172 lines
6.7 KiB
Python
172 lines
6.7 KiB
Python
# At the top of inference.py, make sure you have these imports:
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import argparse
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import os
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from omegaconf import OmegaConf
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import torch
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from diffusers import AutoencoderKL, DDIMScheduler
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from latentsync.models.unet import UNet3DConditionModel
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from latentsync.pipelines.lipsync_pipeline import LipsyncPipeline
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from accelerate.utils import set_seed
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from latentsync.whisper.audio2feature import Audio2Feature
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from DeepCache import DeepCacheSDHelper
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def main(config, args):
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if not os.path.exists(args.video_path):
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raise RuntimeError(f"Video path '{args.video_path}' not found")
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if not os.path.exists(args.audio_path):
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raise RuntimeError(f"Audio path '{args.audio_path}' not found")
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# Check if the GPU supports float16
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is_fp16_supported = torch.cuda.is_available() and torch.cuda.get_device_capability()[0] > 7
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dtype = torch.float16 if is_fp16_supported else torch.float32
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print(f"Input video path: {args.video_path}")
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print(f"Input audio path: {args.audio_path}")
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print(f"Loaded checkpoint path: {args.inference_ckpt_path}")
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# FIXED: Create DDIMScheduler directly (NO HUGGINGFACE)
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scheduler = DDIMScheduler(
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beta_end=0.012,
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beta_schedule="scaled_linear",
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beta_start=0.00085,
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clip_sample=False,
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num_train_timesteps=1000,
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prediction_type="epsilon",
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set_alpha_to_one=False,
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steps_offset=1
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)
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print("✓ Created DDIMScheduler directly (fully offline)")
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if config.model.cross_attention_dim == 768:
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whisper_model_name = "small"
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elif config.model.cross_attention_dim == 384:
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whisper_model_name = "tiny"
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else:
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raise NotImplementedError("cross_attention_dim must be 768 or 384")
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audio_encoder = Audio2Feature(
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model_path=whisper_model_name,
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device="cuda",
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num_frames=config.data.num_frames,
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audio_feat_length=config.data.audio_feat_length,
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)
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# FIXED: Load VAE locally with proper path resolution
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# Get the base directory (where the extension is located)
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if hasattr(args, 'extension_dir'):
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base_dir = args.extension_dir
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else:
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# Fallback: try to determine from script location
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script_dir = os.path.dirname(os.path.abspath(__file__))
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base_dir = os.path.dirname(script_dir) # Go up one level from scripts/ to extension root
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# Try multiple VAE locations in order of preference
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vae_locations = [
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# New vae folder structure
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os.path.join(base_dir, "checkpoints", "vae", "sd-vae-ft-mse.safetensors"),
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os.path.join(base_dir, "checkpoints", "vae"), # Directory with config.json
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# Original locations
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os.path.join(base_dir, "checkpoints", "sd-vae-ft-mse.safetensors"),
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os.path.join(base_dir, "checkpoints", "sd-vae-ft-mse"),
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]
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vae = None
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for vae_path in vae_locations:
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if os.path.exists(vae_path):
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try:
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if vae_path.endswith('.safetensors'):
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print(f"Attempting to load VAE from safetensors file: {vae_path}")
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vae = AutoencoderKL.from_single_file(vae_path, torch_dtype=dtype)
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elif os.path.isdir(vae_path):
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print(f"Attempting to load VAE from directory: {vae_path}")
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vae = AutoencoderKL.from_pretrained(vae_path, torch_dtype=dtype, local_files_only=True)
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if vae is not None:
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print(f"✓ Successfully loaded VAE from: {vae_path}")
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break
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except Exception as e:
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print(f"Failed to load VAE from {vae_path}: {str(e)}")
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vae = None # Reset vae to None if loading failed
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continue
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if vae is None:
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print("Local VAE not found in any location, creating VAE with standard configuration")
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print(f"Searched locations: {vae_locations}")
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# Create VAE with standard SD configuration if local model doesn't exist
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vae = AutoencoderKL(
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in_channels=3,
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out_channels=3,
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down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D", "DownEncoderBlock2D", "DownEncoderBlock2D"],
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up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
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block_out_channels=[128, 256, 512, 512],
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layers_per_block=2,
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act_fn="silu",
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latent_channels=4,
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norm_num_groups=32,
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sample_size=512,
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).to(dtype=dtype)
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print("⚠️ Using default VAE configuration - consider downloading VAE model locally for better results")
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# Set VAE configuration
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vae.config.scaling_factor = 0.18215
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vae.config.shift_factor = 0
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# Rest of the function continues as before...
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unet, _ = UNet3DConditionModel.from_pretrained(
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OmegaConf.to_container(config.model),
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args.inference_ckpt_path,
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device="cpu",
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)
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unet = unet.to(dtype=dtype)
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pipeline = LipsyncPipeline(
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vae=vae,
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audio_encoder=audio_encoder,
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unet=unet,
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scheduler=scheduler,
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).to("cuda")
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# use DeepCache
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helper = DeepCacheSDHelper(pipe=pipeline)
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helper.set_params(cache_interval=3, cache_branch_id=0)
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helper.enable()
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if args.seed != -1:
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set_seed(args.seed)
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else:
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torch.seed()
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print(f"Initial seed: {torch.initial_seed()}")
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pipeline(
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video_path=args.video_path,
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audio_path=args.audio_path,
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video_out_path=args.video_out_path,
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video_mask_path=args.video_out_path.replace(".mp4", "_mask.mp4"),
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num_frames=config.data.num_frames,
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num_inference_steps=args.inference_steps,
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guidance_scale=args.guidance_scale,
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weight_dtype=dtype,
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width=config.data.resolution,
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height=config.data.resolution,
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mask_image_path=config.data.mask_image_path,
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)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--unet_config_path", type=str, default="configs/unet.yaml")
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parser.add_argument("--inference_ckpt_path", type=str, required=True)
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parser.add_argument("--video_path", type=str, required=True)
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parser.add_argument("--audio_path", type=str, required=True)
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parser.add_argument("--video_out_path", type=str, required=True)
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parser.add_argument("--inference_steps", type=int, default=20)
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parser.add_argument("--guidance_scale", type=float, default=1.0)
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parser.add_argument("--seed", type=int, default=1247)
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args = parser.parse_args()
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config = OmegaConf.load(args.unet_config_path)
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main(config, args) |