+ with gr.Blocks() as demo:
+ gr.Markdown(
+ """
+ # Moore-AnimateAnyone Demo
+ """
+ )
+ animation = gr.Video(
+ format="mp4",
+ label="Animation Results",
+ height=448,
+ autoplay=True,
+ )
+
+ with gr.Row():
+ reference_image = gr.Image(label="Reference Image")
+ motion_sequence = gr.Video(
+ format="mp4", label="Motion Sequence", height=512
+ )
+
+ with gr.Column():
+ width_slider = gr.Slider(
+ label="Width", minimum=448, maximum=768, value=512, step=64
+ )
+ height_slider = gr.Slider(
+ label="Height", minimum=512, maximum=1024, value=768, step=64
+ )
+ length_slider = gr.Slider(
+ label="Video Length", minimum=24, maximum=128, value=24, step=24
+ )
+ with gr.Row():
+ seed_textbox = gr.Textbox(label="Seed", value=-1)
+ seed_button = gr.Button(
+ value="\U0001F3B2", elem_classes="toolbutton"
+ )
+ seed_button.click(
+ fn=lambda: gr.Textbox.update(value=random.randint(1, 1e8)),
+ inputs=[],
+ outputs=[seed_textbox],
+ )
+ with gr.Row():
+ sampling_steps = gr.Slider(
+ label="Sampling steps",
+ value=25,
+ info="default: 25",
+ step=5,
+ maximum=30,
+ minimum=10,
+ )
+ guidance_scale = gr.Slider(
+ label="Guidance scale",
+ value=3.5,
+ info="default: 3.5",
+ step=0.5,
+ maximum=10,
+ minimum=2.0,
+ )
+ submit = gr.Button("Animate")
+
+ def read_video(video):
+ return video
+
+ def read_image(image):
+ return Image.fromarray(image)
+
+ # when user uploads a new video
+ motion_sequence.upload(read_video, motion_sequence, motion_sequence)
+ # when `first_frame` is updated
+ reference_image.upload(read_image, reference_image, reference_image)
+ # when the `submit` button is clicked
+ submit.click(
+ controller.animate,
+ [
+ reference_image,
+ motion_sequence,
+ width_slider,
+ height_slider,
+ length_slider,
+ sampling_steps,
+ guidance_scale,
+ seed_textbox,
+ ],
+ animation,
+ )
+
+ # Examples
+ gr.Markdown("## Examples")
+ gr.Examples(
+ examples=[
+ [
+ "./configs/inference/ref_images/anyone-5.png",
+ "./configs/inference/pose_videos/anyone-video-2_kps.mp4",
+ ],
+ [
+ "./configs/inference/ref_images/anyone-10.png",
+ "./configs/inference/pose_videos/anyone-video-1_kps.mp4",
+ ],
+ [
+ "./configs/inference/ref_images/anyone-2.png",
+ "./configs/inference/pose_videos/anyone-video-5_kps.mp4",
+ ],
+ ],
+ inputs=[reference_image, motion_sequence],
+ outputs=animation,
+ )
+
+ return demo
+
+
+demo = ui()
+demo.launch(share=True)
diff --git a/assets/mini_program_maliang.png b/assets/mini_program_maliang.png
new file mode 100644
index 0000000000000000000000000000000000000000..a30800c3cba4c4697787dd431e9ad622f73d85e6
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diff --git a/configs/inference/inference_v1.yaml b/configs/inference/inference_v1.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..e888888b547bf0316e7963a957fa905cb6fe9d65
--- /dev/null
+++ b/configs/inference/inference_v1.yaml
@@ -0,0 +1,23 @@
+unet_additional_kwargs:
+ unet_use_cross_frame_attention: false
+ unet_use_temporal_attention: false
+ use_motion_module: true
+ motion_module_resolutions: [1,2,4,8]
+ motion_module_mid_block: false
+ motion_module_decoder_only: false
+ motion_module_type: "Vanilla"
+
+ motion_module_kwargs:
+ num_attention_heads: 8
+ num_transformer_block: 1
+ attention_block_types: [ "Temporal_Self", "Temporal_Self" ]
+ temporal_position_encoding: true
+ temporal_position_encoding_max_len: 24
+ temporal_attention_dim_div: 1
+
+noise_scheduler_kwargs:
+ beta_start: 0.00085
+ beta_end: 0.012
+ beta_schedule: "linear"
+ steps_offset: 1
+ clip_sample: False
\ No newline at end of file
diff --git a/configs/inference/inference_v2.yaml b/configs/inference/inference_v2.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..d613dca2d2e48a41295a89f47b5a82fd7032dba5
--- /dev/null
+++ b/configs/inference/inference_v2.yaml
@@ -0,0 +1,35 @@
+unet_additional_kwargs:
+ use_inflated_groupnorm: true
+ unet_use_cross_frame_attention: false
+ unet_use_temporal_attention: false
+ use_motion_module: true
+ motion_module_resolutions:
+ - 1
+ - 2
+ - 4
+ - 8
+ motion_module_mid_block: true
+ motion_module_decoder_only: false
+ motion_module_type: Vanilla
+ motion_module_kwargs:
+ num_attention_heads: 8
+ num_transformer_block: 1
+ attention_block_types:
+ - Temporal_Self
+ - Temporal_Self
+ temporal_position_encoding: true
+ temporal_position_encoding_max_len: 32
+ temporal_attention_dim_div: 1
+
+noise_scheduler_kwargs:
+ beta_start: 0.00085
+ beta_end: 0.012
+ beta_schedule: "linear"
+ clip_sample: false
+ steps_offset: 1
+ ### Zero-SNR params
+ prediction_type: "v_prediction"
+ rescale_betas_zero_snr: True
+ timestep_spacing: "trailing"
+
+sampler: DDIM
\ No newline at end of file
diff --git a/configs/inference/pose_images/pose-1.png b/configs/inference/pose_images/pose-1.png
new file mode 100644
index 0000000000000000000000000000000000000000..d8d8dc49f08dcb7142d32e4b0f18453f1f55c0bb
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diff --git a/configs/inference/pose_videos/anyone-video-1_kps.mp4 b/configs/inference/pose_videos/anyone-video-1_kps.mp4
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diff --git a/configs/inference/ref_images/anyone-1.png b/configs/inference/ref_images/anyone-1.png
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diff --git a/configs/inference/ref_images/anyone-2.png b/configs/inference/ref_images/anyone-2.png
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diff --git a/configs/inference/talkinghead_images/1.png b/configs/inference/talkinghead_images/1.png
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diff --git a/configs/inference/talkinghead_videos/3.mp4 b/configs/inference/talkinghead_videos/3.mp4
new file mode 100644
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diff --git a/configs/inference/talkinghead_videos/4.mp4 b/configs/inference/talkinghead_videos/4.mp4
new file mode 100644
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diff --git a/configs/prompts/animation.yaml b/configs/prompts/animation.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..d7895caf6e82ebf699916e07aed6193f1deb72e5
--- /dev/null
+++ b/configs/prompts/animation.yaml
@@ -0,0 +1,26 @@
+pretrained_base_model_path: "./pretrained_weights/stable-diffusion-v1-5/"
+pretrained_vae_path: "./pretrained_weights/sd-vae-ft-mse"
+image_encoder_path: "./pretrained_weights/image_encoder"
+denoising_unet_path: "./pretrained_weights/denoising_unet.pth"
+reference_unet_path: "./pretrained_weights/reference_unet.pth"
+pose_guider_path: "./pretrained_weights/pose_guider.pth"
+motion_module_path: "./pretrained_weights/motion_module.pth"
+
+inference_config: "./configs/inference/inference_v2.yaml"
+weight_dtype: 'fp16'
+
+test_cases:
+ "./configs/inference/ref_images/anyone-2.png":
+ - "./configs/inference/pose_videos/anyone-video-2_kps.mp4"
+ - "./configs/inference/pose_videos/anyone-video-5_kps.mp4"
+ "./configs/inference/ref_images/anyone-10.png":
+ - "./configs/inference/pose_videos/anyone-video-1_kps.mp4"
+ - "./configs/inference/pose_videos/anyone-video-2_kps.mp4"
+ "./configs/inference/ref_images/anyone-11.png":
+ - "./configs/inference/pose_videos/anyone-video-1_kps.mp4"
+ - "./configs/inference/pose_videos/anyone-video-2_kps.mp4"
+ "./configs/inference/ref_images/anyone-3.png":
+ - "./configs/inference/pose_videos/anyone-video-2_kps.mp4"
+ - "./configs/inference/pose_videos/anyone-video-5_kps.mp4"
+ "./configs/inference/ref_images/anyone-5.png":
+ - "./configs/inference/pose_videos/anyone-video-2_kps.mp4"
diff --git a/configs/prompts/inference_reenact.yaml b/configs/prompts/inference_reenact.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..9e299669a107727b03ecca70ae26a4b90aa7ec1e
--- /dev/null
+++ b/configs/prompts/inference_reenact.yaml
@@ -0,0 +1,48 @@
+pretrained_base_model_path: "./pretrained_weights/stable-diffusion-v1-5/"
+pretrained_vae_path: "./pretrained_weights/sd-vae-ft-mse"
+image_encoder_path: "./pretrained_weights/image_encoder"
+denoising_unet_path: "./pretrained_weights/reenact/denoising_unet.pth"
+reference_unet_path: "./pretrained_weights/reenact/reference_unet.pth"
+pose_guider1_path: "./pretrained_weights/reenact/pose_guider1.pth"
+pose_guider2_path: "./pretrained_weights/reenact/pose_guider2.pth"
+unet_additional_kwargs:
+ task_type: "reenact"
+ mode: "read" # "read"
+ use_inflated_groupnorm: true
+ unet_use_cross_frame_attention: false
+ unet_use_temporal_attention: false
+ use_motion_module: true
+ motion_module_resolutions:
+ - 1
+ - 2
+ - 4
+ - 8
+ motion_module_mid_block: true
+ motion_module_decoder_only: false
+ motion_module_type: Vanilla
+ motion_module_kwargs:
+ num_attention_heads: 8
+ num_transformer_block: 1
+ attention_block_types:
+ - Temporal_Self
+ - Temporal_Self
+ temporal_position_encoding: true
+ temporal_position_encoding_max_len: 32
+ temporal_attention_dim_div: 1
+
+noise_scheduler_kwargs:
+ beta_start: 0.00085
+ beta_end: 0.012
+ beta_schedule: "linear"
+ # beta_schedule: "scaled_linear"
+ clip_sample: false
+ # set_alpha_to_one: False
+ # skip_prk_steps: true
+ steps_offset: 1
+ ### Zero-SNR params
+ # prediction_type: "v_prediction"
+ # rescale_betas_zero_snr: True
+ # timestep_spacing: "trailing"
+
+weight_dtype: float16
+sampler: DDIM
diff --git a/configs/prompts/test_cases.py b/configs/prompts/test_cases.py
new file mode 100644
index 0000000000000000000000000000000000000000..8f83e79a519e6556febb0f84039b2c328186afb6
--- /dev/null
+++ b/configs/prompts/test_cases.py
@@ -0,0 +1,33 @@
+TestCasesDict = {
+ 0: [
+ {
+ "./configs/inference/ref_images/anyone-2.png": [
+ "./configs/inference/pose_videos/anyone-video-2_kps.mp4",
+ "./configs/inference/pose_videos/anyone-video-5_kps.mp4",
+ ]
+ },
+ {
+ "./configs/inference/ref_images/anyone-10.png": [
+ "./configs/inference/pose_videos/anyone-video-1_kps.mp4",
+ "./configs/inference/pose_videos/anyone-video-2_kps.mp4",
+ ]
+ },
+ {
+ "./configs/inference/ref_images/anyone-11.png": [
+ "./configs/inference/pose_videos/anyone-video-1_kps.mp4",
+ "./configs/inference/pose_videos/anyone-video-2_kps.mp4",
+ ]
+ },
+ {
+ "./configs/inference/anyone-ref-3.png": [
+ "./configs/inference/pose_videos/anyone-video-2_kps.mp4",
+ "./configs/inference/pose_videos/anyone-video-5_kps.mp4",
+ ]
+ },
+ {
+ "./configs/inference/ref_images/anyone-5.png": [
+ "./configs/inference/pose_videos/anyone-video-2_kps.mp4"
+ ]
+ },
+ ],
+}
diff --git a/configs/train/stage1.yaml b/configs/train/stage1.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..13e7e02277005504754f1f8dccdae6c41f00a3b5
--- /dev/null
+++ b/configs/train/stage1.yaml
@@ -0,0 +1,59 @@
+data:
+ train_bs: 4
+ train_width: 768
+ train_height: 768
+ meta_paths:
+ - "./data/fashion_meta.json"
+ # Margin of frame indexes between ref and tgt images
+ sample_margin: 30
+
+solver:
+ gradient_accumulation_steps: 1
+ mixed_precision: 'fp16'
+ enable_xformers_memory_efficient_attention: True
+ gradient_checkpointing: False
+ max_train_steps: 30000
+ max_grad_norm: 1.0
+ # lr
+ learning_rate: 1.0e-5
+ scale_lr: False
+ lr_warmup_steps: 1
+ lr_scheduler: 'constant'
+
+ # optimizer
+ use_8bit_adam: False
+ adam_beta1: 0.9
+ adam_beta2: 0.999
+ adam_weight_decay: 1.0e-2
+ adam_epsilon: 1.0e-8
+
+val:
+ validation_steps: 200
+
+
+noise_scheduler_kwargs:
+ num_train_timesteps: 1000
+ beta_start: 0.00085
+ beta_end: 0.012
+ beta_schedule: "scaled_linear"
+ steps_offset: 1
+ clip_sample: false
+
+base_model_path: './pretrained_weights/sd-image-variations-diffusers'
+vae_model_path: './pretrained_weights/sd-vae-ft-mse'
+image_encoder_path: './pretrained_weights/sd-image-variations-diffusers/image_encoder'
+controlnet_openpose_path: './pretrained_weights/control_v11p_sd15_openpose/diffusion_pytorch_model.bin'
+
+weight_dtype: 'fp16' # [fp16, fp32]
+uncond_ratio: 0.1
+noise_offset: 0.05
+snr_gamma: 5.0
+enable_zero_snr: True
+pose_guider_pretrain: True
+
+seed: 12580
+resume_from_checkpoint: ''
+checkpointing_steps: 2000
+save_model_epoch_interval: 5
+exp_name: 'stage1'
+output_dir: './exp_output'
\ No newline at end of file
diff --git a/configs/train/stage2.yaml b/configs/train/stage2.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..086fa1e786d1fc8180c882f74c6f35e88a5be65d
--- /dev/null
+++ b/configs/train/stage2.yaml
@@ -0,0 +1,59 @@
+data:
+ train_bs: 1
+ train_width: 512
+ train_height: 512
+ meta_paths:
+ - "./data/fashion_meta.json"
+ sample_rate: 4
+ n_sample_frames: 24
+
+solver:
+ gradient_accumulation_steps: 1
+ mixed_precision: 'fp16'
+ enable_xformers_memory_efficient_attention: True
+ gradient_checkpointing: True
+ max_train_steps: 10000
+ max_grad_norm: 1.0
+ # lr
+ learning_rate: 1e-5
+ scale_lr: False
+ lr_warmup_steps: 1
+ lr_scheduler: 'constant'
+
+ # optimizer
+ use_8bit_adam: True
+ adam_beta1: 0.9
+ adam_beta2: 0.999
+ adam_weight_decay: 1.0e-2
+ adam_epsilon: 1.0e-8
+
+val:
+ validation_steps: 20
+
+
+noise_scheduler_kwargs:
+ num_train_timesteps: 1000
+ beta_start: 0.00085
+ beta_end: 0.012
+ beta_schedule: "linear"
+ steps_offset: 1
+ clip_sample: false
+
+base_model_path: './pretrained_weights/stable-diffusion-v1-5'
+vae_model_path: './pretrained_weights/sd-vae-ft-mse'
+image_encoder_path: './pretrained_weights/sd-image-variations-diffusers/image_encoder'
+mm_path: './pretrained_weights/mm_sd_v15_v2.ckpt'
+
+weight_dtype: 'fp16' # [fp16, fp32]
+uncond_ratio: 0.1
+noise_offset: 0.05
+snr_gamma: 5.0
+enable_zero_snr: True
+stage1_ckpt_dir: './exp_output/stage1'
+stage1_ckpt_step: 980
+
+seed: 12580
+resume_from_checkpoint: ''
+checkpointing_steps: 2000
+exp_name: 'stage2'
+output_dir: './exp_output'
\ No newline at end of file
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diff --git a/pretrained_weights/DWPose/dw-ll_ucoco_384.onnx b/pretrained_weights/DWPose/dw-ll_ucoco_384.onnx
new file mode 100644
index 0000000000000000000000000000000000000000..df84ce34881c5701a29e09badd8c96f5c17bd214
--- /dev/null
+++ b/pretrained_weights/DWPose/dw-ll_ucoco_384.onnx
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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--- /dev/null
+++ b/pretrained_weights/DWPose/yolox_l.onnx
@@ -0,0 +1,3 @@
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diff --git a/pretrained_weights/denoising_unet.pth b/pretrained_weights/denoising_unet.pth
new file mode 100644
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--- /dev/null
+++ b/pretrained_weights/denoising_unet.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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diff --git a/pretrained_weights/image_encoder/config.json b/pretrained_weights/image_encoder/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..251e37d8a59724357a8887da1716fad7b791b9c0
--- /dev/null
+++ b/pretrained_weights/image_encoder/config.json
@@ -0,0 +1,23 @@
+{
+ "_name_or_path": "/home/jpinkney/.cache/huggingface/diffusers/models--lambdalabs--sd-image-variations-diffusers/snapshots/ca6f97f838ae1b5bf764f31363a21f388f4d8f3e/image_encoder",
+ "architectures": [
+ "CLIPVisionModelWithProjection"
+ ],
+ "attention_dropout": 0.0,
+ "dropout": 0.0,
+ "hidden_act": "quick_gelu",
+ "hidden_size": 1024,
+ "image_size": 224,
+ "initializer_factor": 1.0,
+ "initializer_range": 0.02,
+ "intermediate_size": 4096,
+ "layer_norm_eps": 1e-05,
+ "model_type": "clip_vision_model",
+ "num_attention_heads": 16,
+ "num_channels": 3,
+ "num_hidden_layers": 24,
+ "patch_size": 14,
+ "projection_dim": 768,
+ "torch_dtype": "float32",
+ "transformers_version": "4.25.1"
+}
diff --git a/pretrained_weights/image_encoder/pytorch_model.bin b/pretrained_weights/image_encoder/pytorch_model.bin
new file mode 100644
index 0000000000000000000000000000000000000000..167893f2790c143ffda7de008d70cf000136ceed
--- /dev/null
+++ b/pretrained_weights/image_encoder/pytorch_model.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 1215993967
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new file mode 100644
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--- /dev/null
+++ b/pretrained_weights/motion_module.pth
@@ -0,0 +1,3 @@
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diff --git a/pretrained_weights/pose_guider.pth b/pretrained_weights/pose_guider.pth
new file mode 100644
index 0000000000000000000000000000000000000000..f71b567653179a98be41ced378805f7c1cc48025
--- /dev/null
+++ b/pretrained_weights/pose_guider.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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diff --git a/pretrained_weights/reference_unet.pth b/pretrained_weights/reference_unet.pth
new file mode 100644
index 0000000000000000000000000000000000000000..8cc325831535fda0b47fc60b68daa247adf29278
--- /dev/null
+++ b/pretrained_weights/reference_unet.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:beddccb08d49a8b29b0f4d6d456c6521d4382a8d8d48884fa60ba8802509c214
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diff --git a/pretrained_weights/sd-vae-ft-mse/config.json b/pretrained_weights/sd-vae-ft-mse/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..0db26717579be63eb0ddbf15b43faa43700dfe5a
--- /dev/null
+++ b/pretrained_weights/sd-vae-ft-mse/config.json
@@ -0,0 +1,29 @@
+{
+ "_class_name": "AutoencoderKL",
+ "_diffusers_version": "0.4.2",
+ "act_fn": "silu",
+ "block_out_channels": [
+ 128,
+ 256,
+ 512,
+ 512
+ ],
+ "down_block_types": [
+ "DownEncoderBlock2D",
+ "DownEncoderBlock2D",
+ "DownEncoderBlock2D",
+ "DownEncoderBlock2D"
+ ],
+ "in_channels": 3,
+ "latent_channels": 4,
+ "layers_per_block": 2,
+ "norm_num_groups": 32,
+ "out_channels": 3,
+ "sample_size": 256,
+ "up_block_types": [
+ "UpDecoderBlock2D",
+ "UpDecoderBlock2D",
+ "UpDecoderBlock2D",
+ "UpDecoderBlock2D"
+ ]
+}
diff --git a/pretrained_weights/sd-vae-ft-mse/diffusion_pytorch_model.bin b/pretrained_weights/sd-vae-ft-mse/diffusion_pytorch_model.bin
new file mode 100644
index 0000000000000000000000000000000000000000..ba36f34d64ad3be997b7cab94b0b9acd61272851
--- /dev/null
+++ b/pretrained_weights/sd-vae-ft-mse/diffusion_pytorch_model.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1b4889b6b1d4ce7ae320a02dedaeff1780ad77d415ea0d744b476155c6377ddc
+size 334707217
diff --git a/pretrained_weights/sd-vae-ft-mse/diffusion_pytorch_model.safetensors b/pretrained_weights/sd-vae-ft-mse/diffusion_pytorch_model.safetensors
new file mode 100644
index 0000000000000000000000000000000000000000..90464d67ac7303d0ee4696334df13da130a948ea
--- /dev/null
+++ b/pretrained_weights/sd-vae-ft-mse/diffusion_pytorch_model.safetensors
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a1d993488569e928462932c8c38a0760b874d166399b14414135bd9c42df5815
+size 334643276
diff --git a/pretrained_weights/stable-diffusion-v1-5/feature_extractor/preprocessor_config.json b/pretrained_weights/stable-diffusion-v1-5/feature_extractor/preprocessor_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..5294955ff7801083f720b34b55d0f1f51313c5c5
--- /dev/null
+++ b/pretrained_weights/stable-diffusion-v1-5/feature_extractor/preprocessor_config.json
@@ -0,0 +1,20 @@
+{
+ "crop_size": 224,
+ "do_center_crop": true,
+ "do_convert_rgb": true,
+ "do_normalize": true,
+ "do_resize": true,
+ "feature_extractor_type": "CLIPFeatureExtractor",
+ "image_mean": [
+ 0.48145466,
+ 0.4578275,
+ 0.40821073
+ ],
+ "image_std": [
+ 0.26862954,
+ 0.26130258,
+ 0.27577711
+ ],
+ "resample": 3,
+ "size": 224
+}
diff --git a/pretrained_weights/stable-diffusion-v1-5/model_index.json b/pretrained_weights/stable-diffusion-v1-5/model_index.json
new file mode 100644
index 0000000000000000000000000000000000000000..6866dceb3a870b077eb970ecf702ce4e1a83b934
--- /dev/null
+++ b/pretrained_weights/stable-diffusion-v1-5/model_index.json
@@ -0,0 +1,32 @@
+{
+ "_class_name": "StableDiffusionPipeline",
+ "_diffusers_version": "0.6.0",
+ "feature_extractor": [
+ "transformers",
+ "CLIPFeatureExtractor"
+ ],
+ "safety_checker": [
+ "stable_diffusion",
+ "StableDiffusionSafetyChecker"
+ ],
+ "scheduler": [
+ "diffusers",
+ "PNDMScheduler"
+ ],
+ "text_encoder": [
+ "transformers",
+ "CLIPTextModel"
+ ],
+ "tokenizer": [
+ "transformers",
+ "CLIPTokenizer"
+ ],
+ "unet": [
+ "diffusers",
+ "UNet2DConditionModel"
+ ],
+ "vae": [
+ "diffusers",
+ "AutoencoderKL"
+ ]
+}
diff --git a/pretrained_weights/stable-diffusion-v1-5/unet/config.json b/pretrained_weights/stable-diffusion-v1-5/unet/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..6d63242165378f518e00d09c66bd6b30142bbae4
--- /dev/null
+++ b/pretrained_weights/stable-diffusion-v1-5/unet/config.json
@@ -0,0 +1,37 @@
+{
+ "_class_name": "UNet2DConditionModel",
+ "_diffusers_version": "0.6.0",
+ "_name_or_path": "/home/patrick/stable-diffusion-v1-5/unet",
+ "act_fn": "silu",
+ "attention_head_dim": 8,
+ "block_out_channels": [
+ 320,
+ 640,
+ 1280,
+ 1280
+ ],
+ "center_input_sample": false,
+ "cross_attention_dim": 768,
+ "down_block_types": [
+ "CrossAttnDownBlock2D",
+ "CrossAttnDownBlock2D",
+ "CrossAttnDownBlock2D",
+ "DownBlock2D"
+ ],
+ "downsample_padding": 1,
+ "flip_sin_to_cos": true,
+ "freq_shift": 0,
+ "in_channels": 4,
+ "layers_per_block": 2,
+ "mid_block_scale_factor": 1,
+ "norm_eps": 1e-05,
+ "norm_num_groups": 32,
+ "out_channels": 4,
+ "sample_size": 64,
+ "up_block_types": [
+ "UpBlock2D",
+ "CrossAttnUpBlock2D",
+ "CrossAttnUpBlock2D",
+ "CrossAttnUpBlock2D"
+ ]
+}
diff --git a/pretrained_weights/stable-diffusion-v1-5/unet/diffusion_pytorch_model.bin b/pretrained_weights/stable-diffusion-v1-5/unet/diffusion_pytorch_model.bin
new file mode 100644
index 0000000000000000000000000000000000000000..13302c8c5df3ee5ea73bcb10ab4569995eca2c58
--- /dev/null
+++ b/pretrained_weights/stable-diffusion-v1-5/unet/diffusion_pytorch_model.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5019a4fbb455dd9b75192afc3ecf8a8ec875e83812fd51029d2e19277edddebc
+size 1719312805
diff --git a/pretrained_weights/stable-diffusion-v1-5/v1-inference.yaml b/pretrained_weights/stable-diffusion-v1-5/v1-inference.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..d4effe569e897369918625f9d8be5603a0e6a0d6
--- /dev/null
+++ b/pretrained_weights/stable-diffusion-v1-5/v1-inference.yaml
@@ -0,0 +1,70 @@
+model:
+ base_learning_rate: 1.0e-04
+ target: ldm.models.diffusion.ddpm.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.0120
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: "jpg"
+ cond_stage_key: "txt"
+ image_size: 64
+ channels: 4
+ cond_stage_trainable: false # Note: different from the one we trained before
+ conditioning_key: crossattn
+ monitor: val/loss_simple_ema
+ scale_factor: 0.18215
+ use_ema: False
+
+ scheduler_config: # 10000 warmup steps
+ target: ldm.lr_scheduler.LambdaLinearScheduler
+ params:
+ warm_up_steps: [ 10000 ]
+ cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
+ f_start: [ 1.e-6 ]
+ f_max: [ 1. ]
+ f_min: [ 1. ]
+
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+ params:
+ image_size: 32 # unused
+ in_channels: 4
+ out_channels: 4
+ model_channels: 320
+ attention_resolutions: [ 4, 2, 1 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 4, 4 ]
+ num_heads: 8
+ use_spatial_transformer: True
+ transformer_depth: 1
+ context_dim: 768
+ use_checkpoint: True
+ legacy: False
+
+ first_stage_config:
+ target: ldm.models.autoencoder.AutoencoderKL
+ params:
+ embed_dim: 4
+ monitor: val/rec_loss
+ ddconfig:
+ double_z: true
+ z_channels: 4
+ resolution: 256
+ in_channels: 3
+ out_ch: 3
+ ch: 128
+ ch_mult:
+ - 1
+ - 2
+ - 4
+ - 4
+ num_res_blocks: 2
+ attn_resolutions: []
+ dropout: 0.0
+ lossconfig:
+ target: torch.nn.Identity
+
+ cond_stage_config:
+ target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1687ed425946615d32f071cd6d371f64d581c4f7
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,30 @@
+accelerate==0.21.0
+av==11.0.0
+clip @ https://github.com/openai/CLIP/archive/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1.zip#sha256=b5842c25da441d6c581b53a5c60e0c2127ebafe0f746f8e15561a006c6c3be6a
+decord==0.6.0
+diffusers==0.24.0
+einops==0.4.1
+gradio==3.41.2
+gradio_client==0.5.0
+imageio==2.33.0
+imageio-ffmpeg==0.4.9
+numpy==1.23.5
+omegaconf==2.2.3
+onnxruntime-gpu==1.16.3
+open-clip-torch==2.20.0
+opencv-contrib-python==4.8.1.78
+opencv-python==4.8.1.78
+Pillow==9.5.0
+scikit-image==0.21.0
+scikit-learn==1.3.2
+scipy==1.11.4
+torch==2.0.1
+torchdiffeq==0.2.3
+torchmetrics==1.2.1
+torchsde==0.2.5
+torchvision==0.15.2
+tqdm==4.66.1
+transformers==4.30.2
+mlflow==2.9.2
+xformers==0.0.22
+controlnet-aux==0.0.7
\ No newline at end of file
diff --git a/scripts/lmks2vid.py b/scripts/lmks2vid.py
new file mode 100644
index 0000000000000000000000000000000000000000..003c5275d779471d77a70845390b5b82fefbde78
--- /dev/null
+++ b/scripts/lmks2vid.py
@@ -0,0 +1,337 @@
+import argparse
+import os
+import random
+from datetime import datetime
+from pathlib import Path
+from typing import List
+
+import av
+import cv2
+import numpy as np
+import torch
+
+# ๅๅงๅๆจกๅ
+import torchvision
+from diffusers import AutoencoderKL, DDIMScheduler
+from diffusers.pipelines.stable_diffusion import StableDiffusionPipeline
+from einops import rearrange, repeat
+from omegaconf import OmegaConf
+from PIL import Image
+from torchvision import transforms
+from transformers import (
+ CLIPImageProcessor,
+ CLIPTextModel,
+ CLIPTokenizer,
+ CLIPVisionModel,
+ CLIPVisionModelWithProjection,
+)
+
+import sys
+from src.models.unet_3d import UNet3DConditionModel
+from src.pipelines.pipeline_lmks2vid_long import Pose2VideoPipeline
+from src.models.pose_guider import PoseGuider
+from src.utils.util import get_fps, read_frames, save_videos_grid
+from tools.facetracker_api import face_image
+
+
+def parse_args():
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ "--config", type=str, help="Path of inference configs",
+ default="./configs/prompts/inference_reenact.yaml"
+ )
+ parser.add_argument(
+ "--save_dir", type=str, help="Path of save results",
+ default="./output/stage2_infer"
+ )
+ parser.add_argument(
+ "--source_image_path", type=str, help="Path of source image",
+ default="",
+ )
+ parser.add_argument(
+ "--driving_video_path", type=str, help="Path of driving video",
+ default="",
+ )
+ parser.add_argument(
+ "--batch_size",
+ type=int,
+ default=320,
+ help="Checkpoint step of pretrained model",
+ )
+ parser.add_argument("--mask_ratio", type=float, default=0.55) # 0.55~0.6
+ parser.add_argument("-W", type=int, default=512)
+ parser.add_argument("-H", type=int, default=512)
+ parser.add_argument("-L", type=int, default=24)
+ parser.add_argument("--seed", type=int, default=42)
+ parser.add_argument("--cfg", type=float, default=3.5)
+ parser.add_argument("--steps", type=int, default=30)
+ parser.add_argument("--fps", type=int, default=25)
+ args = parser.parse_args()
+
+ return args
+
+
+def lmks_vis(img, lms):
+ # Visualize the mouth, nose, and entire face based on landmarks
+ h, w, c = img.shape
+ lms = lms[:, :2]
+ mouth = lms[48:66]
+ nose = lms[27:36]
+ color = (0, 255, 0)
+ # Center mouth and nose
+ x_c, y_c = np.mean(lms[:, 0]), np.mean(lms[:, 1])
+ h_c, w_c = h // 2, w // 2
+ img_face, img_mouth, img_nose = img.copy(), img.copy(), img.copy()
+ for pt_num, (x, y) in enumerate(mouth):
+ x = x - (x_c - w_c)
+ y = y - (y_c - h_c)
+ x = int(x + 0.5)
+ y = int(y + 0.5)
+ cv2.circle(img_mouth, (y, x), 1, color, -1)
+ for pt_num, (x, y) in enumerate(nose):
+ x = x - (x_c - w_c)
+ y = y - (y_c - h_c)
+ x = int(x + 0.5)
+ y = int(y + 0.5)
+ cv2.circle(img_nose, (y, x), 1, color, -1)
+ for pt_num, (x, y) in enumerate(lms):
+ x = int(x + 0.5)
+ y = int(y + 0.5)
+ if pt_num >= 66:
+ color = (255, 255, 0)
+ else:
+ color = (0, 255, 0)
+ cv2.circle(img_face, (y, x), 1, color, -1)
+ return img_face, img_mouth, img_nose
+
+
+def batch_rearrange(pose_len, batch_size=24):
+ # To rearrange the pose sequence based on batch size
+ batch_ind_list = []
+ for i in range(0, pose_len, batch_size):
+ if i + batch_size < pose_len:
+ batch_ind_list.append(list(range(i, i + batch_size)))
+ else:
+ batch_ind_list.append(list(range(i, min(i + batch_size, pose_len))))
+ return batch_ind_list
+
+
+def lmks_video_extract(video_path):
+ # To extract the landmark sequence of video (single face video)
+ video_stream = cv2.VideoCapture(video_path)
+ lmks_list, frames = [], []
+ while 1:
+ still_reading, frame = video_stream.read()
+ if not still_reading:
+ video_stream.release()
+ break
+ h, w, c = frame.shape
+ lmk_img, lmks = face_image(frame)
+ if lmks is not None:
+ lmks_list.append(lmks)
+ frames.append(frame)
+ return frames, np.array(lmks_list), [h, w]
+
+
+def adjust_pose(src_lms_list, src_size, ref_lms, ref_size):
+ # To align the center of source landmarks based on reference landmark
+ new_src_lms_list = []
+ ref_lms = ref_lms[:, :2]
+ src_lms = src_lms_list[0][:, :2]
+ ref_lms[:, 0] = ref_lms[:, 0] / ref_size[1]
+ ref_lms[:, 1] = ref_lms[:, 1] / ref_size[0]
+ src_lms[:, 0] = src_lms[:, 0] / src_size[1]
+ src_lms[:, 1] = src_lms[:, 1] / src_size[0]
+ ref_cx, ref_cy = np.mean(ref_lms[:, 0]), np.mean(ref_lms[:, 1])
+ src_cx, src_cy = np.mean(src_lms[:, 0]), np.mean(src_lms[:, 1])
+ for item in src_lms_list:
+ item = item[:, :2]
+ item[:, 0] = item[:, 0] - int((src_cx - ref_cx)) * src_size[1]
+ item[:, 1] = item[:, 1] - int((src_cy - ref_cy)) * src_size[0]
+ new_src_lms_list.append(item)
+ return np.array(new_src_lms_list)
+
+
+def main():
+ args = parse_args()
+ infer_config = OmegaConf.load(args.config)
+
+ # base_model_path = "./pretrained_weights/huggingface-models/sd-image-variations-diffusers/"
+ base_model_path = infer_config.pretrained_base_model_path
+ weight_dtype = torch.float16
+
+ image_enc = CLIPVisionModelWithProjection.from_pretrained(
+ # "./pretrained_weights/huggingface-models/sd-image-variations-diffusers/image_encoder"
+ infer_config.image_encoder_path
+ ).to(dtype=weight_dtype, device="cuda")
+ vae = AutoencoderKL.from_pretrained(
+ # "./pretrained_weights/huggingface-models/sd-vae-ft-mse"
+ infer_config.pretrained_vae_path
+ ).to("cuda", dtype=weight_dtype)
+ # initial reference unet, denoise unet, pose guider
+ reference_unet = UNet3DConditionModel.from_pretrained_2d(
+ base_model_path,
+ "",
+ subfolder="unet",
+ unet_additional_kwargs={
+ "task_type": "reenact",
+ "use_motion_module": False,
+ "unet_use_temporal_attention": False,
+ "mode": "write",
+ },
+ ).to(device="cuda", dtype=weight_dtype)
+ denoising_unet = UNet3DConditionModel.from_pretrained_2d(
+ base_model_path,
+ "./pretrained_weights/mm_sd_v15_v2.ckpt",
+ subfolder="unet",
+ unet_additional_kwargs=OmegaConf.to_container(
+ infer_config.unet_additional_kwargs
+ ),
+ # mm_zero_proj_out=True,
+ ).to(device="cuda")
+ pose_guider1 = PoseGuider(
+ conditioning_embedding_channels=320, block_out_channels=(16, 32, 96, 256)
+ ).to(device="cuda", dtype=weight_dtype)
+ pose_guider2 = PoseGuider(
+ conditioning_embedding_channels=320, block_out_channels=(16, 32, 96, 256)
+ ).to(device="cuda", dtype=weight_dtype)
+ print("------------------initial all networks------------------")
+ # load model from pretrained models
+ denoising_unet.load_state_dict(
+ torch.load(
+ infer_config.denoising_unet_path,
+ map_location="cpu",
+ ),
+ strict=True,
+ )
+ reference_unet.load_state_dict(
+ torch.load(
+ infer_config.reference_unet_path,
+ map_location="cpu",
+ )
+ )
+ pose_guider1.load_state_dict(
+ torch.load(
+ infer_config.pose_guider1_path,
+ map_location="cpu",
+ )
+ )
+ pose_guider2.load_state_dict(
+ torch.load(
+ infer_config.pose_guider2_path,
+ map_location="cpu",
+ )
+ )
+ print("---------load pretrained denoising unet, reference unet and pose guider----------")
+ # scheduler
+ enable_zero_snr = True
+ sched_kwargs = OmegaConf.to_container(infer_config.noise_scheduler_kwargs)
+ if enable_zero_snr:
+ sched_kwargs.update(
+ rescale_betas_zero_snr=True,
+ timestep_spacing="trailing",
+ prediction_type="v_prediction",
+ )
+ scheduler = DDIMScheduler(**sched_kwargs)
+ pipe = Pose2VideoPipeline(
+ vae=vae,
+ image_encoder=image_enc,
+ reference_unet=reference_unet,
+ denoising_unet=denoising_unet,
+ pose_guider1=pose_guider1,
+ pose_guider2=pose_guider2,
+ scheduler=scheduler,
+ )
+ pipe = pipe.to("cuda", dtype=weight_dtype)
+ height, width, clip_length = args.H, args.W, args.L
+ generator = torch.manual_seed(42)
+ date_str = datetime.now().strftime("%Y%m%d")
+ save_dir = Path(f"{args.save_dir}/{date_str}")
+ save_dir.mkdir(exist_ok=True, parents=True)
+
+ ref_image_path, pose_video_path = args.source_image_path, args.driving_video_path
+ ref_name = Path(ref_image_path).stem
+ pose_name = Path(pose_video_path).stem
+ ref_image_pil = Image.open(ref_image_path).convert("RGB")
+ ref_image = cv2.imread(ref_image_path)
+ ref_h, ref_w, c = ref_image.shape
+ ref_pose, ref_pose_lms = face_image(ref_image)
+ # To extract landmarks from driving video
+ pose_frames, pose_lms_list, pose_size = lmks_video_extract(pose_video_path)
+ pose_lms_list = adjust_pose(pose_lms_list, pose_size, ref_pose_lms, [ref_h, ref_w])
+ pose_h, pose_w = int(pose_size[0]), int(pose_size[1])
+ pose_len = pose_lms_list.shape[0]
+ # Truncating the video tail if its frames less than 24 to obtain stable effect.
+ pose_len = pose_len // 24 * 24
+ batch_index_list = batch_rearrange(pose_len, args.batch_size)
+ pose_transform = transforms.Compose(
+ [transforms.Resize((height, width)), transforms.ToTensor()]
+ )
+ videos = []
+ zero_map = np.zeros_like(ref_pose)
+ zero_map = cv2.resize(zero_map, (pose_w, pose_h))
+ for batch_index in batch_index_list:
+ pose_list, pose_up_list, pose_down_list = [], [], []
+ pose_frame_list = []
+ pose_tensor_list, pose_up_tensor_list, pose_down_tensor_list = [], [], []
+ batch_len = len(batch_index)
+ for pose_idx in batch_index:
+ pose_lms = pose_lms_list[pose_idx]
+ pose_frame = pose_frames[pose_idx][:, :, ::-1]
+ pose_image, pose_mouth_image, _ = lmks_vis(zero_map, pose_lms)
+ h, w, c = pose_image.shape
+ pose_up_image = pose_image.copy()
+ pose_up_image[int(h * args.mask_ratio):, :, :] = 0.
+ pose_image_pil = Image.fromarray(pose_image)
+ pose_frame = Image.fromarray(pose_frame)
+ pose_up_pil = Image.fromarray(pose_up_image)
+ pose_mouth_pil = Image.fromarray(pose_mouth_image)
+ pose_list.append(pose_image_pil)
+ pose_up_list.append(pose_up_pil)
+ pose_down_list.append(pose_mouth_pil)
+ pose_tensor_list.append(pose_transform(pose_image_pil))
+ pose_up_tensor_list.append(pose_transform(pose_up_pil))
+ pose_down_tensor_list.append(pose_transform(pose_mouth_pil))
+ pose_frame_list.append(pose_transform(pose_frame))
+ pose_tensor = torch.stack(pose_tensor_list, dim=0) # (f, c, h, w)
+ pose_tensor = pose_tensor.transpose(0, 1)
+ pose_tensor = pose_tensor.unsqueeze(0)
+ pose_frames_tensor = torch.stack(pose_frame_list, dim=0) # (f, c, h, w)
+ pose_frames_tensor = pose_frames_tensor.transpose(0, 1)
+ pose_frames_tensor = pose_frames_tensor.unsqueeze(0)
+ ref_image_tensor = pose_transform(ref_image_pil) # (c, h, w)
+ ref_image_tensor = ref_image_tensor.unsqueeze(1).unsqueeze(0) # (1, c, 1, h, w)
+ ref_image_tensor = repeat(
+ ref_image_tensor, "b c f h w -> b c (repeat f) h w", repeat=batch_len
+ )
+ # To disentangle head attitude control (including eyes blink) and mouth motion control
+ pipeline_output = pipe(
+ ref_image_pil,
+ pose_up_list,
+ pose_down_list,
+ width,
+ height,
+ batch_len,
+ 20,
+ 3.5,
+ generator=generator,
+ )
+ video = pipeline_output.videos
+ video = torch.cat([ref_image_tensor, pose_frames_tensor, video], dim=0)
+ videos.append(video)
+ videos = torch.cat(videos, dim=2)
+ time_str = datetime.now().strftime("%H%M")
+ save_video_path = f"{save_dir}/{ref_name}_{pose_name}_{time_str}.mp4"
+ save_videos_grid(
+ videos,
+ save_video_path,
+ n_rows=3,
+ fps=args.fps,
+ )
+ print("infer results: {}".format(save_video_path))
+ del pipe
+ torch.cuda.empty_cache()
+
+
+if __name__ == "__main__":
+ main()
diff --git a/scripts/pose2vid.py b/scripts/pose2vid.py
new file mode 100644
index 0000000000000000000000000000000000000000..826d9a5cf6fdd52ecd83985b0c4475cc1efe27aa
--- /dev/null
+++ b/scripts/pose2vid.py
@@ -0,0 +1,167 @@
+import argparse
+import os
+from datetime import datetime
+from pathlib import Path
+from typing import List
+
+import av
+import numpy as np
+import torch
+import torchvision
+from diffusers import AutoencoderKL, DDIMScheduler
+from diffusers.pipelines.stable_diffusion import StableDiffusionPipeline
+from einops import repeat
+from omegaconf import OmegaConf
+from PIL import Image
+from torchvision import transforms
+from transformers import CLIPVisionModelWithProjection
+
+from configs.prompts.test_cases import TestCasesDict
+from src.models.pose_guider import PoseGuider
+from src.models.unet_2d_condition import UNet2DConditionModel
+from src.models.unet_3d import UNet3DConditionModel
+from src.pipelines.pipeline_pose2vid_long import Pose2VideoPipeline
+from src.utils.util import get_fps, read_frames, save_videos_grid
+
+
+def parse_args():
+ parser = argparse.ArgumentParser()
+ parser.add_argument("--config")
+ parser.add_argument("-W", type=int, default=512)
+ parser.add_argument("-H", type=int, default=784)
+ parser.add_argument("-L", type=int, default=24)
+ parser.add_argument("--seed", type=int, default=42)
+ parser.add_argument("--cfg", type=float, default=3.5)
+ parser.add_argument("--steps", type=int, default=30)
+ parser.add_argument("--fps", type=int)
+ args = parser.parse_args()
+
+ return args
+
+
+def main():
+ args = parse_args()
+
+ config = OmegaConf.load(args.config)
+
+ if config.weight_dtype == "fp16":
+ weight_dtype = torch.float16
+ else:
+ weight_dtype = torch.float32
+
+ vae = AutoencoderKL.from_pretrained(
+ config.pretrained_vae_path,
+ ).to("cuda", dtype=weight_dtype)
+
+ reference_unet = UNet2DConditionModel.from_pretrained(
+ config.pretrained_base_model_path,
+ subfolder="unet",
+ ).to(dtype=weight_dtype, device="cuda")
+
+ inference_config_path = config.inference_config
+ infer_config = OmegaConf.load(inference_config_path)
+ denoising_unet = UNet3DConditionModel.from_pretrained_2d(
+ config.pretrained_base_model_path,
+ config.motion_module_path,
+ subfolder="unet",
+ unet_additional_kwargs=infer_config.unet_additional_kwargs,
+ ).to(dtype=weight_dtype, device="cuda")
+
+ pose_guider = PoseGuider(320, block_out_channels=(16, 32, 96, 256)).to(
+ dtype=weight_dtype, device="cuda"
+ )
+
+ image_enc = CLIPVisionModelWithProjection.from_pretrained(
+ config.image_encoder_path
+ ).to(dtype=weight_dtype, device="cuda")
+
+ sched_kwargs = OmegaConf.to_container(infer_config.noise_scheduler_kwargs)
+ scheduler = DDIMScheduler(**sched_kwargs)
+
+ generator = torch.manual_seed(args.seed)
+
+ width, height = args.W, args.H
+
+ # load pretrained weights
+ denoising_unet.load_state_dict(
+ torch.load(config.denoising_unet_path, map_location="cpu"),
+ strict=False,
+ )
+ reference_unet.load_state_dict(
+ torch.load(config.reference_unet_path, map_location="cpu"),
+ )
+ pose_guider.load_state_dict(
+ torch.load(config.pose_guider_path, map_location="cpu"),
+ )
+
+ pipe = Pose2VideoPipeline(
+ vae=vae,
+ image_encoder=image_enc,
+ reference_unet=reference_unet,
+ denoising_unet=denoising_unet,
+ pose_guider=pose_guider,
+ scheduler=scheduler,
+ )
+ pipe = pipe.to("cuda", dtype=weight_dtype)
+
+ date_str = datetime.now().strftime("%Y%m%d")
+ time_str = datetime.now().strftime("%H%M")
+ save_dir_name = f"{time_str}--seed_{args.seed}-{args.W}x{args.H}"
+
+ save_dir = Path(f"output/{date_str}/{save_dir_name}")
+ save_dir.mkdir(exist_ok=True, parents=True)
+
+ for ref_image_path in config["test_cases"].keys():
+ # Each ref_image may correspond to multiple actions
+ for pose_video_path in config["test_cases"][ref_image_path]:
+ ref_name = Path(ref_image_path).stem
+ pose_name = Path(pose_video_path).stem.replace("_kps", "")
+
+ ref_image_pil = Image.open(ref_image_path).convert("RGB")
+
+ pose_list = []
+ pose_tensor_list = []
+ pose_images = read_frames(pose_video_path)
+ src_fps = get_fps(pose_video_path)
+ print(f"pose video has {len(pose_images)} frames, with {src_fps} fps")
+ pose_transform = transforms.Compose(
+ [transforms.Resize((height, width)), transforms.ToTensor()]
+ )
+ for pose_image_pil in pose_images[: args.L]:
+ pose_tensor_list.append(pose_transform(pose_image_pil))
+ pose_list.append(pose_image_pil)
+
+ ref_image_tensor = pose_transform(ref_image_pil) # (c, h, w)
+ ref_image_tensor = ref_image_tensor.unsqueeze(1).unsqueeze(
+ 0
+ ) # (1, c, 1, h, w)
+ ref_image_tensor = repeat(
+ ref_image_tensor, "b c f h w -> b c (repeat f) h w", repeat=args.L
+ )
+
+ pose_tensor = torch.stack(pose_tensor_list, dim=0) # (f, c, h, w)
+ pose_tensor = pose_tensor.transpose(0, 1)
+ pose_tensor = pose_tensor.unsqueeze(0)
+
+ video = pipe(
+ ref_image_pil,
+ pose_list,
+ width,
+ height,
+ args.L,
+ args.steps,
+ args.cfg,
+ generator=generator,
+ ).videos
+
+ video = torch.cat([ref_image_tensor, pose_tensor, video], dim=0)
+ save_videos_grid(
+ video,
+ f"{save_dir}/{ref_name}_{pose_name}_{args.H}x{args.W}_{int(args.cfg)}_{time_str}.mp4",
+ n_rows=3,
+ fps=src_fps if args.fps is None else args.fps,
+ )
+
+
+if __name__ == "__main__":
+ main()
diff --git a/src/__init__.py b/src/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/src/__pycache__/__init__.cpython-310.pyc b/src/__pycache__/__init__.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..d5fc2d02c2b8c0b672bbdd98db6e61a6def5dec1
Binary files /dev/null and b/src/__pycache__/__init__.cpython-310.pyc differ
diff --git a/src/dataset/dance_image.py b/src/dataset/dance_image.py
new file mode 100644
index 0000000000000000000000000000000000000000..17d390f2a0f8a5c605951d769c33a39de8e4d4cb
--- /dev/null
+++ b/src/dataset/dance_image.py
@@ -0,0 +1,124 @@
+import json
+import random
+
+import torch
+import torchvision.transforms as transforms
+from decord import VideoReader
+from PIL import Image
+from torch.utils.data import Dataset
+from transformers import CLIPImageProcessor
+
+
+class HumanDanceDataset(Dataset):
+ def __init__(
+ self,
+ img_size,
+ img_scale=(1.0, 1.0),
+ img_ratio=(0.9, 1.0),
+ drop_ratio=0.1,
+ data_meta_paths=["./data/fahsion_meta.json"],
+ sample_margin=30,
+ ):
+ super().__init__()
+
+ self.img_size = img_size
+ self.img_scale = img_scale
+ self.img_ratio = img_ratio
+ self.sample_margin = sample_margin
+
+ # -----
+ # vid_meta format:
+ # [{'video_path': , 'kps_path': , 'other':},
+ # {'video_path': , 'kps_path': , 'other':}]
+ # -----
+ vid_meta = []
+ for data_meta_path in data_meta_paths:
+ vid_meta.extend(json.load(open(data_meta_path, "r")))
+ self.vid_meta = vid_meta
+
+ self.clip_image_processor = CLIPImageProcessor()
+
+ self.transform = transforms.Compose(
+ [
+ transforms.RandomResizedCrop(
+ self.img_size,
+ scale=self.img_scale,
+ ratio=self.img_ratio,
+ interpolation=transforms.InterpolationMode.BILINEAR,
+ ),
+ transforms.ToTensor(),
+ transforms.Normalize([0.5], [0.5]),
+ ]
+ )
+
+ self.cond_transform = transforms.Compose(
+ [
+ transforms.RandomResizedCrop(
+ self.img_size,
+ scale=self.img_scale,
+ ratio=self.img_ratio,
+ interpolation=transforms.InterpolationMode.BILINEAR,
+ ),
+ transforms.ToTensor(),
+ ]
+ )
+
+ self.drop_ratio = drop_ratio
+
+ def augmentation(self, image, transform, state=None):
+ if state is not None:
+ torch.set_rng_state(state)
+ return transform(image)
+
+ def __getitem__(self, index):
+ video_meta = self.vid_meta[index]
+ video_path = video_meta["video_path"]
+ kps_path = video_meta["kps_path"]
+
+ video_reader = VideoReader(video_path)
+ kps_reader = VideoReader(kps_path)
+
+ assert len(video_reader) == len(
+ kps_reader
+ ), f"{len(video_reader) = } != {len(kps_reader) = } in {video_path}"
+
+ video_length = len(video_reader)
+
+ margin = min(self.sample_margin, video_length)
+
+ ref_img_idx = random.randint(0, video_length - 1)
+ if ref_img_idx + margin < video_length:
+ tgt_img_idx = random.randint(ref_img_idx + margin, video_length - 1)
+ elif ref_img_idx - margin > 0:
+ tgt_img_idx = random.randint(0, ref_img_idx - margin)
+ else:
+ tgt_img_idx = random.randint(0, video_length - 1)
+
+ ref_img = video_reader[ref_img_idx]
+ ref_img_pil = Image.fromarray(ref_img.asnumpy())
+ tgt_img = video_reader[tgt_img_idx]
+ tgt_img_pil = Image.fromarray(tgt_img.asnumpy())
+
+ tgt_pose = kps_reader[tgt_img_idx]
+ tgt_pose_pil = Image.fromarray(tgt_pose.asnumpy())
+
+ state = torch.get_rng_state()
+ tgt_img = self.augmentation(tgt_img_pil, self.transform, state)
+ tgt_pose_img = self.augmentation(tgt_pose_pil, self.cond_transform, state)
+ ref_img_vae = self.augmentation(ref_img_pil, self.transform, state)
+ clip_image = self.clip_image_processor(
+ images=ref_img_pil, return_tensors="pt"
+ ).pixel_values[0]
+
+ sample = dict(
+ video_dir=video_path,
+ img=tgt_img,
+ tgt_pose=tgt_pose_img,
+ ref_img=ref_img_vae,
+ clip_images=clip_image,
+ )
+
+ return sample
+
+ def __len__(self):
+ return len(self.vid_meta)
diff --git a/src/dataset/dance_video.py b/src/dataset/dance_video.py
new file mode 100644
index 0000000000000000000000000000000000000000..7f68bb0d3c8415eede171031d393d671ec0efd84
--- /dev/null
+++ b/src/dataset/dance_video.py
@@ -0,0 +1,137 @@
+import json
+import random
+from typing import List
+
+import numpy as np
+import pandas as pd
+import torch
+import torchvision.transforms as transforms
+from decord import VideoReader
+from PIL import Image
+from torch.utils.data import Dataset
+from transformers import CLIPImageProcessor
+
+
+class HumanDanceVideoDataset(Dataset):
+ def __init__(
+ self,
+ sample_rate,
+ n_sample_frames,
+ width,
+ height,
+ img_scale=(1.0, 1.0),
+ img_ratio=(0.9, 1.0),
+ drop_ratio=0.1,
+ data_meta_paths=["./data/fashion_meta.json"],
+ ):
+ super().__init__()
+ self.sample_rate = sample_rate
+ self.n_sample_frames = n_sample_frames
+ self.width = width
+ self.height = height
+ self.img_scale = img_scale
+ self.img_ratio = img_ratio
+
+ vid_meta = []
+ for data_meta_path in data_meta_paths:
+ vid_meta.extend(json.load(open(data_meta_path, "r")))
+ self.vid_meta = vid_meta
+
+ self.clip_image_processor = CLIPImageProcessor()
+
+ self.pixel_transform = transforms.Compose(
+ [
+ transforms.RandomResizedCrop(
+ (height, width),
+ scale=self.img_scale,
+ ratio=self.img_ratio,
+ interpolation=transforms.InterpolationMode.BILINEAR,
+ ),
+ transforms.ToTensor(),
+ transforms.Normalize([0.5], [0.5]),
+ ]
+ )
+
+ self.cond_transform = transforms.Compose(
+ [
+ transforms.RandomResizedCrop(
+ (height, width),
+ scale=self.img_scale,
+ ratio=self.img_ratio,
+ interpolation=transforms.InterpolationMode.BILINEAR,
+ ),
+ transforms.ToTensor(),
+ ]
+ )
+
+ self.drop_ratio = drop_ratio
+
+ def augmentation(self, images, transform, state=None):
+ if state is not None:
+ torch.set_rng_state(state)
+ if isinstance(images, List):
+ transformed_images = [transform(img) for img in images]
+ ret_tensor = torch.stack(transformed_images, dim=0) # (f, c, h, w)
+ else:
+ ret_tensor = transform(images) # (c, h, w)
+ return ret_tensor
+
+ def __getitem__(self, index):
+ video_meta = self.vid_meta[index]
+ video_path = video_meta["video_path"]
+ kps_path = video_meta["kps_path"]
+
+ video_reader = VideoReader(video_path)
+ kps_reader = VideoReader(kps_path)
+
+ assert len(video_reader) == len(
+ kps_reader
+ ), f"{len(video_reader) = } != {len(kps_reader) = } in {video_path}"
+
+ video_length = len(video_reader)
+
+ clip_length = min(
+ video_length, (self.n_sample_frames - 1) * self.sample_rate + 1
+ )
+ start_idx = random.randint(0, video_length - clip_length)
+ batch_index = np.linspace(
+ start_idx, start_idx + clip_length - 1, self.n_sample_frames, dtype=int
+ ).tolist()
+
+ # read frames and kps
+ vid_pil_image_list = []
+ pose_pil_image_list = []
+ for index in batch_index:
+ img = video_reader[index]
+ vid_pil_image_list.append(Image.fromarray(img.asnumpy()))
+ img = kps_reader[index]
+ pose_pil_image_list.append(Image.fromarray(img.asnumpy()))
+
+ ref_img_idx = random.randint(0, video_length - 1)
+ ref_img = Image.fromarray(video_reader[ref_img_idx].asnumpy())
+
+ # transform
+ state = torch.get_rng_state()
+ pixel_values_vid = self.augmentation(
+ vid_pil_image_list, self.pixel_transform, state
+ )
+ pixel_values_pose = self.augmentation(
+ pose_pil_image_list, self.cond_transform, state
+ )
+ pixel_values_ref_img = self.augmentation(ref_img, self.pixel_transform, state)
+ clip_ref_img = self.clip_image_processor(
+ images=ref_img, return_tensors="pt"
+ ).pixel_values[0]
+
+ sample = dict(
+ video_dir=video_path,
+ pixel_values_vid=pixel_values_vid,
+ pixel_values_pose=pixel_values_pose,
+ pixel_values_ref_img=pixel_values_ref_img,
+ clip_ref_img=clip_ref_img,
+ )
+
+ return sample
+
+ def __len__(self):
+ return len(self.vid_meta)
diff --git a/src/dwpose/__init__.py b/src/dwpose/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a217b0bcc4b4b8ed3632796ef2a2fe9d212fd268
--- /dev/null
+++ b/src/dwpose/__init__.py
@@ -0,0 +1,123 @@
+# https://github.com/IDEA-Research/DWPose
+# Openpose
+# Original from CMU https://github.com/CMU-Perceptual-Computing-Lab/openpose
+# 2nd Edited by https://github.com/Hzzone/pytorch-openpose
+# 3rd Edited by ControlNet
+# 4th Edited by ControlNet (added face and correct hands)
+
+import copy
+import os
+
+os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
+import cv2
+import numpy as np
+import torch
+from controlnet_aux.util import HWC3, resize_image
+from PIL import Image
+
+from . import util
+from .wholebody import Wholebody
+
+
+def draw_pose(pose, H, W):
+ bodies = pose["bodies"]
+ faces = pose["faces"]
+ hands = pose["hands"]
+ candidate = bodies["candidate"]
+ subset = bodies["subset"]
+ canvas = np.zeros(shape=(H, W, 3), dtype=np.uint8)
+
+ canvas = util.draw_bodypose(canvas, candidate, subset)
+
+ canvas = util.draw_handpose(canvas, hands)
+
+ canvas = util.draw_facepose(canvas, faces)
+
+ return canvas
+
+
+class DWposeDetector:
+ def __init__(self):
+ pass
+
+ def to(self, device):
+ self.pose_estimation = Wholebody(device)
+ return self
+
+ def cal_height(self, input_image):
+ input_image = cv2.cvtColor(
+ np.array(input_image, dtype=np.uint8), cv2.COLOR_RGB2BGR
+ )
+
+ input_image = HWC3(input_image)
+ H, W, C = input_image.shape
+ with torch.no_grad():
+ candidate, subset = self.pose_estimation(input_image)
+ nums, keys, locs = candidate.shape
+ # candidate[..., 0] /= float(W)
+ # candidate[..., 1] /= float(H)
+ body = candidate
+ return body[0, ..., 1].min(), body[..., 1].max() - body[..., 1].min()
+
+ def __call__(
+ self,
+ input_image,
+ detect_resolution=512,
+ image_resolution=512,
+ output_type="pil",
+ **kwargs,
+ ):
+ input_image = cv2.cvtColor(
+ np.array(input_image, dtype=np.uint8), cv2.COLOR_RGB2BGR
+ )
+
+ input_image = HWC3(input_image)
+ input_image = resize_image(input_image, detect_resolution)
+ H, W, C = input_image.shape
+ with torch.no_grad():
+ candidate, subset = self.pose_estimation(input_image)
+ nums, keys, locs = candidate.shape
+ candidate[..., 0] /= float(W)
+ candidate[..., 1] /= float(H)
+ score = subset[:, :18]
+ max_ind = np.mean(score, axis=-1).argmax(axis=0)
+ score = score[[max_ind]]
+ body = candidate[:, :18].copy()
+ body = body[[max_ind]]
+ nums = 1
+ body = body.reshape(nums * 18, locs)
+ body_score = copy.deepcopy(score)
+ for i in range(len(score)):
+ for j in range(len(score[i])):
+ if score[i][j] > 0.3:
+ score[i][j] = int(18 * i + j)
+ else:
+ score[i][j] = -1
+
+ un_visible = subset < 0.3
+ candidate[un_visible] = -1
+
+ foot = candidate[:, 18:24]
+
+ faces = candidate[[max_ind], 24:92]
+
+ hands = candidate[[max_ind], 92:113]
+ hands = np.vstack([hands, candidate[[max_ind], 113:]])
+
+ bodies = dict(candidate=body, subset=score)
+ pose = dict(bodies=bodies, hands=hands, faces=faces)
+
+ detected_map = draw_pose(pose, H, W)
+ detected_map = HWC3(detected_map)
+
+ img = resize_image(input_image, image_resolution)
+ H, W, C = img.shape
+
+ detected_map = cv2.resize(
+ detected_map, (W, H), interpolation=cv2.INTER_LINEAR
+ )
+
+ if output_type == "pil":
+ detected_map = Image.fromarray(detected_map)
+
+ return detected_map, body_score
diff --git a/src/dwpose/onnxdet.py b/src/dwpose/onnxdet.py
new file mode 100644
index 0000000000000000000000000000000000000000..201a4a7fccc2e20032135a4483e14c87ba22c351
--- /dev/null
+++ b/src/dwpose/onnxdet.py
@@ -0,0 +1,130 @@
+# https://github.com/IDEA-Research/DWPose
+import cv2
+import numpy as np
+import onnxruntime
+
+
+def nms(boxes, scores, nms_thr):
+ """Single class NMS implemented in Numpy."""
