aigc-apps-EasyAnimate/predict_t2v.py

import os

import numpy as np
import torch
from diffusers import (AutoencoderKL, DDIMScheduler,
                       DPMSolverMultistepScheduler,
                       EulerAncestralDiscreteScheduler, EulerDiscreteScheduler,
                       PNDMScheduler)
from omegaconf import OmegaConf
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from easyanimate.models.autoencoder_magvit import AutoencoderKLMagvit
from easyanimate.models.transformer3d import Transformer3DModel
from easyanimate.pipeline.pipeline_easyanimate import EasyAnimatePipeline
from easyanimate.pipeline.pipeline_easyanimate_inpaint import \
    EasyAnimateInpaintPipeline
from easyanimate.utils.lora_utils import merge_lora, unmerge_lora
from easyanimate.utils.utils import get_image_to_video_latent, save_videos_grid

# Low gpu memory mode, this is used when the GPU memory is under 16GB
low_gpu_memory_mode = False

# Config and model path
config_path         = "config/easyanimate_video_slicevae_motion_module_v3.yaml"
model_name          = "models/Diffusion_Transformer/EasyAnimateV3-XL-2-InP-512x512"

# Choose the sampler in "Euler" "Euler A" "DPM++" "PNDM" and "DDIM"
sampler_name        = "DPM++"

# Load pretrained model if need
transformer_path    = None
# V2 does not need a motion module
motion_module_path  = None 
vae_path            = None
lora_path           = None

# Other params
sample_size         = [384, 672]
# In EasyAnimateV1, the video_length of video is 40 ~ 80.
# In EasyAnimateV2 and V3, the video_length of video is 1 ~ 144. If u want to generate a image, please set the video_length = 1.
video_length        = 144
fps                 = 24

weight_dtype        = torch.bfloat16
prompt              = "A young woman with beautiful and clear eyes and blonde hair standing and white dress in a forest wearing a crown. She seems to be lost in thought, and the camera focuses on her face. The video is of high quality, and the view is very clear. High quality, masterpiece, best quality, highres, ultra-detailed, fantastic."
negative_prompt     = "The video is not of a high quality, it has a low resolution, and the audio quality is not clear. Strange motion trajectory, a poor composition and deformed video, low resolution, duplicate and ugly, strange body structure, long and strange neck, bad teeth, bad eyes, bad limbs, bad hands, rotating camera, blurry camera, shaking camera. Deformation, low-resolution, blurry, ugly, distortion. " 
guidance_scale      = 7.0
seed                = 43
num_inference_steps = 25
lora_weight         = 0.55
save_path           = "samples/easyanimate-videos"

config = OmegaConf.load(config_path)

# Get Transformer
transformer = Transformer3DModel.from_pretrained_2d(
    model_name, 
    subfolder="transformer",
    transformer_additional_kwargs=OmegaConf.to_container(config['transformer_additional_kwargs'])
).to(weight_dtype)

if transformer_path is not None:
    print(f"From checkpoint: {transformer_path}")
    if transformer_path.endswith("safetensors"):
        from safetensors.torch import load_file, safe_open
        state_dict = load_file(transformer_path)
    else:
        state_dict = torch.load(transformer_path, map_location="cpu")
    state_dict = state_dict["state_dict"] if "state_dict" in state_dict else state_dict

    m, u = transformer.load_state_dict(state_dict, strict=False)
    print(f"missing keys: {len(m)}, unexpected keys: {len(u)}")

if motion_module_path is not None:
    print(f"From Motion Module: {motion_module_path}")
    if motion_module_path.endswith("safetensors"):
        from safetensors.torch import load_file, safe_open
        state_dict = load_file(motion_module_path)
    else:
        state_dict = torch.load(motion_module_path, map_location="cpu")
    state_dict = state_dict["state_dict"] if "state_dict" in state_dict else state_dict

    m, u = transformer.load_state_dict(state_dict, strict=False)
    print(f"missing keys: {len(m)}, unexpected keys: {len(u)}, {u}")

