diff --git a/app.py b/app.py new file mode 100644 index 0000000000000000000000000000000000000000..2a5968de97e5012f308005f457161d3559705fde --- /dev/null +++ b/app.py @@ -0,0 +1,362 @@ +import os, argparse +import sys +import gradio as gr +# from scripts.gradio.i2v_test_application import Image2Video +sys.path.insert(1, os.path.join(sys.path[0], 'lvdm')) +import spaces + + +import os +import time +from omegaconf import OmegaConf +import torch +from scripts.evaluation.funcs import load_model_checkpoint, save_videos, batch_ddim_sampling, get_latent_z +from utils.utils import instantiate_from_config +from huggingface_hub import hf_hub_download +from einops import repeat +import torchvision.transforms as transforms +from pytorch_lightning import seed_everything +from einops import rearrange +import cv2 + +import torch +print("cuda available:", torch.cuda.is_available()) + + +from huggingface_hub import snapshot_download +import os + + + +def download_model(): + REPO_ID = 'fbnnb/tc_1024' + filename_list = ['tc1024_4k.ckpt'] + tar_dir = './checkpoints/tooncrafter_1024_interp_sketch/' + if not os.path.exists(tar_dir): + os.makedirs(tar_dir) + for filename in filename_list: + local_file = os.path.join(tar_dir, filename) + if not os.path.exists(local_file): + hf_hub_download(repo_id=REPO_ID, filename=filename, local_dir=tar_dir, local_dir_use_symlinks=False) + print("downloaded") + + +def get_latent_z_with_hidden_states(model, videos): + b, c, t, h, w = videos.shape + x = rearrange(videos, 'b c t h w -> (b t) c h w') + encoder_posterior, hidden_states = model.first_stage_model.encode(x, return_hidden_states=True) + + hidden_states_first_last = [] + ### use only the first and last hidden states + for hid in hidden_states: + hid = rearrange(hid, '(b t) c h w -> b c t h w', t=t) + hid_new = torch.cat([hid[:, :, 0:1], hid[:, :, -1:]], dim=2) + hidden_states_first_last.append(hid_new) + + z = model.get_first_stage_encoding(encoder_posterior).detach() + z = rearrange(z, '(b t) c h w -> b c t h w', b=b, t=t) + return z, hidden_states_first_last + + + +def extract_frames(video_path): + # 動画ファイルを読み込む + cap = cv2.VideoCapture(video_path) + + frame_list = [] + frame_num = 0 + + while True: + # フレームを読み込む + ret, frame = cap.read() + if not ret: + break + + # フレームをリストに追加 + frame_list.append(frame) + frame_num += 1 + + print("load video length:", len(frame_list)) + # 動画ファイルを閉じる + cap.release() + + return frame_list + + +resolution = '576_1024' +resolution = (576, 1024) +download_model() +print("after download model") +result_dir = "./results/" +if not os.path.exists(result_dir): + os.mkdir(result_dir) + +#ToonCrafterModel +ckpt_path='checkpoints/tooncrafter_1024_interp_sketch/tc1024_4k.ckpt' +# ckpt_path="/group/40005/gzhiwang/tc1024_4k.ckpt" +config_file='configs/inference_1024_v1.0.yaml' +config = OmegaConf.load(config_file) +model_config = config.pop("model", OmegaConf.create()) +model_config['params']['unet_config']['params']['use_checkpoint']=False + +model = instantiate_from_config(model_config) +assert os.path.exists(ckpt_path), "Error: checkpoint Not Found!" +# model = load_model_checkpoint(model, ckpt_path) +state = torch.load(ckpt_path, map_location='cpu') +if 'state_dict' in state: + state = state['state_dict'] +if 'module' in state: + state = state['module'] + +missing, unexpected = model.load_state_dict(state, strict=False) +print("missing:", missing) +print("unexpected:", unexpected) +model.eval() + +# cn_model.load_state_dict(load_state_dict(cn_ckpt_path, location='cpu')) +# cn_model.eval() + +# model.control_model = cn_model +# model_list.append(model) + +save_fps = 8 +print("resolution:", resolution) +print("init done.") + +def transpose_if_needed(tensor): + h = tensor.shape[-2] + w = tensor.shape[-1] + if h > w: + tensor = tensor.permute(0, 2, 1) + return tensor + +def untranspose(tensor): + ndim = tensor.ndim + return tensor.transpose(ndim-1, ndim-2) + +@spaces.GPU(duration=200) +def get_image(image, sketch, prompt, steps=50, cfg_scale=7.5, eta=1.0, fs=3, seed=123, control_scale=0.6): + print("enter fn") + # control_frames = extract_frames(frame_guides) + print("extract frames") + seed_everything(seed) + transform = transforms.Compose([ + transforms.Resize(min(resolution)), + transforms.CenterCrop(resolution), + ]) + print("before empty cache") + torch.cuda.empty_cache() + print('start:', prompt, time.strftime('%Y-%m-%d %H:%M:%S',time.localtime(time.time()))) + start = time.time() + gpu_id=0 + if steps > 60: + steps = 60 + + global model + # model = model_list[gpu_id] + model = model.cuda() + + batch_size=1 + channels = model.model.diffusion_model.out_channels + frames = model.temporal_length + h, w = resolution[0] // 8, resolution[1] // 8 + noise_shape = [batch_size, channels, frames, h, w] + + # text cond + transposed = False + with torch.no_grad(), torch.cuda.amp.autocast(dtype=torch.float16): + text_emb = model.get_learned_conditioning([prompt]) + print("before control") + #control cond + # if frame_guides is not None: + # cn_videos = [] + # for frame in control_frames: + # frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) + # frame = cv2.bitwise_not(frame) + # cn_tensor = torch.from_numpy(frame).unsqueeze(2).permute(2, 0, 1).float().to(model.device) + + # #cn_tensor = (cn_tensor / 255. - 0.5) * 2 + # cn_tensor = ( cn_tensor/255.0 ) + # cn_tensor = transpose_if_needed(cn_tensor) + # cn_tensor_resized = transform(cn_tensor) #3,h,w + + # cn_video = cn_tensor_resized.unsqueeze(0).unsqueeze(2) # bc1hw + # cn_videos.append(cn_video) + + # cn_videos = torch.cat(cn_videos, dim=2) + # if cn_videos.shape[2] > frames: + # idxs = [] + # for i in range(frames): + # index = int((i + 0.5) * cn_videos.shape[2] / frames) + # idxs.append(min(index, cn_videos.shape[2] - 1)) + # cn_videos = cn_videos[:, :, idxs, :, :] + # print("cn_videos.shape after slicing", cn_videos.shape) + # model_list = [] + # for model in model_list: + # model.control_scale = control_scale + # model_list.append(model) + + # else: + cn_videos = None + + print("image cond") + + # img cond + img_tensor = torch.from_numpy(image).permute(2, 0, 1).float().to(model.device) + input_h, input_w = img_tensor.shape[1:] + img_tensor = (img_tensor / 255. - 0.5) * 2 + img_tensor = transpose_if_needed(img_tensor) + + image_tensor_resized = transform(img_tensor) #3,h,w + videos = image_tensor_resized.unsqueeze(0).unsqueeze(2) # bc1hw + print("get latent z") + # z = get_latent_z(model, videos) #bc,1,hw + videos = repeat(videos, 'b c t h w -> b c (repeat t) h w', repeat=frames//2) + + if sketch is not None: + img_tensor2 = torch.from_numpy(sketch).permute(2, 0, 1).float().to(model.device) + img_tensor2 = (img_tensor2 / 255. - 0.5) * 2 + img_tensor2 = transpose_if_needed(img_tensor2) + image_tensor_resized2 = transform(img_tensor2) #3,h,w + videos2 = image_tensor_resized2.unsqueeze(0).unsqueeze(2) # bchw + videos2 = repeat(videos2, 'b c t h w -> b c (repeat t) h w', repeat=frames//2) + + videos = torch.cat([videos, videos2], dim=2) + else: + videos = torch.cat([videos, videos], dim=2) + + z, hs = get_latent_z_with_hidden_states(model, videos) + + img_tensor_repeat = torch.zeros_like(z) + + img_tensor_repeat[:,:,:1,:,:] = z[:,:,:1,:,:] + img_tensor_repeat[:,:,-1:,:,:] = z[:,:,-1:,:,:] + + print("image embedder") + cond_images = model.embedder(img_tensor.unsqueeze(0)) ## blc + img_emb = model.image_proj_model(cond_images) + + imtext_cond = torch.cat([text_emb, img_emb], dim=1) + + fs = torch.tensor([fs], dtype=torch.long, device=model.device) + # print("cn videos:",cn_videos.shape, "img emb:", img_emb.shape) + cond = {"c_crossattn": [imtext_cond], "fs": fs, "c_concat": [img_tensor_repeat], "control_cond": cn_videos} + + print("before sample loop") + ## inference + batch_samples = batch_ddim_sampling(model, cond, noise_shape, n_samples=1, ddim_steps=steps, ddim_eta=eta, cfg_scale=cfg_scale, hs=hs) + + ## remove the last frame + # if image2 is None: + batch_samples = batch_samples[:,:,:,:-1,...] + ## b,samples,c,t,h,w + prompt_str = prompt.replace("/", "_slash_") if "/" in prompt else prompt + prompt_str = prompt_str.replace(" ", "_") if " " in prompt else prompt_str + prompt_str=prompt_str[:40] + if len(prompt_str) == 0: + prompt_str = 'empty_prompt' + + global result_dir + global save_fps + if input_h > input_w: + batch_samples = untranspose(batch_samples) + + save_videos(batch_samples, result_dir, filenames=[prompt_str], fps=save_fps) + print(f"Saved in {prompt_str}. Time used: {(time.time() - start):.2f} seconds") + model = model.cpu() + saved_result_dir = os.path.join(result_dir, f"{prompt_str}.mp4") + print("result saved to:", saved_result_dir) + return saved_result_dir + + + # @spaces.GPU + + + +# i2v_examples_interp_1024 = [ +# ['prompts/1024_interp/frame_000000.jpg', 'prompts/1024_interp/frame_000041.jpg', 'a cat is eating', 50, 7.5, 1.0, 10, 123] +# ] + +i2v_examples_interp_1024 = [ + ['prompts/1024_interp/hall_first.jpg', 'prompts/1024_interp/hall_sketch.jpg', + 'At the start, a still image of a wooden hallway with arched arches, doors, and various furniture. The scene then transitions to an animated version of the hallway, showcasing more details like a bookshelf and a window.', + 50, 7.5, 1.0, 10, 123] +] + + + + +def dynamicrafter_demo(result_dir='./tmp/', res=1024): + if res == 1024: + resolution = '576_1024' + css = """#input_img {max-width: 1024px !important} #output_vid {max-width: 1024px; max-height:576px}""" + elif res == 512: + resolution = '320_512' + css = """#input_img {max-width: 512px !important} #output_vid {max-width: 512px; max-height: 320px} #input_img2 {max-width: 512px !important} #output_vid {max-width: 512px; max-height: 320px}""" + elif res == 256: + resolution = '256_256' + css = """#input_img {max-width: 256px !important} #output_vid {max-width: 256px; max-height: 256px}""" + else: + raise NotImplementedError(f"Unsupported resolution: {res}") + # image2video = Image2Video(result_dir, resolution=resolution) + with gr.Blocks(analytics_enabled=False, css=css) as dynamicrafter_iface: + + + + with gr.Tab(label='ToonCrafter_576x1024'): + with gr.Column(): + with gr.Row(): + with gr.Column(): + with gr.Row(): + i2v_input_image = gr.Image(label="Input Image1",elem_id="input_img") + # frame_guides = gr.Video(label="Input Guidance",elem_id="input_guidance", autoplay=True,show_share_button=True) + with gr.Row(): + i2v_input_text = gr.Text(label='Prompts') + with gr.Row(): + i2v_seed = gr.Slider(label='Random Seed', minimum=0, maximum=50000, step=1, value=123) + i2v_eta = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, label='ETA', value=1.0, elem_id="i2v_eta") + i2v_cfg_scale = gr.Slider(minimum=1.0, maximum=15.0, step=0.5, label='CFG Scale', value=7.5, elem_id="i2v_cfg_scale") + with gr.Row(): + i2v_steps = gr.Slider(minimum=1, maximum=60, step=1, elem_id="i2v_steps", label="Sampling steps", value=50) + i2v_motion = gr.Slider(minimum=5, maximum=30, step=1, elem_id="i2v_motion", label="FPS", value=10) + control_scale = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, elem_id="i2v_ctrl_scale", label="control_scale", value=0.6) + i2v_end_btn = gr.Button("Generate") + with gr.Column(): + with gr.Row(): + i2v_input_sketch = gr.Image(label="Input Image2",elem_id="input_img2") + with gr.Row(): + i2v_output_video = gr.Video(label="Generated Video",elem_id="output_vid",autoplay=True,show_share_button=True) + + gr.Examples(examples=i2v_examples_interp_1024, + inputs=[i2v_input_image, i2v_input_sketch, i2v_input_text, i2v_steps, i2v_cfg_scale, i2v_eta, i2v_motion, i2v_seed, control_scale], + outputs=[i2v_output_video], + fn = get_image, + cache_examples=False, + ) + i2v_end_btn.click(inputs=[i2v_input_image, i2v_input_sketch, i2v_input_text, i2v_steps, i2v_cfg_scale, i2v_eta, i2v_motion, i2v_seed, control_scale], + outputs=[i2v_output_video], + fn = get_image + ) + + + return dynamicrafter_iface + + +def get_parser(): + parser = argparse.ArgumentParser() + return parser + + +if __name__ == "__main__": + parser = get_parser() + args = parser.parse_args() + + result_dir = os.path.join('./', 'results') + dynamicrafter_iface = dynamicrafter_demo(result_dir) + dynamicrafter_iface.queue(max_size=12) + print("launching...") + # dynamicrafter_iface.launch(max_threads=1, share=True) + + dynamicrafter_iface.launch(server_name='0.0.0.0', server_port=12345) + # dynamicrafter_iface.launch() + # print("launched...") \ No newline at end of file diff --git a/configs/.DS_Store b/configs/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..cdc1f682a3c23c9667efb303c6d29e279ca5ffa3 Binary files /dev/null and b/configs/.DS_Store differ diff --git a/configs/inference_1024_v1.0.yaml b/configs/inference_1024_v1.0.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a9c1049fdd254aec7de7639567ed69de61911118 --- /dev/null +++ b/configs/inference_1024_v1.0.yaml @@ -0,0 +1,103 @@ +model: + target: lvdm.models.ddpm3d.LatentVisualDiffusion + params: + rescale_betas_zero_snr: True + parameterization: "v" + linear_start: 0.00085 + linear_end: 0.012 + num_timesteps_cond: 1 + timesteps: 1000 + first_stage_key: video + cond_stage_key: caption + cond_stage_trainable: False + conditioning_key: hybrid + image_size: [72, 128] + channels: 4 + scale_by_std: False + scale_factor: 0.18215 + use_ema: False + uncond_type: 'empty_seq' + use_dynamic_rescale: true + base_scale: 0.7 + fps_condition_type: 'fps' + perframe_ae: True + loop_video: true + unet_config: + target: lvdm.modules.networks.openaimodel3d.UNetModel + params: + in_channels: 8 + out_channels: 4 + model_channels: 320 + attention_resolutions: + - 4 + - 2 + - 1 + num_res_blocks: 2 + channel_mult: + - 1 + - 2 + - 4 + - 4 + dropout: 0.1 + num_head_channels: 64 + transformer_depth: 1 + context_dim: 1024 + use_linear: true + use_checkpoint: True + temporal_conv: True + temporal_attention: True + temporal_selfatt_only: true + use_relative_position: false + use_causal_attention: False + temporal_length: 16 + addition_attention: true + image_cross_attention: true + default_fs: 24 + fs_condition: true + + first_stage_config: + target: lvdm.models.autoencoder.AutoencoderKL_Dualref + 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: lvdm.modules.encoders.condition.FrozenOpenCLIPEmbedder + params: + freeze: true + layer: "penultimate" + + img_cond_stage_config: + target: lvdm.modules.encoders.condition.FrozenOpenCLIPImageEmbedderV2 + params: + freeze: true + + image_proj_stage_config: + target: lvdm.modules.encoders.resampler.Resampler + params: + dim: 1024 + depth: 4 + dim_head: 64 + heads: 12 + num_queries: 16 + embedding_dim: 1280 + output_dim: 1024 + ff_mult: 4 + video_length: 16 diff --git a/configs/inference_512_v1.0.yaml b/configs/inference_512_v1.0.yaml new file mode 100644 index 0000000000000000000000000000000000000000..2ff9a8febcfd51b5ef1b972456f613ef04b25f50 --- /dev/null +++ b/configs/inference_512_v1.0.yaml @@ -0,0 +1,103 @@ +model: + target: lvdm.models.ddpm3d.LatentVisualDiffusion + params: + rescale_betas_zero_snr: True + parameterization: "v" + linear_start: 0.00085 + linear_end: 0.012 + num_timesteps_cond: 1 + timesteps: 1000 + first_stage_key: video + cond_stage_key: caption + cond_stage_trainable: False + conditioning_key: hybrid + image_size: [40, 64] + channels: 4 + scale_by_std: False + scale_factor: 0.18215 + use_ema: False + uncond_type: 'empty_seq' + use_dynamic_rescale: true + base_scale: 0.7 + fps_condition_type: 'fps' + perframe_ae: True + loop_video: true + unet_config: + target: lvdm.modules.networks.openaimodel3d.UNetModel + params: + in_channels: 8 + out_channels: 4 + model_channels: 320 + attention_resolutions: + - 4 + - 2 + - 1 + num_res_blocks: 2 + channel_mult: + - 1 + - 2 + - 4 + - 4 + dropout: 0.1 + num_head_channels: 64 + transformer_depth: 1 + context_dim: 1024 + use_linear: true + use_checkpoint: True + temporal_conv: True + temporal_attention: True + temporal_selfatt_only: true + use_relative_position: false + use_causal_attention: False + temporal_length: 16 + addition_attention: true + image_cross_attention: true + default_fs: 24 + fs_condition: true + + first_stage_config: + target: lvdm.models.autoencoder.AutoencoderKL_Dualref + 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: lvdm.modules.encoders.condition.FrozenOpenCLIPEmbedder + params: + freeze: true + layer: "penultimate" + + img_cond_stage_config: + target: lvdm.modules.encoders.condition.FrozenOpenCLIPImageEmbedderV2 + params: + freeze: true + + image_proj_stage_config: + target: lvdm.modules.encoders.resampler.Resampler + params: + dim: 1024 + depth: 4 + dim_head: 64 + heads: 12 + num_queries: 16 + embedding_dim: 1280 + output_dim: 1024 + ff_mult: 4 + video_length: 16 diff --git a/configs/training_1024_v1.0/config.yaml b/configs/training_1024_v1.0/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..2fa67398d15d66253c1db76261f6ea0a24c40a66 --- /dev/null +++ b/configs/training_1024_v1.0/config.yaml @@ -0,0 +1,166 @@ +model: + pretrained_checkpoint: checkpoints/dynamicrafter_1024_v1/model.ckpt + base_learning_rate: 1.0e-05 + scale_lr: False + target: lvdm.models.ddpm3d.LatentVisualDiffusion + params: + rescale_betas_zero_snr: True + parameterization: "v" + linear_start: 0.00085 + linear_end: 0.012 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: video + cond_stage_key: caption + cond_stage_trainable: False + image_proj_model_trainable: True + conditioning_key: hybrid + image_size: [72, 128] + channels: 4 + scale_by_std: False + scale_factor: 0.18215 + use_ema: False + uncond_prob: 0.05 + uncond_type: 'empty_seq' + rand_cond_frame: true + use_dynamic_rescale: true + base_scale: 0.3 + fps_condition_type: 'fps' + perframe_ae: True + + unet_config: + target: lvdm.modules.networks.openaimodel3d.UNetModel + params: + in_channels: 8 + out_channels: 4 + model_channels: 320 + attention_resolutions: + - 4 + - 2 + - 1 + num_res_blocks: 2 + channel_mult: + - 1 + - 2 + - 4 + - 4 + dropout: 0.1 + num_head_channels: 64 + transformer_depth: 1 + context_dim: 1024 + use_linear: true + use_checkpoint: True + temporal_conv: True + temporal_attention: True + temporal_selfatt_only: true + use_relative_position: false + use_causal_attention: False + temporal_length: 16 + addition_attention: true + image_cross_attention: true + default_fs: 10 + fs_condition: true + + first_stage_config: + target: lvdm.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: lvdm.modules.encoders.condition.FrozenOpenCLIPEmbedder + params: + freeze: true + layer: "penultimate" + + img_cond_stage_config: + target: lvdm.modules.encoders.condition.FrozenOpenCLIPImageEmbedderV2 + params: + freeze: true + + image_proj_stage_config: + target: lvdm.modules.encoders.resampler.Resampler + params: + dim: 1024 + depth: 4 + dim_head: 64 + heads: 12 + num_queries: 16 + embedding_dim: 1280 + output_dim: 1024 + ff_mult: 4 + video_length: 16 + +data: + target: utils_data.DataModuleFromConfig + params: + batch_size: 1 + num_workers: 12 + wrap: false + train: + target: lvdm.data.webvid.WebVid + params: + data_dir: + meta_path: <.csv FILE> + video_length: 16 + frame_stride: 6 + load_raw_resolution: true + resolution: [576, 1024] + spatial_transform: resize_center_crop + random_fs: true ## if true, we uniformly sample fs with max_fs=frame_stride (above) + +lightning: + precision: 16 + # strategy: deepspeed_stage_2 + trainer: + benchmark: True + accumulate_grad_batches: 2 + max_steps: 100000 + # logger + log_every_n_steps: 50 + # val + val_check_interval: 0.5 + gradient_clip_algorithm: 'norm' + gradient_clip_val: 0.5 + callbacks: + model_checkpoint: + target: pytorch_lightning.callbacks.ModelCheckpoint + params: + every_n_train_steps: 9000 #1000 + filename: "{epoch}-{step}" + save_weights_only: True + metrics_over_trainsteps_checkpoint: + target: pytorch_lightning.callbacks.ModelCheckpoint + params: + filename: '{epoch}-{step}' + save_weights_only: True + every_n_train_steps: 10000 #20000 # 3s/step*2w= + batch_logger: + target: callbacks.ImageLogger + params: + batch_frequency: 500 + to_local: False + max_images: 8 + log_images_kwargs: + ddim_steps: 50 + unconditional_guidance_scale: 7.5 + timestep_spacing: uniform_trailing + guidance_rescale: 0.7 \ No newline at end of file diff --git a/configs/training_1024_v1.0/run.sh b/configs/training_1024_v1.0/run.sh new file mode 100644 index 0000000000000000000000000000000000000000..c1f895d85a2307b57f13033c9b17d1f1c639f732 --- /dev/null +++ b/configs/training_1024_v1.0/run.sh @@ -0,0 +1,37 @@ +# NCCL configuration +# export NCCL_DEBUG=INFO +# export NCCL_IB_DISABLE=0 +# export NCCL_IB_GID_INDEX=3 +# export NCCL_NET_GDR_LEVEL=3 +# export NCCL_TOPO_FILE=/tmp/topo.txt + +# args +name="training_1024_v1.0" +config_file=configs/${name}/config.yaml + +# save root dir for logs, checkpoints, tensorboard record, etc. +save_root="" + +mkdir -p $save_root/$name + +## run +CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python3 -m torch.distributed.launch \ +--nproc_per_node=$HOST_GPU_NUM --nnodes=1 --master_addr=127.0.0.1 --master_port=12352 --node_rank=0 \ +./main/trainer.py \ +--base $config_file \ +--train \ +--name $name \ +--logdir $save_root \ +--devices $HOST_GPU_NUM \ +lightning.trainer.num_nodes=1 + +## debugging +# CUDA_VISIBLE_DEVICES=0,1,2,3 python3 -m torch.distributed.launch \ +# --nproc_per_node=4 --nnodes=1 --master_addr=127.0.0.1 --master_port=12352 --node_rank=0 \ +# ./main/trainer.py \ +# --base $config_file \ +# --train \ +# --name $name \ +# --logdir $save_root \ +# --devices 4 \ +# lightning.trainer.num_nodes=1 \ No newline at end of file diff --git a/configs/training_512_v1.0/config.yaml b/configs/training_512_v1.0/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..6cd69c2a39a9e7d9931645724a0496f25ef9ab29 --- /dev/null +++ b/configs/training_512_v1.0/config.yaml @@ -0,0 +1,166 @@ +model: + pretrained_checkpoint: checkpoints/dynamicrafter_512_v1/model.ckpt + base_learning_rate: 1.0e-05 + scale_lr: False + target: lvdm.models.ddpm3d.LatentVisualDiffusion + params: + rescale_betas_zero_snr: True + parameterization: "v" + linear_start: 0.00085 + linear_end: 0.012 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: video + cond_stage_key: caption + cond_stage_trainable: False + image_proj_model_trainable: True + conditioning_key: hybrid + image_size: [40, 64] + channels: 4 + scale_by_std: False + scale_factor: 0.18215 + use_ema: False + uncond_prob: 0.05 + uncond_type: 'empty_seq' + rand_cond_frame: true + use_dynamic_rescale: true + base_scale: 0.7 + fps_condition_type: 'fps' + perframe_ae: True + + unet_config: + target: lvdm.modules.networks.openaimodel3d.UNetModel + params: + in_channels: 8 + out_channels: 4 + model_channels: 320 + attention_resolutions: + - 4 + - 2 + - 1 + num_res_blocks: 2 + channel_mult: + - 1 + - 2 + - 4 + - 4 + dropout: 0.1 + num_head_channels: 64 + transformer_depth: 1 + context_dim: 1024 + use_linear: true + use_checkpoint: True + temporal_conv: True + temporal_attention: True + temporal_selfatt_only: true + use_relative_position: false + use_causal_attention: False + temporal_length: 16 + addition_attention: true + image_cross_attention: true + default_fs: 10 + fs_condition: true + + first_stage_config: + target: lvdm.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: lvdm.modules.encoders.condition.FrozenOpenCLIPEmbedder + params: + freeze: true + layer: "penultimate" + + img_cond_stage_config: + target: lvdm.modules.encoders.condition.FrozenOpenCLIPImageEmbedderV2 + params: + freeze: true + + image_proj_stage_config: + target: lvdm.modules.encoders.resampler.Resampler + params: + dim: 1024 + depth: 4 + dim_head: 64 + heads: 12 + num_queries: 16 + embedding_dim: 1280 + output_dim: 1024 + ff_mult: 4 + video_length: 16 + +data: + target: utils_data.DataModuleFromConfig + params: + batch_size: 2 + num_workers: 12 + wrap: false + train: + target: lvdm.data.webvid.WebVid + params: + data_dir: + meta_path: <.csv FILE> + video_length: 16 + frame_stride: 6 + load_raw_resolution: true + resolution: [320, 512] + spatial_transform: resize_center_crop + random_fs: true ## if true, we uniformly sample fs with max_fs=frame_stride (above) + +lightning: + precision: 16 + # strategy: deepspeed_stage_2 + trainer: + benchmark: True + accumulate_grad_batches: 2 + max_steps: 100000 + # logger + log_every_n_steps: 50 + # val + val_check_interval: 0.5 + gradient_clip_algorithm: 'norm' + gradient_clip_val: 0.5 + callbacks: + model_checkpoint: + target: pytorch_lightning.callbacks.ModelCheckpoint + params: + every_n_train_steps: 9000 #1000 + filename: "{epoch}-{step}" + save_weights_only: True + metrics_over_trainsteps_checkpoint: + target: pytorch_lightning.callbacks.ModelCheckpoint + params: + filename: '{epoch}-{step}' + save_weights_only: True + every_n_train_steps: 10000 #20000 # 3s/step*2w= + batch_logger: + target: callbacks.ImageLogger + params: + batch_frequency: 500 + to_local: False + max_images: 8 + log_images_kwargs: + ddim_steps: 50 + unconditional_guidance_scale: 7.5 + timestep_spacing: uniform_trailing + guidance_rescale: 0.7 \ No newline at end of file diff --git a/configs/training_512_v1.0/run.sh b/configs/training_512_v1.0/run.sh new file mode 100644 index 0000000000000000000000000000000000000000..46320b4a94fb3859e814be903e05ed3d9f1d5cd2 --- /dev/null +++ b/configs/training_512_v1.0/run.sh @@ -0,0 +1,37 @@ +# NCCL configuration +# export NCCL_DEBUG=INFO +# export NCCL_IB_DISABLE=0 +# export NCCL_IB_GID_INDEX=3 +# export NCCL_NET_GDR_LEVEL=3 +# export NCCL_TOPO_FILE=/tmp/topo.txt + +# args +name="training_512_v1.0" +config_file=configs/${name}/config.yaml + +# save root dir for logs, checkpoints, tensorboard record, etc. +save_root="" + +mkdir -p $save_root/$name + +## run +CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python3 -m torch.distributed.launch \ +--nproc_per_node=$HOST_GPU_NUM --nnodes=1 --master_addr=127.0.0.1 --master_port=12352 --node_rank=0 \ +./main/trainer.py \ +--base $config_file \ +--train \ +--name $name \ +--logdir $save_root \ +--devices $HOST_GPU_NUM \ +lightning.trainer.num_nodes=1 + +## debugging +# CUDA_VISIBLE_DEVICES=0,1,2,3 python3 -m torch.distributed.launch \ +# --nproc_per_node=4 --nnodes=1 --master_addr=127.0.0.1 --master_port=12352 --node_rank=0 \ +# ./main/trainer.py \ +# --base $config_file \ +# --train \ +# --name $name \ +# --logdir $save_root \ +# --devices 4 \ +# lightning.trainer.num_nodes=1 \ No newline at end of file diff --git a/lvdm/.DS_Store b/lvdm/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..4b24bc21b99ff26ed7647f23faa48c811417aef6 Binary files /dev/null and b/lvdm/.DS_Store differ diff --git a/lvdm/basics.py b/lvdm/basics.py new file mode 100644 index 0000000000000000000000000000000000000000..65c771d13a7f4a932ac370f08797a8b6ba9e85ff --- /dev/null +++ b/lvdm/basics.py @@ -0,0 +1,100 @@ +# adopted from +# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py +# and +# https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py +# and +# https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py +# +# thanks! + +import torch.nn as nn +from utils.utils import instantiate_from_config + + +def disabled_train(self, mode=True): + """Overwrite model.train with this function to make sure train/eval mode + does not change anymore.""" + return self + +def zero_module(module): + """ + Zero out the parameters of a module and return it. + """ + for p in module.parameters(): + p.detach().zero_() + return module + +def scale_module(module, scale): + """ + Scale the parameters of a module and return it. + """ + for p in module.parameters(): + p.detach().mul_(scale) + return module + + +def conv_nd(dims, *args, **kwargs): + """ + Create a 1D, 2D, or 3D convolution module. + """ + if dims == 1: + return nn.Conv1d(*args, **kwargs) + elif dims == 2: + return nn.Conv2d(*args, **kwargs) + elif dims == 3: + return nn.Conv3d(*args, **kwargs) + raise ValueError(f"unsupported dimensions: {dims}") + + +def linear(*args, **kwargs): + """ + Create a linear module. + """ + return nn.Linear(*args, **kwargs) + + +def avg_pool_nd(dims, *args, **kwargs): + """ + Create a 1D, 2D, or 3D average pooling module. + """ + if dims == 1: + return nn.AvgPool1d(*args, **kwargs) + elif dims == 2: + return nn.AvgPool2d(*args, **kwargs) + elif dims == 3: + return nn.AvgPool3d(*args, **kwargs) + raise ValueError(f"unsupported dimensions: {dims}") + + +def nonlinearity(type='silu'): + if type == 'silu': + return nn.SiLU() + elif type == 'leaky_relu': + return nn.LeakyReLU() + + +class GroupNormSpecific(nn.GroupNorm): + def forward(self, x): + return super().forward(x.float()).type(x.dtype) + + +def normalization(channels, num_groups=32): + """ + Make a standard normalization layer. + :param channels: number of input channels. + :return: an nn.Module for normalization. + """ + return GroupNormSpecific(num_groups, channels) + + +class HybridConditioner(nn.Module): + + def __init__(self, c_concat_config, c_crossattn_config): + super().__init__() + self.concat_conditioner = instantiate_from_config(c_concat_config) + self.crossattn_conditioner = instantiate_from_config(c_crossattn_config) + + def forward(self, c_concat, c_crossattn): + c_concat = self.concat_conditioner(c_concat) + c_crossattn = self.crossattn_conditioner(c_crossattn) + return {'c_concat': [c_concat], 'c_crossattn': [c_crossattn]} \ No newline at end of file diff --git a/lvdm/common.py b/lvdm/common.py new file mode 100644 index 0000000000000000000000000000000000000000..55a150b618e275f01d3a59ad9c7579176c4ea1b8 --- /dev/null +++ b/lvdm/common.py @@ -0,0 +1,94 @@ +import math +from inspect import isfunction +import torch +from torch import nn +import torch.distributed as dist + + +def gather_data(data, return_np=True): + ''' gather data from multiple processes to one list ''' + data_list = [torch.zeros_like(data) for _ in range(dist.get_world_size())] + dist.all_gather(data_list, data) # gather not supported with NCCL + if return_np: + data_list = [data.cpu().numpy() for data in data_list] + return data_list + +def autocast(f): + def do_autocast(*args, **kwargs): + with torch.cuda.amp.autocast(enabled=True, + dtype=torch.get_autocast_gpu_dtype(), + cache_enabled=torch.is_autocast_cache_enabled()): + return f(*args, **kwargs) + return do_autocast + + +def extract_into_tensor(a, t, x_shape): + b, *_ = t.shape + out = a.gather(-1, t) + return out.reshape(b, *((1,) * (len(x_shape) - 1))) + + +def noise_like(shape, device, repeat=False): + repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1))) + noise = lambda: torch.randn(shape, device=device) + return repeat_noise() if repeat else noise() + + +def default(val, d): + if exists(val): + return val + return d() if isfunction(d) else d + +def exists(val): + return val is not None + +def identity(*args, **kwargs): + return nn.Identity() + +def uniq(arr): + return{el: True for el in arr}.keys() + +def mean_flat(tensor): + """ + Take the mean over all non-batch dimensions. + """ + return tensor.mean(dim=list(range(1, len(tensor.shape)))) + +def ismap(x): + if not isinstance(x, torch.Tensor): + return False + return (len(x.shape) == 4) and (x.shape[1] > 3) + +def isimage(x): + if not isinstance(x,torch.Tensor): + return False + return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1) + +def max_neg_value(t): + return -torch.finfo(t.dtype).max + +def shape_to_str(x): + shape_str = "x".join([str(x) for x in x.shape]) + return shape_str + +def init_(tensor): + dim = tensor.shape[-1] + std = 1 / math.sqrt(dim) + tensor.uniform_(-std, std) + return tensor + +ckpt = torch.utils.checkpoint.checkpoint +def checkpoint(func, inputs, params, flag): + """ + Evaluate a function without caching intermediate activations, allowing for + reduced memory at the expense of extra compute in the backward pass. + :param func: the function to evaluate. + :param inputs: the argument sequence to pass to `func`. + :param params: a sequence of parameters `func` depends on but does not + explicitly take as arguments. + :param flag: if False, disable gradient checkpointing. + """ + if flag: + return ckpt(func, *inputs, use_reentrant=False) + else: + return func(*inputs) \ No newline at end of file diff --git a/lvdm/data/base.py b/lvdm/data/base.py new file mode 100644 index 0000000000000000000000000000000000000000..01aac7a81cf970c702d452aeaad7b6ff979d97d1 --- /dev/null +++ b/lvdm/data/base.py @@ -0,0 +1,23 @@ +from abc import abstractmethod +from torch.utils.data import IterableDataset + + +class Txt2ImgIterableBaseDataset(IterableDataset): + ''' + Define an interface to make the IterableDatasets for text2img data chainable + ''' + def __init__(self, num_records=0, valid_ids=None, size=256): + super().__init__() + self.num_records = num_records + self.valid_ids = valid_ids + self.sample_ids = valid_ids + self.size = size + + print(f'{self.__class__.__name__} dataset contains {self.__len__()} examples.') + + def __len__(self): + return self.num_records + + @abstractmethod + def __iter__(self): + pass \ No newline at end of file diff --git a/lvdm/data/webvid.py b/lvdm/data/webvid.py new file mode 100644 index 0000000000000000000000000000000000000000..d8f10692d45eadf2df4440cdb5d0e4418af386b8 --- /dev/null +++ b/lvdm/data/webvid.py @@ -0,0 +1,202 @@ +import os +import random +from tqdm import tqdm +import pandas as pd +from decord import VideoReader, cpu + +import torch +from torch.utils.data import Dataset +from torch.utils.data import DataLoader +from torchvision import transforms + + +class WebVid(Dataset): + """ + WebVid Dataset. + Assumes webvid data is structured as follows. + Webvid/ + videos/ + 000001_000050/ ($page_dir) + 1.mp4 (videoid.mp4) + ... + 5000.mp4 + ... + """ + def __init__(self, + meta_path, + data_dir, + subsample=None, + video_length=16, + resolution=[256, 512], + frame_stride=1, + frame_stride_min=1, + spatial_transform=None, + crop_resolution=None, + fps_max=None, + load_raw_resolution=False, + fixed_fps=None, + random_fs=False, + ): + self.meta_path = meta_path + self.data_dir = data_dir + self.subsample = subsample + self.video_length = video_length + self.resolution = [resolution, resolution] if isinstance(resolution, int) else resolution + self.fps_max = fps_max + self.frame_stride = frame_stride + self.frame_stride_min = frame_stride_min + self.fixed_fps = fixed_fps + self.load_raw_resolution = load_raw_resolution + self.random_fs = random_fs + self._load_metadata() + if spatial_transform is not None: + if spatial_transform == "random_crop": + self.spatial_transform = transforms.RandomCrop(crop_resolution) + elif spatial_transform == "center_crop": + self.spatial_transform = transforms.Compose([ + transforms.CenterCrop(resolution), + ]) + elif spatial_transform == "resize_center_crop": + # assert(self.resolution[0] == self.resolution[1]) + self.spatial_transform = transforms.Compose([ + transforms.Resize(min(self.resolution)), + transforms.CenterCrop(self.resolution), + ]) + elif spatial_transform == "resize": + self.spatial_transform = transforms.Resize(self.resolution) + else: + raise NotImplementedError + else: + self.spatial_transform = None + + def _load_metadata(self): + metadata = pd.read_csv(self.meta_path) + print(f'>>> {len(metadata)} data samples loaded.') + if self.subsample is not None: + metadata = metadata.sample(self.subsample, random_state=0) + + metadata['caption'] = metadata['name'] + del metadata['name'] + self.metadata = metadata + self.metadata.dropna(inplace=True) + + def _get_video_path(self, sample): + rel_video_fp = os.path.join(sample['page_dir'], str(sample['videoid']) + '.mp4') + full_video_fp = os.path.join(self.data_dir, 'videos', rel_video_fp) + return full_video_fp + + def __getitem__(self, index): + if self.random_fs: + frame_stride = random.randint(self.frame_stride_min, self.frame_stride) + else: + frame_stride = self.frame_stride + + ## get frames until success + while True: + index = index % len(self.metadata) + sample = self.metadata.iloc[index] + video_path = self._get_video_path(sample) + ## video_path should be in the format of "....../WebVid/videos/$page_dir/$videoid.mp4" + caption = sample['caption'] + + try: + if self.load_raw_resolution: + video_reader = VideoReader(video_path, ctx=cpu(0)) + else: + video_reader = VideoReader(video_path, ctx=cpu(0), width=530, height=300) + if len(video_reader) < self.video_length: + print(f"video length ({len(video_reader)}) is smaller than target length({self.video_length})") + index += 1 + continue + else: + pass + except: + index += 1 + print(f"Load video failed! path = {video_path}") + continue + + fps_ori = video_reader.get_avg_fps() + if self.fixed_fps is not None: + frame_stride = int(frame_stride * (1.0 * fps_ori / self.fixed_fps)) + + ## to avoid extreme cases when fixed_fps is used + frame_stride = max(frame_stride, 1) + + ## get valid range (adapting case by case) + required_frame_num = frame_stride * (self.video_length-1) + 1 + frame_num = len(video_reader) + if frame_num < required_frame_num: + ## drop extra samples if fixed fps is required + if self.fixed_fps is not None and frame_num < required_frame_num * 0.5: + index += 1 + continue + else: + frame_stride = frame_num // self.video_length + required_frame_num = frame_stride * (self.video_length-1) + 1 + + ## select a random clip + random_range = frame_num - required_frame_num + start_idx = random.randint(0, random_range) if random_range > 0 else 0 + + ## calculate frame indices + frame_indices = [start_idx + frame_stride*i for i in range(self.video_length)] + try: + frames = video_reader.get_batch(frame_indices) + break + except: + print(f"Get frames failed! path = {video_path}; [max_ind vs frame_total:{max(frame_indices)} / {frame_num}]") + index += 1 + continue + + ## process data + assert(frames.shape[0] == self.video_length),f'{len(frames)}, self.video_length={self.video_length}' + frames = torch.tensor(frames.asnumpy()).permute(3, 0, 1, 2).float() # [t,h,w,c] -> [c,t,h,w] + + if self.spatial_transform is not None: + frames = self.spatial_transform(frames) + + if self.resolution is not None: + assert (frames.shape[2], frames.shape[3]) == (self.resolution[0], self.resolution[1]), f'frames={frames.shape}, self.resolution={self.resolution}' + + ## turn frames tensors to [-1,1] + frames = (frames / 255 - 0.5) * 2 + fps_clip = fps_ori // frame_stride + if self.fps_max is not None and fps_clip > self.fps_max: + fps_clip = self.fps_max + + data = {'video': frames, 'caption': caption, 'path': video_path, 'fps': fps_clip, 'frame_stride': frame_stride} + return data + + def __len__(self): + return len(self.metadata) + + +if __name__== "__main__": + meta_path = "" ## path to the meta file + data_dir = "" ## path to the data directory + save_dir = "" ## path to the save directory + dataset = WebVid(meta_path, + data_dir, + subsample=None, + video_length=16, + resolution=[256,448], + frame_stride=4, + spatial_transform="resize_center_crop", + crop_resolution=None, + fps_max=None, + load_raw_resolution=True + ) + dataloader = DataLoader(dataset, + batch_size=1, + num_workers=0, + shuffle=False) + + + import sys + sys.path.insert(1, os.path.join(sys.path[0], '..', '..')) + from utils.save_video import tensor_to_mp4 + for i, batch in tqdm(enumerate(dataloader), desc="Data Batch"): + video = batch['video'] + name = batch['path'][0].split('videos/')[-1].replace('/','_') + tensor_to_mp4(video, save_dir+'/'+name, fps=8) + diff --git a/lvdm/distributions.py b/lvdm/distributions.py new file mode 100644 index 0000000000000000000000000000000000000000..9a2a82ecace3ce27fb7816ddaf088e179c2d5ffd --- /dev/null +++ b/lvdm/distributions.py @@ -0,0 +1,95 @@ +import torch +import numpy as np + + +class AbstractDistribution: + def sample(self): + raise NotImplementedError() + + def mode(self): + raise NotImplementedError() + + +class DiracDistribution(AbstractDistribution): + def __init__(self, value): + self.value = value + + def sample(self): + return self.value + + def mode(self): + return self.value + + +class DiagonalGaussianDistribution(object): + def __init__(self, parameters, deterministic=False): + self.parameters = parameters + self.mean, self.logvar = torch.chunk(parameters, 2, dim=1) + self.logvar = torch.clamp(self.logvar, -30.0, 20.0) + self.deterministic = deterministic + self.std = torch.exp(0.5 * self.logvar) + self.var = torch.exp(self.logvar) + if self.deterministic: + self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device) + + def sample(self, noise=None): + if noise is None: + noise = torch.randn(self.mean.shape) + + x = self.mean + self.std * noise.to(device=self.parameters.device) + return x + + def kl(self, other=None): + if self.deterministic: + return torch.Tensor([0.]) + else: + if other is None: + return 0.5 * torch.sum(torch.pow(self.mean, 2) + + self.var - 1.0 - self.logvar, + dim=[1, 2, 3]) + else: + return 0.5 * torch.sum( + torch.pow(self.mean - other.mean, 2) / other.var + + self.var / other.var - 1.0 - self.logvar + other.logvar, + dim=[1, 2, 3]) + + def nll(self, sample, dims=[1,2,3]): + if self.deterministic: + return torch.Tensor([0.]) + logtwopi = np.log(2.0 * np.pi) + return 0.5 * torch.sum( + logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var, + dim=dims) + + def mode(self): + return self.mean + + +def normal_kl(mean1, logvar1, mean2, logvar2): + """ + source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12 + Compute the KL divergence between two gaussians. + Shapes are automatically broadcasted, so batches can be compared to + scalars, among other use cases. + """ + tensor = None + for obj in (mean1, logvar1, mean2, logvar2): + if isinstance(obj, torch.Tensor): + tensor = obj + break + assert tensor is not None, "at least one argument must be a Tensor" + + # Force variances to be Tensors. Broadcasting helps convert scalars to + # Tensors, but it does not work for torch.exp(). + logvar1, logvar2 = [ + x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor) + for x in (logvar1, logvar2) + ] + + return 0.5 * ( + -1.0 + + logvar2 + - logvar1 + + torch.exp(logvar1 - logvar2) + + ((mean1 - mean2) ** 2) * torch.exp(-logvar2) + ) \ No newline at end of file diff --git a/lvdm/ema.py b/lvdm/ema.py new file mode 100644 index 0000000000000000000000000000000000000000..cd2f8e3115f816b4cac674397238cd8c22de9bc2 --- /dev/null +++ b/lvdm/ema.py @@ -0,0 +1,76 @@ +import torch +from torch import nn + + +class LitEma(nn.Module): + def __init__(self, model, decay=0.9999, use_num_upates=True): + super().__init__() + if decay < 0.0 or decay > 1.0: + raise ValueError('Decay must be between 0 and 1') + + self.m_name2s_name = {} + self.register_buffer('decay', torch.tensor(decay, dtype=torch.float32)) + self.register_buffer('num_updates', torch.tensor(0,dtype=torch.int) if use_num_upates + else torch.tensor(-1,dtype=torch.int)) + + for name, p in model.named_parameters(): + if p.requires_grad: + #remove as '.'