+ x1 = boxes[:, 0]
+ y1 = boxes[:, 1]
+ x2 = boxes[:, 2]
+ y2 = boxes[:, 3]
+
+ areas = (x2 - x1 + 1) * (y2 - y1 + 1)
+ order = scores.argsort()[::-1]
+
+ keep = []
+ while order.size > 0:
+ i = order[0]
+ keep.append(i)
+ xx1 = np.maximum(x1[i], x1[order[1:]])
+ yy1 = np.maximum(y1[i], y1[order[1:]])
+ xx2 = np.minimum(x2[i], x2[order[1:]])
+ yy2 = np.minimum(y2[i], y2[order[1:]])
+
+ w = np.maximum(0.0, xx2 - xx1 + 1)
+ h = np.maximum(0.0, yy2 - yy1 + 1)
+ inter = w * h
+ ovr = inter / (areas[i] + areas[order[1:]] - inter)
+
+ inds = np.where(ovr <= nms_thr)[0]
+ order = order[inds + 1]
+
+ return keep
+
+
+def multiclass_nms(boxes, scores, nms_thr, score_thr):
+ """Multiclass NMS implemented in Numpy. Class-aware version."""
+ final_dets = []
+ num_classes = scores.shape[1]
+ for cls_ind in range(num_classes):
+ cls_scores = scores[:, cls_ind]
+ valid_score_mask = cls_scores > score_thr
+ if valid_score_mask.sum() == 0:
+ continue
+ else:
+ valid_scores = cls_scores[valid_score_mask]
+ valid_boxes = boxes[valid_score_mask]
+ keep = nms(valid_boxes, valid_scores, nms_thr)
+ if len(keep) > 0:
+ cls_inds = np.ones((len(keep), 1)) * cls_ind
+ dets = np.concatenate(
+ [valid_boxes[keep], valid_scores[keep, None], cls_inds], 1
+ )
+ final_dets.append(dets)
+ if len(final_dets) == 0:
+ return None
+ return np.concatenate(final_dets, 0)
+
+
+def demo_postprocess(outputs, img_size, p6=False):
+ grids = []
+ expanded_strides = []
+ strides = [8, 16, 32] if not p6 else [8, 16, 32, 64]
+
+ hsizes = [img_size[0] // stride for stride in strides]
+ wsizes = [img_size[1] // stride for stride in strides]
+
+ for hsize, wsize, stride in zip(hsizes, wsizes, strides):
+ xv, yv = np.meshgrid(np.arange(wsize), np.arange(hsize))
+ grid = np.stack((xv, yv), 2).reshape(1, -1, 2)
+ grids.append(grid)
+ shape = grid.shape[:2]
+ expanded_strides.append(np.full((*shape, 1), stride))
+
+ grids = np.concatenate(grids, 1)
+ expanded_strides = np.concatenate(expanded_strides, 1)
+ outputs[..., :2] = (outputs[..., :2] + grids) * expanded_strides
+ outputs[..., 2:4] = np.exp(outputs[..., 2:4]) * expanded_strides
+
+ return outputs
+
+
+def preprocess(img, input_size, swap=(2, 0, 1)):
+ if len(img.shape) == 3:
+ padded_img = np.ones((input_size[0], input_size[1], 3), dtype=np.uint8) * 114
+ else:
+ padded_img = np.ones(input_size, dtype=np.uint8) * 114
+
+ r = min(input_size[0] / img.shape[0], input_size[1] / img.shape[1])
+ resized_img = cv2.resize(
+ img,
+ (int(img.shape[1] * r), int(img.shape[0] * r)),
+ interpolation=cv2.INTER_LINEAR,
+ ).astype(np.uint8)
+ padded_img[: int(img.shape[0] * r), : int(img.shape[1] * r)] = resized_img
+
+ padded_img = padded_img.transpose(swap)
+ padded_img = np.ascontiguousarray(padded_img, dtype=np.float32)
+ return padded_img, r
+
+
+def inference_detector(session, oriImg):
+ input_shape = (640, 640)
+ img, ratio = preprocess(oriImg, input_shape)
+
+ ort_inputs = {session.get_inputs()[0].name: img[None, :, :, :]}
+ output = session.run(None, ort_inputs)
+ predictions = demo_postprocess(output[0], input_shape)[0]
+
+ boxes = predictions[:, :4]
+ scores = predictions[:, 4:5] * predictions[:, 5:]
+
+ boxes_xyxy = np.ones_like(boxes)
+ boxes_xyxy[:, 0] = boxes[:, 0] - boxes[:, 2] / 2.0
+ boxes_xyxy[:, 1] = boxes[:, 1] - boxes[:, 3] / 2.0
+ boxes_xyxy[:, 2] = boxes[:, 0] + boxes[:, 2] / 2.0
+ boxes_xyxy[:, 3] = boxes[:, 1] + boxes[:, 3] / 2.0
+ boxes_xyxy /= ratio
+ dets = multiclass_nms(boxes_xyxy, scores, nms_thr=0.45, score_thr=0.1)
+ if dets is not None:
+ final_boxes, final_scores, final_cls_inds = dets[:, :4], dets[:, 4], dets[:, 5]
+ isscore = final_scores > 0.3
+ iscat = final_cls_inds == 0
+ isbbox = [i and j for (i, j) in zip(isscore, iscat)]
+ final_boxes = final_boxes[isbbox]
+ else:
+ return []
+
+ return final_boxes
diff --git a/src/dwpose/onnxpose.py b/src/dwpose/onnxpose.py
new file mode 100644
index 0000000000000000000000000000000000000000..b7405383a7dd1a56b1f409c440d0ccb1c121ee73
--- /dev/null
+++ b/src/dwpose/onnxpose.py
@@ -0,0 +1,370 @@
+# https://github.com/IDEA-Research/DWPose
+from typing import List, Tuple
+
+import cv2
+import numpy as np
+import onnxruntime as ort
+
+
+def preprocess(
+ img: np.ndarray, out_bbox, input_size: Tuple[int, int] = (192, 256)
+) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
+ """Do preprocessing for RTMPose model inference.
+
+ Args:
+ img (np.ndarray): Input image in shape.
+ input_size (tuple): Input image size in shape (w, h).
+
+ Returns:
+ tuple:
+ - resized_img (np.ndarray): Preprocessed image.
+ - center (np.ndarray): Center of image.
+ - scale (np.ndarray): Scale of image.
+ """
+ # get shape of image
+ img_shape = img.shape[:2]
+ out_img, out_center, out_scale = [], [], []
+ if len(out_bbox) == 0:
+ out_bbox = [[0, 0, img_shape[1], img_shape[0]]]
+ for i in range(len(out_bbox)):
+ x0 = out_bbox[i][0]
+ y0 = out_bbox[i][1]
+ x1 = out_bbox[i][2]
+ y1 = out_bbox[i][3]
+ bbox = np.array([x0, y0, x1, y1])
+
+ # get center and scale
+ center, scale = bbox_xyxy2cs(bbox, padding=1.25)
+
+ # do affine transformation
+ resized_img, scale = top_down_affine(input_size, scale, center, img)
+
+ # normalize image
+ mean = np.array([123.675, 116.28, 103.53])
+ std = np.array([58.395, 57.12, 57.375])
+ resized_img = (resized_img - mean) / std
+
+ out_img.append(resized_img)
+ out_center.append(center)
+ out_scale.append(scale)
+
+ return out_img, out_center, out_scale
+
+
+def inference(sess: ort.InferenceSession, img: np.ndarray) -> np.ndarray:
+ """Inference RTMPose model.
+
+ Args:
+ sess (ort.InferenceSession): ONNXRuntime session.
+ img (np.ndarray): Input image in shape.
+
+ Returns:
+ outputs (np.ndarray): Output of RTMPose model.
+ """
+ all_out = []
+ # build input
+ for i in range(len(img)):
+ input = [img[i].transpose(2, 0, 1)]
+
+ # build output
+ sess_input = {sess.get_inputs()[0].name: input}
+ sess_output = []
+ for out in sess.get_outputs():
+ sess_output.append(out.name)
+
+ # run model
+ outputs = sess.run(sess_output, sess_input)
+ all_out.append(outputs)
+
+ return all_out
+
+
+def postprocess(
+ outputs: List[np.ndarray],
+ model_input_size: Tuple[int, int],
+ center: Tuple[int, int],
+ scale: Tuple[int, int],
+ simcc_split_ratio: float = 2.0,
+) -> Tuple[np.ndarray, np.ndarray]:
+ """Postprocess for RTMPose model output.
+
+ Args:
+ outputs (np.ndarray): Output of RTMPose model.
+ model_input_size (tuple): RTMPose model Input image size.
+ center (tuple): Center of bbox in shape (x, y).
+ scale (tuple): Scale of bbox in shape (w, h).
+ simcc_split_ratio (float): Split ratio of simcc.
+
+ Returns:
+ tuple:
+ - keypoints (np.ndarray): Rescaled keypoints.
+ - scores (np.ndarray): Model predict scores.
+ """
+ all_key = []
+ all_score = []
+ for i in range(len(outputs)):
+ # use simcc to decode
+ simcc_x, simcc_y = outputs[i]
+ keypoints, scores = decode(simcc_x, simcc_y, simcc_split_ratio)
+
+ # rescale keypoints
+ keypoints = keypoints / model_input_size * scale[i] + center[i] - scale[i] / 2
+ all_key.append(keypoints[0])
+ all_score.append(scores[0])
+
+ return np.array(all_key), np.array(all_score)
+
+
+def bbox_xyxy2cs(
+ bbox: np.ndarray, padding: float = 1.0
+) -> Tuple[np.ndarray, np.ndarray]:
+ """Transform the bbox format from (x,y,w,h) into (center, scale)
+
+ Args:
+ bbox (ndarray): Bounding box(es) in shape (4,) or (n, 4), formatted
+ as (left, top, right, bottom)
+ padding (float): BBox padding factor that will be multilied to scale.
+ Default: 1.0
+
+ Returns:
+ tuple: A tuple containing center and scale.
+ - np.ndarray[float32]: Center (x, y) of the bbox in shape (2,) or
+ (n, 2)
+ - np.ndarray[float32]: Scale (w, h) of the bbox in shape (2,) or
+ (n, 2)
+ """
+ # convert single bbox from (4, ) to (1, 4)
+ dim = bbox.ndim
+ if dim == 1:
+ bbox = bbox[None, :]
+
+ # get bbox center and scale
+ x1, y1, x2, y2 = np.hsplit(bbox, [1, 2, 3])
+ center = np.hstack([x1 + x2, y1 + y2]) * 0.5
+ scale = np.hstack([x2 - x1, y2 - y1]) * padding
+
+ if dim == 1:
+ center = center[0]
+ scale = scale[0]
+
+ return center, scale
+
+
+def _fix_aspect_ratio(bbox_scale: np.ndarray, aspect_ratio: float) -> np.ndarray:
+ """Extend the scale to match the given aspect ratio.
+
+ Args:
+ scale (np.ndarray): The image scale (w, h) in shape (2, )
+ aspect_ratio (float): The ratio of ``w/h``
+
+ Returns:
+ np.ndarray: The reshaped image scale in (2, )
+ """
+ w, h = np.hsplit(bbox_scale, [1])
+ bbox_scale = np.where(
+ w > h * aspect_ratio,
+ np.hstack([w, w / aspect_ratio]),
+ np.hstack([h * aspect_ratio, h]),
+ )
+ return bbox_scale
+
+
+def _rotate_point(pt: np.ndarray, angle_rad: float) -> np.ndarray:
+ """Rotate a point by an angle.
+
+ Args:
+ pt (np.ndarray): 2D point coordinates (x, y) in shape (2, )
+ angle_rad (float): rotation angle in radian
+
+ Returns:
+ np.ndarray: Rotated point in shape (2, )
+ """
+ sn, cs = np.sin(angle_rad), np.cos(angle_rad)
+ rot_mat = np.array([[cs, -sn], [sn, cs]])
+ return rot_mat @ pt
+
+
+def _get_3rd_point(a: np.ndarray, b: np.ndarray) -> np.ndarray:
+ """To calculate the affine matrix, three pairs of points are required. This
+ function is used to get the 3rd point, given 2D points a & b.
+
+ The 3rd point is defined by rotating vector `a - b` by 90 degrees
+ anticlockwise, using b as the rotation center.
+
+ Args:
+ a (np.ndarray): The 1st point (x,y) in shape (2, )
+ b (np.ndarray): The 2nd point (x,y) in shape (2, )
+
+ Returns:
+ np.ndarray: The 3rd point.
+ """
+ direction = a - b
+ c = b + np.r_[-direction[1], direction[0]]
+ return c
+
+
+def get_warp_matrix(
+ center: np.ndarray,
+ scale: np.ndarray,
+ rot: float,
+ output_size: Tuple[int, int],
+ shift: Tuple[float, float] = (0.0, 0.0),
+ inv: bool = False,
+) -> np.ndarray:
+ """Calculate the affine transformation matrix that can warp the bbox area
+ in the input image to the output size.
+
+ Args:
+ center (np.ndarray[2, ]): Center of the bounding box (x, y).
+ scale (np.ndarray[2, ]): Scale of the bounding box
+ wrt [width, height].
+ rot (float): Rotation angle (degree).
+ output_size (np.ndarray[2, ] | list(2,)): Size of the
+ destination heatmaps.
+ shift (0-100%): Shift translation ratio wrt the width/height.
+ Default (0., 0.).
+ inv (bool): Option to inverse the affine transform direction.
+ (inv=False: src->dst or inv=True: dst->src)
+
+ Returns:
+ np.ndarray: A 2x3 transformation matrix
+ """
+ shift = np.array(shift)
+ src_w = scale[0]
+ dst_w = output_size[0]
+ dst_h = output_size[1]
+
+ # compute transformation matrix
+ rot_rad = np.deg2rad(rot)
+ src_dir = _rotate_point(np.array([0.0, src_w * -0.5]), rot_rad)
+ dst_dir = np.array([0.0, dst_w * -0.5])
+
+ # get four corners of the src rectangle in the original image
+ src = np.zeros((3, 2), dtype=np.float32)
+ src[0, :] = center + scale * shift
+ src[1, :] = center + src_dir + scale * shift
+ src[2, :] = _get_3rd_point(src[0, :], src[1, :])
+
+ # get four corners of the dst rectangle in the input image
+ dst = np.zeros((3, 2), dtype=np.float32)
+ dst[0, :] = [dst_w * 0.5, dst_h * 0.5]
+ dst[1, :] = np.array([dst_w * 0.5, dst_h * 0.5]) + dst_dir
+ dst[2, :] = _get_3rd_point(dst[0, :], dst[1, :])
+
+ if inv:
+ warp_mat = cv2.getAffineTransform(np.float32(dst), np.float32(src))
+ else:
+ warp_mat = cv2.getAffineTransform(np.float32(src), np.float32(dst))
+
+ return warp_mat
+
+
+def top_down_affine(
+ input_size: dict, bbox_scale: dict, bbox_center: dict, img: np.ndarray
+) -> Tuple[np.ndarray, np.ndarray]:
+ """Get the bbox image as the model input by affine transform.
+
+ Args:
+ input_size (dict): The input size of the model.
+ bbox_scale (dict): The bbox scale of the img.
+ bbox_center (dict): The bbox center of the img.
+ img (np.ndarray): The original image.
+
+ Returns:
+ tuple: A tuple containing center and scale.
+ - np.ndarray[float32]: img after affine transform.
+ - np.ndarray[float32]: bbox scale after affine transform.
+ """
+ w, h = input_size
+ warp_size = (int(w), int(h))
+
+ # reshape bbox to fixed aspect ratio
+ bbox_scale = _fix_aspect_ratio(bbox_scale, aspect_ratio=w / h)
+
+ # get the affine matrix
+ center = bbox_center
+ scale = bbox_scale
+ rot = 0
+ warp_mat = get_warp_matrix(center, scale, rot, output_size=(w, h))
+
+ # do affine transform
+ img = cv2.warpAffine(img, warp_mat, warp_size, flags=cv2.INTER_LINEAR)
+
+ return img, bbox_scale
+
+
+def get_simcc_maximum(
+ simcc_x: np.ndarray, simcc_y: np.ndarray
+) -> Tuple[np.ndarray, np.ndarray]:
+ """Get maximum response location and value from simcc representations.
+
+ Note:
+ instance number: N
+ num_keypoints: K
+ heatmap height: H
+ heatmap width: W
+
+ Args:
+ simcc_x (np.ndarray): x-axis SimCC in shape (K, Wx) or (N, K, Wx)
+ simcc_y (np.ndarray): y-axis SimCC in shape (K, Wy) or (N, K, Wy)
+
+ Returns:
+ tuple:
+ - locs (np.ndarray): locations of maximum heatmap responses in shape
+ (K, 2) or (N, K, 2)
+ - vals (np.ndarray): values of maximum heatmap responses in shape
+ (K,) or (N, K)
+ """
+ N, K, Wx = simcc_x.shape
+ simcc_x = simcc_x.reshape(N * K, -1)
+ simcc_y = simcc_y.reshape(N * K, -1)
+
+ # get maximum value locations
+ x_locs = np.argmax(simcc_x, axis=1)
+ y_locs = np.argmax(simcc_y, axis=1)
+ locs = np.stack((x_locs, y_locs), axis=-1).astype(np.float32)
+ max_val_x = np.amax(simcc_x, axis=1)
+ max_val_y = np.amax(simcc_y, axis=1)
+
+ # get maximum value across x and y axis
+ mask = max_val_x > max_val_y
+ max_val_x[mask] = max_val_y[mask]
+ vals = max_val_x
+ locs[vals <= 0.0] = -1
+
+ # reshape
+ locs = locs.reshape(N, K, 2)
+ vals = vals.reshape(N, K)
+
+ return locs, vals
+
+
+def decode(
+ simcc_x: np.ndarray, simcc_y: np.ndarray, simcc_split_ratio
+) -> Tuple[np.ndarray, np.ndarray]:
+ """Modulate simcc distribution with Gaussian.
+
+ Args:
+ simcc_x (np.ndarray[K, Wx]): model predicted simcc in x.
+ simcc_y (np.ndarray[K, Wy]): model predicted simcc in y.
+ simcc_split_ratio (int): The split ratio of simcc.
+
+ Returns:
+ tuple: A tuple containing center and scale.
+ - np.ndarray[float32]: keypoints in shape (K, 2) or (n, K, 2)
+ - np.ndarray[float32]: scores in shape (K,) or (n, K)
+ """
+ keypoints, scores = get_simcc_maximum(simcc_x, simcc_y)
+ keypoints /= simcc_split_ratio
+
+ return keypoints, scores
+
+
+def inference_pose(session, out_bbox, oriImg):
+ h, w = session.get_inputs()[0].shape[2:]
+ model_input_size = (w, h)
+ resized_img, center, scale = preprocess(oriImg, out_bbox, model_input_size)
+ outputs = inference(session, resized_img)
+ keypoints, scores = postprocess(outputs, model_input_size, center, scale)
+
+ return keypoints, scores
diff --git a/src/dwpose/util.py b/src/dwpose/util.py
new file mode 100644
index 0000000000000000000000000000000000000000..23dfaf4e254c229fb237c20807863b24025dfd59
--- /dev/null
+++ b/src/dwpose/util.py
@@ -0,0 +1,378 @@
+# https://github.com/IDEA-Research/DWPose
+import math
+import numpy as np
+import matplotlib
+import cv2
+
+
+eps = 0.01
+
+
+def smart_resize(x, s):
+ Ht, Wt = s
+ if x.ndim == 2:
+ Ho, Wo = x.shape
+ Co = 1
+ else:
+ Ho, Wo, Co = x.shape
+ if Co == 3 or Co == 1:
+ k = float(Ht + Wt) / float(Ho + Wo)
+ return cv2.resize(
+ x,
+ (int(Wt), int(Ht)),
+ interpolation=cv2.INTER_AREA if k < 1 else cv2.INTER_LANCZOS4,
+ )
+ else:
+ return np.stack([smart_resize(x[:, :, i], s) for i in range(Co)], axis=2)
+
+
+def smart_resize_k(x, fx, fy):
+ if x.ndim == 2:
+ Ho, Wo = x.shape
+ Co = 1
+ else:
+ Ho, Wo, Co = x.shape
+ Ht, Wt = Ho * fy, Wo * fx
+ if Co == 3 or Co == 1:
+ k = float(Ht + Wt) / float(Ho + Wo)
+ return cv2.resize(
+ x,
+ (int(Wt), int(Ht)),
+ interpolation=cv2.INTER_AREA if k < 1 else cv2.INTER_LANCZOS4,
+ )
+ else:
+ return np.stack([smart_resize_k(x[:, :, i], fx, fy) for i in range(Co)], axis=2)
+
+
+def padRightDownCorner(img, stride, padValue):
+ h = img.shape[0]
+ w = img.shape[1]
+
+ pad = 4 * [None]
+ pad[0] = 0 # up
+ pad[1] = 0 # left
+ pad[2] = 0 if (h % stride == 0) else stride - (h % stride) # down
+ pad[3] = 0 if (w % stride == 0) else stride - (w % stride) # right
+
+ img_padded = img
+ pad_up = np.tile(img_padded[0:1, :, :] * 0 + padValue, (pad[0], 1, 1))
+ img_padded = np.concatenate((pad_up, img_padded), axis=0)
+ pad_left = np.tile(img_padded[:, 0:1, :] * 0 + padValue, (1, pad[1], 1))
+ img_padded = np.concatenate((pad_left, img_padded), axis=1)
+ pad_down = np.tile(img_padded[-2:-1, :, :] * 0 + padValue, (pad[2], 1, 1))
+ img_padded = np.concatenate((img_padded, pad_down), axis=0)
+ pad_right = np.tile(img_padded[:, -2:-1, :] * 0 + padValue, (1, pad[3], 1))
+ img_padded = np.concatenate((img_padded, pad_right), axis=1)
+
+ return img_padded, pad
+
+
+def transfer(model, model_weights):
+ transfered_model_weights = {}
+ for weights_name in model.state_dict().keys():
+ transfered_model_weights[weights_name] = model_weights[
+ ".".join(weights_name.split(".")[1:])
+ ]
+ return transfered_model_weights
+
+
+def draw_bodypose(canvas, candidate, subset):
+ H, W, C = canvas.shape
+ candidate = np.array(candidate)
+ subset = np.array(subset)
+
+ stickwidth = 4
+
+ limbSeq = [
+ [2, 3],
+ [2, 6],
+ [3, 4],
+ [4, 5],
+ [6, 7],
+ [7, 8],
+ [2, 9],
+ [9, 10],
+ [10, 11],
+ [2, 12],
+ [12, 13],
+ [13, 14],
+ [2, 1],
+ [1, 15],
+ [15, 17],
+ [1, 16],
+ [16, 18],
+ [3, 17],
+ [6, 18],
+ ]
+
+ colors = [
+ [255, 0, 0],
+ [255, 85, 0],
+ [255, 170, 0],
+ [255, 255, 0],
+ [170, 255, 0],
+ [85, 255, 0],
+ [0, 255, 0],
+ [0, 255, 85],
+ [0, 255, 170],
+ [0, 255, 255],
+ [0, 170, 255],
+ [0, 85, 255],
+ [0, 0, 255],
+ [85, 0, 255],
+ [170, 0, 255],
+ [255, 0, 255],
+ [255, 0, 170],
+ [255, 0, 85],
+ ]
+
+ for i in range(17):
+ for n in range(len(subset)):
+ index = subset[n][np.array(limbSeq[i]) - 1]
+ if -1 in index:
+ continue
+ Y = candidate[index.astype(int), 0] * float(W)
+ X = candidate[index.astype(int), 1] * float(H)
+ mX = np.mean(X)
+ mY = np.mean(Y)
+ length = ((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2) ** 0.5
+ angle = math.degrees(math.atan2(X[0] - X[1], Y[0] - Y[1]))
+ polygon = cv2.ellipse2Poly(
+ (int(mY), int(mX)), (int(length / 2), stickwidth), int(angle), 0, 360, 1
+ )
+ cv2.fillConvexPoly(canvas, polygon, colors[i])
+
+ canvas = (canvas * 0.6).astype(np.uint8)
+
+ for i in range(18):
+ for n in range(len(subset)):
+ index = int(subset[n][i])
+ if index == -1:
+ continue
+ x, y = candidate[index][0:2]
+ x = int(x * W)
+ y = int(y * H)
+ cv2.circle(canvas, (int(x), int(y)), 4, colors[i], thickness=-1)
+
+ return canvas
+
+
+def draw_handpose(canvas, all_hand_peaks):
+ H, W, C = canvas.shape
+
+ edges = [
+ [0, 1],
+ [1, 2],
+ [2, 3],
+ [3, 4],
+ [0, 5],
+ [5, 6],
+ [6, 7],
+ [7, 8],
+ [0, 9],
+ [9, 10],
+ [10, 11],
+ [11, 12],
+ [0, 13],
+ [13, 14],
+ [14, 15],
+ [15, 16],
+ [0, 17],
+ [17, 18],
+ [18, 19],
+ [19, 20],
+ ]
+
+ for peaks in all_hand_peaks:
+ peaks = np.array(peaks)
+
+ for ie, e in enumerate(edges):
+ x1, y1 = peaks[e[0]]
+ x2, y2 = peaks[e[1]]
+ x1 = int(x1 * W)
+ y1 = int(y1 * H)
+ x2 = int(x2 * W)
+ y2 = int(y2 * H)
+ if x1 > eps and y1 > eps and x2 > eps and y2 > eps:
+ cv2.line(
+ canvas,
+ (x1, y1),
+ (x2, y2),
+ matplotlib.colors.hsv_to_rgb([ie / float(len(edges)), 1.0, 1.0])
+ * 255,
+ thickness=2,
+ )
+
+ for i, keyponit in enumerate(peaks):
+ x, y = keyponit
+ x = int(x * W)
+ y = int(y * H)
+ if x > eps and y > eps:
+ cv2.circle(canvas, (x, y), 4, (0, 0, 255), thickness=-1)
+ return canvas
+
+
+def draw_facepose(canvas, all_lmks):
+ H, W, C = canvas.shape
+ for lmks in all_lmks:
+ lmks = np.array(lmks)
+ for lmk in lmks:
+ x, y = lmk
+ x = int(x * W)
+ y = int(y * H)
+ if x > eps and y > eps:
+ cv2.circle(canvas, (x, y), 3, (255, 255, 255), thickness=-1)
+ return canvas
+
+
+# detect hand according to body pose keypoints
+# please refer to https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/src/openpose/hand/handDetector.cpp
+def handDetect(candidate, subset, oriImg):
+ # right hand: wrist 4, elbow 3, shoulder 2
+ # left hand: wrist 7, elbow 6, shoulder 5
+ ratioWristElbow = 0.33
+ detect_result = []
+ image_height, image_width = oriImg.shape[0:2]
+ for person in subset.astype(int):
+ # if any of three not detected
+ has_left = np.sum(person[[5, 6, 7]] == -1) == 0
+ has_right = np.sum(person[[2, 3, 4]] == -1) == 0
+ if not (has_left or has_right):
+ continue
+ hands = []
+ # left hand
+ if has_left:
+ left_shoulder_index, left_elbow_index, left_wrist_index = person[[5, 6, 7]]
+ x1, y1 = candidate[left_shoulder_index][:2]
+ x2, y2 = candidate[left_elbow_index][:2]
+ x3, y3 = candidate[left_wrist_index][:2]
+ hands.append([x1, y1, x2, y2, x3, y3, True])
+ # right hand
+ if has_right:
+ right_shoulder_index, right_elbow_index, right_wrist_index = person[
+ [2, 3, 4]
+ ]
+ x1, y1 = candidate[right_shoulder_index][:2]
+ x2, y2 = candidate[right_elbow_index][:2]
+ x3, y3 = candidate[right_wrist_index][:2]
+ hands.append([x1, y1, x2, y2, x3, y3, False])
+
+ for x1, y1, x2, y2, x3, y3, is_left in hands:
+ # pos_hand = pos_wrist + ratio * (pos_wrist - pos_elbox) = (1 + ratio) * pos_wrist - ratio * pos_elbox
+ # handRectangle.x = posePtr[wrist*3] + ratioWristElbow * (posePtr[wrist*3] - posePtr[elbow*3]);
+ # handRectangle.y = posePtr[wrist*3+1] + ratioWristElbow * (posePtr[wrist*3+1] - posePtr[elbow*3+1]);
+ # const auto distanceWristElbow = getDistance(poseKeypoints, person, wrist, elbow);
+ # const auto distanceElbowShoulder = getDistance(poseKeypoints, person, elbow, shoulder);
+ # handRectangle.width = 1.5f * fastMax(distanceWristElbow, 0.9f * distanceElbowShoulder);
+ x = x3 + ratioWristElbow * (x3 - x2)
+ y = y3 + ratioWristElbow * (y3 - y2)
+ distanceWristElbow = math.sqrt((x3 - x2) ** 2 + (y3 - y2) ** 2)
+ distanceElbowShoulder = math.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2)
+ width = 1.5 * max(distanceWristElbow, 0.9 * distanceElbowShoulder)
+ # x-y refers to the center --> offset to topLeft point
+ # handRectangle.x -= handRectangle.width / 2.f;
+ # handRectangle.y -= handRectangle.height / 2.f;
+ x -= width / 2
+ y -= width / 2 # width = height
+ # overflow the image
+ if x < 0:
+ x = 0
+ if y < 0:
+ y = 0
+ width1 = width
+ width2 = width
+ if x + width > image_width:
+ width1 = image_width - x
+ if y + width > image_height:
+ width2 = image_height - y
+ width = min(width1, width2)
+ # the max hand box value is 20 pixels
+ if width >= 20:
+ detect_result.append([int(x), int(y), int(width), is_left])
+
+ """
+ return value: [[x, y, w, True if left hand else False]].
+ width=height since the network require squared input.
+ x, y is the coordinate of top left
+ """
+ return detect_result
+
+
+# Written by Lvmin
+def faceDetect(candidate, subset, oriImg):
+ # left right eye ear 14 15 16 17
+ detect_result = []
+ image_height, image_width = oriImg.shape[0:2]
+ for person in subset.astype(int):
+ has_head = person[0] > -1
+ if not has_head:
+ continue
+
+ has_left_eye = person[14] > -1
+ has_right_eye = person[15] > -1
+ has_left_ear = person[16] > -1
+ has_right_ear = person[17] > -1
+
+ if not (has_left_eye or has_right_eye or has_left_ear or has_right_ear):
+ continue
+
+ head, left_eye, right_eye, left_ear, right_ear = person[[0, 14, 15, 16, 17]]
+
+ width = 0.0
+ x0, y0 = candidate[head][:2]
+
+ if has_left_eye:
+ x1, y1 = candidate[left_eye][:2]
+ d = max(abs(x0 - x1), abs(y0 - y1))
+ width = max(width, d * 3.0)
+
+ if has_right_eye:
+ x1, y1 = candidate[right_eye][:2]
+ d = max(abs(x0 - x1), abs(y0 - y1))
+ width = max(width, d * 3.0)
+
+ if has_left_ear:
+ x1, y1 = candidate[left_ear][:2]
+ d = max(abs(x0 - x1), abs(y0 - y1))
+ width = max(width, d * 1.5)
+
+ if has_right_ear:
+ x1, y1 = candidate[right_ear][:2]
+ d = max(abs(x0 - x1), abs(y0 - y1))
+ width = max(width, d * 1.5)
+
+ x, y = x0, y0
+
+ x -= width
+ y -= width
+
+ if x < 0:
+ x = 0
+
+ if y < 0:
+ y = 0
+
+ width1 = width * 2
+ width2 = width * 2
+
+ if x + width > image_width:
+ width1 = image_width - x
+
+ if y + width > image_height:
+ width2 = image_height - y
+
+ width = min(width1, width2)
+
+ if width >= 20:
+ detect_result.append([int(x), int(y), int(width)])
+
+ return detect_result
+
+
+# get max index of 2d array
+def npmax(array):
+ arrayindex = array.argmax(1)
+ arrayvalue = array.max(1)
+ i = arrayvalue.argmax()
+ j = arrayindex[i]
+ return i, j
diff --git a/src/dwpose/wholebody.py b/src/dwpose/wholebody.py
new file mode 100644
index 0000000000000000000000000000000000000000..ec4b5a10cc961c5f2aa67942e83ba231721d6253
--- /dev/null
+++ b/src/dwpose/wholebody.py
@@ -0,0 +1,48 @@
+# https://github.com/IDEA-Research/DWPose
+from pathlib import Path
+
+import cv2
+import numpy as np
+import onnxruntime as ort
+
+from .onnxdet import inference_detector
+from .onnxpose import inference_pose
+
+ModelDataPathPrefix = Path("./pretrained_weights")
+
+
+class Wholebody:
+ def __init__(self, device="cuda:0"):
+ providers = (
+ ["CPUExecutionProvider"] if device == "cpu" else ["CUDAExecutionProvider"]
+ )
+ onnx_det = ModelDataPathPrefix.joinpath("DWPose/yolox_l.onnx")
+ onnx_pose = ModelDataPathPrefix.joinpath("DWPose/dw-ll_ucoco_384.onnx")
+
+ self.session_det = ort.InferenceSession(
+ path_or_bytes=onnx_det, providers=providers
+ )
+ self.session_pose = ort.InferenceSession(
+ path_or_bytes=onnx_pose, providers=providers
+ )
+
+ def __call__(self, oriImg):
+ det_result = inference_detector(self.session_det, oriImg)
+ keypoints, scores = inference_pose(self.session_pose, det_result, oriImg)
+
+ keypoints_info = np.concatenate((keypoints, scores[..., None]), axis=-1)
+ # compute neck joint
+ neck = np.mean(keypoints_info[:, [5, 6]], axis=1)
+ # neck score when visualizing pred
+ neck[:, 2:4] = np.logical_and(
+ keypoints_info[:, 5, 2:4] > 0.3, keypoints_info[:, 6, 2:4] > 0.3
+ ).astype(int)
+ new_keypoints_info = np.insert(keypoints_info, 17, neck, axis=1)
+ mmpose_idx = [17, 6, 8, 10, 7, 9, 12, 14, 16, 13, 15, 2, 1, 4, 3]
+ openpose_idx = [1, 2, 3, 4, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17]
+ new_keypoints_info[:, openpose_idx] = new_keypoints_info[:, mmpose_idx]
+ keypoints_info = new_keypoints_info
+
+ keypoints, scores = keypoints_info[..., :2], keypoints_info[..., 2]
+
+ return keypoints, scores
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diff --git a/src/models/attention.py b/src/models/attention.py
new file mode 100644
index 0000000000000000000000000000000000000000..1d9a25b2e9056bf0b4039cd7b6f990d6e43bc91b
--- /dev/null
+++ b/src/models/attention.py
@@ -0,0 +1,481 @@
+# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention.py
+
+from typing import Any, Dict, Optional
+
+import torch
+from diffusers.models.attention import AdaLayerNorm, Attention, FeedForward
+from diffusers.models.embeddings import SinusoidalPositionalEmbedding
+from einops import rearrange
+from torch import nn
+
+
+class BasicTransformerBlock(nn.Module):
+ r"""
+ A basic Transformer block.
+
+ Parameters:
+ dim (`int`): The number of channels in the input and output.
+ num_attention_heads (`int`): The number of heads to use for multi-head attention.
+ attention_head_dim (`int`): The number of channels in each head.
+ dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
+ cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention.
+ activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward.
+ num_embeds_ada_norm (:
+ obj: `int`, *optional*): The number of diffusion steps used during training. See `Transformer2DModel`.
+ attention_bias (:
+ obj: `bool`, *optional*, defaults to `False`): Configure if the attentions should contain a bias parameter.
+ only_cross_attention (`bool`, *optional*):
+ Whether to use only cross-attention layers. In this case two cross attention layers are used.
+ double_self_attention (`bool`, *optional*):
+ Whether to use two self-attention layers. In this case no cross attention layers are used.
+ upcast_attention (`bool`, *optional*):
+ Whether to upcast the attention computation to float32. This is useful for mixed precision training.
+ norm_elementwise_affine (`bool`, *optional*, defaults to `True`):
+ Whether to use learnable elementwise affine parameters for normalization.
+ norm_type (`str`, *optional*, defaults to `"layer_norm"`):
+ The normalization layer to use. Can be `"layer_norm"`, `"ada_norm"` or `"ada_norm_zero"`.
+ final_dropout (`bool` *optional*, defaults to False):
+ Whether to apply a final dropout after the last feed-forward layer.
+ attention_type (`str`, *optional*, defaults to `"default"`):
+ The type of attention to use. Can be `"default"` or `"gated"` or `"gated-text-image"`.
+ positional_embeddings (`str`, *optional*, defaults to `None`):
+ The type of positional embeddings to apply to.
+ num_positional_embeddings (`int`, *optional*, defaults to `None`):
+ The maximum number of positional embeddings to apply.
+ """
+
+ def __init__(
+ self,
+ dim: int,
+ num_attention_heads: int,
+ attention_head_dim: int,
+ dropout=0.0,
+ cross_attention_dim: Optional[int] = None,
+ activation_fn: str = "geglu",
+ num_embeds_ada_norm: Optional[int] = None,
+ attention_bias: bool = False,
+ only_cross_attention: bool = False,
+ double_self_attention: bool = False,
+ upcast_attention: bool = False,
+ norm_elementwise_affine: bool = True,
+ norm_type: str = "layer_norm", # 'layer_norm', 'ada_norm', 'ada_norm_zero', 'ada_norm_single'
+ norm_eps: float = 1e-5,
+ final_dropout: bool = False,
+ attention_type: str = "default",
+ positional_embeddings: Optional[str] = None,
+ num_positional_embeddings: Optional[int] = None,
+ ):
+ super().__init__()
+ self.only_cross_attention = only_cross_attention
+
+ self.use_ada_layer_norm_zero = (
+ num_embeds_ada_norm is not None
+ ) and norm_type == "ada_norm_zero"
+ self.use_ada_layer_norm = (
+ num_embeds_ada_norm is not None
+ ) and norm_type == "ada_norm"
+ self.use_ada_layer_norm_single = norm_type == "ada_norm_single"
+ self.use_layer_norm = norm_type == "layer_norm"
+
+ if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
+ raise ValueError(
+ f"`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to"
+ f" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}."
+ )
+
+ if positional_embeddings and (num_positional_embeddings is None):
+ raise ValueError(
+ "If `positional_embedding` type is defined, `num_positition_embeddings` must also be defined."
+ )
+
+ if positional_embeddings == "sinusoidal":
+ self.pos_embed = SinusoidalPositionalEmbedding(
+ dim, max_seq_length=num_positional_embeddings
+ )
+ else:
+ self.pos_embed = None
+
+ # Define 3 blocks. Each block has its own normalization layer.
+ # 1. Self-Attn
+ if self.use_ada_layer_norm:
+ self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm)
+ elif self.use_ada_layer_norm_zero:
+ self.norm1 = AdaLayerNormZero(dim, num_embeds_ada_norm)
+ else:
+ self.norm1 = nn.LayerNorm(
+ dim, elementwise_affine=norm_elementwise_affine, eps=norm_eps
+ )
+
+ self.attn1 = Attention(
+ query_dim=dim,
+ heads=num_attention_heads,
+ dim_head=attention_head_dim,
+ dropout=dropout,
+ bias=attention_bias,
+ cross_attention_dim=cross_attention_dim if only_cross_attention else None,
+ upcast_attention=upcast_attention,
+ )
+
+ # 2. Cross-Attn
+ if cross_attention_dim is not None or double_self_attention:
+ # We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
+ # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
+ # the second cross attention block.
+ self.norm2 = (
+ AdaLayerNorm(dim, num_embeds_ada_norm)
+ if self.use_ada_layer_norm
+ else nn.LayerNorm(
+ dim, elementwise_affine=norm_elementwise_affine, eps=norm_eps
+ )
+ )
+ self.attn2 = Attention(
+ query_dim=dim,
+ cross_attention_dim=cross_attention_dim
+ if not double_self_attention
+ else None,
+ heads=num_attention_heads,
+ dim_head=attention_head_dim,
+ dropout=dropout,
+ bias=attention_bias,
+ upcast_attention=upcast_attention,
+ ) # is self-attn if encoder_hidden_states is none
+ else:
+ self.norm2 = None
+ self.attn2 = None
+
+ # 3. Feed-forward
+ if not self.use_ada_layer_norm_single:
+ self.norm3 = nn.LayerNorm(
+ dim, elementwise_affine=norm_elementwise_affine, eps=norm_eps
+ )
+
+ self.ff = FeedForward(
+ dim,
+ dropout=dropout,
+ activation_fn=activation_fn,
+ final_dropout=final_dropout,
+ )
+
+ # 4. Fuser
+ if attention_type == "gated" or attention_type == "gated-text-image":
+ self.fuser = GatedSelfAttentionDense(
+ dim, cross_attention_dim, num_attention_heads, attention_head_dim
+ )
+
+ # 5. Scale-shift for PixArt-Alpha.
+ if self.use_ada_layer_norm_single:
+ self.scale_shift_table = nn.Parameter(torch.randn(6, dim) / dim**0.5)
+
+ # let chunk size default to None
+ self._chunk_size = None
+ self._chunk_dim = 0
+
+ def set_chunk_feed_forward(self, chunk_size: Optional[int], dim: int = 0):
+ # Sets chunk feed-forward
+ self._chunk_size = chunk_size
+ self._chunk_dim = dim
+
+ def forward(
+ self,
+ hidden_states: torch.FloatTensor,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ encoder_hidden_states: Optional[torch.FloatTensor] = None,
+ encoder_attention_mask: Optional[torch.FloatTensor] = None,
+ timestep: Optional[torch.LongTensor] = None,
+ cross_attention_kwargs: Dict[str, Any] = None,
+ class_labels: Optional[torch.LongTensor] = None,
+ ) -> torch.FloatTensor:
+ # Notice that normalization is always applied before the real computation in the following blocks.
+ # 0. Self-Attention
+ batch_size = hidden_states.shape[0]
+
+ if self.use_ada_layer_norm:
+ norm_hidden_states = self.norm1(hidden_states, timestep)
+ elif self.use_ada_layer_norm_zero:
+ norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(
+ hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype
+ )
+ elif self.use_layer_norm:
+ norm_hidden_states = self.norm1(hidden_states)
+ elif self.use_ada_layer_norm_single:
+ shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
+ self.scale_shift_table[None] + timestep.reshape(batch_size, 6, -1)
+ ).chunk(6, dim=1)
+ norm_hidden_states = self.norm1(hidden_states)
+ norm_hidden_states = norm_hidden_states * (1 + scale_msa) + shift_msa
+ norm_hidden_states = norm_hidden_states.squeeze(1)
+ else:
+ raise ValueError("Incorrect norm used")
+
+ if self.pos_embed is not None:
+ norm_hidden_states = self.pos_embed(norm_hidden_states)
+
+ # 1. Retrieve lora scale.
+ lora_scale = (
+ cross_attention_kwargs.get("scale", 1.0)
+ if cross_attention_kwargs is not None
+ else 1.0
+ )
+
+ # 2. Prepare GLIGEN inputs
+ cross_attention_kwargs = (
+ cross_attention_kwargs.copy() if cross_attention_kwargs is not None else {}
+ )
+ gligen_kwargs = cross_attention_kwargs.pop("gligen", None)
+
+ attn_output = self.attn1(
+ norm_hidden_states,
+ encoder_hidden_states=encoder_hidden_states
+ if self.only_cross_attention
+ else None,
+ attention_mask=attention_mask,
+ **cross_attention_kwargs,
+ )
+ if self.use_ada_layer_norm_zero:
+ attn_output = gate_msa.unsqueeze(1) * attn_output
+ elif self.use_ada_layer_norm_single:
+ attn_output = gate_msa * attn_output
+
+ hidden_states = attn_output + hidden_states
+ if hidden_states.ndim == 4:
+ hidden_states = hidden_states.squeeze(1)
+
+ # 2.5 GLIGEN Control
+ if gligen_kwargs is not None:
+ hidden_states = self.fuser(hidden_states, gligen_kwargs["objs"])
+
+ # 3. Cross-Attention
+ if self.attn2 is not None:
+ if self.use_ada_layer_norm:
+ norm_hidden_states = self.norm2(hidden_states, timestep)
+ elif self.use_ada_layer_norm_zero or self.use_layer_norm:
+ norm_hidden_states = self.norm2(hidden_states)
+ elif self.use_ada_layer_norm_single:
+ # For PixArt norm2 isn't applied here:
+ # https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/diffusion/model/nets/PixArtMS.py#L70C1-L76C103
+ norm_hidden_states = hidden_states
+ else:
+ raise ValueError("Incorrect norm")
+
+ if self.pos_embed is not None and self.use_ada_layer_norm_single is False:
+ norm_hidden_states = self.pos_embed(norm_hidden_states)
+
+ attn_output = self.attn2(
+ norm_hidden_states,
+ encoder_hidden_states=encoder_hidden_states,
+ attention_mask=encoder_attention_mask,
+ **cross_attention_kwargs,
+ )
+ hidden_states = attn_output + hidden_states
+
+ # 4. Feed-forward
+ if not self.use_ada_layer_norm_single:
+ norm_hidden_states = self.norm3(hidden_states)
+
+ if self.use_ada_layer_norm_zero:
+ norm_hidden_states = (
+ norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
+ )
+
+ if self.use_ada_layer_norm_single:
+ norm_hidden_states = self.norm2(hidden_states)
+ norm_hidden_states = norm_hidden_states * (1 + scale_mlp) + shift_mlp
+
+ ff_output = self.ff(norm_hidden_states, scale=lora_scale)
+
+ if self.use_ada_layer_norm_zero:
+ ff_output = gate_mlp.unsqueeze(1) * ff_output
+ elif self.use_ada_layer_norm_single:
+ ff_output = gate_mlp * ff_output
+
+ hidden_states = ff_output + hidden_states
+ if hidden_states.ndim == 4:
+ hidden_states = hidden_states.squeeze(1)
+
+ return hidden_states
+
+
+class TemporalBasicTransformerBlock(nn.Module):
+ def __init__(
+ self,
+ dim: int,
+ num_attention_heads: int,
+ attention_head_dim: int,
+ dropout=0.0,
+ cross_attention_dim: Optional[int] = None,
+ activation_fn: str = "geglu",
+ num_embeds_ada_norm: Optional[int] = None,
+ attention_bias: bool = False,
+ only_cross_attention: bool = False,
+ upcast_attention: bool = False,
+ unet_use_cross_frame_attention=None,
+ unet_use_temporal_attention=None,
+ name=None,
+ ):
+ super().__init__()
+ self.only_cross_attention = only_cross_attention
+ self.use_ada_layer_norm = num_embeds_ada_norm is not None
+ self.unet_use_cross_frame_attention = unet_use_cross_frame_attention
+ self.unet_use_temporal_attention = unet_use_temporal_attention
+ self.name=name
+
+ # SC-Attn
+ self.attn1 = Attention(
+ query_dim=dim,
+ heads=num_attention_heads,
+ dim_head=attention_head_dim,
+ dropout=dropout,
+ bias=attention_bias,
+ upcast_attention=upcast_attention,
+ )
+ self.norm1 = (
+ AdaLayerNorm(dim, num_embeds_ada_norm)
+ if self.use_ada_layer_norm
+ else nn.LayerNorm(dim)
+ )
+
+ # Cross-Attn
+ if cross_attention_dim is not None:
+ self.attn2 = Attention(
+ query_dim=dim,
+ cross_attention_dim=cross_attention_dim,
+ heads=num_attention_heads,
+ dim_head=attention_head_dim,
+ dropout=dropout,
+ bias=attention_bias,
+ upcast_attention=upcast_attention,
+ )
+ else:
+ self.attn2 = None
+
+ if cross_attention_dim is not None:
+ self.norm2 = (
+ AdaLayerNorm(dim, num_embeds_ada_norm)
+ if self.use_ada_layer_norm
+ else nn.LayerNorm(dim)
+ )
+ else:
+ self.norm2 = None
+
+ # Feed-forward
+ self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn)
+ self.norm3 = nn.LayerNorm(dim)
+ self.use_ada_layer_norm_zero = False
+ # Temp-Attn
+ assert unet_use_temporal_attention is not None
+ if unet_use_temporal_attention:
+ self.attn_temp = Attention(
+ query_dim=dim,
+ heads=num_attention_heads,
+ dim_head=attention_head_dim,
+ dropout=dropout,
+ bias=attention_bias,
+ upcast_attention=upcast_attention,
+ )
+ nn.init.zeros_(self.attn_temp.to_out[0].weight.data)
+ self.norm_temp = (
+ AdaLayerNorm(dim, num_embeds_ada_norm)
+ if self.use_ada_layer_norm
+ else nn.LayerNorm(dim)
+ )
+
+ def forward(
+ self,
+ hidden_states,
+ encoder_hidden_states=None,
+ timestep=None,
+ attention_mask=None,
+ video_length=None,
+ self_attention_additional_feats=None,
+ mode=None,
+ ):
+ norm_hidden_states = (
+ self.norm1(hidden_states, timestep)
+ if self.use_ada_layer_norm
+ else self.norm1(hidden_states)
+ )
+ if self.name:
+ modify_norm_hidden_states = norm_hidden_states
+ if mode == "write":
+ self_attention_additional_feats[self.name]=norm_hidden_states
+ elif mode == "read" and self_attention_additional_feats:
+ ref_states = self_attention_additional_feats[self.name]
+ bank_fea = [
+ rearrange(
+ ref_states.unsqueeze(1).repeat(1, video_length, 1, 1),
+ "b t l c -> (b t) l c",
+ )
+ ]
+ modify_norm_hidden_states = torch.cat(
+ [norm_hidden_states] + bank_fea, dim=1
+ )
+
+ if self.unet_use_cross_frame_attention:
+ hidden_states = (
+ self.attn1(
+ norm_hidden_states,
+ attention_mask=attention_mask,
+ encoder_hidden_states=modify_norm_hidden_states,
+ video_length=video_length,
+ )
+ + hidden_states
+ )
+ else:
+ hidden_states = (
+ self.attn1(
+ norm_hidden_states,
+ encoder_hidden_states=modify_norm_hidden_states,
+ attention_mask=attention_mask
+ )
+ + hidden_states
+ )
+ else:
+ if self.unet_use_cross_frame_attention:
+ hidden_states = (
+ self.attn1(
+ norm_hidden_states,
+ attention_mask=attention_mask,
+ video_length=video_length,
+ )
+ + hidden_states
+ )
+ else:
+ hidden_states = (
+ self.attn1(norm_hidden_states, attention_mask=attention_mask)
+ + hidden_states
+ )
+
+ if self.attn2 is not None:
+ # Cross-Attention
+ norm_hidden_states = (
+ self.norm2(hidden_states, timestep)
+ if self.use_ada_layer_norm
+ else self.norm2(hidden_states)
+ )
+ hidden_states = (
+ self.attn2(
+ norm_hidden_states,
+ encoder_hidden_states=encoder_hidden_states,
+ attention_mask=attention_mask,
+ )
+ + hidden_states
+ )
+
+ # Feed-forward
+ hidden_states = self.ff(self.norm3(hidden_states)) + hidden_states
+
+ # Temporal-Attention
+ if self.unet_use_temporal_attention:
+ d = hidden_states.shape[1]
+ hidden_states = rearrange(
+ hidden_states, "(b f) d c -> (b d) f c", f=video_length
+ )
+ norm_hidden_states = (
+ self.norm_temp(hidden_states, timestep)
+ if self.use_ada_layer_norm
+ else self.norm_temp(hidden_states)
+ )
+ hidden_states = self.attn_temp(norm_hidden_states) + hidden_states
+ hidden_states = rearrange(hidden_states, "(b d) f c -> (b f) d c", d=d)
+
+ return hidden_states
diff --git a/src/models/motion_module.py b/src/models/motion_module.py
new file mode 100644
index 0000000000000000000000000000000000000000..44232766aed25ea0cc10e141e263fc265ee3aef2
--- /dev/null
+++ b/src/models/motion_module.py
@@ -0,0 +1,388 @@
+# Adapt from https://github.com/guoyww/AnimateDiff/blob/main/animatediff/models/motion_module.py
+import math
+from dataclasses import dataclass
+from typing import Callable, Optional
+
+import torch
+from diffusers.models.attention import FeedForward
+from diffusers.models.attention_processor import Attention, AttnProcessor
+from diffusers.utils import BaseOutput
+from diffusers.utils.import_utils import is_xformers_available
+from einops import rearrange, repeat
+from torch import nn
+
+
+def zero_module(module):
+ # Zero out the parameters of a module and return it.