# Get Vae
if OmegaConf.to_container(config['vae_kwargs'])['enable_magvit']:
    Choosen_AutoencoderKL = AutoencoderKLMagvit
else:
    Choosen_AutoencoderKL = AutoencoderKL
vae = Choosen_AutoencoderKL.from_pretrained(
    model_name, 
    subfolder="vae"
).to(weight_dtype)

if vae_path is not None:
    print(f"From checkpoint: {vae_path}")
    if vae_path.endswith("safetensors"):
        from safetensors.torch import load_file, safe_open
        state_dict = load_file(vae_path)
    else:
        state_dict = torch.load(vae_path, map_location="cpu")
    state_dict = state_dict["state_dict"] if "state_dict" in state_dict else state_dict

    m, u = vae.load_state_dict(state_dict, strict=False)
    print(f"missing keys: {len(m)}, unexpected keys: {len(u)}")

if transformer.config.in_channels == 12:
    clip_image_encoder = CLIPVisionModelWithProjection.from_pretrained(
        model_name, subfolder="image_encoder"
    ).to("cuda", weight_dtype)
    clip_image_processor = CLIPImageProcessor.from_pretrained(
        model_name, subfolder="image_encoder"
    )
else:
    clip_image_encoder = None
    clip_image_processor = None

# Get Scheduler
Choosen_Scheduler = scheduler_dict = {
    "Euler": EulerDiscreteScheduler,
    "Euler A": EulerAncestralDiscreteScheduler,
    "DPM++": DPMSolverMultistepScheduler, 
    "PNDM": PNDMScheduler,
    "DDIM": DDIMScheduler,
}[sampler_name]
scheduler = Choosen_Scheduler(**OmegaConf.to_container(config['noise_scheduler_kwargs']))

if transformer.config.in_channels == 12:
    pipeline = EasyAnimateInpaintPipeline.from_pretrained(
        model_name,
        vae=vae,
        transformer=transformer,
        scheduler=scheduler,
        torch_dtype=weight_dtype,
        clip_image_encoder=clip_image_encoder,
        clip_image_processor=clip_image_processor,
    )
else:
    pipeline = EasyAnimatePipeline.from_pretrained(
        model_name,
        vae=vae,
        transformer=transformer,
        scheduler=scheduler,
        torch_dtype=weight_dtype
    )
if low_gpu_memory_mode:
    pipeline.enable_sequential_cpu_offload()
else:
    pipeline.enable_model_cpu_offload()

generator = torch.Generator(device="cuda").manual_seed(seed)

if lora_path is not None:
    pipeline = merge_lora(pipeline, lora_path, lora_weight)

with torch.no_grad():
    if transformer.config.in_channels == 12:
        video_length = int(video_length // vae.mini_batch_encoder * vae.mini_batch_encoder) if video_length != 1 else 1
        input_video, input_video_mask, clip_image = get_image_to_video_latent(None, None, video_length=video_length, sample_size=sample_size)

        sample = pipeline(
            prompt, 
            video_length = video_length,
            negative_prompt = negative_prompt,
            height      = sample_size[0],
            width       = sample_size[1],
            generator   = generator,
            guidance_scale = guidance_scale,
            num_inference_steps = num_inference_steps,

            video        = input_video,
            mask_video   = input_video_mask,
            clip_image   = clip_image, 
        ).videos
    else:
        sample = pipeline(
            prompt, 
            video_length = video_length,
            negative_prompt = negative_prompt,
            height      = sample_size[0],
            width       = sample_size[1],
            generator   = generator,
            guidance_scale = guidance_scale,
            num_inference_steps = num_inference_steps,
        ).videos

if lora_path is not None:
    pipeline = unmerge_lora(pipeline, lora_path, lora_weight)

if not os.path.exists(save_path):
    os.makedirs(save_path, exist_ok=True)

index = len([path for path in os.listdir(save_path)]) + 1
prefix = str(index).zfill(8)

if video_length == 1:
    video_path = os.path.join(save_path, prefix + ".png")

    image = sample[0, :, 0]
    image = image.transpose(0, 1).transpose(1, 2)
    image = (image * 255).numpy().astype(np.uint8)
    image = Image.fromarray(image)
    image.save(video_path)
else:
    video_path = os.path.join(save_path, prefix + ".mp4")
    save_videos_grid(sample, video_path, fps=fps)