-character is not allowed in buffers + s_name = name.replace('.','') + self.m_name2s_name.update({name:s_name}) + self.register_buffer(s_name,p.clone().detach().data) + + self.collected_params = [] + + def forward(self,model): + decay = self.decay + + if self.num_updates >= 0: + self.num_updates += 1 + decay = min(self.decay,(1 + self.num_updates) / (10 + self.num_updates)) + + one_minus_decay = 1.0 - decay + + with torch.no_grad(): + m_param = dict(model.named_parameters()) + shadow_params = dict(self.named_buffers()) + + for key in m_param: + if m_param[key].requires_grad: + sname = self.m_name2s_name[key] + shadow_params[sname] = shadow_params[sname].type_as(m_param[key]) + shadow_params[sname].sub_(one_minus_decay * (shadow_params[sname] - m_param[key])) + else: + assert not key in self.m_name2s_name + + def copy_to(self, model): + m_param = dict(model.named_parameters()) + shadow_params = dict(self.named_buffers()) + for key in m_param: + if m_param[key].requires_grad: + m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data) + else: + assert not key in self.m_name2s_name + + def store(self, parameters): + """ + Save the current parameters for restoring later. + Args: + parameters: Iterable of `torch.nn.Parameter`; the parameters to be + temporarily stored. + """ + self.collected_params = [param.clone() for param in parameters] + + def restore(self, parameters): + """ + Restore the parameters stored with the `store` method. + Useful to validate the model with EMA parameters without affecting the + original optimization process. Store the parameters before the + `copy_to` method. After validation (or model saving), use this to + restore the former parameters. + Args: + parameters: Iterable of `torch.nn.Parameter`; the parameters to be + updated with the stored parameters. + """ + for c_param, param in zip(self.collected_params, parameters): + param.data.copy_(c_param.data) \ No newline at end of file diff --git a/lvdm/models/autoencoder.py b/lvdm/models/autoencoder.py new file mode 100644 index 0000000000000000000000000000000000000000..799d05c3967075be312df0dcd82da68999c9d201 --- /dev/null +++ b/lvdm/models/autoencoder.py @@ -0,0 +1,275 @@ +import os +from contextlib import contextmanager +import torch +import numpy as np +from einops import rearrange +import torch.nn.functional as F +import pytorch_lightning as pl +from lvdm.modules.networks.ae_modules import Encoder, Decoder +from lvdm.distributions import DiagonalGaussianDistribution +from utils.utils import instantiate_from_config + +TIMESTEPS=16 +class AutoencoderKL(pl.LightningModule): + def __init__(self, + ddconfig, + lossconfig, + embed_dim, + ckpt_path=None, + ignore_keys=[], + image_key="image", + colorize_nlabels=None, + monitor=None, + test=False, + logdir=None, + input_dim=4, + test_args=None, + additional_decode_keys=None, + use_checkpoint=False, + diff_boost_factor=3.0, + ): + super().__init__() + self.image_key = image_key + self.encoder = Encoder(**ddconfig) + self.decoder = Decoder(**ddconfig) + self.loss = instantiate_from_config(lossconfig) + assert ddconfig["double_z"] + self.quant_conv = torch.nn.Conv2d(2*ddconfig["z_channels"], 2*embed_dim, 1) + self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1) + self.embed_dim = embed_dim + self.input_dim = input_dim + self.test = test + self.test_args = test_args + self.logdir = logdir + if colorize_nlabels is not None: + assert type(colorize_nlabels)==int + self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1)) + if monitor is not None: + self.monitor = monitor + if ckpt_path is not None: + self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys) + if self.test: + self.init_test() + + def init_test(self,): + self.test = True + save_dir = os.path.join(self.logdir, "test") + if 'ckpt' in self.test_args: + ckpt_name = os.path.basename(self.test_args.ckpt).split('.ckpt')[0] + f'_epoch{self._cur_epoch}' + self.root = os.path.join(save_dir, ckpt_name) + else: + self.root = save_dir + if 'test_subdir' in self.test_args: + self.root = os.path.join(save_dir, self.test_args.test_subdir) + + self.root_zs = os.path.join(self.root, "zs") + self.root_dec = os.path.join(self.root, "reconstructions") + self.root_inputs = os.path.join(self.root, "inputs") + os.makedirs(self.root, exist_ok=True) + + if self.test_args.save_z: + os.makedirs(self.root_zs, exist_ok=True) + if self.test_args.save_reconstruction: + os.makedirs(self.root_dec, exist_ok=True) + if self.test_args.save_input: + os.makedirs(self.root_inputs, exist_ok=True) + assert(self.test_args is not None) + self.test_maximum = getattr(self.test_args, 'test_maximum', None) + self.count = 0 + self.eval_metrics = {} + self.decodes = [] + self.save_decode_samples = 2048 + + def init_from_ckpt(self, path, ignore_keys=list()): + sd = torch.load(path, map_location="cpu") + try: + self._cur_epoch = sd['epoch'] + sd = sd["state_dict"] + except: + self._cur_epoch = 'null' + keys = list(sd.keys()) + for k in keys: + for ik in ignore_keys: + if k.startswith(ik): + print("Deleting key {} from state_dict.".format(k)) + del sd[k] + self.load_state_dict(sd, strict=False) + # self.load_state_dict(sd, strict=True) + print(f"Restored from {path}") + + def encode(self, x, return_hidden_states=False, **kwargs): + if return_hidden_states: + h, hidden = self.encoder(x, return_hidden_states) + moments = self.quant_conv(h) + posterior = DiagonalGaussianDistribution(moments) + return posterior, hidden + else: + h = self.encoder(x) + moments = self.quant_conv(h) + posterior = DiagonalGaussianDistribution(moments) + return posterior + + def decode(self, z, **kwargs): + if len(kwargs) == 0: ## use the original decoder in AutoencoderKL + z = self.post_quant_conv(z) + dec = self.decoder(z, **kwargs) ##change for SVD decoder by adding **kwargs + return dec + + def forward(self, input, sample_posterior=True, **additional_decode_kwargs): + input_tuple = (input, ) + forward_temp = partial(self._forward, sample_posterior=sample_posterior, **additional_decode_kwargs) + return checkpoint(forward_temp, input_tuple, self.parameters(), self.use_checkpoint) + + + def _forward(self, input, sample_posterior=True, **additional_decode_kwargs): + posterior = self.encode(input) + if sample_posterior: + z = posterior.sample() + else: + z = posterior.mode() + dec = self.decode(z, **additional_decode_kwargs) + ## print(input.shape, dec.shape) torch.Size([16, 3, 256, 256]) torch.Size([16, 3, 256, 256]) + return dec, posterior + + def get_input(self, batch, k): + x = batch[k] + if x.dim() == 5 and self.input_dim == 4: + b,c,t,h,w = x.shape + self.b = b + self.t = t + x = rearrange(x, 'b c t h w -> (b t) c h w') + + return x + + def training_step(self, batch, batch_idx, optimizer_idx): + inputs = self.get_input(batch, self.image_key) + reconstructions, posterior = self(inputs) + + if optimizer_idx == 0: + # train encoder+decoder+logvar + aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step, + last_layer=self.get_last_layer(), split="train") + self.log("aeloss", aeloss, prog_bar=True, logger=True, on_step=True, on_epoch=True) + self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=False) + return aeloss + + if optimizer_idx == 1: + # train the discriminator + discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step, + last_layer=self.get_last_layer(), split="train") + + self.log("discloss", discloss, prog_bar=True, logger=True, on_step=True, on_epoch=True) + self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=False) + return discloss + + def validation_step(self, batch, batch_idx): + inputs = self.get_input(batch, self.image_key) + reconstructions, posterior = self(inputs) + aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, 0, self.global_step, + last_layer=self.get_last_layer(), split="val") + + discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, 1, self.global_step, + last_layer=self.get_last_layer(), split="val") + + self.log("val/rec_loss", log_dict_ae["val/rec_loss"]) + self.log_dict(log_dict_ae) + self.log_dict(log_dict_disc) + return self.log_dict + + def configure_optimizers(self): + lr = self.learning_rate + opt_ae = torch.optim.Adam(list(self.encoder.parameters())+ + list(self.decoder.parameters())+ + list(self.quant_conv.parameters())+ + list(self.post_quant_conv.parameters()), + lr=lr, betas=(0.5, 0.9)) + opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(), + lr=lr, betas=(0.5, 0.9)) + return [opt_ae, opt_disc], [] + + def get_last_layer(self): + return self.decoder.conv_out.weight + + @torch.no_grad() + def log_images(self, batch, only_inputs=False, **kwargs): + log = dict() + x = self.get_input(batch, self.image_key) + x = x.to(self.device) + if not only_inputs: + xrec, posterior = self(x) + if x.shape[1] > 3: + # colorize with random projection + assert xrec.shape[1] > 3 + x = self.to_rgb(x) + xrec = self.to_rgb(xrec) + log["samples"] = self.decode(torch.randn_like(posterior.sample())) + log["reconstructions"] = xrec + log["inputs"] = x + return log + + def to_rgb(self, x): + assert self.image_key == "segmentation" + if not hasattr(self, "colorize"): + self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x)) + x = F.conv2d(x, weight=self.colorize) + x = 2.*(x-x.min())/(x.max()-x.min()) - 1. + return x + +class IdentityFirstStage(torch.nn.Module): + def __init__(self, *args, vq_interface=False, **kwargs): + self.vq_interface = vq_interface # TODO: Should be true by default but check to not break older stuff + super().__init__() + + def encode(self, x, *args, **kwargs): + return x + + def decode(self, x, *args, **kwargs): + return x + + def quantize(self, x, *args, **kwargs): + if self.vq_interface: + return x, None, [None, None, None] + return x + + def forward(self, x, *args, **kwargs): + return x + +from lvdm.models.autoencoder_dualref import VideoDecoder +class AutoencoderKL_Dualref(AutoencoderKL): + def __init__(self, + ddconfig, + lossconfig, + embed_dim, + ckpt_path=None, + ignore_keys=[], + image_key="image", + colorize_nlabels=None, + monitor=None, + test=False, + logdir=None, + input_dim=4, + test_args=None, + additional_decode_keys=None, + use_checkpoint=False, + diff_boost_factor=3.0, + ): + super().__init__(ddconfig, lossconfig, embed_dim, ckpt_path, ignore_keys, image_key, colorize_nlabels, monitor, test, logdir, input_dim, test_args, additional_decode_keys, use_checkpoint, diff_boost_factor) + self.decoder = VideoDecoder(**ddconfig) + + def _forward(self, input, sample_posterior=True, **additional_decode_kwargs): + posterior, hidden_states = self.encode(input, return_hidden_states=True) + + hidden_states_first_last = [] + ### use only the first and last hidden states + for hid in hidden_states: + hid = rearrange(hid, '(b t) c h w -> b c t h w', t=TIMESTEPS) + hid_new = torch.cat([hid[:, :, 0:1], hid[:, :, -1:]], dim=2) + hidden_states_first_last.append(hid_new) + + if sample_posterior: + z = posterior.sample() + else: + z = posterior.mode() + dec = self.decode(z, ref_context=hidden_states_first_last, **additional_decode_kwargs) + ## print(input.shape, dec.shape) torch.Size([16, 3, 256, 256]) torch.Size([16, 3, 256, 256]) + return dec, posterior \ No newline at end of file diff --git a/lvdm/models/autoencoder_dualref.py b/lvdm/models/autoencoder_dualref.py new file mode 100644 index 0000000000000000000000000000000000000000..376d493a7861794a6784a6c0376ee83364a4ed40 --- /dev/null +++ b/lvdm/models/autoencoder_dualref.py @@ -0,0 +1,1178 @@ +#### https://github.com/Stability-AI/generative-models +from einops import rearrange, repeat +import logging +from typing import Any, Callable, Optional, Iterable, Union + +import numpy as np +import torch +import torch.nn as nn +from packaging import version +logpy = logging.getLogger(__name__) + +try: + import xformers + import xformers.ops + + XFORMERS_IS_AVAILABLE = True +except: + XFORMERS_IS_AVAILABLE = False + logpy.warning("no module 'xformers'. Processing without...") + +from lvdm.modules.attention_svd import LinearAttention, MemoryEfficientCrossAttention + + +def nonlinearity(x): + # swish + return x * torch.sigmoid(x) + + +def Normalize(in_channels, num_groups=32): + return torch.nn.GroupNorm( + num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True + ) + + +class ResnetBlock(nn.Module): + def __init__( + self, + *, + in_channels, + out_channels=None, + conv_shortcut=False, + dropout, + temb_channels=512, + ): + super().__init__() + 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.norm1 = Normalize(in_channels) + self.conv1 = torch.nn.Conv2d( + in_channels, out_channels, kernel_size=3, stride=1, padding=1 + ) + if temb_channels > 0: + self.temb_proj = torch.nn.Linear(temb_channels, out_channels) + self.norm2 = Normalize(out_channels) + self.dropout = torch.nn.Dropout(dropout) + self.conv2 = torch.nn.Conv2d( + out_channels, out_channels, kernel_size=3, stride=1, padding=1 + ) + if self.in_channels != self.out_channels: + if self.use_conv_shortcut: + self.conv_shortcut = torch.nn.Conv2d( + in_channels, out_channels, kernel_size=3, stride=1, padding=1 + ) + else: + self.nin_shortcut = torch.nn.Conv2d( + in_channels, out_channels, kernel_size=1, stride=1, padding=0 + ) + + def forward(self, x, temb): + h = x + h = self.norm1(h) + h = nonlinearity(h) + h = self.conv1(h) + + if temb is not None: + h = h + self.temb_proj(nonlinearity(temb))[:, :, None, None] + + h = self.norm2(h) + h = nonlinearity(h) + h = self.dropout(h) + h = self.conv2(h) + + if self.in_channels != self.out_channels: + if self.use_conv_shortcut: + x = self.conv_shortcut(x) + else: + x = self.nin_shortcut(x) + + return x + h + + +class LinAttnBlock(LinearAttention): + """to match AttnBlock usage""" + + def __init__(self, in_channels): + super().__init__(dim=in_channels, heads=1, dim_head=in_channels) + + +class AttnBlock(nn.Module): + def __init__(self, in_channels): + super().__init__() + self.in_channels = in_channels + + self.norm = Normalize(in_channels) + self.q = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.k = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.v = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.proj_out = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + + def attention(self, h_: torch.Tensor) -> torch.Tensor: + h_ = self.norm(h_) + q = self.q(h_) + k = self.k(h_) + v = self.v(h_) + + b, c, h, w = q.shape + q, k, v = map( + lambda x: rearrange(x, "b c h w -> b 1 (h w) c").contiguous(), (q, k, v) + ) + h_ = torch.nn.functional.scaled_dot_product_attention( + q, k, v + ) # scale is dim ** -0.5 per default + # compute attention + + return rearrange(h_, "b 1 (h w) c -> b c h w", h=h, w=w, c=c, b=b) + + def forward(self, x, **kwargs): + h_ = x + h_ = self.attention(h_) + h_ = self.proj_out(h_) + return x + h_ + + +class MemoryEfficientAttnBlock(nn.Module): + """ + Uses xformers efficient implementation, + see https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223 + Note: this is a single-head self-attention operation + """ + + # + def __init__(self, in_channels): + super().__init__() + self.in_channels = in_channels + + self.norm = Normalize(in_channels) + self.q = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.k = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.v = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.proj_out = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.attention_op: Optional[Any] = None + + def attention(self, h_: torch.Tensor) -> torch.Tensor: + h_ = self.norm(h_) + q = self.q(h_) + k = self.k(h_) + v = self.v(h_) + + # compute attention + B, C, H, W = q.shape + q, k, v = map(lambda x: rearrange(x, "b c h w -> b (h w) c"), (q, k, v)) + + q, k, v = map( + lambda t: t.unsqueeze(3) + .reshape(B, t.shape[1], 1, C) + .permute(0, 2, 1, 3) + .reshape(B * 1, t.shape[1], C) + .contiguous(), + (q, k, v), + ) + out = xformers.ops.memory_efficient_attention( + q, k, v, attn_bias=None, op=self.attention_op + ) + + out = ( + out.unsqueeze(0) + .reshape(B, 1, out.shape[1], C) + .permute(0, 2, 1, 3) + .reshape(B, out.shape[1], C) + ) + return rearrange(out, "b (h w) c -> b c h w", b=B, h=H, w=W, c=C) + + def forward(self, x, **kwargs): + h_ = x + h_ = self.attention(h_) + h_ = self.proj_out(h_) + return x + h_ + + +class MemoryEfficientCrossAttentionWrapper(MemoryEfficientCrossAttention): + def forward(self, x, context=None, mask=None, **unused_kwargs): + b, c, h, w = x.shape + x = rearrange(x, "b c h w -> b (h w) c") + out = super().forward(x, context=context, mask=mask) + out = rearrange(out, "b (h w) c -> b c h w", h=h, w=w, c=c) + return x + out + + +def make_attn(in_channels, attn_type="vanilla", attn_kwargs=None): + assert attn_type in [ + "vanilla", + "vanilla-xformers", + "memory-efficient-cross-attn", + "linear", + "none", + "memory-efficient-cross-attn-fusion", + ], f"attn_type {attn_type} unknown" + if ( + version.parse(torch.__version__) < version.parse("2.0.0") + and attn_type != "none" + ): + assert XFORMERS_IS_AVAILABLE, ( + f"We do not support vanilla attention in {torch.__version__} anymore, " + f"as it is too expensive. Please install xformers via e.g. 'pip install xformers==0.0.16'" + ) + # attn_type = "vanilla-xformers" + logpy.info(f"making attention of type '{attn_type}' with {in_channels} in_channels") + if attn_type == "vanilla": + assert attn_kwargs is None + return AttnBlock(in_channels) + elif attn_type == "vanilla-xformers": + logpy.info( + f"building MemoryEfficientAttnBlock with {in_channels} in_channels..." + ) + return MemoryEfficientAttnBlock(in_channels) + elif attn_type == "memory-efficient-cross-attn": + attn_kwargs["query_dim"] = in_channels + return MemoryEfficientCrossAttentionWrapper(**attn_kwargs) + elif attn_type == "memory-efficient-cross-attn-fusion": + attn_kwargs["query_dim"] = in_channels + return MemoryEfficientCrossAttentionWrapperFusion(**attn_kwargs) + elif attn_type == "none": + return nn.Identity(in_channels) + else: + return LinAttnBlock(in_channels) + +class MemoryEfficientCrossAttentionWrapperFusion(MemoryEfficientCrossAttention): + # print('x.shape: ',x.shape, 'context.shape: ',context.shape) ##torch.Size([8, 128, 256, 256]) torch.Size([1, 128, 2, 256, 256]) + def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0, **kwargs): + super().__init__(query_dim, context_dim, heads, dim_head, dropout, **kwargs) + self.norm = Normalize(query_dim) + nn.init.zeros_(self.to_out[0].weight) + nn.init.zeros_(self.to_out[0].bias) + + def forward(self, x, context=None, mask=None): + if self.training: + return checkpoint(self._forward, x, context, mask, use_reentrant=False) + else: + return self._forward(x, context, mask) + + def _forward( + self, + x, + context=None, + mask=None, + ): + bt, c, h, w = x.shape + h_ = self.norm(x) + h_ = rearrange(h_, "b c h w -> b (h w) c") + q = self.to_q(h_) + + + b, c, l, h, w = context.shape + context = rearrange(context, "b c l h w -> (b l) (h w) c") + k = self.to_k(context) + v = self.to_v(context) + k = rearrange(k, "(b l) d c -> b l d c", l=l) + k = torch.cat([k[:, [0] * (bt//b)], k[:, [1]*(bt//b)]], dim=2) + k = rearrange(k, "b l d c -> (b l) d c") + + v = rearrange(v, "(b l) d c -> b l d c", l=l) + v = torch.cat([v[:, [0] * (bt//b)], v[:, [1]*(bt//b)]], dim=2) + v = rearrange(v, "b l d c -> (b l) d c") + + + b, _, _ = q.shape ##actually bt + q, k, v = map( + lambda t: t.unsqueeze(3) + .reshape(b, t.shape[1], self.heads, self.dim_head) + .permute(0, 2, 1, 3) + .reshape(b * self.heads, t.shape[1], self.dim_head) + .contiguous(), + (q, k, v), + ) + + # actually compute the attention, what we cannot get enough of + if version.parse(xformers.__version__) >= version.parse("0.0.21"): + # NOTE: workaround for + # https://github.com/facebookresearch/xformers/issues/845 + max_bs = 32768 + N = q.shape[0] + n_batches = math.ceil(N / max_bs) + out = list() + for i_batch in range(n_batches): + batch = slice(i_batch * max_bs, (i_batch + 1) * max_bs) + out.append( + xformers.ops.memory_efficient_attention( + q[batch], + k[batch], + v[batch], + attn_bias=None, + op=self.attention_op, + ) + ) + out = torch.cat(out, 0) + else: + out = xformers.ops.memory_efficient_attention( + q, k, v, attn_bias=None, op=self.attention_op + ) + + # TODO: Use this directly in the attention operation, as a bias + if exists(mask): + raise NotImplementedError + out = ( + out.unsqueeze(0) + .reshape(b, self.heads, out.shape[1], self.dim_head) + .permute(0, 2, 1, 3) + .reshape(b, out.shape[1], self.heads * self.dim_head) + ) + out = self.to_out(out) + out = rearrange(out, "bt (h w) c -> bt c h w", h=h, w=w, c=c) + return x + out + +class Combiner(nn.Module): + def __init__(self, ch) -> None: + super().__init__() + self.conv = nn.Conv2d(ch,ch,1,padding=0) + + nn.init.zeros_(self.conv.weight) + nn.init.zeros_(self.conv.bias) + + def forward(self, x, context): + if self.training: + return checkpoint(self._forward, x, context, use_reentrant=False) + else: + return self._forward(x, context) + + def _forward(self, x, context): + ## x: b c h w, context: b c 2 h w + b, c, l, h, w = context.shape + bt, c, h, w = x.shape + context = rearrange(context, "b c l h w -> (b l) c h w") + context = self.conv(context) + context = rearrange(context, "(b l) c h w -> b c l h w", l=l) + x = rearrange(x, "(b t) c h w -> b c t h w", t=bt//b) + x[:,:,0] = x[:,:,0] + context[:,:,0] + x[:,:,-1] = x[:,:,-1] + context[:,:,1] + x = rearrange(x, "b c t h w -> (b t) c h w") + return x + + +class Decoder(nn.Module): + def __init__( + self, + *, + ch, + out_ch, + ch_mult=(1, 2, 4, 8), + num_res_blocks, + attn_resolutions, + dropout=0.0, + resamp_with_conv=True, + in_channels, + resolution, + z_channels, + give_pre_end=False, + tanh_out=False, + use_linear_attn=False, + attn_type="vanilla-xformers", + attn_level=[2,3], + **ignorekwargs, + ): + super().__init__() + if use_linear_attn: + attn_type = "linear" + self.ch = ch + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + self.give_pre_end = give_pre_end + self.tanh_out = tanh_out + self.attn_level = attn_level + # compute in_ch_mult, block_in and curr_res at lowest res + in_ch_mult = (1,) + tuple(ch_mult) + block_in = ch * ch_mult[self.num_resolutions - 1] + curr_res = resolution // 2 ** (self.num_resolutions - 1) + self.z_shape = (1, z_channels, curr_res, curr_res) + logpy.info( + "Working with z of shape {} = {} dimensions.".format( + self.z_shape, np.prod(self.z_shape) + ) + ) + + make_attn_cls = self._make_attn() + make_resblock_cls = self._make_resblock() + make_conv_cls = self._make_conv() + # z to block_in + self.conv_in = torch.nn.Conv2d( + z_channels, block_in, kernel_size=3, stride=1, padding=1 + ) + + # middle + self.mid = nn.Module() + self.mid.block_1 = make_resblock_cls( + in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout, + ) + self.mid.attn_1 = make_attn_cls(block_in, attn_type=attn_type) + self.mid.block_2 = make_resblock_cls( + in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout, + ) + + # upsampling + self.up = nn.ModuleList() + self.attn_refinement = nn.ModuleList() + for i_level in reversed(range(self.num_resolutions)): + block = nn.ModuleList() + attn = nn.ModuleList() + block_out = ch * ch_mult[i_level] + for i_block in range(self.num_res_blocks + 1): + block.append( + make_resblock_cls( + in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout, + ) + ) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(make_attn_cls(block_in, attn_type=attn_type)) + up = nn.Module() + up.block = block + up.attn = attn + if i_level != 0: + up.upsample = Upsample(block_in, resamp_with_conv) + curr_res = curr_res * 2 + self.up.insert(0, up) # prepend to get consistent order + + if i_level in self.attn_level: + self.attn_refinement.insert(0, make_attn_cls(block_in, attn_type='memory-efficient-cross-attn-fusion', attn_kwargs={})) + else: + self.attn_refinement.insert(0, Combiner(block_in)) + # end + self.norm_out = Normalize(block_in) + self.attn_refinement.append(Combiner(block_in)) + self.conv_out = make_conv_cls( + block_in, out_ch, kernel_size=3, stride=1, padding=1 + ) + + def _make_attn(self) -> Callable: + return make_attn + + def _make_resblock(self) -> Callable: + return ResnetBlock + + def _make_conv(self) -> Callable: + return torch.nn.Conv2d + + def get_last_layer(self, **kwargs): + return self.conv_out.weight + + def forward(self, z, ref_context=None, **kwargs): + ## ref_context: b c 2 h w, 2 means starting and ending frame + # assert z.shape[1:] == self.z_shape[1:] + # print("ref context:", ref_context) + self.last_z_shape = z.shape + # timestep embedding + temb = None + + # z to block_in + h = self.conv_in(z) + + # middle + h = self.mid.block_1(h, temb, **kwargs) + h = self.mid.attn_1(h, **kwargs) + h = self.mid.block_2(h, temb, **kwargs) + + # upsampling + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks + 1): + h = self.up[i_level].block[i_block](h, temb, **kwargs) + if len(self.up[i_level].attn) > 0: + h = self.up[i_level].attn[i_block](h, **kwargs) + if ref_context: + h = self.attn_refinement[i_level](x=h, context=ref_context[i_level]) + if i_level != 0: + h = self.up[i_level].upsample(h) + + # end + if self.give_pre_end: + return h + + h = self.norm_out(h) + h = nonlinearity(h) + if ref_context: + # print(h.shape, ref_context[i_level].shape) #torch.Size([8, 128, 256, 256]) torch.Size([1, 128, 2, 256, 256]) + h = self.attn_refinement[-1](x=h, context=ref_context[-1]) + h = self.conv_out(h, **kwargs) + if self.tanh_out: + h = torch.tanh(h) + return h + +##### + + +from abc import abstractmethod +from lvdm.models.utils_diffusion import timestep_embedding + +from torch.utils.checkpoint import checkpoint +from lvdm.basics import ( + zero_module, + conv_nd, + linear, + normalization, +) +from lvdm.modules.networks.openaimodel3d import Upsample, Downsample +class TimestepBlock(nn.Module): + """ + Any module where forward() takes timestep embeddings as a second argument. + """ + + @abstractmethod + def forward(self, x: torch.Tensor, emb: torch.Tensor): + """ + Apply the module to `x` given `emb` timestep embeddings. + """ + +class ResBlock(TimestepBlock): + """ + A residual block that can optionally change the number of channels. + :param channels: the number of input channels. + :param emb_channels: the number of timestep embedding channels. + :param dropout: the rate of dropout. + :param out_channels: if specified, the number of out channels. + :param use_conv: if True and out_channels is specified, use a spatial + convolution instead of a smaller 1x1 convolution to change the + channels in the skip connection. + :param dims: determines if the signal is 1D, 2D, or 3D. + :param use_checkpoint: if True, use gradient checkpointing on this module. + :param up: if True, use this block for upsampling. + :param down: if True, use this block for downsampling. + """ + + def __init__( + self, + channels: int, + emb_channels: int, + dropout: float, + out_channels: Optional[int] = None, + use_conv: bool = False, + use_scale_shift_norm: bool = False, + dims: int = 2, + use_checkpoint: bool = False, + up: bool = False, + down: bool = False, + kernel_size: int = 3, + exchange_temb_dims: bool = False, + skip_t_emb: bool = False, + ): + super().__init__() + self.channels = channels + self.emb_channels = emb_channels + self.dropout = dropout + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.use_checkpoint = use_checkpoint + self.use_scale_shift_norm = use_scale_shift_norm + self.exchange_temb_dims = exchange_temb_dims + + if isinstance(kernel_size, Iterable): + padding = [k // 2 for k in kernel_size] + else: + padding = kernel_size // 2 + + self.in_layers = nn.Sequential( + normalization(channels), + nn.SiLU(), + conv_nd(dims, channels, self.out_channels, kernel_size, padding=padding), + ) + + self.updown = up or down + + if up: + self.h_upd = Upsample(channels, False, dims) + self.x_upd = Upsample(channels, False, dims) + elif down: + self.h_upd = Downsample(channels, False, dims) + self.x_upd = Downsample(channels, False, dims) + else: + self.h_upd = self.x_upd = nn.Identity() + + self.skip_t_emb = skip_t_emb + self.emb_out_channels = ( + 2 * self.out_channels if use_scale_shift_norm else self.out_channels + ) + if self.skip_t_emb: + # print(f"Skipping timestep embedding in {self.__class__.__name__}") + assert not self.use_scale_shift_norm + self.emb_layers = None + self.exchange_temb_dims = False + else: + self.emb_layers = nn.Sequential( + nn.SiLU(), + linear( + emb_channels, + self.emb_out_channels, + ), + ) + + self.out_layers = nn.Sequential( + normalization(self.out_channels), + nn.SiLU(), + nn.Dropout(p=dropout), + zero_module( + conv_nd( + dims, + self.out_channels, + self.out_channels, + kernel_size, + padding=padding, + ) + ), + ) + + if self.out_channels == channels: + self.skip_connection = nn.Identity() + elif use_conv: + self.skip_connection = conv_nd( + dims, channels, self.out_channels, kernel_size, padding=padding + ) + else: + self.skip_connection = conv_nd(dims, channels, self.out_channels, 1) + + def forward(self, x: torch.Tensor, emb: torch.Tensor) -> torch.Tensor: + """ + Apply the block to a Tensor, conditioned on a timestep embedding. + :param x: an [N x C x ...] Tensor of features. + :param emb: an [N x emb_channels] Tensor of timestep embeddings. + :return: an [N x C x ...] Tensor of outputs. + """ + if self.use_checkpoint: + return checkpoint(self._forward, x, emb, use_reentrant=False) + else: + return self._forward(x, emb) + + def _forward(self, x: torch.Tensor, emb: torch.Tensor) -> torch.Tensor: + if self.updown: + in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1] + h = in_rest(x) + h = self.h_upd(h) + x = self.x_upd(x) + h = in_conv(h) + else: + h = self.in_layers(x) + + if self.skip_t_emb: + emb_out = torch.zeros_like(h) + else: + emb_out = self.emb_layers(emb).type(h.dtype) + while len(emb_out.shape) < len(h.shape): + emb_out = emb_out[..., None] + if self.use_scale_shift_norm: + out_norm, out_rest = self.out_layers[0], self.out_layers[1:] + scale, shift = torch.chunk(emb_out, 2, dim=1) + h = out_norm(h) * (1 + scale) + shift + h = out_rest(h) + else: + if self.exchange_temb_dims: + emb_out = rearrange(emb_out, "b t c ... -> b c t ...") + h = h + emb_out + h = self.out_layers(h) + return self.skip_connection(x) + h +##### + +##### +from lvdm.modules.attention_svd import * +class VideoTransformerBlock(nn.Module): + ATTENTION_MODES = { + "softmax": CrossAttention, + "softmax-xformers": MemoryEfficientCrossAttention, + } + + def __init__( + self, + dim, + n_heads, + d_head, + dropout=0.0, + context_dim=None, + gated_ff=True, + checkpoint=True, + timesteps=None, + ff_in=False, + inner_dim=None, + attn_mode="softmax", + disable_self_attn=False, + disable_temporal_crossattention=False, + switch_temporal_ca_to_sa=False, + ): + super().__init__() + + attn_cls = self.ATTENTION_MODES[attn_mode] + + self.ff_in = ff_in or inner_dim is not None + if inner_dim is None: + inner_dim = dim + + assert int(n_heads * d_head) == inner_dim + + self.is_res = inner_dim == dim + + if self.ff_in: + self.norm_in = nn.LayerNorm(dim) + self.ff_in = FeedForward( + dim, dim_out=inner_dim, dropout=dropout, glu=gated_ff + ) + + self.timesteps = timesteps + self.disable_self_attn = disable_self_attn + if self.disable_self_attn: + self.attn1 = attn_cls( + query_dim=inner_dim, + heads=n_heads, + dim_head=d_head, + context_dim=context_dim, + dropout=dropout, + ) # is a cross-attention + else: + self.attn1 = attn_cls( + query_dim=inner_dim, heads=n_heads, dim_head=d_head, dropout=dropout + ) # is a self-attention + + self.ff = FeedForward(inner_dim, dim_out=dim, dropout=dropout, glu=gated_ff) + + if disable_temporal_crossattention: + if switch_temporal_ca_to_sa: + raise ValueError + else: + self.attn2 = None + else: + self.norm2 = nn.LayerNorm(inner_dim) + if switch_temporal_ca_to_sa: + self.attn2 = attn_cls( + query_dim=inner_dim, heads=n_heads, dim_head=d_head, dropout=dropout + ) # is a self-attention + else: + self.attn2 = attn_cls( + query_dim=inner_dim, + context_dim=context_dim, + heads=n_heads, + dim_head=d_head, + dropout=dropout, + ) # is self-attn if context is none + + self.norm1 = nn.LayerNorm(inner_dim) + self.norm3 = nn.LayerNorm(inner_dim) + self.switch_temporal_ca_to_sa = switch_temporal_ca_to_sa + + self.checkpoint = checkpoint + if self.checkpoint: + print(f"====>{self.__class__.__name__} is using checkpointing") + else: + print(f"====>{self.__class__.__name__} is NOT using checkpointing") + + def forward( + self, x: torch.Tensor, context: torch.Tensor = None, timesteps: int = None + ) -> torch.Tensor: + if self.checkpoint: + return checkpoint(self._forward, x, context, timesteps, use_reentrant=False) + else: + return self._forward(x, context, timesteps=timesteps) + + def _forward(self, x, context=None, timesteps=None): + assert self.timesteps or timesteps + assert not (self.timesteps and timesteps) or self.timesteps == timesteps + timesteps = self.timesteps or timesteps + B, S, C = x.shape + x = rearrange(x, "(b t) s c -> (b s) t c", t=timesteps) + + if self.ff_in: + x_skip = x + x = self.ff_in(self.norm_in(x)) + if self.is_res: + x += x_skip + + if self.disable_self_attn: + x = self.attn1(self.norm1(x), context=context) + x + else: + x = self.attn1(self.norm1(x)) + x + + if self.attn2 is not None: + if self.switch_temporal_ca_to_sa: + x = self.attn2(self.norm2(x)) + x + else: + x = self.attn2(self.norm2(x), context=context) + x + x_skip = x + x = self.ff(self.norm3(x)) + if self.is_res: + x += x_skip + + x = rearrange( + x, "(b s) t c -> (b t) s c", s=S, b=B // timesteps, c=C, t=timesteps + ) + return x + + def get_last_layer(self): + return self.ff.net[-1].weight + +##### + +##### +import functools +def partialclass(cls, *args, **kwargs): + class NewCls(cls): + __init__ = functools.partialmethod(cls.__init__, *args, **kwargs) + + return NewCls +###### + +class VideoResBlock(ResnetBlock): + def __init__( + self, + out_channels, + *args, + dropout=0.0, + video_kernel_size=3, + alpha=0.0, + merge_strategy="learned", + **kwargs, + ): + super().__init__(out_channels=out_channels, dropout=dropout, *args, **kwargs) + if video_kernel_size is None: + video_kernel_size = [3, 1, 1] + self.time_stack = ResBlock( + channels=out_channels, + emb_channels=0, + dropout=dropout, + dims=3, + use_scale_shift_norm=False, + use_conv=False, + up=False, + down=False, + kernel_size=video_kernel_size, + use_checkpoint=True, + skip_t_emb=True, + ) + + self.merge_strategy = merge_strategy + if self.merge_strategy == "fixed": + self.register_buffer("mix_factor", torch.Tensor([alpha])) + elif self.merge_strategy == "learned": + self.register_parameter( + "mix_factor", torch.nn.Parameter(torch.Tensor([alpha])) + ) + else: + raise ValueError(f"unknown merge strategy {self.merge_strategy}") + + def get_alpha(self, bs): + if self.merge_strategy == "fixed": + return self.mix_factor + elif self.merge_strategy == "learned": + return torch.sigmoid(self.mix_factor) + else: + raise NotImplementedError() + + def forward(self, x, temb, skip_video=False, timesteps=None): + if timesteps is None: + timesteps = self.timesteps + + b, c, h, w = x.shape + + x = super().forward(x, temb) + + if not skip_video: + x_mix = rearrange(x, "(b t) c h w -> b c t h w", t=timesteps) + + x = rearrange(x, "(b t) c h w -> b c t h w", t=timesteps) + + x = self.time_stack(x, temb) + + alpha = self.get_alpha(bs=b // timesteps) + x = alpha * x + (1.0 - alpha) * x_mix + + x = rearrange(x, "b c t h w -> (b t) c h w") + return x + + +class AE3DConv(torch.nn.Conv2d): + def __init__(self, in_channels, out_channels, video_kernel_size=3, *args, **kwargs): + super().__init__(in_channels, out_channels, *args, **kwargs) + if isinstance(video_kernel_size, Iterable): + padding = [int(k // 2) for k in video_kernel_size] + else: + padding = int(video_kernel_size // 2) + + self.time_mix_conv = torch.nn.Conv3d( + in_channels=out_channels, + out_channels=out_channels, + kernel_size=video_kernel_size, + padding=padding, + ) + + def forward(self, input, timesteps, skip_video=False): + x = super().forward(input) + if skip_video: + return x + x = rearrange(x, "(b t) c h w -> b c t h w", t=timesteps) + x = self.time_mix_conv(x) + return rearrange(x, "b c t h w -> (b t) c h w") + + +class VideoBlock(AttnBlock): + def __init__( + self, in_channels: int, alpha: float = 0, merge_strategy: str = "learned" + ): + super().__init__(in_channels) + # no context, single headed, as in base class + self.time_mix_block = VideoTransformerBlock( + dim=in_channels, + n_heads=1, + d_head=in_channels, + checkpoint=True, + ff_in=True, + attn_mode="softmax", + ) + + time_embed_dim = self.in_channels * 4 + self.video_time_embed = torch.nn.Sequential( + torch.nn.Linear(self.in_channels, time_embed_dim), + torch.nn.SiLU(), + torch.nn.Linear(time_embed_dim, self.in_channels), + ) + + self.merge_strategy = merge_strategy + if self.merge_strategy == "fixed": + self.register_buffer("mix_factor", torch.Tensor([alpha])) + elif self.merge_strategy == "learned": + self.register_parameter( + "mix_factor", torch.nn.Parameter(torch.Tensor([alpha])) + ) + else: + raise ValueError(f"unknown merge strategy {self.merge_strategy}") + + def forward(self, x, timesteps, skip_video=False): + if skip_video: + return super().forward(x) + + x_in = x + x = self.attention(x) + h, w = x.shape[2:] + x = rearrange(x, "b c h w -> b (h w) c") + + x_mix = x + num_frames = torch.arange(timesteps, device=x.device) + num_frames = repeat(num_frames, "t -> b t", b=x.shape[0] // timesteps) + num_frames = rearrange(num_frames, "b t -> (b t)") + t_emb = timestep_embedding(num_frames, self.in_channels, repeat_only=False) + emb = self.video_time_embed(t_emb) # b, n_channels + emb = emb[:, None, :] + x_mix = x_mix + emb + + alpha = self.get_alpha() + x_mix = self.time_mix_block(x_mix, timesteps=timesteps) + x = alpha * x + (1.0 - alpha) * x_mix # alpha merge + + x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w) + x = self.proj_out(x) + + return x_in + x + + def get_alpha( + self, + ): + if self.merge_strategy == "fixed": + return self.mix_factor + elif self.merge_strategy == "learned": + return torch.sigmoid(self.mix_factor) + else: + raise NotImplementedError(f"unknown merge strategy {self.merge_strategy}") + + +class MemoryEfficientVideoBlock(MemoryEfficientAttnBlock): + def __init__( + self, in_channels: int, alpha: float = 0, merge_strategy: str = "learned" + ): + super().__init__(in_channels) + # no context, single headed, as in base class + self.time_mix_block = VideoTransformerBlock( + dim=in_channels, + n_heads=1, + d_head=in_channels, + checkpoint=True, + ff_in=True, + attn_mode="softmax-xformers", + ) + + time_embed_dim = self.in_channels * 4 + self.video_time_embed = torch.nn.Sequential( + torch.nn.Linear(self.in_channels, time_embed_dim), + torch.nn.SiLU(), + torch.nn.Linear(time_embed_dim, self.in_channels), + ) + + self.merge_strategy = merge_strategy + if self.merge_strategy == "fixed": + self.register_buffer("mix_factor", torch.Tensor([alpha])) + elif self.merge_strategy == "learned": + self.register_parameter( + "mix_factor", torch.nn.Parameter(torch.Tensor([alpha])) + ) + else: + raise ValueError(f"unknown merge strategy {self.merge_strategy}") + + def forward(self, x, timesteps, skip_time_block=False): + if skip_time_block: + return super().forward(x) + + x_in = x + x = self.attention(x) + h, w = x.shape[2:] + x = rearrange(x, "b c h w -> b (h w) c") + + x_mix = x + num_frames = torch.arange(timesteps, device=x.device) + num_frames = repeat(num_frames, "t -> b t", b=x.shape[0] // timesteps) + num_frames = rearrange(num_frames, "b t -> (b t)") + t_emb = timestep_embedding(num_frames, self.in_channels, repeat_only=False) + emb = self.video_time_embed(t_emb) # b, n_channels + emb = emb[:, None, :] + x_mix = x_mix + emb + + alpha = self.get_alpha() + x_mix = self.time_mix_block(x_mix, timesteps=timesteps) + x = alpha * x + (1.0 - alpha) * x_mix # alpha merge + + x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w) + x = self.proj_out(x) + + return x_in + x + + def get_alpha( + self, + ): + if self.merge_strategy == "fixed": + return self.mix_factor + elif self.merge_strategy == "learned": + return torch.sigmoid(self.mix_factor) + else: + raise NotImplementedError(f"unknown merge strategy {self.merge_strategy}") + + +def make_time_attn( + in_channels, + attn_type="vanilla", + attn_kwargs=None, + alpha: float = 0, + merge_strategy: str = "learned", +): + assert attn_type in [ + "vanilla", + "vanilla-xformers", + ], f"attn_type {attn_type} not supported for spatio-temporal attention" + print( + f"making spatial and temporal attention of type '{attn_type}' with {in_channels} in_channels" + ) + if not XFORMERS_IS_AVAILABLE and attn_type == "vanilla-xformers": + print( + f"Attention mode '{attn_type}' is not available. Falling back to vanilla attention. " + f"This is not a problem in Pytorch >= 2.0. FYI, you are running with PyTorch version {torch.__version__}" + ) + attn_type = "vanilla" + + if attn_type == "vanilla": + assert attn_kwargs is None + return partialclass( + VideoBlock, in_channels, alpha=alpha, merge_strategy=merge_strategy + ) + elif attn_type == "vanilla-xformers": + print(f"building MemoryEfficientAttnBlock with {in_channels} in_channels...") + return partialclass( + MemoryEfficientVideoBlock, + in_channels, + alpha=alpha, + merge_strategy=merge_strategy, + ) + else: + return NotImplementedError() + + +class Conv2DWrapper(torch.nn.Conv2d): + def forward(self, input: torch.Tensor, **kwargs) -> torch.Tensor: + return super().forward(input) + + +class VideoDecoder(Decoder): + available_time_modes = ["all", "conv-only", "attn-only"] + + def __init__( + self, + *args, + video_kernel_size: Union[int, list] = [3,1,1], + alpha: float = 0.0, + merge_strategy: str = "learned", + time_mode: str = "conv-only", + **kwargs, + ): + self.video_kernel_size = video_kernel_size + self.alpha = alpha + self.merge_strategy = merge_strategy + self.time_mode = time_mode + assert ( + self.time_mode in self.available_time_modes + ), f"time_mode parameter has to be in {self.available_time_modes}" + super().__init__(*args, **kwargs) + + def get_last_layer(self, skip_time_mix=False, **kwargs): + if self.time_mode == "attn-only": + raise NotImplementedError("TODO") + else: + return ( + self.conv_out.time_mix_conv.weight + if not skip_time_mix + else self.conv_out.weight + ) + + def _make_attn(self) -> Callable: + if self.time_mode not in ["conv-only", "only-last-conv"]: + return partialclass( + make_time_attn, + alpha=self.alpha, + merge_strategy=self.merge_strategy, + ) + else: + return super()._make_attn() + + def _make_conv(self) -> Callable: + if self.time_mode != "attn-only": + return partialclass(AE3DConv, video_kernel_size=self.video_kernel_size) + else: + return Conv2DWrapper + + def _make_resblock(self) -> Callable: + if self.time_mode not in ["attn-only", "only-last-conv"]: + return partialclass( + VideoResBlock, + video_kernel_size=self.video_kernel_size, + alpha=self.alpha, + merge_strategy=self.merge_strategy, + ) + else: + return super()._make_resblock() \ No newline at end of file diff --git a/lvdm/models/ddpm3d.py b/lvdm/models/ddpm3d.py new file mode 100644 index 0000000000000000000000000000000000000000..e95fdcb754bf72dc45f391788ab4d92082b17401 --- /dev/null +++ b/lvdm/models/ddpm3d.py @@ -0,0 +1,1312 @@ +""" +wild mixture of +https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py +https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py +https://github.com/CompVis/taming-transformers +-- merci +""" + +from functools import partial +from contextlib import contextmanager +import numpy as np +from tqdm import tqdm +from einops import rearrange, repeat +import logging +mainlogger = logging.getLogger('mainlogger') +import random +import torch +import torch.nn as nn +from torch.optim.lr_scheduler import LambdaLR, CosineAnnealingLR +from torchvision.utils import make_grid +import pytorch_lightning as pl +from pytorch_lightning.utilities import rank_zero_only +from utils.utils import instantiate_from_config +from lvdm.ema import LitEma +from lvdm.models.samplers.ddim import DDIMSampler +from lvdm.distributions import DiagonalGaussianDistribution +from lvdm.models.utils_diffusion import make_beta_schedule, rescale_zero_terminal_snr +from lvdm.basics import disabled_train +from lvdm.common import ( + extract_into_tensor, + noise_like, + exists, + default +) +import math +from lvdm.models.autoencoder_dualref import VideoDecoder +__conditioning_keys__ = {'concat': 'c_concat', + 'crossattn': 'c_crossattn', + 'adm': 'y'} + +class DDPM(pl.LightningModule): + # classic DDPM with Gaussian diffusion, in image space + def __init__(self, + unet_config, + timesteps=1000, + beta_schedule="linear", + loss_type="l2", + ckpt_path=None, + ignore_keys=[], + load_only_unet=False, + monitor=None, + use_ema=True, + first_stage_key="image", + image_size=256, + channels=3, + log_every_t=100, + clip_denoised=True, + linear_start=1e-4, + linear_end=2e-2, + cosine_s=8e-3, + given_betas=None, + original_elbo_weight=0., + v_posterior=0., # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta + l_simple_weight=1., + conditioning_key=None, + parameterization="eps", # all assuming fixed variance schedules + scheduler_config=None, + use_positional_encodings=False, + learn_logvar=False, + logvar_init=0., + rescale_betas_zero_snr=False, + ): + super().