animatediff/utils/util.py

import os
import imageio
import numpy as np
from typing import Union

import torch
import torchvision
import torch.distributed as dist

from PIL import Image
from transformers import AutoProcessor, CLIPModel
import torch.nn as nn

from safetensors import safe_open
from tqdm import tqdm
from einops import rearrange
from animatediff.utils.convert_from_ckpt import (
    convert_ldm_unet_checkpoint,
    convert_ldm_clip_checkpoint,
    #convert_ldm_vae_checkpoint,
)
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import convert_ldm_vae_checkpoint
from animatediff.utils.convert_lora_safetensor_to_diffusers import (
    convert_lora,
    convert_motion_lora_ckpt_to_diffusers,
)


def zero_rank_print(s):
    if (not dist.is_initialized()) and (dist.is_initialized() and dist.get_rank() == 0):
        print("### " + s)


def ToImage(videos: torch.Tensor, rescale=False, n_rows=6):
    videos = rearrange(videos, "b c t h w -> t b c h w")
    outputs = []
    for x in videos:
        x = torchvision.utils.make_grid(x, nrow=n_rows)
        x = x.transpose(0, 1).transpose(1, 2).squeeze(-1)
        if rescale:
            x = (x + 1.0) / 2.0  # -1,1 -> 0,1
        x = (x * 255).numpy().astype(np.uint8)
        outputs.append(Image.fromarray(x))
    return outputs


def save_videos_grid(videos: torch.Tensor, path: str, rescale=False, n_rows=6, fps=8):
    videos = rearrange(videos, "b c t h w -> t b c h w")
    outputs = []
    for x in videos:
        x = torchvision.utils.make_grid(x, nrow=n_rows)
        x = x.transpose(0, 1).transpose(1, 2).squeeze(-1)
        if rescale:
            x = (x + 1.0) / 2.0  # -1,1 -> 0,1
        x = (x * 255).numpy().astype(np.uint8)
        outputs.append(x)

    os.makedirs(os.path.dirname(path), exist_ok=True)
    imageio.mimsave(path, outputs, fps=fps)


# DDIM Inversion
@torch.no_grad()
def init_prompt(prompt, pipeline):
    uncond_input = pipeline.tokenizer(
        [""],
        padding="max_length",
        max_length=pipeline.tokenizer.model_max_length,
        return_tensors="pt",
    )
    uncond_embeddings = pipeline.text_encoder(
        uncond_input.input_ids.to(pipeline.device)
    )[0]
    text_input = pipeline.tokenizer(
        [prompt],
        padding="max_length",
        max_length=pipeline.tokenizer.model_max_length,
        truncation=True,
        return_tensors="pt",
    )
    text_embeddings = pipeline.text_encoder(text_input.input_ids.to(pipeline.device))[0]
    context = torch.cat([uncond_embeddings, text_embeddings])

    return context


def next_step(
    model_output: Union[torch.FloatTensor, np.ndarray],
    timestep: int,
    sample: Union[torch.FloatTensor, np.ndarray],
    ddim_scheduler,
):
    timestep, next_timestep = (
        min(
            timestep
            - ddim_scheduler.config.num_train_timesteps
            // ddim_scheduler.num_inference_steps,
            999,
        ),
        timestep,
    )
    alpha_prod_t = (
        ddim_scheduler.alphas_cumprod[timestep]
        if timestep >= 0
        else ddim_scheduler.final_alpha_cumprod
    )
    alpha_prod_t_next = ddim_scheduler.alphas_cumprod[next_timestep]
    beta_prod_t = 1 - alpha_prod_t
    next_original_sample = (
        sample - beta_prod_t**0.5 * model_output
    ) / alpha_prod_t**0.5
    next_sample_direction = (1 - alpha_prod_t_next) ** 0.5 * model_output
    next_sample = alpha_prod_t_next**0.5 * next_original_sample + next_sample_direction
    return next_sample


def get_noise_pred_single(latents, t, context, unet):
    noise_pred = unet(latents, t, encoder_hidden_states=context)["sample"]
    return noise_pred


