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lzy
@@ -7,117 +7,136 @@ from omegaconf import OmegaConf
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import torch
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import diffusers
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from diffusers import AutoencoderKL, DDIMScheduler
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from diffusers import AutoencoderKL, EulerDiscreteScheduler
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from tqdm.auto import tqdm
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from transformers import CLIPTextModel, CLIPTokenizer
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from transformers import CLIPTextModel, CLIPTokenizer, CLIPTextModelWithProjection
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from animatediff.models.unet import UNet3DConditionModel
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from animatediff.pipelines.pipeline_animation import AnimationPipeline
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from animatediff.utils.util import save_videos_grid
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from animatediff.utils.util import load_weights
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from animatediff.utils.util import save_videos_grid, load_weights
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from diffusers.utils.import_utils import is_xformers_available
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from einops import rearrange, repeat
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import csv, pdb, glob
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from safetensors import safe_open
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import math
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from pathlib import Path
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import torchvision
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import torchvision.transforms as transforms
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from PIL import Image
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import numpy as np
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@torch.no_grad()
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def main(args):
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*_, func_args = inspect.getargvalues(inspect.currentframe())
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func_args = dict(func_args)
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time_str = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S")
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savedir = f"samples/{Path(args.config).stem}-{time_str}"
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os.makedirs(savedir)
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*_, func_args = inspect.getargvalues(inspect.currentframe())
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func_args = dict(func_args)
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time_str = datetime.datetime.now().strftime("%Y-%m-%d")
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savedir = f"sample/{Path(args.exp_config).stem}_{args.H}_{args.W}-{time_str}"
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os.makedirs(savedir, exist_ok=True)
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# Load Config
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exp_config = OmegaConf.load(args.exp_config)
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config = OmegaConf.load(args.base_config)
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config = OmegaConf.merge(config, exp_config)
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config = OmegaConf.load(args.config)
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samples = []
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sample_idx = 0
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for model_idx, (config_key, model_config) in enumerate(list(config.items())):
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motion_modules = model_config.motion_module
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motion_modules = [motion_modules] if isinstance(motion_modules, str) else list(motion_modules)
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for motion_module in motion_modules:
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inference_config = OmegaConf.load(model_config.get("inference_config", args.inference_config))
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if config.get('base_model_path', '') != '':
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args.pretrained_model_path = config.base_model_path
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# Load Component
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tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_path, subfolder="tokenizer")
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text_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_path, subfolder="text_encoder")
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vae = AutoencoderKL.from_pretrained(args.pretrained_model_path, subfolder="vae")
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tokenizer_two = CLIPTokenizer.from_pretrained(args.pretrained_model_path, subfolder="tokenizer_2")
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text_encoder_two = CLIPTextModelWithProjection.from_pretrained(args.pretrained_model_path, subfolder="text_encoder_2")
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### >>> create validation pipeline >>> ###
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tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_path, subfolder="tokenizer")
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text_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_path, subfolder="text_encoder")
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vae = AutoencoderKL.from_pretrained(args.pretrained_model_path, subfolder="vae")
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unet = UNet3DConditionModel.from_pretrained_2d(args.pretrained_model_path, subfolder="unet", unet_additional_kwargs=OmegaConf.to_container(inference_config.unet_additional_kwargs))
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# init unet model
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unet = UNet3DConditionModel.from_pretrained_2d(args.pretrained_model_path, subfolder="unet", unet_additional_kwargs=OmegaConf.to_container(config.unet_additional_kwargs))
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if is_xformers_available(): unet.enable_xformers_memory_efficient_attention()
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else: assert False
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# Enable memory efficient attention
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if is_xformers_available() and args.xformers:
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unet.enable_xformers_memory_efficient_attention()
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pipeline = AnimationPipeline(
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vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet,
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scheduler=DDIMScheduler(**OmegaConf.to_container(inference_config.noise_scheduler_kwargs)),
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).to("cuda")
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scheduler = EulerDiscreteScheduler(timestep_spacing='leading', steps_offset=1, **config.noise_scheduler_kwargs)
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pipeline = load_weights(
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pipeline,
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# motion module
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motion_module_path = motion_module,
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motion_module_lora_configs = model_config.get("motion_module_lora_configs", []),
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# image layers
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dreambooth_model_path = model_config.get("dreambooth_path", ""),
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lora_model_path = model_config.get("lora_model_path", ""),
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lora_alpha = model_config.get("lora_alpha", 0.8),
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).to("cuda")
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pipeline = AnimationPipeline(
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unet=unet, vae=vae, tokenizer=tokenizer, text_encoder=text_encoder, scheduler=scheduler,
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text_encoder_2 = text_encoder_two, tokenizer_2=tokenizer_two
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).to("cuda")
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prompts = model_config.prompt
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n_prompts = list(model_config.n_prompt) * len(prompts) if len(model_config.n_prompt) == 1 else model_config.n_prompt
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random_seeds = model_config.get("seed", [-1])
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random_seeds = [random_seeds] if isinstance(random_seeds, int) else list(random_seeds)
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random_seeds = random_seeds * len(prompts) if len(random_seeds) == 1 else random_seeds
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config[config_key].random_seed = []
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for prompt_idx, (prompt, n_prompt, random_seed) in enumerate(zip(prompts, n_prompts, random_seeds)):
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# manually set random seed for reproduction
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if random_seed != -1: torch.manual_seed(random_seed)
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else: torch.seed()
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config[config_key].random_seed.append(torch.initial_seed())
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print(f"current seed: {torch.initial_seed()}")
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print(f"sampling {prompt} ...")