__init__() + assert parameterization in ["eps", "x0", "v"], 'currently only supporting "eps" and "x0" and "v"' + self.parameterization = parameterization + mainlogger.info(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode") + self.cond_stage_model = None + self.clip_denoised = clip_denoised + self.log_every_t = log_every_t + self.first_stage_key = first_stage_key + self.channels = channels + self.temporal_length = unet_config.params.temporal_length + self.image_size = image_size # try conv? + if isinstance(self.image_size, int): + self.image_size = [self.image_size, self.image_size] + self.use_positional_encodings = use_positional_encodings + self.model = DiffusionWrapper(unet_config, conditioning_key) + #count_params(self.model, verbose=True) + self.use_ema = use_ema + self.rescale_betas_zero_snr = rescale_betas_zero_snr + if self.use_ema: + self.model_ema = LitEma(self.model) + mainlogger.info(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.") + + self.use_scheduler = scheduler_config is not None + if self.use_scheduler: + self.scheduler_config = scheduler_config + + self.v_posterior = v_posterior + self.original_elbo_weight = original_elbo_weight + self.l_simple_weight = l_simple_weight + + if monitor is not None: + self.monitor = monitor + if ckpt_path is not None: + self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet) + + self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps, + linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s) + + ## for reschedule + self.given_betas = given_betas + self.beta_schedule = beta_schedule + self.timesteps = timesteps + self.cosine_s = cosine_s + + self.loss_type = loss_type + + self.learn_logvar = learn_logvar + self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,)) + if self.learn_logvar: + self.logvar = nn.Parameter(self.logvar, requires_grad=True) + + def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000, + linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): + if exists(given_betas): + betas = given_betas + else: + betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end, + cosine_s=cosine_s) + if self.rescale_betas_zero_snr: + betas = rescale_zero_terminal_snr(betas) + + alphas = 1. - betas + alphas_cumprod = np.cumprod(alphas, axis=0) + alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1]) + + timesteps, = betas.shape + self.num_timesteps = int(timesteps) + self.linear_start = linear_start + self.linear_end = linear_end + assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep' + + to_torch = partial(torch.tensor, dtype=torch.float32) + + self.register_buffer('betas', to_torch(betas)) + self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) + self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev)) + + # calculations for diffusion q(x_t | x_{t-1}) and others + self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod))) + self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod))) + self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod))) + + if self.parameterization != 'v': + self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod))) + self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1))) + else: + self.register_buffer('sqrt_recip_alphas_cumprod', torch.zeros_like(to_torch(alphas_cumprod))) + self.register_buffer('sqrt_recipm1_alphas_cumprod', torch.zeros_like(to_torch(alphas_cumprod))) + + # calculations for posterior q(x_{t-1} | x_t, x_0) + posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / ( + 1. - alphas_cumprod) + self.v_posterior * betas + # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t) + self.register_buffer('posterior_variance', to_torch(posterior_variance)) + # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain + self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20)))) + self.register_buffer('posterior_mean_coef1', to_torch( + betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod))) + self.register_buffer('posterior_mean_coef2', to_torch( + (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod))) + + if self.parameterization == "eps": + lvlb_weights = self.betas ** 2 / ( + 2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod)) + elif self.parameterization == "x0": + lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod)) + elif self.parameterization == "v": + lvlb_weights = torch.ones_like(self.betas ** 2 / ( + 2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod))) + else: + raise NotImplementedError("mu not supported") + # TODO how to choose this term + lvlb_weights[0] = lvlb_weights[1] + self.register_buffer('lvlb_weights', lvlb_weights, persistent=False) + assert not torch.isnan(self.lvlb_weights).all() + + @contextmanager + def ema_scope(self, context=None): + if self.use_ema: + self.model_ema.store(self.model.parameters()) + self.model_ema.copy_to(self.model) + if context is not None: + mainlogger.info(f"{context}: Switched to EMA weights") + try: + yield None + finally: + if self.use_ema: + self.model_ema.restore(self.model.parameters()) + if context is not None: + mainlogger.info(f"{context}: Restored training weights") + + def init_from_ckpt(self, path, ignore_keys=list(), only_model=False): + sd = torch.load(path, map_location="cpu") + if "state_dict" in list(sd.keys()): + sd = sd["state_dict"] + keys = list(sd.keys()) + for k in keys: + for ik in ignore_keys: + if k.startswith(ik): + mainlogger.info("Deleting key {} from state_dict.".format(k)) + del sd[k] + missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict( + sd, strict=False) + mainlogger.info(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys") + if len(missing) > 0: + mainlogger.info(f"Missing Keys: {missing}") + if len(unexpected) > 0: + mainlogger.info(f"Unexpected Keys: {unexpected}") + + def q_mean_variance(self, x_start, t): + """ + Get the distribution q(x_t | x_0). + :param x_start: the [N x C x ...] tensor of noiseless inputs. + :param t: the number of diffusion steps (minus 1). Here, 0 means one step. + :return: A tuple (mean, variance, log_variance), all of x_start's shape. + """ + mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start) + variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape) + log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape) + return mean, variance, log_variance + + def predict_start_from_noise(self, x_t, t, noise): + return ( + extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - + extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise + ) + + def predict_start_from_z_and_v(self, x_t, t, v): + # self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod))) + # self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod))) + return ( + extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * x_t - + extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * v + ) + + def predict_eps_from_z_and_v(self, x_t, t, v): + return ( + extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * v + + extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * x_t + ) + + def q_posterior(self, x_start, x_t, t): + posterior_mean = ( + extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start + + extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t + ) + posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape) + posterior_log_variance_clipped = extract_into_tensor(self.posterior_log_variance_clipped, t, x_t.shape) + return posterior_mean, posterior_variance, posterior_log_variance_clipped + + def p_mean_variance(self, x, t, clip_denoised: bool): + model_out = self.model(x, t) + if self.parameterization == "eps": + x_recon = self.predict_start_from_noise(x, t=t, noise=model_out) + elif self.parameterization == "x0": + x_recon = model_out + if clip_denoised: + x_recon.clamp_(-1., 1.) + + model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t) + return model_mean, posterior_variance, posterior_log_variance + + @torch.no_grad() + def p_sample(self, x, t, clip_denoised=True, repeat_noise=False): + b, *_, device = *x.shape, x.device + model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised) + noise = noise_like(x.shape, device, repeat_noise) + # no noise when t == 0 + nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1))) + return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise + + @torch.no_grad() + def p_sample_loop(self, shape, return_intermediates=False): + device = self.betas.device + b = shape[0] + img = torch.randn(shape, device=device) + intermediates = [img] + for i in tqdm(reversed(range(0, self.num_timesteps)), desc='Sampling t', total=self.num_timesteps): + img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long), + clip_denoised=self.clip_denoised) + if i % self.log_every_t == 0 or i == self.num_timesteps - 1: + intermediates.append(img) + if return_intermediates: + return img, intermediates + return img + + @torch.no_grad() + def sample(self, batch_size=16, return_intermediates=False): + image_size = self.image_size + channels = self.channels + return self.p_sample_loop((batch_size, channels, image_size, image_size), + return_intermediates=return_intermediates) + + def q_sample(self, x_start, t, noise=None): + noise = default(noise, lambda: torch.randn_like(x_start)) + return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start + + extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise) + + def get_v(self, x, noise, t): + return ( + extract_into_tensor(self.sqrt_alphas_cumprod, t, x.shape) * noise - + extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x.shape) * x + ) + + def get_loss(self, pred, target, mean=True): + if self.loss_type == 'l1': + loss = (target - pred).abs() + if mean: + loss = loss.mean() + elif self.loss_type == 'l2': + if mean: + loss = torch.nn.functional.mse_loss(target, pred) + else: + loss = torch.nn.functional.mse_loss(target, pred, reduction='none') + else: + raise NotImplementedError("unknown loss type '{loss_type}'") + + return loss + + def p_losses(self, x_start, t, noise=None): + noise = default(noise, lambda: torch.randn_like(x_start)) + x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) + model_out = self.model(x_noisy, t) + + loss_dict = {} + if self.parameterization == "eps": + target = noise + elif self.parameterization == "x0": + target = x_start + elif self.parameterization == "v": + target = self.get_v(x_start, noise, t) + else: + raise NotImplementedError(f"Paramterization {self.parameterization} not yet supported") + + loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3]) + + log_prefix = 'train' if self.training else 'val' + + loss_dict.update({f'{log_prefix}/loss_simple': loss.mean()}) + loss_simple = loss.mean() * self.l_simple_weight + + loss_vlb = (self.lvlb_weights[t] * loss).mean() + loss_dict.update({f'{log_prefix}/loss_vlb': loss_vlb}) + + loss = loss_simple + self.original_elbo_weight * loss_vlb + + loss_dict.update({f'{log_prefix}/loss': loss}) + + return loss, loss_dict + + def forward(self, x, *args, **kwargs): + # b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size + # assert h == img_size and w == img_size, f'height and width of image must be {img_size}' + t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long() + return self.p_losses(x, t, *args, **kwargs) + + def get_input(self, batch, k): + x = batch[k] + ''' + if len(x.shape) == 3: + x = x[..., None] + x = rearrange(x, 'b h w c -> b c h w') + ''' + x = x.to(memory_format=torch.contiguous_format).float() + return x + + def shared_step(self, batch): + x = self.get_input(batch, self.first_stage_key) + loss, loss_dict = self(x) + return loss, loss_dict + + def training_step(self, batch, batch_idx): + loss, loss_dict = self.shared_step(batch) + + self.log_dict(loss_dict, prog_bar=True, + logger=True, on_step=True, on_epoch=True) + + self.log("global_step", self.global_step, + prog_bar=True, logger=True, on_step=True, on_epoch=False) + + if self.use_scheduler: + lr = self.optimizers().param_groups[0]['lr'] + self.log('lr_abs', lr, prog_bar=True, logger=True, on_step=True, on_epoch=False) + + return loss + + @torch.no_grad() + def validation_step(self, batch, batch_idx): + _, loss_dict_no_ema = self.shared_step(batch) + with self.ema_scope(): + _, loss_dict_ema = self.shared_step(batch) + loss_dict_ema = {key + '_ema': loss_dict_ema[key] for key in loss_dict_ema} + self.log_dict(loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True) + self.log_dict(loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True) + + def on_train_batch_end(self, *args, **kwargs): + if self.use_ema: + self.model_ema(self.model) + + def _get_rows_from_list(self, samples): + n_imgs_per_row = len(samples) + denoise_grid = rearrange(samples, 'n b c h w -> b n c h w') + denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w') + denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row) + return denoise_grid + + @torch.no_grad() + def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs): + log = dict() + x = self.get_input(batch, self.first_stage_key) + N = min(x.shape[0], N) + n_row = min(x.shape[0], n_row) + x = x.to(self.device)[:N] + log["inputs"] = x + + # get diffusion row + diffusion_row = list() + x_start = x[:n_row] + + for t in range(self.num_timesteps): + if t % self.log_every_t == 0 or t == self.num_timesteps - 1: + t = repeat(torch.tensor([t]), '1 -> b', b=n_row) + t = t.to(self.device).long() + noise = torch.randn_like(x_start) + x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) + diffusion_row.append(x_noisy) + + log["diffusion_row"] = self._get_rows_from_list(diffusion_row) + + if sample: + # get denoise row + with self.ema_scope("Plotting"): + samples, denoise_row = self.sample(batch_size=N, return_intermediates=True) + + log["samples"] = samples + log["denoise_row"] = self._get_rows_from_list(denoise_row) + + if return_keys: + if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0: + return log + else: + return {key: log[key] for key in return_keys} + return log + + def configure_optimizers(self): + lr = self.learning_rate + params = list(self.model.parameters()) + if self.learn_logvar: + params = params + [self.logvar] + opt = torch.optim.AdamW(params, lr=lr) + return opt + +class LatentDiffusion(DDPM): + """main class""" + def __init__(self, + first_stage_config, + cond_stage_config, + num_timesteps_cond=None, + cond_stage_key="caption", + cond_stage_trainable=False, + cond_stage_forward=None, + conditioning_key=None, + uncond_prob=0.2, + uncond_type="empty_seq", + scale_factor=1.0, + scale_by_std=False, + encoder_type="2d", + only_model=False, + noise_strength=0, + use_dynamic_rescale=False, + base_scale=0.7, + turning_step=400, + loop_video=False, + fps_condition_type='fs', + perframe_ae=False, + # added + logdir=None, + rand_cond_frame=False, + en_and_decode_n_samples_a_time=None, + *args, **kwargs): + self.num_timesteps_cond = default(num_timesteps_cond, 1) + self.scale_by_std = scale_by_std + assert self.num_timesteps_cond <= kwargs['timesteps'] + # for backwards compatibility after implementation of DiffusionWrapper + ckpt_path = kwargs.pop("ckpt_path", None) + ignore_keys = kwargs.pop("ignore_keys", []) + conditioning_key = default(conditioning_key, 'crossattn') + super().__init__(conditioning_key=conditioning_key, *args, **kwargs) + + self.cond_stage_trainable = cond_stage_trainable + self.cond_stage_key = cond_stage_key + self.noise_strength = noise_strength + self.use_dynamic_rescale = use_dynamic_rescale + self.loop_video = loop_video + self.fps_condition_type = fps_condition_type + self.perframe_ae = perframe_ae + + self.logdir = logdir + self.rand_cond_frame = rand_cond_frame + self.en_and_decode_n_samples_a_time = en_and_decode_n_samples_a_time + + try: + self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1 + except: + self.num_downs = 0 + if not scale_by_std: + self.scale_factor = scale_factor + else: + self.register_buffer('scale_factor', torch.tensor(scale_factor)) + + if use_dynamic_rescale: + scale_arr1 = np.linspace(1.0, base_scale, turning_step) + scale_arr2 = np.full(self.num_timesteps, base_scale) + scale_arr = np.concatenate((scale_arr1, scale_arr2)) + to_torch = partial(torch.tensor, dtype=torch.float32) + self.register_buffer('scale_arr', to_torch(scale_arr)) + + self.instantiate_first_stage(first_stage_config) + self.instantiate_cond_stage(cond_stage_config) + self.first_stage_config = first_stage_config + self.cond_stage_config = cond_stage_config + self.clip_denoised = False + + self.cond_stage_forward = cond_stage_forward + self.encoder_type = encoder_type + assert(encoder_type in ["2d", "3d"]) + self.uncond_prob = uncond_prob + self.classifier_free_guidance = True if uncond_prob > 0 else False + assert(uncond_type in ["zero_embed", "empty_seq"]) + self.uncond_type = uncond_type + + self.restarted_from_ckpt = False + if ckpt_path is not None: + self.init_from_ckpt(ckpt_path, ignore_keys, only_model=only_model) + self.restarted_from_ckpt = True + + def make_cond_schedule(self, ): + self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long) + ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long() + self.cond_ids[:self.num_timesteps_cond] = ids + + @rank_zero_only + @torch.no_grad() + def on_train_batch_start(self, batch, batch_idx, dataloader_idx=None): + # only for very first batch, reset the self.scale_factor + if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0 and \ + not self.restarted_from_ckpt: + assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously' + # set rescale weight to 1./std of encodings + mainlogger.info("### USING STD-RESCALING ###") + x = super().get_input(batch, self.first_stage_key) + x = x.to(self.device) + encoder_posterior = self.encode_first_stage(x) + z = self.get_first_stage_encoding(encoder_posterior).detach() + del self.scale_factor + self.register_buffer('scale_factor', 1. / z.flatten().std()) + mainlogger.info(f"setting self.scale_factor to {self.scale_factor}") + mainlogger.info("### USING STD-RESCALING ###") + mainlogger.info(f"std={z.flatten().std()}") + + def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000, + linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): + super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s) + + self.shorten_cond_schedule = self.num_timesteps_cond > 1 + if self.shorten_cond_schedule: + self.make_cond_schedule() + + def instantiate_first_stage(self, config): + model = instantiate_from_config(config) + self.first_stage_model = model.eval() + self.first_stage_model.train = disabled_train + for param in self.first_stage_model.parameters(): + param.requires_grad = False + + def instantiate_cond_stage(self, config): + if not self.cond_stage_trainable: + model = instantiate_from_config(config) + self.cond_stage_model = model.eval() + self.cond_stage_model.train = disabled_train + for param in self.cond_stage_model.parameters(): + param.requires_grad = False + else: + model = instantiate_from_config(config) + self.cond_stage_model = model + + def get_learned_conditioning(self, c): + if self.cond_stage_forward is None: + if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode): + c = self.cond_stage_model.encode(c) + if isinstance(c, DiagonalGaussianDistribution): + c = c.mode() + else: + c = self.cond_stage_model(c) + else: + assert hasattr(self.cond_stage_model, self.cond_stage_forward) + c = getattr(self.cond_stage_model, self.cond_stage_forward)(c) + return c + + def get_first_stage_encoding(self, encoder_posterior, noise=None): + if isinstance(encoder_posterior, DiagonalGaussianDistribution): + z = encoder_posterior.sample(noise=noise) + elif isinstance(encoder_posterior, torch.Tensor): + z = encoder_posterior + else: + raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented") + return self.scale_factor * z + + @torch.no_grad() + def encode_first_stage(self, x): + if self.encoder_type == "2d" and x.dim() == 5: + b, _, t, _, _ = x.shape + x = rearrange(x, 'b c t h w -> (b t) c h w') + reshape_back = True + else: + reshape_back = False + + ## consume more GPU memory but faster + if not self.perframe_ae: + encoder_posterior = self.first_stage_model.encode(x) + results = self.get_first_stage_encoding(encoder_posterior).detach() + else: ## consume less GPU memory but slower + results = [] + for index in range(x.shape[0]): + frame_batch = self.first_stage_model.encode(x[index:index+1,:,:,:]) + frame_result = self.get_first_stage_encoding(frame_batch).detach() + results.append(frame_result) + results = torch.cat(results, dim=0) + + if reshape_back: + results = rearrange(results, '(b t) c h w -> b c t h w', b=b,t=t) + + return results + + def decode_core(self, z, **kwargs): + if self.encoder_type == "2d" and z.dim() == 5: + b, _, t, _, _ = z.shape + z = rearrange(z, 'b c t h w -> (b t) c h w') + reshape_back = True + else: + reshape_back = False + + z = 1. / self.scale_factor * z + if not self.perframe_ae: + results = self.first_stage_model.decode(z, **kwargs) + else: + + results = [] + + n_samples = default(self.en_and_decode_n_samples_a_time, self.temporal_length) + n_rounds = math.ceil(z.shape[0] / n_samples) + with torch.autocast("cuda", enabled=True): + for n in range(n_rounds): + if isinstance(self.first_stage_model.decoder, VideoDecoder): + kwargs.update({"timesteps": len(z[n * n_samples : (n + 1) * n_samples])}) + else: + kwargs = {} + + out = self.first_stage_model.decode( + z[n * n_samples : (n + 1) * n_samples], **kwargs + ) + results.append(out) + results = torch.cat(results, dim=0) + + if reshape_back: + results = rearrange(results, '(b t) c h w -> b c t h w', b=b,t=t) + return results + + @torch.no_grad() + def decode_first_stage(self, z, **kwargs): + return self.decode_core(z, **kwargs) + + # same as above but without decorator + def differentiable_decode_first_stage(self, z, **kwargs): + return self.decode_core(z, **kwargs) + + @torch.no_grad() + def get_batch_input(self, batch, random_uncond, return_first_stage_outputs=False, return_original_cond=False): + ## video shape: b, c, t, h, w + x = super().get_input(batch, self.first_stage_key) + + ## encode video frames x to z via a 2D encoder + z = self.encode_first_stage(x) + + ## get caption condition + cond = batch[self.cond_stage_key] + if random_uncond and self.uncond_type == 'empty_seq': + for i, ci in enumerate(cond): + if random.random() < self.uncond_prob: + cond[i] = "" + if isinstance(cond, dict) or isinstance(cond, list): + cond_emb = self.get_learned_conditioning(cond) + else: + cond_emb = self.get_learned_conditioning(cond.to(self.device)) + if random_uncond and self.uncond_type == 'zero_embed': + for i, ci in enumerate(cond): + if random.random() < self.uncond_prob: + cond_emb[i] = torch.zeros_like(cond_emb[i]) + + out = [z, cond_emb] + ## optional output: self-reconst or caption + if return_first_stage_outputs: + xrec = self.decode_first_stage(z) + out.extend([xrec]) + + if return_original_cond: + out.append(cond) + + return out + + def forward(self, x, c, **kwargs): + t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long() + if self.use_dynamic_rescale: + x = x * extract_into_tensor(self.scale_arr, t, x.shape) + return self.p_losses(x, c, t, **kwargs) + + def shared_step(self, batch, random_uncond, **kwargs): + x, c = self.get_batch_input(batch, random_uncond=random_uncond) + loss, loss_dict = self(x, c, **kwargs) + + return loss, loss_dict + + def apply_model(self, x_noisy, t, cond, **kwargs): + if isinstance(cond, dict): + # hybrid case, cond is exptected to be a dict + pass + else: + if not isinstance(cond, list): + cond = [cond] + key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn' + cond = {key: cond} + + x_recon = self.model(x_noisy, t, **cond, **kwargs) + + if isinstance(x_recon, tuple): + return x_recon[0] + else: + return x_recon + + def p_losses(self, x_start, cond, t, noise=None, **kwargs): + if self.noise_strength > 0: + b, c, f, _, _ = x_start.shape + offset_noise = torch.randn(b, c, f, 1, 1, device=x_start.device) + noise = default(noise, lambda: torch.randn_like(x_start) + self.noise_strength * offset_noise) + else: + noise = default(noise, lambda: torch.randn_like(x_start)) + x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) + + model_output = self.apply_model(x_noisy, t, cond, **kwargs) + + loss_dict = {} + prefix = 'train' if self.training else 'val' + + if self.parameterization == "x0": + target = x_start + elif self.parameterization == "eps": + target = noise + elif self.parameterization == "v": + target = self.get_v(x_start, noise, t) + else: + raise NotImplementedError() + + loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3, 4]) + loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()}) + + if self.logvar.device is not self.device: + self.logvar = self.logvar.to(self.device) + logvar_t = self.logvar[t] + # logvar_t = self.logvar[t.item()].to(self.device) # device conflict when ddp shared + loss = loss_simple / torch.exp(logvar_t) + logvar_t + # loss = loss_simple / torch.exp(self.logvar) + self.logvar + if self.learn_logvar: + loss_dict.update({f'{prefix}/loss_gamma': loss.mean()}) + loss_dict.update({'logvar': self.logvar.data.mean()}) + + loss = self.l_simple_weight * loss.mean() + + loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3, 4)) + loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean() + loss_dict.update({f'{prefix}/loss_vlb': loss_vlb}) + loss += (self.original_elbo_weight * loss_vlb) + loss_dict.update({f'{prefix}/loss': loss}) + + return loss, loss_dict + + def training_step(self, batch, batch_idx): + loss, loss_dict = self.shared_step(batch, random_uncond=self.classifier_free_guidance) + ## sync_dist | rank_zero_only + self.log_dict(loss_dict, prog_bar=True, logger=True, on_step=True, on_epoch=True, sync_dist=False) + #self.log("epoch/global_step", self.global_step.float(), prog_bar=True, logger=True, on_step=True, on_epoch=False) + ''' + if self.use_scheduler: + lr = self.optimizers().param_groups[0]['lr'] + self.log('lr_abs', lr, prog_bar=True, logger=True, on_step=True, on_epoch=False, rank_zero_only=True) + ''' + if (batch_idx+1) % self.log_every_t == 0: + mainlogger.info(f"batch:{batch_idx}|epoch:{self.current_epoch} [globalstep:{self.global_step}]: loss={loss}") + return loss + + def _get_denoise_row_from_list(self, samples, desc=''): + denoise_row = [] + for zd in tqdm(samples, desc=desc): + denoise_row.append(self.decode_first_stage(zd.to(self.device))) + n_log_timesteps = len(denoise_row) + + denoise_row = torch.stack(denoise_row) # n_log_timesteps, b, C, H, W + + if denoise_row.dim() == 5: + denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w') + denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w') + denoise_grid = make_grid(denoise_grid, nrow=n_log_timesteps) + elif denoise_row.dim() == 6: + # video, grid_size=[n_log_timesteps*bs, t] + video_length = denoise_row.shape[3] + denoise_grid = rearrange(denoise_row, 'n b c t h w -> b n c t h w') + denoise_grid = rearrange(denoise_grid, 'b n c t h w -> (b n) c t h w') + denoise_grid = rearrange(denoise_grid, 'n c t h w -> (n t) c h w') + denoise_grid = make_grid(denoise_grid, nrow=video_length) + else: + raise ValueError + + return denoise_grid + + @torch.no_grad() + def log_images(self, batch, sample=True, ddim_steps=200, ddim_eta=1., plot_denoise_rows=False, \ + unconditional_guidance_scale=1.0, **kwargs): + """ log images for LatentDiffusion """ + ##### control sampled imgae for logging, larger value may cause OOM + sampled_img_num = 2 + for key in batch.keys(): + batch[key] = batch[key][:sampled_img_num] + + ## TBD: currently, classifier_free_guidance sampling is only supported by DDIM + use_ddim = ddim_steps is not None + log = dict() + z, c, xrec, xc = self.get_batch_input(batch, random_uncond=False, + return_first_stage_outputs=True, + return_original_cond=True) + + N = xrec.shape[0] + log["reconst"] = xrec + log["condition"] = xc + + + if sample: + # get uncond embedding for classifier-free guidance sampling + if unconditional_guidance_scale != 1.0: + if isinstance(c, dict): + c_cat, c_emb = c["c_concat"][0], c["c_crossattn"][0] + log["condition_cat"] = c_cat + else: + c_emb = c + + if self.uncond_type == "empty_seq": + prompts = N * [""] + uc = self.get_learned_conditioning(prompts) + elif self.uncond_type == "zero_embed": + uc = torch.zeros_like(c_emb) + ## hybrid case + if isinstance(c, dict): + uc_hybrid = {"c_concat": [c_cat], "c_crossattn": [uc]} + uc = uc_hybrid + else: + uc = None + + with self.ema_scope("Plotting"): + samples, z_denoise_row = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, + ddim_steps=ddim_steps,eta=ddim_eta, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=uc, x0=z, **kwargs) + x_samples = self.decode_first_stage(samples) + log["samples"] = x_samples + + if plot_denoise_rows: + denoise_grid = self._get_denoise_row_from_list(z_denoise_row) + log["denoise_row"] = denoise_grid + + return log + + def p_mean_variance(self, x, c, t, clip_denoised: bool, return_x0=False, score_corrector=None, corrector_kwargs=None, **kwargs): + t_in = t + model_out = self.apply_model(x, t_in, c, **kwargs) + + if score_corrector is not None: + assert self.parameterization == "eps" + model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs) + + if self.parameterization == "eps": + x_recon = self.predict_start_from_noise(x, t=t, noise=model_out) + elif self.parameterization == "x0": + x_recon = model_out + else: + raise NotImplementedError() + + if clip_denoised: + x_recon.clamp_(-1., 1.) + + model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t) + + if return_x0: + return model_mean, posterior_variance, posterior_log_variance, x_recon + else: + return model_mean, posterior_variance, posterior_log_variance + + @torch.no_grad() + def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False, return_x0=False, \ + temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, **kwargs): + b, *_, device = *x.shape, x.device + outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised, return_x0=return_x0, \ + score_corrector=score_corrector, corrector_kwargs=corrector_kwargs, **kwargs) + if return_x0: + model_mean, _, model_log_variance, x0 = outputs + else: + model_mean, _, model_log_variance = outputs + + noise = noise_like(x.shape, device, repeat_noise) * temperature + if noise_dropout > 0.: + noise = torch.nn.functional.dropout(noise, p=noise_dropout) + # no noise when t == 0 + nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1))) + + if return_x0: + return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0 + else: + return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise + + @torch.no_grad() + def p_sample_loop(self, cond, shape, return_intermediates=False, x_T=None, verbose=True, callback=None, \ + timesteps=None, mask=None, x0=None, img_callback=None, start_T=None, log_every_t=None, **kwargs): + + if not log_every_t: + log_every_t = self.log_every_t + device = self.betas.device + b = shape[0] + # sample an initial noise + if x_T is None: + img = torch.randn(shape, device=device) + else: + img = x_T + + intermediates = [img] + if timesteps is None: + timesteps = self.num_timesteps + if start_T is not None: + timesteps = min(timesteps, start_T) + + iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed(range(0, timesteps)) + + if mask is not None: + assert x0 is not None + assert x0.shape[2:3] == mask.shape[2:3] # spatial size has to match + + for i in iterator: + ts = torch.full((b,), i, device=device, dtype=torch.long) + if self.shorten_cond_schedule: + assert self.model.conditioning_key != 'hybrid' + tc = self.cond_ids[ts].to(cond.device) + cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond)) + + img = self.p_sample(img, cond, ts, clip_denoised=self.clip_denoised, **kwargs) + if mask is not None: + img_orig = self.q_sample(x0, ts) + img = img_orig * mask + (1. - mask) * img + + if i % log_every_t == 0 or i == timesteps - 1: + intermediates.append(img) + if callback: callback(i) + if img_callback: img_callback(img, i) + + if return_intermediates: + return img, intermediates + return img + + @torch.no_grad() + def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None, \ + verbose=True, timesteps=None, mask=None, x0=None, shape=None, **kwargs): + if shape is None: + shape = (batch_size, self.channels, self.temporal_length, *self.image_size) + if cond is not None: + if isinstance(cond, dict): + cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else + list(map(lambda x: x[:batch_size], cond[key])) for key in cond} + else: + cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size] + return self.p_sample_loop(cond, + shape, + return_intermediates=return_intermediates, x_T=x_T, + verbose=verbose, timesteps=timesteps, + mask=mask, x0=x0, **kwargs) + + @torch.no_grad() + def sample_log(self, cond, batch_size, ddim, ddim_steps, **kwargs): + if ddim: + ddim_sampler = DDIMSampler(self) + shape = (self.channels, self.temporal_length, *self.image_size) + samples, intermediates = ddim_sampler.sample(ddim_steps, batch_size, shape, cond, verbose=False, **kwargs) + + else: + samples, intermediates = self.sample(cond=cond, batch_size=batch_size, return_intermediates=True, **kwargs) + + return samples, intermediates + + def configure_schedulers(self, optimizer): + assert 'target' in self.scheduler_config + scheduler_name = self.scheduler_config.target.split('.')[-1] + interval = self.scheduler_config.interval + frequency = self.scheduler_config.frequency + if scheduler_name == "LambdaLRScheduler": + scheduler = instantiate_from_config(self.scheduler_config) + scheduler.start_step = self.global_step + lr_scheduler = { + 'scheduler': LambdaLR(optimizer, lr_lambda=scheduler.schedule), + 'interval': interval, + 'frequency': frequency + } + elif scheduler_name == "CosineAnnealingLRScheduler": + scheduler = instantiate_from_config(self.scheduler_config) + decay_steps = scheduler.decay_steps + last_step = -1 if self.global_step == 0 else scheduler.start_step + lr_scheduler = { + 'scheduler': CosineAnnealingLR(optimizer, T_max=decay_steps, last_epoch=last_step), + 'interval': interval, + 'frequency': frequency + } + else: + raise NotImplementedError + return lr_scheduler + +class LatentVisualDiffusion(LatentDiffusion): + def __init__(self, img_cond_stage_config, image_proj_stage_config, freeze_embedder=True, image_proj_model_trainable=True, *args, **kwargs): + super().__init__(*args, **kwargs) + self.image_proj_model_trainable = image_proj_model_trainable + self._init_embedder(img_cond_stage_config, freeze_embedder) + self._init_img_ctx_projector(image_proj_stage_config, image_proj_model_trainable) + + def _init_img_ctx_projector(self, config, trainable): + self.image_proj_model = instantiate_from_config(config) + if not trainable: + self.image_proj_model.eval() + self.image_proj_model.train = disabled_train + for param in self.image_proj_model.parameters(): + param.requires_grad = False + + def _init_embedder(self, config, freeze=True): + self.embedder = instantiate_from_config(config) + if freeze: + self.embedder.eval() + self.embedder.train = disabled_train + for param in self.embedder.parameters(): + param.requires_grad = False + + def shared_step(self, batch, random_uncond, **kwargs): + x, c, fs = self.get_batch_input(batch, random_uncond=random_uncond, return_fs=True) + kwargs.update({"fs": fs.long()}) + loss, loss_dict = self(x, c, **kwargs) + return loss, loss_dict + + def get_batch_input(self, batch, random_uncond, return_first_stage_outputs=False, return_original_cond=False, return_fs=False, return_cond_frame=False, return_original_input=False, **kwargs): + ## x: b c t h w + x = super().get_input(batch, self.first_stage_key) + ## encode video frames x to z via a 2D encoder + z = self.encode_first_stage(x) + + ## get caption condition + cond_input = batch[self.cond_stage_key] + + if isinstance(cond_input, dict) or isinstance(cond_input, list): + cond_emb = self.get_learned_conditioning(cond_input) + else: + cond_emb = self.get_learned_conditioning(cond_input.to(self.device)) + + cond = {} + ## to support classifier-free guidance, randomly drop out only text conditioning 5%, only image conditioning 5%, and both 5%. + if random_uncond: + random_num = torch.rand(x.size(0), device=x.device) + else: + random_num = torch.ones(x.size(0), device=x.device) ## by doning so, we can get text embedding and complete img emb for inference + prompt_mask = rearrange(random_num < 2 * self.uncond_prob, "n -> n 1 1") + input_mask = 1 - rearrange((random_num >= self.uncond_prob).float() * (random_num < 3 * self.uncond_prob).float(), "n -> n 1 1 1") + + null_prompt = self.get_learned_conditioning([""]) + prompt_imb = torch.where(prompt_mask, null_prompt, cond_emb.detach()) + + ## get conditioning frame + cond_frame_index = 0 + if self.rand_cond_frame: + cond_frame_index = random.randint(0, self.model.diffusion_model.temporal_length-1) + + img = x[:,:,cond_frame_index,...] + img = input_mask * img + ## img: b c h w + img_emb = self.embedder(img) ## b l c + img_emb = self.image_proj_model(img_emb) + + if self.model.conditioning_key == 'hybrid': + ## simply repeat the cond_frame to match the seq_len of z + img_cat_cond = z[:,:,cond_frame_index,:,:] + img_cat_cond = img_cat_cond.unsqueeze(2) + img_cat_cond = repeat(img_cat_cond, 'b c t h w -> b c (repeat t) h w', repeat=z.shape[2]) + + cond["c_concat"] = [img_cat_cond] # b c t h w + cond["c_crossattn"] = [torch.cat([prompt_imb, img_emb], dim=1)] ## concat in the seq_len dim + + out = [z, cond] + if return_first_stage_outputs: + xrec = self.decode_first_stage(z) + out.extend([xrec]) + + if return_original_cond: + out.append(cond_input) + if return_fs: + if self.fps_condition_type == 'fs': + fs = super().get_input(batch, 'frame_stride') + elif self.fps_condition_type == 'fps': + fs = super().get_input(batch, 'fps') + out.append(fs) + if return_cond_frame: + out.append(x[:,:,cond_frame_index,...].unsqueeze(2)) + if return_original_input: + out.append(x) + + return out + + @torch.no_grad() + def log_images(self, batch, sample=True, ddim_steps=50, ddim_eta=1., plot_denoise_rows=False, \ + unconditional_guidance_scale=1.0, mask=None, **kwargs): + """ log images for LatentVisualDiffusion """ + ##### sampled_img_num: control sampled imgae for logging, larger value may cause OOM + sampled_img_num = 1 + for key in batch.keys(): + batch[key] = batch[key][:sampled_img_num] + + ## TBD: currently, classifier_free_guidance sampling is only supported by DDIM + use_ddim = ddim_steps is not None + log = dict() + + z, c, xrec, xc, fs, cond_x = self.get_batch_input(batch, random_uncond=False, + return_first_stage_outputs=True, + return_original_cond=True, + return_fs=True, + return_cond_frame=True) + + N = xrec.shape[0] + log["image_condition"] = cond_x + log["reconst"] = xrec + xc_with_fs = [] + for idx, content in enumerate(xc): + xc_with_fs.append(content + '_fs=' + str(fs[idx].item())) + log["condition"] = xc_with_fs + kwargs.update({"fs": fs.long()}) + + c_cat = None + if sample: + # get uncond embedding for classifier-free guidance sampling + if unconditional_guidance_scale != 1.0: + if isinstance(c, dict): + c_emb = c["c_crossattn"][0] + if 'c_concat' in c.keys(): + c_cat = c["c_concat"][0] + else: + c_emb = c + + if self.uncond_type == "empty_seq": + prompts = N * [""] + uc_prompt = self.get_learned_conditioning(prompts) + elif self.uncond_type == "zero_embed": + uc_prompt = torch.zeros_like(c_emb) + + img = torch.zeros_like(xrec[:,:,0]) ## b c h w + ## img: b c h w + img_emb = self.embedder(img) ## b l c + uc_img = self.image_proj_model(img_emb) + + uc = torch.cat([uc_prompt, uc_img], dim=1) + ## hybrid case + if isinstance(c, dict): + uc_hybrid = {"c_concat": [c_cat], "c_crossattn": [uc]} + uc = uc_hybrid + else: + uc = None + + with self.ema_scope("Plotting"): + samples, z_denoise_row = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, + ddim_steps=ddim_steps,eta=ddim_eta, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=uc, x0=z, **kwargs) + x_samples = self.decode_first_stage(samples) + log["samples"] = x_samples + + if plot_denoise_rows: + denoise_grid = self._get_denoise_row_from_list(z_denoise_row) + log["denoise_row"] = denoise_grid + + return log + + def configure_optimizers(self): + """ configure_optimizers for LatentDiffusion """ + lr = self.learning_rate + + params = list(self.model.parameters()) + mainlogger.info(f"@Training [{len(params)}] Full Paramters.") + + if self.cond_stage_trainable: + params_cond_stage = [p for p in self.cond_stage_model.parameters() if p.requires_grad == True] + mainlogger.info(f"@Training [{len(params_cond_stage)}] Paramters for Cond_stage_model.") + params.extend(params_cond_stage) + + if self.image_proj_model_trainable: + mainlogger.info(f"@Training [{len(list(self.image_proj_model.parameters()))}] Paramters for Image_proj_model.") + params.extend(list(self.image_proj_model.parameters())) + + if self.learn_logvar: + mainlogger.info('Diffusion model optimizing logvar') + if isinstance(params[0], dict): + params.append({"params": [self.logvar]}) + else: + params.append(self.logvar) + + ## optimizer + optimizer = torch.optim.AdamW(params, lr=lr) + + ## lr scheduler + if self.use_scheduler: + mainlogger.info("Setting up scheduler...") + lr_scheduler = self.configure_schedulers(optimizer) + return [optimizer], [lr_scheduler] + + return optimizer + + +class DiffusionWrapper(pl.LightningModule): + def __init__(self, diff_model_config, conditioning_key): + super().__init__() + self.diffusion_model = instantiate_from_config(diff_model_config) + self.conditioning_key = conditioning_key + + def forward(self, x, t, c_concat: list = None, c_crossattn: list = None, + c_adm=None, s=None, mask=None, **kwargs): + # temporal_context = fps is foNone + if self.conditioning_key is None: + out = self.diffusion_model(x, t) + elif self.conditioning_key == 'concat': + xc = torch.cat([x] + c_concat, dim=1) + out = self.diffusion_model(xc, t, **kwargs) + elif self.conditioning_key == 'crossattn': + cc = torch.cat(c_crossattn, 1) + out = self.diffusion_model(x, t, context=cc, **kwargs) + elif self.conditioning_key == 'hybrid': + ## it is just right [b,c,t,h,w]: concatenate in channel dim + xc = torch.cat([x] + c_concat, dim=1) + cc = torch.cat(c_crossattn, 1) + out = self.diffusion_model(xc, t, context=cc, **kwargs) + elif self.conditioning_key == 'resblockcond': + cc = c_crossattn[0] + out = self.diffusion_model(x, t, context=cc) + elif self.conditioning_key == 'adm': + cc = c_crossattn[0] + out = self.diffusion_model(x, t, y=cc) + elif self.conditioning_key == 'hybrid-adm': + assert c_adm is not None + xc = torch.cat([x] + c_concat, dim=1) + cc = torch.cat(c_crossattn, 1) + out = self.diffusion_model(xc, t, context=cc, y=c_adm, **kwargs) + elif self.conditioning_key == 'hybrid-time': + assert s is not None + xc = torch.cat([x] + c_concat, dim=1) + cc = torch.cat(c_crossattn, 1) + out = self.diffusion_model(xc, t, context=cc, s=s) + elif self.conditioning_key == 'concat-time-mask': + # assert s is not None + xc = torch.cat([x] + c_concat, dim=1) + out = self.diffusion_model(xc, t, context=None, s=s, mask=mask) + elif self.conditioning_key == 'concat-adm-mask': + # assert s is not None + if c_concat is not None: + xc = torch.cat([x] + c_concat, dim=1) + else: + xc = x + out = self.diffusion_model(xc, t, context=None, y=s, mask=mask) + elif self.conditioning_key == 'hybrid-adm-mask': + cc = torch.cat(c_crossattn, 1) + if c_concat is not None: + xc = torch.cat([x] + c_concat, dim=1) + else: + xc = x + out = self.diffusion_model(xc, t, context=cc, y=s, mask=mask) + elif self.conditioning_key == 'hybrid-time-adm': # adm means y, e.g., class index + # assert s is not None + assert c_adm is not None + xc = torch.cat([x] + c_concat, dim=1) + cc = torch.cat(c_crossattn, 1) + out = self.diffusion_model(xc, t, context=cc, s=s, y=c_adm) + elif self.conditioning_key == 'crossattn-adm': + assert c_adm is not None + cc = torch.cat(c_crossattn, 1) + out = self.diffusion_model(x, t, context=cc, y=c_adm) + else: + raise NotImplementedError() + + return out \ No newline at end of file diff --git a/lvdm/models/samplers/ddim.py b/lvdm/models/samplers/ddim.py new file mode 100644 index 0000000000000000000000000000000000000000..88006edc2856efc80a5f33839dcaffc27d2930b2 --- /dev/null +++ b/lvdm/models/samplers/ddim.py @@ -0,0 +1,317 @@ +import numpy as np +from tqdm import tqdm +import torch +from lvdm.models.utils_diffusion import make_ddim_sampling_parameters, make_ddim_timesteps, rescale_noise_cfg +from lvdm.common import noise_like +from lvdm.common import extract_into_tensor +import copy + + +class DDIMSampler(object): + def __init__(self, model, schedule="linear", **kwargs): + super().