import torch import gradio as gr import spaces import random import os from diffusers.utils import export_to_video from diffusers import AutoencoderKLWan, WanPipeline from diffusers.schedulers.scheduling_unipc_multistep import UniPCMultistepScheduler from diffusers.schedulers.scheduling_flow_match_euler_discrete import FlowMatchEulerDiscreteScheduler from huggingface_hub import hf_hub_download from lycoris import create_lycoris_from_weights # Define model options MODEL_OPTIONS = { "Wan2.1-T2V-1.3B": "Wan-AI/Wan2.1-T2V-1.3B-Diffusers", "Wan2.1-T2V-14B": "Wan-AI/Wan2.1-T2V-14B-Diffusers" } # Define scheduler options SCHEDULER_OPTIONS = { "UniPCMultistepScheduler": UniPCMultistepScheduler, "FlowMatchEulerDiscreteScheduler": FlowMatchEulerDiscreteScheduler } def download_adapter(repo_id, weight_name=None): """ Download the adapter file from the Hugging Face Hub. If weight_name is not provided, attempts to use pytorch_lora_weights.safetensors """ adapter_filename = weight_name if weight_name else "pytorch_lora_weights.safetensors" cache_dir = os.environ.get('HF_PATH', os.path.expanduser('~/.cache/huggingface/hub/models')) cleaned_adapter_path = repo_id.replace("/", "_").replace("\\", "_").replace(":", "_") path_to_adapter = os.path.join(cache_dir, cleaned_adapter_path) os.makedirs(path_to_adapter, exist_ok=True) try: path_to_adapter_file = hf_hub_download( repo_id=repo_id, filename=adapter_filename, local_dir=path_to_adapter ) return path_to_adapter_file except Exception as e: # If specific file not found, try to get a list of available safetensors files if weight_name is None: raise ValueError(f"Could not download default adapter file: {str(e)}\nPlease specify the exact weight file name.") else: raise ValueError(f"Could not download adapter file {weight_name}: {str(e)}") @spaces.GPU(duration=300) def generate_video( model_choice, prompt, negative_prompt, lycoris_id, lycoris_weight_name, lycoris_scale, scheduler_type, flow_shift, height, width, num_frames, guidance_scale, num_inference_steps, output_fps, seed ): # Get model ID from selection model_id = MODEL_OPTIONS[model_choice] # Set seed for reproducibility if seed == -1 or seed is None or seed == "": seed = random.randint(0, 2147483647) else: seed = int(seed) # Set the seed torch.manual_seed(seed) # Load model vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32) pipe = WanPipeline.from_pretrained(model_id, vae=vae, torch_dtype=torch.float16) # Set scheduler if scheduler_type == "UniPCMultistepScheduler": pipe.scheduler = UniPCMultistepScheduler.from_config( pipe.scheduler.config, flow_shift=flow_shift ) else: pipe.scheduler = FlowMatchEulerDiscreteScheduler(shift=flow_shift) # Move to GPU pipe.to("cuda") # Load LyCORIS weights if provided if lycoris_id and lycoris_id.strip(): try: # Download the adapter file adapter_file_path = download_adapter(repo_id=lycoris_id, weight_name=lycoris_weight_name if lycoris_weight_name and lycoris_weight_name.strip() else None) # Apply LyCORIS adapter wrapper, *_ = create_lycoris_from_weights(lycoris_scale, adapter_file_path, pipe.transformer) wrapper.merge_to() except ValueError as e: # Return informative error if there are issues loading the adapter if "more than one weights file" in str(e) or "Could not download default adapter file" in str(e): return f"Error: The repository '{lycoris_id}' may contain multiple weight files. Please specify a weight name using the 'LyCORIS Weight Name' field.", seed else: return f"Error loading LyCORIS weights: {str(e)}", seed # Enable CPU offload for low VRAM pipe.enable_model_cpu_offload() # Generate video output = pipe( prompt=prompt, negative_prompt=negative_prompt, height=height, width=width, num_frames=num_frames, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps, generator=torch.Generator("cuda").manual_seed(seed) ).frames[0] # Export to video temp_file = "output.mp4" export_to_video(output, temp_file, fps=output_fps) return temp_file, seed # Create the Gradio interface with gr.Blocks() as demo: gr.HTML("""