+ for p in module.parameters():
+ p.detach().zero_()
+ return module
+
+
+@dataclass
+class TemporalTransformer3DModelOutput(BaseOutput):
+ sample: torch.FloatTensor
+
+
+if is_xformers_available():
+ import xformers
+ import xformers.ops
+else:
+ xformers = None
+
+
+def get_motion_module(in_channels, motion_module_type: str, motion_module_kwargs: dict):
+ if motion_module_type == "Vanilla":
+ return VanillaTemporalModule(
+ in_channels=in_channels,
+ **motion_module_kwargs,
+ )
+ else:
+ raise ValueError
+
+
+class VanillaTemporalModule(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ num_attention_heads=8,
+ num_transformer_block=2,
+ attention_block_types=("Temporal_Self", "Temporal_Self"),
+ cross_frame_attention_mode=None,
+ temporal_position_encoding=False,
+ temporal_position_encoding_max_len=24,
+ temporal_attention_dim_div=1,
+ zero_initialize=True,
+ ):
+ super().__init__()
+
+ self.temporal_transformer = TemporalTransformer3DModel(
+ in_channels=in_channels,
+ num_attention_heads=num_attention_heads,
+ attention_head_dim=in_channels
+ // num_attention_heads
+ // temporal_attention_dim_div,
+ num_layers=num_transformer_block,
+ attention_block_types=attention_block_types,
+ cross_frame_attention_mode=cross_frame_attention_mode,
+ temporal_position_encoding=temporal_position_encoding,
+ temporal_position_encoding_max_len=temporal_position_encoding_max_len,
+ )
+
+ if zero_initialize:
+ self.temporal_transformer.proj_out = zero_module(
+ self.temporal_transformer.proj_out
+ )
+
+ def forward(
+ self,
+ input_tensor,
+ temb,
+ encoder_hidden_states,
+ attention_mask=None,
+ anchor_frame_idx=None,
+ ):
+ hidden_states = input_tensor
+ hidden_states = self.temporal_transformer(
+ hidden_states, encoder_hidden_states, attention_mask
+ )
+
+ output = hidden_states
+ return output
+
+
+class TemporalTransformer3DModel(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ num_attention_heads,
+ attention_head_dim,
+ num_layers,
+ attention_block_types=(
+ "Temporal_Self",
+ "Temporal_Self",
+ ),
+ dropout=0.0,
+ norm_num_groups=32,
+ cross_attention_dim=768,
+ activation_fn="geglu",
+ attention_bias=False,
+ upcast_attention=False,
+ cross_frame_attention_mode=None,
+ temporal_position_encoding=False,
+ temporal_position_encoding_max_len=24,
+ ):
+ super().__init__()
+
+ inner_dim = num_attention_heads * attention_head_dim
+
+ self.norm = torch.nn.GroupNorm(
+ num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True
+ )
+ self.proj_in = nn.Linear(in_channels, inner_dim)
+
+ self.transformer_blocks = nn.ModuleList(
+ [
+ TemporalTransformerBlock(
+ dim=inner_dim,
+ num_attention_heads=num_attention_heads,
+ attention_head_dim=attention_head_dim,
+ attention_block_types=attention_block_types,
+ dropout=dropout,
+ norm_num_groups=norm_num_groups,
+ cross_attention_dim=cross_attention_dim,
+ activation_fn=activation_fn,
+ attention_bias=attention_bias,
+ upcast_attention=upcast_attention,
+ cross_frame_attention_mode=cross_frame_attention_mode,
+ temporal_position_encoding=temporal_position_encoding,
+ temporal_position_encoding_max_len=temporal_position_encoding_max_len,
+ )
+ for d in range(num_layers)
+ ]
+ )
+ self.proj_out = nn.Linear(inner_dim, in_channels)
+
+ def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None):
+ assert (
+ hidden_states.dim() == 5
+ ), f"Expected hidden_states to have ndim=5, but got ndim={hidden_states.dim()}."
+ video_length = hidden_states.shape[2]
+ hidden_states = rearrange(hidden_states, "b c f h w -> (b f) c h w")
+
+ batch, channel, height, weight = hidden_states.shape
+ residual = hidden_states
+
+ hidden_states = self.norm(hidden_states)
+ inner_dim = hidden_states.shape[1]
+ hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(
+ batch, height * weight, inner_dim
+ )
+ hidden_states = self.proj_in(hidden_states)
+
+ # Transformer Blocks
+ for block in self.transformer_blocks:
+ hidden_states = block(
+ hidden_states,
+ encoder_hidden_states=encoder_hidden_states,
+ video_length=video_length,
+ )
+
+ # output
+ hidden_states = self.proj_out(hidden_states)
+ hidden_states = (
+ hidden_states.reshape(batch, height, weight, inner_dim)
+ .permute(0, 3, 1, 2)
+ .contiguous()
+ )
+
+ output = hidden_states + residual
+ output = rearrange(output, "(b f) c h w -> b c f h w", f=video_length)
+
+ return output
+
+
+class TemporalTransformerBlock(nn.Module):
+ def __init__(
+ self,
+ dim,
+ num_attention_heads,
+ attention_head_dim,
+ attention_block_types=(
+ "Temporal_Self",
+ "Temporal_Self",
+ ),
+ dropout=0.0,
+ norm_num_groups=32,
+ cross_attention_dim=768,
+ activation_fn="geglu",
+ attention_bias=False,
+ upcast_attention=False,
+ cross_frame_attention_mode=None,
+ temporal_position_encoding=False,
+ temporal_position_encoding_max_len=24,
+ ):
+ super().__init__()
+
+ attention_blocks = []
+ norms = []
+
+ for block_name in attention_block_types:
+ attention_blocks.append(
+ VersatileAttention(
+ attention_mode=block_name.split("_")[0],
+ cross_attention_dim=cross_attention_dim
+ if block_name.endswith("_Cross")
+ else None,
+ query_dim=dim,
+ heads=num_attention_heads,
+ dim_head=attention_head_dim,
+ dropout=dropout,
+ bias=attention_bias,
+ upcast_attention=upcast_attention,
+ cross_frame_attention_mode=cross_frame_attention_mode,
+ temporal_position_encoding=temporal_position_encoding,
+ temporal_position_encoding_max_len=temporal_position_encoding_max_len,
+ )
+ )
+ norms.append(nn.LayerNorm(dim))
+
+ self.attention_blocks = nn.ModuleList(attention_blocks)
+ self.norms = nn.ModuleList(norms)
+
+ self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn)
+ self.ff_norm = nn.LayerNorm(dim)
+
+ def forward(
+ self,
+ hidden_states,
+ encoder_hidden_states=None,
+ attention_mask=None,
+ video_length=None,
+ ):
+ for attention_block, norm in zip(self.attention_blocks, self.norms):
+ norm_hidden_states = norm(hidden_states)
+ hidden_states = (
+ attention_block(
+ norm_hidden_states,
+ encoder_hidden_states=encoder_hidden_states
+ if attention_block.is_cross_attention
+ else None,
+ video_length=video_length,
+ )
+ + hidden_states
+ )
+
+ hidden_states = self.ff(self.ff_norm(hidden_states)) + hidden_states
+
+ output = hidden_states
+ return output
+
+
+class PositionalEncoding(nn.Module):
+ def __init__(self, d_model, dropout=0.0, max_len=24):
+ super().__init__()
+ self.dropout = nn.Dropout(p=dropout)
+ position = torch.arange(max_len).unsqueeze(1)
+ div_term = torch.exp(
+ torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model)
+ )
+ pe = torch.zeros(1, max_len, d_model)
+ pe[0, :, 0::2] = torch.sin(position * div_term)
+ pe[0, :, 1::2] = torch.cos(position * div_term)
+ self.register_buffer("pe", pe)
+
+ def forward(self, x):
+ x = x + self.pe[:, : x.size(1)]
+ return self.dropout(x)
+
+
+class VersatileAttention(Attention):
+ def __init__(
+ self,
+ attention_mode=None,
+ cross_frame_attention_mode=None,
+ temporal_position_encoding=False,
+ temporal_position_encoding_max_len=24,
+ *args,
+ **kwargs,
+ ):
+ super().__init__(*args, **kwargs)
+ assert attention_mode == "Temporal"
+
+ self.attention_mode = attention_mode
+ self.is_cross_attention = kwargs["cross_attention_dim"] is not None
+
+ self.pos_encoder = (
+ PositionalEncoding(
+ kwargs["query_dim"],
+ dropout=0.0,
+ max_len=temporal_position_encoding_max_len,
+ )
+ if (temporal_position_encoding and attention_mode == "Temporal")
+ else None
+ )
+
+ def extra_repr(self):
+ return f"(Module Info) Attention_Mode: {self.attention_mode}, Is_Cross_Attention: {self.is_cross_attention}"
+
+ def set_use_memory_efficient_attention_xformers(
+ self,
+ use_memory_efficient_attention_xformers: bool,
+ attention_op: Optional[Callable] = None,
+ ):
+ if use_memory_efficient_attention_xformers:
+ if not is_xformers_available():
+ raise ModuleNotFoundError(
+ (
+ "Refer to https://github.com/facebookresearch/xformers for more information on how to install"
+ " xformers"
+ ),
+ name="xformers",
+ )
+ elif not torch.cuda.is_available():
+ raise ValueError(
+ "torch.cuda.is_available() should be True but is False. xformers' memory efficient attention is"
+ " only available for GPU "
+ )
+ else:
+ try:
+ # Make sure we can run the memory efficient attention
+ _ = xformers.ops.memory_efficient_attention(
+ torch.randn((1, 2, 40), device="cuda"),
+ torch.randn((1, 2, 40), device="cuda"),
+ torch.randn((1, 2, 40), device="cuda"),
+ )
+ except Exception as e:
+ raise e
+
+ # XFormersAttnProcessor corrupts video generation and work with Pytorch 1.13.
+ # Pytorch 2.0.1 AttnProcessor works the same as XFormersAttnProcessor in Pytorch 1.13.
+ # You don't need XFormersAttnProcessor here.
+ # processor = XFormersAttnProcessor(
+ # attention_op=attention_op,
+ # )
+ processor = AttnProcessor()
+ else:
+ processor = AttnProcessor()
+
+ self.set_processor(processor)
+
+ def forward(
+ self,
+ hidden_states,
+ encoder_hidden_states=None,
+ attention_mask=None,
+ video_length=None,
+ **cross_attention_kwargs,
+ ):
+ if self.attention_mode == "Temporal":
+ d = hidden_states.shape[1] # d means HxW
+ hidden_states = rearrange(
+ hidden_states, "(b f) d c -> (b d) f c", f=video_length
+ )
+
+ if self.pos_encoder is not None:
+ hidden_states = self.pos_encoder(hidden_states)
+
+ encoder_hidden_states = (
+ repeat(encoder_hidden_states, "b n c -> (b d) n c", d=d)
+ if encoder_hidden_states is not None
+ else encoder_hidden_states
+ )
+
+ else:
+ raise NotImplementedError
+
+ hidden_states = self.processor(
+ self,
+ hidden_states,
+ encoder_hidden_states=encoder_hidden_states,
+ attention_mask=attention_mask,
+ **cross_attention_kwargs,
+ )
+
+ if self.attention_mode == "Temporal":
+ hidden_states = rearrange(hidden_states, "(b d) f c -> (b f) d c", d=d)
+
+ return hidden_states
diff --git a/src/models/mutual_self_attention.py b/src/models/mutual_self_attention.py
new file mode 100644
index 0000000000000000000000000000000000000000..a4d5543a2a526733ae77a5c33a75f8a99c7b2c33
--- /dev/null
+++ b/src/models/mutual_self_attention.py
@@ -0,0 +1,365 @@
+# Adapted from https://github.com/magic-research/magic-animate/blob/main/magicanimate/models/mutual_self_attention.py
+from typing import Any, Dict, Optional
+
+import torch
+from einops import rearrange
+
+from src.models.attention import TemporalBasicTransformerBlock
+
+from .attention import BasicTransformerBlock
+
+
+def torch_dfs(model: torch.nn.Module):
+ result = [model]
+ for child in model.children():
+ result += torch_dfs(child)
+ return result
+
+
+class ReferenceAttentionControl:
+ def __init__(
+ self,
+ unet,
+ mode="write",
+ do_classifier_free_guidance=False,
+ attention_auto_machine_weight=float("inf"),
+ gn_auto_machine_weight=1.0,
+ style_fidelity=1.0,
+ reference_attn=True,
+ reference_adain=False,
+ fusion_blocks="midup",
+ batch_size=1,
+ ) -> None:
+ # 10. Modify self attention and group norm
+ self.unet = unet
+ assert mode in ["read", "write"]
+ assert fusion_blocks in ["midup", "full"]
+ self.reference_attn = reference_attn
+ self.reference_adain = reference_adain
+ self.fusion_blocks = fusion_blocks
+ self.register_reference_hooks(
+ mode,
+ do_classifier_free_guidance,
+ attention_auto_machine_weight,
+ gn_auto_machine_weight,
+ style_fidelity,
+ reference_attn,
+ reference_adain,
+ fusion_blocks,
+ batch_size=batch_size,
+ )
+
+ def register_reference_hooks(
+ self,
+ mode,
+ do_classifier_free_guidance,
+ attention_auto_machine_weight,
+ gn_auto_machine_weight,
+ style_fidelity,
+ reference_attn,
+ reference_adain,
+ dtype=torch.float16,
+ batch_size=1,
+ num_images_per_prompt=1,
+ device=torch.device("cpu"),
+ fusion_blocks="midup",
+ ):
+ MODE = mode
+ do_classifier_free_guidance = do_classifier_free_guidance
+ attention_auto_machine_weight = attention_auto_machine_weight
+ gn_auto_machine_weight = gn_auto_machine_weight
+ style_fidelity = style_fidelity
+ reference_attn = reference_attn
+ reference_adain = reference_adain
+ fusion_blocks = fusion_blocks
+ num_images_per_prompt = num_images_per_prompt
+ dtype = dtype
+ if do_classifier_free_guidance:
+ uc_mask = (
+ torch.Tensor(
+ [1] * batch_size * num_images_per_prompt * 16
+ + [0] * batch_size * num_images_per_prompt * 16
+ )
+ .to(device)
+ .bool()
+ )
+ else:
+ uc_mask = (
+ torch.Tensor([0] * batch_size * num_images_per_prompt * 2)
+ .to(device)
+ .bool()
+ )
+
+ def hacked_basic_transformer_inner_forward(
+ self,
+ hidden_states: torch.FloatTensor,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ encoder_hidden_states: Optional[torch.FloatTensor] = None,
+ encoder_attention_mask: Optional[torch.FloatTensor] = None,
+ timestep: Optional[torch.LongTensor] = None,
+ cross_attention_kwargs: Dict[str, Any] = None,
+ class_labels: Optional[torch.LongTensor] = None,
+ video_length=None,
+ self_attention_additional_feats=None,
+ mode=None,
+ ):
+ if self.use_ada_layer_norm: # False
+ norm_hidden_states = self.norm1(hidden_states, timestep)
+ elif self.use_ada_layer_norm_zero:
+ (
+ norm_hidden_states,
+ gate_msa,
+ shift_mlp,
+ scale_mlp,
+ gate_mlp,
+ ) = self.norm1(
+ hidden_states,
+ timestep,
+ class_labels,
+ hidden_dtype=hidden_states.dtype,
+ )
+ else:
+ norm_hidden_states = self.norm1(hidden_states)
+
+ # 1. Self-Attention
+ # self.only_cross_attention = False
+ cross_attention_kwargs = (
+ cross_attention_kwargs if cross_attention_kwargs is not None else {}
+ )
+ if self.only_cross_attention:
+ attn_output = self.attn1(
+ norm_hidden_states,
+ encoder_hidden_states=encoder_hidden_states
+ if self.only_cross_attention
+ else None,
+ attention_mask=attention_mask,
+ **cross_attention_kwargs,
+ )
+ else:
+ if MODE == "write":
+ self.bank.append(norm_hidden_states.clone())
+ attn_output = self.attn1(
+ norm_hidden_states,
+ encoder_hidden_states=encoder_hidden_states
+ if self.only_cross_attention
+ else None,
+ attention_mask=attention_mask,
+ **cross_attention_kwargs,
+ )
+ if MODE == "read":
+ bank_fea = [
+ rearrange(
+ d.unsqueeze(1).repeat(1, video_length, 1, 1),
+ "b t l c -> (b t) l c",
+ )
+ for d in self.bank
+ ]
+ modify_norm_hidden_states = torch.cat(
+ [norm_hidden_states] + bank_fea, dim=1
+ )
+ hidden_states_uc = (
+ self.attn1(
+ norm_hidden_states,
+ encoder_hidden_states=modify_norm_hidden_states,
+ attention_mask=attention_mask,
+ )
+ + hidden_states
+ )
+ if do_classifier_free_guidance:
+ hidden_states_c = hidden_states_uc.clone()
+ _uc_mask = uc_mask.clone()
+ if hidden_states.shape[0] != _uc_mask.shape[0]:
+ _uc_mask = (
+ torch.Tensor(
+ [1] * (hidden_states.shape[0] // 2)
+ + [0] * (hidden_states.shape[0] // 2)
+ )
+ .to(device)
+ .bool()
+ )
+ hidden_states_c[_uc_mask] = (
+ self.attn1(
+ norm_hidden_states[_uc_mask],
+ encoder_hidden_states=norm_hidden_states[_uc_mask],
+ attention_mask=attention_mask,
+ )
+ + hidden_states[_uc_mask]
+ )
+ hidden_states = hidden_states_c.clone()
+ else:
+ hidden_states = hidden_states_uc
+
+ # self.bank.clear()
+ if self.attn2 is not None:
+ # Cross-Attention
+ norm_hidden_states = (
+ self.norm2(hidden_states, timestep)
+ if self.use_ada_layer_norm
+ else self.norm2(hidden_states)
+ )
+ hidden_states = (
+ self.attn2(
+ norm_hidden_states,
+ encoder_hidden_states=encoder_hidden_states,
+ attention_mask=attention_mask,
+ )
+ + hidden_states
+ )
+
+ # Feed-forward
+ hidden_states = self.ff(self.norm3(hidden_states)) + hidden_states
+
+ # Temporal-Attention
+ if self.unet_use_temporal_attention:
+ d = hidden_states.shape[1]
+ hidden_states = rearrange(
+ hidden_states, "(b f) d c -> (b d) f c", f=video_length
+ )
+ norm_hidden_states = (
+ self.norm_temp(hidden_states, timestep)
+ if self.use_ada_layer_norm
+ else self.norm_temp(hidden_states)
+ )
+ hidden_states = (
+ self.attn_temp(norm_hidden_states) + hidden_states
+ )
+ hidden_states = rearrange(
+ hidden_states, "(b d) f c -> (b f) d c", d=d
+ )
+
+ return hidden_states
+
+ if self.use_ada_layer_norm_zero:
+ attn_output = gate_msa.unsqueeze(1) * attn_output
+ hidden_states = attn_output + hidden_states
+
+ if self.attn2 is not None:
+ norm_hidden_states = (
+ self.norm2(hidden_states, timestep)
+ if self.use_ada_layer_norm
+ else self.norm2(hidden_states)
+ )
+
+ # 2. Cross-Attention
+ attn_output = self.attn2(
+ norm_hidden_states,
+ encoder_hidden_states=encoder_hidden_states,
+ attention_mask=encoder_attention_mask,
+ **cross_attention_kwargs,
+ )
+ hidden_states = attn_output + hidden_states
+
+ # 3. Feed-forward
+ norm_hidden_states = self.norm3(hidden_states)
+
+ if self.use_ada_layer_norm_zero:
+ norm_hidden_states = (
+ norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
+ )
+
+ ff_output = self.ff(norm_hidden_states)
+
+ if self.use_ada_layer_norm_zero:
+ ff_output = gate_mlp.unsqueeze(1) * ff_output
+
+ hidden_states = ff_output + hidden_states
+
+ return hidden_states
+
+ if self.reference_attn:
+ if self.fusion_blocks == "midup":
+ attn_modules = [
+ module
+ for module in (
+ torch_dfs(self.unet.mid_block) + torch_dfs(self.unet.up_blocks)
+ )
+ if isinstance(module, BasicTransformerBlock)
+ or isinstance(module, TemporalBasicTransformerBlock)
+ ]
+ elif self.fusion_blocks == "full":
+ attn_modules = [
+ module
+ for module in torch_dfs(self.unet)
+ if isinstance(module, BasicTransformerBlock)
+ or isinstance(module, TemporalBasicTransformerBlock)
+ ]
+ attn_modules = sorted(
+ attn_modules, key=lambda x: -x.norm1.normalized_shape[0]
+ )
+
+ for i, module in enumerate(attn_modules):
+ module._original_inner_forward = module.forward
+ if isinstance(module, BasicTransformerBlock):
+ module.forward = hacked_basic_transformer_inner_forward.__get__(
+ module, BasicTransformerBlock
+ )
+ if isinstance(module, TemporalBasicTransformerBlock):
+ module.forward = hacked_basic_transformer_inner_forward.__get__(
+ module, TemporalBasicTransformerBlock
+ )
+
+ module.bank = []
+ module.attn_weight = float(i) / float(len(attn_modules))
+
+ def update(self, writer, dtype=torch.float16):
+ if self.reference_attn:
+ if self.fusion_blocks == "midup":
+ reader_attn_modules = [
+ module
+ for module in (
+ torch_dfs(self.unet.mid_block) + torch_dfs(self.unet.up_blocks)
+ )
+ if isinstance(module, TemporalBasicTransformerBlock)
+ ]
+ writer_attn_modules = [
+ module
+ for module in (
+ torch_dfs(writer.unet.mid_block)
+ + torch_dfs(writer.unet.up_blocks)
+ )
+ if isinstance(module, BasicTransformerBlock)
+ ]
+ elif self.fusion_blocks == "full":
+ reader_attn_modules = [
+ module
+ for module in torch_dfs(self.unet)
+ if isinstance(module, TemporalBasicTransformerBlock)
+ ]
+ writer_attn_modules = [
+ module
+ for module in torch_dfs(writer.unet)
+ if isinstance(module, BasicTransformerBlock)
+ ]
+ reader_attn_modules = sorted(
+ reader_attn_modules, key=lambda x: -x.norm1.normalized_shape[0]
+ )
+ writer_attn_modules = sorted(
+ writer_attn_modules, key=lambda x: -x.norm1.normalized_shape[0]
+ )
+ for r, w in zip(reader_attn_modules, writer_attn_modules):
+ r.bank = [v.clone().to(dtype) for v in w.bank]
+ # w.bank.clear()
+
+ def clear(self):
+ if self.reference_attn:
+ if self.fusion_blocks == "midup":
+ reader_attn_modules = [
+ module
+ for module in (
+ torch_dfs(self.unet.mid_block) + torch_dfs(self.unet.up_blocks)
+ )
+ if isinstance(module, BasicTransformerBlock)
+ or isinstance(module, TemporalBasicTransformerBlock)
+ ]
+ elif self.fusion_blocks == "full":
+ reader_attn_modules = [
+ module
+ for module in torch_dfs(self.unet)
+ if isinstance(module, BasicTransformerBlock)
+ or isinstance(module, TemporalBasicTransformerBlock)
+ ]
+ reader_attn_modules = sorted(
+ reader_attn_modules, key=lambda x: -x.norm1.normalized_shape[0]
+ )
+ for r in reader_attn_modules:
+ r.bank.clear()
diff --git a/src/models/pose_guider.py b/src/models/pose_guider.py
new file mode 100644
index 0000000000000000000000000000000000000000..f022c90817e2c401e2f4cb738c0a19b27286c259
--- /dev/null
+++ b/src/models/pose_guider.py
@@ -0,0 +1,57 @@
+from typing import Tuple
+
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.nn.init as init
+from diffusers.models.modeling_utils import ModelMixin
+
+from src.models.motion_module import zero_module
+from src.models.resnet import InflatedConv3d
+
+
+class PoseGuider(ModelMixin):
+ def __init__(
+ self,
+ conditioning_embedding_channels: int,
+ conditioning_channels: int = 3,
+ block_out_channels: Tuple[int] = (16, 32, 64, 128),
+ ):
+ super().__init__()
+ self.conv_in = InflatedConv3d(
+ conditioning_channels, block_out_channels[0], kernel_size=3, padding=1
+ )
+
+ self.blocks = nn.ModuleList([])
+
+ for i in range(len(block_out_channels) - 1):
+ channel_in = block_out_channels[i]
+ channel_out = block_out_channels[i + 1]
+ self.blocks.append(
+ InflatedConv3d(channel_in, channel_in, kernel_size=3, padding=1)
+ )
+ self.blocks.append(
+ InflatedConv3d(
+ channel_in, channel_out, kernel_size=3, padding=1, stride=2
+ )
+ )
+
+ self.conv_out = zero_module(
+ InflatedConv3d(
+ block_out_channels[-1],
+ conditioning_embedding_channels,
+ kernel_size=3,
+ padding=1,
+ )
+ )
+
+ def forward(self, conditioning):
+ embedding = self.conv_in(conditioning)
+ embedding = F.silu(embedding)
+
+ for block in self.blocks:
+ embedding = block(embedding)
+ embedding = F.silu(embedding)
+
+ embedding = self.conv_out(embedding)
+
+ return embedding
diff --git a/src/models/resnet.py b/src/models/resnet.py
new file mode 100644
index 0000000000000000000000000000000000000000..b489aee2f28a13954809827b1f2a0e825b893e2e
--- /dev/null
+++ b/src/models/resnet.py
@@ -0,0 +1,252 @@
+# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/resnet.py
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange
+
+
+class InflatedConv3d(nn.Conv2d):
+ def forward(self, x):
+ video_length = x.shape[2]
+
+ x = rearrange(x, "b c f h w -> (b f) c h w")
+ x = super().forward(x)
+ x = rearrange(x, "(b f) c h w -> b c f h w", f=video_length)
+
+ return x
+
+
+class InflatedGroupNorm(nn.GroupNorm):
+ def forward(self, x):
+ video_length = x.shape[2]
+
+ x = rearrange(x, "b c f h w -> (b f) c h w")
+ x = super().forward(x)
+ x = rearrange(x, "(b f) c h w -> b c f h w", f=video_length)
+
+ return x
+
+
+class Upsample3D(nn.Module):
+ def __init__(
+ self,
+ channels,
+ use_conv=False,
+ use_conv_transpose=False,
+ out_channels=None,
+ name="conv",
+ ):
+ super().__init__()
+ self.channels = channels
+ self.out_channels = out_channels or channels
+ self.use_conv = use_conv
+ self.use_conv_transpose = use_conv_transpose
+ self.name = name
+
+ conv = None
+ if use_conv_transpose:
+ raise NotImplementedError
+ elif use_conv:
+ self.conv = InflatedConv3d(self.channels, self.out_channels, 3, padding=1)
+
+ def forward(self, hidden_states, output_size=None):
+ assert hidden_states.shape[1] == self.channels
+
+ if self.use_conv_transpose:
+ raise NotImplementedError
+
+ # Cast to float32 to as 'upsample_nearest2d_out_frame' op does not support bfloat16
+ dtype = hidden_states.dtype
+ if dtype == torch.bfloat16:
+ hidden_states = hidden_states.to(torch.float32)
+
+ # upsample_nearest_nhwc fails with large batch sizes. see https://github.com/huggingface/diffusers/issues/984
+ if hidden_states.shape[0] >= 64:
+ hidden_states = hidden_states.contiguous()
+
+ # if `output_size` is passed we force the interpolation output
+ # size and do not make use of `scale_factor=2`
+ if output_size is None:
+ hidden_states = F.interpolate(
+ hidden_states, scale_factor=[1.0, 2.0, 2.0], mode="nearest"
+ )
+ else:
+ hidden_states = F.interpolate(
+ hidden_states, size=output_size, mode="nearest"
+ )
+
+ # If the input is bfloat16, we cast back to bfloat16
+ if dtype == torch.bfloat16:
+ hidden_states = hidden_states.to(dtype)
+
+ # if self.use_conv:
+ # if self.name == "conv":
+ # hidden_states = self.conv(hidden_states)
+ # else:
+ # hidden_states = self.Conv2d_0(hidden_states)
+ hidden_states = self.conv(hidden_states)
+
+ return hidden_states
+
+
+class Downsample3D(nn.Module):
+ def __init__(
+ self, channels, use_conv=False, out_channels=None, padding=1, name="conv"
+ ):
+ super().__init__()
+ self.channels = channels
+ self.out_channels = out_channels or channels
+ self.use_conv = use_conv
+ self.padding = padding
+ stride = 2
+ self.name = name
+
+ if use_conv:
+ self.conv = InflatedConv3d(
+ self.channels, self.out_channels, 3, stride=stride, padding=padding
+ )
+ else:
+ raise NotImplementedError
+
+ def forward(self, hidden_states):
+ assert hidden_states.shape[1] == self.channels
+ if self.use_conv and self.padding == 0:
+ raise NotImplementedError
+
+ assert hidden_states.shape[1] == self.channels
+ hidden_states = self.conv(hidden_states)
+
+ return hidden_states
+
+
+class ResnetBlock3D(nn.Module):
+ def __init__(
+ self,
+ *,
+ in_channels,
+ out_channels=None,
+ conv_shortcut=False,
+ dropout=0.0,
+ temb_channels=512,
+ groups=32,
+ groups_out=None,
+ pre_norm=True,
+ eps=1e-6,
+ non_linearity="swish",
+ time_embedding_norm="default",
+ output_scale_factor=1.0,
+ use_in_shortcut=None,
+ use_inflated_groupnorm=None,
+ ):
+ super().__init__()
+ self.pre_norm = pre_norm
+ self.pre_norm = True
+ self.in_channels = in_channels
+ out_channels = in_channels if out_channels is None else out_channels
+ self.out_channels = out_channels
+ self.use_conv_shortcut = conv_shortcut
+ self.time_embedding_norm = time_embedding_norm
+ self.output_scale_factor = output_scale_factor
+
+ if groups_out is None:
+ groups_out = groups
+
+ assert use_inflated_groupnorm != None
+ if use_inflated_groupnorm:
+ self.norm1 = InflatedGroupNorm(
+ num_groups=groups, num_channels=in_channels, eps=eps, affine=True
+ )
+ else:
+ self.norm1 = torch.nn.GroupNorm(
+ num_groups=groups, num_channels=in_channels, eps=eps, affine=True
+ )
+
+ self.conv1 = InflatedConv3d(
+ in_channels, out_channels, kernel_size=3, stride=1, padding=1
+ )
+
+ if temb_channels is not None:
+ if self.time_embedding_norm == "default":
+ time_emb_proj_out_channels = out_channels
+ elif self.time_embedding_norm == "scale_shift":
+ time_emb_proj_out_channels = out_channels * 2
+ else:
+ raise ValueError(
+ f"unknown time_embedding_norm : {self.time_embedding_norm} "
+ )
+
+ self.time_emb_proj = torch.nn.Linear(
+ temb_channels, time_emb_proj_out_channels
+ )
+ else:
+ self.time_emb_proj = None
+
+ if use_inflated_groupnorm:
+ self.norm2 = InflatedGroupNorm(
+ num_groups=groups_out, num_channels=out_channels, eps=eps, affine=True
+ )
+ else:
+ self.norm2 = torch.nn.GroupNorm(
+ num_groups=groups_out, num_channels=out_channels, eps=eps, affine=True
+ )
+ self.dropout = torch.nn.Dropout(dropout)
+ self.conv2 = InflatedConv3d(
+ out_channels, out_channels, kernel_size=3, stride=1, padding=1
+ )
+
+ if non_linearity == "swish":
+ self.nonlinearity = lambda x: F.silu(x)
+ elif non_linearity == "mish":
+ self.nonlinearity = Mish()
+ elif non_linearity == "silu":
+ self.nonlinearity = nn.SiLU()
+
+ self.use_in_shortcut = (
+ self.in_channels != self.out_channels
+ if use_in_shortcut is None
+ else use_in_shortcut
+ )
+
+ self.conv_shortcut = None
+ if self.use_in_shortcut:
+ self.conv_shortcut = InflatedConv3d(
+ in_channels, out_channels, kernel_size=1, stride=1, padding=0
+ )
+
+ def forward(self, input_tensor, temb):
+ hidden_states = input_tensor
+
+ hidden_states = self.norm1(hidden_states)
+ hidden_states = self.nonlinearity(hidden_states)
+
+ hidden_states = self.conv1(hidden_states)
+
+ if temb is not None:
+ temb = self.time_emb_proj(self.nonlinearity(temb))[:, :, None, None, None]
+
+ if temb is not None and self.time_embedding_norm == "default":
+ hidden_states = hidden_states + temb
+
+ hidden_states = self.norm2(hidden_states)
+
+ if temb is not None and self.time_embedding_norm == "scale_shift":
+ scale, shift = torch.chunk(temb, 2, dim=1)
+ hidden_states = hidden_states * (1 + scale) + shift
+
+ hidden_states = self.nonlinearity(hidden_states)
+
+ hidden_states = self.dropout(hidden_states)
+ hidden_states = self.conv2(hidden_states)
+
+ if self.conv_shortcut is not None:
+ input_tensor = self.conv_shortcut(input_tensor)
+
+ output_tensor = (input_tensor + hidden_states) / self.output_scale_factor
+
+ return output_tensor
+
+
+class Mish(torch.nn.Module):
+ def forward(self, hidden_states):
+ return hidden_states * torch.tanh(torch.nn.functional.softplus(hidden_states))
diff --git a/src/models/transformer_2d.py b/src/models/transformer_2d.py
new file mode 100644
index 0000000000000000000000000000000000000000..f1f66e948bf31f8aca870fff0225b9194b429fb0
--- /dev/null
+++ b/src/models/transformer_2d.py
@@ -0,0 +1,396 @@
+# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformer_2d.py
+from dataclasses import dataclass
+from typing import Any, Dict, Optional
+
+import torch
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.models.embeddings import CaptionProjection
+from diffusers.models.lora import LoRACompatibleConv, LoRACompatibleLinear
+from diffusers.models.modeling_utils import ModelMixin
+from diffusers.models.normalization import AdaLayerNormSingle
+from diffusers.utils import USE_PEFT_BACKEND, BaseOutput, deprecate, is_torch_version
+from torch import nn
+
+from .attention import BasicTransformerBlock
+
+
+@dataclass
+class Transformer2DModelOutput(BaseOutput):
+ """
+ The output of [`Transformer2DModel`].
+
+ Args:
+ sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` or `(batch size, num_vector_embeds - 1, num_latent_pixels)` if [`Transformer2DModel`] is discrete):
+ The hidden states output conditioned on the `encoder_hidden_states` input. If discrete, returns probability
+ distributions for the unnoised latent pixels.
+ """
+
+ sample: torch.FloatTensor
+ ref_feature: torch.FloatTensor
+
+
+class Transformer2DModel(ModelMixin, ConfigMixin):
+ """
+ A 2D Transformer model for image-like data.
+
+ Parameters:
+ num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention.
+ attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head.
+ in_channels (`int`, *optional*):
+ The number of channels in the input and output (specify if the input is **continuous**).
+ num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use.
+ dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
+ cross_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use.
+ sample_size (`int`, *optional*): The width of the latent images (specify if the input is **discrete**).
+ This is fixed during training since it is used to learn a number of position embeddings.
+ num_vector_embeds (`int`, *optional*):
+ The number of classes of the vector embeddings of the latent pixels (specify if the input is **discrete**).
+ Includes the class for the masked latent pixel.
+ activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to use in feed-forward.
+ num_embeds_ada_norm ( `int`, *optional*):
+ The number of diffusion steps used during training. Pass if at least one of the norm_layers is
+ `AdaLayerNorm`. This is fixed during training since it is used to learn a number of embeddings that are
+ added to the hidden states.
+
+ During inference, you can denoise for up to but not more steps than `num_embeds_ada_norm`.
+ attention_bias (`bool`, *optional*):
+ Configure if the `TransformerBlocks` attention should contain a bias parameter.
+ """
+
+ _supports_gradient_checkpointing = True
+
+ @register_to_config
+ def __init__(
+ self,
+ num_attention_heads: int = 16,
+ attention_head_dim: int = 88,
+ in_channels: Optional[int] = None,
+ out_channels: Optional[int] = None,
+ num_layers: int = 1,
+ dropout: float = 0.0,
+ norm_num_groups: int = 32,
+ cross_attention_dim: Optional[int] = None,
+ attention_bias: bool = False,
+ sample_size: Optional[int] = None,
+ num_vector_embeds: Optional[int] = None,
+ patch_size: Optional[int] = None,
+ activation_fn: str = "geglu",
+ num_embeds_ada_norm: Optional[int] = None,
+ use_linear_projection: bool = False,
+ only_cross_attention: bool = False,
+ double_self_attention: bool = False,
+ upcast_attention: bool = False,
+ norm_type: str = "layer_norm",
+ norm_elementwise_affine: bool = True,
+ norm_eps: float = 1e-5,
+ attention_type: str = "default",
+ caption_channels: int = None,
+ ):
+ super().__init__()
+ self.use_linear_projection = use_linear_projection
+ self.num_attention_heads = num_attention_heads
+ self.attention_head_dim = attention_head_dim
+ inner_dim = num_attention_heads * attention_head_dim
+
+ conv_cls = nn.Conv2d if USE_PEFT_BACKEND else LoRACompatibleConv
+ linear_cls = nn.Linear if USE_PEFT_BACKEND else LoRACompatibleLinear
+
+ # 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`
+ # Define whether input is continuous or discrete depending on configuration
+ self.is_input_continuous = (in_channels is not None) and (patch_size is None)
+ self.is_input_vectorized = num_vector_embeds is not None
+ self.is_input_patches = in_channels is not None and patch_size is not None
+
+ if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
+ deprecation_message = (
+ f"The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or"
+ " incorrectly set to `'layer_norm'`.Make sure to set `norm_type` to `'ada_norm'` in the config."
+ " Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect"
+ " results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it"
+ " would be very nice if you could open a Pull request for the `transformer/config.json` file"
+ )
+ deprecate(
+ "norm_type!=num_embeds_ada_norm",
+ "1.0.0",
+ deprecation_message,
+ standard_warn=False,
+ )
+ norm_type = "ada_norm"
+
+ if self.is_input_continuous and self.is_input_vectorized:
+ raise ValueError(
+ f"Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make"
+ " sure that either `in_channels` or `num_vector_embeds` is None."
+ )
+ elif self.is_input_vectorized and self.is_input_patches:
+ raise ValueError(
+ f"Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make"
+ " sure that either `num_vector_embeds` or `num_patches` is None."
+ )
+ elif (
+ not self.is_input_continuous
+ and not self.is_input_vectorized
+ and not self.is_input_patches
+ ):
+ raise ValueError(
+ f"Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:"
+ f" {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None."
+ )
+
+ # 2. Define input layers
+ self.in_channels = in_channels
+
+ self.norm = torch.nn.GroupNorm(
+ num_groups=norm_num_groups,
+ num_channels=in_channels,
+ eps=1e-6,
+ affine=True,
+ )
+ if use_linear_projection:
+ self.proj_in = linear_cls(in_channels, inner_dim)
+ else:
+ self.proj_in = conv_cls(
+ in_channels, inner_dim, kernel_size=1, stride=1, padding=0
+ )
+
+ # 3. Define transformers blocks
+ self.transformer_blocks = nn.ModuleList(
+ [
+ BasicTransformerBlock(
+ inner_dim,
+ num_attention_heads,
+ attention_head_dim,
+ dropout=dropout,
+ cross_attention_dim=cross_attention_dim,
+ activation_fn=activation_fn,
+ num_embeds_ada_norm=num_embeds_ada_norm,
+ attention_bias=attention_bias,
+ only_cross_attention=only_cross_attention,
+ double_self_attention=double_self_attention,
+ upcast_attention=upcast_attention,
+ norm_type=norm_type,
+ norm_elementwise_affine=norm_elementwise_affine,
+ norm_eps=norm_eps,
+ attention_type=attention_type,
+ )
+ for d in range(num_layers)
+ ]
+ )
+
+ # 4. Define output layers
+ self.out_channels = in_channels if out_channels is None else out_channels
+ # TODO: should use out_channels for continuous projections
+ if use_linear_projection:
+ self.proj_out = linear_cls(inner_dim, in_channels)
+ else:
+ self.proj_out = conv_cls(
+ inner_dim, in_channels, kernel_size=1, stride=1, padding=0
+ )
+
+ # 5. PixArt-Alpha blocks.
+ self.adaln_single = None
+ self.use_additional_conditions = False
+ if norm_type == "ada_norm_single":
+ self.use_additional_conditions = self.config.sample_size == 128
+ # TODO(Sayak, PVP) clean this, for now we use sample size to determine whether to use
+ # additional conditions until we find better name
+ self.adaln_single = AdaLayerNormSingle(
+ inner_dim, use_additional_conditions=self.use_additional_conditions
+ )
+
+ self.caption_projection = None
+ if caption_channels is not None:
+ self.caption_projection = CaptionProjection(
+ in_features=caption_channels, hidden_size=inner_dim
+ )
+
+ self.gradient_checkpointing = False
+
+ def _set_gradient_checkpointing(self, module, value=False):
+ if hasattr(module, "gradient_checkpointing"):
+ module.gradient_checkpointing = value
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ encoder_hidden_states: Optional[torch.Tensor] = None,
+ timestep: Optional[torch.LongTensor] = None,
+ added_cond_kwargs: Dict[str, torch.Tensor] = None,
+ class_labels: Optional[torch.LongTensor] = None,
+ cross_attention_kwargs: Dict[str, Any] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ encoder_attention_mask: Optional[torch.Tensor] = None,
+ return_dict: bool = True,
+ ):
+ """
+ The [`Transformer2DModel`] forward method.
+
+ Args:
+ hidden_states (`torch.LongTensor` of shape `(batch size, num latent pixels)` if discrete, `torch.FloatTensor` of shape `(batch size, channel, height, width)` if continuous):
+ Input `hidden_states`.
+ encoder_hidden_states ( `torch.FloatTensor` of shape `(batch size, sequence len, embed dims)`, *optional*):
+ Conditional embeddings for cross attention layer. If not given, cross-attention defaults to
+ self-attention.
+ timestep ( `torch.LongTensor`, *optional*):
+ Used to indicate denoising step. Optional timestep to be applied as an embedding in `AdaLayerNorm`.
+ class_labels ( `torch.LongTensor` of shape `(batch size, num classes)`, *optional*):
+ Used to indicate class labels conditioning. Optional class labels to be applied as an embedding in
+ `AdaLayerZeroNorm`.
+ cross_attention_kwargs ( `Dict[str, Any]`, *optional*):
+ A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+ `self.processor` in
+ [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+ attention_mask ( `torch.Tensor`, *optional*):
+ An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask
+ is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large
+ negative values to the attention scores corresponding to "discard" tokens.
+ encoder_attention_mask ( `torch.Tensor`, *optional*):
+ Cross-attention mask applied to `encoder_hidden_states`. Two formats supported:
+
+ * Mask `(batch, sequence_length)` True = keep, False = discard.
+ * Bias `(batch, 1, sequence_length)` 0 = keep, -10000 = discard.
+
+ If `ndim == 2`: will be interpreted as a mask, then converted into a bias consistent with the format
+ above. This bias will be added to the cross-attention scores.
+ return_dict (`bool`, *optional*, defaults to `True`):
+ Whether or not to return a [`~models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain
+ tuple.
+
+ Returns:
+ If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
+ `tuple` where the first element is the sample tensor.
+ """
+ # ensure attention_mask is a bias, and give it a singleton query_tokens dimension.
+ # we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward.
+ # we can tell by counting dims; if ndim == 2: it's a mask rather than a bias.
+ # expects mask of shape:
+ # [batch, key_tokens]
+ # adds singleton query_tokens dimension:
+ # [batch, 1, key_tokens]
+ # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
+ # [batch, heads, query_tokens, key_tokens] (e.g. torch sdp attn)
+ # [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
+ if attention_mask is not None and attention_mask.ndim == 2:
+ # assume that mask is expressed as:
+ # (1 = keep, 0 = discard)
+ # convert mask into a bias that can be added to attention scores:
+ # (keep = +0, discard = -10000.0)
+ attention_mask = (1 - attention_mask.to(hidden_states.dtype)) * -10000.0
+ attention_mask = attention_mask.unsqueeze(1)
+
+ # convert encoder_attention_mask to a bias the same way we do for attention_mask
+ if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2:
+ encoder_attention_mask = (
+ 1 - encoder_attention_mask.to(hidden_states.dtype)
+ ) * -10000.0
+ encoder_attention_mask = encoder_attention_mask.unsqueeze(1)
+
+ # Retrieve lora scale.
+ lora_scale = (
+ cross_attention_kwargs.get("scale", 1.0)
+ if cross_attention_kwargs is not None
+ else 1.0
+ )
+
+ # 1. Input
+ batch, _, height, width = hidden_states.shape
+ residual = hidden_states
+
+ hidden_states = self.norm(hidden_states)
+ if not self.use_linear_projection:
+ hidden_states = (
+ self.proj_in(hidden_states, scale=lora_scale)
+ if not USE_PEFT_BACKEND
+ else self.proj_in(hidden_states)
+ )
+ inner_dim = hidden_states.shape[1]
+ hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(
+ batch, height * width, inner_dim
+ )
+ else:
+ inner_dim = hidden_states.shape[1]
+ hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(
+ batch, height * width, inner_dim
+ )
+ hidden_states = (
+ self.proj_in(hidden_states, scale=lora_scale)
+ if not USE_PEFT_BACKEND
+ else self.proj_in(hidden_states)
+ )
+
+ # 2. Blocks
+ if self.caption_projection is not None:
+ batch_size = hidden_states.shape[0]
+ encoder_hidden_states = self.caption_projection(encoder_hidden_states)
+ encoder_hidden_states = encoder_hidden_states.view(
+ batch_size, -1, hidden_states.shape[-1]
+ )
+
+ ref_feature = hidden_states.reshape(batch, height, width, inner_dim)
+ for block in self.transformer_blocks:
+ if self.training and self.gradient_checkpointing:
+
+ def create_custom_forward(module, return_dict=None):
+ def custom_forward(*inputs):
+ if return_dict is not None:
+ return module(*inputs, return_dict=return_dict)
+ else:
+ return module(*inputs)
+
+ return custom_forward
+
+ ckpt_kwargs: Dict[str, Any] = (
+ {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
+ )
+ hidden_states = torch.utils.checkpoint.checkpoint(
+ create_custom_forward(block),
+ hidden_states,
+ attention_mask,
+ encoder_hidden_states,
+ encoder_attention_mask,
+ timestep,
+ cross_attention_kwargs,
+ class_labels,
+ **ckpt_kwargs,
+ )
+ else:
+ hidden_states = block(
+ hidden_states,
+ attention_mask=attention_mask,
+ encoder_hidden_states=encoder_hidden_states,
+ encoder_attention_mask=encoder_attention_mask,
+ timestep=timestep,
+ cross_attention_kwargs=cross_attention_kwargs,
+ class_labels=class_labels,
+ )
+
+ # 3. Output
+ if self.is_input_continuous:
+ if not self.use_linear_projection:
+ hidden_states = (
+ hidden_states.reshape(batch, height, width, inner_dim)
+ .permute(0, 3, 1, 2)
+ .contiguous()
+ )
+ hidden_states = (
+ self.proj_out(hidden_states, scale=lora_scale)
+ if not USE_PEFT_BACKEND
+ else self.proj_out(hidden_states)
+ )
+ else:
+ hidden_states = (
+ self.proj_out(hidden_states, scale=lora_scale)
+ if not USE_PEFT_BACKEND
+ else self.proj_out(hidden_states)
+ )
+ hidden_states = (
+ hidden_states.reshape(batch, height, width, inner_dim)
+ .permute(0, 3, 1, 2)
+ .contiguous()
+ )
+
+ output = hidden_states + residual
+ if not return_dict:
+ return (output, ref_feature)
+
+ return Transformer2DModelOutput(sample=output, ref_feature=ref_feature)
diff --git a/src/models/transformer_3d.py b/src/models/transformer_3d.py
new file mode 100644
index 0000000000000000000000000000000000000000..63367fa844b1ae52f3039ea8042c5a3247ff8cef
--- /dev/null
+++ b/src/models/transformer_3d.py
@@ -0,0 +1,202 @@
+from dataclasses import dataclass
+from typing import Optional
+
+import torch
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.models import ModelMixin
+from diffusers.utils import BaseOutput
+from diffusers.utils.import_utils import is_xformers_available
+from einops import rearrange, repeat
+from torch import nn
+
+from .attention import TemporalBasicTransformerBlock
+
+
+@dataclass
+class Transformer3DModelOutput(BaseOutput):
+ sample: torch.FloatTensor
+
+
+if is_xformers_available():
+ import xformers
+ import xformers.ops
+else:
+ xformers = None
+
+
+class Transformer3DModel(ModelMixin, ConfigMixin):
+ _supports_gradient_checkpointing = True
+
+ @register_to_config
+ def __init__(
+ self,
+ num_attention_heads: int = 16,
+ attention_head_dim: int = 88,
+ in_channels: Optional[int] = None,
+ num_layers: int = 1,
+ dropout: float = 0.0,
+ norm_num_groups: int = 32,
+ cross_attention_dim: Optional[int] = None,
+ attention_bias: bool = False,
+ activation_fn: str = "geglu",
+ num_embeds_ada_norm: Optional[int] = None,
+ use_linear_projection: bool = False,
+ only_cross_attention: bool = False,
+ upcast_attention: bool = False,
+ unet_use_cross_frame_attention=None,
+ unet_use_temporal_attention=None,
+ name=None,
+ ):
+ super().__init__()
+ self.use_linear_projection = use_linear_projection
+ self.num_attention_heads = num_attention_heads
+ self.attention_head_dim = attention_head_dim
+ inner_dim = num_attention_heads * attention_head_dim
+
+ # Define input layers
+ self.in_channels = in_channels
+ self.name=name
+
+ self.norm = torch.nn.GroupNorm(
+ num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True
+ )
+ if use_linear_projection:
+ self.proj_in = nn.Linear(in_channels, inner_dim)
+ else:
+ self.proj_in = nn.Conv2d(
+ in_channels, inner_dim, kernel_size=1, stride=1, padding=0
+ )
+
+ # Define transformers blocks
+ self.transformer_blocks = nn.ModuleList(
+ [
+ TemporalBasicTransformerBlock(
+ inner_dim,
+ num_attention_heads,
+ attention_head_dim,
+ dropout=dropout,
+ cross_attention_dim=cross_attention_dim,
+ activation_fn=activation_fn,
+ num_embeds_ada_norm=num_embeds_ada_norm,
+ attention_bias=attention_bias,
+ only_cross_attention=only_cross_attention,
+ upcast_attention=upcast_attention,
+ unet_use_cross_frame_attention=unet_use_cross_frame_attention,
+ unet_use_temporal_attention=unet_use_temporal_attention,
+ name=f"{self.name}_{d}_TransformerBlock" if self.name else None,
+ )
+ for d in range(num_layers)
+ ]
+ )
+
+ # 4. Define output layers
+ if use_linear_projection:
+ self.proj_out = nn.Linear(in_channels, inner_dim)
+ else:
+ self.proj_out = nn.Conv2d(
+ inner_dim, in_channels, kernel_size=1, stride=1, padding=0
+ )
+
+ self.gradient_checkpointing = False
+
+ def _set_gradient_checkpointing(self, module, value=False):
+ if hasattr(module, "gradient_checkpointing"):
+ module.gradient_checkpointing = value
+
+ def forward(
+ self,
+ hidden_states,
+ encoder_hidden_states=None,
+ self_attention_additional_feats=None,
+ mode=None,
+ timestep=None,
+ return_dict: bool = True,
+ ):
+ # Input
+ assert (
+ hidden_states.dim() == 5
+ ), f"Expected hidden_states to have ndim=5, but got ndim={hidden_states.dim()}."