__init__() + self.model = model + self.ddpm_num_timesteps = model.num_timesteps + self.schedule = schedule + self.counter = 0 + + def register_buffer(self, name, attr): + if type(attr) == torch.Tensor: + if attr.device != torch.device("cuda"): + attr = attr.to(torch.device("cuda")) + setattr(self, name, attr) + + def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True): + self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps, + num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose) + alphas_cumprod = self.model.alphas_cumprod + assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep' + to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device) + + if self.model.use_dynamic_rescale: + self.ddim_scale_arr = self.model.scale_arr[self.ddim_timesteps] + self.ddim_scale_arr_prev = torch.cat([self.ddim_scale_arr[0:1], self.ddim_scale_arr[:-1]]) + + self.register_buffer('betas', to_torch(self.model.betas)) + self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) + self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev)) + + # calculations for diffusion q(x_t | x_{t-1}) and others + self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu()))) + self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu()))) + self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu()))) + self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu()))) + self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1))) + + # ddim sampling parameters + ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(), + ddim_timesteps=self.ddim_timesteps, + eta=ddim_eta,verbose=verbose) + self.register_buffer('ddim_sigmas', ddim_sigmas) + self.register_buffer('ddim_alphas', ddim_alphas) + self.register_buffer('ddim_alphas_prev', ddim_alphas_prev) + self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas)) + sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt( + (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * ( + 1 - self.alphas_cumprod / self.alphas_cumprod_prev)) + self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps) + + @torch.no_grad() + def sample(self, + S, + batch_size, + shape, + conditioning=None, + callback=None, + normals_sequence=None, + img_callback=None, + quantize_x0=False, + eta=0., + mask=None, + x0=None, + temperature=1., + noise_dropout=0., + score_corrector=None, + corrector_kwargs=None, + verbose=True, + schedule_verbose=False, + x_T=None, + log_every_t=100, + unconditional_guidance_scale=1., + unconditional_conditioning=None, + precision=None, + fs=None, + timestep_spacing='uniform', #uniform_trailing for starting from last timestep + guidance_rescale=0.0, + **kwargs + ): + + # check condition bs + if conditioning is not None: + if isinstance(conditioning, dict): + try: + cbs = conditioning[list(conditioning.keys())[0]].shape[0] + except: + cbs = conditioning[list(conditioning.keys())[0]][0].shape[0] + + if cbs != batch_size: + print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}") + else: + if conditioning.shape[0] != batch_size: + print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}") + + self.make_schedule(ddim_num_steps=S, ddim_discretize=timestep_spacing, ddim_eta=eta, verbose=schedule_verbose) + + # make shape + if len(shape) == 3: + C, H, W = shape + size = (batch_size, C, H, W) + elif len(shape) == 4: + C, T, H, W = shape + size = (batch_size, C, T, H, W) + + samples, intermediates = self.ddim_sampling(conditioning, size, + callback=callback, + img_callback=img_callback, + quantize_denoised=quantize_x0, + mask=mask, x0=x0, + ddim_use_original_steps=False, + noise_dropout=noise_dropout, + temperature=temperature, + score_corrector=score_corrector, + corrector_kwargs=corrector_kwargs, + x_T=x_T, + log_every_t=log_every_t, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=unconditional_conditioning, + verbose=verbose, + precision=precision, + fs=fs, + guidance_rescale=guidance_rescale, + **kwargs) + return samples, intermediates + + @torch.no_grad() + def ddim_sampling(self, cond, shape, + x_T=None, ddim_use_original_steps=False, + callback=None, timesteps=None, quantize_denoised=False, + mask=None, x0=None, img_callback=None, log_every_t=100, + temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, + unconditional_guidance_scale=1., unconditional_conditioning=None, verbose=True,precision=None,fs=None,guidance_rescale=0.0, + **kwargs): + device = self.model.betas.device + b = shape[0] + if x_T is None: + img = torch.randn(shape, device=device) + else: + img = x_T + if precision is not None: + if precision == 16: + img = img.to(dtype=torch.float16) + + if timesteps is None: + timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps + elif timesteps is not None and not ddim_use_original_steps: + subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1 + timesteps = self.ddim_timesteps[:subset_end] + + intermediates = {'x_inter': [img], 'pred_x0': [img]} + time_range = reversed(range(0,timesteps)) if ddim_use_original_steps else np.flip(timesteps) + total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0] + if verbose: + iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps) + else: + iterator = time_range + + clean_cond = kwargs.pop("clean_cond", False) + + # cond_copy, unconditional_conditioning_copy = copy.deepcopy(cond), copy.deepcopy(unconditional_conditioning) + for i, step in enumerate(iterator): + index = total_steps - i - 1 + ts = torch.full((b,), step, device=device, dtype=torch.long) + + ## use mask to blend noised original latent (img_orig) & new sampled latent (img) + if mask is not None: + assert x0 is not None + if clean_cond: + img_orig = x0 + else: + img_orig = self.model.q_sample(x0, ts) # TODO: deterministic forward pass? + img = img_orig * mask + (1. - mask) * img # keep original & modify use img + + + + + outs = self.p_sample_ddim(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps, + quantize_denoised=quantize_denoised, temperature=temperature, + noise_dropout=noise_dropout, score_corrector=score_corrector, + corrector_kwargs=corrector_kwargs, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=unconditional_conditioning, + mask=mask,x0=x0,fs=fs,guidance_rescale=guidance_rescale, + **kwargs) + + + img, pred_x0 = outs + if callback: callback(i) + if img_callback: img_callback(pred_x0, i) + + if index % log_every_t == 0 or index == total_steps - 1: + intermediates['x_inter'].append(img) + intermediates['pred_x0'].append(pred_x0) + + return img, intermediates + + @torch.no_grad() + def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False, + temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, + unconditional_guidance_scale=1., unconditional_conditioning=None, + uc_type=None, conditional_guidance_scale_temporal=None,mask=None,x0=None,guidance_rescale=0.0,**kwargs): + b, *_, device = *x.shape, x.device + if x.dim() == 5: + is_video = True + else: + is_video = False + + if unconditional_conditioning is None or unconditional_guidance_scale == 1.: + model_output = self.model.apply_model(x, t, c, **kwargs) # unet denoiser + else: + ### do_classifier_free_guidance + if isinstance(c, torch.Tensor) or isinstance(c, dict): + e_t_cond = self.model.apply_model(x, t, c, **kwargs) + e_t_uncond = self.model.apply_model(x, t, unconditional_conditioning, **kwargs) + else: + raise NotImplementedError + + model_output = e_t_uncond + unconditional_guidance_scale * (e_t_cond - e_t_uncond) + + if guidance_rescale > 0.0: + model_output = rescale_noise_cfg(model_output, e_t_cond, guidance_rescale=guidance_rescale) + + if self.model.parameterization == "v": + e_t = self.model.predict_eps_from_z_and_v(x, t, model_output) + else: + e_t = model_output + + if score_corrector is not None: + assert self.model.parameterization == "eps", 'not implemented' + e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs) + + alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas + alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev + sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas + # sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas + sigmas = self.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas + # select parameters corresponding to the currently considered timestep + + if is_video: + size = (b, 1, 1, 1, 1) + else: + size = (b, 1, 1, 1) + a_t = torch.full(size, alphas[index], device=device) + a_prev = torch.full(size, alphas_prev[index], device=device) + sigma_t = torch.full(size, sigmas[index], device=device) + sqrt_one_minus_at = torch.full(size, sqrt_one_minus_alphas[index],device=device) + + # current prediction for x_0 + if self.model.parameterization != "v": + pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt() + else: + pred_x0 = self.model.predict_start_from_z_and_v(x, t, model_output) + + if self.model.use_dynamic_rescale: + scale_t = torch.full(size, self.ddim_scale_arr[index], device=device) + prev_scale_t = torch.full(size, self.ddim_scale_arr_prev[index], device=device) + rescale = (prev_scale_t / scale_t) + pred_x0 *= rescale + + if quantize_denoised: + pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0) + # direction pointing to x_t + dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t + + noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature + if noise_dropout > 0.: + noise = torch.nn.functional.dropout(noise, p=noise_dropout) + + x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise + + return x_prev, pred_x0 + + @torch.no_grad() + def decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None, + use_original_steps=False, callback=None): + + timesteps = np.arange(self.ddpm_num_timesteps) if use_original_steps else self.ddim_timesteps + timesteps = timesteps[:t_start] + + time_range = np.flip(timesteps) + total_steps = timesteps.shape[0] + print(f"Running DDIM Sampling with {total_steps} timesteps") + + iterator = tqdm(time_range, desc='Decoding image', total=total_steps) + x_dec = x_latent + for i, step in enumerate(iterator): + index = total_steps - i - 1 + ts = torch.full((x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long) + x_dec, _ = self.p_sample_ddim(x_dec, cond, ts, index=index, use_original_steps=use_original_steps, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=unconditional_conditioning) + if callback: callback(i) + return x_dec + + @torch.no_grad() + def stochastic_encode(self, x0, t, use_original_steps=False, noise=None): + # fast, but does not allow for exact reconstruction + # t serves as an index to gather the correct alphas + if use_original_steps: + sqrt_alphas_cumprod = self.sqrt_alphas_cumprod + sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod + else: + sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas) + sqrt_one_minus_alphas_cumprod = self.ddim_sqrt_one_minus_alphas + + if noise is None: + noise = torch.randn_like(x0) + return (extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 + + extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) * noise) diff --git a/lvdm/models/samplers/ddim_multiplecond.py b/lvdm/models/samplers/ddim_multiplecond.py new file mode 100644 index 0000000000000000000000000000000000000000..31c3d89aa2df1ad72dbc8533622ba75d8b5feb16 --- /dev/null +++ b/lvdm/models/samplers/ddim_multiplecond.py @@ -0,0 +1,323 @@ +import numpy as np +from tqdm import tqdm +import torch +from lvdm.models.utils_diffusion import make_ddim_sampling_parameters, make_ddim_timesteps, rescale_noise_cfg +from lvdm.common import noise_like +from lvdm.common import extract_into_tensor +import copy + + +class DDIMSampler(object): + def __init__(self, model, schedule="linear", **kwargs): + super().__init__() + self.model = model + self.ddpm_num_timesteps = model.num_timesteps + self.schedule = schedule + self.counter = 0 + + def register_buffer(self, name, attr): + if type(attr) == torch.Tensor: + if attr.device != torch.device("cuda"): + attr = attr.to(torch.device("cuda")) + setattr(self, name, attr) + + def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True): + self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps, + num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose) + alphas_cumprod = self.model.alphas_cumprod + assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep' + to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device) + + if self.model.use_dynamic_rescale: + self.ddim_scale_arr = self.model.scale_arr[self.ddim_timesteps] + self.ddim_scale_arr_prev = torch.cat([self.ddim_scale_arr[0:1], self.ddim_scale_arr[:-1]]) + + self.register_buffer('betas', to_torch(self.model.betas)) + self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) + self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev)) + + # calculations for diffusion q(x_t | x_{t-1}) and others + self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu()))) + self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu()))) + self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu()))) + self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu()))) + self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1))) + + # ddim sampling parameters + ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(), + ddim_timesteps=self.ddim_timesteps, + eta=ddim_eta,verbose=verbose) + self.register_buffer('ddim_sigmas', ddim_sigmas) + self.register_buffer('ddim_alphas', ddim_alphas) + self.register_buffer('ddim_alphas_prev', ddim_alphas_prev) + self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas)) + sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt( + (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * ( + 1 - self.alphas_cumprod / self.alphas_cumprod_prev)) + self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps) + + @torch.no_grad() + def sample(self, + S, + batch_size, + shape, + conditioning=None, + callback=None, + normals_sequence=None, + img_callback=None, + quantize_x0=False, + eta=0., + mask=None, + x0=None, + temperature=1., + noise_dropout=0., + score_corrector=None, + corrector_kwargs=None, + verbose=True, + schedule_verbose=False, + x_T=None, + log_every_t=100, + unconditional_guidance_scale=1., + unconditional_conditioning=None, + precision=None, + fs=None, + timestep_spacing='uniform', #uniform_trailing for starting from last timestep + guidance_rescale=0.0, + # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ... + **kwargs + ): + + # check condition bs + if conditioning is not None: + if isinstance(conditioning, dict): + try: + cbs = conditioning[list(conditioning.keys())[0]].shape[0] + except: + cbs = conditioning[list(conditioning.keys())[0]][0].shape[0] + + if cbs != batch_size: + print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}") + else: + if conditioning.shape[0] != batch_size: + print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}") + + # print('==> timestep_spacing: ', timestep_spacing, guidance_rescale) + self.make_schedule(ddim_num_steps=S, ddim_discretize=timestep_spacing, ddim_eta=eta, verbose=schedule_verbose) + + # make shape + if len(shape) == 3: + C, H, W = shape + size = (batch_size, C, H, W) + elif len(shape) == 4: + C, T, H, W = shape + size = (batch_size, C, T, H, W) + # print(f'Data shape for DDIM sampling is {size}, eta {eta}') + + samples, intermediates = self.ddim_sampling(conditioning, size, + callback=callback, + img_callback=img_callback, + quantize_denoised=quantize_x0, + mask=mask, x0=x0, + ddim_use_original_steps=False, + noise_dropout=noise_dropout, + temperature=temperature, + score_corrector=score_corrector, + corrector_kwargs=corrector_kwargs, + x_T=x_T, + log_every_t=log_every_t, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=unconditional_conditioning, + verbose=verbose, + precision=precision, + fs=fs, + guidance_rescale=guidance_rescale, + **kwargs) + return samples, intermediates + + @torch.no_grad() + def ddim_sampling(self, cond, shape, + x_T=None, ddim_use_original_steps=False, + callback=None, timesteps=None, quantize_denoised=False, + mask=None, x0=None, img_callback=None, log_every_t=100, + temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, + unconditional_guidance_scale=1., unconditional_conditioning=None, verbose=True,precision=None,fs=None,guidance_rescale=0.0, + **kwargs): + device = self.model.betas.device + b = shape[0] + if x_T is None: + img = torch.randn(shape, device=device) + else: + img = x_T + if precision is not None: + if precision == 16: + img = img.to(dtype=torch.float16) + + + if timesteps is None: + timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps + elif timesteps is not None and not ddim_use_original_steps: + subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1 + timesteps = self.ddim_timesteps[:subset_end] + + intermediates = {'x_inter': [img], 'pred_x0': [img]} + time_range = reversed(range(0,timesteps)) if ddim_use_original_steps else np.flip(timesteps) + total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0] + if verbose: + iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps) + else: + iterator = time_range + + clean_cond = kwargs.pop("clean_cond", False) + + # cond_copy, unconditional_conditioning_copy = copy.deepcopy(cond), copy.deepcopy(unconditional_conditioning) + for i, step in enumerate(iterator): + index = total_steps - i - 1 + ts = torch.full((b,), step, device=device, dtype=torch.long) + + ## use mask to blend noised original latent (img_orig) & new sampled latent (img) + if mask is not None: + assert x0 is not None + if clean_cond: + img_orig = x0 + else: + img_orig = self.model.q_sample(x0, ts) # TODO: deterministic forward pass? + img = img_orig * mask + (1. - mask) * img # keep original & modify use img + + + + + outs = self.p_sample_ddim(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps, + quantize_denoised=quantize_denoised, temperature=temperature, + noise_dropout=noise_dropout, score_corrector=score_corrector, + corrector_kwargs=corrector_kwargs, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=unconditional_conditioning, + mask=mask,x0=x0,fs=fs,guidance_rescale=guidance_rescale, + **kwargs) + + + + img, pred_x0 = outs + if callback: callback(i) + if img_callback: img_callback(pred_x0, i) + + if index % log_every_t == 0 or index == total_steps - 1: + intermediates['x_inter'].append(img) + intermediates['pred_x0'].append(pred_x0) + + return img, intermediates + + @torch.no_grad() + def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False, + temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, + unconditional_guidance_scale=1., unconditional_conditioning=None, + uc_type=None, cfg_img=None,mask=None,x0=None,guidance_rescale=0.0, **kwargs): + b, *_, device = *x.shape, x.device + if x.dim() == 5: + is_video = True + else: + is_video = False + if cfg_img is None: + cfg_img = unconditional_guidance_scale + + unconditional_conditioning_img_nonetext = kwargs['unconditional_conditioning_img_nonetext'] + + + if unconditional_conditioning is None or unconditional_guidance_scale == 1.: + model_output = self.model.apply_model(x, t, c, **kwargs) # unet denoiser + else: + ### with unconditional condition + e_t_cond = self.model.apply_model(x, t, c, **kwargs) + e_t_uncond = self.model.apply_model(x, t, unconditional_conditioning, **kwargs) + e_t_uncond_img = self.model.apply_model(x, t, unconditional_conditioning_img_nonetext, **kwargs) + # text cfg + model_output = e_t_uncond + cfg_img * (e_t_uncond_img - e_t_uncond) + unconditional_guidance_scale * (e_t_cond - e_t_uncond_img) + if guidance_rescale > 0.0: + model_output = rescale_noise_cfg(model_output, e_t_cond, guidance_rescale=guidance_rescale) + + if self.model.parameterization == "v": + e_t = self.model.predict_eps_from_z_and_v(x, t, model_output) + else: + e_t = model_output + + if score_corrector is not None: + assert self.model.parameterization == "eps", 'not implemented' + e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs) + + alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas + alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev + sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas + sigmas = self.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas + # select parameters corresponding to the currently considered timestep + + if is_video: + size = (b, 1, 1, 1, 1) + else: + size = (b, 1, 1, 1) + a_t = torch.full(size, alphas[index], device=device) + a_prev = torch.full(size, alphas_prev[index], device=device) + sigma_t = torch.full(size, sigmas[index], device=device) + sqrt_one_minus_at = torch.full(size, sqrt_one_minus_alphas[index],device=device) + + # current prediction for x_0 + if self.model.parameterization != "v": + pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt() + else: + pred_x0 = self.model.predict_start_from_z_and_v(x, t, model_output) + + if self.model.use_dynamic_rescale: + scale_t = torch.full(size, self.ddim_scale_arr[index], device=device) + prev_scale_t = torch.full(size, self.ddim_scale_arr_prev[index], device=device) + rescale = (prev_scale_t / scale_t) + pred_x0 *= rescale + + if quantize_denoised: + pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0) + # direction pointing to x_t + dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t + + noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature + if noise_dropout > 0.: + noise = torch.nn.functional.dropout(noise, p=noise_dropout) + + x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise + + return x_prev, pred_x0 + + @torch.no_grad() + def decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None, + use_original_steps=False, callback=None): + + timesteps = np.arange(self.ddpm_num_timesteps) if use_original_steps else self.ddim_timesteps + timesteps = timesteps[:t_start] + + time_range = np.flip(timesteps) + total_steps = timesteps.shape[0] + print(f"Running DDIM Sampling with {total_steps} timesteps") + + iterator = tqdm(time_range, desc='Decoding image', total=total_steps) + x_dec = x_latent + for i, step in enumerate(iterator): + index = total_steps - i - 1 + ts = torch.full((x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long) + x_dec, _ = self.p_sample_ddim(x_dec, cond, ts, index=index, use_original_steps=use_original_steps, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=unconditional_conditioning) + if callback: callback(i) + return x_dec + + @torch.no_grad() + def stochastic_encode(self, x0, t, use_original_steps=False, noise=None): + # fast, but does not allow for exact reconstruction + # t serves as an index to gather the correct alphas + if use_original_steps: + sqrt_alphas_cumprod = self.sqrt_alphas_cumprod + sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod + else: + sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas) + sqrt_one_minus_alphas_cumprod = self.ddim_sqrt_one_minus_alphas + + if noise is None: + noise = torch.randn_like(x0) + return (extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 + + extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) * noise) \ No newline at end of file diff --git a/lvdm/models/utils_diffusion.py b/lvdm/models/utils_diffusion.py new file mode 100644 index 0000000000000000000000000000000000000000..f5265d4af6bcc4acb94b9790b903911ff0710cdf --- /dev/null +++ b/lvdm/models/utils_diffusion.py @@ -0,0 +1,158 @@ +import math +import numpy as np +import torch +import torch.nn.functional as F +from einops import repeat + + +def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False): + """ + Create sinusoidal timestep embeddings. + :param timesteps: a 1-D Tensor of N indices, one per batch element. + These may be fractional. + :param dim: the dimension of the output. + :param max_period: controls the minimum frequency of the embeddings. + :return: an [N x dim] Tensor of positional embeddings. + """ + if not repeat_only: + half = dim // 2 + freqs = torch.exp( + -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half + ).to(device=timesteps.device) + args = timesteps[:, None].float() * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) + else: + embedding = repeat(timesteps, 'b -> b d', d=dim) + return embedding + + +def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): + if schedule == "linear": + betas = ( + torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2 + ) + + elif schedule == "cosine": + timesteps = ( + torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s + ) + alphas = timesteps / (1 + cosine_s) * np.pi / 2 + alphas = torch.cos(alphas).pow(2) + alphas = alphas / alphas[0] + betas = 1 - alphas[1:] / alphas[:-1] + betas = np.clip(betas, a_min=0, a_max=0.999) + + elif schedule == "sqrt_linear": + betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) + elif schedule == "sqrt": + betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5 + else: + raise ValueError(f"schedule '{schedule}' unknown.") + return betas.numpy() + + +def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True): + if ddim_discr_method == 'uniform': + c = num_ddpm_timesteps // num_ddim_timesteps + ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c))) + steps_out = ddim_timesteps + 1 + elif ddim_discr_method == 'uniform_trailing': + c = num_ddpm_timesteps / num_ddim_timesteps + ddim_timesteps = np.flip(np.round(np.arange(num_ddpm_timesteps, 0, -c))).astype(np.int64) + steps_out = ddim_timesteps - 1 + elif ddim_discr_method == 'quad': + ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int) + steps_out = ddim_timesteps + 1 + else: + raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"') + + # assert ddim_timesteps.shape[0] == num_ddim_timesteps + # add one to get the final alpha values right (the ones from first scale to data during sampling) + # steps_out = ddim_timesteps + 1 + if verbose: + print(f'Selected timesteps for ddim sampler: {steps_out}') + return steps_out + + +def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True): + # select alphas for computing the variance schedule + # print(f'ddim_timesteps={ddim_timesteps}, len_alphacums={len(alphacums)}') + alphas = alphacums[ddim_timesteps] + alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist()) + + # according the formula provided in https://arxiv.org/abs/2010.02502 + sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev)) + if verbose: + print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}') + print(f'For the chosen value of eta, which is {eta}, ' + f'this results in the following sigma_t schedule for ddim sampler {sigmas}') + return sigmas, alphas, alphas_prev + + +def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999): + """ + Create a beta schedule that discretizes the given alpha_t_bar function, + which defines the cumulative product of (1-beta) over time from t = [0,1]. + :param num_diffusion_timesteps: the number of betas to produce. + :param alpha_bar: a lambda that takes an argument t from 0 to 1 and + produces the cumulative product of (1-beta) up to that + part of the diffusion process. + :param max_beta: the maximum beta to use; use values lower than 1 to + prevent singularities. + """ + betas = [] + for i in range(num_diffusion_timesteps): + t1 = i / num_diffusion_timesteps + t2 = (i + 1) / num_diffusion_timesteps + betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta)) + return np.array(betas) + +def rescale_zero_terminal_snr(betas): + """ + Rescales betas to have zero terminal SNR Based on https://arxiv.org/pdf/2305.08891.pdf (Algorithm 1) + + Args: + betas (`numpy.ndarray`): + the betas that the scheduler is being initialized with. + + Returns: + `numpy.ndarray`: rescaled betas with zero terminal SNR + """ + # Convert betas to alphas_bar_sqrt + alphas = 1.0 - betas + alphas_cumprod = np.cumprod(alphas, axis=0) + alphas_bar_sqrt = np.sqrt(alphas_cumprod) + + # Store old values. + alphas_bar_sqrt_0 = alphas_bar_sqrt[0].copy() + alphas_bar_sqrt_T = alphas_bar_sqrt[-1].copy() + + # Shift so the last timestep is zero. + alphas_bar_sqrt -= alphas_bar_sqrt_T + + # Scale so the first timestep is back to the old value. + alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T) + + # Convert alphas_bar_sqrt to betas + alphas_bar = alphas_bar_sqrt**2 # Revert sqrt + alphas = alphas_bar[1:] / alphas_bar[:-1] # Revert cumprod + alphas = np.concatenate([alphas_bar[0:1], alphas]) + betas = 1 - alphas + + return betas + + +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 + return noise_cfg \ No newline at end of file diff --git a/lvdm/modules/attention.py b/lvdm/modules/attention.py new file mode 100644 index 0000000000000000000000000000000000000000..176885d9ff6f5675c413523a38b78845ce04bd97 --- /dev/null +++ b/lvdm/modules/attention.py @@ -0,0 +1,514 @@ +import torch +from torch import nn, einsum +import torch.nn.functional as F +from einops import rearrange, repeat +from functools import partial +try: + import xformers + import xformers.ops + XFORMERS_IS_AVAILBLE = True +except: + XFORMERS_IS_AVAILBLE = False +from lvdm.common import ( + checkpoint, + exists, + default, +) +from lvdm.basics import zero_module + + +class RelativePosition(nn.Module): + """ https://github.com/evelinehong/Transformer_Relative_Position_PyTorch/blob/master/relative_position.py """ + + def __init__(self, num_units, max_relative_position): + super().__init__() + self.num_units = num_units + self.max_relative_position = max_relative_position + self.embeddings_table = nn.Parameter(torch.Tensor(max_relative_position * 2 + 1, num_units)) + nn.init.xavier_uniform_(self.embeddings_table) + + def forward(self, length_q, length_k): + device = self.embeddings_table.device + range_vec_q = torch.arange(length_q, device=device) + range_vec_k = torch.arange(length_k, device=device) + distance_mat = range_vec_k[None, :] - range_vec_q[:, None] + distance_mat_clipped = torch.clamp(distance_mat, -self.max_relative_position, self.max_relative_position) + final_mat = distance_mat_clipped + self.max_relative_position + final_mat = final_mat.long() + embeddings = self.embeddings_table[final_mat] + return embeddings + + +class CrossAttention(nn.Module): + + def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., + relative_position=False, temporal_length=None, video_length=None, image_cross_attention=False, image_cross_attention_scale=1.0, image_cross_attention_scale_learnable=False, text_context_len=77): + super().__init__() + inner_dim = dim_head * heads + context_dim = default(context_dim, query_dim) + + self.scale = dim_head**-0.5 + self.heads = heads + self.dim_head = dim_head + self.to_q = nn.Linear(query_dim, inner_dim, bias=False) + self.to_k = nn.Linear(context_dim, inner_dim, bias=False) + self.to_v = nn.Linear(context_dim, inner_dim, bias=False) + + self.to_out = nn.Sequential(nn.Linear(inner_dim, query_dim), nn.Dropout(dropout)) + + self.relative_position = relative_position + if self.relative_position: + assert(temporal_length is not None) + self.relative_position_k = RelativePosition(num_units=dim_head, max_relative_position=temporal_length) + self.relative_position_v = RelativePosition(num_units=dim_head, max_relative_position=temporal_length) + else: + ## only used for spatial attention, while NOT for temporal attention + if XFORMERS_IS_AVAILBLE and temporal_length is None: + self.forward = self.efficient_forward + + self.video_length = video_length + self.image_cross_attention = image_cross_attention + self.image_cross_attention_scale = image_cross_attention_scale + self.text_context_len = text_context_len + self.image_cross_attention_scale_learnable = image_cross_attention_scale_learnable + if self.image_cross_attention: + self.to_k_ip = nn.Linear(context_dim, inner_dim, bias=False) + self.to_v_ip = nn.Linear(context_dim, inner_dim, bias=False) + if image_cross_attention_scale_learnable: + self.register_parameter('alpha', nn.Parameter(torch.tensor(0.)) ) + + + def forward(self, x, context=None, mask=None): + spatial_self_attn = (context is None) + k_ip, v_ip, out_ip = None, None, None + + h = self.heads + q = self.to_q(x) + context = default(context, x) + + if self.image_cross_attention and not spatial_self_attn: + context, context_image = context[:,:self.text_context_len,:], context[:,self.text_context_len:,:] + k = self.to_k(context) + v = self.to_v(context) + k_ip = self.to_k_ip(context_image) + v_ip = self.to_v_ip(context_image) + else: + if not spatial_self_attn: + context = context[:,:self.text_context_len,:] + k = self.to_k(context) + v = self.to_v(context) + + q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v)) + + sim = torch.einsum('b i d, b j d -> b i j', q, k) * self.scale + if self.relative_position: + len_q, len_k, len_v = q.shape[1], k.shape[1], v.shape[1] + k2 = self.relative_position_k(len_q, len_k) + sim2 = einsum('b t d, t s d -> b t s', q, k2) * self.scale # TODO check + sim += sim2 + del k + + if exists(mask): + ## feasible for causal attention mask only + max_neg_value = -torch.finfo(sim.dtype).max + mask = repeat(mask, 'b i j -> (b h) i j', h=h) + sim.masked_fill_(~(mask>0.5), max_neg_value) + + # attention, what we cannot get enough of + sim = sim.softmax(dim=-1) + + out = torch.einsum('b i j, b j d -> b i d', sim, v) + if self.relative_position: + v2 = self.relative_position_v(len_q, len_v) + out2 = einsum('b t s, t s d -> b t d', sim, v2) # TODO check + out += out2 + out = rearrange(out, '(b h) n d -> b n (h d)', h=h) + + + ## for image cross-attention + if k_ip is not None: + k_ip, v_ip = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (k_ip, v_ip)) + sim_ip = torch.einsum('b i d, b j d -> b i j', q, k_ip) * self.scale + del k_ip + sim_ip = sim_ip.softmax(dim=-1) + out_ip = torch.einsum('b i j, b j d -> b i d', sim_ip, v_ip) + out_ip = rearrange(out_ip, '(b h) n d -> b n (h d)', h=h) + + + if out_ip is not None: + if self.image_cross_attention_scale_learnable: + out = out + self.image_cross_attention_scale * out_ip * (torch.tanh(self.alpha)+1) + else: + out = out + self.image_cross_attention_scale * out_ip + + return self.to_out(out) + + def efficient_forward(self, x, context=None, mask=None): + spatial_self_attn = (context is None) + k_ip, v_ip, out_ip = None, None, None + + q = self.to_q(x) + context = default(context, x) + + if self.image_cross_attention and not spatial_self_attn: + context, context_image = context[:,:self.text_context_len,:], context[:,self.text_context_len:,:] + k = self.to_k(context) + v = self.to_v(context) + k_ip = self.to_k_ip(context_image) + v_ip = self.to_v_ip(context_image) + else: + if not spatial_self_attn: + context = context[:,:self.text_context_len,:] + k = self.to_k(context) + v = self.to_v(context) + + b, _, _ = q.shape + q, k, v = map( + lambda t: t.unsqueeze(3) + .reshape(b, t.shape[1], self.heads, self.dim_head) + .permute(0, 2, 1, 3) + .reshape(b * self.heads, t.shape[1], self.dim_head) + .contiguous(), + (q, k, v), + ) + # actually compute the attention, what we cannot get enough of + out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=None) + + ## for image cross-attention + if k_ip is not None: + k_ip, v_ip = map( + lambda t: t.unsqueeze(3) + .reshape(b, t.shape[1], self.heads, self.dim_head) + .permute(0, 2, 1, 3) + .reshape(b * self.heads, t.shape[1], self.dim_head) + .contiguous(), + (k_ip, v_ip), + ) + out_ip = xformers.ops.memory_efficient_attention(q, k_ip, v_ip, attn_bias=None, op=None) + out_ip = ( + out_ip.unsqueeze(0) + .reshape(b, self.heads, out.shape[1], self.dim_head) + .permute(0, 2, 1, 3) + .reshape(b, out.shape[1], self.heads * self.dim_head) + ) + + if exists(mask): + raise NotImplementedError + out = ( + out.unsqueeze(0) + .reshape(b, self.heads, out.shape[1], self.dim_head) + .permute(0, 2, 1, 3) + .reshape(b, out.shape[1], self.heads * self.dim_head) + ) + if out_ip is not None: + if self.image_cross_attention_scale_learnable: + out = out + self.image_cross_attention_scale * out_ip * (torch.tanh(self.alpha)+1) + else: + out = out + self.image_cross_attention_scale * out_ip + + return self.to_out(out) + + +class BasicTransformerBlock(nn.Module): + + def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True, + disable_self_attn=False, attention_cls=None, video_length=None, image_cross_attention=False, image_cross_attention_scale=1.0, image_cross_attention_scale_learnable=False, text_context_len=77): + super().__init__() + attn_cls = CrossAttention if attention_cls is None else attention_cls + self.disable_self_attn = disable_self_attn + self.attn1 = attn_cls(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout, + context_dim=context_dim if self.disable_self_attn else None) + self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff) + self.attn2 = attn_cls(query_dim=dim, context_dim=context_dim, heads=n_heads, dim_head=d_head, dropout=dropout, video_length=video_length, image_cross_attention=image_cross_attention, image_cross_attention_scale=image_cross_attention_scale, image_cross_attention_scale_learnable=image_cross_attention_scale_learnable,text_context_len=text_context_len) + self.image_cross_attention = image_cross_attention + + self.norm1 = nn.LayerNorm(dim) + self.norm2 = nn.LayerNorm(dim) + self.norm3 = nn.LayerNorm(dim) + self.checkpoint = checkpoint + + + def forward(self, x, context=None, mask=None, **kwargs): + ## implementation tricks: because checkpointing doesn't support non-tensor (e.g. None or scalar) arguments + input_tuple = (x,) ## should not be (x), otherwise *input_tuple will decouple x into multiple arguments + if context is not None: + input_tuple = (x, context) + if mask is not None: + forward_mask = partial(self._forward, mask=mask) + return checkpoint(forward_mask, (x,), self.parameters(), self.checkpoint) + return checkpoint(self._forward, input_tuple, self.parameters(), self.checkpoint) + + + def _forward(self, x, context=None, mask=None): + x = self.attn1(self.norm1(x), context=context if self.disable_self_attn else None, mask=mask) + x + x = self.attn2(self.norm2(x), context=context, mask=mask) + x + x = self.ff(self.norm3(x)) + x + return x + + +class SpatialTransformer(nn.Module): + """ + Transformer block for image-like data in spatial axis. + First, project the input (aka embedding) + and reshape to b, t, d. + Then apply standard transformer action. + Finally, reshape to image + NEW: use_linear for more efficiency instead of the 1x1 convs + """ + + def __init__(self, in_channels, n_heads, d_head, depth=1, dropout=0., context_dim=None, + use_checkpoint=True, disable_self_attn=False, use_linear=False, video_length=None, + image_cross_attention=False, image_cross_attention_scale_learnable=False): + super().__init__() + self.in_channels = in_channels + inner_dim = n_heads * d_head + self.norm = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) + if not use_linear: + self.proj_in = nn.Conv2d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0) + else: + self.proj_in = nn.Linear(in_channels, inner_dim) + + attention_cls = None + self.transformer_blocks = nn.ModuleList([ + BasicTransformerBlock( + inner_dim, + n_heads, + d_head, + dropout=dropout, + context_dim=context_dim, + disable_self_attn=disable_self_attn, + checkpoint=use_checkpoint, + attention_cls=attention_cls, + video_length=video_length, + image_cross_attention=image_cross_attention, + image_cross_attention_scale_learnable=image_cross_attention_scale_learnable, + ) for d in range(depth) + ]) + if not use_linear: + self.proj_out = zero_module(nn.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0)) + else: + self.proj_out = zero_module(nn.Linear(inner_dim, in_channels)) + self.use_linear = use_linear + + + def forward(self, x, context=None, **kwargs): + b, c, h, w = x.shape + x_in = x + x = self.norm(x) + if not self.use_linear: + x = self.proj_in(x) + x = rearrange(x, 'b c h w -> b (h w) c').contiguous() + if self.use_linear: + x = self.proj_in(x) + for i, block in enumerate(self.transformer_blocks): + x = block(x, context=context, **kwargs) + if self.use_linear: + x = self.proj_out(x) + x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w).contiguous() + if not self.use_linear: + x = self.proj_out(x) + return x + x_in + + +class TemporalTransformer(nn.Module): + """ + Transformer block for image-like data in temporal axis. + First, reshape to b, t, d. + Then apply standard transformer action. + Finally, reshape to image + """ + def __init__(self, in_channels, n_heads, d_head, depth=1, dropout=0., context_dim=None, + use_checkpoint=True, use_linear=False, only_self_att=True, causal_attention=False, causal_block_size=1, + relative_position=False, temporal_length=None): + super().__init__() + self.only_self_att = only_self_att + self.relative_position = relative_position + self.causal_attention = causal_attention + self.causal_block_size = causal_block_size + + self.in_channels = in_channels + inner_dim = n_heads * d_head + self.norm = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) + self.proj_in = nn.Conv1d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0) + if not use_linear: + self.proj_in = nn.Conv1d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0) + else: + self.proj_in = nn.Linear(in_channels, inner_dim) + + if relative_position: + assert(temporal_length is not None) + attention_cls = partial(CrossAttention, relative_position=True, temporal_length=temporal_length) + else: + attention_cls = partial(CrossAttention, temporal_length=temporal_length) + if self.causal_attention: + assert(temporal_length is not None) + self.mask = torch.tril(torch.ones([1, temporal_length, temporal_length])) + + if self.only_self_att: + context_dim = None + self.transformer_blocks = nn.ModuleList([ + BasicTransformerBlock( + inner_dim, + n_heads, + d_head, + dropout=dropout, + context_dim=context_dim, + attention_cls=attention_cls, + checkpoint=use_checkpoint) for d in range(depth) + ]) + if not use_linear: + self.proj_out = zero_module(nn.Conv1d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0)) + else: + self.proj_out = zero_module(nn.Linear(inner_dim, in_channels)) + self.use_linear = use_linear + + def forward(self, x, context=None): + b, c, t, h, w = x.shape + x_in = x + x = self.norm(x) + x = rearrange(x, 'b c t h w -> (b h w) c t').contiguous() + if not self.use_linear: + x = self.proj_in(x) + x = rearrange(x, 'bhw c t -> bhw t c').contiguous() + if self.use_linear: + x = self.proj_in(x) + + temp_mask = None + if self.causal_attention: + # slice the from mask map + temp_mask = self.mask[:,:t,:t].to(x.device) + + if temp_mask is not None: + mask = temp_mask.to(x.device) + mask = repeat(mask, 'l i j -> (l bhw) i j', bhw=b*h*w) + else: + mask = None + + if self.only_self_att: + ## note: if no context is given, cross-attention defaults to self-attention + for i, block in enumerate(self.transformer_blocks): + x = block(x, mask=mask) + x = rearrange(x, '(b hw) t c -> b hw t c', b=b).contiguous() + else: + x = rearrange(x, '(b hw) t c -> b hw t c', b=b).contiguous() + context = rearrange(context, '(b t) l con -> b t l con', t=t).contiguous() + for i, block in enumerate(self.transformer_blocks): + # calculate each batch one by one (since number in shape could not greater then 65,535 for some package) + for j in range(b): + context_j = repeat( + context[j], + 't l con -> (t r) l con', r=(h * w) // t, t=t).contiguous() + ## note: causal mask will not applied in cross-attention case + x[j] = block(x[j], context=context_j) + + if self.use_linear: + x = self.proj_out(x) + x = rearrange(x, 'b (h w) t c -> b c t h w', h=h, w=w).contiguous() + if not self.use_linear: + x = rearrange(x, 'b hw t c -> (b hw) c t').contiguous() + x = self.proj_out(x) + x = rearrange(x, '(b h w) c t -> b c t h w', b=b, h=h, w=w).contiguous() + + return x + x_in + + +class GEGLU(nn.Module): + def __init__(self, dim_in, dim_out): + super().