""") gr.Markdown("# Wan 2.1 T2V with LyCORIS") with gr.Row(): with gr.Column(scale=1): model_choice = gr.Dropdown( choices=list(MODEL_OPTIONS.keys()), value="Wan2.1-T2V-1.3B", label="Model" ) prompt = gr.Textbox( label="Prompt", value="", lines=3 ) negative_prompt = gr.Textbox( label="Negative Prompt", value="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿", lines=3 ) with gr.Row(): lycoris_id = gr.Textbox( label="LyCORIS Model Repo (e.g., TheBulge/AndroWan-2.1-T2V-1.3B)", value="TheBulge/AndroWan-2.1-T2V-1.3B" ) with gr.Row(): lycoris_weight_name = gr.Textbox( label="LyCORIS Weight Path in Repo (optional)", value="safetensors/AndroWan_v32-0036_ema.safetensors", info="Specify for repos with multiple .safetensors files, e.g.: adapter_model.safetensors, pytorch_lora_weights.safetensors, etc." ) lycoris_scale = gr.Slider( label="LyCORIS Scale", minimum=0.0, maximum=2.0, value=1.00, step=0.05 ) with gr.Row(): scheduler_type = gr.Dropdown( choices=list(SCHEDULER_OPTIONS.keys()), value="UniPCMultistepScheduler", label="Scheduler" ) flow_shift = gr.Slider( label="Flow Shift", minimum=1.0, maximum=12.0, value=3.0, step=0.5, info="2.0-5.0 for smaller videos, 7.0-12.0 for larger videos" ) with gr.Row(): height = gr.Slider( label="Height", minimum=256, maximum=1024, value=832, step=32 ) width = gr.Slider( label="Width", minimum=256, maximum=1792, value=480, step=30 ) with gr.Row(): num_frames = gr.Slider( label="Number of Frames (4k+1 is recommended, e.g. 33)", minimum=17, maximum=129, value=33, step=4 ) output_fps = gr.Slider( label="Output FPS", minimum=8, maximum=30, value=16, step=1 ) with gr.Row(): guidance_scale = gr.Slider( label="Guidance Scale (CFG)", minimum=1.0, maximum=15.0, value=4.0, step=0.5 ) num_inference_steps = gr.Slider( label="Inference Steps", minimum=10, maximum=100, value=24, step=1 ) seed = gr.Number( label="Seed (-1 for random)", value=-1, precision=0, info="Set a specific seed for deterministic results" ) generate_btn = gr.Button("Generate Video") with gr.Column(scale=1): output_video = gr.Video(label="Generated Video") used_seed = gr.Number(label="Seed", precision=0) generate_btn.click( fn=generate_video, inputs=[ model_choice, prompt, negative_prompt, lycoris_id, lycoris_weight_name, lycoris_scale, scheduler_type, flow_shift, height, width, num_frames, guidance_scale, num_inference_steps, output_fps, seed ], outputs=[output_video, used_seed] ) gr.Markdown(""" ## Tips for best results: - For smaller resolution videos, try lower values of flow shift (2.0-5.0) - For larger resolution videos, try higher values of flow shift (7.0-12.0) - Number of frames should be of the form 4k+1 (e.g., 33, 81) - Stick to lower frame counts. Even at 480p, an 81 frame sequence at 30 steps will nearly time out the request in this space. ## Using LyCORIS with multiple safetensors files: If you encounter an error stating "Could not download default adapter file", you need to specify the exact weight file name in the "LyCORIS Weight Name" field. You can find this by browsing the repository on Hugging Face and looking for the safetensors files (common names include: adapter_model.safetensors, pytorch_lora_weights.safetensors). """) demo.launch()