+ video_length = hidden_states.shape[2]
+ hidden_states = rearrange(hidden_states, "b c f h w -> (b f) c h w")
+ if encoder_hidden_states.shape[0] != hidden_states.shape[0]:
+ encoder_hidden_states = repeat(
+ encoder_hidden_states, "b n c -> (b f) n c", f=video_length
+ )
+
+ batch, channel, height, weight = hidden_states.shape
+ residual = hidden_states
+
+ hidden_states = self.norm(hidden_states)
+ if not self.use_linear_projection:
+ hidden_states = self.proj_in(hidden_states)
+ inner_dim = hidden_states.shape[1]
+ hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(
+ batch, height * weight, inner_dim
+ )
+ else:
+ inner_dim = hidden_states.shape[1]
+ hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(
+ batch, height * weight, inner_dim
+ )
+ hidden_states = self.proj_in(hidden_states)
+
+ # Blocks
+ for i, block in enumerate(self.transformer_blocks):
+
+ if self.training and self.gradient_checkpointing:
+
+ def create_custom_forward(module, return_dict=None):
+ def custom_forward(*inputs):
+ if return_dict is not None:
+ return module(*inputs, return_dict=return_dict)
+ else:
+ return module(*inputs)
+
+ return custom_forward
+
+ # if hasattr(self.block, 'bank') and len(self.block.bank) > 0:
+ # hidden_states
+ hidden_states = torch.utils.checkpoint.checkpoint(
+ create_custom_forward(block),
+ hidden_states,
+ encoder_hidden_states=encoder_hidden_states,
+ timestep=timestep,
+ attention_mask=None,
+ video_length=video_length,
+ self_attention_additional_feats=self_attention_additional_feats,
+ mode=mode,
+ )
+ else:
+
+ hidden_states = block(
+ hidden_states,
+ encoder_hidden_states=encoder_hidden_states,
+ timestep=timestep,
+ self_attention_additional_feats=self_attention_additional_feats,
+ mode=mode,
+ video_length=video_length,
+ )
+
+ # Output
+ if not self.use_linear_projection:
+ hidden_states = (
+ hidden_states.reshape(batch, height, weight, inner_dim)
+ .permute(0, 3, 1, 2)
+ .contiguous()
+ )
+ hidden_states = self.proj_out(hidden_states)
+ else:
+ hidden_states = self.proj_out(hidden_states)
+ hidden_states = (
+ hidden_states.reshape(batch, height, weight, inner_dim)
+ .permute(0, 3, 1, 2)
+ .contiguous()
+ )
+
+ output = hidden_states + residual
+
+ output = rearrange(output, "(b f) c h w -> b c f h w", f=video_length)
+ if not return_dict:
+ return (output,)
+
+ return Transformer3DModelOutput(sample=output)
diff --git a/src/models/unet_2d_blocks.py b/src/models/unet_2d_blocks.py
new file mode 100644
index 0000000000000000000000000000000000000000..fd3d607f92f810baa053153a3c6192f6c2241f19
--- /dev/null
+++ b/src/models/unet_2d_blocks.py
@@ -0,0 +1,1074 @@
+# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unet_2d_blocks.py
+from typing import Any, Dict, Optional, Tuple, Union
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from diffusers.models.activations import get_activation
+from diffusers.models.attention_processor import Attention
+from diffusers.models.dual_transformer_2d import DualTransformer2DModel
+from diffusers.models.resnet import Downsample2D, ResnetBlock2D, Upsample2D
+from diffusers.utils import is_torch_version, logging
+from diffusers.utils.torch_utils import apply_freeu
+from torch import nn
+
+from .transformer_2d import Transformer2DModel
+
+logger = logging.get_logger(__name__) # pylint: disable=invalid-name
+
+
+def get_down_block(
+ down_block_type: str,
+ num_layers: int,
+ in_channels: int,
+ out_channels: int,
+ temb_channels: int,
+ add_downsample: bool,
+ resnet_eps: float,
+ resnet_act_fn: str,
+ transformer_layers_per_block: int = 1,
+ num_attention_heads: Optional[int] = None,
+ resnet_groups: Optional[int] = None,
+ cross_attention_dim: Optional[int] = None,
+ downsample_padding: Optional[int] = None,
+ dual_cross_attention: bool = False,
+ use_linear_projection: bool = False,
+ only_cross_attention: bool = False,
+ upcast_attention: bool = False,
+ resnet_time_scale_shift: str = "default",
+ attention_type: str = "default",
+ resnet_skip_time_act: bool = False,
+ resnet_out_scale_factor: float = 1.0,
+ cross_attention_norm: Optional[str] = None,
+ attention_head_dim: Optional[int] = None,
+ downsample_type: Optional[str] = None,
+ dropout: float = 0.0,
+):
+ # If attn head dim is not defined, we default it to the number of heads
+ if attention_head_dim is None:
+ logger.warn(
+ f"It is recommended to provide `attention_head_dim` when calling `get_down_block`. Defaulting `attention_head_dim` to {num_attention_heads}."
+ )
+ attention_head_dim = num_attention_heads
+
+ down_block_type = (
+ down_block_type[7:]
+ if down_block_type.startswith("UNetRes")
+ else down_block_type
+ )
+ if down_block_type == "DownBlock2D":
+ return DownBlock2D(
+ num_layers=num_layers,
+ in_channels=in_channels,
+ out_channels=out_channels,
+ temb_channels=temb_channels,
+ dropout=dropout,
+ add_downsample=add_downsample,
+ resnet_eps=resnet_eps,
+ resnet_act_fn=resnet_act_fn,
+ resnet_groups=resnet_groups,
+ downsample_padding=downsample_padding,
+ resnet_time_scale_shift=resnet_time_scale_shift,
+ )
+ elif down_block_type == "CrossAttnDownBlock2D":
+ if cross_attention_dim is None:
+ raise ValueError(
+ "cross_attention_dim must be specified for CrossAttnDownBlock2D"
+ )
+ return CrossAttnDownBlock2D(
+ num_layers=num_layers,
+ transformer_layers_per_block=transformer_layers_per_block,
+ in_channels=in_channels,
+ out_channels=out_channels,
+ temb_channels=temb_channels,
+ dropout=dropout,
+ add_downsample=add_downsample,
+ resnet_eps=resnet_eps,
+ resnet_act_fn=resnet_act_fn,
+ resnet_groups=resnet_groups,
+ downsample_padding=downsample_padding,
+ cross_attention_dim=cross_attention_dim,
+ num_attention_heads=num_attention_heads,
+ dual_cross_attention=dual_cross_attention,
+ use_linear_projection=use_linear_projection,
+ only_cross_attention=only_cross_attention,
+ upcast_attention=upcast_attention,
+ resnet_time_scale_shift=resnet_time_scale_shift,
+ attention_type=attention_type,
+ )
+ raise ValueError(f"{down_block_type} does not exist.")
+
+
+def get_up_block(
+ up_block_type: str,
+ num_layers: int,
+ in_channels: int,
+ out_channels: int,
+ prev_output_channel: int,
+ temb_channels: int,
+ add_upsample: bool,
+ resnet_eps: float,
+ resnet_act_fn: str,
+ resolution_idx: Optional[int] = None,
+ transformer_layers_per_block: int = 1,
+ num_attention_heads: Optional[int] = None,
+ resnet_groups: Optional[int] = None,
+ cross_attention_dim: Optional[int] = None,
+ dual_cross_attention: bool = False,
+ use_linear_projection: bool = False,
+ only_cross_attention: bool = False,
+ upcast_attention: bool = False,
+ resnet_time_scale_shift: str = "default",
+ attention_type: str = "default",
+ resnet_skip_time_act: bool = False,
+ resnet_out_scale_factor: float = 1.0,
+ cross_attention_norm: Optional[str] = None,
+ attention_head_dim: Optional[int] = None,
+ upsample_type: Optional[str] = None,
+ dropout: float = 0.0,
+) -> nn.Module:
+ # If attn head dim is not defined, we default it to the number of heads
+ if attention_head_dim is None:
+ logger.warn(
+ f"It is recommended to provide `attention_head_dim` when calling `get_up_block`. Defaulting `attention_head_dim` to {num_attention_heads}."
+ )
+ attention_head_dim = num_attention_heads
+
+ up_block_type = (
+ up_block_type[7:] if up_block_type.startswith("UNetRes") else up_block_type
+ )
+ if up_block_type == "UpBlock2D":
+ return UpBlock2D(
+ num_layers=num_layers,
+ in_channels=in_channels,
+ out_channels=out_channels,
+ prev_output_channel=prev_output_channel,
+ temb_channels=temb_channels,
+ resolution_idx=resolution_idx,
+ dropout=dropout,
+ add_upsample=add_upsample,
+ resnet_eps=resnet_eps,
+ resnet_act_fn=resnet_act_fn,
+ resnet_groups=resnet_groups,
+ resnet_time_scale_shift=resnet_time_scale_shift,
+ )
+ elif up_block_type == "CrossAttnUpBlock2D":
+ if cross_attention_dim is None:
+ raise ValueError(
+ "cross_attention_dim must be specified for CrossAttnUpBlock2D"
+ )
+ return CrossAttnUpBlock2D(
+ num_layers=num_layers,
+ transformer_layers_per_block=transformer_layers_per_block,
+ in_channels=in_channels,
+ out_channels=out_channels,
+ prev_output_channel=prev_output_channel,
+ temb_channels=temb_channels,
+ resolution_idx=resolution_idx,
+ dropout=dropout,
+ add_upsample=add_upsample,
+ resnet_eps=resnet_eps,
+ resnet_act_fn=resnet_act_fn,
+ resnet_groups=resnet_groups,
+ cross_attention_dim=cross_attention_dim,
+ num_attention_heads=num_attention_heads,
+ dual_cross_attention=dual_cross_attention,
+ use_linear_projection=use_linear_projection,
+ only_cross_attention=only_cross_attention,
+ upcast_attention=upcast_attention,
+ resnet_time_scale_shift=resnet_time_scale_shift,
+ attention_type=attention_type,
+ )
+
+ raise ValueError(f"{up_block_type} does not exist.")
+
+
+class AutoencoderTinyBlock(nn.Module):
+ """
+ Tiny Autoencoder block used in [`AutoencoderTiny`]. It is a mini residual module consisting of plain conv + ReLU
+ blocks.
+
+ Args:
+ in_channels (`int`): The number of input channels.
+ out_channels (`int`): The number of output channels.
+ act_fn (`str`):
+ ` The activation function to use. Supported values are `"swish"`, `"mish"`, `"gelu"`, and `"relu"`.
+
+ Returns:
+ `torch.FloatTensor`: A tensor with the same shape as the input tensor, but with the number of channels equal to
+ `out_channels`.
+ """
+
+ def __init__(self, in_channels: int, out_channels: int, act_fn: str):
+ super().__init__()
+ act_fn = get_activation(act_fn)
+ self.conv = nn.Sequential(
+ nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
+ act_fn,
+ nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
+ act_fn,
+ nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
+ )
+ self.skip = (
+ nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False)
+ if in_channels != out_channels
+ else nn.Identity()
+ )
+ self.fuse = nn.ReLU()
+
+ def forward(self, x: torch.FloatTensor) -> torch.FloatTensor:
+ return self.fuse(self.conv(x) + self.skip(x))
+
+
+class UNetMidBlock2D(nn.Module):
+ """
+ A 2D UNet mid-block [`UNetMidBlock2D`] with multiple residual blocks and optional attention blocks.
+
+ Args:
+ in_channels (`int`): The number of input channels.
+ temb_channels (`int`): The number of temporal embedding channels.
+ dropout (`float`, *optional*, defaults to 0.0): The dropout rate.
+ num_layers (`int`, *optional*, defaults to 1): The number of residual blocks.
+ resnet_eps (`float`, *optional*, 1e-6 ): The epsilon value for the resnet blocks.
+ resnet_time_scale_shift (`str`, *optional*, defaults to `default`):
+ The type of normalization to apply to the time embeddings. This can help to improve the performance of the
+ model on tasks with long-range temporal dependencies.
+ resnet_act_fn (`str`, *optional*, defaults to `swish`): The activation function for the resnet blocks.
+ resnet_groups (`int`, *optional*, defaults to 32):
+ The number of groups to use in the group normalization layers of the resnet blocks.
+ attn_groups (`Optional[int]`, *optional*, defaults to None): The number of groups for the attention blocks.
+ resnet_pre_norm (`bool`, *optional*, defaults to `True`):
+ Whether to use pre-normalization for the resnet blocks.
+ add_attention (`bool`, *optional*, defaults to `True`): Whether to add attention blocks.
+ attention_head_dim (`int`, *optional*, defaults to 1):
+ Dimension of a single attention head. The number of attention heads is determined based on this value and
+ the number of input channels.
+ output_scale_factor (`float`, *optional*, defaults to 1.0): The output scale factor.
+
+ Returns:
+ `torch.FloatTensor`: The output of the last residual block, which is a tensor of shape `(batch_size,
+ in_channels, height, width)`.
+
+ """
+
+ def __init__(
+ self,
+ in_channels: int,
+ temb_channels: int,
+ dropout: float = 0.0,
+ num_layers: int = 1,
+ resnet_eps: float = 1e-6,
+ resnet_time_scale_shift: str = "default", # default, spatial
+ resnet_act_fn: str = "swish",
+ resnet_groups: int = 32,
+ attn_groups: Optional[int] = None,
+ resnet_pre_norm: bool = True,
+ add_attention: bool = True,
+ attention_head_dim: int = 1,
+ output_scale_factor: float = 1.0,
+ ):
+ super().__init__()
+ resnet_groups = (
+ resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)
+ )
+ self.add_attention = add_attention
+
+ if attn_groups is None:
+ attn_groups = (
+ resnet_groups if resnet_time_scale_shift == "default" else None
+ )
+
+ # there is always at least one resnet
+ resnets = [
+ ResnetBlock2D(
+ in_channels=in_channels,
+ out_channels=in_channels,
+ temb_channels=temb_channels,
+ eps=resnet_eps,
+ groups=resnet_groups,
+ dropout=dropout,
+ time_embedding_norm=resnet_time_scale_shift,
+ non_linearity=resnet_act_fn,
+ output_scale_factor=output_scale_factor,
+ pre_norm=resnet_pre_norm,
+ )
+ ]
+ attentions = []
+
+ if attention_head_dim is None:
+ logger.warn(
+ f"It is not recommend to pass `attention_head_dim=None`. Defaulting `attention_head_dim` to `in_channels`: {in_channels}."
+ )
+ attention_head_dim = in_channels
+
+ for _ in range(num_layers):
+ if self.add_attention:
+ attentions.append(
+ Attention(
+ in_channels,
+ heads=in_channels // attention_head_dim,
+ dim_head=attention_head_dim,
+ rescale_output_factor=output_scale_factor,
+ eps=resnet_eps,
+ norm_num_groups=attn_groups,
+ spatial_norm_dim=temb_channels
+ if resnet_time_scale_shift == "spatial"
+ else None,
+ residual_connection=True,
+ bias=True,
+ upcast_softmax=True,
+ _from_deprecated_attn_block=True,
+ )
+ )
+ else:
+ attentions.append(None)
+
+ resnets.append(
+ ResnetBlock2D(
+ in_channels=in_channels,
+ out_channels=in_channels,
+ temb_channels=temb_channels,
+ eps=resnet_eps,
+ groups=resnet_groups,
+ dropout=dropout,
+ time_embedding_norm=resnet_time_scale_shift,
+ non_linearity=resnet_act_fn,
+ output_scale_factor=output_scale_factor,
+ pre_norm=resnet_pre_norm,
+ )
+ )
+
+ self.attentions = nn.ModuleList(attentions)
+ self.resnets = nn.ModuleList(resnets)
+
+ def forward(
+ self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None
+ ) -> torch.FloatTensor:
+ hidden_states = self.resnets[0](hidden_states, temb)
+ for attn, resnet in zip(self.attentions, self.resnets[1:]):
+ if attn is not None:
+ hidden_states = attn(hidden_states, temb=temb)
+ hidden_states = resnet(hidden_states, temb)
+
+ return hidden_states
+
+
+class UNetMidBlock2DCrossAttn(nn.Module):
+ def __init__(
+ self,
+ in_channels: int,
+ temb_channels: int,
+ dropout: float = 0.0,
+ num_layers: int = 1,
+ transformer_layers_per_block: Union[int, Tuple[int]] = 1,
+ resnet_eps: float = 1e-6,
+ resnet_time_scale_shift: str = "default",
+ resnet_act_fn: str = "swish",
+ resnet_groups: int = 32,
+ resnet_pre_norm: bool = True,
+ num_attention_heads: int = 1,
+ output_scale_factor: float = 1.0,
+ cross_attention_dim: int = 1280,
+ dual_cross_attention: bool = False,
+ use_linear_projection: bool = False,
+ upcast_attention: bool = False,
+ attention_type: str = "default",
+ ):
+ super().__init__()
+
+ self.has_cross_attention = True
+ self.num_attention_heads = num_attention_heads
+ resnet_groups = (
+ resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)
+ )
+
+ # support for variable transformer layers per block
+ if isinstance(transformer_layers_per_block, int):
+ transformer_layers_per_block = [transformer_layers_per_block] * num_layers
+
+ # there is always at least one resnet
+ resnets = [
+ ResnetBlock2D(
+ in_channels=in_channels,
+ out_channels=in_channels,
+ temb_channels=temb_channels,
+ eps=resnet_eps,
+ groups=resnet_groups,
+ dropout=dropout,
+ time_embedding_norm=resnet_time_scale_shift,
+ non_linearity=resnet_act_fn,
+ output_scale_factor=output_scale_factor,
+ pre_norm=resnet_pre_norm,
+ )
+ ]
+ attentions = []
+
+ for i in range(num_layers):
+ if not dual_cross_attention:
+ attentions.append(
+ Transformer2DModel(
+ num_attention_heads,
+ in_channels // num_attention_heads,
+ in_channels=in_channels,
+ num_layers=transformer_layers_per_block[i],
+ cross_attention_dim=cross_attention_dim,
+ norm_num_groups=resnet_groups,
+ use_linear_projection=use_linear_projection,
+ upcast_attention=upcast_attention,
+ attention_type=attention_type,
+ )
+ )
+ else:
+ attentions.append(
+ DualTransformer2DModel(
+ num_attention_heads,
+ in_channels // num_attention_heads,
+ in_channels=in_channels,
+ num_layers=1,
+ cross_attention_dim=cross_attention_dim,
+ norm_num_groups=resnet_groups,
+ )
+ )
+ resnets.append(
+ ResnetBlock2D(
+ in_channels=in_channels,
+ out_channels=in_channels,
+ temb_channels=temb_channels,
+ eps=resnet_eps,
+ groups=resnet_groups,
+ dropout=dropout,
+ time_embedding_norm=resnet_time_scale_shift,
+ non_linearity=resnet_act_fn,
+ output_scale_factor=output_scale_factor,
+ pre_norm=resnet_pre_norm,
+ )
+ )
+
+ self.attentions = nn.ModuleList(attentions)
+ self.resnets = nn.ModuleList(resnets)
+
+ self.gradient_checkpointing = False
+
+ def forward(
+ self,
+ hidden_states: torch.FloatTensor,
+ temb: Optional[torch.FloatTensor] = None,
+ encoder_hidden_states: Optional[torch.FloatTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+ encoder_attention_mask: Optional[torch.FloatTensor] = None,
+ ) -> torch.FloatTensor:
+ lora_scale = (
+ cross_attention_kwargs.get("scale", 1.0)
+ if cross_attention_kwargs is not None
+ else 1.0
+ )
+ hidden_states = self.resnets[0](hidden_states, temb, scale=lora_scale)
+ for attn, resnet in zip(self.attentions, self.resnets[1:]):
+ if self.training and self.gradient_checkpointing:
+
+ def create_custom_forward(module, return_dict=None):
+ def custom_forward(*inputs):
+ if return_dict is not None:
+ return module(*inputs, return_dict=return_dict)
+ else:
+ return module(*inputs)
+
+ return custom_forward
+
+ ckpt_kwargs: Dict[str, Any] = (
+ {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
+ )
+ hidden_states, ref_feature = attn(
+ hidden_states,
+ encoder_hidden_states=encoder_hidden_states,
+ cross_attention_kwargs=cross_attention_kwargs,
+ attention_mask=attention_mask,
+ encoder_attention_mask=encoder_attention_mask,
+ return_dict=False,
+ )
+ hidden_states = torch.utils.checkpoint.checkpoint(
+ create_custom_forward(resnet),
+ hidden_states,
+ temb,
+ **ckpt_kwargs,
+ )
+ else:
+ hidden_states, ref_feature = attn(
+ hidden_states,
+ encoder_hidden_states=encoder_hidden_states,
+ cross_attention_kwargs=cross_attention_kwargs,
+ attention_mask=attention_mask,
+ encoder_attention_mask=encoder_attention_mask,
+ return_dict=False,
+ )
+ hidden_states = resnet(hidden_states, temb, scale=lora_scale)
+
+ return hidden_states
+
+
+class CrossAttnDownBlock2D(nn.Module):
+ def __init__(
+ self,
+ in_channels: int,
+ out_channels: int,
+ temb_channels: int,
+ dropout: float = 0.0,
+ num_layers: int = 1,
+ transformer_layers_per_block: Union[int, Tuple[int]] = 1,
+ resnet_eps: float = 1e-6,
+ resnet_time_scale_shift: str = "default",
+ resnet_act_fn: str = "swish",
+ resnet_groups: int = 32,
+ resnet_pre_norm: bool = True,
+ num_attention_heads: int = 1,
+ cross_attention_dim: int = 1280,
+ output_scale_factor: float = 1.0,
+ downsample_padding: int = 1,
+ add_downsample: bool = True,
+ dual_cross_attention: bool = False,
+ use_linear_projection: bool = False,
+ only_cross_attention: bool = False,
+ upcast_attention: bool = False,
+ attention_type: str = "default",
+ ):
+ super().__init__()
+ resnets = []
+ attentions = []
+
+ self.has_cross_attention = True
+ self.num_attention_heads = num_attention_heads
+ if isinstance(transformer_layers_per_block, int):
+ transformer_layers_per_block = [transformer_layers_per_block] * num_layers
+
+ for i in range(num_layers):
+ in_channels = in_channels if i == 0 else out_channels
+ resnets.append(
+ ResnetBlock2D(
+ in_channels=in_channels,
+ out_channels=out_channels,
+ temb_channels=temb_channels,
+ eps=resnet_eps,
+ groups=resnet_groups,
+ dropout=dropout,
+ time_embedding_norm=resnet_time_scale_shift,
+ non_linearity=resnet_act_fn,
+ output_scale_factor=output_scale_factor,
+ pre_norm=resnet_pre_norm,
+ )
+ )
+ if not dual_cross_attention:
+ attentions.append(
+ Transformer2DModel(
+ num_attention_heads,
+ out_channels // num_attention_heads,
+ in_channels=out_channels,
+ num_layers=transformer_layers_per_block[i],
+ cross_attention_dim=cross_attention_dim,
+ norm_num_groups=resnet_groups,
+ use_linear_projection=use_linear_projection,
+ only_cross_attention=only_cross_attention,
+ upcast_attention=upcast_attention,
+ attention_type=attention_type,
+ )
+ )
+ else:
+ attentions.append(
+ DualTransformer2DModel(
+ num_attention_heads,
+ out_channels // num_attention_heads,
+ in_channels=out_channels,
+ num_layers=1,
+ cross_attention_dim=cross_attention_dim,
+ norm_num_groups=resnet_groups,
+ )
+ )
+ self.attentions = nn.ModuleList(attentions)
+ self.resnets = nn.ModuleList(resnets)
+
+ if add_downsample:
+ self.downsamplers = nn.ModuleList(
+ [
+ Downsample2D(
+ out_channels,
+ use_conv=True,
+ out_channels=out_channels,
+ padding=downsample_padding,
+ name="op",
+ )
+ ]
+ )
+ else:
+ self.downsamplers = None
+
+ self.gradient_checkpointing = False
+
+ def forward(
+ self,
+ hidden_states: torch.FloatTensor,
+ temb: Optional[torch.FloatTensor] = None,
+ encoder_hidden_states: Optional[torch.FloatTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+ encoder_attention_mask: Optional[torch.FloatTensor] = None,
+ additional_residuals: Optional[torch.FloatTensor] = None,
+ ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]:
+ output_states = ()
+
+ lora_scale = (
+ cross_attention_kwargs.get("scale", 1.0)
+ if cross_attention_kwargs is not None
+ else 1.0
+ )
+
+ blocks = list(zip(self.resnets, self.attentions))
+
+ for i, (resnet, attn) in enumerate(blocks):
+ if self.training and self.gradient_checkpointing:
+
+ def create_custom_forward(module, return_dict=None):
+ def custom_forward(*inputs):
+ if return_dict is not None:
+ return module(*inputs, return_dict=return_dict)
+ else:
+ return module(*inputs)
+
+ return custom_forward
+
+ ckpt_kwargs: Dict[str, Any] = (
+ {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
+ )
+ hidden_states = torch.utils.checkpoint.checkpoint(
+ create_custom_forward(resnet),
+ hidden_states,
+ temb,
+ **ckpt_kwargs,
+ )
+ hidden_states, ref_feature = attn(
+ hidden_states,
+ encoder_hidden_states=encoder_hidden_states,
+ cross_attention_kwargs=cross_attention_kwargs,
+ attention_mask=attention_mask,
+ encoder_attention_mask=encoder_attention_mask,
+ return_dict=False,
+ )
+ else:
+ hidden_states = resnet(hidden_states, temb, scale=lora_scale)
+ hidden_states, ref_feature = attn(
+ hidden_states,
+ encoder_hidden_states=encoder_hidden_states,
+ cross_attention_kwargs=cross_attention_kwargs,
+ attention_mask=attention_mask,
+ encoder_attention_mask=encoder_attention_mask,
+ return_dict=False,
+ )
+
+ # apply additional residuals to the output of the last pair of resnet and attention blocks
+ if i == len(blocks) - 1 and additional_residuals is not None:
+ hidden_states = hidden_states + additional_residuals
+
+ output_states = output_states + (hidden_states,)
+
+ if self.downsamplers is not None:
+ for downsampler in self.downsamplers:
+ hidden_states = downsampler(hidden_states, scale=lora_scale)
+
+ output_states = output_states + (hidden_states,)
+
+ return hidden_states, output_states
+
+
+class DownBlock2D(nn.Module):
+ def __init__(
+ self,
+ in_channels: int,
+ out_channels: int,
+ temb_channels: int,
+ dropout: float = 0.0,
+ num_layers: int = 1,
+ resnet_eps: float = 1e-6,
+ resnet_time_scale_shift: str = "default",
+ resnet_act_fn: str = "swish",
+ resnet_groups: int = 32,
+ resnet_pre_norm: bool = True,
+ output_scale_factor: float = 1.0,
+ add_downsample: bool = True,
+ downsample_padding: int = 1,
+ ):
+ super().__init__()
+ resnets = []
+
+ for i in range(num_layers):
+ in_channels = in_channels if i == 0 else out_channels
+ resnets.append(
+ ResnetBlock2D(
+ in_channels=in_channels,
+ out_channels=out_channels,
+ temb_channels=temb_channels,
+ eps=resnet_eps,
+ groups=resnet_groups,
+ dropout=dropout,
+ time_embedding_norm=resnet_time_scale_shift,
+ non_linearity=resnet_act_fn,
+ output_scale_factor=output_scale_factor,
+ pre_norm=resnet_pre_norm,
+ )
+ )
+
+ self.resnets = nn.ModuleList(resnets)
+
+ if add_downsample:
+ self.downsamplers = nn.ModuleList(
+ [
+ Downsample2D(
+ out_channels,
+ use_conv=True,
+ out_channels=out_channels,
+ padding=downsample_padding,
+ name="op",
+ )
+ ]
+ )
+ else:
+ self.downsamplers = None
+
+ self.gradient_checkpointing = False
+
+ def forward(
+ self,
+ hidden_states: torch.FloatTensor,
+ temb: Optional[torch.FloatTensor] = None,
+ scale: float = 1.0,
+ ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]:
+ output_states = ()
+
+ for resnet in self.resnets:
+ if self.training and self.gradient_checkpointing:
+
+ def create_custom_forward(module):
+ def custom_forward(*inputs):
+ return module(*inputs)
+
+ return custom_forward
+
+ if is_torch_version(">=", "1.11.0"):
+ hidden_states = torch.utils.checkpoint.checkpoint(
+ create_custom_forward(resnet),
+ hidden_states,
+ temb,
+ use_reentrant=False,
+ )
+ else:
+ hidden_states = torch.utils.checkpoint.checkpoint(
+ create_custom_forward(resnet), hidden_states, temb
+ )
+ else:
+ hidden_states = resnet(hidden_states, temb, scale=scale)
+
+ output_states = output_states + (hidden_states,)
+
+ if self.downsamplers is not None:
+ for downsampler in self.downsamplers:
+ hidden_states = downsampler(hidden_states, scale=scale)
+
+ output_states = output_states + (hidden_states,)
+
+ return hidden_states, output_states
+
+
+class CrossAttnUpBlock2D(nn.Module):
+ def __init__(
+ self,
+ in_channels: int,
+ out_channels: int,
+ prev_output_channel: int,
+ temb_channels: int,
+ resolution_idx: Optional[int] = None,
+ dropout: float = 0.0,
+ num_layers: int = 1,
+ transformer_layers_per_block: Union[int, Tuple[int]] = 1,
+ resnet_eps: float = 1e-6,
+ resnet_time_scale_shift: str = "default",
+ resnet_act_fn: str = "swish",
+ resnet_groups: int = 32,
+ resnet_pre_norm: bool = True,
+ num_attention_heads: int = 1,
+ cross_attention_dim: int = 1280,
+ output_scale_factor: float = 1.0,
+ add_upsample: bool = True,
+ dual_cross_attention: bool = False,
+ use_linear_projection: bool = False,
+ only_cross_attention: bool = False,
+ upcast_attention: bool = False,
+ attention_type: str = "default",
+ ):
+ super().__init__()
+ resnets = []
+ attentions = []
+
+ self.has_cross_attention = True
+ self.num_attention_heads = num_attention_heads
+
+ if isinstance(transformer_layers_per_block, int):
+ transformer_layers_per_block = [transformer_layers_per_block] * num_layers
+
+ for i in range(num_layers):
+ res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
+ resnet_in_channels = prev_output_channel if i == 0 else out_channels
+
+ resnets.append(
+ ResnetBlock2D(
+ in_channels=resnet_in_channels + res_skip_channels,
+ out_channels=out_channels,
+ temb_channels=temb_channels,
+ eps=resnet_eps,
+ groups=resnet_groups,
+ dropout=dropout,
+ time_embedding_norm=resnet_time_scale_shift,
+ non_linearity=resnet_act_fn,
+ output_scale_factor=output_scale_factor,
+ pre_norm=resnet_pre_norm,
+ )
+ )
+ if not dual_cross_attention:
+ attentions.append(
+ Transformer2DModel(
+ num_attention_heads,
+ out_channels // num_attention_heads,
+ in_channels=out_channels,
+ num_layers=transformer_layers_per_block[i],
+ cross_attention_dim=cross_attention_dim,
+ norm_num_groups=resnet_groups,
+ use_linear_projection=use_linear_projection,
+ only_cross_attention=only_cross_attention,
+ upcast_attention=upcast_attention,
+ attention_type=attention_type,
+ )
+ )
+ else:
+ attentions.append(
+ DualTransformer2DModel(
+ num_attention_heads,
+ out_channels // num_attention_heads,
+ in_channels=out_channels,
+ num_layers=1,
+ cross_attention_dim=cross_attention_dim,
+ norm_num_groups=resnet_groups,
+ )
+ )
+ self.attentions = nn.ModuleList(attentions)
+ self.resnets = nn.ModuleList(resnets)
+
+ if add_upsample:
+ self.upsamplers = nn.ModuleList(
+ [Upsample2D(out_channels, use_conv=True, out_channels=out_channels)]
+ )
+ else:
+ self.upsamplers = None
+
+ self.gradient_checkpointing = False
+ self.resolution_idx = resolution_idx
+
+ def forward(
+ self,
+ hidden_states: torch.FloatTensor,
+ res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],
+ temb: Optional[torch.FloatTensor] = None,
+ encoder_hidden_states: Optional[torch.FloatTensor] = None,
+ cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+ upsample_size: Optional[int] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ encoder_attention_mask: Optional[torch.FloatTensor] = None,
+ ) -> torch.FloatTensor:
+ lora_scale = (
+ cross_attention_kwargs.get("scale", 1.0)
+ if cross_attention_kwargs is not None
+ else 1.0
+ )
+ is_freeu_enabled = (
+ getattr(self, "s1", None)
+ and getattr(self, "s2", None)
+ and getattr(self, "b1", None)
+ and getattr(self, "b2", None)
+ )
+
+ for resnet, attn in zip(self.resnets, self.attentions):
+ # pop res hidden states
+ res_hidden_states = res_hidden_states_tuple[-1]
+ res_hidden_states_tuple = res_hidden_states_tuple[:-1]
+
+ # FreeU: Only operate on the first two stages
+ if is_freeu_enabled:
+ hidden_states, res_hidden_states = apply_freeu(
+ self.resolution_idx,
+ hidden_states,
+ res_hidden_states,
+ s1=self.s1,
+ s2=self.s2,
+ b1=self.b1,
+ b2=self.b2,
+ )
+
+ hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
+
+ if self.training and self.gradient_checkpointing:
+
+ def create_custom_forward(module, return_dict=None):
+ def custom_forward(*inputs):
+ if return_dict is not None:
+ return module(*inputs, return_dict=return_dict)
+ else:
+ return module(*inputs)
+
+ return custom_forward
+
+ ckpt_kwargs: Dict[str, Any] = (
+ {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
+ )
+ hidden_states = torch.utils.checkpoint.checkpoint(
+ create_custom_forward(resnet),
+ hidden_states,
+ temb,
+ **ckpt_kwargs,
+ )
+ hidden_states, ref_feature = attn(
+ hidden_states,
+ encoder_hidden_states=encoder_hidden_states,
+ cross_attention_kwargs=cross_attention_kwargs,
+ attention_mask=attention_mask,
+ encoder_attention_mask=encoder_attention_mask,
+ return_dict=False,
+ )
+ else:
+ hidden_states = resnet(hidden_states, temb, scale=lora_scale)
+ hidden_states, ref_feature = attn(
+ hidden_states,
+ encoder_hidden_states=encoder_hidden_states,
+ cross_attention_kwargs=cross_attention_kwargs,
+ attention_mask=attention_mask,
+ encoder_attention_mask=encoder_attention_mask,
+ return_dict=False,
+ )
+
+ if self.upsamplers is not None:
+ for upsampler in self.upsamplers:
+ hidden_states = upsampler(
+ hidden_states, upsample_size, scale=lora_scale
+ )
+
+ return hidden_states
+
+
+class UpBlock2D(nn.Module):
+ def __init__(
+ self,
+ in_channels: int,
+ prev_output_channel: int,
+ out_channels: int,
+ temb_channels: int,
+ resolution_idx: Optional[int] = None,
+ dropout: float = 0.0,
+ num_layers: int = 1,
+ resnet_eps: float = 1e-6,
+ resnet_time_scale_shift: str = "default",
+ resnet_act_fn: str = "swish",
+ resnet_groups: int = 32,
+ resnet_pre_norm: bool = True,
+ output_scale_factor: float = 1.0,
+ add_upsample: bool = True,
+ ):
+ super().__init__()
+ resnets = []
+
+ for i in range(num_layers):
+ res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
+ resnet_in_channels = prev_output_channel if i == 0 else out_channels
+
+ resnets.append(
+ ResnetBlock2D(
+ in_channels=resnet_in_channels + res_skip_channels,
+ out_channels=out_channels,
+ temb_channels=temb_channels,
+ eps=resnet_eps,
+ groups=resnet_groups,
+ dropout=dropout,
+ time_embedding_norm=resnet_time_scale_shift,
+ non_linearity=resnet_act_fn,
+ output_scale_factor=output_scale_factor,
+ pre_norm=resnet_pre_norm,
+ )
+ )
+
+ self.resnets = nn.ModuleList(resnets)
+
+ if add_upsample:
+ self.upsamplers = nn.ModuleList(
+ [Upsample2D(out_channels, use_conv=True, out_channels=out_channels)]
+ )
+ else:
+ self.upsamplers = None
+
+ self.gradient_checkpointing = False
+ self.resolution_idx = resolution_idx
+
+ def forward(
+ self,
+ hidden_states: torch.FloatTensor,
+ res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],
+ temb: Optional[torch.FloatTensor] = None,
+ upsample_size: Optional[int] = None,
+ scale: float = 1.0,
+ ) -> torch.FloatTensor:
+ is_freeu_enabled = (
+ getattr(self, "s1", None)
+ and getattr(self, "s2", None)
+ and getattr(self, "b1", None)
+ and getattr(self, "b2", None)
+ )
+
+ for resnet in self.resnets:
+ # pop res hidden states
+ res_hidden_states = res_hidden_states_tuple[-1]
+ res_hidden_states_tuple = res_hidden_states_tuple[:-1]
+
+ # FreeU: Only operate on the first two stages
+ if is_freeu_enabled:
+ hidden_states, res_hidden_states = apply_freeu(
+ self.resolution_idx,
+ hidden_states,
+ res_hidden_states,
+ s1=self.s1,
+ s2=self.s2,
+ b1=self.b1,
+ b2=self.b2,
+ )
+
+ hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
+
+ if self.training and self.gradient_checkpointing:
+
+ def create_custom_forward(module):
+ def custom_forward(*inputs):
+ return module(*inputs)
+
+ return custom_forward
+
+ if is_torch_version(">=", "1.11.0"):
+ hidden_states = torch.utils.checkpoint.checkpoint(
+ create_custom_forward(resnet),
+ hidden_states,
+ temb,
+ use_reentrant=False,
+ )
+ else:
+ hidden_states = torch.utils.checkpoint.checkpoint(
+ create_custom_forward(resnet), hidden_states, temb
+ )
+ else:
+ hidden_states = resnet(hidden_states, temb, scale=scale)
+
+ if self.upsamplers is not None:
+ for upsampler in self.upsamplers:
+ hidden_states = upsampler(hidden_states, upsample_size, scale=scale)
+
+ return hidden_states
diff --git a/src/models/unet_2d_condition.py b/src/models/unet_2d_condition.py
new file mode 100644
index 0000000000000000000000000000000000000000..2b77c45e2baac28e36cd60cdd478ee4fd6ce8634
--- /dev/null
+++ b/src/models/unet_2d_condition.py
@@ -0,0 +1,1308 @@
+# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unet_2d_condition.py
+from dataclasses import dataclass
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+import torch.utils.checkpoint
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.loaders import UNet2DConditionLoadersMixin
+from diffusers.models.activations import get_activation
+from diffusers.models.attention_processor import (
+ ADDED_KV_ATTENTION_PROCESSORS,
+ CROSS_ATTENTION_PROCESSORS,
+ AttentionProcessor,
+ AttnAddedKVProcessor,
+ AttnProcessor,
+)
+from diffusers.models.embeddings import (
+ GaussianFourierProjection,
+ ImageHintTimeEmbedding,
+ ImageProjection,
+ ImageTimeEmbedding,
+ PositionNet,
+ TextImageProjection,
+ TextImageTimeEmbedding,
+ TextTimeEmbedding,
+ TimestepEmbedding,
+ Timesteps,
+)
+from diffusers.models.modeling_utils import ModelMixin
+from diffusers.utils import (
+ USE_PEFT_BACKEND,
+ BaseOutput,
+ deprecate,
+ logging,
+ scale_lora_layers,
+ unscale_lora_layers,
+)
+
+from .unet_2d_blocks import (
+ UNetMidBlock2D,
+ UNetMidBlock2DCrossAttn,
+ get_down_block,
+ get_up_block,
+)
+
+logger = logging.get_logger(__name__) # pylint: disable=invalid-name
+
+
+@dataclass
+class UNet2DConditionOutput(BaseOutput):
+ """
+ The output of [`UNet2DConditionModel`].
+
+ Args:
+ sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
+ The hidden states output conditioned on `encoder_hidden_states` input. Output of last layer of model.
+ """
+
+ sample: torch.FloatTensor = None
+ ref_features: Tuple[torch.FloatTensor] = None
+
+
+class UNet2DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
+ r"""
+ A conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a sample
+ shaped output.
+
+ This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
+ for all models (such as downloading or saving).
+
+ Parameters:
+ sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`):
+ Height and width of input/output sample.
+ in_channels (`int`, *optional*, defaults to 4): Number of channels in the input sample.
+ out_channels (`int`, *optional*, defaults to 4): Number of channels in the output.
+ center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample.
+ flip_sin_to_cos (`bool`, *optional*, defaults to `False`):
+ Whether to flip the sin to cos in the time embedding.
+ freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding.
+ down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`):
+ The tuple of downsample blocks to use.
+ mid_block_type (`str`, *optional*, defaults to `"UNetMidBlock2DCrossAttn"`):
+ Block type for middle of UNet, it can be one of `UNetMidBlock2DCrossAttn`, `UNetMidBlock2D`, or
+ `UNetMidBlock2DSimpleCrossAttn`. If `None`, the mid block layer is skipped.
+ up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")`):
+ The tuple of upsample blocks to use.
+ only_cross_attention(`bool` or `Tuple[bool]`, *optional*, default to `False`):
+ Whether to include self-attention in the basic transformer blocks, see
+ [`~models.attention.BasicTransformerBlock`].
+ block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):
+ The tuple of output channels for each block.
+ layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block.
+ downsample_padding (`int`, *optional*, defaults to 1): The padding to use for the downsampling convolution.
+ mid_block_scale_factor (`float`, *optional*, defaults to 1.0): The scale factor to use for the mid block.
+ dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
+ act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
+ norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization.
+ If `None`, normalization and activation layers is skipped in post-processing.
+ norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon to use for the normalization.
+ cross_attention_dim (`int` or `Tuple[int]`, *optional*, defaults to 1280):
+ The dimension of the cross attention features.
+ transformer_layers_per_block (`int`, `Tuple[int]`, or `Tuple[Tuple]` , *optional*, defaults to 1):
+ The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for
+ [`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`],
+ [`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`].
+ reverse_transformer_layers_per_block : (`Tuple[Tuple]`, *optional*, defaults to None):
+ The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`], in the upsampling
+ blocks of the U-Net. Only relevant if `transformer_layers_per_block` is of type `Tuple[Tuple]` and for
+ [`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`],
+ [`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`].
+ encoder_hid_dim (`int`, *optional*, defaults to None):
+ If `encoder_hid_dim_type` is defined, `encoder_hidden_states` will be projected from `encoder_hid_dim`
+ dimension to `cross_attention_dim`.
+ encoder_hid_dim_type (`str`, *optional*, defaults to `None`):
+ If given, the `encoder_hidden_states` and potentially other embeddings are down-projected to text
+ embeddings of dimension `cross_attention` according to `encoder_hid_dim_type`.
+ attention_head_dim (`int`, *optional*, defaults to 8): The dimension of the attention heads.
+ num_attention_heads (`int`, *optional*):
+ The number of attention heads. If not defined, defaults to `attention_head_dim`
+ resnet_time_scale_shift (`str`, *optional*, defaults to `"default"`): Time scale shift config
+ for ResNet blocks (see [`~models.resnet.ResnetBlock2D`]). Choose from `default` or `scale_shift`.
+ class_embed_type (`str`, *optional*, defaults to `None`):
+ The type of class embedding to use which is ultimately summed with the time embeddings. Choose from `None`,
+ `"timestep"`, `"identity"`, `"projection"`, or `"simple_projection"`.
+ addition_embed_type (`str`, *optional*, defaults to `None`):
+ Configures an optional embedding which will be summed with the time embeddings. Choose from `None` or
+ "text". "text" will use the `TextTimeEmbedding` layer.
+ addition_time_embed_dim: (`int`, *optional*, defaults to `None`):
+ Dimension for the timestep embeddings.
+ num_class_embeds (`int`, *optional*, defaults to `None`):
+ Input dimension of the learnable embedding matrix to be projected to `time_embed_dim`, when performing
+ class conditioning with `class_embed_type` equal to `None`.
+ time_embedding_type (`str`, *optional*, defaults to `positional`):
+ The type of position embedding to use for timesteps. Choose from `positional` or `fourier`.
+ time_embedding_dim (`int`, *optional*, defaults to `None`):
+ An optional override for the dimension of the projected time embedding.
+ time_embedding_act_fn (`str`, *optional*, defaults to `None`):
+ Optional activation function to use only once on the time embeddings before they are passed to the rest of
+ the UNet. Choose from `silu`, `mish`, `gelu`, and `swish`.
+ timestep_post_act (`str`, *optional*, defaults to `None`):
+ The second activation function to use in timestep embedding. Choose from `silu`, `mish` and `gelu`.
+ time_cond_proj_dim (`int`, *optional*, defaults to `None`):
+ The dimension of `cond_proj` layer in the timestep embedding.
+ conv_in_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_in` layer. conv_out_kernel (`int`,
+ *optional*, default to `3`): The kernel size of `conv_out` layer. projection_class_embeddings_input_dim (`int`,
+ *optional*): The dimension of the `class_labels` input when
+ `class_embed_type="projection"`. Required when `class_embed_type="projection"`.
+ class_embeddings_concat (`bool`, *optional*, defaults to `False`): Whether to concatenate the time
+ embeddings with the class embeddings.
+ mid_block_only_cross_attention (`bool`, *optional*, defaults to `None`):
+ Whether to use cross attention with the mid block when using the `UNetMidBlock2DSimpleCrossAttn`. If
+ `only_cross_attention` is given as a single boolean and `mid_block_only_cross_attention` is `None`, the
+ `only_cross_attention` value is used as the value for `mid_block_only_cross_attention`. Default to `False`
+ otherwise.
+ """
+
+ _supports_gradient_checkpointing = True
+
+ @register_to_config
+ def __init__(
+ self,
+ sample_size: Optional[int] = None,
+ in_channels: int = 4,
+ out_channels: int = 4,
+ center_input_sample: bool = False,
+ flip_sin_to_cos: bool = True,
+ freq_shift: int = 0,
+ down_block_types: Tuple[str] = (
+ "CrossAttnDownBlock2D",
+ "CrossAttnDownBlock2D",
+ "CrossAttnDownBlock2D",
+ "DownBlock2D",
+ ),
+ mid_block_type: Optional[str] = "UNetMidBlock2DCrossAttn",
+ up_block_types: Tuple[str] = (
+ "UpBlock2D",
+ "CrossAttnUpBlock2D",
+ "CrossAttnUpBlock2D",
+ "CrossAttnUpBlock2D",
+ ),
+ only_cross_attention: Union[bool, Tuple[bool]] = False,
+ block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
+ layers_per_block: Union[int, Tuple[int]] = 2,
+ downsample_padding: int = 1,
+ mid_block_scale_factor: float = 1,
+ dropout: float = 0.0,
+ act_fn: str = "silu",
+ norm_num_groups: Optional[int] = 32,
+ norm_eps: float = 1e-5,
+ cross_attention_dim: Union[int, Tuple[int]] = 1280,
+ transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple]] = 1,
+ reverse_transformer_layers_per_block: Optional[Tuple[Tuple[int]]] = None,
+ encoder_hid_dim: Optional[int] = None,
+ encoder_hid_dim_type: Optional[str] = None,
+ attention_head_dim: Union[int, Tuple[int]] = 8,
+ num_attention_heads: Optional[Union[int, Tuple[int]]] = None,
+ dual_cross_attention: bool = False,
+ use_linear_projection: bool = False,
+ class_embed_type: Optional[str] = None,
+ addition_embed_type: Optional[str] = None,
+ addition_time_embed_dim: Optional[int] = None,
+ num_class_embeds: Optional[int] = None,
+ upcast_attention: bool = False,
+ resnet_time_scale_shift: str = "default",
+ resnet_skip_time_act: bool = False,
+ resnet_out_scale_factor: int = 1.0,
+ time_embedding_type: str = "positional",
+ time_embedding_dim: Optional[int] = None,
+ time_embedding_act_fn: Optional[str] = None,
+ timestep_post_act: Optional[str] = None,
+ time_cond_proj_dim: Optional[int] = None,
+ conv_in_kernel: int = 3,
+ conv_out_kernel: int = 3,
+ projection_class_embeddings_input_dim: Optional[int] = None,
+ attention_type: str = "default",
+ class_embeddings_concat: bool = False,
+ mid_block_only_cross_attention: Optional[bool] = None,
+ cross_attention_norm: Optional[str] = None,
+ addition_embed_type_num_heads=64,
+ ):
+ super().__init__()
+
+ self.sample_size = sample_size
+
+ if num_attention_heads is not None:
+ raise ValueError(
+ "At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19."
+ )
+
+ # If `num_attention_heads` is not defined (which is the case for most models)
+ # it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
+ # The reason for this behavior is to correct for incorrectly named variables that were introduced
+ # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
+ # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
+ # which is why we correct for the naming here.
+ num_attention_heads = num_attention_heads or attention_head_dim
+
+ # Check inputs
+ if len(down_block_types) != len(up_block_types):
+ raise ValueError(
+ f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}."
+ )
+
+ if len(block_out_channels) != len(down_block_types):
+ raise ValueError(
+ f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."
+ )
+
+ if not isinstance(only_cross_attention, bool) and len(
+ only_cross_attention
+ ) != len(down_block_types):
+ raise ValueError(
+ f"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}."
+ )
+
+ if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(
+ down_block_types
+ ):
+ raise ValueError(
+ f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}."
+ )
+
+ if not isinstance(attention_head_dim, int) and len(attention_head_dim) != len(
+ down_block_types
+ ):
+ raise ValueError(
+ f"Must provide the same number of `attention_head_dim` as `down_block_types`. `attention_head_dim`: {attention_head_dim}. `down_block_types`: {down_block_types}."
+ )
+
+ if isinstance(cross_attention_dim, list) and len(cross_attention_dim) != len(
+ down_block_types
+ ):
+ raise ValueError(
+ f"Must provide the same number of `cross_attention_dim` as `down_block_types`. `cross_attention_dim`: {cross_attention_dim}. `down_block_types`: {down_block_types}."
+ )
+
+ if not isinstance(layers_per_block, int) and len(layers_per_block) != len(
+ down_block_types
+ ):
+ raise ValueError(
+ f"Must provide the same number of `layers_per_block` as `down_block_types`. `layers_per_block`: {layers_per_block}. `down_block_types`: {down_block_types}."
+ )
+ if (
+ isinstance(transformer_layers_per_block, list)
+ and reverse_transformer_layers_per_block is None
+ ):
+ for layer_number_per_block in transformer_layers_per_block:
+ if isinstance(layer_number_per_block, list):
+ raise ValueError(
+ "Must provide 'reverse_transformer_layers_per_block` if using asymmetrical UNet."
+ )
+
+ # input
+ conv_in_padding = (conv_in_kernel - 1) // 2
+ self.conv_in = nn.Conv2d(
+ in_channels,
+ block_out_channels[0],
+ kernel_size=conv_in_kernel,
+ padding=conv_in_padding,
+ )
+
+ # time
+ if time_embedding_type == "fourier":
+ time_embed_dim = time_embedding_dim or block_out_channels[0] * 2
+ if time_embed_dim % 2 != 0:
+ raise ValueError(
+ f"`time_embed_dim` should be divisible by 2, but is {time_embed_dim}."
+ )
+ self.time_proj = GaussianFourierProjection(
+ time_embed_dim // 2,
+ set_W_to_weight=False,
+ log=False,
+ flip_sin_to_cos=flip_sin_to_cos,
+ )
+ timestep_input_dim = time_embed_dim
+ elif time_embedding_type == "positional":
+ time_embed_dim = time_embedding_dim or block_out_channels[0] * 4
+
+ self.time_proj = Timesteps(
+ block_out_channels[0], flip_sin_to_cos, freq_shift
+ )
+ timestep_input_dim = block_out_channels[0]
+ else:
+ raise ValueError(
+ f"{time_embedding_type} does not exist. Please make sure to use one of `fourier` or `positional`."
+ )
+
+ self.time_embedding = TimestepEmbedding(
+ timestep_input_dim,
+ time_embed_dim,
+ act_fn=act_fn,
+ post_act_fn=timestep_post_act,
+ cond_proj_dim=time_cond_proj_dim,
+ )
+
+ if encoder_hid_dim_type is None and encoder_hid_dim is not None:
+ encoder_hid_dim_type = "text_proj"
+ self.register_to_config(encoder_hid_dim_type=encoder_hid_dim_type)
+ logger.info(
+ "encoder_hid_dim_type defaults to 'text_proj' as `encoder_hid_dim` is defined."
+ )
+
+ if encoder_hid_dim is None and encoder_hid_dim_type is not None:
+ raise ValueError(
+ f"`encoder_hid_dim` has to be defined when `encoder_hid_dim_type` is set to {encoder_hid_dim_type}."
+ )
+
+ if encoder_hid_dim_type == "text_proj":
+ self.encoder_hid_proj = nn.Linear(encoder_hid_dim, cross_attention_dim)
+ elif encoder_hid_dim_type == "text_image_proj":
+ # image_embed_dim DOESN'T have to be `cross_attention_dim`. To not clutter the __init__ too much
+ # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use
+ # case when `addition_embed_type == "text_image_proj"` (Kadinsky 2.1)`
+ self.encoder_hid_proj = TextImageProjection(
+ text_embed_dim=encoder_hid_dim,
+ image_embed_dim=cross_attention_dim,
+ cross_attention_dim=cross_attention_dim,
+ )
+ elif encoder_hid_dim_type == "image_proj":
+ # Kandinsky 2.2
+ self.encoder_hid_proj = ImageProjection(
+ image_embed_dim=encoder_hid_dim,
+ cross_attention_dim=cross_attention_dim,
+ )
+ elif encoder_hid_dim_type is not None:
+ raise ValueError(
+ f"encoder_hid_dim_type: {encoder_hid_dim_type} must be None, 'text_proj' or 'text_image_proj'."
+ )
+ else:
+ self.encoder_hid_proj = None
+
+ # class embedding
+ if class_embed_type is None and num_class_embeds is not None:
+ self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
+ elif class_embed_type == "timestep":
+ self.class_embedding = TimestepEmbedding(
+ timestep_input_dim, time_embed_dim, act_fn=act_fn
+ )
+ elif class_embed_type == "identity":
+ self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)
+ elif class_embed_type == "projection":
+ if projection_class_embeddings_input_dim is None:
+ raise ValueError(
+ "`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set"
+ )
+ # The projection `class_embed_type` is the same as the timestep `class_embed_type` except
+ # 1. the `class_labels` inputs are not first converted to sinusoidal embeddings
+ # 2. it projects from an arbitrary input dimension.
+ #
+ # Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations.
+ # When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings.
+ # As a result, `TimestepEmbedding` can be passed arbitrary vectors.
+ self.class_embedding = TimestepEmbedding(
+ projection_class_embeddings_input_dim, time_embed_dim
+ )
+ elif class_embed_type == "simple_projection":
+ if projection_class_embeddings_input_dim is None:
+ raise ValueError(
+ "`class_embed_type`: 'simple_projection' requires `projection_class_embeddings_input_dim` be set"
+ )
+ self.class_embedding = nn.Linear(
+ projection_class_embeddings_input_dim, time_embed_dim
+ )
+ else:
+ self.class_embedding = None
+
+ if addition_embed_type == "text":
+ if encoder_hid_dim is not None:
+ text_time_embedding_from_dim = encoder_hid_dim
+ else:
+ text_time_embedding_from_dim = cross_attention_dim
+
+ self.add_embedding = TextTimeEmbedding(
+ text_time_embedding_from_dim,
+ time_embed_dim,
+ num_heads=addition_embed_type_num_heads,
+ )
+ elif addition_embed_type == "text_image":
+ # text_embed_dim and image_embed_dim DON'T have to be `cross_attention_dim`. To not clutter the __init__ too much
+ # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use
+ # case when `addition_embed_type == "text_image"` (Kadinsky 2.1)`
+ self.add_embedding = TextImageTimeEmbedding(
+ text_embed_dim=cross_attention_dim,
+ image_embed_dim=cross_attention_dim,
+ time_embed_dim=time_embed_dim,
+ )
+ elif addition_embed_type == "text_time":
+ self.add_time_proj = Timesteps(
+ addition_time_embed_dim, flip_sin_to_cos, freq_shift
+ )
+ self.add_embedding = TimestepEmbedding(
+ projection_class_embeddings_input_dim, time_embed_dim
+ )
+ elif addition_embed_type == "image":
+ # Kandinsky 2.2
+ self.add_embedding = ImageTimeEmbedding(
+ image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim
+ )
+ elif addition_embed_type == "image_hint":
+ # Kandinsky 2.2 ControlNet
+ self.add_embedding = ImageHintTimeEmbedding(
+ image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim
+ )
+ elif addition_embed_type is not None:
+ raise ValueError(
+ f"addition_embed_type: {addition_embed_type} must be None, 'text' or 'text_image'."
