144 lines
5.7 KiB
Python
144 lines
5.7 KiB
Python
import os
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import torch
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from diffusers import (AutoencoderKL, DDIMScheduler,
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DPMSolverMultistepScheduler,
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EulerAncestralDiscreteScheduler, EulerDiscreteScheduler,
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PNDMScheduler)
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from omegaconf import OmegaConf
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from easyanimate.models.autoencoder_magvit import AutoencoderKLMagvit
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from easyanimate.models.transformer3d import Transformer3DModel
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from easyanimate.pipeline.pipeline_easyanimate import EasyAnimatePipeline
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from easyanimate.utils.lora_utils import merge_lora, unmerge_lora
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from easyanimate.utils.utils import save_videos_grid
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# Config and model path
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config_path = "config/easyanimate_video_magvit_motion_module_v2.yaml"
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model_name = "models/Diffusion_Transformer/EasyAnimateV2-XL-2-512x512"
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# Choose the sampler in "Euler" "Euler A" "DPM++" "PNDM" and "DDIM"
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sampler_name = "DPM++"
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# Load pretrained model if need
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transformer_path = None
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# V2 does not need a motion module
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motion_module_path = None
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vae_path = None
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lora_path = None
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# Other params
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sample_size = [384, 672]
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# In EasyAnimateV1, the video_length of video is 40 ~ 80.
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# In EasyAnimateV2, the video_length of video is 1 ~ 144. If u want to generate a image, please set the video_length = 1.
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video_length = 144
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fps = 24
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weight_dtype = torch.bfloat16
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prompt = "The video features a young woman with with black eyes and blonde hair standing in a forest wearing a crown. She seems to be lost in thought, and the camera focuses on her face. The atmosphere is serene, and the shot is in slow motion. The video is of high quality, and the view is very clear. High quality, masterpiece, best quality, highres, ultra-detailed, fantastic. "
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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. "
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guidance_scale = 6.0
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seed = 43
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num_inference_steps = 50
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lora_weight = 0.55
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save_path = "samples/easyanimate-videos"
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config = OmegaConf.load(config_path)
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# Get Transformer
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transformer = Transformer3DModel.from_pretrained_2d(
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model_name,
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subfolder="transformer",
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transformer_additional_kwargs=OmegaConf.to_container(config['transformer_additional_kwargs'])
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).to(weight_dtype)
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if transformer_path is not None:
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print(f"From checkpoint: {transformer_path}")
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if transformer_path.endswith("safetensors"):
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from safetensors.torch import load_file, safe_open
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state_dict = load_file(transformer_path)
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else:
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state_dict = torch.load(transformer_path, map_location="cpu")
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state_dict = state_dict["state_dict"] if "state_dict" in state_dict else state_dict
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m, u = transformer.load_state_dict(state_dict, strict=False)
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print(f"missing keys: {len(m)}, unexpected keys: {len(u)}")
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if motion_module_path is not None:
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print(f"From Motion Module: {motion_module_path}")
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if motion_module_path.endswith("safetensors"):
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from safetensors.torch import load_file, safe_open
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state_dict = load_file(motion_module_path)
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else:
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state_dict = torch.load(motion_module_path, map_location="cpu")
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state_dict = state_dict["state_dict"] if "state_dict" in state_dict else state_dict
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m, u = transformer.load_state_dict(state_dict, strict=False)
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print(f"missing keys: {len(m)}, unexpected keys: {len(u)}, {u}")
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# Get Vae
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if OmegaConf.to_container(config['vae_kwargs'])['enable_magvit']:
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Choosen_AutoencoderKL = AutoencoderKLMagvit
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else:
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Choosen_AutoencoderKL = AutoencoderKL
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vae = Choosen_AutoencoderKL.from_pretrained(
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model_name,
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subfolder="vae"
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).to(weight_dtype)
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if vae_path is not None:
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print(f"From checkpoint: {vae_path}")
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if vae_path.endswith("safetensors"):
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from safetensors.torch import load_file, safe_open
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state_dict = load_file(vae_path)
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else:
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state_dict = torch.load(vae_path, map_location="cpu")
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state_dict = state_dict["state_dict"] if "state_dict" in state_dict else state_dict
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m, u = vae.load_state_dict(state_dict, strict=False)
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print(f"missing keys: {len(m)}, unexpected keys: {len(u)}")
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# Get Scheduler
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Choosen_Scheduler = scheduler_dict = {
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"Euler": EulerDiscreteScheduler,
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"Euler A": EulerAncestralDiscreteScheduler,
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"DPM++": DPMSolverMultistepScheduler,
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"PNDM": PNDMScheduler,
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"DDIM": DDIMScheduler,
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}[sampler_name]
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scheduler = Choosen_Scheduler(**OmegaConf.to_container(config['noise_scheduler_kwargs']))
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pipeline = EasyAnimatePipeline.from_pretrained(
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model_name,
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vae=vae,
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transformer=transformer,
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scheduler=scheduler,
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torch_dtype=weight_dtype
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)
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pipeline.to("cuda")
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pipeline.enable_model_cpu_offload()
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generator = torch.Generator(device="cuda").manual_seed(seed)
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if lora_path is not None:
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pipeline = merge_lora(pipeline, lora_path, lora_weight)
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with torch.no_grad():
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sample = pipeline(
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prompt,
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video_length = video_length,
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negative_prompt = negative_prompt,
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height = sample_size[0],
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width = sample_size[1],
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generator = generator,
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guidance_scale = guidance_scale,
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num_inference_steps = num_inference_steps,
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).videos
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if not os.path.exists(save_path):
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os.makedirs(save_path, exist_ok=True)
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index = len([path for path in os.listdir(save_path)]) + 1
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prefix = str(index).zfill(8)
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video_path = os.path.join(save_path, prefix + ".gif")
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save_videos_grid(sample, video_path, fps=fps) |