__init__() + self.proj = nn.Linear(dim_in, dim_out * 2) + + def forward(self, x): + x, gate = self.proj(x).chunk(2, dim=-1) + return x * F.gelu(gate) + + +class FeedForward(nn.Module): + def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.): + super().__init__() + inner_dim = int(dim * mult) + dim_out = default(dim_out, dim) + project_in = nn.Sequential( + nn.Linear(dim, inner_dim), + nn.GELU() + ) if not glu else GEGLU(dim, inner_dim) + + self.net = nn.Sequential( + project_in, + nn.Dropout(dropout), + nn.Linear(inner_dim, dim_out) + ) + + def forward(self, x): + return self.net(x) + + +class LinearAttention(nn.Module): + def __init__(self, dim, heads=4, dim_head=32): + super().__init__() + self.heads = heads + hidden_dim = dim_head * heads + self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias = False) + self.to_out = nn.Conv2d(hidden_dim, dim, 1) + + def forward(self, x): + b, c, h, w = x.shape + qkv = self.to_qkv(x) + q, k, v = rearrange(qkv, 'b (qkv heads c) h w -> qkv b heads c (h w)', heads = self.heads, qkv=3) + k = k.softmax(dim=-1) + context = torch.einsum('bhdn,bhen->bhde', k, v) + out = torch.einsum('bhde,bhdn->bhen', context, q) + out = rearrange(out, 'b heads c (h w) -> b (heads c) h w', heads=self.heads, h=h, w=w) + return self.to_out(out) + + +class SpatialSelfAttention(nn.Module): + def __init__(self, in_channels): + super().__init__() + self.in_channels = in_channels + + self.norm = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) + self.q = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.k = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.v = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.proj_out = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + + def forward(self, x): + h_ = x + h_ = self.norm(h_) + q = self.q(h_) + k = self.k(h_) + v = self.v(h_) + + # compute attention + b,c,h,w = q.shape + q = rearrange(q, 'b c h w -> b (h w) c') + k = rearrange(k, 'b c h w -> b c (h w)') + w_ = torch.einsum('bij,bjk->bik', q, k) + + w_ = w_ * (int(c)**(-0.5)) + w_ = torch.nn.functional.softmax(w_, dim=2) + + # attend to values + v = rearrange(v, 'b c h w -> b c (h w)') + w_ = rearrange(w_, 'b i j -> b j i') + h_ = torch.einsum('bij,bjk->bik', v, w_) + h_ = rearrange(h_, 'b c (h w) -> b c h w', h=h) + h_ = self.proj_out(h_) + + return x+h_ diff --git a/lvdm/modules/attention_svd.py b/lvdm/modules/attention_svd.py new file mode 100644 index 0000000000000000000000000000000000000000..92ceb3c978025c9bb9a640d63558a20a4989d377 --- /dev/null +++ b/lvdm/modules/attention_svd.py @@ -0,0 +1,759 @@ +import logging +import math +from inspect import isfunction +from typing import Any, Optional + +import torch +import torch.nn.functional as F +from einops import rearrange, repeat +from packaging import version +from torch import nn +from torch.utils.checkpoint import checkpoint + +logpy = logging.getLogger(__name__) + +if version.parse(torch.__version__) >= version.parse("2.0.0"): + SDP_IS_AVAILABLE = True + from torch.backends.cuda import SDPBackend, sdp_kernel + + BACKEND_MAP = { + SDPBackend.MATH: { + "enable_math": True, + "enable_flash": False, + "enable_mem_efficient": False, + }, + SDPBackend.FLASH_ATTENTION: { + "enable_math": False, + "enable_flash": True, + "enable_mem_efficient": False, + }, + SDPBackend.EFFICIENT_ATTENTION: { + "enable_math": False, + "enable_flash": False, + "enable_mem_efficient": True, + }, + None: {"enable_math": True, "enable_flash": True, "enable_mem_efficient": True}, + } +else: + from contextlib import nullcontext + + SDP_IS_AVAILABLE = False + sdp_kernel = nullcontext + BACKEND_MAP = {} + logpy.warn( + f"No SDP backend available, likely because you are running in pytorch " + f"versions < 2.0. In fact, you are using PyTorch {torch.__version__}. " + f"You might want to consider upgrading." + ) + +try: + import xformers + import xformers.ops + + XFORMERS_IS_AVAILABLE = True +except: + XFORMERS_IS_AVAILABLE = False + logpy.warn("no module 'xformers'. Processing without...") + +# from .diffusionmodules.util import mixed_checkpoint as checkpoint + + +def exists(val): + return val is not None + + +def uniq(arr): + return {el: True for el in arr}.keys() + + +def default(val, d): + if exists(val): + return val + return d() if isfunction(d) else d + + +def max_neg_value(t): + return -torch.finfo(t.dtype).max + + +def init_(tensor): + dim = tensor.shape[-1] + std = 1 / math.sqrt(dim) + tensor.uniform_(-std, std) + return tensor + + +# feedforward +class GEGLU(nn.Module): + def __init__(self, dim_in, dim_out): + super().__init__() + self.proj = nn.Linear(dim_in, dim_out * 2) + + def forward(self, x): + x, gate = self.proj(x).chunk(2, dim=-1) + return x * F.gelu(gate) + + +class FeedForward(nn.Module): + def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.0): + super().__init__() + inner_dim = int(dim * mult) + dim_out = default(dim_out, dim) + project_in = ( + nn.Sequential(nn.Linear(dim, inner_dim), nn.GELU()) + if not glu + else GEGLU(dim, inner_dim) + ) + + self.net = nn.Sequential( + project_in, nn.Dropout(dropout), nn.Linear(inner_dim, dim_out) + ) + + def forward(self, x): + return self.net(x) + + +def zero_module(module): + """ + Zero out the parameters of a module and return it. + """ + for p in module.parameters(): + p.detach().zero_() + return module + + +def Normalize(in_channels): + return torch.nn.GroupNorm( + num_groups=32, num_channels=in_channels, eps=1e-6, affine=True + ) + + +class LinearAttention(nn.Module): + def __init__(self, dim, heads=4, dim_head=32): + super().__init__() + self.heads = heads + hidden_dim = dim_head * heads + self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias=False) + self.to_out = nn.Conv2d(hidden_dim, dim, 1) + + def forward(self, x): + b, c, h, w = x.shape + qkv = self.to_qkv(x) + q, k, v = rearrange( + qkv, "b (qkv heads c) h w -> qkv b heads c (h w)", heads=self.heads, qkv=3 + ) + k = k.softmax(dim=-1) + context = torch.einsum("bhdn,bhen->bhde", k, v) + out = torch.einsum("bhde,bhdn->bhen", context, q) + out = rearrange( + out, "b heads c (h w) -> b (heads c) h w", heads=self.heads, h=h, w=w + ) + return self.to_out(out) + + +class SelfAttention(nn.Module): + ATTENTION_MODES = ("xformers", "torch", "math") + + def __init__( + self, + dim: int, + num_heads: int = 8, + qkv_bias: bool = False, + qk_scale: Optional[float] = None, + attn_drop: float = 0.0, + proj_drop: float = 0.0, + attn_mode: str = "xformers", + ): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = qk_scale or head_dim**-0.5 + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + assert attn_mode in self.ATTENTION_MODES + self.attn_mode = attn_mode + + def forward(self, x: torch.Tensor) -> torch.Tensor: + B, L, C = x.shape + + qkv = self.qkv(x) + if self.attn_mode == "torch": + qkv = rearrange( + qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads + ).float() + q, k, v = qkv[0], qkv[1], qkv[2] # B H L D + x = torch.nn.functional.scaled_dot_product_attention(q, k, v) + x = rearrange(x, "B H L D -> B L (H D)") + elif self.attn_mode == "xformers": + qkv = rearrange(qkv, "B L (K H D) -> K B L H D", K=3, H=self.num_heads) + q, k, v = qkv[0], qkv[1], qkv[2] # B L H D + x = xformers.ops.memory_efficient_attention(q, k, v) + x = rearrange(x, "B L H D -> B L (H D)", H=self.num_heads) + elif self.attn_mode == "math": + qkv = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads) + q, k, v = qkv[0], qkv[1], qkv[2] # B H L D + attn = (q @ k.transpose(-2, -1)) * self.scale + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + x = (attn @ v).transpose(1, 2).reshape(B, L, C) + else: + raise NotImplemented + + x = self.proj(x) + x = self.proj_drop(x) + return x + + +class SpatialSelfAttention(nn.Module): + def __init__(self, in_channels): + super().__init__() + self.in_channels = in_channels + + self.norm = Normalize(in_channels) + self.q = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.k = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.v = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.proj_out = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + + def forward(self, x): + h_ = x + h_ = self.norm(h_) + q = self.q(h_) + k = self.k(h_) + v = self.v(h_) + + # compute attention + b, c, h, w = q.shape + q = rearrange(q, "b c h w -> b (h w) c") + k = rearrange(k, "b c h w -> b c (h w)") + w_ = torch.einsum("bij,bjk->bik", q, k) + + w_ = w_ * (int(c) ** (-0.5)) + w_ = torch.nn.functional.softmax(w_, dim=2) + + # attend to values + v = rearrange(v, "b c h w -> b c (h w)") + w_ = rearrange(w_, "b i j -> b j i") + h_ = torch.einsum("bij,bjk->bik", v, w_) + h_ = rearrange(h_, "b c (h w) -> b c h w", h=h) + h_ = self.proj_out(h_) + + return x + h_ + + +class CrossAttention(nn.Module): + def __init__( + self, + query_dim, + context_dim=None, + heads=8, + dim_head=64, + dropout=0.0, + backend=None, + ): + super().__init__() + inner_dim = dim_head * heads + context_dim = default(context_dim, query_dim) + + self.scale = dim_head**-0.5 + self.heads = heads + + self.to_q = nn.Linear(query_dim, inner_dim, bias=False) + self.to_k = nn.Linear(context_dim, inner_dim, bias=False) + self.to_v = nn.Linear(context_dim, inner_dim, bias=False) + + self.to_out = nn.Sequential( + nn.Linear(inner_dim, query_dim), nn.Dropout(dropout) + ) + self.backend = backend + + def forward( + self, + x, + context=None, + mask=None, + additional_tokens=None, + n_times_crossframe_attn_in_self=0, + ): + h = self.heads + + if additional_tokens is not None: + # get the number of masked tokens at the beginning of the output sequence + n_tokens_to_mask = additional_tokens.shape[1] + # add additional token + x = torch.cat([additional_tokens, x], dim=1) + + q = self.to_q(x) + context = default(context, x) + k = self.to_k(context) + v = self.to_v(context) + + if n_times_crossframe_attn_in_self: + # reprogramming cross-frame attention as in https://arxiv.org/abs/2303.13439 + assert x.shape[0] % n_times_crossframe_attn_in_self == 0 + n_cp = x.shape[0] // n_times_crossframe_attn_in_self + k = repeat( + k[::n_times_crossframe_attn_in_self], "b ... -> (b n) ...", n=n_cp + ) + v = repeat( + v[::n_times_crossframe_attn_in_self], "b ... -> (b n) ...", n=n_cp + ) + + q, k, v = map(lambda t: rearrange(t, "b n (h d) -> b h n d", h=h), (q, k, v)) + + ## old + """ + sim = einsum('b i d, b j d -> b i j', q, k) * self.scale + del q, k + + if exists(mask): + mask = rearrange(mask, 'b ... -> b (...)') + max_neg_value = -torch.finfo(sim.dtype).max + mask = repeat(mask, 'b j -> (b h) () j', h=h) + sim.masked_fill_(~mask, max_neg_value) + + # attention, what we cannot get enough of + sim = sim.softmax(dim=-1) + + out = einsum('b i j, b j d -> b i d', sim, v) + """ + ## new + with sdp_kernel(**BACKEND_MAP[self.backend]): + # print("dispatching into backend", self.backend, "q/k/v shape: ", q.shape, k.shape, v.shape) + out = F.scaled_dot_product_attention( + q, k, v, attn_mask=mask + ) # scale is dim_head ** -0.5 per default + + del q, k, v + out = rearrange(out, "b h n d -> b n (h d)", h=h) + + if additional_tokens is not None: + # remove additional token + out = out[:, n_tokens_to_mask:] + return self.to_out(out) + + +class MemoryEfficientCrossAttention(nn.Module): + # https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223 + def __init__( + self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0, **kwargs + ): + super().__init__() + logpy.debug( + f"Setting up {self.__class__.__name__}. Query dim is {query_dim}, " + f"context_dim is {context_dim} and using {heads} heads with a " + f"dimension of {dim_head}." + ) + inner_dim = dim_head * heads + context_dim = default(context_dim, query_dim) + + self.heads = heads + self.dim_head = dim_head + + self.to_q = nn.Linear(query_dim, inner_dim, bias=False) + self.to_k = nn.Linear(context_dim, inner_dim, bias=False) + self.to_v = nn.Linear(context_dim, inner_dim, bias=False) + + self.to_out = nn.Sequential( + nn.Linear(inner_dim, query_dim), nn.Dropout(dropout) + ) + self.attention_op: Optional[Any] = None + + def forward( + self, + x, + context=None, + mask=None, + additional_tokens=None, + n_times_crossframe_attn_in_self=0, + ): + if additional_tokens is not None: + # get the number of masked tokens at the beginning of the output sequence + n_tokens_to_mask = additional_tokens.shape[1] + # add additional token + x = torch.cat([additional_tokens, x], dim=1) + q = self.to_q(x) + context = default(context, x) + k = self.to_k(context) + v = self.to_v(context) + + if n_times_crossframe_attn_in_self: + # reprogramming cross-frame attention as in https://arxiv.org/abs/2303.13439 + assert x.shape[0] % n_times_crossframe_attn_in_self == 0 + # n_cp = x.shape[0]//n_times_crossframe_attn_in_self + k = repeat( + k[::n_times_crossframe_attn_in_self], + "b ... -> (b n) ...", + n=n_times_crossframe_attn_in_self, + ) + v = repeat( + v[::n_times_crossframe_attn_in_self], + "b ... -> (b n) ...", + n=n_times_crossframe_attn_in_self, + ) + + b, _, _ = q.shape + q, k, v = map( + lambda t: t.unsqueeze(3) + .reshape(b, t.shape[1], self.heads, self.dim_head) + .permute(0, 2, 1, 3) + .reshape(b * self.heads, t.shape[1], self.dim_head) + .contiguous(), + (q, k, v), + ) + + # actually compute the attention, what we cannot get enough of + if version.parse(xformers.__version__) >= version.parse("0.0.21"): + # NOTE: workaround for + # https://github.com/facebookresearch/xformers/issues/845 + max_bs = 32768 + N = q.shape[0] + n_batches = math.ceil(N / max_bs) + out = list() + for i_batch in range(n_batches): + batch = slice(i_batch * max_bs, (i_batch + 1) * max_bs) + out.append( + xformers.ops.memory_efficient_attention( + q[batch], + k[batch], + v[batch], + attn_bias=None, + op=self.attention_op, + ) + ) + out = torch.cat(out, 0) + else: + out = xformers.ops.memory_efficient_attention( + q, k, v, attn_bias=None, op=self.attention_op + ) + + # TODO: Use this directly in the attention operation, as a bias + if exists(mask): + raise NotImplementedError + out = ( + out.unsqueeze(0) + .reshape(b, self.heads, out.shape[1], self.dim_head) + .permute(0, 2, 1, 3) + .reshape(b, out.shape[1], self.heads * self.dim_head) + ) + if additional_tokens is not None: + # remove additional token + out = out[:, n_tokens_to_mask:] + return self.to_out(out) + + +class BasicTransformerBlock(nn.Module): + ATTENTION_MODES = { + "softmax": CrossAttention, # vanilla attention + "softmax-xformers": MemoryEfficientCrossAttention, # ampere + } + + def __init__( + self, + dim, + n_heads, + d_head, + dropout=0.0, + context_dim=None, + gated_ff=True, + checkpoint=True, + disable_self_attn=False, + attn_mode="softmax", + sdp_backend=None, + ): + super().__init__() + assert attn_mode in self.ATTENTION_MODES + if attn_mode != "softmax" and not XFORMERS_IS_AVAILABLE: + logpy.warn( + f"Attention mode '{attn_mode}' is not available. Falling " + f"back to native attention. This is not a problem in " + f"Pytorch >= 2.0. FYI, you are running with PyTorch " + f"version {torch.__version__}." + ) + attn_mode = "softmax" + elif attn_mode == "softmax" and not SDP_IS_AVAILABLE: + logpy.warn( + "We do not support vanilla attention anymore, as it is too " + "expensive. Sorry." + ) + if not XFORMERS_IS_AVAILABLE: + assert ( + False + ), "Please install xformers via e.g. 'pip install xformers==0.0.16'" + else: + logpy.info("Falling back to xformers efficient attention.") + attn_mode = "softmax-xformers" + attn_cls = self.ATTENTION_MODES[attn_mode] + if version.parse(torch.__version__) >= version.parse("2.0.0"): + assert sdp_backend is None or isinstance(sdp_backend, SDPBackend) + else: + assert sdp_backend is None + self.disable_self_attn = disable_self_attn + self.attn1 = attn_cls( + query_dim=dim, + heads=n_heads, + dim_head=d_head, + dropout=dropout, + context_dim=context_dim if self.disable_self_attn else None, + backend=sdp_backend, + ) # is a self-attention if not self.disable_self_attn + self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff) + self.attn2 = attn_cls( + query_dim=dim, + context_dim=context_dim, + heads=n_heads, + dim_head=d_head, + dropout=dropout, + backend=sdp_backend, + ) # is self-attn if context is none + self.norm1 = nn.LayerNorm(dim) + self.norm2 = nn.LayerNorm(dim) + self.norm3 = nn.LayerNorm(dim) + self.checkpoint = checkpoint + if self.checkpoint: + logpy.debug(f"{self.__class__.__name__} is using checkpointing") + + def forward( + self, x, context=None, additional_tokens=None, n_times_crossframe_attn_in_self=0 + ): + kwargs = {"x": x} + + if context is not None: + kwargs.update({"context": context}) + + if additional_tokens is not None: + kwargs.update({"additional_tokens": additional_tokens}) + + if n_times_crossframe_attn_in_self: + kwargs.update( + {"n_times_crossframe_attn_in_self": n_times_crossframe_attn_in_self} + ) + + # return mixed_checkpoint(self._forward, kwargs, self.parameters(), self.checkpoint) + if self.checkpoint: + # inputs = {"x": x, "context": context} + return checkpoint(self._forward, x, context) + # return checkpoint(self._forward, inputs, self.parameters(), self.checkpoint) + else: + return self._forward(**kwargs) + + def _forward( + self, x, context=None, additional_tokens=None, n_times_crossframe_attn_in_self=0 + ): + x = ( + self.attn1( + self.norm1(x), + context=context if self.disable_self_attn else None, + additional_tokens=additional_tokens, + n_times_crossframe_attn_in_self=n_times_crossframe_attn_in_self + if not self.disable_self_attn + else 0, + ) + + x + ) + x = ( + self.attn2( + self.norm2(x), context=context, additional_tokens=additional_tokens + ) + + x + ) + x = self.ff(self.norm3(x)) + x + return x + + +class BasicTransformerSingleLayerBlock(nn.Module): + ATTENTION_MODES = { + "softmax": CrossAttention, # vanilla attention + "softmax-xformers": MemoryEfficientCrossAttention # on the A100s not quite as fast as the above version + # (todo might depend on head_dim, check, falls back to semi-optimized kernels for dim!=[16,32,64,128]) + } + + def __init__( + self, + dim, + n_heads, + d_head, + dropout=0.0, + context_dim=None, + gated_ff=True, + checkpoint=True, + attn_mode="softmax", + ): + super().__init__() + assert attn_mode in self.ATTENTION_MODES + attn_cls = self.ATTENTION_MODES[attn_mode] + self.attn1 = attn_cls( + query_dim=dim, + heads=n_heads, + dim_head=d_head, + dropout=dropout, + context_dim=context_dim, + ) + self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff) + self.norm1 = nn.LayerNorm(dim) + self.norm2 = nn.LayerNorm(dim) + self.checkpoint = checkpoint + + def forward(self, x, context=None): + # inputs = {"x": x, "context": context} + # return checkpoint(self._forward, inputs, self.parameters(), self.checkpoint) + return checkpoint(self._forward, x, context) + + def _forward(self, x, context=None): + x = self.attn1(self.norm1(x), context=context) + x + x = self.ff(self.norm2(x)) + x + return x + + +class SpatialTransformer(nn.Module): + """ + Transformer block for image-like data. + First, project the input (aka embedding) + and reshape to b, t, d. + Then apply standard transformer action. + Finally, reshape to image + NEW: use_linear for more efficiency instead of the 1x1 convs + """ + + def __init__( + self, + in_channels, + n_heads, + d_head, + depth=1, + dropout=0.0, + context_dim=None, + disable_self_attn=False, + use_linear=False, + attn_type="softmax", + use_checkpoint=True, + # sdp_backend=SDPBackend.FLASH_ATTENTION + sdp_backend=None, + ): + super().__init__() + logpy.debug( + f"constructing {self.__class__.__name__} of depth {depth} w/ " + f"{in_channels} channels and {n_heads} heads." + ) + + if exists(context_dim) and not isinstance(context_dim, list): + context_dim = [context_dim] + if exists(context_dim) and isinstance(context_dim, list): + if depth != len(context_dim): + logpy.warn( + f"{self.__class__.__name__}: Found context dims " + f"{context_dim} of depth {len(context_dim)}, which does not " + f"match the specified 'depth' of {depth}. Setting context_dim " + f"to {depth * [context_dim[0]]} now." + ) + # depth does not match context dims. + assert all( + map(lambda x: x == context_dim[0], context_dim) + ), "need homogenous context_dim to match depth automatically" + context_dim = depth * [context_dim[0]] + elif context_dim is None: + context_dim = [None] * depth + self.in_channels = in_channels + inner_dim = n_heads * d_head + self.norm = Normalize(in_channels) + if not use_linear: + self.proj_in = nn.Conv2d( + in_channels, inner_dim, kernel_size=1, stride=1, padding=0 + ) + else: + self.proj_in = nn.Linear(in_channels, inner_dim) + + self.transformer_blocks = nn.ModuleList( + [ + BasicTransformerBlock( + inner_dim, + n_heads, + d_head, + dropout=dropout, + context_dim=context_dim[d], + disable_self_attn=disable_self_attn, + attn_mode=attn_type, + checkpoint=use_checkpoint, + sdp_backend=sdp_backend, + ) + for d in range(depth) + ] + ) + if not use_linear: + self.proj_out = zero_module( + nn.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0) + ) + else: + # self.proj_out = zero_module(nn.Linear(in_channels, inner_dim)) + self.proj_out = zero_module(nn.Linear(inner_dim, in_channels)) + self.use_linear = use_linear + + def forward(self, x, context=None): + # note: if no context is given, cross-attention defaults to self-attention + if not isinstance(context, list): + context = [context] + b, c, h, w = x.shape + x_in = x + x = self.norm(x) + if not self.use_linear: + x = self.proj_in(x) + x = rearrange(x, "b c h w -> b (h w) c").contiguous() + if self.use_linear: + x = self.proj_in(x) + for i, block in enumerate(self.transformer_blocks): + if i > 0 and len(context) == 1: + i = 0 # use same context for each block + x = block(x, context=context[i]) + if self.use_linear: + x = self.proj_out(x) + x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w).contiguous() + if not self.use_linear: + x = self.proj_out(x) + return x + x_in + + +class SimpleTransformer(nn.Module): + def __init__( + self, + dim: int, + depth: int, + heads: int, + dim_head: int, + context_dim: Optional[int] = None, + dropout: float = 0.0, + checkpoint: bool = True, + ): + super().__init__() + self.layers = nn.ModuleList([]) + for _ in range(depth): + self.layers.append( + BasicTransformerBlock( + dim, + heads, + dim_head, + dropout=dropout, + context_dim=context_dim, + attn_mode="softmax-xformers", + checkpoint=checkpoint, + ) + ) + + def forward( + self, + x: torch.Tensor, + context: Optional[torch.Tensor] = None, + ) -> torch.Tensor: + for layer in self.layers: + x = layer(x, context) + return x \ No newline at end of file diff --git a/lvdm/modules/encoders/condition.py b/lvdm/modules/encoders/condition.py new file mode 100644 index 0000000000000000000000000000000000000000..443ce9a9e7e015dad5c05db737a189da83dbbb4f --- /dev/null +++ b/lvdm/modules/encoders/condition.py @@ -0,0 +1,389 @@ +import torch +import torch.nn as nn +import kornia +import open_clip +from torch.utils.checkpoint import checkpoint +from transformers import T5Tokenizer, T5EncoderModel, CLIPTokenizer, CLIPTextModel +from lvdm.common import autocast +from utils.utils import count_params + + +class AbstractEncoder(nn.Module): + def __init__(self): + super().__init__() + + def encode(self, *args, **kwargs): + raise NotImplementedError + + +class IdentityEncoder(AbstractEncoder): + def encode(self, x): + return x + + +class ClassEmbedder(nn.Module): + def __init__(self, embed_dim, n_classes=1000, key='class', ucg_rate=0.1): + super().__init__() + self.key = key + self.embedding = nn.Embedding(n_classes, embed_dim) + self.n_classes = n_classes + self.ucg_rate = ucg_rate + + def forward(self, batch, key=None, disable_dropout=False): + if key is None: + key = self.key + # this is for use in crossattn + c = batch[key][:, None] + if self.ucg_rate > 0. and not disable_dropout: + mask = 1. - torch.bernoulli(torch.ones_like(c) * self.ucg_rate) + c = mask * c + (1 - mask) * torch.ones_like(c) * (self.n_classes - 1) + c = c.long() + c = self.embedding(c) + return c + + def get_unconditional_conditioning(self, bs, device="cuda"): + uc_class = self.n_classes - 1 # 1000 classes --> 0 ... 999, one extra class for ucg (class 1000) + uc = torch.ones((bs,), device=device) * uc_class + uc = {self.key: uc} + return uc + + +def disabled_train(self, mode=True): + """Overwrite model.train with this function to make sure train/eval mode + does not change anymore.""" + return self + + +class FrozenT5Embedder(AbstractEncoder): + """Uses the T5 transformer encoder for text""" + + def __init__(self, version="google/t5-v1_1-large", device="cuda", max_length=77, + freeze=True): # others are google/t5-v1_1-xl and google/t5-v1_1-xxl + super().__init__() + self.tokenizer = T5Tokenizer.from_pretrained(version) + self.transformer = T5EncoderModel.from_pretrained(version) + self.device = device + self.max_length = max_length # TODO: typical value? + if freeze: + self.freeze() + + def freeze(self): + self.transformer = self.transformer.eval() + # self.train = disabled_train + for param in self.parameters(): + param.requires_grad = False + + def forward(self, text): + batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True, + return_overflowing_tokens=False, padding="max_length", return_tensors="pt") + tokens = batch_encoding["input_ids"].to(self.device) + outputs = self.transformer(input_ids=tokens) + + z = outputs.last_hidden_state + return z + + def encode(self, text): + return self(text) + + +class FrozenCLIPEmbedder(AbstractEncoder): + """Uses the CLIP transformer encoder for text (from huggingface)""" + LAYERS = [ + "last", + "pooled", + "hidden" + ] + + def __init__(self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77, + freeze=True, layer="last", layer_idx=None): # clip-vit-base-patch32 + super().__init__() + assert layer in self.LAYERS + self.tokenizer = CLIPTokenizer.from_pretrained(version) + self.transformer = CLIPTextModel.from_pretrained(version) + self.device = device + self.max_length = max_length + if freeze: + self.freeze() + self.layer = layer + self.layer_idx = layer_idx + if layer == "hidden": + assert layer_idx is not None + assert 0 <= abs(layer_idx) <= 12 + + def freeze(self): + self.transformer = self.transformer.eval() + # self.train = disabled_train + for param in self.parameters(): + param.requires_grad = False + + def forward(self, text): + batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True, + return_overflowing_tokens=False, padding="max_length", return_tensors="pt") + tokens = batch_encoding["input_ids"].to(self.device) + outputs = self.transformer(input_ids=tokens, output_hidden_states=self.layer == "hidden") + if self.layer == "last": + z = outputs.last_hidden_state + elif self.layer == "pooled": + z = outputs.pooler_output[:, None, :] + else: + z = outputs.hidden_states[self.layer_idx] + return z + + def encode(self, text): + return self(text) + + +class ClipImageEmbedder(nn.Module): + def __init__( + self, + model, + jit=False, + device='cuda' if torch.cuda.is_available() else 'cpu', + antialias=True, + ucg_rate=0. + ): + super().__init__() + from clip import load as load_clip + self.model, _ = load_clip(name=model, device=device, jit=jit) + + self.antialias = antialias + + self.register_buffer('mean', torch.Tensor([0.48145466, 0.4578275, 0.40821073]), persistent=False) + self.register_buffer('std', torch.Tensor([0.26862954, 0.26130258, 0.27577711]), persistent=False) + self.ucg_rate = ucg_rate + + def preprocess(self, x): + # normalize to [0,1] + x = kornia.geometry.resize(x, (224, 224), + interpolation='bicubic', align_corners=True, + antialias=self.antialias) + x = (x + 1.) / 2. + # re-normalize according to clip + x = kornia.enhance.normalize(x, self.mean, self.std) + return x + + def forward(self, x, no_dropout=False): + # x is assumed to be in range [-1,1] + out = self.model.encode_image(self.preprocess(x)) + out = out.to(x.dtype) + if self.ucg_rate > 0. and not no_dropout: + out = torch.bernoulli((1. - self.ucg_rate) * torch.ones(out.shape[0], device=out.device))[:, None] * out + return out + + +class FrozenOpenCLIPEmbedder(AbstractEncoder): + """ + Uses the OpenCLIP transformer encoder for text + """ + LAYERS = [ + # "pooled", + "last", + "penultimate" + ] + + def __init__(self, arch="ViT-H-14", version="laion2b_s32b_b79k", device="cuda", max_length=77, + freeze=True, layer="last"): + super().__init__() + assert layer in self.LAYERS + model, _, _ = open_clip.create_model_and_transforms(arch, device=torch.device('cpu'), pretrained=version) + del model.visual + self.model = model + + self.device = device + self.max_length = max_length + if freeze: + self.freeze() + self.layer = layer + if self.layer == "last": + self.layer_idx = 0 + elif self.layer == "penultimate": + self.layer_idx = 1 + else: + raise NotImplementedError() + + def freeze(self): + self.model = self.model.eval() + for param in self.parameters(): + param.requires_grad = False + + def forward(self, text): + tokens = open_clip.tokenize(text) ## all clip models use 77 as context length + z = self.encode_with_transformer(tokens.to(self.device)) + return z + + def encode_with_transformer(self, text): + x = self.model.token_embedding(text) # [batch_size, n_ctx, d_model] + x = x + self.model.positional_embedding + x = x.permute(1, 0, 2) # NLD -> LND + x = self.text_transformer_forward(x, attn_mask=self.model.attn_mask) + x = x.permute(1, 0, 2) # LND -> NLD + x = self.model.ln_final(x) + return x + + def text_transformer_forward(self, x: torch.Tensor, attn_mask=None): + for i, r in enumerate(self.model.transformer.resblocks): + if i == len(self.model.transformer.resblocks) - self.layer_idx: + break + if self.model.transformer.grad_checkpointing and not torch.jit.is_scripting(): + x = checkpoint(r, x, attn_mask) + else: + x = r(x, attn_mask=attn_mask) + return x + + def encode(self, text): + return self(text) + + +class FrozenOpenCLIPImageEmbedder(AbstractEncoder): + """ + Uses the OpenCLIP vision transformer encoder for images + """ + + def __init__(self, arch="ViT-H-14", version="laion2b_s32b_b79k", device="cuda", max_length=77, + freeze=True, layer="pooled", antialias=True, ucg_rate=0.): + super().__init__() + model, _, _ = open_clip.create_model_and_transforms(arch, device=torch.device('cpu'), + pretrained=version, ) + del model.transformer + self.model = model + # self.mapper = torch.nn.Linear(1280, 1024) + self.device = device + self.max_length = max_length + if freeze: + self.freeze() + self.layer = layer + if self.layer == "penultimate": + raise NotImplementedError() + self.layer_idx = 1 + + self.antialias = antialias + + self.register_buffer('mean', torch.Tensor([0.48145466, 0.4578275, 0.40821073]), persistent=False) + self.register_buffer('std', torch.Tensor([0.26862954, 0.26130258, 0.27577711]), persistent=False) + self.ucg_rate = ucg_rate + + def preprocess(self, x): + # normalize to [0,1] + x = kornia.geometry.resize(x, (224, 224), + interpolation='bicubic', align_corners=True, + antialias=self.antialias) + x = (x + 1.) / 2. + # renormalize according to clip + x = kornia.enhance.normalize(x, self.mean, self.std) + return x + + def freeze(self): + self.model = self.model.eval() + for param in self.model.parameters(): + param.requires_grad = False + + @autocast + def forward(self, image, no_dropout=False): + z = self.encode_with_vision_transformer(image) + if self.ucg_rate > 0. and not no_dropout: + z = torch.bernoulli((1. - self.ucg_rate) * torch.ones(z.shape[0], device=z.device))[:, None] * z + return z + + def encode_with_vision_transformer(self, img): + img = self.preprocess(img) + x = self.model.visual(img) + return x + + def encode(self, text): + return self(text) + +class FrozenOpenCLIPImageEmbedderV2(AbstractEncoder): + """ + Uses the OpenCLIP vision transformer encoder for images + """ + + def __init__(self, arch="ViT-H-14", version="laion2b_s32b_b79k", device="cuda", + freeze=True, layer="pooled", antialias=True): + super().__init__() + model, _, _ = open_clip.create_model_and_transforms(arch, device=torch.device('cpu'), + pretrained=version, ) + del model.transformer + self.model = model + self.device = device + + if freeze: + self.freeze() + self.layer = layer + if self.layer == "penultimate": + raise NotImplementedError() + self.layer_idx = 1 + + self.antialias = antialias + + self.register_buffer('mean', torch.Tensor([0.48145466, 0.4578275, 0.40821073]), persistent=False) + self.register_buffer('std', torch.Tensor([0.26862954, 0.26130258, 0.27577711]), persistent=False) + + + def preprocess(self, x): + # normalize to [0,1] + x = kornia.geometry.resize(x, (224, 224), + interpolation='bicubic', align_corners=True, + antialias=self.antialias) + x = (x + 1.) / 2. + # renormalize according to clip + x = kornia.enhance.normalize(x, self.mean, self.std) + return x + + def freeze(self): + self.model = self.model.eval() + for param in self.model.parameters(): + param.requires_grad = False + + def forward(self, image, no_dropout=False): + ## image: b c h w + z = self.encode_with_vision_transformer(image) + return z + + def encode_with_vision_transformer(self, x): + x = self.preprocess(x) + + # to patches - whether to use dual patchnorm - https://arxiv.org/abs/2302.01327v1 + if self.model.visual.input_patchnorm: + # einops - rearrange(x, 'b c (h p1) (w p2) -> b (h w) (c p1 p2)') + x = x.reshape(x.shape[0], x.shape[1], self.model.visual.grid_size[0], self.model.visual.patch_size[0], self.model.visual.grid_size[1], self.model.visual.patch_size[1]) + x = x.permute(0, 2, 4, 1, 3, 5) + x = x.reshape(x.shape[0], self.model.visual.grid_size[0] * self.model.visual.grid_size[1], -1) + x = self.model.visual.patchnorm_pre_ln(x) + x = self.model.visual.conv1(x) + else: + x = self.model.visual.conv1(x) # shape = [*, width, grid, grid] + x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2] + x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width] + + # class embeddings and positional embeddings + x = torch.cat( + [self.model.visual.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), + x], dim=1) # shape = [*, grid ** 2 + 1, width] + x = x + self.model.visual.positional_embedding.to(x.dtype) + + # a patch_dropout of 0. would mean it is disabled and this function would do nothing but return what was passed in + x = self.model.visual.patch_dropout(x) + x = self.model.visual.ln_pre(x) + + x = x.permute(1, 0, 2) # NLD -> LND + x = self.model.visual.transformer(x) + x = x.permute(1, 0, 2) # LND -> NLD + + return x + +class FrozenCLIPT5Encoder(AbstractEncoder): + def __init__(self, clip_version="openai/clip-vit-large-patch14", t5_version="google/t5-v1_1-xl", device="cuda", + clip_max_length=77, t5_max_length=77): + super().__init__() + self.clip_encoder = FrozenCLIPEmbedder(clip_version, device, max_length=clip_max_length) + self.t5_encoder = FrozenT5Embedder(t5_version, device, max_length=t5_max_length) + print(f"{self.clip_encoder.__class__.__name__} has {count_params(self.clip_encoder) * 1.e-6:.2f} M parameters, " + f"{self.t5_encoder.__class__.__name__} comes with {count_params(self.t5_encoder) * 1.e-6:.2f} M params.") + + def encode(self, text): + return self(text) + + def forward(self, text): + clip_z = self.clip_encoder.encode(text) + t5_z = self.t5_encoder.encode(text) + return [clip_z, t5_z] diff --git a/lvdm/modules/encoders/resampler.py b/lvdm/modules/encoders/resampler.py new file mode 100644 index 0000000000000000000000000000000000000000..d9abd23f681510d757acdf4dbb31794fe49ab5b1 --- /dev/null +++ b/lvdm/modules/encoders/resampler.py @@ -0,0 +1,145 @@ +# modified from https://github.com/mlfoundations/open_flamingo/blob/main/open_flamingo/src/helpers.py +# and https://github.com/lucidrains/imagen-pytorch/blob/main/imagen_pytorch/imagen_pytorch.py +# and https://github.com/tencent-ailab/IP-Adapter/blob/main/ip_adapter/resampler.py +import math +import torch +import torch.nn as nn + + +class ImageProjModel(nn.Module): + """Projection Model""" + def __init__(self, cross_attention_dim=1024, clip_embeddings_dim=1024, clip_extra_context_tokens=4): + super().__init__() + self.cross_attention_dim = cross_attention_dim + self.clip_extra_context_tokens = clip_extra_context_tokens + self.proj = nn.Linear(clip_embeddings_dim, self.clip_extra_context_tokens * cross_attention_dim) + self.norm = nn.LayerNorm(cross_attention_dim) + + def forward(self, image_embeds): + #embeds = image_embeds + embeds = image_embeds.type(list(self.proj.parameters())[0].dtype) + clip_extra_context_tokens = self.proj(embeds).reshape(-1, self.clip_extra_context_tokens, self.cross_attention_dim) + clip_extra_context_tokens = self.norm(clip_extra_context_tokens) + return clip_extra_context_tokens + + +# FFN +def FeedForward(dim, mult=4): + inner_dim = int(dim * mult) + return nn.Sequential( + nn.LayerNorm(dim), + nn.Linear(dim, inner_dim, bias=False), + nn.GELU(), + nn.Linear(inner_dim, dim, bias=False), + ) + + +def reshape_tensor(x, heads): + bs, length, width = x.shape + #(bs, length, width) --> (bs, length, n_heads, dim_per_head) + x = x.view(bs, length, heads, -1) + # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head) + x = x.transpose(1, 2) + # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head) + x = x.reshape(bs, heads, length, -1) + return x + + +class PerceiverAttention(nn.Module): + def __init__(self, *, dim, dim_head=64, heads=8): + super().__init__() + self.scale = dim_head**-0.5 + self.dim_head = dim_head + self.heads = heads + inner_dim = dim_head * heads + + self.norm1 = nn.LayerNorm(dim) + self.norm2 = nn.LayerNorm(dim) + + self.to_q = nn.Linear(dim, inner_dim, bias=False) + self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False) + self.to_out = nn.Linear(inner_dim, dim, bias=False) + + + def forward(self, x, latents): + """ + Args: + x (torch.Tensor): image features + shape (b, n1, D) + latent (torch.Tensor): latent features + shape (b, n2, D) + """ + x = self.norm1(x) + latents = self.norm2(latents) + + b, l, _ = latents.shape + + q = self.to_q(latents) + kv_input = torch.cat((x, latents), dim=-2) + k, v = self.to_kv(kv_input).chunk(2, dim=-1) + + q = reshape_tensor(q, self.heads) + k = reshape_tensor(k, self.heads) + v = reshape_tensor(v, self.heads) + + # attention + scale = 1 / math.sqrt(math.sqrt(self.dim_head)) + weight = (q * scale) @ (k * scale).transpose(-2, -1) # More stable with f16 than dividing afterwards + weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype) + out = weight @ v + + out = out.permute(0, 2, 1, 3).reshape(b, l, -1) + + return self.to_out(out) + + +class Resampler(nn.Module): + def __init__( + self, + dim=1024, + depth=8, + dim_head=64, + heads=16, + num_queries=8, + embedding_dim=768, + output_dim=1024, + ff_mult=4, + video_length=None, # using frame-wise version or not + ): + super().__init__() + ## queries for a single frame / image + self.num_queries = num_queries + self.video_length = video_length + + ## queries for each frame + if video_length is not None: + num_queries = num_queries * video_length + + self.latents = nn.Parameter(torch.randn(1, num_queries, dim) / dim**0.5) + self.proj_in = nn.Linear(embedding_dim, dim) + self.proj_out = nn.Linear(dim, output_dim) + self.norm_out = nn.LayerNorm(output_dim) + + self.layers = nn.ModuleList([]) + for _ in range(depth): + self.layers.append( + nn.ModuleList( + [ + PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads), + FeedForward(dim=dim, mult=ff_mult), + ] + ) + ) + + def forward(self, x): + latents = self.latents.repeat(x.size(0), 1, 1) ## B (T L) C + x = self.proj_in(x) + + for attn, ff in self.layers: + latents = attn(x, latents) + latents + latents = ff(latents) + latents + + latents = self.proj_out(latents) + latents = self.norm_out(latents) # B L C or B (T L) C + + return latents \ No newline at end of file diff --git a/lvdm/modules/networks/ae_modules.py b/lvdm/modules/networks/ae_modules.py new file mode 100644 index 0000000000000000000000000000000000000000..043e4ff2613d973ea76fb10319a4a2d738942155 --- /dev/null +++ b/lvdm/modules/networks/ae_modules.py @@ -0,0 +1,856 @@ +# pytorch_diffusion + derived encoder decoder +import math + +import torch +import numpy as np +import torch.nn as nn +from einops import rearrange + +from utils.utils import instantiate_from_config +from lvdm.modules.attention import LinearAttention + +def nonlinearity(x): + # swish + return x*torch.sigmoid(x) + + +def Normalize(in_channels, num_groups=32): + return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True) + + + +class LinAttnBlock(LinearAttention): + """to match AttnBlock usage""" + def __init__(self, in_channels): + super().__init__(dim=in_channels, heads=1, dim_head=in_channels) + + +class AttnBlock(nn.Module): + def __init__(self, in_channels): + super().__init__() + self.in_channels = in_channels + + self.norm = Normalize(in_channels) + self.q = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.k = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.v = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.proj_out = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + + def forward(self, x): + h_ = x + h_ = self.norm(h_) + q = self.q(h_) + k = self.k(h_) + v = self.v(h_) + + # compute attention + b,c,h,w = q.shape + q = q.reshape(b,c,h*w) # bcl + q = q.permute(0,2,1) # bcl -> blc l=hw + k = k.reshape(b,c,h*w) # bcl + + w_ = torch.bmm(q,k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j] + w_ = w_ * (int(c)**(-0.5)) + w_ = torch.nn.functional.softmax(w_, dim=2) + + # attend to values + v = v.reshape(b,c,h*w) + w_ = w_.permute(0,2,1) # b,hw,hw (first hw of k, second of q) + h_ = torch.bmm(v,w_) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j] + h_ = h_.reshape(b,c,h,w) + + h_ = self.proj_out(h_) + + return x+h_ + +def make_attn(in_channels, attn_type="vanilla"): + assert attn_type in ["vanilla", "linear", "none"], f'attn_type {attn_type} unknown' + #print(f"making attention of type '{attn_type}' with {in_channels} in_channels") + if attn_type == "vanilla": + return AttnBlock(in_channels) + elif attn_type == "none": + return nn.Identity(in_channels) + else: + return LinAttnBlock(in_channels) + +class Downsample(nn.Module): + def __init__(self, in_channels, with_conv): + super().__init__() + self.with_conv = with_conv + self.in_channels = in_channels + if self.with_conv: + # no asymmetric padding in torch conv, must do it ourselves + self.conv = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=3, + stride=2, + padding=0) + def forward(self, x): + if self.with_conv: + pad = (0,1,0,1) + x = torch.nn.functional.pad(x, pad, mode="constant", value=0) + x = self.conv(x) + else: + x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2) + return x + +class Upsample(nn.Module): + def __init__(self, in_channels, with_conv): + super().__init__() + self.with_conv = with_conv + self.in_channels = in_channels + if self.with_conv: + self.conv = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, x): + x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest") + if self.with_conv: + x = self.conv(x) + return x + +def get_timestep_embedding(timesteps, embedding_dim): + """ + This matches the implementation in Denoising Diffusion Probabilistic Models: + From Fairseq. + Build sinusoidal embeddings. + This matches the implementation in tensor2tensor, but differs slightly + from the description in Section 3.5 of "Attention Is All You Need". + """ + assert len(timesteps.shape) == 1 + + half_dim = embedding_dim // 2 + emb = math.log(10000) / (half_dim - 1) + emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb) + emb = emb.to(device=timesteps.device) + emb = timesteps.float()[:, None] * emb[None, :] + emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1) + if embedding_dim % 2 == 1: # zero pad + emb = torch.nn.functional.pad(emb, (0,1,0,0)) + return emb + + + +class ResnetBlock(nn.Module): + def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False, + dropout, temb_channels=512): + super().