250 lines
9.8 KiB
Python
250 lines
9.8 KiB
Python
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import os
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import numpy as np
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import torch
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import json
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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 PIL import Image
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from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
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from easyanimate.models.autoencoder_magvit import AutoencoderKLMagvit
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from easyanimate.models.transformer3d import Transformer3DModel, HunyuanTransformer3DModel
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from easyanimate.pipeline.pipeline_easyanimate_multi_text_encoder import EasyAnimatePipeline_Multi_Text_Encoder
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from easyanimate.pipeline.pipeline_easyanimate_multi_text_encoder_inpaint import EasyAnimatePipeline_Multi_Text_Encoder_Inpaint
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from easyanimate.pipeline.pipeline_easyanimate import EasyAnimatePipeline
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from easyanimate.pipeline.pipeline_easyanimate_inpaint import \
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EasyAnimateInpaintPipeline
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from easyanimate.utils.lora_utils import merge_lora, unmerge_lora
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from easyanimate.utils.utils import get_image_to_video_latent, save_videos_grid
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# Low gpu memory mode, this is used when the GPU memory is under 16GB
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low_gpu_memory_mode = False
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# Config and model path
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config_path = "config/easyanimate_video_slicevae_motion_module_v3.yaml"
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model_name = "models/Diffusion_Transformer/EasyAnimateV3-XL-2-InP-512x512"
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# Choose the sampler in "Euler" "Euler A" "DPM++" "PNDM" and "DDIM"
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sampler_name = "Euler"
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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 and V3, 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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# Use torch.float16 if GPU does not support torch.bfloat16
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# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
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weight_dtype = torch.bfloat16
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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."
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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 = 7.0
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seed = 43
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num_inference_steps = 25
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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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if config.get('enable_multi_text_encoder', False):
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Choosen_Transformer3DModel = HunyuanTransformer3DModel
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else:
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Choosen_Transformer3DModel = Transformer3DModel
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transformer_additional_kwargs = OmegaConf.to_container(config['transformer_additional_kwargs'])
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if weight_dtype == torch.float16:
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transformer_additional_kwargs["upcast_attention"] = True
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transformer = Choosen_Transformer3DModel.from_pretrained_2d(
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model_name,
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subfolder="transformer",
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transformer_additional_kwargs=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 OmegaConf.to_container(config['vae_kwargs'])['enable_magvit'] and weight_dtype == torch.float16:
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vae.upcast_vae = True
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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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if transformer.config.in_channels != vae.config.latent_channels:
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clip_image_encoder = CLIPVisionModelWithProjection.from_pretrained(
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model_name, subfolder="image_encoder"
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).to("cuda", weight_dtype)
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clip_image_processor = CLIPImageProcessor.from_pretrained(
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model_name, subfolder="image_encoder"
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)
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else:
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clip_image_encoder = None
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clip_image_processor = None
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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.from_pretrained(
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model_name,
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subfolder="scheduler"
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)
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# scheduler = Choosen_Scheduler(**OmegaConf.to_container(config['noise_scheduler_kwargs']))
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if config.get('enable_multi_text_encoder', False):
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if transformer.config.in_channels != vae.config.latent_channels:
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pipeline = EasyAnimatePipeline_Multi_Text_Encoder_Inpaint.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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clip_image_encoder=clip_image_encoder,
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clip_image_processor=clip_image_processor,
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)
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else:
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pipeline = EasyAnimatePipeline_Multi_Text_Encoder.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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else:
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if transformer.config.in_channels != vae.config.latent_channels:
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pipeline = EasyAnimateInpaintPipeline.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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clip_image_encoder=clip_image_encoder,
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clip_image_processor=clip_image_processor,
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)
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else:
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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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if low_gpu_memory_mode:
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pipeline.enable_sequential_cpu_offload()
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else:
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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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if transformer.config.in_channels != vae.config.latent_channels:
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video_length = int(video_length // vae.mini_batch_encoder * vae.mini_batch_encoder) if video_length != 1 else 1
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input_video, input_video_mask, clip_image = get_image_to_video_latent(None, None, video_length=video_length, sample_size=sample_size)
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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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video = input_video,
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mask_video = input_video_mask,
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clip_image = clip_image,
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).videos
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else:
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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 lora_path is not None:
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pipeline = unmerge_lora(pipeline, lora_path, lora_weight)
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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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if video_length == 1:
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video_path = os.path.join(save_path, prefix + ".png")
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image = sample[0, :, 0]
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image = image.transpose(0, 1).transpose(1, 2)
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image = (image * 255).numpy().astype(np.uint8)
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image = Image.fromarray(image)
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image.save(video_path)
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else:
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video_path = os.path.join(save_path, prefix + ".mp4")
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save_videos_grid(sample, video_path, fps=fps) |