+ )
+
+ if time_embedding_act_fn is None:
+ self.time_embed_act = None
+ else:
+ self.time_embed_act = get_activation(time_embedding_act_fn)
+
+ self.down_blocks = nn.ModuleList([])
+ self.up_blocks = nn.ModuleList([])
+
+ if isinstance(only_cross_attention, bool):
+ if mid_block_only_cross_attention is None:
+ mid_block_only_cross_attention = only_cross_attention
+
+ only_cross_attention = [only_cross_attention] * len(down_block_types)
+
+ if mid_block_only_cross_attention is None:
+ mid_block_only_cross_attention = False
+
+ if isinstance(num_attention_heads, int):
+ num_attention_heads = (num_attention_heads,) * len(down_block_types)
+
+ if isinstance(attention_head_dim, int):
+ attention_head_dim = (attention_head_dim,) * len(down_block_types)
+
+ if isinstance(cross_attention_dim, int):
+ cross_attention_dim = (cross_attention_dim,) * len(down_block_types)
+
+ if isinstance(layers_per_block, int):
+ layers_per_block = [layers_per_block] * len(down_block_types)
+
+ if isinstance(transformer_layers_per_block, int):
+ transformer_layers_per_block = [transformer_layers_per_block] * len(
+ down_block_types
+ )
+
+ if class_embeddings_concat:
+ # The time embeddings are concatenated with the class embeddings. The dimension of the
+ # time embeddings passed to the down, middle, and up blocks is twice the dimension of the
+ # regular time embeddings
+ blocks_time_embed_dim = time_embed_dim * 2
+ else:
+ blocks_time_embed_dim = time_embed_dim
+
+ # down
+ output_channel = block_out_channels[0]
+ for i, down_block_type in enumerate(down_block_types):
+ input_channel = output_channel
+ output_channel = block_out_channels[i]
+ is_final_block = i == len(block_out_channels) - 1
+
+ down_block = get_down_block(
+ down_block_type,
+ num_layers=layers_per_block[i],
+ transformer_layers_per_block=transformer_layers_per_block[i],
+ in_channels=input_channel,
+ out_channels=output_channel,
+ temb_channels=blocks_time_embed_dim,
+ add_downsample=not is_final_block,
+ resnet_eps=norm_eps,
+ resnet_act_fn=act_fn,
+ resnet_groups=norm_num_groups,
+ cross_attention_dim=cross_attention_dim[i],
+ num_attention_heads=num_attention_heads[i],
+ downsample_padding=downsample_padding,
+ dual_cross_attention=dual_cross_attention,
+ use_linear_projection=use_linear_projection,
+ only_cross_attention=only_cross_attention[i],
+ upcast_attention=upcast_attention,
+ resnet_time_scale_shift=resnet_time_scale_shift,
+ attention_type=attention_type,
+ resnet_skip_time_act=resnet_skip_time_act,
+ resnet_out_scale_factor=resnet_out_scale_factor,
+ cross_attention_norm=cross_attention_norm,
+ attention_head_dim=attention_head_dim[i]
+ if attention_head_dim[i] is not None
+ else output_channel,
+ dropout=dropout,
+ )
+ self.down_blocks.append(down_block)
+
+ # mid
+ if mid_block_type == "UNetMidBlock2DCrossAttn":
+ self.mid_block = UNetMidBlock2DCrossAttn(
+ transformer_layers_per_block=transformer_layers_per_block[-1],
+ in_channels=block_out_channels[-1],
+ temb_channels=blocks_time_embed_dim,
+ dropout=dropout,
+ resnet_eps=norm_eps,
+ resnet_act_fn=act_fn,
+ output_scale_factor=mid_block_scale_factor,
+ resnet_time_scale_shift=resnet_time_scale_shift,
+ cross_attention_dim=cross_attention_dim[-1],
+ num_attention_heads=num_attention_heads[-1],
+ resnet_groups=norm_num_groups,
+ dual_cross_attention=dual_cross_attention,
+ use_linear_projection=use_linear_projection,
+ upcast_attention=upcast_attention,
+ attention_type=attention_type,
+ )
+ elif mid_block_type == "UNetMidBlock2DSimpleCrossAttn":
+ raise NotImplementedError(f"Unsupport mid_block_type: {mid_block_type}")
+ elif mid_block_type == "UNetMidBlock2D":
+ self.mid_block = UNetMidBlock2D(
+ in_channels=block_out_channels[-1],
+ temb_channels=blocks_time_embed_dim,
+ dropout=dropout,
+ num_layers=0,
+ resnet_eps=norm_eps,
+ resnet_act_fn=act_fn,
+ output_scale_factor=mid_block_scale_factor,
+ resnet_groups=norm_num_groups,
+ resnet_time_scale_shift=resnet_time_scale_shift,
+ add_attention=False,
+ )
+ elif mid_block_type is None:
+ self.mid_block = None
+ else:
+ raise ValueError(f"unknown mid_block_type : {mid_block_type}")
+
+ # count how many layers upsample the images
+ self.num_upsamplers = 0
+
+ # up
+ reversed_block_out_channels = list(reversed(block_out_channels))
+ reversed_num_attention_heads = list(reversed(num_attention_heads))
+ reversed_layers_per_block = list(reversed(layers_per_block))
+ reversed_cross_attention_dim = list(reversed(cross_attention_dim))
+ reversed_transformer_layers_per_block = (
+ list(reversed(transformer_layers_per_block))
+ if reverse_transformer_layers_per_block is None
+ else reverse_transformer_layers_per_block
+ )
+ only_cross_attention = list(reversed(only_cross_attention))
+
+ output_channel = reversed_block_out_channels[0]
+ for i, up_block_type in enumerate(up_block_types):
+ is_final_block = i == len(block_out_channels) - 1
+
+ prev_output_channel = output_channel
+ output_channel = reversed_block_out_channels[i]
+ input_channel = reversed_block_out_channels[
+ min(i + 1, len(block_out_channels) - 1)
+ ]
+
+ # add upsample block for all BUT final layer
+ if not is_final_block:
+ add_upsample = True
+ self.num_upsamplers += 1
+ else:
+ add_upsample = False
+
+ up_block = get_up_block(
+ up_block_type,
+ num_layers=reversed_layers_per_block[i] + 1,
+ transformer_layers_per_block=reversed_transformer_layers_per_block[i],
+ in_channels=input_channel,
+ out_channels=output_channel,
+ prev_output_channel=prev_output_channel,
+ temb_channels=blocks_time_embed_dim,
+ add_upsample=add_upsample,
+ resnet_eps=norm_eps,
+ resnet_act_fn=act_fn,
+ resolution_idx=i,
+ resnet_groups=norm_num_groups,
+ cross_attention_dim=reversed_cross_attention_dim[i],
+ num_attention_heads=reversed_num_attention_heads[i],
+ dual_cross_attention=dual_cross_attention,
+ use_linear_projection=use_linear_projection,
+ only_cross_attention=only_cross_attention[i],
+ upcast_attention=upcast_attention,
+ resnet_time_scale_shift=resnet_time_scale_shift,
+ attention_type=attention_type,
+ resnet_skip_time_act=resnet_skip_time_act,
+ resnet_out_scale_factor=resnet_out_scale_factor,
+ cross_attention_norm=cross_attention_norm,
+ attention_head_dim=attention_head_dim[i]
+ if attention_head_dim[i] is not None
+ else output_channel,
+ dropout=dropout,
+ )
+ self.up_blocks.append(up_block)
+ prev_output_channel = output_channel
+
+ # out
+ if norm_num_groups is not None:
+ self.conv_norm_out = nn.GroupNorm(
+ num_channels=block_out_channels[0],
+ num_groups=norm_num_groups,
+ eps=norm_eps,
+ )
+
+ self.conv_act = get_activation(act_fn)
+
+ else:
+ self.conv_norm_out = None
+ self.conv_act = None
+ self.conv_norm_out = None
+
+ conv_out_padding = (conv_out_kernel - 1) // 2
+ # self.conv_out = nn.Conv2d(
+ # block_out_channels[0],
+ # out_channels,
+ # kernel_size=conv_out_kernel,
+ # padding=conv_out_padding,
+ # )
+
+ if attention_type in ["gated", "gated-text-image"]:
+ positive_len = 768
+ if isinstance(cross_attention_dim, int):
+ positive_len = cross_attention_dim
+ elif isinstance(cross_attention_dim, tuple) or isinstance(
+ cross_attention_dim, list
+ ):
+ positive_len = cross_attention_dim[0]
+
+ feature_type = "text-only" if attention_type == "gated" else "text-image"
+ self.position_net = PositionNet(
+ positive_len=positive_len,
+ out_dim=cross_attention_dim,
+ feature_type=feature_type,
+ )
+
+ @property
+ def attn_processors(self) -> Dict[str, AttentionProcessor]:
+ r"""
+ Returns:
+ `dict` of attention processors: A dictionary containing all attention processors used in the model with
+ indexed by its weight name.
+ """
+ # set recursively
+ processors = {}
+
+ def fn_recursive_add_processors(
+ name: str,
+ module: torch.nn.Module,
+ processors: Dict[str, AttentionProcessor],
+ ):
+ if hasattr(module, "get_processor"):
+ processors[f"{name}.processor"] = module.get_processor(
+ return_deprecated_lora=True
+ )
+
+ for sub_name, child in module.named_children():
+ fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
+
+ return processors
+
+ for name, module in self.named_children():
+ fn_recursive_add_processors(name, module, processors)
+
+ return processors
+
+ def set_attn_processor(
+ self,
+ processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]],
+ _remove_lora=False,
+ ):
+ r"""
+ Sets the attention processor to use to compute attention.
+
+ Parameters:
+ processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
+ The instantiated processor class or a dictionary of processor classes that will be set as the processor
+ for **all** `Attention` layers.
+
+ If `processor` is a dict, the key needs to define the path to the corresponding cross attention
+ processor. This is strongly recommended when setting trainable attention processors.
+
+ """
+ count = len(self.attn_processors.keys())
+
+ if isinstance(processor, dict) and len(processor) != count:
+ raise ValueError(
+ f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
+ f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
+ )
+
+ def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
+ if hasattr(module, "set_processor"):
+ if not isinstance(processor, dict):
+ module.set_processor(processor, _remove_lora=_remove_lora)
+ else:
+ module.set_processor(
+ processor.pop(f"{name}.processor"), _remove_lora=_remove_lora
+ )
+
+ for sub_name, child in module.named_children():
+ fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
+
+ for name, module in self.named_children():
+ fn_recursive_attn_processor(name, module, processor)
+
+ def set_default_attn_processor(self):
+ """
+ Disables custom attention processors and sets the default attention implementation.
+ """
+ if all(
+ proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS
+ for proc in self.attn_processors.values()
+ ):
+ processor = AttnAddedKVProcessor()
+ elif all(
+ proc.__class__ in CROSS_ATTENTION_PROCESSORS
+ for proc in self.attn_processors.values()
+ ):
+ processor = AttnProcessor()
+ else:
+ raise ValueError(
+ f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}"
+ )
+
+ self.set_attn_processor(processor, _remove_lora=True)
+
+ def set_attention_slice(self, slice_size):
+ r"""
+ Enable sliced attention computation.
+
+ When this option is enabled, the attention module splits the input tensor in slices to compute attention in
+ several steps. This is useful for saving some memory in exchange for a small decrease in speed.
+
+ Args:
+ slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`):
+ When `"auto"`, input to the attention heads is halved, so attention is computed in two steps. If
+ `"max"`, maximum amount of memory is saved by running only one slice at a time. If a number is
+ provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
+ must be a multiple of `slice_size`.
+ """
+ sliceable_head_dims = []
+
+ def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module):
+ if hasattr(module, "set_attention_slice"):
+ sliceable_head_dims.append(module.sliceable_head_dim)
+
+ for child in module.children():
+ fn_recursive_retrieve_sliceable_dims(child)
+
+ # retrieve number of attention layers
+ for module in self.children():
+ fn_recursive_retrieve_sliceable_dims(module)
+
+ num_sliceable_layers = len(sliceable_head_dims)
+
+ if slice_size == "auto":
+ # half the attention head size is usually a good trade-off between
+ # speed and memory
+ slice_size = [dim // 2 for dim in sliceable_head_dims]
+ elif slice_size == "max":
+ # make smallest slice possible
+ slice_size = num_sliceable_layers * [1]
+
+ slice_size = (
+ num_sliceable_layers * [slice_size]
+ if not isinstance(slice_size, list)
+ else slice_size
+ )
+
+ if len(slice_size) != len(sliceable_head_dims):
+ raise ValueError(
+ f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different"
+ f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}."
+ )
+
+ for i in range(len(slice_size)):
+ size = slice_size[i]
+ dim = sliceable_head_dims[i]
+ if size is not None and size > dim:
+ raise ValueError(f"size {size} has to be smaller or equal to {dim}.")
+
+ # Recursively walk through all the children.
+ # Any children which exposes the set_attention_slice method
+ # gets the message
+ def fn_recursive_set_attention_slice(
+ module: torch.nn.Module, slice_size: List[int]
+ ):
+ if hasattr(module, "set_attention_slice"):
+ module.set_attention_slice(slice_size.pop())
+
+ for child in module.children():
+ fn_recursive_set_attention_slice(child, slice_size)
+
+ reversed_slice_size = list(reversed(slice_size))
+ for module in self.children():
+ fn_recursive_set_attention_slice(module, reversed_slice_size)
+
+ def _set_gradient_checkpointing(self, module, value=False):
+ if hasattr(module, "gradient_checkpointing"):
+ module.gradient_checkpointing = value
+
+ def enable_freeu(self, s1, s2, b1, b2):
+ r"""Enables the FreeU mechanism from https://arxiv.org/abs/2309.11497.
+
+ The suffixes after the scaling factors represent the stage blocks where they are being applied.
+
+ Please refer to the [official repository](https://github.com/ChenyangSi/FreeU) for combinations of values that
+ are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL.
+
+ Args:
+ s1 (`float`):
+ Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to
+ mitigate the "oversmoothing effect" in the enhanced denoising process.
+ s2 (`float`):
+ Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to
+ mitigate the "oversmoothing effect" in the enhanced denoising process.
+ b1 (`float`): Scaling factor for stage 1 to amplify the contributions of backbone features.
+ b2 (`float`): Scaling factor for stage 2 to amplify the contributions of backbone features.
+ """
+ for i, upsample_block in enumerate(self.up_blocks):
+ setattr(upsample_block, "s1", s1)
+ setattr(upsample_block, "s2", s2)
+ setattr(upsample_block, "b1", b1)
+ setattr(upsample_block, "b2", b2)
+
+ def disable_freeu(self):
+ """Disables the FreeU mechanism."""
+ freeu_keys = {"s1", "s2", "b1", "b2"}
+ for i, upsample_block in enumerate(self.up_blocks):
+ for k in freeu_keys:
+ if (
+ hasattr(upsample_block, k)
+ or getattr(upsample_block, k, None) is not None
+ ):
+ setattr(upsample_block, k, None)
+
+ def forward(
+ self,
+ sample: torch.FloatTensor,
+ timestep: Union[torch.Tensor, float, int],
+ encoder_hidden_states: torch.Tensor,
+ class_labels: Optional[torch.Tensor] = None,
+ timestep_cond: Optional[torch.Tensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+ added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
+ down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
+ mid_block_additional_residual: Optional[torch.Tensor] = None,
+ down_intrablock_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
+ encoder_attention_mask: Optional[torch.Tensor] = None,
+ return_dict: bool = True,
+ ) -> Union[UNet2DConditionOutput, Tuple]:
+ r"""
+ The [`UNet2DConditionModel`] forward method.
+
+ Args:
+ sample (`torch.FloatTensor`):
+ The noisy input tensor with the following shape `(batch, channel, height, width)`.
+ timestep (`torch.FloatTensor` or `float` or `int`): The number of timesteps to denoise an input.
+ encoder_hidden_states (`torch.FloatTensor`):
+ The encoder hidden states with shape `(batch, sequence_length, feature_dim)`.
+ class_labels (`torch.Tensor`, *optional*, defaults to `None`):
+ Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings.
+ timestep_cond: (`torch.Tensor`, *optional*, defaults to `None`):
+ Conditional embeddings for timestep. If provided, the embeddings will be summed with the samples passed
+ through the `self.time_embedding` layer to obtain the timestep embeddings.
+ attention_mask (`torch.Tensor`, *optional*, defaults to `None`):
+ An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask
+ is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large
+ negative values to the attention scores corresponding to "discard" tokens.
+ cross_attention_kwargs (`dict`, *optional*):
+ A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+ `self.processor` in
+ [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+ added_cond_kwargs: (`dict`, *optional*):
+ A kwargs dictionary containing additional embeddings that if specified are added to the embeddings that
+ are passed along to the UNet blocks.
+ down_block_additional_residuals: (`tuple` of `torch.Tensor`, *optional*):
+ A tuple of tensors that if specified are added to the residuals of down unet blocks.
+ mid_block_additional_residual: (`torch.Tensor`, *optional*):
+ A tensor that if specified is added to the residual of the middle unet block.
+ encoder_attention_mask (`torch.Tensor`):
+ A cross-attention mask of shape `(batch, sequence_length)` is applied to `encoder_hidden_states`. If
+ `True` the mask is kept, otherwise if `False` it is discarded. Mask will be converted into a bias,
+ which adds large negative values to the attention scores corresponding to "discard" tokens.
+ return_dict (`bool`, *optional*, defaults to `True`):
+ Whether or not to return a [`~models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain
+ tuple.
+ cross_attention_kwargs (`dict`, *optional*):
+ A kwargs dictionary that if specified is passed along to the [`AttnProcessor`].
+ added_cond_kwargs: (`dict`, *optional*):
+ A kwargs dictionary containin additional embeddings that if specified are added to the embeddings that
+ are passed along to the UNet blocks.
+ down_block_additional_residuals (`tuple` of `torch.Tensor`, *optional*):
+ additional residuals to be added to UNet long skip connections from down blocks to up blocks for
+ example from ControlNet side model(s)
+ mid_block_additional_residual (`torch.Tensor`, *optional*):
+ additional residual to be added to UNet mid block output, for example from ControlNet side model
+ down_intrablock_additional_residuals (`tuple` of `torch.Tensor`, *optional*):
+ additional residuals to be added within UNet down blocks, for example from T2I-Adapter side model(s)
+
+ Returns:
+ [`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:
+ If `return_dict` is True, an [`~models.unet_2d_condition.UNet2DConditionOutput`] is returned, otherwise
+ a `tuple` is returned where the first element is the sample tensor.
+ """
+ # By default samples have to be AT least a multiple of the overall upsampling factor.
+ # The overall upsampling factor is equal to 2 ** (# num of upsampling layers).
+ # However, the upsampling interpolation output size can be forced to fit any upsampling size
+ # on the fly if necessary.
+ default_overall_up_factor = 2**self.num_upsamplers
+
+ # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`
+ forward_upsample_size = False
+ upsample_size = None
+
+ for dim in sample.shape[-2:]:
+ if dim % default_overall_up_factor != 0:
+ # Forward upsample size to force interpolation output size.
+ forward_upsample_size = True
+ break
+
+ # ensure attention_mask is a bias, and give it a singleton query_tokens dimension
+ # expects mask of shape:
+ # [batch, key_tokens]
+ # adds singleton query_tokens dimension:
+ # [batch, 1, key_tokens]
+ # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
+ # [batch, heads, query_tokens, key_tokens] (e.g. torch sdp attn)
+ # [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
+ if attention_mask is not None:
+ # assume that mask is expressed as:
+ # (1 = keep, 0 = discard)
+ # convert mask into a bias that can be added to attention scores:
+ # (keep = +0, discard = -10000.0)
+ attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
+ attention_mask = attention_mask.unsqueeze(1)
+
+ # convert encoder_attention_mask to a bias the same way we do for attention_mask
+ if encoder_attention_mask is not None:
+ encoder_attention_mask = (
+ 1 - encoder_attention_mask.to(sample.dtype)
+ ) * -10000.0
+ encoder_attention_mask = encoder_attention_mask.unsqueeze(1)
+
+ # 0. center input if necessary
+ if self.config.center_input_sample:
+ sample = 2 * sample - 1.0
+
+ # 1. time
+ timesteps = timestep
+ if not torch.is_tensor(timesteps):
+ # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
+ # This would be a good case for the `match` statement (Python 3.10+)
+ is_mps = sample.device.type == "mps"
+ if isinstance(timestep, float):
+ dtype = torch.float32 if is_mps else torch.float64
+ else:
+ dtype = torch.int32 if is_mps else torch.int64
+ timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
+ elif len(timesteps.shape) == 0:
+ timesteps = timesteps[None].to(sample.device)
+
+ # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+ timesteps = timesteps.expand(sample.shape[0])
+
+ t_emb = self.time_proj(timesteps)
+
+ # `Timesteps` does not contain any weights and will always return f32 tensors
+ # but time_embedding might actually be running in fp16. so we need to cast here.
+ # there might be better ways to encapsulate this.
+ t_emb = t_emb.to(dtype=sample.dtype)
+
+ emb = self.time_embedding(t_emb, timestep_cond)
+ aug_emb = None
+
+ if self.class_embedding is not None:
+ if class_labels is None:
+ raise ValueError(
+ "class_labels should be provided when num_class_embeds > 0"
+ )
+
+ if self.config.class_embed_type == "timestep":
+ class_labels = self.time_proj(class_labels)
+
+ # `Timesteps` does not contain any weights and will always return f32 tensors
+ # there might be better ways to encapsulate this.
+ class_labels = class_labels.to(dtype=sample.dtype)
+
+ class_emb = self.class_embedding(class_labels).to(dtype=sample.dtype)
+
+ if self.config.class_embeddings_concat:
+ emb = torch.cat([emb, class_emb], dim=-1)
+ else:
+ emb = emb + class_emb
+
+ if self.config.addition_embed_type == "text":
+ aug_emb = self.add_embedding(encoder_hidden_states)
+ elif self.config.addition_embed_type == "text_image":
+ # Kandinsky 2.1 - style
+ if "image_embeds" not in added_cond_kwargs:
+ raise ValueError(
+ f"{self.__class__} has the config param `addition_embed_type` set to 'text_image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`"
+ )
+
+ image_embs = added_cond_kwargs.get("image_embeds")
+ text_embs = added_cond_kwargs.get("text_embeds", encoder_hidden_states)
+ aug_emb = self.add_embedding(text_embs, image_embs)
+ elif self.config.addition_embed_type == "text_time":
+ # SDXL - style
+ if "text_embeds" not in added_cond_kwargs:
+ raise ValueError(
+ f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `text_embeds` to be passed in `added_cond_kwargs`"
+ )
+ text_embeds = added_cond_kwargs.get("text_embeds")
+ if "time_ids" not in added_cond_kwargs:
+ raise ValueError(
+ f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `time_ids` to be passed in `added_cond_kwargs`"
+ )
+ time_ids = added_cond_kwargs.get("time_ids")
+ time_embeds = self.add_time_proj(time_ids.flatten())
+ time_embeds = time_embeds.reshape((text_embeds.shape[0], -1))
+ add_embeds = torch.concat([text_embeds, time_embeds], dim=-1)
+ add_embeds = add_embeds.to(emb.dtype)
+ aug_emb = self.add_embedding(add_embeds)
+ elif self.config.addition_embed_type == "image":
+ # Kandinsky 2.2 - style
+ if "image_embeds" not in added_cond_kwargs:
+ raise ValueError(
+ f"{self.__class__} has the config param `addition_embed_type` set to 'image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`"
+ )
+ image_embs = added_cond_kwargs.get("image_embeds")
+ aug_emb = self.add_embedding(image_embs)
+ elif self.config.addition_embed_type == "image_hint":
+ # Kandinsky 2.2 - style
+ if (
+ "image_embeds" not in added_cond_kwargs
+ or "hint" not in added_cond_kwargs
+ ):
+ raise ValueError(
+ f"{self.__class__} has the config param `addition_embed_type` set to 'image_hint' which requires the keyword arguments `image_embeds` and `hint` to be passed in `added_cond_kwargs`"
+ )
+ image_embs = added_cond_kwargs.get("image_embeds")
+ hint = added_cond_kwargs.get("hint")
+ aug_emb, hint = self.add_embedding(image_embs, hint)
+ sample = torch.cat([sample, hint], dim=1)
+
+ emb = emb + aug_emb if aug_emb is not None else emb
+
+ if self.time_embed_act is not None:
+ emb = self.time_embed_act(emb)
+
+ if (
+ self.encoder_hid_proj is not None
+ and self.config.encoder_hid_dim_type == "text_proj"
+ ):
+ encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states)
+ elif (
+ self.encoder_hid_proj is not None
+ and self.config.encoder_hid_dim_type == "text_image_proj"
+ ):
+ # Kadinsky 2.1 - style
+ if "image_embeds" not in added_cond_kwargs:
+ raise ValueError(
+ f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'text_image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`"
+ )
+
+ image_embeds = added_cond_kwargs.get("image_embeds")
+ encoder_hidden_states = self.encoder_hid_proj(
+ encoder_hidden_states, image_embeds
+ )
+ elif (
+ self.encoder_hid_proj is not None
+ and self.config.encoder_hid_dim_type == "image_proj"
+ ):
+ # Kandinsky 2.2 - style
+ if "image_embeds" not in added_cond_kwargs:
+ raise ValueError(
+ f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`"
+ )
+ image_embeds = added_cond_kwargs.get("image_embeds")
+ encoder_hidden_states = self.encoder_hid_proj(image_embeds)
+ elif (
+ self.encoder_hid_proj is not None
+ and self.config.encoder_hid_dim_type == "ip_image_proj"
+ ):
+ if "image_embeds" not in added_cond_kwargs:
+ raise ValueError(
+ f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'ip_image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`"
+ )
+ image_embeds = added_cond_kwargs.get("image_embeds")
+ image_embeds = self.encoder_hid_proj(image_embeds).to(
+ encoder_hidden_states.dtype
+ )
+ encoder_hidden_states = torch.cat(
+ [encoder_hidden_states, image_embeds], dim=1
+ )
+
+ # 2. pre-process
+ sample = self.conv_in(sample)
+
+ # 2.5 GLIGEN position net
+ if (
+ cross_attention_kwargs is not None
+ and cross_attention_kwargs.get("gligen", None) is not None
+ ):
+ cross_attention_kwargs = cross_attention_kwargs.copy()
+ gligen_args = cross_attention_kwargs.pop("gligen")
+ cross_attention_kwargs["gligen"] = {
+ "objs": self.position_net(**gligen_args)
+ }
+
+ # 3. down
+ lora_scale = (
+ cross_attention_kwargs.get("scale", 1.0)
+ if cross_attention_kwargs is not None
+ else 1.0
+ )
+ if USE_PEFT_BACKEND:
+ # weight the lora layers by setting `lora_scale` for each PEFT layer
+ scale_lora_layers(self, lora_scale)
+
+ is_controlnet = (
+ mid_block_additional_residual is not None
+ and down_block_additional_residuals is not None
+ )
+ # using new arg down_intrablock_additional_residuals for T2I-Adapters, to distinguish from controlnets
+ is_adapter = down_intrablock_additional_residuals is not None
+ # maintain backward compatibility for legacy usage, where
+ # T2I-Adapter and ControlNet both use down_block_additional_residuals arg
+ # but can only use one or the other
+ if (
+ not is_adapter
+ and mid_block_additional_residual is None
+ and down_block_additional_residuals is not None
+ ):
+ deprecate(
+ "T2I should not use down_block_additional_residuals",
+ "1.3.0",
+ "Passing intrablock residual connections with `down_block_additional_residuals` is deprecated \
+ and will be removed in diffusers 1.3.0. `down_block_additional_residuals` should only be used \
+ for ControlNet. Please make sure use `down_intrablock_additional_residuals` instead. ",
+ standard_warn=False,
+ )
+ down_intrablock_additional_residuals = down_block_additional_residuals
+ is_adapter = True
+
+ down_block_res_samples = (sample,)
+ tot_referece_features = ()
+ for downsample_block in self.down_blocks:
+ if (
+ hasattr(downsample_block, "has_cross_attention")
+ and downsample_block.has_cross_attention
+ ):
+ # For t2i-adapter CrossAttnDownBlock2D
+ additional_residuals = {}
+ if is_adapter and len(down_intrablock_additional_residuals) > 0:
+ additional_residuals[
+ "additional_residuals"
+ ] = down_intrablock_additional_residuals.pop(0)
+
+ sample, res_samples = downsample_block(
+ hidden_states=sample,
+ temb=emb,
+ encoder_hidden_states=encoder_hidden_states,
+ attention_mask=attention_mask,
+ cross_attention_kwargs=cross_attention_kwargs,
+ encoder_attention_mask=encoder_attention_mask,
+ **additional_residuals,
+ )
+ else:
+ sample, res_samples = downsample_block(
+ hidden_states=sample, temb=emb, scale=lora_scale
+ )
+ if is_adapter and len(down_intrablock_additional_residuals) > 0:
+ sample += down_intrablock_additional_residuals.pop(0)
+
+ down_block_res_samples += res_samples
+
+ if is_controlnet:
+ new_down_block_res_samples = ()
+
+ for down_block_res_sample, down_block_additional_residual in zip(
+ down_block_res_samples, down_block_additional_residuals
+ ):
+ down_block_res_sample = (
+ down_block_res_sample + down_block_additional_residual
+ )
+ new_down_block_res_samples = new_down_block_res_samples + (
+ down_block_res_sample,
+ )
+
+ down_block_res_samples = new_down_block_res_samples
+
+ # 4. mid
+ if self.mid_block is not None:
+ if (
+ hasattr(self.mid_block, "has_cross_attention")
+ and self.mid_block.has_cross_attention
+ ):
+ sample = self.mid_block(
+ sample,
+ emb,
+ encoder_hidden_states=encoder_hidden_states,
+ attention_mask=attention_mask,
+ cross_attention_kwargs=cross_attention_kwargs,
+ encoder_attention_mask=encoder_attention_mask,
+ )
+ else:
+ sample = self.mid_block(sample, emb)
+
+ # To support T2I-Adapter-XL
+ if (
+ is_adapter
+ and len(down_intrablock_additional_residuals) > 0
+ and sample.shape == down_intrablock_additional_residuals[0].shape
+ ):
+ sample += down_intrablock_additional_residuals.pop(0)
+
+ if is_controlnet:
+ sample = sample + mid_block_additional_residual
+
+ # 5. up
+ for i, upsample_block in enumerate(self.up_blocks):
+ is_final_block = i == len(self.up_blocks) - 1
+
+ res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
+ down_block_res_samples = down_block_res_samples[
+ : -len(upsample_block.resnets)
+ ]
+
+ # if we have not reached the final block and need to forward the
+ # upsample size, we do it here
+ if not is_final_block and forward_upsample_size:
+ upsample_size = down_block_res_samples[-1].shape[2:]
+
+ if (
+ hasattr(upsample_block, "has_cross_attention")
+ and upsample_block.has_cross_attention
+ ):
+ sample = upsample_block(
+ hidden_states=sample,
+ temb=emb,
+ res_hidden_states_tuple=res_samples,
+ encoder_hidden_states=encoder_hidden_states,
+ cross_attention_kwargs=cross_attention_kwargs,
+ upsample_size=upsample_size,
+ attention_mask=attention_mask,
+ encoder_attention_mask=encoder_attention_mask,
+ )
+ else:
+ sample = upsample_block(
+ hidden_states=sample,
+ temb=emb,
+ res_hidden_states_tuple=res_samples,
+ upsample_size=upsample_size,
+ scale=lora_scale,
+ )
+
+ # 6. post-process
+ # if self.conv_norm_out:
+ # sample = self.conv_norm_out(sample)
+ # sample = self.conv_act(sample)
+ # sample = self.conv_out(sample)
+
+ if USE_PEFT_BACKEND:
+ # remove `lora_scale` from each PEFT layer
+ unscale_lora_layers(self, lora_scale)
+
+ if not return_dict:
+ return (sample,)
+
+ return UNet2DConditionOutput(sample=sample)
diff --git a/src/models/unet_3d.py b/src/models/unet_3d.py
new file mode 100644
index 0000000000000000000000000000000000000000..49d7d04048d02393102b441def6d5decb5066c77
--- /dev/null
+++ b/src/models/unet_3d.py
@@ -0,0 +1,695 @@
+# Adapted from https://github.com/guoyww/AnimateDiff/blob/main/animatediff/models/unet_blocks.py
+
+from collections import OrderedDict
+from dataclasses import dataclass
+from os import PathLike
+from pathlib import Path
+from typing import Dict, List, Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+import torch.utils.checkpoint
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.models.attention_processor import AttentionProcessor
+from diffusers.models.embeddings import TimestepEmbedding, Timesteps
+try:
+ from diffusers.modeling_utils import ModelMixin
+except:
+ from diffusers.models.modeling_utils import ModelMixin
+from diffusers.utils import SAFETENSORS_WEIGHTS_NAME, WEIGHTS_NAME, BaseOutput, logging
+from safetensors.torch import load_file
+
+from .resnet import InflatedConv3d, InflatedGroupNorm
+from .unet_3d_blocks import UNetMidBlock3DCrossAttn, get_down_block, get_up_block
+
+logger = logging.get_logger(__name__) # pylint: disable=invalid-name
+
+
+@dataclass
+class UNet3DConditionOutput(BaseOutput):
+ sample: torch.FloatTensor
+
+
+class UNet3DConditionModel(ModelMixin, ConfigMixin):
+ _supports_gradient_checkpointing = True
+
+ @register_to_config
+ def __init__(
+ self,
+ sample_size: Optional[int] = None,
+ in_channels: int = 4,
+ out_channels: int = 4,
+ center_input_sample: bool = False,
+ flip_sin_to_cos: bool = True,
+ freq_shift: int = 0,
+ down_block_types: Tuple[str] = (
+ "CrossAttnDownBlock3D",
+ "CrossAttnDownBlock3D",
+ "CrossAttnDownBlock3D",
+ "DownBlock3D",
+ ),
+ mid_block_type: str = "UNetMidBlock3DCrossAttn",
+ up_block_types: Tuple[str] = (
+ "UpBlock3D",
+ "CrossAttnUpBlock3D",
+ "CrossAttnUpBlock3D",
+ "CrossAttnUpBlock3D",
+ ),
+ only_cross_attention: Union[bool, Tuple[bool]] = False,
+ block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
+ layers_per_block: int = 2,
+ downsample_padding: int = 1,
+ mid_block_scale_factor: float = 1,
+ act_fn: str = "silu",
+ norm_num_groups: int = 32,
+ norm_eps: float = 1e-5,
+ cross_attention_dim: int = 1280,
+ attention_head_dim: Union[int, Tuple[int]] = 8,
+ dual_cross_attention: bool = False,
+ use_linear_projection: bool = False,
+ class_embed_type: Optional[str] = None,
+ num_class_embeds: Optional[int] = None,
+ upcast_attention: bool = False,
+ resnet_time_scale_shift: str = "default",
+ use_inflated_groupnorm=False,
+ # Additional
+ use_motion_module=False,
+ motion_module_resolutions=(1, 2, 4, 8),
+ motion_module_mid_block=False,
+ motion_module_decoder_only=False,
+ motion_module_type=None,
+ motion_module_kwargs={},
+ unet_use_cross_frame_attention=None,
+ unet_use_temporal_attention=None,
+ mode=None,
+ task_type="action",
+ ):
+ super().__init__()
+
+ self.sample_size = sample_size
+ time_embed_dim = block_out_channels[0] * 4
+
+ # input
+ self.conv_in = InflatedConv3d(
+ in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1)
+ )
+
+ # time
+ self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
+ timestep_input_dim = block_out_channels[0]
+
+ self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
+
+ # class embedding
+ if class_embed_type is None and num_class_embeds is not None:
+ self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
+ elif class_embed_type == "timestep":
+ self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
+ elif class_embed_type == "identity":
+ self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)
+ else:
+ self.class_embedding = None
+
+ self.down_blocks = nn.ModuleList([])
+ self.mid_block = None
+ self.up_blocks = nn.ModuleList([])
+
+ if isinstance(only_cross_attention, bool):
+ only_cross_attention = [only_cross_attention] * len(down_block_types)
+
+ if isinstance(attention_head_dim, int):
+ attention_head_dim = (attention_head_dim,) * len(down_block_types)
+
+ # down
+ output_channel = block_out_channels[0]
+ for i, down_block_type in enumerate(down_block_types):
+ if task_type == "action":
+ name_index, mid_name = None, None
+ else:
+ name_index, mid_name = i, "MidBlock"
+ res = 2**i
+ input_channel = output_channel
+ output_channel = block_out_channels[i]
+ is_final_block = i == len(block_out_channels) - 1
+
+ down_block = get_down_block(
+ down_block_type,
+ num_layers=layers_per_block,
+ in_channels=input_channel,
+ out_channels=output_channel,
+ temb_channels=time_embed_dim,
+ add_downsample=not is_final_block,
+ resnet_eps=norm_eps,
+ resnet_act_fn=act_fn,
+ resnet_groups=norm_num_groups,
+ cross_attention_dim=cross_attention_dim,
+ attn_num_head_channels=attention_head_dim[i],
+ downsample_padding=downsample_padding,
+ dual_cross_attention=dual_cross_attention,
+ use_linear_projection=use_linear_projection,
+ only_cross_attention=only_cross_attention[i],
+ upcast_attention=upcast_attention,
+ resnet_time_scale_shift=resnet_time_scale_shift,
+ unet_use_cross_frame_attention=unet_use_cross_frame_attention,
+ unet_use_temporal_attention=unet_use_temporal_attention,
+ use_inflated_groupnorm=use_inflated_groupnorm,
+ use_motion_module=use_motion_module
+ and (res in motion_module_resolutions)
+ and (not motion_module_decoder_only),
+ motion_module_type=motion_module_type,
+ motion_module_kwargs=motion_module_kwargs,
+ name_index=name_index,
+ )
+ self.down_blocks.append(down_block)
+
+ # mid
+
+ if mid_block_type == "UNetMidBlock3DCrossAttn":
+ self.mid_block = UNetMidBlock3DCrossAttn(
+ in_channels=block_out_channels[-1],
+ temb_channels=time_embed_dim,
+ resnet_eps=norm_eps,
+ resnet_act_fn=act_fn,
+ output_scale_factor=mid_block_scale_factor,
+ resnet_time_scale_shift=resnet_time_scale_shift,
+ cross_attention_dim=cross_attention_dim,
+ attn_num_head_channels=attention_head_dim[-1],
+ resnet_groups=norm_num_groups,
+ dual_cross_attention=dual_cross_attention,
+ use_linear_projection=use_linear_projection,
+ upcast_attention=upcast_attention,
+ unet_use_cross_frame_attention=unet_use_cross_frame_attention,
+ unet_use_temporal_attention=unet_use_temporal_attention,
+ use_inflated_groupnorm=use_inflated_groupnorm,
+ use_motion_module=use_motion_module and motion_module_mid_block,
+ motion_module_type=motion_module_type,
+ motion_module_kwargs=motion_module_kwargs,
+ name=mid_name,
+ )
+ else:
+ raise ValueError(f"unknown mid_block_type : {mid_block_type}")
+
+ # count how many layers upsample the videos
+ self.num_upsamplers = 0
+
+ # up
+ reversed_block_out_channels = list(reversed(block_out_channels))
+ reversed_attention_head_dim = list(reversed(attention_head_dim))
+ only_cross_attention = list(reversed(only_cross_attention))
+ output_channel = reversed_block_out_channels[0]
+ for i, up_block_type in enumerate(up_block_types):
+ res = 2 ** (3 - i)
+ is_final_block = i == len(block_out_channels) - 1
+
+ if task_type == "action":
+ name_index = None
+ else:
+ name_index = i
+
+ prev_output_channel = output_channel
+ output_channel = reversed_block_out_channels[i]
+ input_channel = reversed_block_out_channels[
+ min(i + 1, len(block_out_channels) - 1)
+ ]
+
+ # add upsample block for all BUT final layer
+ if not is_final_block:
+ add_upsample = True
+ self.num_upsamplers += 1
+ else:
+ add_upsample = False
+
+ up_block = get_up_block(
+ up_block_type,
+ num_layers=layers_per_block + 1,
+ in_channels=input_channel,
+ out_channels=output_channel,
+ prev_output_channel=prev_output_channel,
+ temb_channels=time_embed_dim,
+ add_upsample=add_upsample,
+ resnet_eps=norm_eps,
+ resnet_act_fn=act_fn,
+ resnet_groups=norm_num_groups,
+ cross_attention_dim=cross_attention_dim,
+ attn_num_head_channels=reversed_attention_head_dim[i],
+ dual_cross_attention=dual_cross_attention,
+ use_linear_projection=use_linear_projection,
+ only_cross_attention=only_cross_attention[i],
+ upcast_attention=upcast_attention,
+ resnet_time_scale_shift=resnet_time_scale_shift,
+ unet_use_cross_frame_attention=unet_use_cross_frame_attention,
+ unet_use_temporal_attention=unet_use_temporal_attention,
+ use_inflated_groupnorm=use_inflated_groupnorm,
+ use_motion_module=use_motion_module
+ and (res in motion_module_resolutions),
+ motion_module_type=motion_module_type,
+ motion_module_kwargs=motion_module_kwargs,
+ name_index=name_index,
+ )
+ self.up_blocks.append(up_block)
+ prev_output_channel = output_channel
+
+ # out
+ if use_inflated_groupnorm:
+ self.conv_norm_out = InflatedGroupNorm(
+ num_channels=block_out_channels[0],
+ num_groups=norm_num_groups,
+ eps=norm_eps,
+ )
+ else:
+ self.conv_norm_out = nn.GroupNorm(
+ num_channels=block_out_channels[0],
+ num_groups=norm_num_groups,
+ eps=norm_eps,
+ )
+ self.conv_act = nn.SiLU()
+ self.conv_out = InflatedConv3d(
+ block_out_channels[0], out_channels, kernel_size=3, padding=1
+ )
+
+ self.mode = mode
+
+ @property
+ # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
+ def attn_processors(self) -> Dict[str, AttentionProcessor]:
+ r"""
+ Returns:
+ `dict` of attention processors: A dictionary containing all attention processors used in the model with
+ indexed by its weight name.
+ """
+ # set recursively
+ processors = {}
+
+ def fn_recursive_add_processors(
+ name: str,
+ module: torch.nn.Module,
+ processors: Dict[str, AttentionProcessor],
+ ):
+ if hasattr(module, "set_processor"):
+ processors[f"{name}.processor"] = module.processor
+
+ for sub_name, child in module.named_children():
+ if "temporal_transformer" not in sub_name:
+ fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
+
+ return processors
+
+ for name, module in self.named_children():
+ if "temporal_transformer" not in name:
+ fn_recursive_add_processors(name, module, processors)
+
+ return processors
+
+ def set_attention_slice(self, slice_size):
+ r"""
+ Enable sliced attention computation.
+
+ When this option is enabled, the attention module will split the input tensor in slices, to compute attention
+ in several steps. This is useful to save some memory in exchange for a small speed decrease.
+
+ Args:
+ slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`):
+ When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
+ `"max"`, maxium amount of memory will be saved by running only one slice at a time. If a number is
+ provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
+ must be a multiple of `slice_size`.
+ """
+ sliceable_head_dims = []
+
+ def fn_recursive_retrieve_slicable_dims(module: torch.nn.Module):
+ if hasattr(module, "set_attention_slice"):
+ sliceable_head_dims.append(module.sliceable_head_dim)
+
+ for child in module.children():
+ fn_recursive_retrieve_slicable_dims(child)
+
+ # retrieve number of attention layers
+ for module in self.children():
+ fn_recursive_retrieve_slicable_dims(module)
+
+ num_slicable_layers = len(sliceable_head_dims)
+
+ if slice_size == "auto":
+ # half the attention head size is usually a good trade-off between
+ # speed and memory
+ slice_size = [dim // 2 for dim in sliceable_head_dims]
+ elif slice_size == "max":
+ # make smallest slice possible
+ slice_size = num_slicable_layers * [1]
+
+ slice_size = (
+ num_slicable_layers * [slice_size]
+ if not isinstance(slice_size, list)
+ else slice_size
+ )
+
+ if len(slice_size) != len(sliceable_head_dims):
+ raise ValueError(
+ f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different"
+ f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}."
+ )
+
+ for i in range(len(slice_size)):
+ size = slice_size[i]
+ dim = sliceable_head_dims[i]
+ if size is not None and size > dim:
+ raise ValueError(f"size {size} has to be smaller or equal to {dim}.")
+
+ # Recursively walk through all the children.
+ # Any children which exposes the set_attention_slice method
+ # gets the message
+ def fn_recursive_set_attention_slice(
+ module: torch.nn.Module, slice_size: List[int]
+ ):
+ if hasattr(module, "set_attention_slice"):
+ module.set_attention_slice(slice_size.pop())
+
+ for child in module.children():
+ fn_recursive_set_attention_slice(child, slice_size)
+
+ reversed_slice_size = list(reversed(slice_size))
+ for module in self.children():
+ fn_recursive_set_attention_slice(module, reversed_slice_size)
+
+ def _set_gradient_checkpointing(self, module, value=False):
+ if hasattr(module, "gradient_checkpointing"):
+ module.gradient_checkpointing = value
+
+ # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_attn_processor
+ def set_attn_processor(
+ self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]
+ ):
+ r"""
+ Sets the attention processor to use to compute attention.
+
+ Parameters:
+ processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
+ The instantiated processor class or a dictionary of processor classes that will be set as the processor
+ for **all** `Attention` layers.
+
+ If `processor` is a dict, the key needs to define the path to the corresponding cross attention
+ processor. This is strongly recommended when setting trainable attention processors.
+
+ """
+ count = len(self.attn_processors.keys())
+
+ if isinstance(processor, dict) and len(processor) != count:
+ raise ValueError(
+ f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
+ f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
+ )
+
+ def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
+ if hasattr(module, "set_processor"):
+ if not isinstance(processor, dict):
+ module.set_processor(processor)
+ else:
+ module.set_processor(processor.pop(f"{name}.processor"))
+
+ for sub_name, child in module.named_children():
+ if "temporal_transformer" not in sub_name:
+ fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
+
+ for name, module in self.named_children():
+ if "temporal_transformer" not in name:
+ fn_recursive_attn_processor(name, module, processor)
+
+ def forward(
+ self,
+ sample: torch.FloatTensor,
+ timestep: Union[torch.Tensor, float, int],
+ encoder_hidden_states: torch.Tensor,
+ class_labels: Optional[torch.Tensor] = None,
+ pose_cond_fea: Optional[torch.Tensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
+ mid_block_additional_residual: Optional[torch.Tensor] = None,
+ return_dict: bool = True,
+ self_attention_additional_feats = None,
+ ) -> Union[UNet3DConditionOutput, Tuple]:
+ r"""
+ Args:
+ sample (`torch.FloatTensor`): (batch, channel, height, width) noisy inputs tensor
+ timestep (`torch.FloatTensor` or `float` or `int`): (batch) timesteps
+ encoder_hidden_states (`torch.FloatTensor`): (batch, sequence_length, feature_dim) encoder hidden states
+ return_dict (`bool`, *optional*, defaults to `True`):
+ Whether or not to return a [`models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain tuple.
+
+ Returns:
+ [`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:
+ [`~models.unet_2d_condition.UNet2DConditionOutput`] if `return_dict` is True, otherwise a `tuple`. When
+ returning a tuple, the first element is the sample tensor.
+ """
+ # By default samples have to be AT least a multiple of the overall upsampling factor.
+ # The overall upsampling factor is equal to 2 ** (# num of upsampling layears).
+ # However, the upsampling interpolation output size can be forced to fit any upsampling size
+ # on the fly if necessary.
+ default_overall_up_factor = 2**self.num_upsamplers
+
+ # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`
+ forward_upsample_size = False
+ upsample_size = None
+
+ if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):
+ logger.info("Forward upsample size to force interpolation output size.")
+ forward_upsample_size = True
+
+ # prepare attention_mask
+ if attention_mask is not None:
+ attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
+ attention_mask = attention_mask.unsqueeze(1)
+
+ # center input if necessary
+ if self.config.center_input_sample:
+ sample = 2 * sample - 1.0
+
+ # time
+ timesteps = timestep
+ if not torch.is_tensor(timesteps):
+ # This would be a good case for the `match` statement (Python 3.10+)
+ is_mps = sample.device.type == "mps"
+ if isinstance(timestep, float):
+ dtype = torch.float32 if is_mps else torch.float64
+ else:
+ dtype = torch.int32 if is_mps else torch.int64
+ timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
+ elif len(timesteps.shape) == 0:
+ timesteps = timesteps[None].to(sample.device)
+
+ # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+ timesteps = timesteps.expand(sample.shape[0])
+
+ t_emb = self.time_proj(timesteps)
+
+ # timesteps does not contain any weights and will always return f32 tensors
+ # but time_embedding might actually be running in fp16. so we need to cast here.
+ # there might be better ways to encapsulate this.
+ t_emb = t_emb.to(dtype=self.dtype)
+ emb = self.time_embedding(t_emb)
+
+ if self.class_embedding is not None:
+ if class_labels is None:
+ raise ValueError(
+ "class_labels should be provided when num_class_embeds > 0"
+ )
+
+ if self.config.class_embed_type == "timestep":
+ class_labels = self.time_proj(class_labels)
+
+ class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)
+ emb = emb + class_emb
+
+ # pre-process
+ sample = self.conv_in(sample)
+ if pose_cond_fea is not None:
+ sample = sample + pose_cond_fea
+
+ # down
+ down_block_res_samples = (sample,)
+ for downsample_block in self.down_blocks:
+ if (
+ hasattr(downsample_block, "has_cross_attention")
+ and downsample_block.has_cross_attention
+ ):
+ sample, res_samples = downsample_block(
+ hidden_states=sample,
+ temb=emb,
+ encoder_hidden_states=encoder_hidden_states,
+ attention_mask=attention_mask,
+ self_attention_additional_feats=self_attention_additional_feats,
+ mode=self.mode,
+ )
+ else:
+ sample, res_samples = downsample_block(
+ hidden_states=sample,
+ temb=emb,
+ encoder_hidden_states=encoder_hidden_states,
+ )
+
+ down_block_res_samples += res_samples
+
+ if down_block_additional_residuals is not None:
+ new_down_block_res_samples = ()
+
+ for down_block_res_sample, down_block_additional_residual in zip(
+ down_block_res_samples, down_block_additional_residuals
+ ):
+ down_block_res_sample = (
+ down_block_res_sample + down_block_additional_residual
+ )
+ new_down_block_res_samples += (down_block_res_sample,)
+
+ down_block_res_samples = new_down_block_res_samples
+
+ # mid
+ sample = self.mid_block(
+ sample,
+ emb,
+ encoder_hidden_states=encoder_hidden_states,
+ attention_mask=attention_mask,
+ self_attention_additional_feats=self_attention_additional_feats,
+ mode=self.mode,
+ )
+
+ if mid_block_additional_residual is not None:
+ sample = sample + mid_block_additional_residual
+
+ # up
+ for i, upsample_block in enumerate(self.up_blocks):
+ is_final_block = i == len(self.up_blocks) - 1
+
+ res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
+ down_block_res_samples = down_block_res_samples[
+ : -len(upsample_block.resnets)
+ ]
+
+ # if we have not reached the final block and need to forward the
+ # upsample size, we do it here
+ if not is_final_block and forward_upsample_size:
+ upsample_size = down_block_res_samples[-1].shape[2:]
+
+ if (
+ hasattr(upsample_block, "has_cross_attention")
+ and upsample_block.has_cross_attention
+ ):
+ sample = upsample_block(
+ hidden_states=sample,
+ temb=emb,
+ res_hidden_states_tuple=res_samples,
+ encoder_hidden_states=encoder_hidden_states,
+ upsample_size=upsample_size,
+ attention_mask=attention_mask,
+ self_attention_additional_feats=self_attention_additional_feats,
+ mode=self.mode,
+ )
+ else:
+ sample = upsample_block(
+ hidden_states=sample,
+ temb=emb,
+ res_hidden_states_tuple=res_samples,
+ upsample_size=upsample_size,
+ encoder_hidden_states=encoder_hidden_states,
+ )
+
+ # post-process
+ sample = self.conv_norm_out(sample)
+ sample = self.conv_act(sample)
+ sample = self.conv_out(sample)
+
+ if not return_dict:
+ return (sample,)
+
+ return UNet3DConditionOutput(sample=sample)
+
+ @classmethod
+ def from_pretrained_2d(
+ cls,
+ pretrained_model_path: PathLike,
+ motion_module_path: PathLike,
+ subfolder=None,
+ unet_additional_kwargs=None,
+ mm_zero_proj_out=False,
+ ):
+ pretrained_model_path = Path(pretrained_model_path)
+ motion_module_path = Path(motion_module_path)
+ if subfolder is not None:
+ pretrained_model_path = pretrained_model_path.joinpath(subfolder)
+ logger.info(
+ f"loaded temporal unet's pretrained weights from {pretrained_model_path} ..."
+ )
+
+ config_file = pretrained_model_path / "config.json"
+ if not (config_file.exists() and config_file.is_file()):
+ raise RuntimeError(f"{config_file} does not exist or is not a file")
+
+ unet_config = cls.load_config(config_file)
+ unet_config["_class_name"] = cls.__name__
+ unet_config["down_block_types"] = [
+ "CrossAttnDownBlock3D",
+ "CrossAttnDownBlock3D",
+ "CrossAttnDownBlock3D",
+ "DownBlock3D",
+ ]
+ unet_config["up_block_types"] = [
+ "UpBlock3D",
+ "CrossAttnUpBlock3D",
+ "CrossAttnUpBlock3D",
+ "CrossAttnUpBlock3D",
+ ]
+ unet_config["mid_block_type"] = "UNetMidBlock3DCrossAttn"
+
+ model = cls.from_config(unet_config, **unet_additional_kwargs)
+ # load the vanilla weights
+ if pretrained_model_path.joinpath(SAFETENSORS_WEIGHTS_NAME).exists():
+ logger.debug(
+ f"loading safeTensors weights from {pretrained_model_path} ..."
+ )
+ state_dict = load_file(
+ pretrained_model_path.joinpath(SAFETENSORS_WEIGHTS_NAME), device="cpu"
+ )
+
+ elif pretrained_model_path.joinpath(WEIGHTS_NAME).exists():
+ logger.debug(f"loading weights from {pretrained_model_path} ...")
+ state_dict = torch.load(
+ pretrained_model_path.joinpath(WEIGHTS_NAME),
+ map_location="cpu",
+ weights_only=True,
+ )
+ else:
+ raise FileNotFoundError(f"no weights file found in {pretrained_model_path}")
+
+ # load the motion module weights
+ if motion_module_path.exists() and motion_module_path.is_file():
+ if motion_module_path.suffix.lower() in [".pth", ".pt", ".ckpt"]:
+ logger.info(f"Load motion module params from {motion_module_path}")
+ motion_state_dict = torch.load(
+ motion_module_path, map_location="cpu", weights_only=True
+ )
+ elif motion_module_path.suffix.lower() == ".safetensors":
+ motion_state_dict = load_file(motion_module_path, device="cpu")
+ else:
+ raise RuntimeError(
+ f"unknown file format for motion module weights: {motion_module_path.suffix}"
+ )
+ if mm_zero_proj_out:
+ logger.info(f"Zero initialize proj_out layers in motion module...")