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sample = pipeline(
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prompt,
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negative_prompt = n_prompt,
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num_inference_steps = model_config.steps,
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guidance_scale = model_config.guidance_scale,
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width = args.W,
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height = args.H,
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video_length = args.L,
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).videos
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samples.append(sample)
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# Load model weights
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pipeline = load_weights(
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pipeline = pipeline,
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motion_module_path = config.get("motion_module_path", ""),
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ckpt_path = config.get("ckpt_path", ""),
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lora_path = config.get("lora_path", ""),
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lora_alpha = config.get("lora_alpha", 0.8)
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)
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prompt = "-".join((prompt.replace("/", "").split(" ")[:10]))
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save_videos_grid(sample, f"{savedir}/sample/{sample_idx}-{prompt}.gif")
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print(f"save to {savedir}/sample/{prompt}.gif")
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sample_idx += 1
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pipeline.unet = pipeline.unet.half()
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pipeline.text_encoder = pipeline.text_encoder.half()
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pipeline.text_encoder_2 = pipeline.text_encoder_2.half()
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pipeline.enable_model_cpu_offload()
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pipeline.enable_vae_slicing()
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samples = torch.concat(samples)
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save_videos_grid(samples, f"{savedir}/sample.gif", n_rows=4)
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prompts = config.prompt
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n_prompts = config.n_prompt
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OmegaConf.save(config, f"{savedir}/config.yaml")
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random_seeds = config.get("seed", [-1])
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random_seeds = [random_seeds] if isinstance(random_seeds, int) else list(random_seeds)
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random_seeds = random_seeds * len(prompts) if len(random_seeds) == 1 else random_seeds
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seeds = []
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samples = []
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with torch.inference_mode():
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for prompt_idx, (prompt, n_prompt, random_seed) in enumerate(zip(prompts, n_prompts, random_seeds)):
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# manually set random seed for reproduction
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if random_seed != -1: torch.manual_seed(random_seed)
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else: torch.seed()
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seeds.append(torch.initial_seed())
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print(f"current seed: {torch.initial_seed()}")
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print(f"sampling {prompt} ...")
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sample = pipeline(
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prompt,
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negative_prompt = n_prompt,
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num_inference_steps = config.get('steps', 100),
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guidance_scale = config.get('guidance_scale', 10),
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width = args.W,
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height = args.H,
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single_model_length = args.L,
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).videos
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samples.append(sample)
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prompt = "-".join((prompt.replace("/", "").split(" ")[:10]))
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prompt = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S")
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# save video
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save_videos_grid(sample, f"{savedir}/sample/{prompt}.mp4")
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print(f"save to {savedir}/sample/{prompt}.mp4")
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samples = torch.concat(samples)
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save_videos_grid(samples, f"{savedir}/sample-{datetime.datetime.now().strftime('%Y-%m-%dT%H-%M-%S')}.mp4", n_rows=4)
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config.seed = seeds
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OmegaConf.save(config, f"{savedir}/config.yaml")
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--pretrained_model_path", type=str, default="models/StableDiffusion/stable-diffusion-v1-5",)
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parser.add_argument("--inference_config", type=str, default="configs/inference/inference-v1.yaml")
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parser.add_argument("--config", type=str, required=True)
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parser.add_argument("--L", type=int, default=16 )
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parser.add_argument("--W", type=int, default=512)
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parser.add_argument("--H", type=int, default=512)
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parser = argparse.ArgumentParser()
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args = parser.parse_args()
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main(args)
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parser.add_argument("--pretrained_model_path", type=str, default="models/StableDiffusion/stable-diffusion-xl-base-1.0",)
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parser.add_argument("--base_config", type=str, default="configs/inference/inference.yaml")
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parser.add_argument("--exp_config", type=str, required=True)
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parser.add_argument("--L", type=int, default=16 )
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parser.add_argument("--W", type=int, default=1024)
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parser.add_argument("--H", type=int, default=1024)
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parser.add_argument("--xformers", action="store_true")
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args = parser.parse_args()
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main(args)
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