@torch.no_grad()
def ddim_loop(pipeline, ddim_scheduler, latent, num_inv_steps, prompt):
    context = init_prompt(prompt, pipeline)
    uncond_embeddings, cond_embeddings = context.chunk(2)
    all_latent = [latent]
    latent = latent.clone().detach()
    for i in tqdm(range(num_inv_steps)):
        t = ddim_scheduler.timesteps[len(ddim_scheduler.timesteps) - i - 1]
        noise_pred = get_noise_pred_single(latent, t, cond_embeddings, pipeline.unet)
        latent = next_step(noise_pred, t, latent, ddim_scheduler)
        all_latent.append(latent)
    return all_latent


@torch.no_grad()
def ddim_inversion(pipeline, ddim_scheduler, video_latent, num_inv_steps, prompt=""):
    ddim_latents = ddim_loop(
        pipeline, ddim_scheduler, video_latent, num_inv_steps, prompt
    )
    return ddim_latents


def load_weights(
    animation_pipeline,
    # motion module
    motion_module_path="",
    motion_module_lora_configs=[],
    # image layers
    dreambooth_model_path="",
    lora_model_path="",
    lora_alpha=0.8,
):
    # 1.1 motion module
    unet_state_dict = {}
    if motion_module_path != "":
        print(f"load motion module from {motion_module_path}")
        motion_module_state_dict = torch.load(motion_module_path, map_location="cpu")
        motion_module_state_dict = (
            motion_module_state_dict["state_dict"]
            if "state_dict" in motion_module_state_dict
            else motion_module_state_dict
        )
        unet_state_dict.update(
            {
                name: param
                for name, param in motion_module_state_dict.items()
                if "motion_modules." in name
            }
        )

    missing, unexpected = animation_pipeline.unet.load_state_dict(
        unet_state_dict, strict=False
    )
    assert len(unexpected) == 0
    del unet_state_dict

    if dreambooth_model_path != "":
        print(f"load dreambooth model from {dreambooth_model_path}")
        if dreambooth_model_path.endswith(".safetensors"):
            dreambooth_state_dict = {}
            with safe_open(dreambooth_model_path, framework="pt", device="cpu") as f:
                for key in f.keys():
                    dreambooth_state_dict[key] = f.get_tensor(key)
        elif dreambooth_model_path.endswith(".ckpt"):
            dreambooth_state_dict = torch.load(
                dreambooth_model_path, map_location="cpu"
            )

        # 1. vae
        converted_vae_checkpoint = convert_ldm_vae_checkpoint(
            dreambooth_state_dict, animation_pipeline.vae.config
        )
        animation_pipeline.vae.load_state_dict(converted_vae_checkpoint)
        # 2. unet
        converted_unet_checkpoint = convert_ldm_unet_checkpoint(
            dreambooth_state_dict, animation_pipeline.unet.config
        )
        animation_pipeline.unet.load_state_dict(converted_unet_checkpoint, strict=False)
        # 3. text_model
        animation_pipeline.text_encoder = convert_ldm_clip_checkpoint(
            dreambooth_state_dict
        )
        del dreambooth_state_dict

    if lora_model_path != "":
        print(f"load lora model from {lora_model_path}")
        assert lora_model_path.endswith(".safetensors")
        lora_state_dict = {}
        with safe_open(lora_model_path, framework="pt", device="cpu") as f:
            for key in f.keys():
                lora_state_dict[key] = f.get_tensor(key)

        animation_pipeline = convert_lora(
            animation_pipeline, lora_state_dict, alpha=lora_alpha
        )
        del lora_state_dict

    for motion_module_lora_config in motion_module_lora_configs:
        path, alpha = (
            motion_module_lora_config["path"],
            motion_module_lora_config["alpha"],
        )
        print(f"load motion LoRA from {path}")

        motion_lora_state_dict = torch.load(path, map_location="cpu")
        motion_lora_state_dict = (
            motion_lora_state_dict["state_dict"]
            if "state_dict" in motion_lora_state_dict
            else motion_lora_state_dict
        )

        animation_pipeline = convert_motion_lora_ckpt_to_diffusers(
            animation_pipeline, motion_lora_state_dict, alpha
        )

    return animation_pipeline