+ new_motion_state_dict = OrderedDict()
+ for k in motion_state_dict:
+ if "proj_out" in k:
+ continue
+ new_motion_state_dict[k] = motion_state_dict[k]
+ motion_state_dict = new_motion_state_dict
+
+ # merge the state dicts
+ state_dict.update(motion_state_dict)
+
+ # load the weights into the model
+ m, u = model.load_state_dict(state_dict, strict=False)
+ logger.debug(f"### missing keys: {len(m)}; \n### unexpected keys: {len(u)};")
+
+ params = [
+ p.numel() if "temporal" in n else 0 for n, p in model.named_parameters()
+ ]
+ logger.info(f"Loaded {sum(params) / 1e6}M-parameter motion module")
+
+ return model
diff --git a/src/models/unet_3d_blocks.py b/src/models/unet_3d_blocks.py
new file mode 100644
index 0000000000000000000000000000000000000000..dee275f2249146dbfe8f645718a1a1d8d2357eac
--- /dev/null
+++ b/src/models/unet_3d_blocks.py
@@ -0,0 +1,906 @@
+# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unet_2d_blocks.py
+
+import pdb
+
+import torch
+from torch import nn
+
+from .motion_module import get_motion_module
+
+# from .motion_module import get_motion_module
+from .resnet import Downsample3D, ResnetBlock3D, Upsample3D
+from .transformer_3d import Transformer3DModel
+
+
+def get_down_block(
+ down_block_type,
+ num_layers,
+ in_channels,
+ out_channels,
+ temb_channels,
+ add_downsample,
+ resnet_eps,
+ resnet_act_fn,
+ attn_num_head_channels,
+ resnet_groups=None,
+ cross_attention_dim=None,
+ downsample_padding=None,
+ dual_cross_attention=False,
+ use_linear_projection=False,
+ only_cross_attention=False,
+ upcast_attention=False,
+ resnet_time_scale_shift="default",
+ unet_use_cross_frame_attention=None,
+ unet_use_temporal_attention=None,
+ use_inflated_groupnorm=None,
+ use_motion_module=None,
+ motion_module_type=None,
+ motion_module_kwargs=None,
+ name_index=None,
+):
+ down_block_type = (
+ down_block_type[7:]
+ if down_block_type.startswith("UNetRes")
+ else down_block_type
+ )
+ if down_block_type == "DownBlock3D":
+ return DownBlock3D(
+ num_layers=num_layers,
+ in_channels=in_channels,
+ out_channels=out_channels,
+ temb_channels=temb_channels,
+ add_downsample=add_downsample,
+ resnet_eps=resnet_eps,
+ resnet_act_fn=resnet_act_fn,
+ resnet_groups=resnet_groups,
+ downsample_padding=downsample_padding,
+ resnet_time_scale_shift=resnet_time_scale_shift,
+ use_inflated_groupnorm=use_inflated_groupnorm,
+ use_motion_module=use_motion_module,
+ motion_module_type=motion_module_type,
+ motion_module_kwargs=motion_module_kwargs,
+ )
+ elif down_block_type == "CrossAttnDownBlock3D":
+ if cross_attention_dim is None:
+ raise ValueError(
+ "cross_attention_dim must be specified for CrossAttnDownBlock3D"
+ )
+ if name_index is not None:
+ name_index = f"CrossAttnDownBlock_{name_index}_"
+ return CrossAttnDownBlock3D(
+ num_layers=num_layers,
+ in_channels=in_channels,
+ out_channels=out_channels,
+ temb_channels=temb_channels,
+ add_downsample=add_downsample,
+ resnet_eps=resnet_eps,
+ resnet_act_fn=resnet_act_fn,
+ resnet_groups=resnet_groups,
+ downsample_padding=downsample_padding,
+ cross_attention_dim=cross_attention_dim,
+ attn_num_head_channels=attn_num_head_channels,
+ dual_cross_attention=dual_cross_attention,
+ use_linear_projection=use_linear_projection,
+ only_cross_attention=only_cross_attention,
+ upcast_attention=upcast_attention,
+ resnet_time_scale_shift=resnet_time_scale_shift,
+ unet_use_cross_frame_attention=unet_use_cross_frame_attention,
+ unet_use_temporal_attention=unet_use_temporal_attention,
+ use_inflated_groupnorm=use_inflated_groupnorm,
+ use_motion_module=use_motion_module,
+ motion_module_type=motion_module_type,
+ motion_module_kwargs=motion_module_kwargs,
+ name=name_index,
+ )
+ raise ValueError(f"{down_block_type} does not exist.")
+
+
+def get_up_block(
+ up_block_type,
+ num_layers,
+ in_channels,
+ out_channels,
+ prev_output_channel,
+ temb_channels,
+ add_upsample,
+ resnet_eps,
+ resnet_act_fn,
+ attn_num_head_channels,
+ resnet_groups=None,
+ cross_attention_dim=None,
+ dual_cross_attention=False,
+ use_linear_projection=False,
+ only_cross_attention=False,
+ upcast_attention=False,
+ resnet_time_scale_shift="default",
+ unet_use_cross_frame_attention=None,
+ unet_use_temporal_attention=None,
+ use_inflated_groupnorm=None,
+ use_motion_module=None,
+ motion_module_type=None,
+ motion_module_kwargs=None,
+ name_index=None,
+):
+ up_block_type = (
+ up_block_type[7:] if up_block_type.startswith("UNetRes") else up_block_type
+ )
+ if up_block_type == "UpBlock3D":
+ return UpBlock3D(
+ num_layers=num_layers,
+ in_channels=in_channels,
+ out_channels=out_channels,
+ prev_output_channel=prev_output_channel,
+ temb_channels=temb_channels,
+ add_upsample=add_upsample,
+ resnet_eps=resnet_eps,
+ resnet_act_fn=resnet_act_fn,
+ resnet_groups=resnet_groups,
+ resnet_time_scale_shift=resnet_time_scale_shift,
+ use_inflated_groupnorm=use_inflated_groupnorm,
+ use_motion_module=use_motion_module,
+ motion_module_type=motion_module_type,
+ motion_module_kwargs=motion_module_kwargs,
+ )
+ elif up_block_type == "CrossAttnUpBlock3D":
+ if cross_attention_dim is None:
+ raise ValueError(
+ "cross_attention_dim must be specified for CrossAttnUpBlock3D"
+ )
+ if name_index is not None:
+ name_index = f"CrossAttnUpBlock_{name_index}_"
+ return CrossAttnUpBlock3D(
+ num_layers=num_layers,
+ in_channels=in_channels,
+ out_channels=out_channels,
+ prev_output_channel=prev_output_channel,
+ temb_channels=temb_channels,
+ add_upsample=add_upsample,
+ resnet_eps=resnet_eps,
+ resnet_act_fn=resnet_act_fn,
+ resnet_groups=resnet_groups,
+ cross_attention_dim=cross_attention_dim,
+ attn_num_head_channels=attn_num_head_channels,
+ dual_cross_attention=dual_cross_attention,
+ use_linear_projection=use_linear_projection,
+ only_cross_attention=only_cross_attention,
+ upcast_attention=upcast_attention,
+ resnet_time_scale_shift=resnet_time_scale_shift,
+ unet_use_cross_frame_attention=unet_use_cross_frame_attention,
+ unet_use_temporal_attention=unet_use_temporal_attention,
+ use_inflated_groupnorm=use_inflated_groupnorm,
+ use_motion_module=use_motion_module,
+ motion_module_type=motion_module_type,
+ motion_module_kwargs=motion_module_kwargs,
+ name=name_index,
+ )
+ raise ValueError(f"{up_block_type} does not exist.")
+
+
+class UNetMidBlock3DCrossAttn(nn.Module):
+ def __init__(
+ self,
+ in_channels: int,
+ temb_channels: int,
+ dropout: float = 0.0,
+ num_layers: int = 1,
+ resnet_eps: float = 1e-6,
+ resnet_time_scale_shift: str = "default",
+ resnet_act_fn: str = "swish",
+ resnet_groups: int = 32,
+ resnet_pre_norm: bool = True,
+ attn_num_head_channels=1,
+ output_scale_factor=1.0,
+ cross_attention_dim=1280,
+ dual_cross_attention=False,
+ use_linear_projection=False,
+ upcast_attention=False,
+ unet_use_cross_frame_attention=None,
+ unet_use_temporal_attention=None,
+ use_inflated_groupnorm=None,
+ use_motion_module=None,
+ motion_module_type=None,
+ motion_module_kwargs=None,
+ name=None
+ ):
+ super().__init__()
+
+ self.has_cross_attention = True
+ self.attn_num_head_channels = attn_num_head_channels
+ resnet_groups = (
+ resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)
+ )
+ self.name = name
+ # there is always at least one resnet
+ resnets = [
+ ResnetBlock3D(
+ in_channels=in_channels,
+ out_channels=in_channels,
+ temb_channels=temb_channels,
+ eps=resnet_eps,
+ groups=resnet_groups,
+ dropout=dropout,
+ time_embedding_norm=resnet_time_scale_shift,
+ non_linearity=resnet_act_fn,
+ output_scale_factor=output_scale_factor,
+ pre_norm=resnet_pre_norm,
+ use_inflated_groupnorm=use_inflated_groupnorm,
+ )
+ ]
+ attentions = []
+ motion_modules = []
+ for i in range(num_layers):
+ if dual_cross_attention:
+ raise NotImplementedError
+ if self.name is not None:
+ attn_name = f"{self.name}_{i}_TransformerModel"
+ else:
+ attn_name = None
+ attentions.append(
+ Transformer3DModel(
+ attn_num_head_channels,
+ in_channels // attn_num_head_channels,
+ in_channels=in_channels,
+ num_layers=1,
+ cross_attention_dim=cross_attention_dim,
+ norm_num_groups=resnet_groups,
+ use_linear_projection=use_linear_projection,
+ upcast_attention=upcast_attention,
+ unet_use_cross_frame_attention=unet_use_cross_frame_attention,
+ unet_use_temporal_attention=unet_use_temporal_attention,
+ name=attn_name,
+ )
+ )
+ motion_modules.append(
+ get_motion_module(
+ in_channels=in_channels,
+ motion_module_type=motion_module_type,
+ motion_module_kwargs=motion_module_kwargs,
+ )
+ if use_motion_module
+ else None
+ )
+ resnets.append(
+ ResnetBlock3D(
+ in_channels=in_channels,
+ out_channels=in_channels,
+ temb_channels=temb_channels,
+ eps=resnet_eps,
+ groups=resnet_groups,
+ dropout=dropout,
+ time_embedding_norm=resnet_time_scale_shift,
+ non_linearity=resnet_act_fn,
+ output_scale_factor=output_scale_factor,
+ pre_norm=resnet_pre_norm,
+ use_inflated_groupnorm=use_inflated_groupnorm,
+ )
+ )
+
+ self.attentions = nn.ModuleList(attentions)
+ self.resnets = nn.ModuleList(resnets)
+ self.motion_modules = nn.ModuleList(motion_modules)
+
+ def forward(
+ self,
+ hidden_states,
+ temb=None,
+ encoder_hidden_states=None,
+ attention_mask=None,
+ self_attention_additional_feats=None,
+ mode=None,
+ ):
+ hidden_states = self.resnets[0](hidden_states, temb)
+ for attn, resnet, motion_module in zip(
+ self.attentions, self.resnets[1:], self.motion_modules
+ ):
+ hidden_states = attn(
+ hidden_states,
+ encoder_hidden_states=encoder_hidden_states,
+ self_attention_additional_feats=self_attention_additional_feats,
+ mode=mode,
+ ).sample
+ hidden_states = (
+ motion_module(
+ hidden_states, temb, encoder_hidden_states=encoder_hidden_states
+ )
+ if motion_module is not None
+ else hidden_states
+ )
+ hidden_states = resnet(hidden_states, temb)
+
+ return hidden_states
+
+
+class CrossAttnDownBlock3D(nn.Module):
+ def __init__(
+ self,
+ in_channels: int,
+ out_channels: int,
+ temb_channels: int,
+ dropout: float = 0.0,
+ num_layers: int = 1,
+ resnet_eps: float = 1e-6,
+ resnet_time_scale_shift: str = "default",
+ resnet_act_fn: str = "swish",
+ resnet_groups: int = 32,
+ resnet_pre_norm: bool = True,
+ attn_num_head_channels=1,
+ cross_attention_dim=1280,
+ output_scale_factor=1.0,
+ downsample_padding=1,
+ add_downsample=True,
+ dual_cross_attention=False,
+ use_linear_projection=False,
+ only_cross_attention=False,
+ upcast_attention=False,
+ unet_use_cross_frame_attention=None,
+ unet_use_temporal_attention=None,
+ use_inflated_groupnorm=None,
+ use_motion_module=None,
+ motion_module_type=None,
+ motion_module_kwargs=None,
+ name=None,
+ ):
+ super().__init__()
+ resnets = []
+ attentions = []
+ motion_modules = []
+
+ self.has_cross_attention = True
+ self.attn_num_head_channels = attn_num_head_channels
+ self.name=name
+
+ for i in range(num_layers):
+ in_channels = in_channels if i == 0 else out_channels
+ resnets.append(
+ ResnetBlock3D(
+ in_channels=in_channels,
+ out_channels=out_channels,
+ temb_channels=temb_channels,
+ eps=resnet_eps,
+ groups=resnet_groups,
+ dropout=dropout,
+ time_embedding_norm=resnet_time_scale_shift,
+ non_linearity=resnet_act_fn,
+ output_scale_factor=output_scale_factor,
+ pre_norm=resnet_pre_norm,
+ use_inflated_groupnorm=use_inflated_groupnorm,
+ )
+ )
+ if dual_cross_attention:
+ raise NotImplementedError
+ if self.name is not None:
+ attn_name = f"{self.name}_{i}_TransformerModel"
+ else:
+ attn_name = None
+ attentions.append(
+ Transformer3DModel(
+ attn_num_head_channels,
+ out_channels // attn_num_head_channels,
+ in_channels=out_channels,
+ num_layers=1,
+ cross_attention_dim=cross_attention_dim,
+ norm_num_groups=resnet_groups,
+ use_linear_projection=use_linear_projection,
+ only_cross_attention=only_cross_attention,
+ upcast_attention=upcast_attention,
+ unet_use_cross_frame_attention=unet_use_cross_frame_attention,
+ unet_use_temporal_attention=unet_use_temporal_attention,
+ name=attn_name,
+ )
+ )
+ motion_modules.append(
+ get_motion_module(
+ in_channels=out_channels,
+ motion_module_type=motion_module_type,
+ motion_module_kwargs=motion_module_kwargs,
+ )
+ if use_motion_module
+ else None
+ )
+
+ self.attentions = nn.ModuleList(attentions)
+ self.resnets = nn.ModuleList(resnets)
+ self.motion_modules = nn.ModuleList(motion_modules)
+
+ if add_downsample:
+ self.downsamplers = nn.ModuleList(
+ [
+ Downsample3D(
+ out_channels,
+ use_conv=True,
+ out_channels=out_channels,
+ padding=downsample_padding,
+ name="op",
+ )
+ ]
+ )
+ else:
+ self.downsamplers = None
+
+ self.gradient_checkpointing = False
+
+ def forward(
+ self,
+ hidden_states,
+ temb=None,
+ encoder_hidden_states=None,
+ attention_mask=None,
+ self_attention_additional_feats=None,
+ mode=None,
+ ):
+ output_states = ()
+
+ for i, (resnet, attn, motion_module) in enumerate(
+ zip(self.resnets, self.attentions, self.motion_modules)
+ ):
+ # self.gradient_checkpointing = False
+ if self.training and self.gradient_checkpointing:
+
+ def create_custom_forward(module, return_dict=None):
+ def custom_forward(*inputs):
+ if return_dict is not None:
+ return module(*inputs, return_dict=return_dict)
+ else:
+ return module(*inputs)
+
+ return custom_forward
+
+ hidden_states = torch.utils.checkpoint.checkpoint(
+ create_custom_forward(resnet), hidden_states, temb
+ )
+ hidden_states = torch.utils.checkpoint.checkpoint(
+ create_custom_forward(attn, return_dict=False),
+ hidden_states,
+ encoder_hidden_states,
+ self_attention_additional_feats,
+ mode,
+ )[0]
+
+ # add motion module
+ if motion_module is not None:
+ hidden_states = torch.utils.checkpoint.checkpoint(
+ create_custom_forward(motion_module),
+ hidden_states.requires_grad_(),
+ temb,
+ encoder_hidden_states,
+ )
+
+ else:
+ hidden_states = resnet(hidden_states, temb)
+ hidden_states = attn(
+ hidden_states,
+ encoder_hidden_states=encoder_hidden_states,
+ self_attention_additional_feats=self_attention_additional_feats,
+ mode=mode,
+ ).sample
+
+ # add motion module
+ hidden_states = (
+ motion_module(
+ hidden_states, temb, encoder_hidden_states=encoder_hidden_states
+ )
+ if motion_module is not None
+ else hidden_states
+ )
+
+ output_states += (hidden_states,)
+
+ if self.downsamplers is not None:
+ for downsampler in self.downsamplers:
+ hidden_states = downsampler(hidden_states)
+
+ output_states += (hidden_states,)
+
+ return hidden_states, output_states
+
+
+class DownBlock3D(nn.Module):
+ def __init__(
+ self,
+ in_channels: int,
+ out_channels: int,
+ temb_channels: int,
+ dropout: float = 0.0,
+ num_layers: int = 1,
+ resnet_eps: float = 1e-6,
+ resnet_time_scale_shift: str = "default",
+ resnet_act_fn: str = "swish",
+ resnet_groups: int = 32,
+ resnet_pre_norm: bool = True,
+ output_scale_factor=1.0,
+ add_downsample=True,
+ downsample_padding=1,
+ use_inflated_groupnorm=None,
+ use_motion_module=None,
+ motion_module_type=None,
+ motion_module_kwargs=None,
+ ):
+ super().__init__()
+ resnets = []
+ motion_modules = []
+
+ # use_motion_module = False
+ for i in range(num_layers):
+ in_channels = in_channels if i == 0 else out_channels
+ resnets.append(
+ ResnetBlock3D(
+ in_channels=in_channels,
+ out_channels=out_channels,
+ temb_channels=temb_channels,
+ eps=resnet_eps,
+ groups=resnet_groups,
+ dropout=dropout,
+ time_embedding_norm=resnet_time_scale_shift,
+ non_linearity=resnet_act_fn,
+ output_scale_factor=output_scale_factor,
+ pre_norm=resnet_pre_norm,
+ use_inflated_groupnorm=use_inflated_groupnorm,
+ )
+ )
+ motion_modules.append(
+ get_motion_module(
+ in_channels=out_channels,
+ motion_module_type=motion_module_type,
+ motion_module_kwargs=motion_module_kwargs,
+ )
+ if use_motion_module
+ else None
+ )
+
+ self.resnets = nn.ModuleList(resnets)
+ self.motion_modules = nn.ModuleList(motion_modules)
+
+ if add_downsample:
+ self.downsamplers = nn.ModuleList(
+ [
+ Downsample3D(
+ out_channels,
+ use_conv=True,
+ out_channels=out_channels,
+ padding=downsample_padding,
+ name="op",
+ )
+ ]
+ )
+ else:
+ self.downsamplers = None
+
+ self.gradient_checkpointing = False
+
+ def forward(self, hidden_states, temb=None, encoder_hidden_states=None):
+ output_states = ()
+
+ for resnet, motion_module in zip(self.resnets, self.motion_modules):
+ # print(f"DownBlock3D {self.gradient_checkpointing = }")
+ if self.training and self.gradient_checkpointing:
+
+ def create_custom_forward(module):
+ def custom_forward(*inputs):
+ return module(*inputs)
+
+ return custom_forward
+
+ hidden_states = torch.utils.checkpoint.checkpoint(
+ create_custom_forward(resnet), hidden_states, temb
+ )
+ if motion_module is not None:
+ hidden_states = torch.utils.checkpoint.checkpoint(
+ create_custom_forward(motion_module),
+ hidden_states.requires_grad_(),
+ temb,
+ encoder_hidden_states,
+ )
+ else:
+ hidden_states = resnet(hidden_states, temb)
+
+ # add motion module
+ hidden_states = (
+ motion_module(
+ hidden_states, temb, encoder_hidden_states=encoder_hidden_states
+ )
+ if motion_module is not None
+ else hidden_states
+ )
+
+ output_states += (hidden_states,)
+
+ if self.downsamplers is not None:
+ for downsampler in self.downsamplers:
+ hidden_states = downsampler(hidden_states)
+
+ output_states += (hidden_states,)
+
+ return hidden_states, output_states
+
+
+class CrossAttnUpBlock3D(nn.Module):
+ def __init__(
+ self,
+ in_channels: int,
+ out_channels: int,
+ prev_output_channel: int,
+ temb_channels: int,
+ dropout: float = 0.0,
+ num_layers: int = 1,
+ resnet_eps: float = 1e-6,
+ resnet_time_scale_shift: str = "default",
+ resnet_act_fn: str = "swish",
+ resnet_groups: int = 32,
+ resnet_pre_norm: bool = True,
+ attn_num_head_channels=1,
+ cross_attention_dim=1280,
+ output_scale_factor=1.0,
+ add_upsample=True,
+ dual_cross_attention=False,
+ use_linear_projection=False,
+ only_cross_attention=False,
+ upcast_attention=False,
+ unet_use_cross_frame_attention=None,
+ unet_use_temporal_attention=None,
+ use_motion_module=None,
+ use_inflated_groupnorm=None,
+ motion_module_type=None,
+ motion_module_kwargs=None,
+ name=None
+ ):
+ super().__init__()
+ resnets = []
+ attentions = []
+ motion_modules = []
+
+ self.has_cross_attention = True
+ self.attn_num_head_channels = attn_num_head_channels
+ self.name = name
+
+ for i in range(num_layers):
+ res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
+ resnet_in_channels = prev_output_channel if i == 0 else out_channels
+
+ resnets.append(
+ ResnetBlock3D(
+ in_channels=resnet_in_channels + res_skip_channels,
+ out_channels=out_channels,
+ temb_channels=temb_channels,
+ eps=resnet_eps,
+ groups=resnet_groups,
+ dropout=dropout,
+ time_embedding_norm=resnet_time_scale_shift,
+ non_linearity=resnet_act_fn,
+ output_scale_factor=output_scale_factor,
+ pre_norm=resnet_pre_norm,
+ use_inflated_groupnorm=use_inflated_groupnorm,
+ )
+ )
+ if dual_cross_attention:
+ raise NotImplementedError
+ if self.name is not None:
+ attn_name = f"{self.name}_{i}_TransformerModel"
+ else:
+ attn_name = None
+ attentions.append(
+ Transformer3DModel(
+ attn_num_head_channels,
+ out_channels // attn_num_head_channels,
+ in_channels=out_channels,
+ num_layers=1,
+ cross_attention_dim=cross_attention_dim,
+ norm_num_groups=resnet_groups,
+ use_linear_projection=use_linear_projection,
+ only_cross_attention=only_cross_attention,
+ upcast_attention=upcast_attention,
+ unet_use_cross_frame_attention=unet_use_cross_frame_attention,
+ unet_use_temporal_attention=unet_use_temporal_attention,
+ name=attn_name,
+ )
+ )
+ motion_modules.append(
+ get_motion_module(
+ in_channels=out_channels,
+ motion_module_type=motion_module_type,
+ motion_module_kwargs=motion_module_kwargs,
+ )
+ if use_motion_module
+ else None
+ )
+
+ self.attentions = nn.ModuleList(attentions)
+ self.resnets = nn.ModuleList(resnets)
+ self.motion_modules = nn.ModuleList(motion_modules)
+
+ if add_upsample:
+ self.upsamplers = nn.ModuleList(
+ [Upsample3D(out_channels, use_conv=True, out_channels=out_channels)]
+ )
+ else:
+ self.upsamplers = None
+
+ self.gradient_checkpointing = False
+
+ def forward(
+ self,
+ hidden_states,
+ res_hidden_states_tuple,
+ temb=None,
+ encoder_hidden_states=None,
+ upsample_size=None,
+ attention_mask=None,
+ self_attention_additional_feats=None,
+ mode=None,
+ ):
+ for i, (resnet, attn, motion_module) in enumerate(
+ zip(self.resnets, self.attentions, self.motion_modules)
+ ):
+ # pop res hidden states
+ res_hidden_states = res_hidden_states_tuple[-1]
+ res_hidden_states_tuple = res_hidden_states_tuple[:-1]
+ hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
+
+ if self.training and self.gradient_checkpointing:
+
+ def create_custom_forward(module, return_dict=None):
+ def custom_forward(*inputs):
+ if return_dict is not None:
+ return module(*inputs, return_dict=return_dict)
+ else:
+ return module(*inputs)
+
+ return custom_forward
+
+ hidden_states = torch.utils.checkpoint.checkpoint(
+ create_custom_forward(resnet), hidden_states, temb
+ )
+ hidden_states = torch.utils.checkpoint.checkpoint(
+ create_custom_forward(attn, return_dict=False),
+ hidden_states,
+ encoder_hidden_states,
+ self_attention_additional_feats,
+ mode,
+ )[0]
+ if motion_module is not None:
+ hidden_states = torch.utils.checkpoint.checkpoint(
+ create_custom_forward(motion_module),
+ hidden_states.requires_grad_(),
+ temb,
+ encoder_hidden_states,
+ )
+
+ else:
+ hidden_states = resnet(hidden_states, temb)
+ hidden_states = attn(
+ hidden_states,
+ encoder_hidden_states=encoder_hidden_states,
+ self_attention_additional_feats=self_attention_additional_feats,
+ mode=mode,
+ ).sample
+
+ # add motion module
+ hidden_states = (
+ motion_module(
+ hidden_states, temb, encoder_hidden_states=encoder_hidden_states
+ )
+ if motion_module is not None
+ else hidden_states
+ )
+
+ if self.upsamplers is not None:
+ for upsampler in self.upsamplers:
+ hidden_states = upsampler(hidden_states, upsample_size)
+
+ return hidden_states
+
+
+class UpBlock3D(nn.Module):
+ def __init__(
+ self,
+ in_channels: int,
+ prev_output_channel: int,
+ out_channels: int,
+ temb_channels: int,
+ dropout: float = 0.0,
+ num_layers: int = 1,
+ resnet_eps: float = 1e-6,
+ resnet_time_scale_shift: str = "default",
+ resnet_act_fn: str = "swish",
+ resnet_groups: int = 32,
+ resnet_pre_norm: bool = True,
+ output_scale_factor=1.0,
+ add_upsample=True,
+ use_inflated_groupnorm=None,
+ use_motion_module=None,
+ motion_module_type=None,
+ motion_module_kwargs=None,
+ ):
+ super().__init__()
+ resnets = []
+ motion_modules = []
+
+ # use_motion_module = False
+ for i in range(num_layers):
+ res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
+ resnet_in_channels = prev_output_channel if i == 0 else out_channels
+
+ resnets.append(
+ ResnetBlock3D(
+ in_channels=resnet_in_channels + res_skip_channels,
+ out_channels=out_channels,
+ temb_channels=temb_channels,
+ eps=resnet_eps,
+ groups=resnet_groups,
+ dropout=dropout,
+ time_embedding_norm=resnet_time_scale_shift,
+ non_linearity=resnet_act_fn,
+ output_scale_factor=output_scale_factor,
+ pre_norm=resnet_pre_norm,
+ use_inflated_groupnorm=use_inflated_groupnorm,
+ )
+ )
+ motion_modules.append(
+ get_motion_module(
+ in_channels=out_channels,
+ motion_module_type=motion_module_type,
+ motion_module_kwargs=motion_module_kwargs,
+ )
+ if use_motion_module
+ else None
+ )
+
+ self.resnets = nn.ModuleList(resnets)
+ self.motion_modules = nn.ModuleList(motion_modules)
+
+ if add_upsample:
+ self.upsamplers = nn.ModuleList(
+ [Upsample3D(out_channels, use_conv=True, out_channels=out_channels)]
+ )
+ else:
+ self.upsamplers = None
+
+ self.gradient_checkpointing = False
+
+ def forward(
+ self,
+ hidden_states,
+ res_hidden_states_tuple,
+ temb=None,
+ upsample_size=None,
+ encoder_hidden_states=None,
+ ):
+ for resnet, motion_module in zip(self.resnets, self.motion_modules):
+ # pop res hidden states
+ res_hidden_states = res_hidden_states_tuple[-1]
+ res_hidden_states_tuple = res_hidden_states_tuple[:-1]
+ hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
+
+ # print(f"UpBlock3D {self.gradient_checkpointing = }")
+ if self.training and self.gradient_checkpointing:
+
+ def create_custom_forward(module):
+ def custom_forward(*inputs):
+ return module(*inputs)
+
+ return custom_forward
+
+ hidden_states = torch.utils.checkpoint.checkpoint(
+ create_custom_forward(resnet), hidden_states, temb
+ )
+ if motion_module is not None:
+ hidden_states = torch.utils.checkpoint.checkpoint(
+ create_custom_forward(motion_module),
+ hidden_states.requires_grad_(),
+ temb,
+ encoder_hidden_states,
+ )
+ else:
+ hidden_states = resnet(hidden_states, temb)
+ hidden_states = (
+ motion_module(
+ hidden_states, temb, encoder_hidden_states=encoder_hidden_states
+ )
+ if motion_module is not None
+ else hidden_states
+ )
+
+ if self.upsamplers is not None:
+ for upsampler in self.upsamplers:
+ hidden_states = upsampler(hidden_states, upsample_size)
+
+ return hidden_states
diff --git a/src/pipelines/__init__.py b/src/pipelines/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/src/pipelines/__pycache__/__init__.cpython-310.pyc b/src/pipelines/__pycache__/__init__.cpython-310.pyc
new file mode 100644
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diff --git a/src/pipelines/__pycache__/context.cpython-310.pyc b/src/pipelines/__pycache__/context.cpython-310.pyc
new file mode 100644
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diff --git a/src/pipelines/__pycache__/pipeline_pose2vid_long.cpython-310.pyc b/src/pipelines/__pycache__/pipeline_pose2vid_long.cpython-310.pyc
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index 0000000000000000000000000000000000000000..0a33bb9092296a33152457ef99c0cfe1a2db3c49
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diff --git a/src/pipelines/__pycache__/utils.cpython-310.pyc b/src/pipelines/__pycache__/utils.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..82556c96e173cc1cfd466165d5f118e9d95061d4
Binary files /dev/null and b/src/pipelines/__pycache__/utils.cpython-310.pyc differ
diff --git a/src/pipelines/context.py b/src/pipelines/context.py
new file mode 100644
index 0000000000000000000000000000000000000000..c00274c8861b5fae3b86af437b5f14998045a5dc
--- /dev/null
+++ b/src/pipelines/context.py
@@ -0,0 +1,76 @@
+# TODO: Adapted from cli
+from typing import Callable, List, Optional
+
+import numpy as np
+
+
+def ordered_halving(val):
+ bin_str = f"{val:064b}"
+ bin_flip = bin_str[::-1]
+ as_int = int(bin_flip, 2)
+
+ return as_int / (1 << 64)
+
+
+def uniform(
+ step: int = ...,
+ num_steps: Optional[int] = None,
+ num_frames: int = ...,
+ context_size: Optional[int] = None,
+ context_stride: int = 3,
+ context_overlap: int = 4,
+ closed_loop: bool = True,
+):
+ if num_frames <= context_size:
+ yield list(range(num_frames))
+ return
+
+ context_stride = min(
+ context_stride, int(np.ceil(np.log2(num_frames / context_size))) + 1
+ )
+
+ for context_step in 1 << np.arange(context_stride):
+ pad = int(round(num_frames * ordered_halving(step)))
+ for j in range(
+ int(ordered_halving(step) * context_step) + pad,
+ num_frames + pad + (0 if closed_loop else -context_overlap),
+ (context_size * context_step - context_overlap),
+ ):
+ yield [
+ e % num_frames
+ for e in range(j, j + context_size * context_step, context_step)
+ ]
+
+
+def get_context_scheduler(name: str) -> Callable:
+ if name == "uniform":
+ return uniform
+ else:
+ raise ValueError(f"Unknown context_overlap policy {name}")
+
+
+def get_total_steps(
+ scheduler,
+ timesteps: List[int],
+ num_steps: Optional[int] = None,
+ num_frames: int = ...,
+ context_size: Optional[int] = None,
+ context_stride: int = 3,
+ context_overlap: int = 4,
+ closed_loop: bool = True,
+):
+ return sum(
+ len(
+ list(
+ scheduler(
+ i,
+ num_steps,
+ num_frames,
+ context_size,
+ context_stride,
+ context_overlap,
+ )
+ )
+ )
+ for i in range(len(timesteps))
+ )
diff --git a/src/pipelines/pipeline_lmks2vid_long.py b/src/pipelines/pipeline_lmks2vid_long.py
new file mode 100644
index 0000000000000000000000000000000000000000..2f03c5e6ee9220866b6ca32593318c06e522433f
--- /dev/null
+++ b/src/pipelines/pipeline_lmks2vid_long.py
@@ -0,0 +1,622 @@
+import inspect
+import math
+from dataclasses import dataclass
+from typing import Callable, List, Optional, Union
+
+import numpy as np
+import torch
+from diffusers import DiffusionPipeline
+from diffusers.image_processor import VaeImageProcessor
+from diffusers.schedulers import (
+ DDIMScheduler,
+ DPMSolverMultistepScheduler,
+ EulerAncestralDiscreteScheduler,
+ EulerDiscreteScheduler,
+ LMSDiscreteScheduler,
+ PNDMScheduler,
+)
+from diffusers.utils import BaseOutput, deprecate, is_accelerate_available, logging
+from diffusers.utils.torch_utils import randn_tensor
+from einops import rearrange
+from tqdm import tqdm
+from transformers import CLIPImageProcessor
+
+from src.models.mutual_self_attention import ReferenceAttentionControl
+from src.pipelines.context import get_context_scheduler
+from src.pipelines.utils import get_tensor_interpolation_method
+
+
+def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
+ """
+ Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and
+ Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4
+ """
+ std_text = noise_pred_text.std(
+ dim=list(range(1, noise_pred_text.ndim)), keepdim=True
+ )
+ std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
+ # rescale the results from guidance (fixes overexposure)
+ noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
+ # mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images
+ noise_cfg = (
+ guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg
+ )
+ print(f"{(std_text / std_cfg) = }")
+ return noise_cfg
+
+
+@dataclass
+class Pose2VideoPipelineOutput(BaseOutput):
+ videos: Union[torch.Tensor, np.ndarray]
+
+
+class Pose2VideoPipeline(DiffusionPipeline):
+ _optional_components = []
+
+ def __init__(
+ self,
+ vae,
+ image_encoder,
+ reference_unet,
+ denoising_unet,
+ pose_guider1,
+ pose_guider2,
+ scheduler: Union[
+ DDIMScheduler,
+ PNDMScheduler,
+ LMSDiscreteScheduler,
+ EulerDiscreteScheduler,
+ EulerAncestralDiscreteScheduler,
+ DPMSolverMultistepScheduler,
+ ],
+ audio_guider=None,
+ image_proj_model=None,
+ tokenizer=None,
+ text_encoder=None,
+ ):
+ super().__init__()
+
+ self.register_modules(
+ vae=vae,
+ image_encoder=image_encoder,
+ reference_unet=reference_unet,
+ denoising_unet=denoising_unet,
+ pose_guider1=pose_guider1,
+ pose_guider2=pose_guider2,
+ scheduler=scheduler,
+ audio_guider=audio_guider,
+ image_proj_model=image_proj_model,
+ tokenizer=tokenizer,
+ text_encoder=text_encoder,
+ )
+ self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+ self.clip_image_processor = CLIPImageProcessor()
+ self.ref_image_processor = VaeImageProcessor(
+ vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True
+ )
+ self.cond_image_processor = VaeImageProcessor(
+ vae_scale_factor=self.vae_scale_factor,
+ do_convert_rgb=True,
+ do_normalize=False,
+ )
+
+ def enable_vae_slicing(self):
+ self.vae.enable_slicing()
+
+ def disable_vae_slicing(self):
+ self.vae.disable_slicing()
+
+ def enable_sequential_cpu_offload(self, gpu_id=0):
+ if is_accelerate_available():
+ from accelerate import cpu_offload
+ else:
+ raise ImportError("Please install accelerate via `pip install accelerate`")
+
+ device = torch.device(f"cuda:{gpu_id}")
+
+ for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]:
+ if cpu_offloaded_model is not None:
+ cpu_offload(cpu_offloaded_model, device)
+
+ @property
+ def _execution_device(self):
+ if self.device != torch.device("meta") or not hasattr(self.unet, "_hf_hook"):
+ return self.device
+ for module in self.unet.modules():
+ if (
+ hasattr(module, "_hf_hook")
+ and hasattr(module._hf_hook, "execution_device")
+ and module._hf_hook.execution_device is not None
+ ):
+ return torch.device(module._hf_hook.execution_device)
+ return self.device
+
+ def decode_latents(self, latents):
+ video_length = latents.shape[2]
+ latents = 1 / 0.18215 * latents
+ latents = rearrange(latents, "b c f h w -> (b f) c h w")
+ # video = self.vae.decode(latents).sample
+ video = []
+ for frame_idx in tqdm(range(latents.shape[0])):
+ video.append(self.vae.decode(latents[frame_idx : frame_idx + 1]).sample)
+ video = torch.cat(video)
+ video = rearrange(video, "(b f) c h w -> b c f h w", f=video_length)
+ video = (video / 2 + 0.5).clamp(0, 1)
+ # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
+ video = video.cpu().float().numpy()
+ return video
+
+ def prepare_extra_step_kwargs(self, generator, eta):
+ # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+ # eta (ฮท) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+ # eta corresponds to ฮท in DDIM paper: https://arxiv.org/abs/2010.02502
+ # and should be between [0, 1]
+
+ accepts_eta = "eta" in set(
+ inspect.signature(self.scheduler.step).parameters.keys()
+ )
+ extra_step_kwargs = {}
+ if accepts_eta:
+ extra_step_kwargs["eta"] = eta
+
+ # check if the scheduler accepts generator
+ accepts_generator = "generator" in set(
+ inspect.signature(self.scheduler.step).parameters.keys()
+ )
+ if accepts_generator:
+ extra_step_kwargs["generator"] = generator
+ return extra_step_kwargs
+
+ def prepare_latents(
+ self,
+ batch_size,
+ num_channels_latents,
+ width,
+ height,
+ video_length,
+ dtype,
+ device,
+ generator,
+ latents=None,
+ ):
+ shape = (
+ batch_size,
+ num_channels_latents,
+ video_length,
+ height // self.vae_scale_factor,
+ width // self.vae_scale_factor,
+ )
+ if isinstance(generator, list) and len(generator) != batch_size:
+ raise ValueError(
+ f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+ f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+ )
+
+ if latents is None:
+ latents = randn_tensor(
+ shape, generator=generator, device=device, dtype=dtype
+ )
+ else:
+ latents = latents.to(device)
+
+ # scale the initial noise by the standard deviation required by the scheduler
+ latents = latents * self.scheduler.init_noise_sigma
+ return latents
+
+ def _encode_prompt(
+ self,
+ prompt,
+ device,
+ num_videos_per_prompt,
+ do_classifier_free_guidance,
+ negative_prompt,
+ ):
+ batch_size = len(prompt) if isinstance(prompt, list) else 1
+
+ text_inputs = self.tokenizer(
+ prompt,
+ padding="max_length",
+ max_length=self.tokenizer.model_max_length,
+ truncation=True,
+ return_tensors="pt",
+ )
+ text_input_ids = text_inputs.input_ids
+ untruncated_ids = self.tokenizer(
+ prompt, padding="longest", return_tensors="pt"
+ ).input_ids
+
+ if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
+ text_input_ids, untruncated_ids
+ ):
+ removed_text = self.tokenizer.batch_decode(
+ untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
+ )
+
+ if (
+ hasattr(self.text_encoder.config, "use_attention_mask")
+ and self.text_encoder.config.use_attention_mask
+ ):
+ attention_mask = text_inputs.attention_mask.to(device)
+ else:
+ attention_mask = None
+
+ text_embeddings = self.text_encoder(
+ text_input_ids.to(device),
+ attention_mask=attention_mask,
+ )
+ text_embeddings = text_embeddings[0]
+
+ # duplicate text embeddings for each generation per prompt, using mps friendly method
+ bs_embed, seq_len, _ = text_embeddings.shape
+ text_embeddings = text_embeddings.repeat(1, num_videos_per_prompt, 1)
+ text_embeddings = text_embeddings.view(
+ bs_embed * num_videos_per_prompt, seq_len, -1
+ )
+
+ # get unconditional embeddings for classifier free guidance
+ if do_classifier_free_guidance:
+ uncond_tokens: List[str]
+ if negative_prompt is None:
+ uncond_tokens = [""] * batch_size
+ elif type(prompt) is not type(negative_prompt):
+ raise TypeError(
+ f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+ f" {type(prompt)}."
+ )
+ elif isinstance(negative_prompt, str):
+ uncond_tokens = [negative_prompt]
+ elif batch_size != len(negative_prompt):
+ raise ValueError(
+ f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+ f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+ " the batch size of `prompt`."
+ )
+ else:
+ uncond_tokens = negative_prompt
+
+ max_length = text_input_ids.shape[-1]
+ uncond_input = self.tokenizer(
+ uncond_tokens,
+ padding="max_length",
+ max_length=max_length,
+ truncation=True,
+ return_tensors="pt",
+ )
+
+ if (
+ hasattr(self.text_encoder.config, "use_attention_mask")
+ and self.text_encoder.config.use_attention_mask
+ ):
+ attention_mask = uncond_input.attention_mask.to(device)
+ else:
+ attention_mask = None
+
+ uncond_embeddings = self.text_encoder(
+ uncond_input.input_ids.to(device),
+ attention_mask=attention_mask,
+ )
+ uncond_embeddings = uncond_embeddings[0]
+
+ # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+ seq_len = uncond_embeddings.shape[1]
+ uncond_embeddings = uncond_embeddings.repeat(1, num_videos_per_prompt, 1)
+ uncond_embeddings = uncond_embeddings.view(
+ batch_size * num_videos_per_prompt, seq_len, -1
+ )
+
+ # For classifier free guidance, we need to do two forward passes.
+ # Here we concatenate the unconditional and text embeddings into a single batch
+ # to avoid doing two forward passes
+ text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
+
+ return text_embeddings
+
+ def interpolate_latents(
+ self, latents: torch.Tensor, interpolation_factor: int, device
+ ):
+ if interpolation_factor < 2:
+ return latents
+
+ new_latents = torch.zeros(
+ (
+ latents.shape[0],
+ latents.shape[1],
+ ((latents.shape[2] - 1) * interpolation_factor) + 1,
+ latents.shape[3],
+ latents.shape[4],
+ ),
+ device=latents.device,
+ dtype=latents.dtype,
+ )
+
+ org_video_length = latents.shape[2]
+ rate = [i / interpolation_factor for i in range(interpolation_factor)][1:]
+
+ new_index = 0
+
+ v0 = None
+ v1 = None
+
+ for i0, i1 in zip(range(org_video_length), range(org_video_length)[1:]):
+ v0 = latents[:, :, i0, :, :]
+ v1 = latents[:, :, i1, :, :]
+
+ new_latents[:, :, new_index, :, :] = v0
+ new_index += 1
+
+ for f in rate:
+ v = get_tensor_interpolation_method()(
+ v0.to(device=device), v1.to(device=device), f
+ )
+ new_latents[:, :, new_index, :, :] = v.to(latents.device)
+ new_index += 1
+
+ new_latents[:, :, new_index, :, :] = v1
+ new_index += 1
+
+ return new_latents
+
+ @torch.no_grad()
+ def __call__(
+ self,
+ ref_image,
+ pose_up_images,
+ pose_down_images,
+ width,
+ height,
+ video_length,
+ num_inference_steps,
+ guidance_scale,
+ audio_features=None,
+ num_images_per_prompt=1,
+ eta: float = 0.0,
+ generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+ output_type: Optional[str] = "tensor",
+ return_dict: bool = True,
+ callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+ callback_steps: Optional[int] = 1,
+ guidance_rescale=0., # 0.7
+ **kwargs,
+ ):
+ # Default height and width to unet
+ height = height or self.unet.config.sample_size * self.vae_scale_factor
+ width = width or self.unet.config.sample_size * self.vae_scale_factor
+
+ device = self._execution_device
+
+ do_classifier_free_guidance = guidance_scale > 1.0
+
+ # Prepare timesteps
+ self.scheduler.set_timesteps(num_inference_steps, device=device)
+ timesteps = self.scheduler.timesteps
+
+ batch_size = 1
+
+ # Prepare clip image embeds
+ # NOTE: ่ฟ้ๆฏๅฆ้่ฆ resize ๅฐ (224, 224) ้่ฆ่งๅฏ
+ clip_image = self.clip_image_processor.preprocess(
+ ref_image.resize((224, 224)), return_tensors="pt"
+ ).pixel_values
+ # If image_proj_model is not None, means enable ip-adapter
+ if self.image_proj_model is not None:
+ clip_image_embeds = self.image_encoder(
+ clip_image.to(device, dtype=self.image_encoder.dtype),
+ output_hidden_states=True,
+ ).hidden_states[-2]
+ image_prompt_embeds = self.image_proj_model(clip_image_embeds)
+ uncond_image_prompt_embeds = self.image_proj_model(
+ torch.zeros_like(clip_image_embeds)
+ )
+ text_prompt_embeds = self._encode_prompt(
+ "best quality, high quality",
+ device,
+ num_images_per_prompt,
+ do_classifier_free_guidance,
+ negative_prompt="monochrome, lowres, bad anatomy, worst quality, low quality",
+ )
+ # Concat image and text embeddings
+ if do_classifier_free_guidance:
+ (
+ uncond_text_prompt_embeds,
+ text_prompt_embeds,
+ ) = text_prompt_embeds.chunk(2)
+ uncond_encoder_hidden_states = torch.cat(
+ [uncond_text_prompt_embeds, uncond_image_prompt_embeds], dim=1
+ )
+ encoder_hidden_states = torch.cat(
+ [text_prompt_embeds, image_prompt_embeds], dim=1
+ )
+
+ else:
+ clip_image_embeds = self.image_encoder(
+ clip_image.to(device, dtype=self.image_encoder.dtype)
+ ).image_embeds
+ encoder_hidden_states = clip_image_embeds.unsqueeze(1)
+ uncond_encoder_hidden_states = torch.zeros_like(encoder_hidden_states)
+
+ if do_classifier_free_guidance:
+ encoder_hidden_states = torch.cat(
+ [uncond_encoder_hidden_states, encoder_hidden_states], dim=0
+ )
+
+ num_channels_latents = self.denoising_unet.in_channels
+
+ latents = self.prepare_latents(
+ batch_size * num_images_per_prompt,
+ num_channels_latents,
+ width,
+ height,
+ video_length,
+ clip_image_embeds.dtype,
+ device,
+ generator,
+ )
+
+ # Prepare extra step kwargs.
+ extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+ # Prepare ref image latents
+ ref_image_tensor = self.ref_image_processor.preprocess(
+ ref_image, height=height, width=width
+ ) # (bs, c, width, height)
+ ref_image_tensor = ref_image_tensor.to(
+ dtype=self.vae.dtype, device=self.vae.device
+ )
+ ref_image_latents = self.vae.encode(ref_image_tensor).latent_dist.mean
+ ref_image_latents = ref_image_latents * 0.18215 # (b, 4, h, w)
+ ref_image_latents = ref_image_latents.unsqueeze(2)
+
+ # Prepare a list of pose condition images
+ pose_up_cond_tensor_list, pose_down_cond_tensor_list = [], []
+ for i, pose_up_image in enumerate(pose_up_images):
+ pose_up_cond_tensor = self.cond_image_processor.preprocess(
+ pose_up_image, height=height, width=width
+ )
+ pose_up_cond_tensor = pose_up_cond_tensor.unsqueeze(2) # (bs, c, 1, h, w)
+ pose_up_cond_tensor_list.append(pose_up_cond_tensor)
+ pose_down_cond_tensor = self.cond_image_processor.preprocess(
+ pose_down_images[i], height=height, width=width
+ )
+ pose_down_cond_tensor = pose_down_cond_tensor.unsqueeze(2) # (bs, c, 1, h, w)
+ pose_down_cond_tensor_list.append(pose_down_cond_tensor)
+ pose_up_cond_tensor = torch.cat(pose_up_cond_tensor_list, dim=2) # (bs, c, t, h, w)
+ pose_up_cond_tensor = pose_up_cond_tensor.to(
+ device=device, dtype=self.pose_guider1.dtype
+ )
+ pose_down_cond_tensor = torch.cat(pose_down_cond_tensor_list, dim=2) # (bs, c, t, h, w)
+ pose_up_fea = self.pose_guider1(pose_up_cond_tensor)
+ pose_down_cond_tensor = pose_down_cond_tensor.to(device=device, dtype=self.pose_guider2.dtype)
+ pose_down_fea = self.pose_guider2(pose_down_cond_tensor)
+ pose_fea = pose_up_fea + pose_down_fea
+
+ context_schedule = "uniform"
+ context_frames = 24
+ context_stride = 1
+ context_overlap = 4 # 4
+ context_batch_size = 1
+ context_scheduler = get_context_scheduler(context_schedule)
+
+ # denoising loop
+ num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
+ middle_results = []
+ with self.progress_bar(total=num_inference_steps) as progress_bar:
+ self_attention_additional_feats = {}
+ for i, t in enumerate(timesteps):
+ noise_pred = torch.zeros(
+ (
+ latents.shape[0] * (2 if do_classifier_free_guidance else 1),
+ *latents.shape[1:],
+ ),
+ device=latents.device,
+ dtype=latents.dtype,
+ )
+ counter = torch.zeros(
+ (1, 1, latents.shape[2], 1, 1),
+ device=latents.device,
+ dtype=latents.dtype,
+ )
+
+ # 1. Forward reference image
+ if i == 0:
+ self.reference_unet(
+ # ref_image_latents.repeat(
+ # (2 if do_classifier_free_guidance else 1), 1, 1, 1
+ # ),
+ torch.cat([torch.zeros_like(ref_image_latents), ref_image_latents]) \
+ if do_classifier_free_guidance else \
+ ref_image_latents,
+ torch.zeros_like(t),
+ # t,
+ encoder_hidden_states=encoder_hidden_states,
+ self_attention_additional_feats=self_attention_additional_feats,
+ return_dict=False,
+ )
+ context_queue = list(
+ context_scheduler(
+ 0,
+ num_inference_steps,
+ latents.shape[2],
+ context_frames,
+ context_stride,
+ context_overlap,
+ )
+ )
+
+ num_context_batches = math.ceil(len(context_queue) / context_batch_size)
+ global_context = []
+ for i in range(num_context_batches):
+ global_context.append(
+ context_queue[
+ i * context_batch_size : (i + 1) * context_batch_size
+ ]
+ )
+
+ for context in global_context:
+ # 3.1 expand the latents if we are doing classifier free guidance
+ latent_model_input = (
+ torch.cat([latents[:, :, c] for c in context])
+ .to(device)
+ .repeat(2 if do_classifier_free_guidance else 1, 1, 1, 1, 1)
+ )
+ latent_model_input = self.scheduler.scale_model_input(
+ latent_model_input, t
+ )
+ b, c, f, h, w = latent_model_input.shape
+ latent_pose_input = torch.cat(
+ [pose_fea[:, :, c] for c in context]
+ ).repeat(2 if do_classifier_free_guidance else 1, 1, 1, 1, 1)
+
+ pred = self.denoising_unet(
+ latent_model_input,
+ t,
+ encoder_hidden_states=encoder_hidden_states[:b],
+ pose_cond_fea=latent_pose_input,
+ self_attention_additional_feats=self_attention_additional_feats,
+ return_dict=False,
+ )[0]
+
+ for j, c in enumerate(context):
+ noise_pred[:, :, c] = noise_pred[:, :, c] + pred
+ counter[:, :, c] = counter[:, :, c] + 1
+
+ # perform guidance
+ if do_classifier_free_guidance:
+ noise_pred_uncond, noise_pred_text = (noise_pred / counter).chunk(2)
+ noise_pred = noise_pred_uncond + guidance_scale * (
+ noise_pred_text - noise_pred_uncond
+ )
+
+ if do_classifier_free_guidance and guidance_rescale > 0.0:
+ # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
+ noise_pred = rescale_noise_cfg(
+ noise_pred,
+ noise_pred_text,
+ guidance_rescale=guidance_rescale,
+ )
+
+ latents = self.scheduler.step(
+ noise_pred, t, latents, **extra_step_kwargs
+ ).prev_sample
+
+ if i == len(timesteps) - 1 or (
+ (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0
+ ):
+ progress_bar.update()
+ if callback is not None and i % callback_steps == 0:
+ step_idx = i // getattr(self.scheduler, "order", 1)
+ callback(step_idx, t, latents)
+
+
+ interpolation_factor = 1
+ latents = self.interpolate_latents(latents, interpolation_factor, device)
+ # Post-processing
+ images = self.decode_latents(latents) # (b, c, f, h, w)
+
+ # Convert to tensor
+ if output_type == "tensor":
+ images = torch.from_numpy(images)
+
+ if not return_dict:
+ return images
+
+ return Pose2VideoPipelineOutput(videos=images)
diff --git a/src/pipelines/pipeline_pose2img.py b/src/pipelines/pipeline_pose2img.py
new file mode 100644
index 0000000000000000000000000000000000000000..72a3d66b278a758eacce29dc0df3f34896d6ca32
--- /dev/null
+++ b/src/pipelines/pipeline_pose2img.py
@@ -0,0 +1,360 @@
+import inspect
+from dataclasses import dataclass
+from typing import Callable, List, Optional, Union
+
+import numpy as np
+import torch
+from diffusers import DiffusionPipeline
+from diffusers.image_processor import VaeImageProcessor
+from diffusers.schedulers import (
+ DDIMScheduler,
+ DPMSolverMultistepScheduler,
+ EulerAncestralDiscreteScheduler,
+ EulerDiscreteScheduler,
+ LMSDiscreteScheduler,
+ PNDMScheduler,
+)
+from diffusers.utils import BaseOutput, is_accelerate_available
+from diffusers.utils.torch_utils import randn_tensor
+from einops import rearrange
+from tqdm import tqdm
+from transformers import CLIPImageProcessor
+
+from src.models.mutual_self_attention import ReferenceAttentionControl
+
+
+@dataclass
+class Pose2ImagePipelineOutput(BaseOutput):
+ images: Union[torch.Tensor, np.ndarray]
+
+
+class Pose2ImagePipeline(DiffusionPipeline):
+ _optional_components = []
+
+ def __init__(
+ self,
+ vae,
+ image_encoder,
+ reference_unet,
+ denoising_unet,
+ pose_guider,
+ scheduler: Union[
+ DDIMScheduler,
+ PNDMScheduler,
+ LMSDiscreteScheduler,
+ EulerDiscreteScheduler,
+ EulerAncestralDiscreteScheduler,
+ DPMSolverMultistepScheduler,
+ ],
+ ):
+ super().__init__()
+
+ self.register_modules(
+ vae=vae,
+ image_encoder=image_encoder,
+ reference_unet=reference_unet,
+ denoising_unet=denoising_unet,
+ pose_guider=pose_guider,
+ scheduler=scheduler,
+ )
+ self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+ self.clip_image_processor = CLIPImageProcessor()
+ self.ref_image_processor = VaeImageProcessor(
+ vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True
+ )
+ self.cond_image_processor = VaeImageProcessor(
+ vae_scale_factor=self.vae_scale_factor,
+ do_convert_rgb=True,
+ do_normalize=False,
+ )
+
+ def enable_vae_slicing(self):
+ self.vae.enable_slicing()
+
+ def disable_vae_slicing(self):
+ self.vae.disable_slicing()
+
+ def enable_sequential_cpu_offload(self, gpu_id=0):
+ if is_accelerate_available():
+ from accelerate import cpu_offload
+ else:
+ raise ImportError("Please install accelerate via `pip install accelerate`")
+
+ device = torch.device(f"cuda:{gpu_id}")
+
+ for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]:
+ if cpu_offloaded_model is not None:
+ cpu_offload(cpu_offloaded_model, device)
+
+ @property
+ def _execution_device(self):
+ if self.device != torch.device("meta") or not hasattr(self.unet, "_hf_hook"):
+ return self.device
+ for module in self.unet.modules():
+ if (
+ hasattr(module, "_hf_hook")
+ and hasattr(module._hf_hook, "execution_device")
+ and module._hf_hook.execution_device is not None
+ ):
+ return torch.device(module._hf_hook.execution_device)
+ return self.device
+
+ def decode_latents(self, latents):
+ video_length = latents.shape[2]
+ latents = 1 / 0.18215 * latents
+ latents = rearrange(latents, "b c f h w -> (b f) c h w")
+ # video = self.vae.decode(latents).sample
+ video = []
+ for frame_idx in tqdm(range(latents.shape[0])):
+ video.append(self.vae.decode(latents[frame_idx : frame_idx + 1]).sample)
+ video = torch.cat(video)
+ video = rearrange(video, "(b f) c h w -> b c f h w", f=video_length)
+ video = (video / 2 + 0.5).clamp(0, 1)
+ # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
+ video = video.cpu().float().numpy()
+ return video
+
+ def prepare_extra_step_kwargs(self, generator, eta):
+ # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+ # eta (ฮท) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+ # eta corresponds to ฮท in DDIM paper: https://arxiv.org/abs/2010.02502
+ # and should be between [0, 1]
+
+ accepts_eta = "eta" in set(
+ inspect.signature(self.scheduler.step).parameters.keys()
+ )
+ extra_step_kwargs = {}
+ if accepts_eta:
+ extra_step_kwargs["eta"] = eta
+
+ # check if the scheduler accepts generator
+ accepts_generator = "generator" in set(
+ inspect.signature(self.scheduler.step).parameters.keys()
+ )
+ if accepts_generator:
+ extra_step_kwargs["generator"] = generator
+ return extra_step_kwargs
+
+ def prepare_latents(
+ self,
+ batch_size,
+ num_channels_latents,
+ width,
+ height,
+ dtype,
+ device,
+ generator,
+ latents=None,
+ ):
+ shape = (
+ batch_size,
+ num_channels_latents,
+ height // self.vae_scale_factor,
+ width // self.vae_scale_factor,
+ )
+ if isinstance(generator, list) and len(generator) != batch_size:
+ raise ValueError(
+ f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+ f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+ )
+
+ if latents is None:
+ latents = randn_tensor(
+ shape, generator=generator, device=device, dtype=dtype
+ )
+ else:
+ latents = latents.to(device)
+
+ # scale the initial noise by the standard deviation required by the scheduler
+ latents = latents * self.scheduler.init_noise_sigma
+ return latents
+
+ def prepare_condition(
+ self,
+ cond_image,
+ width,
+ height,
+ device,
+ dtype,
+ do_classififer_free_guidance=False,
+ ):
+ image = self.cond_image_processor.preprocess(
+ cond_image, height=height, width=width
+ ).to(dtype=torch.float32)
+
+ image = image.to(device=device, dtype=dtype)
+
+ if do_classififer_free_guidance:
+ image = torch.cat([image] * 2)
+
+ return image
+
+ @torch.no_grad()
+ def __call__(
+ self,
+ ref_image,
+ pose_image,
+ width,
+ height,
+ num_inference_steps,
+ guidance_scale,
+ num_images_per_prompt=1,
+ eta: float = 0.0,
+ generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+ output_type: Optional[str] = "tensor",
+ return_dict: bool = True,
+ callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+ callback_steps: Optional[int] = 1,
+ **kwargs,
+ ):
+ # Default height and width to unet
+ height = height or self.unet.config.sample_size * self.vae_scale_factor
+ width = width or self.unet.config.sample_size * self.vae_scale_factor
+
+ device = self._execution_device
+
+ do_classifier_free_guidance = guidance_scale > 1.0
+
+ # Prepare timesteps
+ self.scheduler.set_timesteps(num_inference_steps, device=device)
+ timesteps = self.scheduler.timesteps
+
+ batch_size = 1
+
+ # Prepare clip image embeds
+ clip_image = self.clip_image_processor.preprocess(
+ ref_image.resize((224, 224)), return_tensors="pt"
+ ).pixel_values
+ clip_image_embeds = self.image_encoder(
+ clip_image.to(device, dtype=self.image_encoder.dtype)
+ ).image_embeds
+ image_prompt_embeds = clip_image_embeds.unsqueeze(1)
+ uncond_image_prompt_embeds = torch.zeros_like(image_prompt_embeds)
+
+ if do_classifier_free_guidance:
+ image_prompt_embeds = torch.cat(
+ [uncond_image_prompt_embeds, image_prompt_embeds], dim=0
+ )
+
+ reference_control_writer = ReferenceAttentionControl(
+ self.reference_unet,
+ do_classifier_free_guidance=do_classifier_free_guidance,
+ mode="write",
+ batch_size=batch_size,
+ fusion_blocks="full",
+ )
+ reference_control_reader = ReferenceAttentionControl(
+ self.denoising_unet,
+ do_classifier_free_guidance=do_classifier_free_guidance,
+ mode="read",
+ batch_size=batch_size,
+ fusion_blocks="full",
+ )
+
+ num_channels_latents = self.denoising_unet.in_channels
+ latents = self.prepare_latents(
+ batch_size * num_images_per_prompt,
+ num_channels_latents,
+ width,
+ height,
+ clip_image_embeds.dtype,
+ device,
+ generator,
+ )
+ latents = latents.unsqueeze(2) # (bs, c, 1, h', w')
+ latents_dtype = latents.dtype
+
+ # Prepare extra step kwargs.
+ extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+ # Prepare ref image latents
+ ref_image_tensor = self.ref_image_processor.preprocess(
+ ref_image, height=height, width=width
+ ) # (bs, c, width, height)
+ ref_image_tensor = ref_image_tensor.to(
+ dtype=self.vae.dtype, device=self.vae.device
+ )
+ ref_image_latents = self.vae.encode(ref_image_tensor).latent_dist.mean
+ ref_image_latents = ref_image_latents * 0.18215 # (b, 4, h, w)
+
+ # Prepare pose condition image
+ pose_cond_tensor = self.cond_image_processor.preprocess(
+ pose_image, height=height, width=width
+ )
+ pose_cond_tensor = pose_cond_tensor.unsqueeze(2) # (bs, c, 1, h, w)
+ pose_cond_tensor = pose_cond_tensor.to(
+ device=device, dtype=self.pose_guider.dtype
+ )
+ pose_fea = self.pose_guider(pose_cond_tensor)
+ pose_fea = (
+ torch.cat([pose_fea] * 2) if do_classifier_free_guidance else pose_fea
+ )
+
+ # denoising loop
+ num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
+ with self.progress_bar(total=num_inference_steps) as progress_bar:
+ for i, t in enumerate(timesteps):
+ # 1. Forward reference image
+ if i == 0:
+ self.reference_unet(
+ ref_image_latents.repeat(
+ (2 if do_classifier_free_guidance else 1), 1, 1, 1
+ ),
+ torch.zeros_like(t),
+ encoder_hidden_states=image_prompt_embeds,
+ return_dict=False,
+ )
+
+ # 2. Update reference unet feature into denosing net
+ reference_control_reader.update(reference_control_writer)
+
+ # 3.1 expand the latents if we are doing classifier free guidance
+ latent_model_input = (
+ torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+ )
+ latent_model_input = self.scheduler.scale_model_input(
+ latent_model_input, t
+ )
+
+ noise_pred = self.denoising_unet(
+ latent_model_input,
+ t,
+ encoder_hidden_states=image_prompt_embeds,
+ pose_cond_fea=pose_fea,
+ return_dict=False,
+ )[0]
+
+ # perform guidance
+ if do_classifier_free_guidance:
+ noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+ noise_pred = noise_pred_uncond + guidance_scale * (
+ noise_pred_text - noise_pred_uncond
+ )
+
+ # compute the previous noisy sample x_t -> x_t-1
+ latents = self.scheduler.step(
+ noise_pred, t, latents, **extra_step_kwargs, return_dict=False
+ )[0]
+
+ # call the callback, if provided
+ if i == len(timesteps) - 1 or (
+ (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0
+ ):
+ progress_bar.update()
+ if callback is not None and i % callback_steps == 0:
+ step_idx = i // getattr(self.scheduler, "order", 1)
+ callback(step_idx, t, latents)
+ reference_control_reader.clear()
+ reference_control_writer.clear()
+
+ # Post-processing
+ image = self.decode_latents(latents) # (b, c, 1, h, w)
+
+ # Convert to tensor
+ if output_type == "tensor":
+ image = torch.from_numpy(image)
+
+ if not return_dict:
+ return image
+
+ return Pose2ImagePipelineOutput(images=image)
diff --git a/src/pipelines/pipeline_pose2vid.py b/src/pipelines/pipeline_pose2vid.py
new file mode 100644
index 0000000000000000000000000000000000000000..51356f5ff5fba61385388cb847fd8a400f1fd914
--- /dev/null
+++ b/src/pipelines/pipeline_pose2vid.py
@@ -0,0 +1,458 @@
+import inspect
+from dataclasses import dataclass
+from typing import Callable, List, Optional, Union
+
+import numpy as np
+import torch
+from diffusers import DiffusionPipeline
+from diffusers.image_processor import VaeImageProcessor
+from diffusers.schedulers import (DDIMScheduler, DPMSolverMultistepScheduler,
+ EulerAncestralDiscreteScheduler,
+ EulerDiscreteScheduler, LMSDiscreteScheduler,
+ PNDMScheduler)
+from diffusers.utils import BaseOutput, is_accelerate_available
+from diffusers.utils.torch_utils import randn_tensor
+from einops import rearrange
+from tqdm import tqdm
+from transformers import CLIPImageProcessor
+
+from src.models.mutual_self_attention import ReferenceAttentionControl
+
+
+@dataclass
+class Pose2VideoPipelineOutput(BaseOutput):
+ videos: Union[torch.Tensor, np.ndarray]
+
+
+class Pose2VideoPipeline(DiffusionPipeline):
+ _optional_components = []
+
+ def __init__(
+ self,
+ vae,
+ image_encoder,
+ reference_unet,
+ denoising_unet,
+ pose_guider,
+ scheduler: Union[
+ DDIMScheduler,
+ PNDMScheduler,
+ LMSDiscreteScheduler,
+ EulerDiscreteScheduler,
+ EulerAncestralDiscreteScheduler,
+ DPMSolverMultistepScheduler,
+ ],
+ image_proj_model=None,
+ tokenizer=None,
+ text_encoder=None,
+ ):
+ super().__init__()
+
+ self.register_modules(
+ vae=vae,
+ image_encoder=image_encoder,
+ reference_unet=reference_unet,
+ denoising_unet=denoising_unet,
+ pose_guider=pose_guider,
+ scheduler=scheduler,
+ image_proj_model=image_proj_model,
+ tokenizer=tokenizer,
+ text_encoder=text_encoder,
+ )
+ self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+ self.clip_image_processor = CLIPImageProcessor()
+ self.ref_image_processor = VaeImageProcessor(
+ vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True
+ )
+ self.cond_image_processor = VaeImageProcessor(
+ vae_scale_factor=self.vae_scale_factor,
+ do_convert_rgb=True,
+ do_normalize=False,
+ )
+
+ def enable_vae_slicing(self):
+ self.vae.enable_slicing()
+
+ def disable_vae_slicing(self):
+ self.vae.disable_slicing()
+
+ def enable_sequential_cpu_offload(self, gpu_id=0):
+ if is_accelerate_available():
+ from accelerate import cpu_offload
+ else:
+ raise ImportError("Please install accelerate via `pip install accelerate`")
+
+ device = torch.device(f"cuda:{gpu_id}")
+
+ for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]:
+ if cpu_offloaded_model is not None:
+ cpu_offload(cpu_offloaded_model, device)
+
+ @property
+ def _execution_device(self):
+ if self.device != torch.device("meta") or not hasattr(self.unet, "_hf_hook"):
+ return self.device
+ for module in self.unet.modules():
+ if (
+ hasattr(module, "_hf_hook")
+ and hasattr(module._hf_hook, "execution_device")
+ and module._hf_hook.execution_device is not None
+ ):
+ return torch.device(module._hf_hook.execution_device)
+ return self.device
+
+ def decode_latents(self, latents):
+ video_length = latents.shape[2]
+ latents = 1 / 0.18215 * latents
+ latents = rearrange(latents, "b c f h w -> (b f) c h w")
+ # video = self.vae.decode(latents).sample
+ video = []
+ for frame_idx in tqdm(range(latents.shape[0])):
+ video.append(self.vae.decode(latents[frame_idx : frame_idx + 1]).sample)
+ video = torch.cat(video)
+ video = rearrange(video, "(b f) c h w -> b c f h w", f=video_length)
+ video = (video / 2 + 0.5).clamp(0, 1)
+ # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
+ video = video.cpu().float().numpy()
+ return video
+
+ def prepare_extra_step_kwargs(self, generator, eta):
+ # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+ # eta (ฮท) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+ # eta corresponds to ฮท in DDIM paper: https://arxiv.org/abs/2010.02502
+ # and should be between [0, 1]
+
+ accepts_eta = "eta" in set(
+ inspect.signature(self.scheduler.step).parameters.keys()
+ )
+ extra_step_kwargs = {}
+ if accepts_eta:
+ extra_step_kwargs["eta"] = eta
+
+ # check if the scheduler accepts generator
+ accepts_generator = "generator" in set(
+ inspect.signature(self.scheduler.step).parameters.keys()
+ )
+ if accepts_generator:
+ extra_step_kwargs["generator"] = generator
+ return extra_step_kwargs
+
+ def prepare_latents(
+ self,
+ batch_size,
+ num_channels_latents,
+ width,
+ height,
+ video_length,
+ dtype,
+ device,
+ generator,
+ latents=None,
+ ):
+ shape = (
+ batch_size,
+ num_channels_latents,
+ video_length,
+ height // self.vae_scale_factor,
+ width // self.vae_scale_factor,
+ )
+ if isinstance(generator, list) and len(generator) != batch_size:
+ raise ValueError(
+ f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+ f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+ )
+
+ if latents is None:
+ latents = randn_tensor(
+ shape, generator=generator, device=device, dtype=dtype
+ )
+ else:
+ latents = latents.to(device)
+
+ # scale the initial noise by the standard deviation required by the scheduler
+ latents = latents * self.scheduler.init_noise_sigma
+ return latents
+
+ def _encode_prompt(
+ self,
+ prompt,
+ device,
+ num_videos_per_prompt,
+ do_classifier_free_guidance,
+ negative_prompt,
+ ):
+ batch_size = len(prompt) if isinstance(prompt, list) else 1
+
+ text_inputs = self.tokenizer(
+ prompt,
+ padding="max_length",
+ max_length=self.tokenizer.model_max_length,
+ truncation=True,
+ return_tensors="pt",
+ )
+ text_input_ids = text_inputs.input_ids
+ untruncated_ids = self.tokenizer(
+ prompt, padding="longest", return_tensors="pt"
+ ).input_ids
+
+ if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
+ text_input_ids, untruncated_ids
+ ):
+ removed_text = self.tokenizer.batch_decode(
+ untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
+ )
+
+ if (
+ hasattr(self.text_encoder.config, "use_attention_mask")
+ and self.text_encoder.config.use_attention_mask
+ ):
+ attention_mask = text_inputs.attention_mask.to(device)
+ else:
+ attention_mask = None
+
+ text_embeddings = self.text_encoder(
+ text_input_ids.to(device),
+ attention_mask=attention_mask,
+ )
+ text_embeddings = text_embeddings[0]
+
+ # duplicate text embeddings for each generation per prompt, using mps friendly method
+ bs_embed, seq_len, _ = text_embeddings.shape
+ text_embeddings = text_embeddings.repeat(1, num_videos_per_prompt, 1)
+ text_embeddings = text_embeddings.view(
+ bs_embed * num_videos_per_prompt, seq_len, -1
+ )
+
+ # get unconditional embeddings for classifier free guidance
+ if do_classifier_free_guidance:
+ uncond_tokens: List[str]
+ if negative_prompt is None:
+ uncond_tokens = [""] * batch_size
+ elif type(prompt) is not type(negative_prompt):
+ raise TypeError(
+ f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+ f" {type(prompt)}."
+ )
+ elif isinstance(negative_prompt, str):
+ uncond_tokens = [negative_prompt]
+ elif batch_size != len(negative_prompt):
+ raise ValueError(
+ f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+ f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+ " the batch size of `prompt`."
+ )
+ else:
+ uncond_tokens = negative_prompt
+
+ max_length = text_input_ids.shape[-1]
+ uncond_input = self.tokenizer(
+ uncond_tokens,
+ padding="max_length",
+ max_length=max_length,
+ truncation=True,
+ return_tensors="pt",
+ )
+
+ if (
+ hasattr(self.text_encoder.config, "use_attention_mask")
+ and self.text_encoder.config.use_attention_mask
+ ):
+ attention_mask = uncond_input.attention_mask.to(device)
+ else:
+ attention_mask = None
+
+ uncond_embeddings = self.text_encoder(
+ uncond_input.input_ids.to(device),
+ attention_mask=attention_mask,
+ )
+ uncond_embeddings = uncond_embeddings[0]
+
+ # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+ seq_len = uncond_embeddings.shape[1]
+ uncond_embeddings = uncond_embeddings.repeat(1, num_videos_per_prompt, 1)
+ uncond_embeddings = uncond_embeddings.view(
+ batch_size * num_videos_per_prompt, seq_len, -1
+ )
+
+ # For classifier free guidance, we need to do two forward passes.
+ # Here we concatenate the unconditional and text embeddings into a single batch
+ # to avoid doing two forward passes
+ text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
+
+ return text_embeddings
+
+ @torch.no_grad()
+ def __call__(
+ self,
+ ref_image,
+ pose_images,
+ width,
+ height,
+ video_length,
+ num_inference_steps,
+ guidance_scale,
+ num_images_per_prompt=1,
+ eta: float = 0.0,
+ generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+ output_type: Optional[str] = "tensor",
+ return_dict: bool = True,
+ callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+ callback_steps: Optional[int] = 1,
+ **kwargs,
+ ):
+ # Default height and width to unet
+ height = height or self.unet.config.sample_size * self.vae_scale_factor
+ width = width or self.unet.config.sample_size * self.vae_scale_factor
+
+ device = self._execution_device
+
+ do_classifier_free_guidance = guidance_scale > 1.0
+
+ # Prepare timesteps
+ self.scheduler.set_timesteps(num_inference_steps, device=device)
+ timesteps = self.scheduler.timesteps
+
+ batch_size = 1
+
+ # Prepare clip image embeds
+ clip_image = self.clip_image_processor.preprocess(
+ ref_image, return_tensors="pt"
+ ).pixel_values
+ clip_image_embeds = self.image_encoder(
+ clip_image.to(device, dtype=self.image_encoder.dtype)
+ ).image_embeds
+ encoder_hidden_states = clip_image_embeds.unsqueeze(1)
+ uncond_encoder_hidden_states = torch.zeros_like(encoder_hidden_states)
+
+ if do_classifier_free_guidance:
+ encoder_hidden_states = torch.cat(
+ [uncond_encoder_hidden_states, encoder_hidden_states], dim=0
+ )
+ reference_control_writer = ReferenceAttentionControl(
+ self.reference_unet,
+ do_classifier_free_guidance=do_classifier_free_guidance,
+ mode="write",
+ batch_size=batch_size,
+ fusion_blocks="full",
+ )
+ reference_control_reader = ReferenceAttentionControl(
+ self.denoising_unet,
+ do_classifier_free_guidance=do_classifier_free_guidance,
+ mode="read",
+ batch_size=batch_size,
+ fusion_blocks="full",
+ )
+
+ num_channels_latents = self.denoising_unet.in_channels
+ latents = self.prepare_latents(
+ batch_size * num_images_per_prompt,
+ num_channels_latents,
+ width,
+ height,
+ video_length,
+ clip_image_embeds.dtype,
+ device,
+ generator,
+ )
+
+ # Prepare extra step kwargs.
+ extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+ # Prepare ref image latents
+ ref_image_tensor = self.ref_image_processor.preprocess(
+ ref_image, height=height, width=width
+ ) # (bs, c, width, height)
+ ref_image_tensor = ref_image_tensor.to(
+ dtype=self.vae.dtype, device=self.vae.device
+ )
+ ref_image_latents = self.vae.encode(ref_image_tensor).latent_dist.mean
+ ref_image_latents = ref_image_latents * 0.18215 # (b, 4, h, w)
+
+ # Prepare a list of pose condition images
+ pose_cond_tensor_list = []
+ for pose_image in pose_images:
+ pose_cond_tensor = (
+ torch.from_numpy(np.array(pose_image.resize((width, height)))) / 255.0
+ )
+ pose_cond_tensor = pose_cond_tensor.permute(2, 0, 1).unsqueeze(
+ 1
+ ) # (c, 1, h, w)
+ pose_cond_tensor_list.append(pose_cond_tensor)
+ pose_cond_tensor = torch.cat(pose_cond_tensor_list, dim=1) # (c, t, h, w)
+ pose_cond_tensor = pose_cond_tensor.unsqueeze(0)
+ pose_cond_tensor = pose_cond_tensor.to(
+ device=device, dtype=self.pose_guider.dtype
+ )
+ pose_fea = self.pose_guider(pose_cond_tensor)
+ pose_fea = (
+ torch.cat([pose_fea] * 2) if do_classifier_free_guidance else pose_fea
+ )
+
+ # denoising loop
+ num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
+ with self.progress_bar(total=num_inference_steps) as progress_bar:
+ for i, t in enumerate(timesteps):
+ # 1. Forward reference image
+ if i == 0:
+ self.reference_unet(
+ ref_image_latents.repeat(
+ (2 if do_classifier_free_guidance else 1), 1, 1, 1
+ ),
+ torch.zeros_like(t),
+ # t,
+ encoder_hidden_states=encoder_hidden_states,
+ return_dict=False,
+ )
+ reference_control_reader.update(reference_control_writer)
+
+ # 3.1 expand the latents if we are doing classifier free guidance
+ latent_model_input = (
+ torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+ )
+ latent_model_input = self.scheduler.scale_model_input(
+ latent_model_input, t
+ )
+
+ noise_pred = self.denoising_unet(
+ latent_model_input,
+ t,
+ encoder_hidden_states=encoder_hidden_states,
+ pose_cond_fea=pose_fea,
+ return_dict=False,
+ )[0]
+
+ # perform guidance
+ if do_classifier_free_guidance:
+ noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+ noise_pred = noise_pred_uncond + guidance_scale * (
+ noise_pred_text - noise_pred_uncond
+ )
+
+ # compute the previous noisy sample x_t -> x_t-1
+ latents = self.scheduler.step(
+ noise_pred, t, latents, **extra_step_kwargs, return_dict=False
+ )[0]
+
+ # call the callback, if provided
+ if i == len(timesteps) - 1 or (
+ (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0
+ ):
+ progress_bar.update()
+ if callback is not None and i % callback_steps == 0:
+ step_idx = i // getattr(self.scheduler, "order", 1)
+ callback(step_idx, t, latents)
+
+ reference_control_reader.clear()
+ reference_control_writer.clear()
+
+ # Post-processing
+ images = self.decode_latents(latents) # (b, c, f, h, w)
+
+ # Convert to tensor
+ if output_type == "tensor":
+ images = torch.from_numpy(images)
+
+ if not return_dict:
+ return images
+
+ return Pose2VideoPipelineOutput(videos=images)
diff --git a/src/pipelines/pipeline_pose2vid_long.py b/src/pipelines/pipeline_pose2vid_long.py
new file mode 100644
index 0000000000000000000000000000000000000000..1a79410acd03c3d0aaf0fd6c57becf669c5f6bec
--- /dev/null
+++ b/src/pipelines/pipeline_pose2vid_long.py
@@ -0,0 +1,571 @@
+# Adapted from https://github.com/magic-research/magic-animate/blob/main/magicanimate/pipelines/pipeline_animation.py
+import inspect
+import math
+from dataclasses import dataclass
+from typing import Callable, List, Optional, Union
+
+import numpy as np
+import torch
+from diffusers import DiffusionPipeline
+from diffusers.image_processor import VaeImageProcessor
+from diffusers.schedulers import (
+ DDIMScheduler,
+ DPMSolverMultistepScheduler,
+ EulerAncestralDiscreteScheduler,
+ EulerDiscreteScheduler,
+ LMSDiscreteScheduler,
+ PNDMScheduler,
+)
+from diffusers.utils import BaseOutput, deprecate, is_accelerate_available, logging
+from diffusers.utils.torch_utils import randn_tensor
+from einops import rearrange
+from tqdm import tqdm
+from transformers import CLIPImageProcessor
+
+from src.models.mutual_self_attention import ReferenceAttentionControl
+from src.pipelines.context import get_context_scheduler
+from src.pipelines.utils import get_tensor_interpolation_method
+
+
+@dataclass
+class Pose2VideoPipelineOutput(BaseOutput):
+ videos: Union[torch.Tensor, np.ndarray]
+
+
+class Pose2VideoPipeline(DiffusionPipeline):
+ _optional_components = []
+
+ def __init__(
+ self,
+ vae,
+ image_encoder,
+ reference_unet,
+ denoising_unet,
+ pose_guider,
+ scheduler: Union[
+ DDIMScheduler,
+ PNDMScheduler,
+ LMSDiscreteScheduler,
+ EulerDiscreteScheduler,
+ EulerAncestralDiscreteScheduler,
+ DPMSolverMultistepScheduler,
+ ],
+ image_proj_model=None,
+ tokenizer=None,
+ text_encoder=None,
+ ):
+ super().__init__()
+
+ self.register_modules(
+ vae=vae,
+ image_encoder=image_encoder,
+ reference_unet=reference_unet,
+ denoising_unet=denoising_unet,
+ pose_guider=pose_guider,
+ scheduler=scheduler,
+ image_proj_model=image_proj_model,
+ tokenizer=tokenizer,
+ text_encoder=text_encoder,
+ )
+ self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+ self.clip_image_processor = CLIPImageProcessor()
+ self.ref_image_processor = VaeImageProcessor(
+ vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True
+ )
+ self.cond_image_processor = VaeImageProcessor(
+ vae_scale_factor=self.vae_scale_factor,
+ do_convert_rgb=True,
+ do_normalize=False,
+ )
+
+ def enable_vae_slicing(self):
+ self.vae.enable_slicing()
+
+ def disable_vae_slicing(self):
+ self.vae.disable_slicing()
+
+ def enable_sequential_cpu_offload(self, gpu_id=0):
+ if is_accelerate_available():
+ from accelerate import cpu_offload
+ else:
+ raise ImportError("Please install accelerate via `pip install accelerate`")
+
+ device = torch.device(f"cuda:{gpu_id}")
+
+ for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]:
+ if cpu_offloaded_model is not None:
+ cpu_offload(cpu_offloaded_model, device)
+
+ @property
+ def _execution_device(self):
+ if self.device != torch.device("meta") or not hasattr(self.unet, "_hf_hook"):
+ return self.device
+ for module in self.unet.modules():
+ if (
+ hasattr(module, "_hf_hook")
+ and hasattr(module._hf_hook, "execution_device")
+ and module._hf_hook.execution_device is not None
+ ):
+ return torch.device(module._hf_hook.execution_device)
+ return self.device
+
+ def decode_latents(self, latents):
+ video_length = latents.shape[2]
+ latents = 1 / 0.18215 * latents
+ latents = rearrange(latents, "b c f h w -> (b f) c h w")
+ # video = self.vae.decode(latents).sample
+ video = []
+ for frame_idx in tqdm(range(latents.shape[0])):
+ video.append(self.vae.decode(latents[frame_idx : frame_idx + 1]).sample)
+ video = torch.cat(video)
+ video = rearrange(video, "(b f) c h w -> b c f h w", f=video_length)
+ video = (video / 2 + 0.5).clamp(0, 1)
+ # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
+ video = video.cpu().float().numpy()
+ return video
+
+ def prepare_extra_step_kwargs(self, generator, eta):
+ # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+ # eta (ฮท) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+ # eta corresponds to ฮท in DDIM paper: https://arxiv.org/abs/2010.02502
+ # and should be between [0, 1]
+
+ accepts_eta = "eta" in set(
+ inspect.signature(self.scheduler.step).parameters.keys()
+ )
+ extra_step_kwargs = {}
+ if accepts_eta:
+ extra_step_kwargs["eta"] = eta
+
+ # check if the scheduler accepts generator
+ accepts_generator = "generator" in set(
+ inspect.signature(self.scheduler.step).parameters.keys()
+ )
+ if accepts_generator:
+ extra_step_kwargs["generator"] = generator
+ return extra_step_kwargs
+
+ def prepare_latents(
+ self,
+ batch_size,
+ num_channels_latents,
+ width,
+ height,
+ video_length,
+ dtype,
+ device,
+ generator,
+ latents=None,
+ ):
+ shape = (
+ batch_size,
+ num_channels_latents,
+ video_length,
+ height // self.vae_scale_factor,
+ width // self.vae_scale_factor,
+ )
+ if isinstance(generator, list) and len(generator) != batch_size:
+ raise ValueError(
+ f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+ f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+ )
+
+ if latents is None:
+ latents = randn_tensor(
+ shape, generator=generator, device=device, dtype=dtype
+ )
+ else:
+ latents = latents.to(device)
+
+ # scale the initial noise by the standard deviation required by the scheduler
+ latents = latents * self.scheduler.init_noise_sigma
+ return latents
+
+ def _encode_prompt(
+ self,
+ prompt,
+ device,
+ num_videos_per_prompt,
+ do_classifier_free_guidance,
+ negative_prompt,
+ ):
+ batch_size = len(prompt) if isinstance(prompt, list) else 1
+
+ text_inputs = self.tokenizer(
+ prompt,
+ padding="max_length",
+ max_length=self.tokenizer.model_max_length,
+ truncation=True,
+ return_tensors="pt",
+ )
+ text_input_ids = text_inputs.input_ids
+ untruncated_ids = self.tokenizer(
+ prompt, padding="longest", return_tensors="pt"
+ ).input_ids
+
+ if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
+ text_input_ids, untruncated_ids
+ ):
+ removed_text = self.tokenizer.batch_decode(
+ untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
+ )
+
+ if (
+ hasattr(self.text_encoder.config, "use_attention_mask")
+ and self.text_encoder.config.use_attention_mask
+ ):
+ attention_mask = text_inputs.attention_mask.to(device)
+ else:
+ attention_mask = None
+
+ text_embeddings = self.text_encoder(
+ text_input_ids.to(device),
+ attention_mask=attention_mask,
+ )
+ text_embeddings = text_embeddings[0]
+
+ # duplicate text embeddings for each generation per prompt, using mps friendly method
+ bs_embed, seq_len, _ = text_embeddings.shape
+ text_embeddings = text_embeddings.repeat(1, num_videos_per_prompt, 1)
+ text_embeddings = text_embeddings.view(
+ bs_embed * num_videos_per_prompt, seq_len, -1
+ )
+
+ # get unconditional embeddings for classifier free guidance
+ if do_classifier_free_guidance:
+ uncond_tokens: List[str]
+ if negative_prompt is None:
+ uncond_tokens = [""] * batch_size
+ elif type(prompt) is not type(negative_prompt):
+ raise TypeError(
+ f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+ f" {type(prompt)}."
+ )
+ elif isinstance(negative_prompt, str):
+ uncond_tokens = [negative_prompt]
+ elif batch_size != len(negative_prompt):
+ raise ValueError(
+ f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+ f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+ " the batch size of `prompt`."
+ )
+ else:
+ uncond_tokens = negative_prompt
+
+ max_length = text_input_ids.shape[-1]
+ uncond_input = self.tokenizer(
+ uncond_tokens,
+ padding="max_length",
+ max_length=max_length,
+ truncation=True,
+ return_tensors="pt",
+ )
+
+ if (
+ hasattr(self.text_encoder.config, "use_attention_mask")
+ and self.text_encoder.config.use_attention_mask
+ ):
+ attention_mask = uncond_input.attention_mask.to(device)
+ else:
+ attention_mask = None
+
+ uncond_embeddings = self.text_encoder(
+ uncond_input.input_ids.to(device),
+ attention_mask=attention_mask,
+ )
+ uncond_embeddings = uncond_embeddings[0]
+
+ # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+ seq_len = uncond_embeddings.shape[1]
+ uncond_embeddings = uncond_embeddings.repeat(1, num_videos_per_prompt, 1)
+ uncond_embeddings = uncond_embeddings.view(
+ batch_size * num_videos_per_prompt, seq_len, -1
+ )
+
+ # For classifier free guidance, we need to do two forward passes.
+ # Here we concatenate the unconditional and text embeddings into a single batch
+ # to avoid doing two forward passes
+ text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
+
+ return text_embeddings
+
+ def interpolate_latents(
+ self, latents: torch.Tensor, interpolation_factor: int, device
+ ):
+ if interpolation_factor < 2:
+ return latents
+
+ new_latents = torch.zeros(
+ (
+ latents.shape[0],
+ latents.shape[1],
+ ((latents.shape[2] - 1) * interpolation_factor) + 1,
+ latents.shape[3],
+ latents.shape[4],
+ ),
+ device=latents.device,
+ dtype=latents.dtype,
+ )
+
+ org_video_length = latents.shape[2]
+ rate = [i / interpolation_factor for i in range(interpolation_factor)][1:]
+
+ new_index = 0
+
+ v0 = None
+ v1 = None
+
+ for i0, i1 in zip(range(org_video_length), range(org_video_length)[1:]):
+ v0 = latents[:, :, i0, :, :]
+ v1 = latents[:, :, i1, :, :]
+
+ new_latents[:, :, new_index, :, :] = v0
+ new_index += 1
+
+ for f in rate:
+ v = get_tensor_interpolation_method()(
+ v0.to(device=device), v1.to(device=device), f
+ )
+ new_latents[:, :, new_index, :, :] = v.to(latents.device)
+ new_index += 1
+
+ new_latents[:, :, new_index, :, :] = v1
+ new_index += 1
+
+ return new_latents
+
+ @torch.no_grad()
+ def __call__(
+ self,
+ ref_image,
+ pose_images,
+ width,
+ height,
+ video_length,
+ num_inference_steps,
+ guidance_scale,
+ num_images_per_prompt=1,
+ eta: float = 0.0,
+ generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+ output_type: Optional[str] = "tensor",
+ return_dict: bool = True,
+ callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+ callback_steps: Optional[int] = 1,
+ context_schedule="uniform",
+ context_frames=24,
+ context_stride=1,
+ context_overlap=4,
+ context_batch_size=1,
+ interpolation_factor=1,
+ **kwargs,
+ ):
+ # Default height and width to unet
+ height = height or self.unet.config.sample_size * self.vae_scale_factor
+ width = width or self.unet.config.sample_size * self.vae_scale_factor
+
+ device = self._execution_device
+
+ do_classifier_free_guidance = guidance_scale > 1.0
+
+ # Prepare timesteps
+ self.scheduler.set_timesteps(num_inference_steps, device=device)
+ timesteps = self.scheduler.timesteps
+
+ batch_size = 1
+
+ # Prepare clip image embeds
+ clip_image = self.clip_image_processor.preprocess(
+ ref_image.resize((224, 224)), return_tensors="pt"
+ ).pixel_values
+ clip_image_embeds = self.image_encoder(
+ clip_image.to(device, dtype=self.image_encoder.dtype)
+ ).image_embeds
+ encoder_hidden_states = clip_image_embeds.unsqueeze(1)
+ uncond_encoder_hidden_states = torch.zeros_like(encoder_hidden_states)
+
+ if do_classifier_free_guidance:
+ encoder_hidden_states = torch.cat(
+ [uncond_encoder_hidden_states, encoder_hidden_states], dim=0
+ )
+
+ reference_control_writer = ReferenceAttentionControl(
+ self.reference_unet,
+ do_classifier_free_guidance=do_classifier_free_guidance,
+ mode="write",
+ batch_size=batch_size,
+ fusion_blocks="full",
+ )
+ reference_control_reader = ReferenceAttentionControl(
+ self.denoising_unet,
+ do_classifier_free_guidance=do_classifier_free_guidance,
+ mode="read",
+ batch_size=batch_size,
+ fusion_blocks="full",
+ )
+
+ num_channels_latents = self.denoising_unet.in_channels
+ latents = self.prepare_latents(
+ batch_size * num_images_per_prompt,
+ num_channels_latents,
+ width,
+ height,
+ video_length,
+ clip_image_embeds.dtype,
+ device,
+ generator,
+ )
+
+ # Prepare extra step kwargs.
+ extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+ # Prepare ref image latents
+ ref_image_tensor = self.ref_image_processor.preprocess(
+ ref_image, height=height, width=width
+ ) # (bs, c, width, height)
+ ref_image_tensor = ref_image_tensor.to(
+ dtype=self.vae.dtype, device=self.vae.device
+ )
+ ref_image_latents = self.vae.encode(ref_image_tensor).latent_dist.mean
+ ref_image_latents = ref_image_latents * 0.18215 # (b, 4, h, w)
+
+ # Prepare a list of pose condition images
+ pose_cond_tensor_list = []
+ for pose_image in pose_images:
+ pose_cond_tensor = self.cond_image_processor.preprocess(
+ pose_image, height=height, width=width
+ )
+ pose_cond_tensor = pose_cond_tensor.unsqueeze(2) # (bs, c, 1, h, w)
+ pose_cond_tensor_list.append(pose_cond_tensor)
+ pose_cond_tensor = torch.cat(pose_cond_tensor_list, dim=2) # (bs, c, t, h, w)
+ pose_cond_tensor = pose_cond_tensor.to(
+ device=device, dtype=self.pose_guider.dtype
+ )
+ pose_fea = self.pose_guider(pose_cond_tensor)
+
+ context_scheduler = get_context_scheduler(context_schedule)
+
+ # denoising loop
+ num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
+ with self.progress_bar(total=num_inference_steps) as progress_bar:
+ for i, t in enumerate(timesteps):
+ noise_pred = torch.zeros(
+ (
+ latents.shape[0] * (2 if do_classifier_free_guidance else 1),
+ *latents.shape[1:],
+ ),
+ device=latents.device,
+ dtype=latents.dtype,
+ )
+ counter = torch.zeros(
+ (1, 1, latents.shape[2], 1, 1),
+ device=latents.device,
+ dtype=latents.dtype,
+ )
+
+ # 1. Forward reference image
+ if i == 0:
+ self.reference_unet(
+ ref_image_latents.repeat(
+ (2 if do_classifier_free_guidance else 1), 1, 1, 1
+ ),
+ torch.zeros_like(t),
+ # t,
+ encoder_hidden_states=encoder_hidden_states,
+ return_dict=False,
+ )
+ reference_control_reader.update(reference_control_writer)
+
+ context_queue = list(
+ context_scheduler(
+ 0,
+ num_inference_steps,
+ latents.shape[2],
+ context_frames,
+ context_stride,
+ 0,
+ )
+ )
+ num_context_batches = math.ceil(len(context_queue) / context_batch_size)
+
+ context_queue = list(
+ context_scheduler(
+ 0,
+ num_inference_steps,
+ latents.shape[2],
+ context_frames,
+ context_stride,
+ context_overlap,
+ )
+ )
+
+ num_context_batches = math.ceil(len(context_queue) / context_batch_size)
+ global_context = []
+ for i in range(num_context_batches):
+ global_context.append(
+ context_queue[
+ i * context_batch_size : (i + 1) * context_batch_size
+ ]
+ )
+
+ for context in global_context:
+ # 3.1 expand the latents if we are doing classifier free guidance
+ latent_model_input = (
+ torch.cat([latents[:, :, c] for c in context])
+ .to(device)
+ .repeat(2 if do_classifier_free_guidance else 1, 1, 1, 1, 1)
+ )
+ latent_model_input = self.scheduler.scale_model_input(
+ latent_model_input, t
+ )
+ b, c, f, h, w = latent_model_input.shape
+ latent_pose_input = torch.cat(
+ [pose_fea[:, :, c] for c in context]
+ ).repeat(2 if do_classifier_free_guidance else 1, 1, 1, 1, 1)
+
+ pred = self.denoising_unet(
+ latent_model_input,
+ t,
+ encoder_hidden_states=encoder_hidden_states[:b],
+ pose_cond_fea=latent_pose_input,
+ return_dict=False,
+ )[0]
+
+ for j, c in enumerate(context):
+ noise_pred[:, :, c] = noise_pred[:, :, c] + pred
+ counter[:, :, c] = counter[:, :, c] + 1
+
+ # perform guidance
+ if do_classifier_free_guidance:
+ noise_pred_uncond, noise_pred_text = (noise_pred / counter).chunk(2)
+ noise_pred = noise_pred_uncond + guidance_scale * (
+ noise_pred_text - noise_pred_uncond
+ )
+
+ latents = self.scheduler.step(
+ noise_pred, t, latents, **extra_step_kwargs
+ ).prev_sample
+
+ if i == len(timesteps) - 1 or (
+ (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0
+ ):
+ progress_bar.update()
+ if callback is not None and i % callback_steps == 0:
+ step_idx = i // getattr(self.scheduler, "order", 1)
+ callback(step_idx, t, latents)
+
+ reference_control_reader.clear()
+ reference_control_writer.clear()
+
+ if interpolation_factor > 0:
+ latents = self.interpolate_latents(latents, interpolation_factor, device)
+ # Post-processing
+ images = self.decode_latents(latents) # (b, c, f, h, w)
+
+ # Convert to tensor
+ if output_type == "tensor":
+ images = torch.from_numpy(images)
+
+ if not return_dict:
+ return images
+
+ return Pose2VideoPipelineOutput(videos=images)
diff --git a/src/pipelines/utils.py b/src/pipelines/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..3cd5076748d15de4fc4b872bff7ba173f346478d
--- /dev/null
+++ b/src/pipelines/utils.py
@@ -0,0 +1,29 @@
+import torch
+
+tensor_interpolation = None
+
+
+def get_tensor_interpolation_method():
+ return tensor_interpolation
+
+
+def set_tensor_interpolation_method(is_slerp):
+ global tensor_interpolation
+ tensor_interpolation = slerp if is_slerp else linear
+
+
+def linear(v1, v2, t):
+ return (1.0 - t) * v1 + t * v2
+
+
+def slerp(
+ v0: torch.Tensor, v1: torch.Tensor, t: float, DOT_THRESHOLD: float = 0.9995
+) -> torch.Tensor:
+ u0 = v0 / v0.norm()
+ u1 = v1 / v1.norm()
+ dot = (u0 * u1).sum()
+ if dot.abs() > DOT_THRESHOLD:
+ # logger.info(f'warning: v0 and v1 close to parallel, using linear interpolation instead.')
+ return (1.0 - t) * v0 + t * v1
+ omega = dot.acos()
+ return (((1.0 - t) * omega).sin() * v0 + (t * omega).sin() * v1) / omega.sin()
diff --git a/src/utils/__pycache__/util.cpython-310.pyc b/src/utils/__pycache__/util.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..2ac3be271ac422290c1dab259242b95b22f300a4
Binary files /dev/null and b/src/utils/__pycache__/util.cpython-310.pyc differ
diff --git a/src/utils/util.py b/src/utils/util.py
new file mode 100644
index 0000000000000000000000000000000000000000..3298b2164e54dae7bc9170e04ac381bdad9d755d
--- /dev/null
+++ b/src/utils/util.py
@@ -0,0 +1,128 @@
+import importlib
+import os
+import os.path as osp
+import shutil
+import sys
+from pathlib import Path
+
+import av
+import numpy as np
+import torch
+import torchvision
+from einops import rearrange
+from PIL import Image
+
+
+def seed_everything(seed):
+ import random
+
+ import numpy as np
+
+ torch.manual_seed(seed)
+ torch.cuda.manual_seed_all(seed)
+ np.random.seed(seed % (2**32))
+ random.seed(seed)
+
+
+def import_filename(filename):
+ spec = importlib.util.spec_from_file_location("mymodule", filename)
+ module = importlib.util.module_from_spec(spec)
+ sys.modules[spec.name] = module
+ spec.loader.exec_module(module)
+ return module
+
+
+def delete_additional_ckpt(base_path, num_keep):
+ dirs = []
+ for d in os.listdir(base_path):
+ if d.startswith("checkpoint-"):
+ dirs.append(d)
+ num_tot = len(dirs)
+ if num_tot <= num_keep:
+ return
+ # ensure ckpt is sorted and delete the ealier!
+ del_dirs = sorted(dirs, key=lambda x: int(x.split("-")[-1]))[: num_tot - num_keep]
+ for d in del_dirs:
+ path_to_dir = osp.join(base_path, d)
+ if osp.exists(path_to_dir):
+ shutil.rmtree(path_to_dir)
+
+
+def save_videos_from_pil(pil_images, path, fps=8):
+ import av
+
+ save_fmt = Path(path).suffix
+ os.makedirs(os.path.dirname(path), exist_ok=True)
+ width, height = pil_images[0].size
+
+ if save_fmt == ".mp4":
+ codec = "libx264"
+ container = av.open(path, "w")
+ stream = container.add_stream(codec, rate=fps)
+
+ stream.width = width
+ stream.height = height
+
+ for pil_image in pil_images:
+ # pil_image = Image.fromarray(image_arr).convert("RGB")
+ av_frame = av.VideoFrame.from_image(pil_image)
+ container.mux(stream.encode(av_frame))
+ container.mux(stream.encode())
+ container.close()
+
+ elif save_fmt == ".gif":
+ pil_images[0].save(
+ fp=path,
+ format="GIF",
+ append_images=pil_images[1:],
+ save_all=True,
+ duration=(1 / fps * 1000),
+ loop=0,
+ )
+ else:
+ raise ValueError("Unsupported file type. Use .mp4 or .gif.")
+
+
+def save_videos_grid(videos: torch.Tensor, path: str, rescale=False, n_rows=6, fps=8):
+ videos = rearrange(videos, "b c t h w -> t b c h w")
+ height, width = videos.shape[-2:]
+ outputs = []
+
+ for x in videos:
+ x = torchvision.utils.make_grid(x, nrow=n_rows) # (c h w)
+ x = x.transpose(0, 1).transpose(1, 2).squeeze(-1) # (h w c)
+ if rescale:
+ x = (x + 1.0) / 2.0 # -1,1 -> 0,1
+ x = (x * 255).numpy().astype(np.uint8)
+ x = Image.fromarray(x)
+
+ outputs.append(x)
+
+ os.makedirs(os.path.dirname(path), exist_ok=True)
+
+ save_videos_from_pil(outputs, path, fps)
+
+
+def read_frames(video_path):
+ container = av.open(video_path)
+
+ video_stream = next(s for s in container.streams if s.type == "video")
+ frames = []
+ for packet in container.demux(video_stream):
+ for frame in packet.decode():
+ image = Image.frombytes(
+ "RGB",
+ (frame.width, frame.height),
+ frame.to_rgb().to_ndarray(),
+ )
+ frames.append(image)
+
+ return frames
+
+
+def get_fps(video_path):
+ container = av.open(video_path)
+ video_stream = next(s for s in container.streams if s.type == "video")
+ fps = video_stream.average_rate
+ container.close()
+ return fps
diff --git a/tools/download_weights.py b/tools/download_weights.py
new file mode 100644
index 0000000000000000000000000000000000000000..dab5fcf850745688bfebd91f30e500a9aa275854
--- /dev/null
+++ b/tools/download_weights.py
@@ -0,0 +1,111 @@
+import os
+from pathlib import Path, PurePosixPath
+
+from huggingface_hub import hf_hub_download
+
+
+def prepare_base_model():
+ print(f'Preparing base stable-diffusion-v1-5 weights...')
+ local_dir = "./pretrained_weights/stable-diffusion-v1-5"
+ os.makedirs(local_dir, exist_ok=True)
+ for hub_file in ["unet/config.json", "unet/diffusion_pytorch_model.bin"]:
+ path = Path(hub_file)
+ saved_path = local_dir / path
+ if os.path.exists(saved_path):
+ continue
+ hf_hub_download(
+ repo_id="runwayml/stable-diffusion-v1-5",
+ subfolder=PurePosixPath(path.parent),
+ filename=PurePosixPath(path.name),
+ local_dir=local_dir,
+ )
+
+
+def prepare_image_encoder():
+ print(f"Preparing image encoder weights...")
+ local_dir = "./pretrained_weights"
+ os.makedirs(local_dir, exist_ok=True)
+ for hub_file in ["image_encoder/config.json", "image_encoder/pytorch_model.bin"]:
+ path = Path(hub_file)
+ saved_path = local_dir / path
+ if os.path.exists(saved_path):
+ continue
+ hf_hub_download(
+ repo_id="lambdalabs/sd-image-variations-diffusers",
+ subfolder=PurePosixPath(path.parent),
+ filename=PurePosixPath(path.name),
+ local_dir=local_dir,
+ )
+
+
+def prepare_dwpose():
+ print(f"Preparing DWPose weights...")
+ local_dir = "./pretrained_weights/DWPose"
+ os.makedirs(local_dir, exist_ok=True)
+ for hub_file in [
+ "dw-ll_ucoco_384.onnx",
+ "yolox_l.onnx",
+ ]:
+ path = Path(hub_file)
+ saved_path = local_dir / path
+ if os.path.exists(saved_path):
+ continue
+
+ hf_hub_download(
+ repo_id="yzd-v/DWPose",
+ subfolder=PurePosixPath(path.parent),
+ filename=PurePosixPath(path.name),
+ local_dir=local_dir,
+ )
+
+
+def prepare_vae():
+ print(f"Preparing vae weights...")
+ local_dir = "./pretrained_weights/sd-vae-ft-mse"
+ os.makedirs(local_dir, exist_ok=True)
+ for hub_file in [
+ "config.json",
+ "diffusion_pytorch_model.bin",
+ ]:
+ path = Path(hub_file)
+ saved_path = local_dir / path
+ if os.path.exists(saved_path):
+ continue
+
+ hf_hub_download(
+ repo_id="stabilityai/sd-vae-ft-mse",
+ subfolder=PurePosixPath(path.parent),
+ filename=PurePosixPath(path.name),
+ local_dir=local_dir,
+ )
+
+
+def prepare_anyone():
+ print(f"Preparing AnimateAnyone weights...")
+ local_dir = "./pretrained_weights"
+ os.makedirs(local_dir, exist_ok=True)
+ for hub_file in [
+ "denoising_unet.pth",
+ "motion_module.pth",
+ "pose_guider.pth",
+ "reference_unet.pth",
+ ]:
+ path = Path(hub_file)
+ saved_path = local_dir / path
+ if os.path.exists(saved_path):
+ continue
+
+ hf_hub_download(
+ repo_id="patrolli/AnimateAnyone",
+ subfolder=PurePosixPath(path.parent),
+ filename=PurePosixPath(path.name),
+ local_dir=local_dir,
+ )
+
+if __name__ == '__main__':
+ prepare_base_model()
+ prepare_image_encoder()
+ prepare_dwpose()
+ prepare_vae()
+ prepare_anyone()
+
\ No newline at end of file
diff --git a/tools/extract_dwpose_from_vid.py b/tools/extract_dwpose_from_vid.py
new file mode 100644
index 0000000000000000000000000000000000000000..1ca3ccc7691431cbbd3894b52f17464abed37e33
--- /dev/null
+++ b/tools/extract_dwpose_from_vid.py
@@ -0,0 +1,101 @@
+import concurrent.futures
+import os
+import random
+from pathlib import Path
+
+import numpy as np
+
+from src.dwpose import DWposeDetector
+from src.utils.util import get_fps, read_frames, save_videos_from_pil
+
+# Extract dwpose mp4 videos from raw videos
+# /path/to/video_dataset/*/*.mp4 -> /path/to/video_dataset_dwpose/*/*.mp4
+
+
+def process_single_video(video_path, detector, root_dir, save_dir):
+ relative_path = os.path.relpath(video_path, root_dir)
+ print(relative_path, video_path, root_dir)
+ out_path = os.path.join(save_dir, relative_path)
+ if os.path.exists(out_path):
+ return
+
+ output_dir = Path(os.path.dirname(os.path.join(save_dir, relative_path)))
+ if not output_dir.exists():
+ output_dir.mkdir(parents=True, exist_ok=True)
+
+ fps = get_fps(video_path)
+ frames = read_frames(video_path)
+ kps_results = []
+ for i, frame_pil in enumerate(frames):
+ result, score = detector(frame_pil)
+ score = np.mean(score, axis=-1)
+
+ kps_results.append(result)
+
+ save_videos_from_pil(kps_results, out_path, fps=fps)
+
+
+def process_batch_videos(video_list, detector, root_dir, save_dir):
+ for i, video_path in enumerate(video_list):
+ print(f"Process {i}/{len(video_list)} video")
+ process_single_video(video_path, detector, root_dir, save_dir)
+
+
+if __name__ == "__main__":
+ # -----
+ # NOTE:
+ # python tools/extract_dwpose_from_vid.py --video_root /path/to/video_dir
+ # -----
+ import argparse
+
+ parser = argparse.ArgumentParser()
+ parser.add_argument("--video_root", type=str)
+ parser.add_argument(
+ "--save_dir", type=str, help="Path to save extracted pose videos"
+ )
+ parser.add_argument("-j", type=int, default=4, help="Num workers")
+ args = parser.parse_args()
+ num_workers = args.j
+ if args.save_dir is None:
+ save_dir = args.video_root + "_dwpose"
+ else:
+ save_dir = args.save_dir
+ if not os.path.exists(save_dir):
+ os.makedirs(save_dir)
+ cuda_visible_devices = os.environ.get("CUDA_VISIBLE_DEVICES", "0")
+ gpu_ids = [int(id) for id in range(len(cuda_visible_devices.split(",")))]
+ print(f"avaliable gpu ids: {gpu_ids}")
+
+ # collect all video_folder paths
+ video_mp4_paths = set()
+ for root, dirs, files in os.walk(args.video_root):
+ for name in files:
+ if name.endswith(".mp4"):
+ video_mp4_paths.add(os.path.join(root, name))
+ video_mp4_paths = list(video_mp4_paths)
+ random.shuffle(video_mp4_paths)
+
+ # split into chunks,
+ batch_size = (len(video_mp4_paths) + num_workers - 1) // num_workers
+ print(f"Num videos: {len(video_mp4_paths)} {batch_size = }")
+ video_chunks = [
+ video_mp4_paths[i : i + batch_size]
+ for i in range(0, len(video_mp4_paths), batch_size)
+ ]
+
+ with concurrent.futures.ThreadPoolExecutor() as executor:
+ futures = []
+ for i, chunk in enumerate(video_chunks):
+ # init detector
+ gpu_id = gpu_ids[i % len(gpu_ids)]
+ detector = DWposeDetector()
+ # torch.cuda.set_device(gpu_id)
+ detector = detector.to(f"cuda:{gpu_id}")
+
+ futures.append(
+ executor.submit(
+ process_batch_videos, chunk, detector, args.video_root, save_dir
+ )
+ )
+ for future in concurrent.futures.as_completed(futures):
+ future.result()
diff --git a/tools/extract_meta_info.py b/tools/extract_meta_info.py
new file mode 100644
index 0000000000000000000000000000000000000000..89437c62660994129cc56c73ecbc3fb458bdb7db
--- /dev/null
+++ b/tools/extract_meta_info.py
@@ -0,0 +1,37 @@
+import argparse
+import json
+import os
+
+# -----
+# [{'vid': , 'kps': , 'other':},
+# {'vid': , 'kps': , 'other':}]
+# -----
+# python tools/extract_meta_info.py --root_path /path/to/video_dir --dataset_name fashion
+# -----
+parser = argparse.ArgumentParser()
+parser.add_argument("--root_path", type=str)
+parser.add_argument("--dataset_name", type=str)
+parser.add_argument("--meta_info_name", type=str)
+
+args = parser.parse_args()
+
+if args.meta_info_name is None:
+ args.meta_info_name = args.dataset_name
+
+pose_dir = args.root_path + "_dwpose"
+
+# collect all video_folder paths
+video_mp4_paths = set()
+for root, dirs, files in os.walk(args.root_path):
+ for name in files:
+ if name.endswith(".mp4"):
+ video_mp4_paths.add(os.path.join(root, name))
+video_mp4_paths = list(video_mp4_paths)
+
+meta_infos = []
+for video_mp4_path in video_mp4_paths:
+ relative_video_name = os.path.relpath(video_mp4_path, args.root_path)
+ kps_path = os.path.join(pose_dir, relative_video_name)
+ meta_infos.append({"video_path": video_mp4_path, "kps_path": kps_path})
+
+json.dump(meta_infos, open(f"./data/{args.meta_info_name}_meta.json", "w"))
diff --git a/tools/facetracker_api.py b/tools/facetracker_api.py
new file mode 100644
index 0000000000000000000000000000000000000000..82140269cc24158e6b9377247c42e06275935b44
--- /dev/null
+++ b/tools/facetracker_api.py
@@ -0,0 +1,62 @@
+import copy
+import os, sys
+import math
+import numpy as np
+import cv2
+sys.path.append("OpenSeeFace/")
+from tracker import Tracker, get_model_base_path
+
+features = ["eye_l", "eye_r", "eyebrow_steepness_l", "eyebrow_updown_l", "eyebrow_quirk_l", "eyebrow_steepness_r", "eyebrow_updown_r", "eyebrow_quirk_r", "mouth_corner_updown_l", "mouth_corner_inout_l", "mouth_corner_updown_r", "mouth_corner_inout_r", "mouth_open", "mouth_wide"]
+
+
+def face_image(frame, save_path=None):
+ height, width, c = frame.shape
+ tracker = Tracker(width, height, threshold=None, max_threads=1, max_faces=1, discard_after=10, scan_every=3, silent=False, model_type=3, model_dir=None,
+ no_gaze=False, detection_threshold=0.4, use_retinaface=0, max_feature_updates=900, static_model=True, try_hard=False)
+ faces = tracker.predict(frame)
+ frame = np.zeros_like(frame)
+ detected = False
+ face_lms = None
+ for face_num, f in enumerate(faces):
+ f = copy.copy(f)
+ if f.eye_blink is None:
+ f.eye_blink = [1, 1]
+ right_state = "O" if f.eye_blink[0] > 0.30 else "-"
+ left_state = "O" if f.eye_blink[1] > 0.30 else "-"
+ detected = True
+ if not f.success:
+ pts_3d = np.zeros((70, 3), np.float32)
+ if face_num == 0:
+ face_lms = f.lms
+ for pt_num, (x,y,c) in enumerate(f.lms):
+ if pt_num == 66 and (f.eye_blink[0] < 0.30 or c < 0.20):
+ continue
+ if pt_num == 67 and (f.eye_blink[1] < 0.30 or c < 0.20):
+ continue
+ x = int(x + 0.5)
+ y = int(y + 0.5)
+
+ color = (0, 255, 0)
+ if pt_num >= 66:
+ color = (255, 255, 0)
+ if not (x < 0 or y < 0 or x >= height or y >= width):
+ cv2.circle(frame, (y, x), 1, color, -1)
+ if f.rotation is not None:
+ projected = cv2.projectPoints(f.contour, f.rotation, f.translation, tracker.camera, tracker.dist_coeffs)
+ for [(x,y)] in projected[0]:
+ x = int(x + 0.5)
+ y = int(y + 0.5)
+ if not (x < 0 or y < 0 or x >= height or y >= width):
+ frame[int(x), int(y)] = (0, 255, 255)
+ x += 1
+ if not (x < 0 or y < 0 or x >= height or y >= width):
+ frame[int(x), int(y)] = (0, 255, 255)
+ y += 1
+ if not (x < 0 or y < 0 or x >= height or y >= width):
+ frame[int(x), int(y)] = (0, 255, 255)
+ x -= 1
+ if not (x < 0 or y < 0 or x >= height or y >= width):
+ frame[int(x), int(y)] = (0, 255, 255)
+ if save_path is not None:
+ cv2.imwrite(save_path, frame)
+ return frame, face_lms
diff --git a/tools/vid2pose.py b/tools/vid2pose.py
new file mode 100644
index 0000000000000000000000000000000000000000..6813fe10da0d1210dbe5ce45b2c6843a58f3a0ba
--- /dev/null
+++ b/tools/vid2pose.py
@@ -0,0 +1,38 @@
+from src.dwpose import DWposeDetector
+import os
+from pathlib import Path
+
+from src.utils.util import get_fps, read_frames, save_videos_from_pil
+import numpy as np
+
+
+if __name__ == "__main__":
+ import argparse
+
+ parser = argparse.ArgumentParser()
+ parser.add_argument("--video_path", type=str)
+ args = parser.parse_args()
+
+ if not os.path.exists(args.video_path):
+ raise ValueError(f"Path: {args.video_path} not exists")
+
+ dir_path, video_name = (
+ os.path.dirname(args.video_path),
+ os.path.splitext(os.path.basename(args.video_path))[0],
+ )
+ out_path = os.path.join(dir_path, video_name + "_kps.mp4")
+
+ detector = DWposeDetector()
+ detector = detector.to(f"cuda")
+
+ fps = get_fps(args.video_path)
+ frames = read_frames(args.video_path)
+ kps_results = []
+ for i, frame_pil in enumerate(frames):
+ result, score = detector(frame_pil)
+ score = np.mean(score, axis=-1)
+
+ kps_results.append(result)
+
+ print(out_path)
+ save_videos_from_pil(kps_results, out_path, fps=fps)
diff --git a/train_stage_1.py b/train_stage_1.py
new file mode 100644
index 0000000000000000000000000000000000000000..c6f465c15f5d13130e70ebf7b4b67b10e68b4168
--- /dev/null
+++ b/train_stage_1.py
@@ -0,0 +1,726 @@
+import argparse
+import logging
+import math
+import os
+import os.path as osp
+import random
+import warnings
+from datetime import datetime
+from pathlib import Path
+from tempfile import TemporaryDirectory
+
+import diffusers
+import mlflow
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.checkpoint
+import transformers
+from accelerate import Accelerator
+from accelerate.logging import get_logger
+from accelerate.utils import DistributedDataParallelKwargs
+from diffusers import AutoencoderKL, DDIMScheduler
+from diffusers.optimization import get_scheduler
+from diffusers.utils import check_min_version
+from diffusers.utils.import_utils import is_xformers_available
+from omegaconf import OmegaConf
+from PIL import Image
+from tqdm.auto import tqdm
+from transformers import CLIPVisionModelWithProjection
+
+from src.dataset.dance_image import HumanDanceDataset
+from src.dwpose import DWposeDetector
+from src.models.mutual_self_attention import ReferenceAttentionControl
+from src.models.pose_guider import PoseGuider
+from src.models.unet_2d_condition import UNet2DConditionModel
+from src.models.unet_3d import UNet3DConditionModel
+from src.pipelines.pipeline_pose2img import Pose2ImagePipeline
+from src.utils.util import delete_additional_ckpt, import_filename, seed_everything
+
+warnings.filterwarnings("ignore")
+
+# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
+check_min_version("0.10.0.dev0")
+
+logger = get_logger(__name__, log_level="INFO")
+
+
+class Net(nn.Module):
+ def __init__(
+ self,
+ reference_unet: UNet2DConditionModel,
+ denoising_unet: UNet3DConditionModel,
+ pose_guider: PoseGuider,
+ reference_control_writer,
+ reference_control_reader,
+ ):
+ super().__init__()
+ self.reference_unet = reference_unet
+ self.denoising_unet = denoising_unet
+ self.pose_guider = pose_guider
+ self.reference_control_writer = reference_control_writer
+ self.reference_control_reader = reference_control_reader
+
+ def forward(
+ self,
+ noisy_latents,
+ timesteps,
+ ref_image_latents,
+ clip_image_embeds,
+ pose_img,
+ uncond_fwd: bool = False,
+ ):
+ pose_cond_tensor = pose_img.to(device="cuda")
+ pose_fea = self.pose_guider(pose_cond_tensor)
+
+ if not uncond_fwd:
+ ref_timesteps = torch.zeros_like(timesteps)
+ self.reference_unet(
+ ref_image_latents,
+ ref_timesteps,
+ encoder_hidden_states=clip_image_embeds,
+ return_dict=False,
+ )
+ self.reference_control_reader.update(self.reference_control_writer)
+
+ model_pred = self.denoising_unet(
+ noisy_latents,
+ timesteps,
+ pose_cond_fea=pose_fea,
+ encoder_hidden_states=clip_image_embeds,
+ ).sample
+
+ return model_pred
+
+
+def compute_snr(noise_scheduler, timesteps):
+ """
+ Computes SNR as per
+ https://github.com/TiankaiHang/Min-SNR-Diffusion-Training/blob/521b624bd70c67cee4bdf49225915f5945a872e3/guided_diffusion/gaussian_diffusion.py#L847-L849
+ """
+ alphas_cumprod = noise_scheduler.alphas_cumprod
+ sqrt_alphas_cumprod = alphas_cumprod**0.5
+ sqrt_one_minus_alphas_cumprod = (1.0 - alphas_cumprod) ** 0.5
+
+ # Expand the tensors.