__init__() + 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.norm1 = Normalize(in_channels) + self.conv1 = torch.nn.Conv2d(in_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1) + if temb_channels > 0: + self.temb_proj = torch.nn.Linear(temb_channels, + out_channels) + self.norm2 = Normalize(out_channels) + self.dropout = torch.nn.Dropout(dropout) + self.conv2 = torch.nn.Conv2d(out_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1) + if self.in_channels != self.out_channels: + if self.use_conv_shortcut: + self.conv_shortcut = torch.nn.Conv2d(in_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1) + else: + self.nin_shortcut = torch.nn.Conv2d(in_channels, + out_channels, + kernel_size=1, + stride=1, + padding=0) + + def forward(self, x, temb): + h = x + h = self.norm1(h) + h = nonlinearity(h) + h = self.conv1(h) + + if temb is not None: + h = h + self.temb_proj(nonlinearity(temb))[:,:,None,None] + + h = self.norm2(h) + h = nonlinearity(h) + h = self.dropout(h) + h = self.conv2(h) + + if self.in_channels != self.out_channels: + if self.use_conv_shortcut: + x = self.conv_shortcut(x) + else: + x = self.nin_shortcut(x) + + return x+h + +class Model(nn.Module): + def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, + attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, + resolution, use_timestep=True, use_linear_attn=False, attn_type="vanilla"): + super().__init__() + if use_linear_attn: attn_type = "linear" + self.ch = ch + self.temb_ch = self.ch*4 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + + self.use_timestep = use_timestep + if self.use_timestep: + # timestep embedding + self.temb = nn.Module() + self.temb.dense = nn.ModuleList([ + torch.nn.Linear(self.ch, + self.temb_ch), + torch.nn.Linear(self.temb_ch, + self.temb_ch), + ]) + + # downsampling + self.conv_in = torch.nn.Conv2d(in_channels, + self.ch, + kernel_size=3, + stride=1, + padding=1) + + curr_res = resolution + in_ch_mult = (1,)+tuple(ch_mult) + self.down = nn.ModuleList() + for i_level in range(self.num_resolutions): + block = nn.ModuleList() + attn = nn.ModuleList() + block_in = ch*in_ch_mult[i_level] + block_out = ch*ch_mult[i_level] + for i_block in range(self.num_res_blocks): + block.append(ResnetBlock(in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(make_attn(block_in, attn_type=attn_type)) + down = nn.Module() + down.block = block + down.attn = attn + if i_level != self.num_resolutions-1: + down.downsample = Downsample(block_in, resamp_with_conv) + curr_res = curr_res // 2 + self.down.append(down) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + self.mid.attn_1 = make_attn(block_in, attn_type=attn_type) + self.mid.block_2 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + + # upsampling + self.up = nn.ModuleList() + for i_level in reversed(range(self.num_resolutions)): + block = nn.ModuleList() + attn = nn.ModuleList() + block_out = ch*ch_mult[i_level] + skip_in = ch*ch_mult[i_level] + for i_block in range(self.num_res_blocks+1): + if i_block == self.num_res_blocks: + skip_in = ch*in_ch_mult[i_level] + block.append(ResnetBlock(in_channels=block_in+skip_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(make_attn(block_in, attn_type=attn_type)) + up = nn.Module() + up.block = block + up.attn = attn + if i_level != 0: + up.upsample = Upsample(block_in, resamp_with_conv) + curr_res = curr_res * 2 + self.up.insert(0, up) # prepend to get consistent order + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d(block_in, + out_ch, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, x, t=None, context=None): + #assert x.shape[2] == x.shape[3] == self.resolution + if context is not None: + # assume aligned context, cat along channel axis + x = torch.cat((x, context), dim=1) + if self.use_timestep: + # timestep embedding + assert t is not None + temb = get_timestep_embedding(t, self.ch) + temb = self.temb.dense[0](temb) + temb = nonlinearity(temb) + temb = self.temb.dense[1](temb) + else: + temb = None + + # downsampling + hs = [self.conv_in(x)] + for i_level in range(self.num_resolutions): + for i_block in range(self.num_res_blocks): + h = self.down[i_level].block[i_block](hs[-1], temb) + if len(self.down[i_level].attn) > 0: + h = self.down[i_level].attn[i_block](h) + hs.append(h) + if i_level != self.num_resolutions-1: + hs.append(self.down[i_level].downsample(hs[-1])) + + # middle + h = hs[-1] + h = self.mid.block_1(h, temb) + h = self.mid.attn_1(h) + h = self.mid.block_2(h, temb) + + # upsampling + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks+1): + h = self.up[i_level].block[i_block]( + torch.cat([h, hs.pop()], dim=1), temb) + if len(self.up[i_level].attn) > 0: + h = self.up[i_level].attn[i_block](h) + if i_level != 0: + h = self.up[i_level].upsample(h) + + # end + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + + def get_last_layer(self): + return self.conv_out.weight + + +class Encoder(nn.Module): + def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, + attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, + resolution, z_channels, double_z=True, use_linear_attn=False, attn_type="vanilla", + **ignore_kwargs): + super().__init__() + if use_linear_attn: attn_type = "linear" + self.ch = ch + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + + # downsampling + self.conv_in = torch.nn.Conv2d(in_channels, + self.ch, + kernel_size=3, + stride=1, + padding=1) + + curr_res = resolution + in_ch_mult = (1,)+tuple(ch_mult) + self.in_ch_mult = in_ch_mult + self.down = nn.ModuleList() + for i_level in range(self.num_resolutions): + block = nn.ModuleList() + attn = nn.ModuleList() + block_in = ch*in_ch_mult[i_level] + block_out = ch*ch_mult[i_level] + for i_block in range(self.num_res_blocks): + block.append(ResnetBlock(in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(make_attn(block_in, attn_type=attn_type)) + down = nn.Module() + down.block = block + down.attn = attn + if i_level != self.num_resolutions-1: + down.downsample = Downsample(block_in, resamp_with_conv) + curr_res = curr_res // 2 + self.down.append(down) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + self.mid.attn_1 = make_attn(block_in, attn_type=attn_type) + self.mid.block_2 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d(block_in, + 2*z_channels if double_z else z_channels, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, x, return_hidden_states=False): + # timestep embedding + temb = None + + # print(f'encoder-input={x.shape}') + # downsampling + hs = [self.conv_in(x)] + + ## if we return hidden states for decoder usage, we will store them in a list + if return_hidden_states: + hidden_states = [] + # print(f'encoder-conv in feat={hs[0].shape}') + for i_level in range(self.num_resolutions): + for i_block in range(self.num_res_blocks): + h = self.down[i_level].block[i_block](hs[-1], temb) + # print(f'encoder-down feat={h.shape}') + if len(self.down[i_level].attn) > 0: + h = self.down[i_level].attn[i_block](h) + hs.append(h) + if return_hidden_states: + hidden_states.append(h) + if i_level != self.num_resolutions-1: + # print(f'encoder-downsample (input)={hs[-1].shape}') + hs.append(self.down[i_level].downsample(hs[-1])) + # print(f'encoder-downsample (output)={hs[-1].shape}') + if return_hidden_states: + hidden_states.append(hs[0]) + # middle + h = hs[-1] + h = self.mid.block_1(h, temb) + # print(f'encoder-mid1 feat={h.shape}') + h = self.mid.attn_1(h) + h = self.mid.block_2(h, temb) + # print(f'encoder-mid2 feat={h.shape}') + + # end + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + # print(f'end feat={h.shape}') + if return_hidden_states: + return h, hidden_states + else: + return h + + +class Decoder(nn.Module): + def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, + attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, + resolution, z_channels, give_pre_end=False, tanh_out=False, use_linear_attn=False, + attn_type="vanilla", **ignorekwargs): + super().__init__() + if use_linear_attn: attn_type = "linear" + self.ch = ch + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + self.give_pre_end = give_pre_end + self.tanh_out = tanh_out + + # compute in_ch_mult, block_in and curr_res at lowest res + in_ch_mult = (1,)+tuple(ch_mult) + block_in = ch*ch_mult[self.num_resolutions-1] + curr_res = resolution // 2**(self.num_resolutions-1) + self.z_shape = (1,z_channels,curr_res,curr_res) + print("AE working on z of shape {} = {} dimensions.".format( + self.z_shape, np.prod(self.z_shape))) + + # z to block_in + self.conv_in = torch.nn.Conv2d(z_channels, + block_in, + kernel_size=3, + stride=1, + padding=1) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + self.mid.attn_1 = make_attn(block_in, attn_type=attn_type) + self.mid.block_2 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + + # upsampling + self.up = nn.ModuleList() + for i_level in reversed(range(self.num_resolutions)): + block = nn.ModuleList() + attn = nn.ModuleList() + block_out = ch*ch_mult[i_level] + for i_block in range(self.num_res_blocks+1): + block.append(ResnetBlock(in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(make_attn(block_in, attn_type=attn_type)) + up = nn.Module() + up.block = block + up.attn = attn + if i_level != 0: + up.upsample = Upsample(block_in, resamp_with_conv) + curr_res = curr_res * 2 + self.up.insert(0, up) # prepend to get consistent order + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d(block_in, + out_ch, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, z): + #assert z.shape[1:] == self.z_shape[1:] + self.last_z_shape = z.shape + + # print(f'decoder-input={z.shape}') + # timestep embedding + temb = None + + # z to block_in + h = self.conv_in(z) + # print(f'decoder-conv in feat={h.shape}') + + # middle + h = self.mid.block_1(h, temb) + h = self.mid.attn_1(h) + h = self.mid.block_2(h, temb) + # print(f'decoder-mid feat={h.shape}') + + # upsampling + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks+1): + h = self.up[i_level].block[i_block](h, temb) + if len(self.up[i_level].attn) > 0: + h = self.up[i_level].attn[i_block](h) + # print(f'decoder-up feat={h.shape}') + if i_level != 0: + h = self.up[i_level].upsample(h) + # print(f'decoder-upsample feat={h.shape}') + + # end + if self.give_pre_end: + return h + + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + # print(f'decoder-conv_out feat={h.shape}') + if self.tanh_out: + h = torch.tanh(h) + return h + + +class SimpleDecoder(nn.Module): + def __init__(self, in_channels, out_channels, *args, **kwargs): + super().__init__() + self.model = nn.ModuleList([nn.Conv2d(in_channels, in_channels, 1), + ResnetBlock(in_channels=in_channels, + out_channels=2 * in_channels, + temb_channels=0, dropout=0.0), + ResnetBlock(in_channels=2 * in_channels, + out_channels=4 * in_channels, + temb_channels=0, dropout=0.0), + ResnetBlock(in_channels=4 * in_channels, + out_channels=2 * in_channels, + temb_channels=0, dropout=0.0), + nn.Conv2d(2*in_channels, in_channels, 1), + Upsample(in_channels, with_conv=True)]) + # end + self.norm_out = Normalize(in_channels) + self.conv_out = torch.nn.Conv2d(in_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, x): + for i, layer in enumerate(self.model): + if i in [1,2,3]: + x = layer(x, None) + else: + x = layer(x) + + h = self.norm_out(x) + h = nonlinearity(h) + x = self.conv_out(h) + return x + + +class UpsampleDecoder(nn.Module): + def __init__(self, in_channels, out_channels, ch, num_res_blocks, resolution, + ch_mult=(2,2), dropout=0.0): + super().__init__() + # upsampling + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + block_in = in_channels + curr_res = resolution // 2 ** (self.num_resolutions - 1) + self.res_blocks = nn.ModuleList() + self.upsample_blocks = nn.ModuleList() + for i_level in range(self.num_resolutions): + res_block = [] + block_out = ch * ch_mult[i_level] + for i_block in range(self.num_res_blocks + 1): + res_block.append(ResnetBlock(in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + self.res_blocks.append(nn.ModuleList(res_block)) + if i_level != self.num_resolutions - 1: + self.upsample_blocks.append(Upsample(block_in, True)) + curr_res = curr_res * 2 + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d(block_in, + out_channels, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, x): + # upsampling + h = x + for k, i_level in enumerate(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks + 1): + h = self.res_blocks[i_level][i_block](h, None) + if i_level != self.num_resolutions - 1: + h = self.upsample_blocks[k](h) + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + + +class LatentRescaler(nn.Module): + def __init__(self, factor, in_channels, mid_channels, out_channels, depth=2): + super().__init__() + # residual block, interpolate, residual block + self.factor = factor + self.conv_in = nn.Conv2d(in_channels, + mid_channels, + kernel_size=3, + stride=1, + padding=1) + self.res_block1 = nn.ModuleList([ResnetBlock(in_channels=mid_channels, + out_channels=mid_channels, + temb_channels=0, + dropout=0.0) for _ in range(depth)]) + self.attn = AttnBlock(mid_channels) + self.res_block2 = nn.ModuleList([ResnetBlock(in_channels=mid_channels, + out_channels=mid_channels, + temb_channels=0, + dropout=0.0) for _ in range(depth)]) + + self.conv_out = nn.Conv2d(mid_channels, + out_channels, + kernel_size=1, + ) + + def forward(self, x): + x = self.conv_in(x) + for block in self.res_block1: + x = block(x, None) + x = torch.nn.functional.interpolate(x, size=(int(round(x.shape[2]*self.factor)), int(round(x.shape[3]*self.factor)))) + x = self.attn(x) + for block in self.res_block2: + x = block(x, None) + x = self.conv_out(x) + return x + + +class MergedRescaleEncoder(nn.Module): + def __init__(self, in_channels, ch, resolution, out_ch, num_res_blocks, + attn_resolutions, dropout=0.0, resamp_with_conv=True, + ch_mult=(1,2,4,8), rescale_factor=1.0, rescale_module_depth=1): + super().__init__() + intermediate_chn = ch * ch_mult[-1] + self.encoder = Encoder(in_channels=in_channels, num_res_blocks=num_res_blocks, ch=ch, ch_mult=ch_mult, + z_channels=intermediate_chn, double_z=False, resolution=resolution, + attn_resolutions=attn_resolutions, dropout=dropout, resamp_with_conv=resamp_with_conv, + out_ch=None) + self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=intermediate_chn, + mid_channels=intermediate_chn, out_channels=out_ch, depth=rescale_module_depth) + + def forward(self, x): + x = self.encoder(x) + x = self.rescaler(x) + return x + + +class MergedRescaleDecoder(nn.Module): + def __init__(self, z_channels, out_ch, resolution, num_res_blocks, attn_resolutions, ch, ch_mult=(1,2,4,8), + dropout=0.0, resamp_with_conv=True, rescale_factor=1.0, rescale_module_depth=1): + super().__init__() + tmp_chn = z_channels*ch_mult[-1] + self.decoder = Decoder(out_ch=out_ch, z_channels=tmp_chn, attn_resolutions=attn_resolutions, dropout=dropout, + resamp_with_conv=resamp_with_conv, in_channels=None, num_res_blocks=num_res_blocks, + ch_mult=ch_mult, resolution=resolution, ch=ch) + self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=z_channels, mid_channels=tmp_chn, + out_channels=tmp_chn, depth=rescale_module_depth) + + def forward(self, x): + x = self.rescaler(x) + x = self.decoder(x) + return x + + +class Upsampler(nn.Module): + def __init__(self, in_size, out_size, in_channels, out_channels, ch_mult=2): + super().__init__() + assert out_size >= in_size + num_blocks = int(np.log2(out_size//in_size))+1 + factor_up = 1.+ (out_size % in_size) + print(f"Building {self.__class__.__name__} with in_size: {in_size} --> out_size {out_size} and factor {factor_up}") + self.rescaler = LatentRescaler(factor=factor_up, in_channels=in_channels, mid_channels=2*in_channels, + out_channels=in_channels) + self.decoder = Decoder(out_ch=out_channels, resolution=out_size, z_channels=in_channels, num_res_blocks=2, + attn_resolutions=[], in_channels=None, ch=in_channels, + ch_mult=[ch_mult for _ in range(num_blocks)]) + + def forward(self, x): + x = self.rescaler(x) + x = self.decoder(x) + return x + + +class Resize(nn.Module): + def __init__(self, in_channels=None, learned=False, mode="bilinear"): + super().__init__() + self.with_conv = learned + self.mode = mode + if self.with_conv: + print(f"Note: {self.__class__.__name} uses learned downsampling and will ignore the fixed {mode} mode") + raise NotImplementedError() + assert in_channels is not None + # no asymmetric padding in torch conv, must do it ourselves + self.conv = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=4, + stride=2, + padding=1) + + def forward(self, x, scale_factor=1.0): + if scale_factor==1.0: + return x + else: + x = torch.nn.functional.interpolate(x, mode=self.mode, align_corners=False, scale_factor=scale_factor) + return x + +class FirstStagePostProcessor(nn.Module): + + def __init__(self, ch_mult:list, in_channels, + pretrained_model:nn.Module=None, + reshape=False, + n_channels=None, + dropout=0., + pretrained_config=None): + super().__init__() + if pretrained_config is None: + assert pretrained_model is not None, 'Either "pretrained_model" or "pretrained_config" must not be None' + self.pretrained_model = pretrained_model + else: + assert pretrained_config is not None, 'Either "pretrained_model" or "pretrained_config" must not be None' + self.instantiate_pretrained(pretrained_config) + + self.do_reshape = reshape + + if n_channels is None: + n_channels = self.pretrained_model.encoder.ch + + self.proj_norm = Normalize(in_channels,num_groups=in_channels//2) + self.proj = nn.Conv2d(in_channels,n_channels,kernel_size=3, + stride=1,padding=1) + + blocks = [] + downs = [] + ch_in = n_channels + for m in ch_mult: + blocks.append(ResnetBlock(in_channels=ch_in,out_channels=m*n_channels,dropout=dropout)) + ch_in = m * n_channels + downs.append(Downsample(ch_in, with_conv=False)) + + self.model = nn.ModuleList(blocks) + self.downsampler = nn.ModuleList(downs) + + + def instantiate_pretrained(self, config): + model = instantiate_from_config(config) + self.pretrained_model = model.eval() + # self.pretrained_model.train = False + for param in self.pretrained_model.parameters(): + param.requires_grad = False + + + @torch.no_grad() + def encode_with_pretrained(self,x): + c = self.pretrained_model.encode(x) + if isinstance(c, DiagonalGaussianDistribution): + c = c.mode() + return c + + def forward(self,x): + z_fs = self.encode_with_pretrained(x) + z = self.proj_norm(z_fs) + z = self.proj(z) + z = nonlinearity(z) + + for submodel, downmodel in zip(self.model,self.downsampler): + z = submodel(z,temb=None) + z = downmodel(z) + + if self.do_reshape: + z = rearrange(z,'b c h w -> b (h w) c') + return z \ No newline at end of file diff --git a/lvdm/modules/networks/openaimodel3d.py b/lvdm/modules/networks/openaimodel3d.py new file mode 100644 index 0000000000000000000000000000000000000000..49245da8ff896d938cf13c6cf6cb23548383c6dc --- /dev/null +++ b/lvdm/modules/networks/openaimodel3d.py @@ -0,0 +1,603 @@ +from functools import partial +from abc import abstractmethod +import torch +import torch.nn as nn +from einops import rearrange +import torch.nn.functional as F +from lvdm.models.utils_diffusion import timestep_embedding +from lvdm.common import checkpoint +from lvdm.basics import ( + zero_module, + conv_nd, + linear, + avg_pool_nd, + normalization +) +from lvdm.modules.attention import SpatialTransformer, TemporalTransformer + + +class TimestepBlock(nn.Module): + """ + Any module where forward() takes timestep embeddings as a second argument. + """ + @abstractmethod + def forward(self, x, emb): + """ + Apply the module to `x` given `emb` timestep embeddings. + """ + + +class TimestepEmbedSequential(nn.Sequential, TimestepBlock): + """ + A sequential module that passes timestep embeddings to the children that + support it as an extra input. + """ + + def forward(self, x, emb, context=None, batch_size=None): + for layer in self: + if isinstance(layer, TimestepBlock): + x = layer(x, emb, batch_size=batch_size) + elif isinstance(layer, SpatialTransformer): + x = layer(x, context) + elif isinstance(layer, TemporalTransformer): + x = rearrange(x, '(b f) c h w -> b c f h w', b=batch_size) + x = layer(x, context) + x = rearrange(x, 'b c f h w -> (b f) c h w') + else: + x = layer(x) + return x + + +class Downsample(nn.Module): + """ + A downsampling layer with an optional convolution. + :param channels: channels in the inputs and outputs. + :param use_conv: a bool determining if a convolution is applied. + :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then + downsampling occurs in the inner-two dimensions. + """ + + def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.dims = dims + stride = 2 if dims != 3 else (1, 2, 2) + if use_conv: + self.op = conv_nd( + dims, self.channels, self.out_channels, 3, stride=stride, padding=padding + ) + else: + assert self.channels == self.out_channels + self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride) + + def forward(self, x): + assert x.shape[1] == self.channels + return self.op(x) + + +class Upsample(nn.Module): + """ + An upsampling layer with an optional convolution. + :param channels: channels in the inputs and outputs. + :param use_conv: a bool determining if a convolution is applied. + :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then + upsampling occurs in the inner-two dimensions. + """ + + def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.dims = dims + if use_conv: + self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=padding) + + def forward(self, x): + assert x.shape[1] == self.channels + if self.dims == 3: + x = F.interpolate(x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode='nearest') + else: + x = F.interpolate(x, scale_factor=2, mode='nearest') + if self.use_conv: + x = self.conv(x) + return x + + +class ResBlock(TimestepBlock): + """ + A residual block that can optionally change the number of channels. + :param channels: the number of input channels. + :param emb_channels: the number of timestep embedding channels. + :param dropout: the rate of dropout. + :param out_channels: if specified, the number of out channels. + :param use_conv: if True and out_channels is specified, use a spatial + convolution instead of a smaller 1x1 convolution to change the + channels in the skip connection. + :param dims: determines if the signal is 1D, 2D, or 3D. + :param up: if True, use this block for upsampling. + :param down: if True, use this block for downsampling. + :param use_temporal_conv: if True, use the temporal convolution. + :param use_image_dataset: if True, the temporal parameters will not be optimized. + """ + + def __init__( + self, + channels, + emb_channels, + dropout, + out_channels=None, + use_scale_shift_norm=False, + dims=2, + use_checkpoint=False, + use_conv=False, + up=False, + down=False, + use_temporal_conv=False, + tempspatial_aware=False + ): + super().__init__() + self.channels = channels + self.emb_channels = emb_channels + self.dropout = dropout + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.use_checkpoint = use_checkpoint + self.use_scale_shift_norm = use_scale_shift_norm + self.use_temporal_conv = use_temporal_conv + + self.in_layers = nn.Sequential( + normalization(channels), + nn.SiLU(), + conv_nd(dims, channels, self.out_channels, 3, padding=1), + ) + + self.updown = up or down + + if up: + self.h_upd = Upsample(channels, False, dims) + self.x_upd = Upsample(channels, False, dims) + elif down: + self.h_upd = Downsample(channels, False, dims) + self.x_upd = Downsample(channels, False, dims) + else: + self.h_upd = self.x_upd = nn.Identity() + + self.emb_layers = nn.Sequential( + nn.SiLU(), + nn.Linear( + emb_channels, + 2 * self.out_channels if use_scale_shift_norm else self.out_channels, + ), + ) + self.out_layers = nn.Sequential( + normalization(self.out_channels), + nn.SiLU(), + nn.Dropout(p=dropout), + zero_module(nn.Conv2d(self.out_channels, self.out_channels, 3, padding=1)), + ) + + if self.out_channels == channels: + self.skip_connection = nn.Identity() + elif use_conv: + self.skip_connection = conv_nd(dims, channels, self.out_channels, 3, padding=1) + else: + self.skip_connection = conv_nd(dims, channels, self.out_channels, 1) + + if self.use_temporal_conv: + self.temopral_conv = TemporalConvBlock( + self.out_channels, + self.out_channels, + dropout=0.1, + spatial_aware=tempspatial_aware + ) + + def forward(self, x, emb, batch_size=None): + """ + Apply the block to a Tensor, conditioned on a timestep embedding. + :param x: an [N x C x ...] Tensor of features. + :param emb: an [N x emb_channels] Tensor of timestep embeddings. + :return: an [N x C x ...] Tensor of outputs. + """ + input_tuple = (x, emb) + if batch_size: + forward_batchsize = partial(self._forward, batch_size=batch_size) + return checkpoint(forward_batchsize, input_tuple, self.parameters(), self.use_checkpoint) + return checkpoint(self._forward, input_tuple, self.parameters(), self.use_checkpoint) + + def _forward(self, x, emb, batch_size=None): + if self.updown: + in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1] + h = in_rest(x) + h = self.h_upd(h) + x = self.x_upd(x) + h = in_conv(h) + else: + h = self.in_layers(x) + emb_out = self.emb_layers(emb).type(h.dtype) + while len(emb_out.shape) < len(h.shape): + emb_out = emb_out[..., None] + if self.use_scale_shift_norm: + out_norm, out_rest = self.out_layers[0], self.out_layers[1:] + scale, shift = torch.chunk(emb_out, 2, dim=1) + h = out_norm(h) * (1 + scale) + shift + h = out_rest(h) + else: + h = h + emb_out + h = self.out_layers(h) + h = self.skip_connection(x) + h + + if self.use_temporal_conv and batch_size: + h = rearrange(h, '(b t) c h w -> b c t h w', b=batch_size) + h = self.temopral_conv(h) + h = rearrange(h, 'b c t h w -> (b t) c h w') + return h + + +class TemporalConvBlock(nn.Module): + """ + Adapted from modelscope: https://github.com/modelscope/modelscope/blob/master/modelscope/models/multi_modal/video_synthesis/unet_sd.py + """ + def __init__(self, in_channels, out_channels=None, dropout=0.0, spatial_aware=False): + super(TemporalConvBlock, self).__init__() + if out_channels is None: + out_channels = in_channels + self.in_channels = in_channels + self.out_channels = out_channels + th_kernel_shape = (3, 1, 1) if not spatial_aware else (3, 3, 1) + th_padding_shape = (1, 0, 0) if not spatial_aware else (1, 1, 0) + tw_kernel_shape = (3, 1, 1) if not spatial_aware else (3, 1, 3) + tw_padding_shape = (1, 0, 0) if not spatial_aware else (1, 0, 1) + + # conv layers + self.conv1 = nn.Sequential( + nn.GroupNorm(32, in_channels), nn.SiLU(), + nn.Conv3d(in_channels, out_channels, th_kernel_shape, padding=th_padding_shape)) + self.conv2 = nn.Sequential( + nn.GroupNorm(32, out_channels), nn.SiLU(), nn.Dropout(dropout), + nn.Conv3d(out_channels, in_channels, tw_kernel_shape, padding=tw_padding_shape)) + self.conv3 = nn.Sequential( + nn.GroupNorm(32, out_channels), nn.SiLU(), nn.Dropout(dropout), + nn.Conv3d(out_channels, in_channels, th_kernel_shape, padding=th_padding_shape)) + self.conv4 = nn.Sequential( + nn.GroupNorm(32, out_channels), nn.SiLU(), nn.Dropout(dropout), + nn.Conv3d(out_channels, in_channels, tw_kernel_shape, padding=tw_padding_shape)) + + # zero out the last layer params,so the conv block is identity + nn.init.zeros_(self.conv4[-1].weight) + nn.init.zeros_(self.conv4[-1].bias) + + def forward(self, x): + identity = x + x = self.conv1(x) + x = self.conv2(x) + x = self.conv3(x) + x = self.conv4(x) + + return identity + x + +class UNetModel(nn.Module): + """ + The full UNet model with attention and timestep embedding. + :param in_channels: in_channels in the input Tensor. + :param model_channels: base channel count for the model. + :param out_channels: channels in the output Tensor. + :param num_res_blocks: number of residual blocks per downsample. + :param attention_resolutions: a collection of downsample rates at which + attention will take place. May be a set, list, or tuple. + For example, if this contains 4, then at 4x downsampling, attention + will be used. + :param dropout: the dropout probability. + :param channel_mult: channel multiplier for each level of the UNet. + :param conv_resample: if True, use learned convolutions for upsampling and + downsampling. + :param dims: determines if the signal is 1D, 2D, or 3D. + :param num_classes: if specified (as an int), then this model will be + class-conditional with `num_classes` classes. + :param use_checkpoint: use gradient checkpointing to reduce memory usage. + :param num_heads: the number of attention heads in each attention layer. + :param num_heads_channels: if specified, ignore num_heads and instead use + a fixed channel width per attention head. + :param num_heads_upsample: works with num_heads to set a different number + of heads for upsampling. Deprecated. + :param use_scale_shift_norm: use a FiLM-like conditioning mechanism. + :param resblock_updown: use residual blocks for up/downsampling. + :param use_new_attention_order: use a different attention pattern for potentially + increased efficiency. + """ + + def __init__(self, + in_channels, + model_channels, + out_channels, + num_res_blocks, + attention_resolutions, + dropout=0.0, + channel_mult=(1, 2, 4, 8), + conv_resample=True, + dims=2, + context_dim=None, + use_scale_shift_norm=False, + resblock_updown=False, + num_heads=-1, + num_head_channels=-1, + transformer_depth=1, + use_linear=False, + use_checkpoint=False, + temporal_conv=False, + tempspatial_aware=False, + temporal_attention=True, + use_relative_position=True, + use_causal_attention=False, + temporal_length=None, + use_fp16=False, + addition_attention=False, + temporal_selfatt_only=True, + image_cross_attention=False, + image_cross_attention_scale_learnable=False, + default_fs=4, + fs_condition=False, + ): + super(UNetModel, self).__init__() + if num_heads == -1: + assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set' + if num_head_channels == -1: + assert num_heads != -1, 'Either num_heads or num_head_channels has to be set' + + self.in_channels = in_channels + self.model_channels = model_channels + self.out_channels = out_channels + self.num_res_blocks = num_res_blocks + self.attention_resolutions = attention_resolutions + self.dropout = dropout + self.channel_mult = channel_mult + self.conv_resample = conv_resample + self.temporal_attention = temporal_attention + time_embed_dim = model_channels * 4 + self.use_checkpoint = use_checkpoint + self.dtype = torch.float16 if use_fp16 else torch.float32 + temporal_self_att_only = True + self.addition_attention = addition_attention + self.temporal_length = temporal_length + self.image_cross_attention = image_cross_attention + self.image_cross_attention_scale_learnable = image_cross_attention_scale_learnable + self.default_fs = default_fs + self.fs_condition = fs_condition + + ## Time embedding blocks + self.time_embed = nn.Sequential( + linear(model_channels, time_embed_dim), + nn.SiLU(), + linear(time_embed_dim, time_embed_dim), + ) + if fs_condition: + self.fps_embedding = nn.Sequential( + linear(model_channels, time_embed_dim), + nn.SiLU(), + linear(time_embed_dim, time_embed_dim), + ) + nn.init.zeros_(self.fps_embedding[-1].weight) + nn.init.zeros_(self.fps_embedding[-1].bias) + ## Input Block + self.input_blocks = nn.ModuleList( + [ + TimestepEmbedSequential(conv_nd(dims, in_channels, model_channels, 3, padding=1)) + ] + ) + if self.addition_attention: + self.init_attn=TimestepEmbedSequential( + TemporalTransformer( + model_channels, + n_heads=8, + d_head=num_head_channels, + depth=transformer_depth, + context_dim=context_dim, + use_checkpoint=use_checkpoint, only_self_att=temporal_selfatt_only, + causal_attention=False, relative_position=use_relative_position, + temporal_length=temporal_length)) + + input_block_chans = [model_channels] + ch = model_channels + ds = 1 + for level, mult in enumerate(channel_mult): + for _ in range(num_res_blocks): + layers = [ + ResBlock(ch, time_embed_dim, dropout, + out_channels=mult * model_channels, dims=dims, use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, tempspatial_aware=tempspatial_aware, + use_temporal_conv=temporal_conv + ) + ] + ch = mult * model_channels + if ds in attention_resolutions: + if num_head_channels == -1: + dim_head = ch // num_heads + else: + num_heads = ch // num_head_channels + dim_head = num_head_channels + layers.append( + SpatialTransformer(ch, num_heads, dim_head, + depth=transformer_depth, context_dim=context_dim, use_linear=use_linear, + use_checkpoint=use_checkpoint, disable_self_attn=False, + video_length=temporal_length, image_cross_attention=self.image_cross_attention, + image_cross_attention_scale_learnable=self.image_cross_attention_scale_learnable, + ) + ) + if self.temporal_attention: + layers.append( + TemporalTransformer(ch, num_heads, dim_head, + depth=transformer_depth, context_dim=context_dim, use_linear=use_linear, + use_checkpoint=use_checkpoint, only_self_att=temporal_self_att_only, + causal_attention=use_causal_attention, relative_position=use_relative_position, + temporal_length=temporal_length + ) + ) + self.input_blocks.append(TimestepEmbedSequential(*layers)) + input_block_chans.append(ch) + if level != len(channel_mult) - 1: + out_ch = ch + self.input_blocks.append( + TimestepEmbedSequential( + ResBlock(ch, time_embed_dim, dropout, + out_channels=out_ch, dims=dims, use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + down=True + ) + if resblock_updown + else Downsample(ch, conv_resample, dims=dims, out_channels=out_ch) + ) + ) + ch = out_ch + input_block_chans.append(ch) + ds *= 2 + + if num_head_channels == -1: + dim_head = ch // num_heads + else: + num_heads = ch // num_head_channels + dim_head = num_head_channels + layers = [ + ResBlock(ch, time_embed_dim, dropout, + dims=dims, use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, tempspatial_aware=tempspatial_aware, + use_temporal_conv=temporal_conv + ), + SpatialTransformer(ch, num_heads, dim_head, + depth=transformer_depth, context_dim=context_dim, use_linear=use_linear, + use_checkpoint=use_checkpoint, disable_self_attn=False, video_length=temporal_length, + image_cross_attention=self.image_cross_attention,image_cross_attention_scale_learnable=self.image_cross_attention_scale_learnable + ) + ] + if self.temporal_attention: + layers.append( + TemporalTransformer(ch, num_heads, dim_head, + depth=transformer_depth, context_dim=context_dim, use_linear=use_linear, + use_checkpoint=use_checkpoint, only_self_att=temporal_self_att_only, + causal_attention=use_causal_attention, relative_position=use_relative_position, + temporal_length=temporal_length + ) + ) + layers.append( + ResBlock(ch, time_embed_dim, dropout, + dims=dims, use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, tempspatial_aware=tempspatial_aware, + use_temporal_conv=temporal_conv + ) + ) + + ## Middle Block + self.middle_block = TimestepEmbedSequential(*layers) + + ## Output Block + self.output_blocks = nn.ModuleList([]) + for level, mult in list(enumerate(channel_mult))[::-1]: + for i in range(num_res_blocks + 1): + ich = input_block_chans.pop() + layers = [ + ResBlock(ch + ich, time_embed_dim, dropout, + out_channels=mult * model_channels, dims=dims, use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, tempspatial_aware=tempspatial_aware, + use_temporal_conv=temporal_conv + ) + ] + ch = model_channels * mult + if ds in attention_resolutions: + if num_head_channels == -1: + dim_head = ch // num_heads + else: + num_heads = ch // num_head_channels + dim_head = num_head_channels + layers.append( + SpatialTransformer(ch, num_heads, dim_head, + depth=transformer_depth, context_dim=context_dim, use_linear=use_linear, + use_checkpoint=use_checkpoint, disable_self_attn=False, video_length=temporal_length, + image_cross_attention=self.image_cross_attention,image_cross_attention_scale_learnable=self.image_cross_attention_scale_learnable + ) + ) + if self.temporal_attention: + layers.append( + TemporalTransformer(ch, num_heads, dim_head, + depth=transformer_depth, context_dim=context_dim, use_linear=use_linear, + use_checkpoint=use_checkpoint, only_self_att=temporal_self_att_only, + causal_attention=use_causal_attention, relative_position=use_relative_position, + temporal_length=temporal_length + ) + ) + if level and i == num_res_blocks: + out_ch = ch + layers.append( + ResBlock(ch, time_embed_dim, dropout, + out_channels=out_ch, dims=dims, use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + up=True + ) + if resblock_updown + else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch) + ) + ds //= 2 + self.output_blocks.append(TimestepEmbedSequential(*layers)) + + self.out = nn.Sequential( + normalization(ch), + nn.SiLU(), + zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)), + ) + + def forward(self, x, timesteps, context=None, features_adapter=None, fs=None, **kwargs): + b,_,t,_,_ = x.shape + t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).type(x.dtype) + emb = self.time_embed(t_emb) + + ## repeat t times for context [(b t) 77 768] & time embedding + ## check if we use per-frame image conditioning + _, l_context, _ = context.shape + if l_context == 77 + t*16: ## !!! HARD CODE here + context_text, context_img = context[:,:77,:], context[:,77:,:] + context_text = context_text.repeat_interleave(repeats=t, dim=0) + context_img = rearrange(context_img, 'b (t l) c -> (b t) l c', t=t) + context = torch.cat([context_text, context_img], dim=1) + else: + context = context.repeat_interleave(repeats=t, dim=0) + emb = emb.repeat_interleave(repeats=t, dim=0) + + ## always in shape (b t) c h w, except for temporal layer + x = rearrange(x, 'b c t h w -> (b t) c h w') + + ## combine emb + if self.fs_condition: + if fs is None: + fs = torch.tensor( + [self.default_fs] * b, dtype=torch.long, device=x.device) + fs_emb = timestep_embedding(fs, self.model_channels, repeat_only=False).type(x.dtype) + + fs_embed = self.fps_embedding(fs_emb) + fs_embed = fs_embed.repeat_interleave(repeats=t, dim=0) + emb = emb + fs_embed + + h = x.type(self.dtype) + adapter_idx = 0 + hs = [] + for id, module in enumerate(self.input_blocks): + h = module(h, emb, context=context, batch_size=b) + if id ==0 and self.addition_attention: + h = self.init_attn(h, emb, context=context, batch_size=b) + ## plug-in adapter features + if ((id+1)%3 == 0) and features_adapter is not None: + h = h + features_adapter[adapter_idx] + adapter_idx += 1 + hs.append(h) + if features_adapter is not None: + assert len(features_adapter)==adapter_idx, 'Wrong features_adapter' + + h = self.middle_block(h, emb, context=context, batch_size=b) + for module in self.output_blocks: + h = torch.cat([h, hs.pop()], dim=1) + h = module(h, emb, context=context, batch_size=b) + h = h.type(x.dtype) + y = self.out(h) + + # reshape back to (b c t h w) + y = rearrange(y, '(b t) c h w -> b c t h w', b=b) + return y \ No newline at end of file diff --git a/lvdm/modules/x_transformer.py b/lvdm/modules/x_transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..5321012f860a8fb06850c1ddf495db934addecae --- /dev/null +++ b/lvdm/modules/x_transformer.py @@ -0,0 +1,639 @@ +"""shout-out to https://github.com/lucidrains/x-transformers/tree/main/x_transformers""" +from functools import partial +from inspect import isfunction +from collections import namedtuple +from einops import rearrange, repeat +import torch +from torch import nn, einsum +import torch.nn.functional as F + +# constants +DEFAULT_DIM_HEAD = 64 + +Intermediates = namedtuple('Intermediates', [ + 'pre_softmax_attn', + 'post_softmax_attn' +]) + +LayerIntermediates = namedtuple('Intermediates', [ + 'hiddens', + 'attn_intermediates' +]) + + +class AbsolutePositionalEmbedding(nn.Module): + def __init__(self, dim, max_seq_len): + super().__init__() + self.emb = nn.Embedding(max_seq_len, dim) + self.init_() + + def init_(self): + nn.init.normal_(self.emb.weight, std=0.02) + + def forward(self, x): + n = torch.arange(x.shape[1], device=x.device) + return self.emb(n)[None, :, :] + + +class FixedPositionalEmbedding(nn.Module): + def __init__(self, dim): + super().__init__() + inv_freq = 1. / (10000 ** (torch.arange(0, dim, 2).float() / dim)) + self.register_buffer('inv_freq', inv_freq) + + def forward(self, x, seq_dim=1, offset=0): + t = torch.arange(x.shape[seq_dim], device=x.device).type_as(self.inv_freq) + offset + sinusoid_inp = torch.einsum('i , j -> i j', t, self.inv_freq) + emb = torch.cat((sinusoid_inp.sin(), sinusoid_inp.cos()), dim=-1) + return emb[None, :, :] + + +# helpers + +def exists(val): + return val is not None + + +def default(val, d): + if exists(val): + return val + return d() if isfunction(d) else d + + +def always(val): + def inner(*args, **kwargs): + return val + return inner + + +def not_equals(val): + def inner(x): + return x != val + return inner + + +def equals(val): + def inner(x): + return x == val + return inner + + +def max_neg_value(tensor): + return -torch.finfo(tensor.dtype).max + + +# keyword argument helpers + +def pick_and_pop(keys, d): + values = list(map(lambda key: d.pop(key), keys)) + return dict(zip(keys, values)) + + +def group_dict_by_key(cond, d): + return_val = [dict(), dict()] + for key in d.keys(): + match = bool(cond(key)) + ind = int(not match) + return_val[ind][key] = d[key] + return (*return_val,) + + +def string_begins_with(prefix, str): + return str.startswith(prefix) + + +def group_by_key_prefix(prefix, d): + return group_dict_by_key(partial(string_begins_with, prefix), d) + + +def groupby_prefix_and_trim(prefix, d): + kwargs_with_prefix, kwargs = group_dict_by_key(partial(string_begins_with, prefix), d) + kwargs_without_prefix = dict(map(lambda x: (x[0][len(prefix):], x[1]), tuple(kwargs_with_prefix.items()))) + return kwargs_without_prefix, kwargs + + +# classes +class Scale(nn.Module): + def __init__(self, value, fn): + super().__init__() + self.value = value + self.fn = fn + + def forward(self, x, **kwargs): + x, *rest = self.fn(x, **kwargs) + return (x * self.value, *rest) + + +class Rezero(nn.Module): + def __init__(self, fn): + super().__init__() + self.fn = fn + self.g = nn.Parameter(torch.zeros(1)) + + def forward(self, x, **kwargs): + x, *rest = self.fn(x, **kwargs) + return (x * self.g, *rest) + + +class ScaleNorm(nn.Module): + def __init__(self, dim, eps=1e-5): + super().__init__() + self.scale = dim ** -0.5 + self.eps = eps + self.g = nn.Parameter(torch.ones(1)) + + def forward(self, x): + norm = torch.norm(x, dim=-1, keepdim=True) * self.scale + return x / norm.clamp(min=self.eps) * self.g + + +class RMSNorm(nn.Module): + def __init__(self, dim, eps=1e-8): + super().__init__() + self.scale = dim ** -0.5 + self.eps = eps + self.g = nn.Parameter(torch.ones(dim)) + + def forward(self, x): + norm = torch.norm(x, dim=-1, keepdim=True) * self.scale + return x / norm.clamp(min=self.eps) * self.g + + +class Residual(nn.Module): + def forward(self, x, residual): + return x + residual + + +class GRUGating(nn.Module): + def __init__(self, dim): + super().__init__() + self.gru = nn.GRUCell(dim, dim) + + def forward(self, x, residual): + gated_output = self.gru( + rearrange(x, 'b n d -> (b n) d'), + rearrange(residual, 'b n d -> (b n) d') + ) + + return gated_output.reshape_as(x) + + +# feedforward + +class GEGLU(nn.Module): + def __init__(self, dim_in, dim_out): + super().__init__() + self.proj = nn.Linear(dim_in, dim_out * 2) + + def forward(self, x): + x, gate = self.proj(x).chunk(2, dim=-1) + return x * F.gelu(gate) + + +class FeedForward(nn.Module): + def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.): + super().__init__() + inner_dim = int(dim * mult) + dim_out = default(dim_out, dim) + project_in = nn.Sequential( + nn.Linear(dim, inner_dim), + nn.GELU() + ) if not glu else GEGLU(dim, inner_dim) + + self.net = nn.Sequential( + project_in, + nn.Dropout(dropout), + nn.Linear(inner_dim, dim_out) + ) + + def forward(self, x): + return self.net(x) + + +# attention. +class Attention(nn.Module): + def __init__( + self, + dim, + dim_head=DEFAULT_DIM_HEAD, + heads=8, + causal=False, + mask=None, + talking_heads=False, + sparse_topk=None, + use_entmax15=False, + num_mem_kv=0, + dropout=0., + on_attn=False + ): + super().