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Running
on
Zero
| import torch | |
| import gradio as gr | |
| import spaces | |
| import random | |
| 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 | |
| # 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 generate_video( | |
| model_choice, | |
| prompt, | |
| negative_prompt, | |
| lora_id, | |
| lora_weight_name, | |
| lora_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 LoRA weights if provided | |
| if lora_id and lora_id.strip(): | |
| try: | |
| # If a specific weight name is provided, use it | |
| if lora_weight_name and lora_weight_name.strip(): | |
| pipe.load_lora_weights(lora_id, weight_name=lora_weight_name) | |
| else: | |
| pipe.load_lora_weights(lora_id) | |
| # Set lora scale if applicable | |
| if hasattr(pipe, "set_adapters_scale") and lora_scale is not None: | |
| pipe.set_adapters_scale(lora_scale) | |
| except ValueError as e: | |
| # Return informative error if there are multiple safetensors and no weight name | |
| if "more than one weights file" in str(e): | |
| return f"Error: The repository '{lora_id}' contains multiple safetensors files. Please specify a weight name using the 'LoRA Weight Name' field.", seed | |
| else: | |
| return f"Error loading LoRA 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(""" | |
| <p align="center"> | |
| <svg version="1.1" viewBox="0 0 1200 295" xmlns="http://www.w3.org/2000/svg" xmlns:v="https://vecta.io/nano" width="400"> | |
| <path d='M195.43 109.93a.18.18 0 0 1-.27-.03l-.17-.26a.13.12-43.2 0 1 .01-.16q21.87-21.98 42.02-37.95c17.57-13.93 36.61-26.36 57.23-34.01q13.26-4.92 24.23-4.1c10.91.81 18.01 8.05 19.12 18.82.79 7.76-.64 15.73-3.29 23.37q-5.87 16.92-15.46 32.47a1.05 1.02-40.3 0 0-.09.91q11.79 32.3 2.48 63.6c-13.06 43.95-53.78 72.83-99.49 71.06q-16.32-.64-30.92-6.27a1.88 1.88 0 0 0-1.65.15c-12.54 7.69-26.93 14.55-41.16 17.68-4.99 1.1-9.54.6-16.14.62a6.31 6.04 50.1 0 1-1.35-.15c-19.69-4.51-18.03-25.43-12.71-40.65q5.95-17.05 15.9-32.34a.89.88 50.1 0 0 .07-.82c-13.76-32.93-7.8-71.64 14.32-99.05q20.75-25.73 51.75-33.99 29.88-7.96 59.38 2.25a.18.18 0 0 1 .04.32l-11.17 7.52a1.18 1.17-55 0 1-.93.17q-26.82-6.3-51.8 2.83c-24.65 9.02-43.94 28.53-52.73 53.27q-8.3 23.33-2.99 48.49.7 3.32 3.15 10.35c7.87 22.55 24.1 40.02 45.62 50.35a.52.51 43.2 0 1 .03.91l-10.31 5.96a.7.68-44.3 0 1-.69 0q-27.53-15.57-41.3-43.86a.1.09-44.1 0 0-.17 0q-6.59 11.46-11.16 23.67c-4.39 11.73-11.35 38.04 10.45 38.5q6.38.13 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| </svg> | |
| </p> | |
| """) | |
| gr.Markdown("# Wan 2.1 T2V 1.3B with LoRA") | |
| 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(): | |
| lora_id = gr.Textbox( | |
| label="LoRA Model Repo (e.g., TheBulge/AndroWan-2.1-T2V-1.3B)", | |
| value="TheBulge/AndroWan-2.1-T2V-1.3B" | |
| ) | |
| with gr.Row(): | |
| lora_weight_name = gr.Textbox( | |
| label="LoRA 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." | |
| ) | |
| lora_scale = gr.Slider( | |
| label="LoRA 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, | |
| lora_id, | |
| lora_weight_name, | |
| lora_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 LoRAs with multiple safetensors files: | |
| If you encounter an error stating "more than one weights file", you need to specify the exact weight file name in the "LoRA 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() |