+ # Adapted from https://github.com/TiankaiHang/Min-SNR-Diffusion-Training/blob/521b624bd70c67cee4bdf49225915f5945a872e3/guided_diffusion/gaussian_diffusion.py#L1026
+ sqrt_alphas_cumprod = sqrt_alphas_cumprod.to(device=timesteps.device)[
+ timesteps
+ ].float()
+ while len(sqrt_alphas_cumprod.shape) < len(timesteps.shape):
+ sqrt_alphas_cumprod = sqrt_alphas_cumprod[..., None]
+ alpha = sqrt_alphas_cumprod.expand(timesteps.shape)
+
+ sqrt_one_minus_alphas_cumprod = sqrt_one_minus_alphas_cumprod.to(
+ device=timesteps.device
+ )[timesteps].float()
+ while len(sqrt_one_minus_alphas_cumprod.shape) < len(timesteps.shape):
+ sqrt_one_minus_alphas_cumprod = sqrt_one_minus_alphas_cumprod[..., None]
+ sigma = sqrt_one_minus_alphas_cumprod.expand(timesteps.shape)
+
+ # Compute SNR.
+ snr = (alpha / sigma) ** 2
+ return snr
+
+
+def log_validation(
+ vae,
+ image_enc,
+ net,
+ scheduler,
+ accelerator,
+ width,
+ height,
+):
+ logger.info("Running validation... ")
+
+ ori_net = accelerator.unwrap_model(net)
+ reference_unet = ori_net.reference_unet
+ denoising_unet = ori_net.denoising_unet
+ pose_guider = ori_net.pose_guider
+
+ # generator = torch.manual_seed(42)
+ generator = torch.Generator().manual_seed(42)
+ # cast unet dtype
+ vae = vae.to(dtype=torch.float32)
+ image_enc = image_enc.to(dtype=torch.float32)
+
+ pose_detector = DWposeDetector()
+ pose_detector.to(accelerator.device)
+
+ pipe = Pose2ImagePipeline(
+ vae=vae,
+ image_encoder=image_enc,
+ reference_unet=reference_unet,
+ denoising_unet=denoising_unet,
+ pose_guider=pose_guider,
+ scheduler=scheduler,
+ )
+ pipe = pipe.to(accelerator.device)
+
+ ref_image_paths = [
+ "./configs/inference/ref_images/anyone-2.png",
+ "./configs/inference/ref_images/anyone-3.png",
+ ]
+ pose_image_paths = [
+ "./configs/inference/pose_images/pose-1.png",
+ "./configs/inference/pose_images/pose-1.png",
+ ]
+
+ pil_images = []
+ for ref_image_path in ref_image_paths:
+ for pose_image_path in pose_image_paths:
+ pose_name = pose_image_path.split("/")[-1].replace(".png", "")
+ ref_name = ref_image_path.split("/")[-1].replace(".png", "")
+ ref_image_pil = Image.open(ref_image_path).convert("RGB")
+ pose_image_pil = Image.open(pose_image_path).convert("RGB")
+
+ image = pipe(
+ ref_image_pil,
+ pose_image_pil,
+ width,
+ height,
+ 20,
+ 3.5,
+ generator=generator,
+ ).images
+ image = image[0, :, 0].permute(1, 2, 0).cpu().numpy() # (3, 512, 512)
+ res_image_pil = Image.fromarray((image * 255).astype(np.uint8))
+ # Save ref_image, src_image and the generated_image
+ w, h = res_image_pil.size
+ canvas = Image.new("RGB", (w * 3, h), "white")
+ ref_image_pil = ref_image_pil.resize((w, h))
+ pose_image_pil = pose_image_pil.resize((w, h))
+ canvas.paste(ref_image_pil, (0, 0))
+ canvas.paste(pose_image_pil, (w, 0))
+ canvas.paste(res_image_pil, (w * 2, 0))
+
+ pil_images.append({"name": f"{ref_name}_{pose_name}", "img": canvas})
+
+ vae = vae.to(dtype=torch.float16)
+ image_enc = image_enc.to(dtype=torch.float16)
+
+ del pipe
+ torch.cuda.empty_cache()
+
+ return pil_images
+
+
+def main(cfg):
+ kwargs = DistributedDataParallelKwargs(find_unused_parameters=True)
+ accelerator = Accelerator(
+ gradient_accumulation_steps=cfg.solver.gradient_accumulation_steps,
+ mixed_precision=cfg.solver.mixed_precision,
+ log_with="mlflow",
+ project_dir="./mlruns",
+ kwargs_handlers=[kwargs],
+ )
+
+ # Make one log on every process with the configuration for debugging.
+ logging.basicConfig(
+ format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+ datefmt="%m/%d/%Y %H:%M:%S",
+ level=logging.INFO,
+ )
+ logger.info(accelerator.state, main_process_only=False)
+ if accelerator.is_local_main_process:
+ transformers.utils.logging.set_verbosity_warning()
+ diffusers.utils.logging.set_verbosity_info()
+ else:
+ transformers.utils.logging.set_verbosity_error()
+ diffusers.utils.logging.set_verbosity_error()
+
+ # If passed along, set the training seed now.
+ if cfg.seed is not None:
+ seed_everything(cfg.seed)
+
+ exp_name = cfg.exp_name
+ save_dir = f"{cfg.output_dir}/{exp_name}"
+ if accelerator.is_main_process and not os.path.exists(save_dir):
+ os.makedirs(save_dir)
+
+ if cfg.weight_dtype == "fp16":
+ weight_dtype = torch.float16
+ elif cfg.weight_dtype == "fp32":
+ weight_dtype = torch.float32
+ else:
+ raise ValueError(
+ f"Do not support weight dtype: {cfg.weight_dtype} during training"
+ )
+
+ sched_kwargs = OmegaConf.to_container(cfg.noise_scheduler_kwargs)
+ if cfg.enable_zero_snr:
+ sched_kwargs.update(
+ rescale_betas_zero_snr=True,
+ timestep_spacing="trailing",
+ prediction_type="v_prediction",
+ )
+ val_noise_scheduler = DDIMScheduler(**sched_kwargs)
+ sched_kwargs.update({"beta_schedule": "scaled_linear"})
+ train_noise_scheduler = DDIMScheduler(**sched_kwargs)
+ vae = AutoencoderKL.from_pretrained(cfg.vae_model_path).to(
+ "cuda", dtype=weight_dtype
+ )
+
+ reference_unet = UNet2DConditionModel.from_pretrained(
+ cfg.base_model_path,
+ subfolder="unet",
+ ).to(device="cuda")
+ denoising_unet = UNet3DConditionModel.from_pretrained_2d(
+ cfg.base_model_path,
+ "",
+ subfolder="unet",
+ unet_additional_kwargs={
+ "use_motion_module": False,
+ "unet_use_temporal_attention": False,
+ },
+ ).to(device="cuda")
+
+ image_enc = CLIPVisionModelWithProjection.from_pretrained(
+ cfg.image_encoder_path,
+ ).to(dtype=weight_dtype, device="cuda")
+
+ if cfg.pose_guider_pretrain:
+ pose_guider = PoseGuider(
+ conditioning_embedding_channels=320, block_out_channels=(16, 32, 96, 256)
+ ).to(device="cuda")
+ # load pretrained controlnet-openpose params for pose_guider
+ controlnet_openpose_state_dict = torch.load(cfg.controlnet_openpose_path)
+ state_dict_to_load = {}
+ for k in controlnet_openpose_state_dict.keys():
+ if k.startswith("controlnet_cond_embedding.") and k.find("conv_out") < 0:
+ new_k = k.replace("controlnet_cond_embedding.", "")
+ state_dict_to_load[new_k] = controlnet_openpose_state_dict[k]
+ miss, _ = pose_guider.load_state_dict(state_dict_to_load, strict=False)
+ logger.info(f"Missing key for pose guider: {len(miss)}")
+ else:
+ pose_guider = PoseGuider(
+ conditioning_embedding_channels=320,
+ ).to(device="cuda")
+
+ # Freeze
+ vae.requires_grad_(False)
+ image_enc.requires_grad_(False)
+
+ # Explictly declare training models
+ denoising_unet.requires_grad_(True)
+ # Some top layer parames of reference_unet don't need grad
+ for name, param in reference_unet.named_parameters():
+ if "up_blocks.3" in name:
+ param.requires_grad_(False)
+ else:
+ param.requires_grad_(True)
+
+ pose_guider.requires_grad_(True)
+
+ reference_control_writer = ReferenceAttentionControl(
+ reference_unet,
+ do_classifier_free_guidance=False,
+ mode="write",
+ fusion_blocks="full",
+ )
+ reference_control_reader = ReferenceAttentionControl(
+ denoising_unet,
+ do_classifier_free_guidance=False,
+ mode="read",
+ fusion_blocks="full",
+ )
+
+ net = Net(
+ reference_unet,
+ denoising_unet,
+ pose_guider,
+ reference_control_writer,
+ reference_control_reader,
+ )
+
+ if cfg.solver.enable_xformers_memory_efficient_attention:
+ if is_xformers_available():
+ reference_unet.enable_xformers_memory_efficient_attention()
+ denoising_unet.enable_xformers_memory_efficient_attention()
+ else:
+ raise ValueError(
+ "xformers is not available. Make sure it is installed correctly"
+ )
+
+ if cfg.solver.gradient_checkpointing:
+ reference_unet.enable_gradient_checkpointing()
+ denoising_unet.enable_gradient_checkpointing()
+
+ if cfg.solver.scale_lr:
+ learning_rate = (
+ cfg.solver.learning_rate
+ * cfg.solver.gradient_accumulation_steps
+ * cfg.data.train_bs
+ * accelerator.num_processes
+ )
+ else:
+ learning_rate = cfg.solver.learning_rate
+
+ # Initialize the optimizer
+ if cfg.solver.use_8bit_adam:
+ try:
+ import bitsandbytes as bnb
+ except ImportError:
+ raise ImportError(
+ "Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`"
+ )
+
+ optimizer_cls = bnb.optim.AdamW8bit
+ else:
+ optimizer_cls = torch.optim.AdamW
+
+ trainable_params = list(filter(lambda p: p.requires_grad, net.parameters()))
+ optimizer = optimizer_cls(
+ trainable_params,
+ lr=learning_rate,
+ betas=(cfg.solver.adam_beta1, cfg.solver.adam_beta2),
+ weight_decay=cfg.solver.adam_weight_decay,
+ eps=cfg.solver.adam_epsilon,
+ )
+
+ # Scheduler
+ lr_scheduler = get_scheduler(
+ cfg.solver.lr_scheduler,
+ optimizer=optimizer,
+ num_warmup_steps=cfg.solver.lr_warmup_steps
+ * cfg.solver.gradient_accumulation_steps,
+ num_training_steps=cfg.solver.max_train_steps
+ * cfg.solver.gradient_accumulation_steps,
+ )
+
+ train_dataset = HumanDanceDataset(
+ img_size=(cfg.data.train_width, cfg.data.train_height),
+ img_scale=(0.9, 1.0),
+ data_meta_paths=cfg.data.meta_paths,
+ sample_margin=cfg.data.sample_margin,
+ )
+ train_dataloader = torch.utils.data.DataLoader(
+ train_dataset, batch_size=cfg.data.train_bs, shuffle=True, num_workers=4
+ )
+
+ # Prepare everything with our `accelerator`.
+ (
+ net,
+ optimizer,
+ train_dataloader,
+ lr_scheduler,
+ ) = accelerator.prepare(
+ net,
+ optimizer,
+ train_dataloader,
+ lr_scheduler,
+ )
+
+ # We need to recalculate our total training steps as the size of the training dataloader may have changed.
+ num_update_steps_per_epoch = math.ceil(
+ len(train_dataloader) / cfg.solver.gradient_accumulation_steps
+ )
+ # Afterwards we recalculate our number of training epochs
+ num_train_epochs = math.ceil(
+ cfg.solver.max_train_steps / num_update_steps_per_epoch
+ )
+
+ # We need to initialize the trackers we use, and also store our configuration.
+ # The trackers initializes automatically on the main process.
+ if accelerator.is_main_process:
+ run_time = datetime.now().strftime("%Y%m%d-%H%M")
+ accelerator.init_trackers(
+ cfg.exp_name,
+ init_kwargs={"mlflow": {"run_name": run_time}},
+ )
+ # dump config file
+ mlflow.log_dict(OmegaConf.to_container(cfg), "config.yaml")
+
+ # Train!
+ total_batch_size = (
+ cfg.data.train_bs
+ * accelerator.num_processes
+ * cfg.solver.gradient_accumulation_steps
+ )
+
+ logger.info("***** Running training *****")
+ logger.info(f" Num examples = {len(train_dataset)}")
+ logger.info(f" Num Epochs = {num_train_epochs}")
+ logger.info(f" Instantaneous batch size per device = {cfg.data.train_bs}")
+ logger.info(
+ f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}"
+ )
+ logger.info(
+ f" Gradient Accumulation steps = {cfg.solver.gradient_accumulation_steps}"
+ )
+ logger.info(f" Total optimization steps = {cfg.solver.max_train_steps}")
+ global_step = 0
+ first_epoch = 0
+
+ # Potentially load in the weights and states from a previous save
+ if cfg.resume_from_checkpoint:
+ if cfg.resume_from_checkpoint != "latest":
+ resume_dir = cfg.resume_from_checkpoint
+ else:
+ resume_dir = save_dir
+ # Get the most recent checkpoint
+ dirs = os.listdir(resume_dir)
+ dirs = [d for d in dirs if d.startswith("checkpoint")]
+ dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
+ path = dirs[-1]
+ accelerator.load_state(os.path.join(resume_dir, path))
+ accelerator.print(f"Resuming from checkpoint {path}")
+ global_step = int(path.split("-")[1])
+
+ first_epoch = global_step // num_update_steps_per_epoch
+ resume_step = global_step % num_update_steps_per_epoch
+
+ # Only show the progress bar once on each machine.
+ progress_bar = tqdm(
+ range(global_step, cfg.solver.max_train_steps),
+ disable=not accelerator.is_local_main_process,
+ )
+ progress_bar.set_description("Steps")
+
+ for epoch in range(first_epoch, num_train_epochs):
+ train_loss = 0.0
+ for step, batch in enumerate(train_dataloader):
+ with accelerator.accumulate(net):
+ # Convert videos to latent space
+ pixel_values = batch["img"].to(weight_dtype)
+ with torch.no_grad():
+ latents = vae.encode(pixel_values).latent_dist.sample()
+ latents = latents.unsqueeze(2) # (b, c, 1, h, w)
+ latents = latents * 0.18215
+
+ noise = torch.randn_like(latents)
+ if cfg.noise_offset > 0.0:
+ noise += cfg.noise_offset * torch.randn(
+ (noise.shape[0], noise.shape[1], 1, 1, 1),
+ device=noise.device,
+ )
+
+ bsz = latents.shape[0]
+ # Sample a random timestep for each video
+ timesteps = torch.randint(
+ 0,
+ train_noise_scheduler.num_train_timesteps,
+ (bsz,),
+ device=latents.device,
+ )
+ timesteps = timesteps.long()
+
+ tgt_pose_img = batch["tgt_pose"]
+ tgt_pose_img = tgt_pose_img.unsqueeze(2) # (bs, 3, 1, 512, 512)
+
+ uncond_fwd = random.random() < cfg.uncond_ratio
+ clip_image_list = []
+ ref_image_list = []
+ for batch_idx, (ref_img, clip_img) in enumerate(
+ zip(
+ batch["ref_img"],
+ batch["clip_images"],
+ )
+ ):
+ if uncond_fwd:
+ clip_image_list.append(torch.zeros_like(clip_img))
+ else:
+ clip_image_list.append(clip_img)
+ ref_image_list.append(ref_img)
+
+ with torch.no_grad():
+ ref_img = torch.stack(ref_image_list, dim=0).to(
+ dtype=vae.dtype, device=vae.device
+ )
+ ref_image_latents = vae.encode(
+ ref_img
+ ).latent_dist.sample() # (bs, d, 64, 64)
+ ref_image_latents = ref_image_latents * 0.18215
+
+ clip_img = torch.stack(clip_image_list, dim=0).to(
+ dtype=image_enc.dtype, device=image_enc.device
+ )
+ clip_image_embeds = image_enc(
+ clip_img.to("cuda", dtype=weight_dtype)
+ ).image_embeds
+ image_prompt_embeds = clip_image_embeds.unsqueeze(1) # (bs, 1, d)
+
+ # add noise
+ noisy_latents = train_noise_scheduler.add_noise(
+ latents, noise, timesteps
+ )
+
+ # Get the target for loss depending on the prediction type
+ if train_noise_scheduler.prediction_type == "epsilon":
+ target = noise
+ elif train_noise_scheduler.prediction_type == "v_prediction":
+ target = train_noise_scheduler.get_velocity(
+ latents, noise, timesteps
+ )
+ else:
+ raise ValueError(
+ f"Unknown prediction type {train_noise_scheduler.prediction_type}"
+ )
+
+ model_pred = net(
+ noisy_latents,
+ timesteps,
+ ref_image_latents,
+ image_prompt_embeds,
+ tgt_pose_img,
+ uncond_fwd,
+ )
+
+ if cfg.snr_gamma == 0:
+ loss = F.mse_loss(
+ model_pred.float(), target.float(), reduction="mean"
+ )
+ else:
+ snr = compute_snr(train_noise_scheduler, timesteps)
+ if train_noise_scheduler.config.prediction_type == "v_prediction":
+ # Velocity objective requires that we add one to SNR values before we divide by them.
+ snr = snr + 1
+ mse_loss_weights = (
+ torch.stack(
+ [snr, cfg.snr_gamma * torch.ones_like(timesteps)], dim=1
+ ).min(dim=1)[0]
+ / snr
+ )
+ loss = F.mse_loss(
+ model_pred.float(), target.float(), reduction="none"
+ )
+ loss = (
+ loss.mean(dim=list(range(1, len(loss.shape))))
+ * mse_loss_weights
+ )
+ loss = loss.mean()
+
+ # Gather the losses across all processes for logging (if we use distributed training).
+ avg_loss = accelerator.gather(loss.repeat(cfg.data.train_bs)).mean()
+ train_loss += avg_loss.item() / cfg.solver.gradient_accumulation_steps
+
+ # Backpropagate
+ accelerator.backward(loss)
+ if accelerator.sync_gradients:
+ accelerator.clip_grad_norm_(
+ trainable_params,
+ cfg.solver.max_grad_norm,
+ )
+ optimizer.step()
+ lr_scheduler.step()
+ optimizer.zero_grad()
+
+ if accelerator.sync_gradients:
+ reference_control_reader.clear()
+ reference_control_writer.clear()
+ progress_bar.update(1)
+ global_step += 1
+ accelerator.log({"train_loss": train_loss}, step=global_step)
+ train_loss = 0.0
+ if global_step % cfg.checkpointing_steps == 0:
+ if accelerator.is_main_process:
+ save_path = os.path.join(save_dir, f"checkpoint-{global_step}")
+ delete_additional_ckpt(save_dir, 1)
+ accelerator.save_state(save_path)
+
+ if global_step % cfg.val.validation_steps == 0:
+ if accelerator.is_main_process:
+ generator = torch.Generator(device=accelerator.device)
+ generator.manual_seed(cfg.seed)
+
+ sample_dicts = log_validation(
+ vae=vae,
+ image_enc=image_enc,
+ net=net,
+ scheduler=val_noise_scheduler,
+ accelerator=accelerator,
+ width=cfg.data.train_width,
+ height=cfg.data.train_height,
+ )
+
+ for sample_id, sample_dict in enumerate(sample_dicts):
+ sample_name = sample_dict["name"]
+ img = sample_dict["img"]
+ with TemporaryDirectory() as temp_dir:
+ out_file = Path(
+ f"{temp_dir}/{global_step:06d}-{sample_name}.gif"
+ )
+ img.save(out_file)
+ mlflow.log_artifact(out_file)
+
+ logs = {
+ "step_loss": loss.detach().item(),
+ "lr": lr_scheduler.get_last_lr()[0],
+ }
+ progress_bar.set_postfix(**logs)
+
+ if global_step >= cfg.solver.max_train_steps:
+ break
+
+ # save model after each epoch
+ if (
+ epoch + 1
+ ) % cfg.save_model_epoch_interval == 0 and accelerator.is_main_process:
+ unwrap_net = accelerator.unwrap_model(net)
+ save_checkpoint(
+ unwrap_net.reference_unet,
+ save_dir,
+ "reference_unet",
+ global_step,
+ total_limit=3,
+ )
+ save_checkpoint(
+ unwrap_net.denoising_unet,
+ save_dir,
+ "denoising_unet",
+ global_step,
+ total_limit=3,
+ )
+ save_checkpoint(
+ unwrap_net.pose_guider,
+ save_dir,
+ "pose_guider",
+ global_step,
+ total_limit=3,
+ )
+
+ # Create the pipeline using the trained modules and save it.
+ accelerator.wait_for_everyone()
+ accelerator.end_training()
+
+
+def save_checkpoint(model, save_dir, prefix, ckpt_num, total_limit=None):
+ save_path = osp.join(save_dir, f"{prefix}-{ckpt_num}.pth")
+
+ if total_limit is not None:
+ checkpoints = os.listdir(save_dir)
+ checkpoints = [d for d in checkpoints if d.startswith(prefix)]
+ checkpoints = sorted(
+ checkpoints, key=lambda x: int(x.split("-")[1].split(".")[0])
+ )
+
+ if len(checkpoints) >= total_limit:
+ num_to_remove = len(checkpoints) - total_limit + 1
+ removing_checkpoints = checkpoints[0:num_to_remove]
+ logger.info(
+ f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints"
+ )
+ logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}")
+
+ for removing_checkpoint in removing_checkpoints:
+ removing_checkpoint = os.path.join(save_dir, removing_checkpoint)
+ os.remove(removing_checkpoint)
+
+ state_dict = model.state_dict()
+ torch.save(state_dict, save_path)
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument("--config", type=str, default="./configs/training/stage1.yaml")
+ args = parser.parse_args()
+
+ if args.config[-5:] == ".yaml":
+ config = OmegaConf.load(args.config)
+ elif args.config[-3:] == ".py":
+ config = import_filename(args.config).cfg
+ else:
+ raise ValueError("Do not support this format config file")
+ main(config)
diff --git a/train_stage_2.py b/train_stage_2.py
new file mode 100644
index 0000000000000000000000000000000000000000..d7d94988252675d5dfed75f12957d5972f63e740
--- /dev/null
+++ b/train_stage_2.py
@@ -0,0 +1,773 @@
+import argparse
+import copy
+import logging
+import math
+import os
+import os.path as osp
+import random
+import time
+import warnings
+from collections import OrderedDict
+from datetime import datetime
+from pathlib import Path
+from tempfile import TemporaryDirectory
+
+import diffusers
+import mlflow
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.checkpoint
+import transformers
+from accelerate import Accelerator
+from accelerate.logging import get_logger
+from accelerate.utils import DistributedDataParallelKwargs
+from diffusers import AutoencoderKL, DDIMScheduler
+from diffusers.optimization import get_scheduler
+from diffusers.utils import check_min_version
+from diffusers.utils.import_utils import is_xformers_available
+from einops import rearrange
+from omegaconf import OmegaConf
+from PIL import Image
+from torchvision import transforms
+from tqdm.auto import tqdm
+from transformers import CLIPVisionModelWithProjection
+
+from src.dataset.dance_video import HumanDanceVideoDataset
+from src.models.mutual_self_attention import ReferenceAttentionControl
+from src.models.pose_guider import PoseGuider
+from src.models.unet_2d_condition import UNet2DConditionModel
+from src.models.unet_3d import UNet3DConditionModel
+from src.pipelines.pipeline_pose2vid import Pose2VideoPipeline
+from src.utils.util import (
+ delete_additional_ckpt,
+ import_filename,
+ read_frames,
+ save_videos_grid,
+ seed_everything,
+)
+
+warnings.filterwarnings("ignore")
+
+# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
+check_min_version("0.10.0.dev0")
+
+logger = get_logger(__name__, log_level="INFO")
+
+
+class Net(nn.Module):
+ def __init__(
+ self,
+ reference_unet: UNet2DConditionModel,
+ denoising_unet: UNet3DConditionModel,
+ pose_guider: PoseGuider,
+ reference_control_writer,
+ reference_control_reader,
+ ):
+ super().__init__()
+ self.reference_unet = reference_unet
+ self.denoising_unet = denoising_unet
+ self.pose_guider = pose_guider
+ self.reference_control_writer = reference_control_writer
+ self.reference_control_reader = reference_control_reader
+
+ def forward(
+ self,
+ noisy_latents,
+ timesteps,
+ ref_image_latents,
+ clip_image_embeds,
+ pose_img,
+ uncond_fwd: bool = False,
+ ):
+ pose_cond_tensor = pose_img.to(device="cuda")
+ pose_fea = self.pose_guider(pose_cond_tensor)
+
+ if not uncond_fwd:
+ ref_timesteps = torch.zeros_like(timesteps)
+ self.reference_unet(
+ ref_image_latents,
+ ref_timesteps,
+ encoder_hidden_states=clip_image_embeds,
+ return_dict=False,
+ )
+ self.reference_control_reader.update(self.reference_control_writer)
+
+ model_pred = self.denoising_unet(
+ noisy_latents,
+ timesteps,
+ pose_cond_fea=pose_fea,
+ encoder_hidden_states=clip_image_embeds,
+ ).sample
+
+ return model_pred
+
+
+def compute_snr(noise_scheduler, timesteps):
+ """
+ Computes SNR as per
+ https://github.com/TiankaiHang/Min-SNR-Diffusion-Training/blob/521b624bd70c67cee4bdf49225915f5945a872e3/guided_diffusion/gaussian_diffusion.py#L847-L849
+ """
+ alphas_cumprod = noise_scheduler.alphas_cumprod
+ sqrt_alphas_cumprod = alphas_cumprod**0.5
+ sqrt_one_minus_alphas_cumprod = (1.0 - alphas_cumprod) ** 0.5
+
+ # Expand the tensors.
+ # Adapted from https://github.com/TiankaiHang/Min-SNR-Diffusion-Training/blob/521b624bd70c67cee4bdf49225915f5945a872e3/guided_diffusion/gaussian_diffusion.py#L1026
+ sqrt_alphas_cumprod = sqrt_alphas_cumprod.to(device=timesteps.device)[
+ timesteps
+ ].float()
+ while len(sqrt_alphas_cumprod.shape) < len(timesteps.shape):
+ sqrt_alphas_cumprod = sqrt_alphas_cumprod[..., None]
+ alpha = sqrt_alphas_cumprod.expand(timesteps.shape)
+
+ sqrt_one_minus_alphas_cumprod = sqrt_one_minus_alphas_cumprod.to(
+ device=timesteps.device
+ )[timesteps].float()
+ while len(sqrt_one_minus_alphas_cumprod.shape) < len(timesteps.shape):
+ sqrt_one_minus_alphas_cumprod = sqrt_one_minus_alphas_cumprod[..., None]
+ sigma = sqrt_one_minus_alphas_cumprod.expand(timesteps.shape)
+
+ # Compute SNR.
+ snr = (alpha / sigma) ** 2
+ return snr
+
+
+def log_validation(
+ vae,
+ image_enc,
+ net,
+ scheduler,
+ accelerator,
+ width,
+ height,
+ clip_length=24,
+ generator=None,
+):
+ logger.info("Running validation... ")
+
+ ori_net = accelerator.unwrap_model(net)
+ reference_unet = ori_net.reference_unet
+ denoising_unet = ori_net.denoising_unet
+ pose_guider = ori_net.pose_guider
+
+ if generator is None:
+ generator = torch.manual_seed(42)
+ tmp_denoising_unet = copy.deepcopy(denoising_unet)
+ tmp_denoising_unet = tmp_denoising_unet.to(dtype=torch.float16)
+
+ pipe = Pose2VideoPipeline(
+ vae=vae,
+ image_encoder=image_enc,
+ reference_unet=reference_unet,
+ denoising_unet=tmp_denoising_unet,
+ pose_guider=pose_guider,
+ scheduler=scheduler,
+ )
+ pipe = pipe.to(accelerator.device)
+
+ test_cases = [
+ (
+ "./configs/inference/ref_images/anyone-3.png",
+ "./configs/inference/pose_videos/anyone-video-1_kps.mp4",
+ ),
+ (
+ "./configs/inference/ref_images/anyone-2.png",
+ "./configs/inference/pose_videos/anyone-video-2_kps.mp4",
+ ),
+ ]
+
+ results = []
+ for test_case in test_cases:
+ ref_image_path, pose_video_path = test_case
+ ref_name = Path(ref_image_path).stem
+ pose_name = Path(pose_video_path).stem
+ ref_image_pil = Image.open(ref_image_path).convert("RGB")
+
+ pose_list = []
+ pose_tensor_list = []
+ pose_images = read_frames(pose_video_path)
+ pose_transform = transforms.Compose(
+ [transforms.Resize((height, width)), transforms.ToTensor()]
+ )
+ for pose_image_pil in pose_images[:clip_length]:
+ pose_tensor_list.append(pose_transform(pose_image_pil))
+ pose_list.append(pose_image_pil)
+
+ pose_tensor = torch.stack(pose_tensor_list, dim=0) # (f, c, h, w)
+ pose_tensor = pose_tensor.transpose(0, 1)
+
+ pipeline_output = pipe(
+ ref_image_pil,
+ pose_list,
+ width,
+ height,
+ clip_length,
+ 20,
+ 3.5,
+ generator=generator,
+ )
+ video = pipeline_output.videos
+
+ # Concat it with pose tensor
+ pose_tensor = pose_tensor.unsqueeze(0)
+ video = torch.cat([video, pose_tensor], dim=0)
+
+ results.append({"name": f"{ref_name}_{pose_name}", "vid": video})
+
+ del tmp_denoising_unet
+ del pipe
+ torch.cuda.empty_cache()
+
+ return results
+
+
+def main(cfg):
+ kwargs = DistributedDataParallelKwargs(find_unused_parameters=False)
+ accelerator = Accelerator(
+ gradient_accumulation_steps=cfg.solver.gradient_accumulation_steps,
+ mixed_precision=cfg.solver.mixed_precision,
+ log_with="mlflow",
+ project_dir="./mlruns",
+ kwargs_handlers=[kwargs],
+ )
+
+ # Make one log on every process with the configuration for debugging.
+ logging.basicConfig(
+ format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+ datefmt="%m/%d/%Y %H:%M:%S",
+ level=logging.INFO,
+ )
+ logger.info(accelerator.state, main_process_only=False)
+ if accelerator.is_local_main_process:
+ transformers.utils.logging.set_verbosity_warning()
+ diffusers.utils.logging.set_verbosity_info()
+ else:
+ transformers.utils.logging.set_verbosity_error()
+ diffusers.utils.logging.set_verbosity_error()
+
+ # If passed along, set the training seed now.
+ if cfg.seed is not None:
+ seed_everything(cfg.seed)
+
+ exp_name = cfg.exp_name
+ save_dir = f"{cfg.output_dir}/{exp_name}"
+ if accelerator.is_main_process:
+ if not os.path.exists(save_dir):
+ os.makedirs(save_dir)
+
+ inference_config_path = "./configs/inference/inference_v2.yaml"
+ infer_config = OmegaConf.load(inference_config_path)
+
+ if cfg.weight_dtype == "fp16":
+ weight_dtype = torch.float16
+ elif cfg.weight_dtype == "fp32":
+ weight_dtype = torch.float32
+ else:
+ raise ValueError(
+ f"Do not support weight dtype: {cfg.weight_dtype} during training"
+ )
+
+ sched_kwargs = OmegaConf.to_container(cfg.noise_scheduler_kwargs)
+ if cfg.enable_zero_snr:
+ sched_kwargs.update(
+ rescale_betas_zero_snr=True,
+ timestep_spacing="trailing",
+ prediction_type="v_prediction",
+ )
+ val_noise_scheduler = DDIMScheduler(**sched_kwargs)
+ sched_kwargs.update({"beta_schedule": "scaled_linear"})
+ train_noise_scheduler = DDIMScheduler(**sched_kwargs)
+
+ image_enc = CLIPVisionModelWithProjection.from_pretrained(
+ cfg.image_encoder_path,
+ ).to(dtype=weight_dtype, device="cuda")
+ vae = AutoencoderKL.from_pretrained(cfg.vae_model_path).to(
+ "cuda", dtype=weight_dtype
+ )
+ reference_unet = UNet2DConditionModel.from_pretrained(
+ cfg.base_model_path,
+ subfolder="unet",
+ ).to(device="cuda", dtype=weight_dtype)
+
+ denoising_unet = UNet3DConditionModel.from_pretrained_2d(
+ cfg.base_model_path,
+ cfg.mm_path,
+ subfolder="unet",
+ unet_additional_kwargs=OmegaConf.to_container(
+ infer_config.unet_additional_kwargs
+ ),
+ ).to(device="cuda")
+
+ pose_guider = PoseGuider(
+ conditioning_embedding_channels=320, block_out_channels=(16, 32, 96, 256)
+ ).to(device="cuda", dtype=weight_dtype)
+
+ stage1_ckpt_dir = cfg.stage1_ckpt_dir
+ stage1_ckpt_step = cfg.stage1_ckpt_step
+ denoising_unet.load_state_dict(
+ torch.load(
+ os.path.join(stage1_ckpt_dir, f"denoising_unet-{stage1_ckpt_step}.pth"),
+ map_location="cpu",
+ ),
+ strict=False,
+ )
+ reference_unet.load_state_dict(
+ torch.load(
+ os.path.join(stage1_ckpt_dir, f"reference_unet-{stage1_ckpt_step}.pth"),
+ map_location="cpu",
+ ),
+ strict=False,
+ )
+ pose_guider.load_state_dict(
+ torch.load(
+ os.path.join(stage1_ckpt_dir, f"pose_guider-{stage1_ckpt_step}.pth"),
+ map_location="cpu",
+ ),
+ strict=False,
+ )
+
+ # Freeze
+ vae.requires_grad_(False)
+ image_enc.requires_grad_(False)
+ reference_unet.requires_grad_(False)
+ denoising_unet.requires_grad_(False)
+ pose_guider.requires_grad_(False)
+
+ # Set motion module learnable
+ for name, module in denoising_unet.named_modules():
+ if "motion_modules" in name:
+ for params in module.parameters():
+ params.requires_grad = True
+
+ reference_control_writer = ReferenceAttentionControl(
+ reference_unet,
+ do_classifier_free_guidance=False,
+ mode="write",
+ fusion_blocks="full",
+ )
+ reference_control_reader = ReferenceAttentionControl(
+ denoising_unet,
+ do_classifier_free_guidance=False,
+ mode="read",
+ fusion_blocks="full",
+ )
+
+ net = Net(
+ reference_unet,
+ denoising_unet,
+ pose_guider,
+ reference_control_writer,
+ reference_control_reader,
+ )
+
+ if cfg.solver.enable_xformers_memory_efficient_attention:
+ if is_xformers_available():
+ reference_unet.enable_xformers_memory_efficient_attention()
+ denoising_unet.enable_xformers_memory_efficient_attention()
+ else:
+ raise ValueError(
+ "xformers is not available. Make sure it is installed correctly"
+ )
+
+ if cfg.solver.gradient_checkpointing:
+ reference_unet.enable_gradient_checkpointing()
+ denoising_unet.enable_gradient_checkpointing()
+
+ if cfg.solver.scale_lr:
+ learning_rate = (
+ cfg.solver.learning_rate
+ * cfg.solver.gradient_accumulation_steps
+ * cfg.data.train_bs
+ * accelerator.num_processes
+ )
+ else:
+ learning_rate = cfg.solver.learning_rate
+
+ # Initialize the optimizer
+ if cfg.solver.use_8bit_adam:
+ try:
+ import bitsandbytes as bnb
+ except ImportError:
+ raise ImportError(
+ "Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`"
+ )
+
+ optimizer_cls = bnb.optim.AdamW8bit
+ else:
+ optimizer_cls = torch.optim.AdamW
+
+ trainable_params = list(filter(lambda p: p.requires_grad, net.parameters()))
+ logger.info(f"Total trainable params {len(trainable_params)}")
+ optimizer = optimizer_cls(
+ trainable_params,
+ lr=learning_rate,
+ betas=(cfg.solver.adam_beta1, cfg.solver.adam_beta2),
+ weight_decay=cfg.solver.adam_weight_decay,
+ eps=cfg.solver.adam_epsilon,
+ )
+
+ # Scheduler
+ lr_scheduler = get_scheduler(
+ cfg.solver.lr_scheduler,
+ optimizer=optimizer,
+ num_warmup_steps=cfg.solver.lr_warmup_steps
+ * cfg.solver.gradient_accumulation_steps,
+ num_training_steps=cfg.solver.max_train_steps
+ * cfg.solver.gradient_accumulation_steps,
+ )
+
+ train_dataset = HumanDanceVideoDataset(
+ width=cfg.data.train_width,
+ height=cfg.data.train_height,
+ n_sample_frames=cfg.data.n_sample_frames,
+ sample_rate=cfg.data.sample_rate,
+ img_scale=(1.0, 1.0),
+ data_meta_paths=cfg.data.meta_paths,
+ )
+ train_dataloader = torch.utils.data.DataLoader(
+ train_dataset, batch_size=cfg.data.train_bs, shuffle=True, num_workers=4
+ )
+
+ # Prepare everything with our `accelerator`.
+ (
+ net,
+ optimizer,
+ train_dataloader,
+ lr_scheduler,
+ ) = accelerator.prepare(
+ net,
+ optimizer,
+ train_dataloader,
+ lr_scheduler,
+ )
+
+ # We need to recalculate our total training steps as the size of the training dataloader may have changed.
+ num_update_steps_per_epoch = math.ceil(
+ len(train_dataloader) / cfg.solver.gradient_accumulation_steps
+ )
+ # Afterwards we recalculate our number of training epochs
+ num_train_epochs = math.ceil(
+ cfg.solver.max_train_steps / num_update_steps_per_epoch
+ )
+
+ # We need to initialize the trackers we use, and also store our configuration.
+ # The trackers initializes automatically on the main process.
+ if accelerator.is_main_process:
+ run_time = datetime.now().strftime("%Y%m%d-%H%M")
+ accelerator.init_trackers(
+ exp_name,
+ init_kwargs={"mlflow": {"run_name": run_time}},
+ )
+ # dump config file
+ mlflow.log_dict(OmegaConf.to_container(cfg), "config.yaml")
+
+ # Train!
+ total_batch_size = (
+ cfg.data.train_bs
+ * accelerator.num_processes
+ * cfg.solver.gradient_accumulation_steps
+ )
+
+ logger.info("***** Running training *****")
+ logger.info(f" Num examples = {len(train_dataset)}")
+ logger.info(f" Num Epochs = {num_train_epochs}")
+ logger.info(f" Instantaneous batch size per device = {cfg.data.train_bs}")
+ logger.info(
+ f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}"
+ )
+ logger.info(
+ f" Gradient Accumulation steps = {cfg.solver.gradient_accumulation_steps}"
+ )
+ logger.info(f" Total optimization steps = {cfg.solver.max_train_steps}")
+ global_step = 0
+ first_epoch = 0
+
+ # Potentially load in the weights and states from a previous save
+ if cfg.resume_from_checkpoint:
+ if cfg.resume_from_checkpoint != "latest":
+ resume_dir = cfg.resume_from_checkpoint
+ else:
+ resume_dir = save_dir
+ # Get the most recent checkpoint
+ dirs = os.listdir(resume_dir)
+ dirs = [d for d in dirs if d.startswith("checkpoint")]
+ dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
+ path = dirs[-1]
+ accelerator.load_state(os.path.join(resume_dir, path))
+ accelerator.print(f"Resuming from checkpoint {path}")
+ global_step = int(path.split("-")[1])
+
+ first_epoch = global_step // num_update_steps_per_epoch
+ resume_step = global_step % num_update_steps_per_epoch
+
+ # Only show the progress bar once on each machine.
+ progress_bar = tqdm(
+ range(global_step, cfg.solver.max_train_steps),
+ disable=not accelerator.is_local_main_process,
+ )
+ progress_bar.set_description("Steps")
+
+ for epoch in range(first_epoch, num_train_epochs):
+ train_loss = 0.0
+ t_data_start = time.time()
+ for step, batch in enumerate(train_dataloader):
+ t_data = time.time() - t_data_start
+ with accelerator.accumulate(net):
+ # Convert videos to latent space
+ pixel_values_vid = batch["pixel_values_vid"].to(weight_dtype)
+ with torch.no_grad():
+ video_length = pixel_values_vid.shape[1]
+ pixel_values_vid = rearrange(
+ pixel_values_vid, "b f c h w -> (b f) c h w"
+ )
+ latents = vae.encode(pixel_values_vid).latent_dist.sample()
+ latents = rearrange(
+ latents, "(b f) c h w -> b c f h w", f=video_length
+ )
+ latents = latents * 0.18215
+
+ noise = torch.randn_like(latents)
+ if cfg.noise_offset > 0:
+ noise += cfg.noise_offset * torch.randn(
+ (latents.shape[0], latents.shape[1], 1, 1, 1),
+ device=latents.device,
+ )
+ bsz = latents.shape[0]
+ # Sample a random timestep for each video
+ timesteps = torch.randint(
+ 0,
+ train_noise_scheduler.num_train_timesteps,
+ (bsz,),
+ device=latents.device,
+ )
+ timesteps = timesteps.long()
+
+ pixel_values_pose = batch["pixel_values_pose"] # (bs, f, c, H, W)
+ pixel_values_pose = pixel_values_pose.transpose(
+ 1, 2
+ ) # (bs, c, f, H, W)
+
+ uncond_fwd = random.random() < cfg.uncond_ratio
+ clip_image_list = []
+ ref_image_list = []
+ for batch_idx, (ref_img, clip_img) in enumerate(
+ zip(
+ batch["pixel_values_ref_img"],
+ batch["clip_ref_img"],
+ )
+ ):
+ if uncond_fwd:
+ clip_image_list.append(torch.zeros_like(clip_img))
+ else:
+ clip_image_list.append(clip_img)
+ ref_image_list.append(ref_img)
+
+ with torch.no_grad():
+ ref_img = torch.stack(ref_image_list, dim=0).to(
+ dtype=vae.dtype, device=vae.device
+ )
+ ref_image_latents = vae.encode(
+ ref_img
+ ).latent_dist.sample() # (bs, d, 64, 64)
+ ref_image_latents = ref_image_latents * 0.18215
+
+ clip_img = torch.stack(clip_image_list, dim=0).to(
+ dtype=image_enc.dtype, device=image_enc.device
+ )
+ clip_img = clip_img.to(device="cuda", dtype=weight_dtype)
+ clip_image_embeds = image_enc(
+ clip_img.to("cuda", dtype=weight_dtype)
+ ).image_embeds
+ clip_image_embeds = clip_image_embeds.unsqueeze(1) # (bs, 1, d)
+
+ # add noise
+ noisy_latents = train_noise_scheduler.add_noise(
+ latents, noise, timesteps
+ )
+
+ # Get the target for loss depending on the prediction type
+ if train_noise_scheduler.prediction_type == "epsilon":
+ target = noise
+ elif train_noise_scheduler.prediction_type == "v_prediction":
+ target = train_noise_scheduler.get_velocity(
+ latents, noise, timesteps
+ )
+ else:
+ raise ValueError(
+ f"Unknown prediction type {train_noise_scheduler.prediction_type}"
+ )
+
+ # ---- Forward!!! -----
+ model_pred = net(
+ noisy_latents,
+ timesteps,
+ ref_image_latents,
+ clip_image_embeds,
+ pixel_values_pose,
+ uncond_fwd=uncond_fwd,
+ )
+
+ if cfg.snr_gamma == 0:
+ loss = F.mse_loss(
+ model_pred.float(), target.float(), reduction="mean"
+ )
+ else:
+ snr = compute_snr(train_noise_scheduler, timesteps)
+ if train_noise_scheduler.config.prediction_type == "v_prediction":
+ # Velocity objective requires that we add one to SNR values before we divide by them.
+ snr = snr + 1
+ mse_loss_weights = (
+ torch.stack(
+ [snr, cfg.snr_gamma * torch.ones_like(timesteps)], dim=1
+ ).min(dim=1)[0]
+ / snr
+ )
+ loss = F.mse_loss(
+ model_pred.float(), target.float(), reduction="none"
+ )
+ loss = (
+ loss.mean(dim=list(range(1, len(loss.shape))))
+ * mse_loss_weights
+ )
+ loss = loss.mean()
+
+ # Gather the losses across all processes for logging (if we use distributed training).
+ avg_loss = accelerator.gather(loss.repeat(cfg.data.train_bs)).mean()
+ train_loss += avg_loss.item() / cfg.solver.gradient_accumulation_steps
+
+ # Backpropagate
+ accelerator.backward(loss)
+ if accelerator.sync_gradients:
+ accelerator.clip_grad_norm_(
+ trainable_params,
+ cfg.solver.max_grad_norm,
+ )
+ optimizer.step()
+ lr_scheduler.step()
+ optimizer.zero_grad()
+
+ if accelerator.sync_gradients:
+ reference_control_reader.clear()
+ reference_control_writer.clear()
+ progress_bar.update(1)
+ global_step += 1
+ accelerator.log({"train_loss": train_loss}, step=global_step)
+ train_loss = 0.0
+
+ if global_step % cfg.val.validation_steps == 0:
+ if accelerator.is_main_process:
+ generator = torch.Generator(device=accelerator.device)
+ generator.manual_seed(cfg.seed)
+
+ sample_dicts = log_validation(
+ vae=vae,
+ image_enc=image_enc,
+ net=net,
+ scheduler=val_noise_scheduler,
+ accelerator=accelerator,
+ width=cfg.data.train_width,
+ height=cfg.data.train_height,
+ clip_length=cfg.data.n_sample_frames,
+ generator=generator,
+ )
+
+ for sample_id, sample_dict in enumerate(sample_dicts):
+ sample_name = sample_dict["name"]
+ vid = sample_dict["vid"]
+ with TemporaryDirectory() as temp_dir:
+ out_file = Path(
+ f"{temp_dir}/{global_step:06d}-{sample_name}.gif"
+ )
+ save_videos_grid(vid, out_file, n_rows=2)
+ mlflow.log_artifact(out_file)
+
+ logs = {
+ "step_loss": loss.detach().item(),
+ "lr": lr_scheduler.get_last_lr()[0],
+ "td": f"{t_data:.2f}s",
+ }
+ t_data_start = time.time()
+ progress_bar.set_postfix(**logs)
+
+ if global_step >= cfg.solver.max_train_steps:
+ break
+ # save model after each epoch
+ if accelerator.is_main_process:
+ save_path = os.path.join(save_dir, f"checkpoint-{global_step}")
+ delete_additional_ckpt(save_dir, 1)
+ accelerator.save_state(save_path)
+ # save motion module only
+ unwrap_net = accelerator.unwrap_model(net)
+ save_checkpoint(
+ unwrap_net.denoising_unet,
+ save_dir,
+ "motion_module",
+ global_step,
+ total_limit=3,
+ )
+
+ # Create the pipeline using the trained modules and save it.
+ accelerator.wait_for_everyone()
+ accelerator.end_training()
+
+
+def save_checkpoint(model, save_dir, prefix, ckpt_num, total_limit=None):
+ save_path = osp.join(save_dir, f"{prefix}-{ckpt_num}.pth")
+
+ if total_limit is not None:
+ checkpoints = os.listdir(save_dir)
+ checkpoints = [d for d in checkpoints if d.startswith(prefix)]
+ checkpoints = sorted(
+ checkpoints, key=lambda x: int(x.split("-")[1].split(".")[0])
+ )
+
+ if len(checkpoints) >= total_limit:
+ num_to_remove = len(checkpoints) - total_limit + 1
+ removing_checkpoints = checkpoints[0:num_to_remove]
+ logger.info(
+ f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints"
+ )
+ logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}")
+
+ for removing_checkpoint in removing_checkpoints:
+ removing_checkpoint = os.path.join(save_dir, removing_checkpoint)
+ os.remove(removing_checkpoint)
+
+ mm_state_dict = OrderedDict()
+ state_dict = model.state_dict()
+ for key in state_dict:
+ if "motion_module" in key:
+ mm_state_dict[key] = state_dict[key]
+
+ torch.save(mm_state_dict, save_path)
+
+
+def decode_latents(vae, latents):
+ video_length = latents.shape[2]
+ latents = 1 / 0.18215 * latents
+ latents = rearrange(latents, "b c f h w -> (b f) c h w")
+ # video = self.vae.decode(latents).sample
+ video = []
+ for frame_idx in tqdm(range(latents.shape[0])):
+ video.append(vae.decode(latents[frame_idx : frame_idx + 1]).sample)
+ video = torch.cat(video)
+ video = rearrange(video, "(b f) c h w -> b c f h w", f=video_length)
+ video = (video / 2 + 0.5).clamp(0, 1)
+ # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
+ video = video.cpu().float().numpy()
+ return video
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument("--config", type=str, default="./configs/training/stage2.yaml")
+ args = parser.parse_args()
+
+ if args.config[-5:] == ".yaml":
+ config = OmegaConf.load(args.config)
+ elif args.config[-3:] == ".py":
+ config = import_filename(args.config).cfg
+ else:
+ raise ValueError("Do not support this format config file")
+ main(config)