__init__() + if use_entmax15: + raise NotImplementedError("Check out entmax activation instead of softmax activation!") + self.scale = dim_head ** -0.5 + self.heads = heads + self.causal = causal + self.mask = mask + + inner_dim = dim_head * heads + + self.to_q = nn.Linear(dim, inner_dim, bias=False) + self.to_k = nn.Linear(dim, inner_dim, bias=False) + self.to_v = nn.Linear(dim, inner_dim, bias=False) + self.dropout = nn.Dropout(dropout) + + # talking heads + self.talking_heads = talking_heads + if talking_heads: + self.pre_softmax_proj = nn.Parameter(torch.randn(heads, heads)) + self.post_softmax_proj = nn.Parameter(torch.randn(heads, heads)) + + # explicit topk sparse attention + self.sparse_topk = sparse_topk + + # entmax + #self.attn_fn = entmax15 if use_entmax15 else F.softmax + self.attn_fn = F.softmax + + # add memory key / values + self.num_mem_kv = num_mem_kv + if num_mem_kv > 0: + self.mem_k = nn.Parameter(torch.randn(heads, num_mem_kv, dim_head)) + self.mem_v = nn.Parameter(torch.randn(heads, num_mem_kv, dim_head)) + + # attention on attention + self.attn_on_attn = on_attn + self.to_out = nn.Sequential(nn.Linear(inner_dim, dim * 2), nn.GLU()) if on_attn else nn.Linear(inner_dim, dim) + + def forward( + self, + x, + context=None, + mask=None, + context_mask=None, + rel_pos=None, + sinusoidal_emb=None, + prev_attn=None, + mem=None + ): + b, n, _, h, talking_heads, device = *x.shape, self.heads, self.talking_heads, x.device + kv_input = default(context, x) + + q_input = x + k_input = kv_input + v_input = kv_input + + if exists(mem): + k_input = torch.cat((mem, k_input), dim=-2) + v_input = torch.cat((mem, v_input), dim=-2) + + if exists(sinusoidal_emb): + # in shortformer, the query would start at a position offset depending on the past cached memory + offset = k_input.shape[-2] - q_input.shape[-2] + q_input = q_input + sinusoidal_emb(q_input, offset=offset) + k_input = k_input + sinusoidal_emb(k_input) + + q = self.to_q(q_input) + k = self.to_k(k_input) + v = self.to_v(v_input) + + q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h=h), (q, k, v)) + + input_mask = None + if any(map(exists, (mask, context_mask))): + q_mask = default(mask, lambda: torch.ones((b, n), device=device).bool()) + k_mask = q_mask if not exists(context) else context_mask + k_mask = default(k_mask, lambda: torch.ones((b, k.shape[-2]), device=device).bool()) + q_mask = rearrange(q_mask, 'b i -> b () i ()') + k_mask = rearrange(k_mask, 'b j -> b () () j') + input_mask = q_mask * k_mask + + if self.num_mem_kv > 0: + mem_k, mem_v = map(lambda t: repeat(t, 'h n d -> b h n d', b=b), (self.mem_k, self.mem_v)) + k = torch.cat((mem_k, k), dim=-2) + v = torch.cat((mem_v, v), dim=-2) + if exists(input_mask): + input_mask = F.pad(input_mask, (self.num_mem_kv, 0), value=True) + + dots = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale + mask_value = max_neg_value(dots) + + if exists(prev_attn): + dots = dots + prev_attn + + pre_softmax_attn = dots + + if talking_heads: + dots = einsum('b h i j, h k -> b k i j', dots, self.pre_softmax_proj).contiguous() + + if exists(rel_pos): + dots = rel_pos(dots) + + if exists(input_mask): + dots.masked_fill_(~input_mask, mask_value) + del input_mask + + if self.causal: + i, j = dots.shape[-2:] + r = torch.arange(i, device=device) + mask = rearrange(r, 'i -> () () i ()') < rearrange(r, 'j -> () () () j') + mask = F.pad(mask, (j - i, 0), value=False) + dots.masked_fill_(mask, mask_value) + del mask + + if exists(self.sparse_topk) and self.sparse_topk < dots.shape[-1]: + top, _ = dots.topk(self.sparse_topk, dim=-1) + vk = top[..., -1].unsqueeze(-1).expand_as(dots) + mask = dots < vk + dots.masked_fill_(mask, mask_value) + del mask + + attn = self.attn_fn(dots, dim=-1) + post_softmax_attn = attn + + attn = self.dropout(attn) + + if talking_heads: + attn = einsum('b h i j, h k -> b k i j', attn, self.post_softmax_proj).contiguous() + + out = einsum('b h i j, b h j d -> b h i d', attn, v) + out = rearrange(out, 'b h n d -> b n (h d)') + + intermediates = Intermediates( + pre_softmax_attn=pre_softmax_attn, + post_softmax_attn=post_softmax_attn + ) + + return self.to_out(out), intermediates + + +class AttentionLayers(nn.Module): + def __init__( + self, + dim, + depth, + heads=8, + causal=False, + cross_attend=False, + only_cross=False, + use_scalenorm=False, + use_rmsnorm=False, + use_rezero=False, + rel_pos_num_buckets=32, + rel_pos_max_distance=128, + position_infused_attn=False, + custom_layers=None, + sandwich_coef=None, + par_ratio=None, + residual_attn=False, + cross_residual_attn=False, + macaron=False, + pre_norm=True, + gate_residual=False, + **kwargs + ): + super().__init__() + ff_kwargs, kwargs = groupby_prefix_and_trim('ff_', kwargs) + attn_kwargs, _ = groupby_prefix_and_trim('attn_', kwargs) + + dim_head = attn_kwargs.get('dim_head', DEFAULT_DIM_HEAD) + + self.dim = dim + self.depth = depth + self.layers = nn.ModuleList([]) + + self.has_pos_emb = position_infused_attn + self.pia_pos_emb = FixedPositionalEmbedding(dim) if position_infused_attn else None + self.rotary_pos_emb = always(None) + + assert rel_pos_num_buckets <= rel_pos_max_distance, 'number of relative position buckets must be less than the relative position max distance' + self.rel_pos = None + + self.pre_norm = pre_norm + + self.residual_attn = residual_attn + self.cross_residual_attn = cross_residual_attn + + norm_class = ScaleNorm if use_scalenorm else nn.LayerNorm + norm_class = RMSNorm if use_rmsnorm else norm_class + norm_fn = partial(norm_class, dim) + + norm_fn = nn.Identity if use_rezero else norm_fn + branch_fn = Rezero if use_rezero else None + + if cross_attend and not only_cross: + default_block = ('a', 'c', 'f') + elif cross_attend and only_cross: + default_block = ('c', 'f') + else: + default_block = ('a', 'f') + + if macaron: + default_block = ('f',) + default_block + + if exists(custom_layers): + layer_types = custom_layers + elif exists(par_ratio): + par_depth = depth * len(default_block) + assert 1 < par_ratio <= par_depth, 'par ratio out of range' + default_block = tuple(filter(not_equals('f'), default_block)) + par_attn = par_depth // par_ratio + depth_cut = par_depth * 2 // 3 # 2 / 3 attention layer cutoff suggested by PAR paper + par_width = (depth_cut + depth_cut // par_attn) // par_attn + assert len(default_block) <= par_width, 'default block is too large for par_ratio' + par_block = default_block + ('f',) * (par_width - len(default_block)) + par_head = par_block * par_attn + layer_types = par_head + ('f',) * (par_depth - len(par_head)) + elif exists(sandwich_coef): + assert sandwich_coef > 0 and sandwich_coef <= depth, 'sandwich coefficient should be less than the depth' + layer_types = ('a',) * sandwich_coef + default_block * (depth - sandwich_coef) + ('f',) * sandwich_coef + else: + layer_types = default_block * depth + + self.layer_types = layer_types + self.num_attn_layers = len(list(filter(equals('a'), layer_types))) + + for layer_type in self.layer_types: + if layer_type == 'a': + layer = Attention(dim, heads=heads, causal=causal, **attn_kwargs) + elif layer_type == 'c': + layer = Attention(dim, heads=heads, **attn_kwargs) + elif layer_type == 'f': + layer = FeedForward(dim, **ff_kwargs) + layer = layer if not macaron else Scale(0.5, layer) + else: + raise Exception(f'invalid layer type {layer_type}') + + if isinstance(layer, Attention) and exists(branch_fn): + layer = branch_fn(layer) + + if gate_residual: + residual_fn = GRUGating(dim) + else: + residual_fn = Residual() + + self.layers.append(nn.ModuleList([ + norm_fn(), + layer, + residual_fn + ])) + + def forward( + self, + x, + context=None, + mask=None, + context_mask=None, + mems=None, + return_hiddens=False + ): + hiddens = [] + intermediates = [] + prev_attn = None + prev_cross_attn = None + + mems = mems.copy() if exists(mems) else [None] * self.num_attn_layers + + for ind, (layer_type, (norm, block, residual_fn)) in enumerate(zip(self.layer_types, self.layers)): + is_last = ind == (len(self.layers) - 1) + + if layer_type == 'a': + hiddens.append(x) + layer_mem = mems.pop(0) + + residual = x + + if self.pre_norm: + x = norm(x) + + if layer_type == 'a': + out, inter = block(x, mask=mask, sinusoidal_emb=self.pia_pos_emb, rel_pos=self.rel_pos, + prev_attn=prev_attn, mem=layer_mem) + elif layer_type == 'c': + out, inter = block(x, context=context, mask=mask, context_mask=context_mask, prev_attn=prev_cross_attn) + elif layer_type == 'f': + out = block(x) + + x = residual_fn(out, residual) + + if layer_type in ('a', 'c'): + intermediates.append(inter) + + if layer_type == 'a' and self.residual_attn: + prev_attn = inter.pre_softmax_attn + elif layer_type == 'c' and self.cross_residual_attn: + prev_cross_attn = inter.pre_softmax_attn + + if not self.pre_norm and not is_last: + x = norm(x) + + if return_hiddens: + intermediates = LayerIntermediates( + hiddens=hiddens, + attn_intermediates=intermediates + ) + + return x, intermediates + + return x + + +class Encoder(AttentionLayers): + def __init__(self, **kwargs): + assert 'causal' not in kwargs, 'cannot set causality on encoder' + super().__init__(causal=False, **kwargs) + + + +class TransformerWrapper(nn.Module): + def __init__( + self, + *, + num_tokens, + max_seq_len, + attn_layers, + emb_dim=None, + max_mem_len=0., + emb_dropout=0., + num_memory_tokens=None, + tie_embedding=False, + use_pos_emb=True + ): + super().__init__() + assert isinstance(attn_layers, AttentionLayers), 'attention layers must be one of Encoder or Decoder' + + dim = attn_layers.dim + emb_dim = default(emb_dim, dim) + + self.max_seq_len = max_seq_len + self.max_mem_len = max_mem_len + self.num_tokens = num_tokens + + self.token_emb = nn.Embedding(num_tokens, emb_dim) + self.pos_emb = AbsolutePositionalEmbedding(emb_dim, max_seq_len) if ( + use_pos_emb and not attn_layers.has_pos_emb) else always(0) + self.emb_dropout = nn.Dropout(emb_dropout) + + self.project_emb = nn.Linear(emb_dim, dim) if emb_dim != dim else nn.Identity() + self.attn_layers = attn_layers + self.norm = nn.LayerNorm(dim) + + self.init_() + + self.to_logits = nn.Linear(dim, num_tokens) if not tie_embedding else lambda t: t @ self.token_emb.weight.t() + + # memory tokens (like [cls]) from Memory Transformers paper + num_memory_tokens = default(num_memory_tokens, 0) + self.num_memory_tokens = num_memory_tokens + if num_memory_tokens > 0: + self.memory_tokens = nn.Parameter(torch.randn(num_memory_tokens, dim)) + + # let funnel encoder know number of memory tokens, if specified + if hasattr(attn_layers, 'num_memory_tokens'): + attn_layers.num_memory_tokens = num_memory_tokens + + def init_(self): + nn.init.normal_(self.token_emb.weight, std=0.02) + + def forward( + self, + x, + return_embeddings=False, + mask=None, + return_mems=False, + return_attn=False, + mems=None, + **kwargs + ): + b, n, device, num_mem = *x.shape, x.device, self.num_memory_tokens + x = self.token_emb(x) + x += self.pos_emb(x) + x = self.emb_dropout(x) + + x = self.project_emb(x) + + if num_mem > 0: + mem = repeat(self.memory_tokens, 'n d -> b n d', b=b) + x = torch.cat((mem, x), dim=1) + + # auto-handle masking after appending memory tokens + if exists(mask): + mask = F.pad(mask, (num_mem, 0), value=True) + + x, intermediates = self.attn_layers(x, mask=mask, mems=mems, return_hiddens=True, **kwargs) + x = self.norm(x) + + mem, x = x[:, :num_mem], x[:, num_mem:] + + out = self.to_logits(x) if not return_embeddings else x + + if return_mems: + hiddens = intermediates.hiddens + new_mems = list(map(lambda pair: torch.cat(pair, dim=-2), zip(mems, hiddens))) if exists(mems) else hiddens + new_mems = list(map(lambda t: t[..., -self.max_mem_len:, :].detach(), new_mems)) + return out, new_mems + + if return_attn: + attn_maps = list(map(lambda t: t.post_softmax_attn, intermediates.attn_intermediates)) + return out, attn_maps + + return out \ No newline at end of file diff --git a/main/callbacks.py b/main/callbacks.py new file mode 100644 index 0000000000000000000000000000000000000000..b03c583acf4f5b8caba85e8272b31ea1b77d80c2 --- /dev/null +++ b/main/callbacks.py @@ -0,0 +1,133 @@ +import os +import time +import logging +mainlogger = logging.getLogger('mainlogger') + +import torch +import torchvision +import pytorch_lightning as pl +from pytorch_lightning.callbacks import Callback +from pytorch_lightning.utilities import rank_zero_only +from pytorch_lightning.utilities import rank_zero_info +from utils.save_video import log_local, prepare_to_log + + +class ImageLogger(Callback): + def __init__(self, batch_frequency, max_images=8, clamp=True, rescale=True, save_dir=None, \ + to_local=False, log_images_kwargs=None): + super().__init__() + self.rescale = rescale + self.batch_freq = batch_frequency + self.max_images = max_images + self.to_local = to_local + self.clamp = clamp + self.log_images_kwargs = log_images_kwargs if log_images_kwargs else {} + if self.to_local: + ## default save dir + self.save_dir = os.path.join(save_dir, "images") + os.makedirs(os.path.join(self.save_dir, "train"), exist_ok=True) + os.makedirs(os.path.join(self.save_dir, "val"), exist_ok=True) + + def log_to_tensorboard(self, pl_module, batch_logs, filename, split, save_fps=8): + """ log images and videos to tensorboard """ + global_step = pl_module.global_step + for key in batch_logs: + value = batch_logs[key] + tag = "gs%d-%s/%s-%s"%(global_step, split, filename, key) + if isinstance(value, list) and isinstance(value[0], str): + captions = ' |------| '.join(value) + pl_module.logger.experiment.add_text(tag, captions, global_step=global_step) + elif isinstance(value, torch.Tensor) and value.dim() == 5: + video = value + n = video.shape[0] + video = video.permute(2, 0, 1, 3, 4) # t,n,c,h,w + frame_grids = [torchvision.utils.make_grid(framesheet, nrow=int(n), padding=0) for framesheet in video] #[3, n*h, 1*w] + grid = torch.stack(frame_grids, dim=0) # stack in temporal dim [t, 3, n*h, w] + grid = (grid + 1.0) / 2.0 + grid = grid.unsqueeze(dim=0) + pl_module.logger.experiment.add_video(tag, grid, fps=save_fps, global_step=global_step) + elif isinstance(value, torch.Tensor) and value.dim() == 4: + img = value + grid = torchvision.utils.make_grid(img, nrow=int(n), padding=0) + grid = (grid + 1.0) / 2.0 # -1,1 -> 0,1; c,h,w + pl_module.logger.experiment.add_image(tag, grid, global_step=global_step) + else: + pass + + @rank_zero_only + def log_batch_imgs(self, pl_module, batch, batch_idx, split="train"): + """ generate images, then save and log to tensorboard """ + skip_freq = self.batch_freq if split == "train" else 5 + if (batch_idx+1) % skip_freq == 0: + is_train = pl_module.training + if is_train: + pl_module.eval() + torch.cuda.empty_cache() + with torch.no_grad(): + log_func = pl_module.log_images + batch_logs = log_func(batch, split=split, **self.log_images_kwargs) + + ## process: move to CPU and clamp + batch_logs = prepare_to_log(batch_logs, self.max_images, self.clamp) + torch.cuda.empty_cache() + + filename = "ep{}_idx{}_rank{}".format( + pl_module.current_epoch, + batch_idx, + pl_module.global_rank) + if self.to_local: + mainlogger.info("Log [%s] batch <%s> to local ..."%(split, filename)) + filename = "gs{}_".format(pl_module.global_step) + filename + log_local(batch_logs, os.path.join(self.save_dir, split), filename, save_fps=10) + else: + mainlogger.info("Log [%s] batch <%s> to tensorboard ..."%(split, filename)) + self.log_to_tensorboard(pl_module, batch_logs, filename, split, save_fps=10) + mainlogger.info('Finish!') + + if is_train: + pl_module.train() + + def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx=None): + if self.batch_freq != -1 and pl_module.logdir: + self.log_batch_imgs(pl_module, batch, batch_idx, split="train") + + def on_validation_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx=None): + ## different with validation_step() that saving the whole validation set and only keep the latest, + ## it records the performance of every validation (without overwritten) by only keep a subset + if self.batch_freq != -1 and pl_module.logdir: + self.log_batch_imgs(pl_module, batch, batch_idx, split="val") + if hasattr(pl_module, 'calibrate_grad_norm'): + if (pl_module.calibrate_grad_norm and batch_idx % 25 == 0) and batch_idx > 0: + self.log_gradients(trainer, pl_module, batch_idx=batch_idx) + + +class CUDACallback(Callback): + # see https://github.com/SeanNaren/minGPT/blob/master/mingpt/callback.py + def on_train_epoch_start(self, trainer, pl_module): + # Reset the memory use counter + # lightning update + if int((pl.__version__).split('.')[1])>=7: + gpu_index = trainer.strategy.root_device.index + else: + gpu_index = trainer.root_gpu + torch.cuda.reset_peak_memory_stats(gpu_index) + torch.cuda.synchronize(gpu_index) + self.start_time = time.time() + + def on_train_epoch_end(self, trainer, pl_module): + if int((pl.__version__).split('.')[1])>=7: + gpu_index = trainer.strategy.root_device.index + else: + gpu_index = trainer.root_gpu + torch.cuda.synchronize(gpu_index) + max_memory = torch.cuda.max_memory_allocated(gpu_index) / 2 ** 20 + epoch_time = time.time() - self.start_time + + try: + max_memory = trainer.training_type_plugin.reduce(max_memory) + epoch_time = trainer.training_type_plugin.reduce(epoch_time) + + rank_zero_info(f"Average Epoch time: {epoch_time:.2f} seconds") + rank_zero_info(f"Average Peak memory {max_memory:.2f}MiB") + except AttributeError: + pass diff --git a/main/trainer.py b/main/trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..8082563975292d528b5dd1ab879310a33899b7d1 --- /dev/null +++ b/main/trainer.py @@ -0,0 +1,168 @@ +import argparse, os, sys, datetime +from omegaconf import OmegaConf +from transformers import logging as transf_logging +import pytorch_lightning as pl +from pytorch_lightning import seed_everything +from pytorch_lightning.trainer import Trainer +import torch +sys.path.insert(1, os.path.join(sys.path[0], '..')) +from utils.utils import instantiate_from_config +from utils_train import get_trainer_callbacks, get_trainer_logger, get_trainer_strategy +from utils_train import set_logger, init_workspace, load_checkpoints + + +def get_parser(**parser_kwargs): + parser = argparse.ArgumentParser(**parser_kwargs) + parser.add_argument("--seed", "-s", type=int, default=20230211, help="seed for seed_everything") + parser.add_argument("--name", "-n", type=str, default="", help="experiment name, as saving folder") + + parser.add_argument("--base", "-b", nargs="*", metavar="base_config.yaml", help="paths to base configs. Loaded from left-to-right. " + "Parameters can be overwritten or added with command-line options of the form `--key value`.", default=list()) + + parser.add_argument("--train", "-t", action='store_true', default=False, help='train') + parser.add_argument("--val", "-v", action='store_true', default=False, help='val') + parser.add_argument("--test", action='store_true', default=False, help='test') + + parser.add_argument("--logdir", "-l", type=str, default="logs", help="directory for logging dat shit") + parser.add_argument("--auto_resume", action='store_true', default=False, help="resume from full-info checkpoint") + parser.add_argument("--auto_resume_weight_only", action='store_true', default=False, help="resume from weight-only checkpoint") + parser.add_argument("--debug", "-d", action='store_true', default=False, help="enable post-mortem debugging") + + return parser + +def get_nondefault_trainer_args(args): + parser = argparse.ArgumentParser() + parser = Trainer.add_argparse_args(parser) + default_trainer_args = parser.parse_args([]) + return sorted(k for k in vars(default_trainer_args) if getattr(args, k) != getattr(default_trainer_args, k)) + + +if __name__ == "__main__": + now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S") + local_rank = int(os.environ.get('LOCAL_RANK')) + global_rank = int(os.environ.get('RANK')) + num_rank = int(os.environ.get('WORLD_SIZE')) + + parser = get_parser() + ## Extends existing argparse by default Trainer attributes + parser = Trainer.add_argparse_args(parser) + args, unknown = parser.parse_known_args() + ## disable transformer warning + transf_logging.set_verbosity_error() + seed_everything(args.seed) + + ## yaml configs: "model" | "data" | "lightning" + configs = [OmegaConf.load(cfg) for cfg in args.base] + cli = OmegaConf.from_dotlist(unknown) + config = OmegaConf.merge(*configs, cli) + lightning_config = config.pop("lightning", OmegaConf.create()) + trainer_config = lightning_config.get("trainer", OmegaConf.create()) + + ## setup workspace directories + workdir, ckptdir, cfgdir, loginfo = init_workspace(args.name, args.logdir, config, lightning_config, global_rank) + logger = set_logger(logfile=os.path.join(loginfo, 'log_%d:%s.txt'%(global_rank, now))) + logger.info("@lightning version: %s [>=1.8 required]"%(pl.__version__)) + + ## MODEL CONFIG >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> + logger.info("***** Configing Model *****") + config.model.params.logdir = workdir + model = instantiate_from_config(config.model) + + ## load checkpoints + model = load_checkpoints(model, config.model) + + ## register_schedule again to make ZTSNR work + if model.rescale_betas_zero_snr: + model.register_schedule(given_betas=model.given_betas, beta_schedule=model.beta_schedule, timesteps=model.timesteps, + linear_start=model.linear_start, linear_end=model.linear_end, cosine_s=model.cosine_s) + + ## update trainer config + for k in get_nondefault_trainer_args(args): + trainer_config[k] = getattr(args, k) + + num_nodes = trainer_config.num_nodes + ngpu_per_node = trainer_config.devices + logger.info(f"Running on {num_rank}={num_nodes}x{ngpu_per_node} GPUs") + + ## setup learning rate + base_lr = config.model.base_learning_rate + bs = config.data.params.batch_size + if getattr(config.model, 'scale_lr', True): + model.learning_rate = num_rank * bs * base_lr + else: + model.learning_rate = base_lr + + + ## DATA CONFIG >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> + logger.info("***** Configing Data *****") + data = instantiate_from_config(config.data) + data.setup() + for k in data.datasets: + logger.info(f"{k}, {data.datasets[k].__class__.__name__}, {len(data.datasets[k])}") + + + ## TRAINER CONFIG >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> + logger.info("***** Configing Trainer *****") + if "accelerator" not in trainer_config: + trainer_config["accelerator"] = "gpu" + + ## setup trainer args: pl-logger and callbacks + trainer_kwargs = dict() + trainer_kwargs["num_sanity_val_steps"] = 0 + logger_cfg = get_trainer_logger(lightning_config, workdir, args.debug) + trainer_kwargs["logger"] = instantiate_from_config(logger_cfg) + + ## setup callbacks + callbacks_cfg = get_trainer_callbacks(lightning_config, config, workdir, ckptdir, logger) + trainer_kwargs["callbacks"] = [instantiate_from_config(callbacks_cfg[k]) for k in callbacks_cfg] + strategy_cfg = get_trainer_strategy(lightning_config) + trainer_kwargs["strategy"] = strategy_cfg if type(strategy_cfg) == str else instantiate_from_config(strategy_cfg) + trainer_kwargs['precision'] = lightning_config.get('precision', 32) + trainer_kwargs["sync_batchnorm"] = False + + ## trainer config: others + + trainer_args = argparse.Namespace(**trainer_config) + trainer = Trainer.from_argparse_args(trainer_args, **trainer_kwargs) + + ## allow checkpointing via USR1 + def melk(*args, **kwargs): + ## run all checkpoint hooks + if trainer.global_rank == 0: + print("Summoning checkpoint.") + ckpt_path = os.path.join(ckptdir, "last_summoning.ckpt") + trainer.save_checkpoint(ckpt_path) + + def divein(*args, **kwargs): + if trainer.global_rank == 0: + import pudb; + pudb.set_trace() + + import signal + signal.signal(signal.SIGUSR1, melk) + signal.signal(signal.SIGUSR2, divein) + + ## Running LOOP >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> + logger.info("***** Running the Loop *****") + if args.train: + try: + if "strategy" in lightning_config and lightning_config['strategy'].startswith('deepspeed'): + logger.info("") + ## deepspeed + if trainer_kwargs['precision'] == 16: + with torch.cuda.amp.autocast(): + trainer.fit(model, data) + else: + trainer.fit(model, data) + else: + logger.info("") ## this is default + ## ddpsharded + trainer.fit(model, data) + except Exception: + #melk() + raise + + # if args.val: + # trainer.validate(model, data) + # if args.test or not trainer.interrupted: + # trainer.test(model, data) \ No newline at end of file diff --git a/main/utils_data.py b/main/utils_data.py new file mode 100644 index 0000000000000000000000000000000000000000..412216a729612230c3492eafe2b5b51666fdc33c --- /dev/null +++ b/main/utils_data.py @@ -0,0 +1,136 @@ +from functools import partial +import numpy as np + +import torch +import pytorch_lightning as pl +from torch.utils.data import DataLoader, Dataset + +import os, sys +os.chdir(sys.path[0]) +sys.path.append("..") +from lvdm.data.base import Txt2ImgIterableBaseDataset +from utils.utils import instantiate_from_config + + +def worker_init_fn(_): + worker_info = torch.utils.data.get_worker_info() + + dataset = worker_info.dataset + worker_id = worker_info.id + + if isinstance(dataset, Txt2ImgIterableBaseDataset): + split_size = dataset.num_records // worker_info.num_workers + # reset num_records to the true number to retain reliable length information + dataset.sample_ids = dataset.valid_ids[worker_id * split_size:(worker_id + 1) * split_size] + current_id = np.random.choice(len(np.random.get_state()[1]), 1) + return np.random.seed(np.random.get_state()[1][current_id] + worker_id) + else: + return np.random.seed(np.random.get_state()[1][0] + worker_id) + + +class WrappedDataset(Dataset): + """Wraps an arbitrary object with __len__ and __getitem__ into a pytorch dataset""" + + def __init__(self, dataset): + self.data = dataset + + def __len__(self): + return len(self.data) + + def __getitem__(self, idx): + return self.data[idx] + + +class DataModuleFromConfig(pl.LightningDataModule): + def __init__(self, batch_size, train=None, validation=None, test=None, predict=None, + wrap=False, num_workers=None, shuffle_test_loader=False, use_worker_init_fn=False, + shuffle_val_dataloader=False, train_img=None, + test_max_n_samples=None): + super().__init__() + self.batch_size = batch_size + self.dataset_configs = dict() + self.num_workers = num_workers if num_workers is not None else batch_size * 2 + self.use_worker_init_fn = use_worker_init_fn + if train is not None: + self.dataset_configs["train"] = train + self.train_dataloader = self._train_dataloader + if validation is not None: + self.dataset_configs["validation"] = validation + self.val_dataloader = partial(self._val_dataloader, shuffle=shuffle_val_dataloader) + if test is not None: + self.dataset_configs["test"] = test + self.test_dataloader = partial(self._test_dataloader, shuffle=shuffle_test_loader) + if predict is not None: + self.dataset_configs["predict"] = predict + self.predict_dataloader = self._predict_dataloader + + self.img_loader = None + self.wrap = wrap + self.test_max_n_samples = test_max_n_samples + self.collate_fn = None + + def prepare_data(self): + pass + + def setup(self, stage=None): + self.datasets = dict((k, instantiate_from_config(self.dataset_configs[k])) for k in self.dataset_configs) + if self.wrap: + for k in self.datasets: + self.datasets[k] = WrappedDataset(self.datasets[k]) + + def _train_dataloader(self): + is_iterable_dataset = isinstance(self.datasets['train'], Txt2ImgIterableBaseDataset) + if is_iterable_dataset or self.use_worker_init_fn: + init_fn = worker_init_fn + else: + init_fn = None + loader = DataLoader(self.datasets["train"], batch_size=self.batch_size, + num_workers=self.num_workers, shuffle=False if is_iterable_dataset else True, + worker_init_fn=init_fn, collate_fn=self.collate_fn, + ) + return loader + + def _val_dataloader(self, shuffle=False): + if isinstance(self.datasets['validation'], Txt2ImgIterableBaseDataset) or self.use_worker_init_fn: + init_fn = worker_init_fn + else: + init_fn = None + return DataLoader(self.datasets["validation"], + batch_size=self.batch_size, + num_workers=self.num_workers, + worker_init_fn=init_fn, + shuffle=shuffle, + collate_fn=self.collate_fn, + ) + + def _test_dataloader(self, shuffle=False): + try: + is_iterable_dataset = isinstance(self.datasets['train'], Txt2ImgIterableBaseDataset) + except: + is_iterable_dataset = isinstance(self.datasets['test'], Txt2ImgIterableBaseDataset) + + if is_iterable_dataset or self.use_worker_init_fn: + init_fn = worker_init_fn + else: + init_fn = None + + # do not shuffle dataloader for iterable dataset + shuffle = shuffle and (not is_iterable_dataset) + if self.test_max_n_samples is not None: + dataset = torch.utils.data.Subset(self.datasets["test"], list(range(self.test_max_n_samples))) + else: + dataset = self.datasets["test"] + return DataLoader(dataset, batch_size=self.batch_size, + num_workers=self.num_workers, worker_init_fn=init_fn, shuffle=shuffle, + collate_fn=self.collate_fn, + ) + + def _predict_dataloader(self, shuffle=False): + if isinstance(self.datasets['predict'], Txt2ImgIterableBaseDataset) or self.use_worker_init_fn: + init_fn = worker_init_fn + else: + init_fn = None + return DataLoader(self.datasets["predict"], batch_size=self.batch_size, + num_workers=self.num_workers, worker_init_fn=init_fn, + collate_fn=self.collate_fn, + ) diff --git a/main/utils_train.py b/main/utils_train.py new file mode 100644 index 0000000000000000000000000000000000000000..505d005de928921e68ea32b1e6fdc380d1660c06 --- /dev/null +++ b/main/utils_train.py @@ -0,0 +1,173 @@ +import os, re +from omegaconf import OmegaConf +import logging +mainlogger = logging.getLogger('mainlogger') + +import torch +from collections import OrderedDict + +def init_workspace(name, logdir, model_config, lightning_config, rank=0): + workdir = os.path.join(logdir, name) + ckptdir = os.path.join(workdir, "checkpoints") + cfgdir = os.path.join(workdir, "configs") + loginfo = os.path.join(workdir, "loginfo") + + # Create logdirs and save configs (all ranks will do to avoid missing directory error if rank:0 is slower) + os.makedirs(workdir, exist_ok=True) + os.makedirs(ckptdir, exist_ok=True) + os.makedirs(cfgdir, exist_ok=True) + os.makedirs(loginfo, exist_ok=True) + + if rank == 0: + if "callbacks" in lightning_config and 'metrics_over_trainsteps_checkpoint' in lightning_config.callbacks: + os.makedirs(os.path.join(ckptdir, 'trainstep_checkpoints'), exist_ok=True) + OmegaConf.save(model_config, os.path.join(cfgdir, "model.yaml")) + OmegaConf.save(OmegaConf.create({"lightning": lightning_config}), os.path.join(cfgdir, "lightning.yaml")) + return workdir, ckptdir, cfgdir, loginfo + +def check_config_attribute(config, name): + if name in config: + value = getattr(config, name) + return value + else: + return None + +def get_trainer_callbacks(lightning_config, config, logdir, ckptdir, logger): + default_callbacks_cfg = { + "model_checkpoint": { + "target": "pytorch_lightning.callbacks.ModelCheckpoint", + "params": { + "dirpath": ckptdir, + "filename": "{epoch}", + "verbose": True, + "save_last": False, + } + }, + "batch_logger": { + "target": "callbacks.ImageLogger", + "params": { + "save_dir": logdir, + "batch_frequency": 1000, + "max_images": 4, + "clamp": True, + } + }, + "learning_rate_logger": { + "target": "pytorch_lightning.callbacks.LearningRateMonitor", + "params": { + "logging_interval": "step", + "log_momentum": False + } + }, + "cuda_callback": { + "target": "callbacks.CUDACallback" + }, + } + + ## optional setting for saving checkpoints + monitor_metric = check_config_attribute(config.model.params, "monitor") + if monitor_metric is not None: + mainlogger.info(f"Monitoring {monitor_metric} as checkpoint metric.") + default_callbacks_cfg["model_checkpoint"]["params"]["monitor"] = monitor_metric + default_callbacks_cfg["model_checkpoint"]["params"]["save_top_k"] = 3 + default_callbacks_cfg["model_checkpoint"]["params"]["mode"] = "min" + + if 'metrics_over_trainsteps_checkpoint' in lightning_config.callbacks: + mainlogger.info('Caution: Saving checkpoints every n train steps without deleting. This might require some free space.') + default_metrics_over_trainsteps_ckpt_dict = { + 'metrics_over_trainsteps_checkpoint': {"target": 'pytorch_lightning.callbacks.ModelCheckpoint', + 'params': { + "dirpath": os.path.join(ckptdir, 'trainstep_checkpoints'), + "filename": "{epoch}-{step}", + "verbose": True, + 'save_top_k': -1, + 'every_n_train_steps': 10000, + 'save_weights_only': True + } + } + } + default_callbacks_cfg.update(default_metrics_over_trainsteps_ckpt_dict) + + if "callbacks" in lightning_config: + callbacks_cfg = lightning_config.callbacks + else: + callbacks_cfg = OmegaConf.create() + callbacks_cfg = OmegaConf.merge(default_callbacks_cfg, callbacks_cfg) + + return callbacks_cfg + +def get_trainer_logger(lightning_config, logdir, on_debug): + default_logger_cfgs = { + "tensorboard": { + "target": "pytorch_lightning.loggers.TensorBoardLogger", + "params": { + "save_dir": logdir, + "name": "tensorboard", + } + }, + "testtube": { + "target": "pytorch_lightning.loggers.CSVLogger", + "params": { + "name": "testtube", + "save_dir": logdir, + } + }, + } + os.makedirs(os.path.join(logdir, "tensorboard"), exist_ok=True) + default_logger_cfg = default_logger_cfgs["tensorboard"] + if "logger" in lightning_config: + logger_cfg = lightning_config.logger + else: + logger_cfg = OmegaConf.create() + logger_cfg = OmegaConf.merge(default_logger_cfg, logger_cfg) + return logger_cfg + +def get_trainer_strategy(lightning_config): + default_strategy_dict = { + "target": "pytorch_lightning.strategies.DDPShardedStrategy" + } + if "strategy" in lightning_config: + strategy_cfg = lightning_config.strategy + return strategy_cfg + else: + strategy_cfg = OmegaConf.create() + + strategy_cfg = OmegaConf.merge(default_strategy_dict, strategy_cfg) + return strategy_cfg + +def load_checkpoints(model, model_cfg): + if check_config_attribute(model_cfg, "pretrained_checkpoint"): + pretrained_ckpt = model_cfg.pretrained_checkpoint + assert os.path.exists(pretrained_ckpt), "Error: Pre-trained checkpoint NOT found at:%s"%pretrained_ckpt + mainlogger.info(">>> Load weights from pretrained checkpoint") + + pl_sd = torch.load(pretrained_ckpt, map_location="cpu") + try: + if 'state_dict' in pl_sd.keys(): + model.load_state_dict(pl_sd["state_dict"], strict=True) + mainlogger.info(">>> Loaded weights from pretrained checkpoint: %s"%pretrained_ckpt) + else: + # deepspeed + new_pl_sd = OrderedDict() + for key in pl_sd['module'].keys(): + new_pl_sd[key[16:]]=pl_sd['module'][key] + model.load_state_dict(new_pl_sd, strict=True) + except: + model.load_state_dict(pl_sd) + else: + mainlogger.info(">>> Start training from scratch") + + return model + +def set_logger(logfile, name='mainlogger'): + logger = logging.getLogger(name) + logger.setLevel(logging.INFO) + fh = logging.FileHandler(logfile, mode='w') + fh.setLevel(logging.INFO) + ch = logging.StreamHandler() + ch.setLevel(logging.DEBUG) + fh.setFormatter(logging.Formatter("%(asctime)s-%(levelname)s: %(message)s")) + ch.setFormatter(logging.Formatter("%(message)s")) + logger.addHandler(fh) + logger.addHandler(ch) + return logger \ No newline at end of file diff --git a/prompts/.DS_Store b/prompts/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..39260ad0e5e0a5c6195ea222d7de19f4c48278b9 Binary files /dev/null and b/prompts/.DS_Store differ diff --git a/prompts/1024_interp/74906_1462_frame1.png b/prompts/1024_interp/74906_1462_frame1.png new file mode 100644 index 0000000000000000000000000000000000000000..7474541a0ca9604951b0de3f344ada3aba018636 Binary files /dev/null and b/prompts/1024_interp/74906_1462_frame1.png differ diff --git a/prompts/1024_interp/74906_1462_frame3.png b/prompts/1024_interp/74906_1462_frame3.png new file mode 100644 index 0000000000000000000000000000000000000000..95997cf76dca00a4a2f4f4cdbe408c52b6ec9fe9 Binary files /dev/null and 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b/prompts/512_interp/Japan_v2_3_119235_s2_frame3.png new file mode 100644 index 0000000000000000000000000000000000000000..4bc78cd19d2ed487c418748cc148918379cbcbaf Binary files /dev/null and b/prompts/512_interp/Japan_v2_3_119235_s2_frame3.png differ diff --git a/prompts/512_interp/prompts.txt b/prompts/512_interp/prompts.txt new file mode 100644 index 0000000000000000000000000000000000000000..21ff33243e165bef9977ae7e389c6a98fa8e4cee --- /dev/null +++ b/prompts/512_interp/prompts.txt @@ -0,0 +1,3 @@ +walking man +an anime scene +an anime scene \ No newline at end of file diff --git a/prompts/other_exps/interp_1_1.png b/prompts/other_exps/interp_1_1.png new file mode 100644 index 0000000000000000000000000000000000000000..b875686659b151a87eff55f84a9e4502bedeab1d Binary files /dev/null and b/prompts/other_exps/interp_1_1.png differ diff --git a/prompts/other_exps/interp_1_2.png b/prompts/other_exps/interp_1_2.png new file mode 100644 index 0000000000000000000000000000000000000000..7564a86c2ee8b48613452bfd0cbff79a4d39bc04 Binary files /dev/null and b/prompts/other_exps/interp_1_2.png differ diff --git a/prompts/other_exps/interp_2_1.png b/prompts/other_exps/interp_2_1.png new file mode 100644 index 0000000000000000000000000000000000000000..6b1d1ecdcf8ee5693a1076797e812eeefbff1084 Binary files /dev/null and b/prompts/other_exps/interp_2_1.png differ diff --git a/prompts/other_exps/interp_2_2.png b/prompts/other_exps/interp_2_2.png new file mode 100644 index 0000000000000000000000000000000000000000..64837a531f7c8fe5c08c1300956d2385971a32a7 Binary files /dev/null and b/prompts/other_exps/interp_2_2.png differ diff --git a/prompts/other_exps/prompts.txt b/prompts/other_exps/prompts.txt new file mode 100644 index 0000000000000000000000000000000000000000..14b4e87d1f79cdc954271db60d26981f8c347b18 --- /dev/null +++ b/prompts/other_exps/prompts.txt @@ -0,0 +1,2 @@ +an anime scene +an anime scene \ No newline at end of file diff --git a/prompts/resize.py b/prompts/resize.py new file mode 100644 index 0000000000000000000000000000000000000000..084dc23bb1322e7eb73acb4f95e748be727667a9 --- /dev/null +++ b/prompts/resize.py @@ -0,0 +1,16 @@ +import os +from PIL import Image +from torchvision import transforms + +src_dir = "other_exps" +tar_dir = "1024_interp" +image_dir = os.listdir(src_dir) +for image in image_dir: + if 'txt' not in image: + img = Image.open(os.path.join(src_dir, image)) + T = transforms.Compose([ + transforms.Resize(576), + transforms.CenterCrop((576, 1024)) + ]) + img = T(img) + img.save(os.path.join(tar_dir, image)) \ No newline at end of file diff --git a/scripts/.DS_Store b/scripts/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..af79778dad3113ef381d2f847c27e05cf8b2a857 Binary files /dev/null and b/scripts/.DS_Store differ diff --git a/scripts/evaluation/ddp_wrapper.py b/scripts/evaluation/ddp_wrapper.py new file mode 100644 index 0000000000000000000000000000000000000000..3caec49623b852fadd34feead1574df53f077332 --- /dev/null +++ b/scripts/evaluation/ddp_wrapper.py @@ -0,0 +1,47 @@ +import datetime +import argparse, importlib +from pytorch_lightning import seed_everything + +import torch +import torch.distributed as dist + +def setup_dist(local_rank): + if dist.is_initialized(): + return + torch.cuda.set_device(local_rank) + torch.distributed.init_process_group('nccl', init_method='env://') + + +def get_dist_info(): + if dist.is_available(): + initialized = dist.is_initialized() + else: + initialized = False + if initialized: + rank = dist.get_rank() + world_size = dist.get_world_size() + else: + rank = 0 + world_size = 1 + return rank, world_size + + +if __name__ == '__main__': + now = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S") + parser = argparse.ArgumentParser() + parser.add_argument("--module", type=str, help="module name", default="inference") + parser.add_argument("--local_rank", type=int, nargs="?", help="for ddp", default=0) + args, unknown = parser.parse_known_args() + inference_api = importlib.import_module(args.module, package=None) + + inference_parser = inference_api.get_parser() + inference_args, unknown = inference_parser.parse_known_args() + + seed_everything(inference_args.seed) + setup_dist(args.local_rank) + torch.backends.cudnn.benchmark = True + rank, gpu_num = get_dist_info() + + # inference_args.savedir = inference_args.savedir+str('_seed')+str(inference_args.seed) + print("@DynamiCrafter Inference [rank%d]: %s"%(rank, now)) + inference_api.run_inference(inference_args, gpu_num, rank) \ No newline at end of file diff --git a/scripts/evaluation/funcs.py b/scripts/evaluation/funcs.py new file mode 100644 index 0000000000000000000000000000000000000000..2ee5b779ad907fd2b5a4e4e8368744bb445284e1 --- /dev/null +++ b/scripts/evaluation/funcs.py @@ -0,0 +1,243 @@ +import os, sys, glob +import numpy as np +from collections import OrderedDict +from decord import VideoReader, cpu +import cv2 + +import torch +import torchvision +sys.path.insert(1, os.path.join(sys.path[0], '..', '..')) +from lvdm.models.samplers.ddim import DDIMSampler +from einops import rearrange + + +def batch_ddim_sampling(model, cond, noise_shape, n_samples=1, ddim_steps=50, ddim_eta=1.0,\ + cfg_scale=1.0, hs=None, temporal_cfg_scale=None, **kwargs): + ddim_sampler = DDIMSampler(model) + uncond_type = model.uncond_type + batch_size = noise_shape[0] + fs = cond["fs"] + del cond["fs"] + if noise_shape[-1] == 32: + timestep_spacing = "uniform" + guidance_rescale = 0.0 + else: + timestep_spacing = "uniform_trailing" + guidance_rescale = 0.7 + ## construct unconditional guidance + if cfg_scale != 1.0: + if uncond_type == "empty_seq": + prompts = batch_size * [""] + #prompts = N * T * [""] ## if is_imgbatch=True + uc_emb = model.get_learned_conditioning(prompts) + elif uncond_type == "zero_embed": + c_emb = cond["c_crossattn"][0] if isinstance(cond, dict) else cond + uc_emb = torch.zeros_like(c_emb) + + ## process image embedding token + if hasattr(model, 'embedder'): + uc_img = torch.zeros(noise_shape[0],3,224,224).to(model.device) + ## img: b c h w >> b l c + uc_img = model.embedder(uc_img) + uc_img = model.image_proj_model(uc_img) + uc_emb = torch.cat([uc_emb, uc_img], dim=1) + + if isinstance(cond, dict): + uc = {key:cond[key] for key in cond.keys()} + uc.update({'c_crossattn': [uc_emb]}) + else: + uc = uc_emb + else: + uc = None + + + additional_decode_kwargs = {'ref_context': hs} + x_T = None + batch_variants = [] + + for _ in range(n_samples): + if ddim_sampler is not None: + kwargs.update({"clean_cond": True}) + samples, _ = ddim_sampler.sample(S=ddim_steps, + conditioning=cond, + batch_size=noise_shape[0], + shape=noise_shape[1:], + verbose=False, + unconditional_guidance_scale=cfg_scale, + unconditional_conditioning=uc, + eta=ddim_eta, + temporal_length=noise_shape[2], + conditional_guidance_scale_temporal=temporal_cfg_scale, + x_T=x_T, + fs=fs, + timestep_spacing=timestep_spacing, + guidance_rescale=guidance_rescale, + **kwargs + ) + ## reconstruct from latent to pixel space + idx = torch.arange(0, 16, 2) + print("samples shape:", samples.shape) + samples = samples[:, :, idx] + batch_images = model.decode_first_stage(samples, **additional_decode_kwargs) + + index = list(range(samples.shape[2])) + del index[1] + del index[-2] + samples = samples[:,:,index,:,:] + ## reconstruct from latent to pixel space + batch_images_middle = model.decode_first_stage(samples, **additional_decode_kwargs) + batch_images[:,:,batch_images.shape[2]//2-1:batch_images.shape[2]//2+1] = batch_images_middle[:,:,batch_images.shape[2]//2-2:batch_images.shape[2]//2] + + + + batch_variants.append(batch_images) + ## batch, , c, t, h, w + batch_variants = torch.stack(batch_variants, dim=1) + return batch_variants + + +def get_filelist(data_dir, ext='*'): + file_list = glob.glob(os.path.join(data_dir, '*.%s'%ext)) + file_list.sort() + return file_list + +def get_dirlist(path): + list = [] + if (os.path.exists(path)): + files = os.listdir(path) + for file in files: + m = os.path.join(path,file) + if (os.path.isdir(m)): + list.append(m) + list.sort() + return list + + +def load_model_checkpoint(model, ckpt): + def load_checkpoint(model, ckpt, full_strict): + state_dict = torch.load(ckpt, map_location="cpu") + if "state_dict" in list(state_dict.keys()): + state_dict = state_dict["state_dict"] + try: + model.load_state_dict(state_dict, strict=full_strict) + except: + ## rename the keys for 256x256 model + new_pl_sd = OrderedDict() + for k,v in state_dict.items(): + new_pl_sd[k] = v + + for k in list(new_pl_sd.keys()): + if "framestride_embed" in k: + new_key = k.replace("framestride_embed", "fps_embedding") + new_pl_sd[new_key] = new_pl_sd[k] + del new_pl_sd[k] + model.load_state_dict(new_pl_sd, strict=full_strict) + else: + ## deepspeed + new_pl_sd = OrderedDict() + for key in state_dict['module'].keys(): + new_pl_sd[key[16:]]=state_dict['module'][key] + model.load_state_dict(new_pl_sd, strict=full_strict) + + return model + load_checkpoint(model, ckpt, full_strict=True) + print('>>> model checkpoint loaded.') + return model + + +def load_prompts(prompt_file): + f = open(prompt_file, 'r') + prompt_list = [] + for idx, line in enumerate(f.readlines()): + l = line.strip() + if len(l) != 0: + prompt_list.append(l) + f.close() + return prompt_list + + +def load_video_batch(filepath_list, frame_stride, video_size=(256,256), video_frames=16): + ''' + Notice about some special cases: + 1. video_frames=-1 means to take all the frames (with fs=1) + 2. when the total video frames is less than required, padding strategy will be used (repeated last frame) + ''' + fps_list = [] + batch_tensor = [] + assert frame_stride > 0, "valid frame stride should be a positive interge!" + for filepath in filepath_list: + padding_num = 0 + vidreader = VideoReader(filepath, ctx=cpu(0), width=video_size[1], height=video_size[0]) + fps = vidreader.get_avg_fps() + total_frames = len(vidreader) + max_valid_frames = (total_frames-1) // frame_stride + 1 + if video_frames < 0: + ## all frames are collected: fs=1 is a must + required_frames = total_frames + frame_stride = 1 + else: + required_frames = video_frames + query_frames = min(required_frames, max_valid_frames) + frame_indices = [frame_stride*i for i in range(query_frames)] + + ## [t,h,w,c] -> [c,t,h,w] + frames = vidreader.get_batch(frame_indices) + frame_tensor = torch.tensor(frames.asnumpy()).permute(3, 0, 1, 2).float() + frame_tensor = (frame_tensor / 255. - 0.5) * 2 + if max_valid_frames < required_frames: + padding_num = required_frames - max_valid_frames + frame_tensor = torch.cat([frame_tensor, *([frame_tensor[:,-1:,:,:]]*padding_num)], dim=1) + print(f'{os.path.split(filepath)[1]} is not long enough: {padding_num} frames padded.') + batch_tensor.append(frame_tensor) + sample_fps = int(fps/frame_stride) + fps_list.append(sample_fps) + + return torch.stack(batch_tensor, dim=0) + +from PIL import Image +def load_image_batch(filepath_list, image_size=(256,256)): + batch_tensor = [] + for filepath in filepath_list: + _, filename = os.path.split(filepath) + _, ext = os.path.splitext(filename) + if ext == '.mp4': + vidreader = VideoReader(filepath, ctx=cpu(0), width=image_size[1], height=image_size[0]) + frame = vidreader.get_batch([0]) + img_tensor = torch.tensor(frame.asnumpy()).squeeze(0).permute(2, 0, 1).float() + elif ext == '.png' or ext == '.jpg': + img = Image.open(filepath).convert("RGB") + rgb_img = np.array(img, np.float32) + #bgr_img = cv2.imread(filepath, cv2.IMREAD_COLOR) + #bgr_img = cv2.cvtColor(bgr_img, cv2.COLOR_BGR2RGB) + rgb_img = cv2.resize(rgb_img, (image_size[1],image_size[0]), interpolation=cv2.INTER_LINEAR) + img_tensor = torch.from_numpy(rgb_img).permute(2, 0, 1).float() + else: + print(f'ERROR: <{ext}> image loading only support format: [mp4], [png], [jpg]') + raise NotImplementedError + img_tensor = (img_tensor / 255. - 0.5) * 2 + batch_tensor.append(img_tensor) + return torch.stack(batch_tensor, dim=0) + + +def save_videos(batch_tensors, savedir, filenames, fps=10): + # b,samples,c,t,h,w + n_samples = batch_tensors.shape[1] + for idx, vid_tensor in enumerate(batch_tensors): + video = vid_tensor.detach().cpu() + video = torch.clamp(video.float(), -1., 1.) + video = video.permute(2, 0, 1, 3, 4) # t,n,c,h,w + frame_grids = [torchvision.utils.make_grid(framesheet, nrow=int(n_samples)) for framesheet in video] #[3, 1*h, n*w] + grid = torch.stack(frame_grids, dim=0) # stack in temporal dim [t, 3, n*h, w] + grid = (grid + 1.0) / 2.0 + grid = (grid * 255).to(torch.uint8).permute(0, 2, 3, 1) + savepath = os.path.join(savedir, f"{filenames[idx]}.mp4") + torchvision.io.write_video(savepath, grid, fps=fps, video_codec='h264', options={'crf': '10'}) + + +def get_latent_z(model, videos): + b, c, t, h, w = videos.shape + x = rearrange(videos, 'b c t h w -> (b t) c h w') + z = model.encode_first_stage(x) + z = rearrange(z, '(b t) c h w -> b c t h w', b=b, t=t) + return z + diff --git a/scripts/evaluation/inference.py b/scripts/evaluation/inference.py new file mode 100644 index 0000000000000000000000000000000000000000..2c4825833ad3d2d7fe000f15e939da618314ba63 --- /dev/null +++ b/scripts/evaluation/inference.py @@ -0,0 +1,402 @@ +import argparse, os, sys, glob +import datetime, time +from omegaconf import OmegaConf +from tqdm import tqdm +from einops import rearrange, repeat +from collections import OrderedDict + +import torch +import torchvision +import torchvision.transforms as transforms +from pytorch_lightning import seed_everything +from PIL import Image +sys.path.insert(1, os.path.join(sys.path[0], '..', '..')) +from lvdm.models.samplers.ddim import DDIMSampler +from lvdm.models.samplers.ddim_multiplecond import DDIMSampler as DDIMSampler_multicond +from utils.utils import instantiate_from_config + + +def get_filelist(data_dir, postfixes): + patterns = [os.path.join(data_dir, f"*.{postfix}") for postfix in postfixes] + file_list = [] + for pattern in patterns: + file_list.extend(glob.glob(pattern)) + file_list.sort() + return file_list + +def load_model_checkpoint(model, ckpt): + state_dict = torch.load(ckpt, map_location="cpu") + if "state_dict" in list(state_dict.keys()): + state_dict = state_dict["state_dict"] + try: + model.load_state_dict(state_dict, strict=True) + except: + ## rename the keys for 256x256 model + new_pl_sd = OrderedDict() + for k,v in state_dict.items(): + new_pl_sd[k] = v + + for k in list(new_pl_sd.keys()): + if "framestride_embed" in k: + new_key = k.replace("framestride_embed", "fps_embedding") + new_pl_sd[new_key] = new_pl_sd[k] + del new_pl_sd[k] + missing, unexpected = model.load_state_dict(new_pl_sd, strict=False) + print("missing:", missing) + print("unexpected:", unexpected) + elif 'module' in state_dict: + # deepspeed + new_pl_sd = OrderedDict() + for key in state_dict['module'].keys(): + new_pl_sd[key[16:]]=state_dict['module'][key] + model.load_state_dict(new_pl_sd) + else: + model.load_state_dict(state_dict) + print('>>> model checkpoint loaded.') + return model + +def load_prompts(prompt_file): + f = open(prompt_file, 'r') + prompt_list = [] + for idx, line in enumerate(f.readlines()): + l = line.strip() + if len(l) != 0: + prompt_list.append(l) + f.close() + return prompt_list + +def load_data_prompts(data_dir, video_size=(256,256), video_frames=16, interp=False): + transform = transforms.Compose([ + transforms.Resize(min(video_size)), + transforms.CenterCrop(video_size), + transforms.ToTensor(), + transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))]) + ## load prompts + prompt_file = get_filelist(data_dir, ['txt']) + assert len(prompt_file) > 0, "Error: found NO prompt file!" + ###### default prompt + default_idx = 0 + default_idx = min(default_idx, len(prompt_file)-1) + if len(prompt_file) > 1: + print(f"Warning: multiple prompt files exist. The one {os.path.split(prompt_file[default_idx])[1]} is used.") + ## only use the first one (sorted by name) if multiple exist + + ## load video + file_list = get_filelist(data_dir, ['jpg', 'png', 'jpeg', 'JPEG', 'PNG']) + # assert len(file_list) == n_samples, "Error: data and prompts are NOT paired!" + data_list = [] + filename_list = [] + prompt_list = load_prompts(prompt_file[default_idx]) + n_samples = len(prompt_list) + for idx in range(n_samples): + if interp: + image1 = Image.open(file_list[2*idx]).convert('RGB') + image_tensor1 = transform(image1).unsqueeze(1) # [c,1,h,w] + image2 = Image.open(file_list[2*idx+1]).convert('RGB') + image_tensor2 = transform(image2).unsqueeze(1) # [c,1,h,w] + frame_tensor1 = repeat(image_tensor1, 'c t h w -> c (repeat t) h w', repeat=15) + frame_tensor2 = repeat(image_tensor2, 'c t h w -> c (repeat t) h w', repeat=1) + frame_tensor = torch.cat([frame_tensor1, frame_tensor2], dim=1) + # frame_tensor = torch.cat([frame_tensor1, frame_tensor1], dim=1) + _, filename = os.path.split(file_list[idx*2]) + else: + image = Image.open(file_list[idx]).convert('RGB') + image_tensor = transform(image).unsqueeze(1) # [c,1,h,w] + frame_tensor = repeat(image_tensor, 'c t h w -> c (repeat t) h w', repeat=video_frames) + _, filename = os.path.split(file_list[idx]) + + data_list.append(frame_tensor) + filename_list.append(filename) + + return filename_list, data_list, prompt_list + + +def save_results(prompt, samples, filename, fakedir, fps=8, loop=False): + filename = filename.split('.')[0]+'.mp4' + prompt = prompt[0] if isinstance(prompt, list) else prompt + + ## save video + videos = [samples] + savedirs = [fakedir] + for idx, video in enumerate(videos): + if video is None: + continue + # b,c,t,h,w + video = video.detach().cpu() + video = torch.clamp(video.float(), -1., 1.) + n = video.shape[0] + video = video.permute(2, 0, 1, 3, 4) # t,n,c,h,w + if loop: + video = video[:-1,...] + + frame_grids = [torchvision.utils.make_grid(framesheet, nrow=int(n), padding=0) for framesheet in video] #[3, 1*h, n*w] + grid = torch.stack(frame_grids, dim=0) # stack in temporal dim [t, 3, h, n*w] + grid = (grid + 1.0) / 2.0 + grid = (grid * 255).to(torch.uint8).permute(0, 2, 3, 1) + path = os.path.join(savedirs[idx], filename) + torchvision.io.write_video(path, grid, fps=fps, video_codec='h264', options={'crf': '10'}) ## crf indicates the quality + + +def save_results_seperate(prompt, samples, filename, fakedir, fps=10, loop=False): + prompt = prompt[0] if isinstance(prompt, list) else prompt + + ## save video + videos = [samples] + savedirs = [fakedir] + for idx, video in enumerate(videos): + if video is None: + continue + # b,c,t,h,w + video = video.detach().cpu() + if loop: # remove the last frame + video = video[:,:,:-1,...] + video = torch.clamp(video.float(), -1., 1.) + n = video.shape[0] + for i in range(n): + grid = video[i,...] + grid = (grid + 1.0) / 2.0 + grid = (grid * 255).to(torch.uint8).permute(1, 2, 3, 0) #thwc + path = os.path.join(savedirs[idx].replace('samples', 'samples_separate'), f'{filename.split(".")[0]}_sample{i}.mp4') + torchvision.io.write_video(path, grid, fps=fps, video_codec='h264', options={'crf': '10'}) + +def get_latent_z(model, videos): + b, c, t, h, w = videos.shape + x = rearrange(videos, 'b c t h w -> (b t) c h w') + z = model.encode_first_stage(x) + z = rearrange(z, '(b t) c h w -> b c t h w', b=b, t=t) + return z + +def get_latent_z_with_hidden_states(model, videos): + b, c, t, h, w = videos.shape + x = rearrange(videos, 'b c t h w -> (b t) c h w') + encoder_posterior, hidden_states = model.first_stage_model.encode(x, return_hidden_states=True) + + hidden_states_first_last = [] + ### use only the first and last hidden states + for hid in hidden_states: + hid = rearrange(hid, '(b t) c h w -> b c t h w', t=t) + hid_new = torch.cat([hid[:, :, 0:1], hid[:, :, -1:]], dim=2) + hidden_states_first_last.append(hid_new) + + z = model.get_first_stage_encoding(encoder_posterior).detach() + z = rearrange(z, '(b t) c h w -> b c t h w', b=b, t=t) + return z, hidden_states_first_last + +@torch.no_grad() +def image_guided_synthesis(model, prompts, videos, noise_shape, n_samples=1, ddim_steps=50, ddim_eta=1., \ + unconditional_guidance_scale=1.0, cfg_img=None, fs=None, text_input=False, multiple_cond_cfg=False, loop=False, interp=False, timestep_spacing='uniform', guidance_rescale=0.0, **kwargs): + model = model.cuda() + ddim_sampler = DDIMSampler(model) if not multiple_cond_cfg else DDIMSampler_multicond(model) + batch_size = noise_shape[0] + fs = torch.tensor([fs] * batch_size, dtype=torch.long, device=model.device) + + if not text_input: + prompts = [""]*batch_size + + img = videos[:,:,0] #bchw + img_emb = model.embedder(img) ## blc + img_emb = model.image_proj_model(img_emb) + + cond_emb = model.get_learned_conditioning(prompts) + cond = {"c_crossattn": [torch.cat([cond_emb,img_emb], dim=1)]} + if model.model.conditioning_key == 'hybrid': + z, hs = get_latent_z_with_hidden_states(model, videos) # b c t h w + if loop or interp: + img_cat_cond = torch.zeros_like(z) + img_cat_cond[:,:,0,:,:] = z[:,:,0,:,:] + img_cat_cond[:,:,-1,:,:] = z[:,:,-1,:,:] + else: + img_cat_cond = z[:,:,:1,:,:] + img_cat_cond = repeat(img_cat_cond, 'b c t h w -> b c (repeat t) h w', repeat=z.shape[2]) + cond["c_concat"] = [img_cat_cond] # b c 1 h w + + if unconditional_guidance_scale != 1.0: + if model.uncond_type == "empty_seq": + prompts = batch_size * [""] + uc_emb = model.get_learned_conditioning(prompts) + elif model.uncond_type == "zero_embed": + uc_emb = torch.zeros_like(cond_emb) + uc_img_emb = model.embedder(torch.zeros_like(img)) ## b l c + uc_img_emb = model.image_proj_model(uc_img_emb) + uc = {"c_crossattn": [torch.cat([uc_emb,uc_img_emb],dim=1)]} + if model.model.conditioning_key == 'hybrid': + uc["c_concat"] = [img_cat_cond] + else: + uc = None +# + # for i, h in enumerate(hs): + # print("h:", h.shape) + # hs[i] = hs[i][:,:,0,:,:].unsqueeze(2) + additional_decode_kwargs = {'ref_context': hs} + # additional_decode_kwargs = {'ref_context': None} + + ## we need one more unconditioning image=yes, text="" + if multiple_cond_cfg and cfg_img != 1.0: + uc_2 = {"c_crossattn": [torch.cat([uc_emb,img_emb],dim=1)]} + if model.model.conditioning_key == 'hybrid': + uc_2["c_concat"] = [img_cat_cond] + kwargs.update({"unconditional_conditioning_img_nonetext": uc_2}) + else: + kwargs.update({"unconditional_conditioning_img_nonetext": None}) + + z0 = None + cond_mask = None + + model.image_proj_model.cpu() + model.embedder.cpu() + + batch_variants = [] + for _ in range(n_samples): + + if z0 is not None: + cond_z0 = z0.clone() + kwargs.update({"clean_cond": True}) + else: + cond_z0 = None + if ddim_sampler is not None: + + samples, _ = ddim_sampler.sample(S=ddim_steps, + conditioning=cond, + batch_size=batch_size, + shape=noise_shape[1:], + verbose=False, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=uc, + eta=ddim_eta, + cfg_img=cfg_img, + mask=cond_mask, + x0=cond_z0, + fs=fs, + timestep_spacing=timestep_spacing, + guidance_rescale=guidance_rescale, + **kwargs + ) + + model.cpu() + torch.cuda.empty_cache() + model.first_stage_model.decoder = model.first_stage_model.decoder.cuda() + print("put into cpu") + + ## reconstruct from latent to pixel space + batch_images = model.decode_first_stage(samples, **additional_decode_kwargs) + batch_images.cpu() + + index = list(range(samples.shape[2])) + del index[1] + del index[-2] + samples = samples[:,:,index,:,:] + ## reconstruct from latent to pixel space + batch_images_middle = model.decode_first_stage(samples, **additional_decode_kwargs).cpu() + batch_images[:,:,batch_images.shape[2]//2-1:batch_images.shape[2]//2+1] = batch_images_middle[:,:,batch_images.shape[2]//2-2:batch_images.shape[2]//2] + batch_variants.append(batch_images) + ## variants, batch, c, t, h, w + batch_variants = torch.stack(batch_variants) + return batch_variants.permute(1, 0, 2, 3, 4, 5) #[:, :, :, :-1, :, :] + + +def run_inference(args, gpu_num, gpu_no): + ## model config + config = OmegaConf.load(args.config) + model_config = config.pop("model", OmegaConf.create()) + + ## set use_checkpoint as False as when using deepspeed, it encounters an error "deepspeed backend not set" + model_config['params']['unet_config']['params']['use_checkpoint'] = False + model = instantiate_from_config(model_config) + model = model.cuda(gpu_no) + model.perframe_ae = args.perframe_ae + assert os.path.exists(args.ckpt_path), "Error: checkpoint Not Found!" + model = load_model_checkpoint(model, args.ckpt_path) + model.eval() + + ## run over data + assert (args.height % 16 == 0) and (args.width % 16 == 0), "Error: image size [h,w] should be multiples of 16!" + assert args.bs == 1, "Current implementation only support [batch size = 1]!" + ## latent noise shape + h, w = args.height // 8, args.width // 8 + channels = model.model.diffusion_model.out_channels + n_frames = args.video_length + print(f'Inference with {n_frames} frames') + noise_shape = [args.bs, channels, n_frames, h, w] + + fakedir = os.path.join(args.savedir, "samples") + fakedir_separate = os.path.join(args.savedir, "samples_separate") + + # os.makedirs(fakedir, exist_ok=True) + os.makedirs(fakedir_separate, exist_ok=True) + + ## prompt file setting + assert os.path.exists(args.prompt_dir), "Error: prompt file Not Found!" + filename_list, data_list, prompt_list = load_data_prompts(args.prompt_dir, video_size=(args.height, args.width), video_frames=n_frames, interp=args.interp) + num_samples = len(prompt_list) + samples_split = num_samples // gpu_num + print('Prompts testing [rank:%d] %d/%d samples loaded.'%(gpu_no, samples_split, num_samples)) + #indices = random.choices(list(range(0, num_samples)), k=samples_per_device) + indices = list(range(samples_split*gpu_no, samples_split*(gpu_no+1))) + prompt_list_rank = [prompt_list[i] for i in indices] + data_list_rank = [data_list[i] for i in indices] + filename_list_rank = [filename_list[i] for i in indices] + + start = time.time() + with torch.no_grad(), torch.cuda.amp.autocast(): + for idx, indice in tqdm(enumerate(range(0, len(prompt_list_rank), args.bs)), desc='Sample Batch'): + prompts = prompt_list_rank[indice:indice+args.bs] + videos = data_list_rank[indice:indice+args.bs] + filenames = filename_list_rank[indice:indice+args.bs] + if isinstance(videos, list): + videos = torch.stack(videos, dim=0).to("cuda") + else: + videos = videos.unsqueeze(0).to("cuda") + + batch_samples = image_guided_synthesis(model, prompts, videos, noise_shape, args.n_samples, args.ddim_steps, args.ddim_eta, \ + args.unconditional_guidance_scale, args.cfg_img, args.frame_stride, args.text_input, args.multiple_cond_cfg, args.loop, args.interp, args.timestep_spacing, args.guidance_rescale) + + ## save each example individually + for nn, samples in enumerate(batch_samples): + ## samples : [n_samples,c,t,h,w] + prompt = prompts[nn] + filename = filenames[nn] + # save_results(prompt, samples, filename, fakedir, fps=8, loop=args.loop) + save_results_seperate(prompt, samples, filename, fakedir, fps=8, loop=args.loop) + + print(f"Saved in {args.savedir}. Time used: {(time.time() - start):.2f} seconds") + + +def get_parser(): + parser = argparse.ArgumentParser() + parser.add_argument("--savedir", type=str, default=None, help="results saving path") + parser.add_argument("--ckpt_path", type=str, default=None, help="checkpoint path") + parser.add_argument("--config", type=str, help="config (yaml) path") + parser.add_argument("--prompt_dir", type=str, default=None, help="a data dir containing videos and prompts") + parser.add_argument("--n_samples", type=int, default=1, help="num of samples per prompt",) + parser.add_argument("--ddim_steps", type=int, default=50, help="steps of ddim if positive, otherwise use DDPM",) + parser.add_argument("--ddim_eta", type=float, default=1.0, help="eta for ddim sampling (0.0 yields deterministic sampling)",) + parser.add_argument("--bs", type=int, default=1, help="batch size for inference, should be one") + parser.add_argument("--height", type=int, default=512, help="image height, in pixel space") + parser.add_argument("--width", type=int, default=512, help="image width, in pixel space") + parser.add_argument("--frame_stride", type=int, default=3, help="frame stride control for 256 model (larger->larger motion), FPS control for 512 or 1024 model (smaller->larger motion)") + parser.add_argument("--unconditional_guidance_scale", type=float, default=1.0, help="prompt classifier-free guidance") + parser.add_argument("--seed", type=int, default=123, help="seed for seed_everything") + parser.add_argument("--video_length", type=int, default=16, help="inference video length") + parser.add_argument("--negative_prompt", action='store_true', default=False, help="negative prompt") + parser.add_argument("--text_input", action='store_true', default=False, help="input text to I2V model or not") + parser.add_argument("--multiple_cond_cfg", action='store_true', default=False, help="use multi-condition cfg or not") + parser.add_argument("--cfg_img", type=float, default=None, help="guidance scale for image conditioning") + parser.add_argument("--timestep_spacing", type=str, default="uniform", help="The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information.") + parser.add_argument("--guidance_rescale", type=float, default=0.0, help="guidance rescale in [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://huggingface.co/papers/2305.08891)") + parser.add_argument("--perframe_ae", action='store_true', default=False, help="if we use per-frame AE decoding, set it to True to save GPU memory, especially for the model of 576x1024") + + ## currently not support looping video and generative frame interpolation + parser.add_argument("--loop", action='store_true', default=False, help="generate looping videos or not") + parser.add_argument("--interp", action='store_true', default=False, help="generate generative frame interpolation or not") + return parser + + +if __name__ == '__main__': + now = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S") + print("@DynamiCrafter cond-Inference: %s"%now) + parser = get_parser() + args = parser.parse_args() + + seed_everything(args.seed) + rank, gpu_num = 0, 1 + run_inference(args, gpu_num, rank) \ No newline at end of file diff --git a/scripts/gradio/i2v_test.py b/scripts/gradio/i2v_test.py new file mode 100644 index 0000000000000000000000000000000000000000..9cb620acafd60a2d3e32c30025cc102429805a72 --- /dev/null +++ b/scripts/gradio/i2v_test.py @@ -0,0 +1,107 @@ +import os +import time +from omegaconf import OmegaConf +import torch +from scripts.evaluation.funcs import load_model_checkpoint, save_videos, batch_ddim_sampling, get_latent_z +from utils.utils import instantiate_from_config +from huggingface_hub import hf_hub_download +from einops import repeat +import torchvision.transforms as transforms +from pytorch_lightning import seed_everything + + +class Image2Video(): + def __init__(self,result_dir='./tmp/',gpu_num=1,resolution='256_256') -> None: + self.resolution = (int(resolution.split('_')[0]), int(resolution.split('_')[1])) #hw + self.download_model() + + self.result_dir = result_dir + if not os.path.exists(self.result_dir): + os.mkdir(self.result_dir) + ckpt_path='checkpoints/dynamicrafter_'+resolution.split('_')[1]+'_v1/model.ckpt' + config_file='configs/inference_'+resolution.split('_')[1]+'_v1.0.yaml' + config = OmegaConf.load(config_file) + model_config = config.pop("model", OmegaConf.create()) + model_config['params']['unet_config']['params']['use_checkpoint']=False + model_list = [] + for gpu_id in range(gpu_num): + model = instantiate_from_config(model_config) + # model = model.cuda(gpu_id) + assert os.path.exists(ckpt_path), "Error: checkpoint Not Found!" + model = load_model_checkpoint(model, ckpt_path) + model.eval() + model_list.append(model) + self.model_list = model_list + self.save_fps = 8 + + def get_image(self, image, prompt, steps=50, cfg_scale=7.5, eta=1.0, fs=3, seed=123): + seed_everything(seed) + transform = transforms.Compose([ + transforms.Resize(min(self.resolution)), + transforms.CenterCrop(self.resolution), + ]) + torch.cuda.empty_cache() + print('start:', prompt, time.strftime('%Y-%m-%d %H:%M:%S',time.localtime(time.time()))) + start = time.time() + gpu_id=0 + if steps > 60: + steps = 60 + model = self.model_list[gpu_id] + model = model.cuda() + batch_size=1 + channels = model.model.diffusion_model.out_channels + frames = model.temporal_length + h, w = self.resolution[0] // 8, self.resolution[1] // 8 + noise_shape = [batch_size, channels, frames, h, w] + + # text cond + with torch.no_grad(), torch.cuda.amp.autocast(): + text_emb = model.get_learned_conditioning([prompt]) + + # img cond + img_tensor = torch.from_numpy(image).permute(2, 0, 1).float().to(model.device) + img_tensor = (img_tensor / 255. - 0.5) * 2 + + image_tensor_resized = transform(img_tensor) #3,h,w + videos = image_tensor_resized.unsqueeze(0) # bchw + + z = get_latent_z(model, videos.unsqueeze(2)) #bc,1,hw + + img_tensor_repeat = repeat(z, 'b c t h w -> b c (repeat t) h w', repeat=frames) + + cond_images = model.embedder(img_tensor.unsqueeze(0)) ## blc + img_emb = model.image_proj_model(cond_images) + + imtext_cond = torch.cat([text_emb, img_emb], dim=1) + + fs = torch.tensor([fs], dtype=torch.long, device=model.device) + cond = {"c_crossattn": [imtext_cond], "fs": fs, "c_concat": [img_tensor_repeat]} + + ## inference + batch_samples = batch_ddim_sampling(model, cond, noise_shape, n_samples=1, ddim_steps=steps, ddim_eta=eta, cfg_scale=cfg_scale) + ## b,samples,c,t,h,w + prompt_str = prompt.replace("/", "_slash_") if "/" in prompt else prompt + prompt_str = prompt_str.replace(" ", "_") if " " in prompt else prompt_str + prompt_str=prompt_str[:40] + if len(prompt_str) == 0: + prompt_str = 'empty_prompt' + + save_videos(batch_samples, self.result_dir, filenames=[prompt_str], fps=self.save_fps) + print(f"Saved in {prompt_str}. Time used: {(time.time() - start):.2f} seconds") + model = model.cpu() + return os.path.join(self.result_dir, f"{prompt_str}.mp4") + + def download_model(self): + REPO_ID = 'Doubiiu/DynamiCrafter_'+str(self.resolution[1]) if self.resolution[1]!=256 else 'Doubiiu/DynamiCrafter' + filename_list = ['model.ckpt'] + if not os.path.exists('./checkpoints/dynamicrafter_'+str(self.resolution[1])+'_v1/'): + os.makedirs('./checkpoints/dynamicrafter_'+str(self.resolution[1])+'_v1/') + for filename in filename_list: + local_file = os.path.join('./checkpoints/dynamicrafter_'+str(self.resolution[1])+'_v1/', filename) + if not os.path.exists(local_file): + hf_hub_download(repo_id=REPO_ID, filename=filename, local_dir='./checkpoints/dynamicrafter_'+str(self.resolution[1])+'_v1/', local_dir_use_symlinks=False) + +if __name__ == '__main__': + i2v = Image2Video() + video_path = i2v.get_image('prompts/art.png','man fishing in a boat at sunset') + print('done', video_path) \ No newline at end of file diff --git a/scripts/gradio/i2v_test_application.py b/scripts/gradio/i2v_test_application.py new file mode 100644 index 0000000000000000000000000000000000000000..68f64809b7e9ae365e0d5d6fdf60d57de8999e0f --- /dev/null +++ b/scripts/gradio/i2v_test_application.py @@ -0,0 +1,145 @@ +import os +import time +from omegaconf import OmegaConf +import torch +from scripts.evaluation.funcs import load_model_checkpoint, save_videos, batch_ddim_sampling, get_latent_z +from utils.utils import instantiate_from_config +from huggingface_hub import hf_hub_download +from einops import repeat +import torchvision.transforms as transforms +from pytorch_lightning import seed_everything +from einops import rearrange + +class Image2Video(): + def __init__(self,result_dir='./tmp/',gpu_num=1,resolution='256_256') -> None: + self.resolution = (int(resolution.split('_')[0]), int(resolution.split('_')[1])) #hw + self.download_model() + + self.result_dir = result_dir + if not os.path.exists(self.result_dir): + os.mkdir(self.result_dir) + ckpt_path='checkpoints/tooncrafter_'+resolution.split('_')[1]+'_interp_v1/model.ckpt' + config_file='configs/inference_'+resolution.split('_')[1]+'_v1.0.yaml' + config = OmegaConf.load(config_file) + model_config = config.pop("model", OmegaConf.create()) + model_config['params']['unet_config']['params']['use_checkpoint']=False + model_list = [] + for gpu_id in range(gpu_num): + model = instantiate_from_config(model_config) + # model = model.cuda(gpu_id) + print(ckpt_path) + assert os.path.exists(ckpt_path), "Error: checkpoint Not Found!" + model = load_model_checkpoint(model, ckpt_path) + model.eval() + model_list.append(model) + self.model_list = model_list + self.save_fps = 8 + + def get_image(self, image, prompt, steps=50, cfg_scale=7.5, eta=1.0, fs=3, seed=123, image2=None): + seed_everything(seed) + transform = transforms.Compose([ + transforms.Resize(min(self.resolution)), + transforms.CenterCrop(self.resolution), + ]) + torch.cuda.empty_cache() + print('start:', prompt, time.strftime('%Y-%m-%d %H:%M:%S',time.localtime(time.time()))) + start = time.time() + gpu_id=0 + if steps > 60: + steps = 60 + model = self.model_list[gpu_id] + model = model.cuda() + batch_size=1 + channels = model.model.diffusion_model.out_channels + frames = model.temporal_length + h, w = self.resolution[0] // 8, self.resolution[1] // 8 + noise_shape = [batch_size, channels, frames, h, w] + + # text cond + with torch.no_grad(), torch.cuda.amp.autocast(): + text_emb = model.get_learned_conditioning([prompt]) + + # img cond + img_tensor = torch.from_numpy(image).permute(2, 0, 1).float().to(model.device) + img_tensor = (img_tensor / 255. - 0.5) * 2 + + image_tensor_resized = transform(img_tensor) #3,h,w + videos = image_tensor_resized.unsqueeze(0).unsqueeze(2) # bc1hw + + # z = get_latent_z(model, videos) #bc,1,hw + videos = repeat(videos, 'b c t h w -> b c (repeat t) h w', repeat=frames//2) + + + + + img_tensor2 = torch.from_numpy(image2).permute(2, 0, 1).float().to(model.device) + img_tensor2 = (img_tensor2 / 255. - 0.5) * 2 + image_tensor_resized2 = transform(img_tensor2) #3,h,w + videos2 = image_tensor_resized2.unsqueeze(0).unsqueeze(2) # bchw + videos2 = repeat(videos2, 'b c t h w -> b c (repeat t) h w', repeat=frames//2) + + + videos = torch.cat([videos, videos2], dim=2) + z, hs = self.get_latent_z_with_hidden_states(model, videos) + + img_tensor_repeat = torch.zeros_like(z) + + img_tensor_repeat[:,:,:1,:,:] = z[:,:,:1,:,:] + img_tensor_repeat[:,:,-1:,:,:] = z[:,:,-1:,:,:] + + + cond_images = model.embedder(img_tensor.unsqueeze(0)) ## blc + img_emb = model.image_proj_model(cond_images) + + imtext_cond = torch.cat([text_emb, img_emb], dim=1) + + fs = torch.tensor([fs], dtype=torch.long, device=model.device) + cond = {"c_crossattn": [imtext_cond], "fs": fs, "c_concat": [img_tensor_repeat]} + + ## inference + batch_samples = batch_ddim_sampling(model, cond, noise_shape, n_samples=1, ddim_steps=steps, ddim_eta=eta, cfg_scale=cfg_scale, hs=hs) + + ## remove the last frame + if image2 is None: + batch_samples = batch_samples[:,:,:,:-1,...] + ## b,samples,c,t,h,w + prompt_str = prompt.replace("/", "_slash_") if "/" in prompt else prompt + prompt_str = prompt_str.replace(" ", "_") if " " in prompt else prompt_str + prompt_str=prompt_str[:40] + if len(prompt_str) == 0: + prompt_str = 'empty_prompt' + + save_videos(batch_samples, self.result_dir, filenames=[prompt_str], fps=self.save_fps) + print(f"Saved in {prompt_str}. Time used: {(time.time() - start):.2f} seconds") + model = model.cpu() + return os.path.join(self.result_dir, f"{prompt_str}.mp4") + + def download_model(self): + REPO_ID = 'Doubiiu/ToonCrafter' + filename_list = ['model.ckpt'] + if not os.path.exists('./checkpoints/tooncrafter_'+str(self.resolution[1])+'_interp_v1/'): + os.makedirs('./checkpoints/tooncrafter_'+str(self.resolution[1])+'_interp_v1/') + for filename in filename_list: + local_file = os.path.join('./checkpoints/tooncrafter_'+str(self.resolution[1])+'_interp_v1/', filename) + if not os.path.exists(local_file): + hf_hub_download(repo_id=REPO_ID, filename=filename, local_dir='./checkpoints/tooncrafter_'+str(self.resolution[1])+'_interp_v1/', local_dir_use_symlinks=False) + + def get_latent_z_with_hidden_states(self, model, videos): + b, c, t, h, w = videos.shape + x = rearrange(videos, 'b c t h w -> (b t) c h w') + encoder_posterior, hidden_states = model.first_stage_model.encode(x, return_hidden_states=True) + + hidden_states_first_last = [] + ### use only the first and last hidden states + for hid in hidden_states: + hid = rearrange(hid, '(b t) c h w -> b c t h w', t=t) + hid_new = torch.cat([hid[:, :, 0:1], hid[:, :, -1:]], dim=2) + hidden_states_first_last.append(hid_new) + + z = model.get_first_stage_encoding(encoder_posterior).detach() + z = rearrange(z, '(b t) c h w -> b c t h w', b=b, t=t) + return z, hidden_states_first_last +if __name__ == '__main__': + i2v = Image2Video() + video_path = i2v.get_image('prompts/art.png','man fishing in a boat at sunset') + print('done', video_path) \ No newline at end of file diff --git a/scripts/run.sh b/scripts/run.sh new file mode 100644 index 0000000000000000000000000000000000000000..2198147ef518ac6d9f5efb0e9900a56072ed6958 --- /dev/null +++ b/scripts/run.sh @@ -0,0 +1,33 @@ + +# ckpt=/group/40005/gzhiwang/tooncrafter_1024_4k.ckpt +ckpt=/group/40005/gzhiwang/tc_sketch.pt +config=configs/inference_1024_v1.0.yaml + +# prompt_dir="/group/40005/gzhiwang/ToonCrafter_with_SketchGuidance_1024/ToonCrafter/prompts/other_exps" +prompt_dir="/group/40005/gzhiwang/ToonCrafter_with_SketchGuidance_1024/prompts/1024_kk_rotate" +res_dir="resulst_interp_1024_sketch_15_1" + +sudo mkdir ${res_dir} +sudo chmod 777 ${res_dir} + +FS=10 ## This model adopts FPS=5, range recommended: 5-30 (smaller value -> larger motion) + + + +seed=123 +name=tooncrafter_512_interp_seed${seed} +CUDA_VISIBLE_DEVICES=$1 python3 scripts/evaluation/inference.py \ +--seed ${seed} \ +--ckpt_path $ckpt \ +--config $config \ +--savedir $res_dir/$name \ +--n_samples 1 \ +--bs 1 --height 576 --width 1024 \ +--unconditional_guidance_scale 7.5 \ +--ddim_steps 1 \ +--ddim_eta 1.0 \ +--prompt_dir $prompt_dir \ +--text_input \ +--video_length 16 \ +--frame_stride ${FS} \ +--timestep_spacing 'uniform_trailing' --guidance_rescale 0.7 --perframe_ae --interp diff --git a/utils/save_video.py b/utils/save_video.py new file mode 100644 index 0000000000000000000000000000000000000000..3915f57a7790f3f3a59c513d59794d52158658d4 --- /dev/null +++ b/utils/save_video.py @@ -0,0 +1,195 @@ +import os +import numpy as np +from tqdm import tqdm +from PIL import Image +from einops import rearrange + +import torch +import torchvision +from torch import Tensor +from torchvision.utils import make_grid +from torchvision.transforms.functional import to_tensor + + +def frames_to_mp4(frame_dir,output_path,fps): + def read_first_n_frames(d: os.PathLike, num_frames: int): + if num_frames: + images = [Image.open(os.path.join(d, f)) for f in sorted(os.listdir(d))[:num_frames]] + else: + images = [Image.open(os.path.join(d, f)) for f in sorted(os.listdir(d))] + images = [to_tensor(x) for x in images] + return torch.stack(images) + videos = read_first_n_frames(frame_dir, num_frames=None) + videos = videos.mul(255).to(torch.uint8).permute(0, 2, 3, 1) + torchvision.io.write_video(output_path, videos, fps=fps, video_codec='h264', options={'crf': '10'}) + + +def tensor_to_mp4(video, savepath, fps, rescale=True, nrow=None): + """ + video: torch.Tensor, b,c,t,h,w, 0-1 + if -1~1, enable rescale=True + """ + n = video.shape[0] + video = video.permute(2, 0, 1, 3, 4) # t,n,c,h,w + nrow = int(np.sqrt(n)) if nrow is None else nrow + frame_grids = [torchvision.utils.make_grid(framesheet, nrow=nrow, padding=0) for framesheet in video] # [3, grid_h, grid_w] + grid = torch.stack(frame_grids, dim=0) # stack in temporal dim [T, 3, grid_h, grid_w] + grid = torch.clamp(grid.float(), -1., 1.) + if rescale: + grid = (grid + 1.0) / 2.0 + grid = (grid * 255).to(torch.uint8).permute(0, 2, 3, 1) # [T, 3, grid_h, grid_w] -> [T, grid_h, grid_w, 3] + torchvision.io.write_video(savepath, grid, fps=fps, video_codec='h264', options={'crf': '10'}) + + +def tensor2videogrids(video, root, filename, fps, rescale=True, clamp=True): + assert(video.dim() == 5) # b,c,t,h,w + assert(isinstance(video, torch.Tensor)) + + video = video.detach().cpu() + if clamp: + video = torch.clamp(video, -1., 1.) + n = video.shape[0] + video = video.permute(2, 0, 1, 3, 4) # t,n,c,h,w + frame_grids = [torchvision.utils.make_grid(framesheet, nrow=int(np.sqrt(n))) for framesheet in video] # [3, grid_h, grid_w] + grid = torch.stack(frame_grids, dim=0) # stack in temporal dim [T, 3, grid_h, grid_w] + if rescale: + grid = (grid + 1.0) / 2.0 + grid = (grid * 255).to(torch.uint8).permute(0, 2, 3, 1) # [T, 3, grid_h, grid_w] -> [T, grid_h, grid_w, 3] + path = os.path.join(root, filename) + torchvision.io.write_video(path, grid, fps=fps, video_codec='h264', options={'crf': '10'}) + + +def log_local(batch_logs, save_dir, filename, save_fps=10, rescale=True): + if batch_logs is None: + return None + """ save images and videos from images dict """ + def save_img_grid(grid, path, rescale): + if rescale: + grid = (grid + 1.0) / 2.0 # -1,1 -> 0,1; c,h,w + grid = grid.transpose(0, 1).transpose(1, 2).squeeze(-1) + grid = grid.numpy() + grid = (grid * 255).astype(np.uint8) + os.makedirs(os.path.split(path)[0], exist_ok=True) + Image.fromarray(grid).save(path) + + for key in batch_logs: + value = batch_logs[key] + if isinstance(value, list) and isinstance(value[0], str): + ## a batch of captions + path = os.path.join(save_dir, "%s-%s.txt"%(key, filename)) + with open(path, 'w') as f: + for i, txt in enumerate(value): + f.write(f'idx={i}, txt={txt}\n') + f.close() + elif isinstance(value, torch.Tensor) and value.dim() == 5: + ## save video grids + video = value # b,c,t,h,w + ## only save grayscale or rgb mode + if video.shape[1] != 1 and video.shape[1] != 3: + continue + n = video.shape[0] + video = video.permute(2, 0, 1, 3, 4) # t,n,c,h,w + frame_grids = [torchvision.utils.make_grid(framesheet, nrow=int(1), padding=0) for framesheet in video] #[3, n*h, 1*w] + grid = torch.stack(frame_grids, dim=0) # stack in temporal dim [t, 3, n*h, w] + if rescale: + grid = (grid + 1.0) / 2.0 + grid = (grid * 255).to(torch.uint8).permute(0, 2, 3, 1) + path = os.path.join(save_dir, "%s-%s.mp4"%(key, filename)) + torchvision.io.write_video(path, grid, fps=save_fps, video_codec='h264', options={'crf': '10'}) + + ## save frame sheet + img = value + video_frames = rearrange(img, 'b c t h w -> (b t) c h w') + t = img.shape[2] + grid = torchvision.utils.make_grid(video_frames, nrow=t, padding=0) + path = os.path.join(save_dir, "%s-%s.jpg"%(key, filename)) + #save_img_grid(grid, path, rescale) + elif isinstance(value, torch.Tensor) and value.dim() == 4: + ## save image grids + img = value + ## only save grayscale or rgb mode + if img.shape[1] != 1 and img.shape[1] != 3: + continue + n = img.shape[0] + grid = torchvision.utils.make_grid(img, nrow=1, padding=0) + path = os.path.join(save_dir, "%s-%s.jpg"%(key, filename)) + save_img_grid(grid, path, rescale) + else: + pass + +def prepare_to_log(batch_logs, max_images=100000, clamp=True): + if batch_logs is None: + return None + # process + for key in batch_logs: + N = batch_logs[key].shape[0] if hasattr(batch_logs[key], 'shape') else len(batch_logs[key]) + N = min(N, max_images) + batch_logs[key] = batch_logs[key][:N] + ## in batch_logs: images & caption + if isinstance(batch_logs[key], torch.Tensor): + batch_logs[key] = batch_logs[key].detach().cpu() + if clamp: + try: + batch_logs[key] = torch.clamp(batch_logs[key].float(), -1., 1.) + except RuntimeError: + print("clamp_scalar_cpu not implemented for Half") + return batch_logs + +# ---------------------------------------------------------------------------------------------- + +def fill_with_black_squares(video, desired_len: int) -> Tensor: + if len(video) >= desired_len: + return video + + return torch.cat([ + video, + torch.zeros_like(video[0]).unsqueeze(0).repeat(desired_len - len(video), 1, 1, 1), + ], dim=0) + +# ---------------------------------------------------------------------------------------------- +def load_num_videos(data_path, num_videos): + # first argument can be either data_path of np array + if isinstance(data_path, str): + videos = np.load(data_path)['arr_0'] # NTHWC + elif isinstance(data_path, np.ndarray): + videos = data_path + else: + raise Exception + + if num_videos is not None: + videos = videos[:num_videos, :, :, :, :] + return videos + +def npz_to_video_grid(data_path, out_path, num_frames, fps, num_videos=None, nrow=None, verbose=True): + # videos = torch.tensor(np.load(data_path)['arr_0']).permute(0,1,4,2,3).div_(255).mul_(2) - 1.0 # NTHWC->NTCHW, np int -> torch tensor 0-1 + if isinstance(data_path, str): + videos = load_num_videos(data_path, num_videos) + elif isinstance(data_path, np.ndarray): + videos = data_path + else: + raise Exception + n,t,h,w,c = videos.shape + videos_th = [] + for i in range(n): + video = videos[i, :,:,:,:] + images = [video[j, :,:,:] for j in range(t)] + images = [to_tensor(img) for img in images] + video = torch.stack(images) + videos_th.append(video) + if verbose: + videos = [fill_with_black_squares(v, num_frames) for v in tqdm(videos_th, desc='Adding empty frames')] # NTCHW + else: + videos = [fill_with_black_squares(v, num_frames) for v in videos_th] # NTCHW + + frame_grids = torch.stack(videos).permute(1, 0, 2, 3, 4) # [T, N, C, H, W] + if nrow is None: + nrow = int(np.ceil(np.sqrt(n))) + if verbose: + frame_grids = [make_grid(fs, nrow=nrow) for fs in tqdm(frame_grids, desc='Making grids')] + else: + frame_grids = [make_grid(fs, nrow=nrow) for fs in frame_grids] + + if os.path.dirname(out_path) != "": + os.makedirs(os.path.dirname(out_path), exist_ok=True) + frame_grids = (torch.stack(frame_grids) * 255).to(torch.uint8).permute(0, 2, 3, 1) # [T, H, W, C] + torchvision.io.write_video(out_path, frame_grids, fps=fps, video_codec='h264', options={'crf': '10'}) diff --git a/utils/utils.py b/utils/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..ef116e56f6f61dce0cf4942986ac659bf93fcd24 --- /dev/null +++ b/utils/utils.py @@ -0,0 +1,77 @@ +import importlib +import numpy as np +import cv2 +import torch +import torch.distributed as dist + + +def count_params(model, verbose=False): + total_params = sum(p.numel() for p in model.parameters()) + if verbose: + print(f"{model.__class__.__name__} has {total_params*1.e-6:.2f} M params.") + return total_params + + +def check_istarget(name, para_list): + """ + name: full name of source para + para_list: partial name of target para + """ + istarget=False + for para in para_list: + if para in name: + return True + return istarget + + +def instantiate_from_config(config): + if not "target" in config: + if config == '__is_first_stage__': + return None + elif config == "__is_unconditional__": + return None + raise KeyError("Expected key `target` to instantiate.") + return get_obj_from_str(config["target"])(**config.get("params", dict())) + + +def get_obj_from_str(string, reload=False): + module, cls = string.rsplit(".", 1) + if reload: + module_imp = importlib.import_module(module) + importlib.reload(module_imp) + return getattr(importlib.import_module(module, package=None), cls) + + +def load_npz_from_dir(data_dir): + data = [np.load(os.path.join(data_dir, data_name))['arr_0'] for data_name in os.listdir(data_dir)] + data = np.concatenate(data, axis=0) + return data + + +def load_npz_from_paths(data_paths): + data = [np.load(data_path)['arr_0'] for data_path in data_paths] + data = np.concatenate(data, axis=0) + return data + + +def resize_numpy_image(image, max_resolution=512 * 512, resize_short_edge=None): + h, w = image.shape[:2] + if resize_short_edge is not None: + k = resize_short_edge / min(h, w) + else: + k = max_resolution / (h * w) + k = k**0.5 + h = int(np.round(h * k / 64)) * 64 + w = int(np.round(w * k / 64)) * 64 + image = cv2.resize(image, (w, h), interpolation=cv2.INTER_LANCZOS4) + return image + + +def setup_dist(args): + if dist.is_initialized(): + return + torch.cuda.set_device(args.local_rank) + torch.distributed.init_process_group( + 'nccl', + init_method='env://' + ) \ No newline at end of file