# PyTorch 2.8 (temporary hack) import os os.system('pip install --upgrade --pre --extra-index-url https://download.pytorch.org/whl/nightly/cu126 "torch<2.9" spaces') # Actual demo code import spaces import torch from diffusers import WanPipeline, AutoencoderKLWan from diffusers.models.transformers.transformer_wan import WanTransformer3DModel from diffusers.utils.export_utils import export_to_video import gradio as gr import tempfile import numpy as np from PIL import Image import random import gc from optimization import optimize_pipeline_ MODEL_ID = "Wan-AI/Wan2.2-T2V-A14B-Diffusers" LANDSCAPE_WIDTH = 1024 LANDSCAPE_HEIGHT = 1024 MAX_SEED = np.iinfo(np.int32).max FIXED_FPS = 16 MIN_FRAMES_MODEL = 8 MAX_FRAMES_MODEL = 81 MIN_DURATION = round(MIN_FRAMES_MODEL/FIXED_FPS,1) MAX_DURATION = round(MAX_FRAMES_MODEL/FIXED_FPS,1) vae = AutoencoderKLWan.from_pretrained("Wan-AI/Wan2.2-T2V-A14B-Diffusers", subfolder="vae", torch_dtype=torch.float32) pipe = WanPipeline.from_pretrained(MODEL_ID, transformer=WanTransformer3DModel.from_pretrained('linoyts/Wan2.2-T2V-A14B-Diffusers-BF16', subfolder='transformer', torch_dtype=torch.bfloat16, device_map='cuda', ), transformer_2=WanTransformer3DModel.from_pretrained('linoyts/Wan2.2-T2V-A14B-Diffusers-BF16', subfolder='transformer_2', torch_dtype=torch.bfloat16, device_map='cuda', ), vae=vae, torch_dtype=torch.bfloat16, ).to('cuda') for i in range(3): gc.collect() torch.cuda.synchronize() torch.cuda.empty_cache() optimize_pipeline_(pipe, prompt='prompt', height=LANDSCAPE_HEIGHT, width=LANDSCAPE_WIDTH, num_frames=MAX_FRAMES_MODEL, ) default_prompt_i2v = "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage." default_negative_prompt = "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, 背景人很多, 倒着走" def get_duration( prompt, negative_prompt, guidance_scale, guidance_scale_2, steps, seed, randomize_seed, progress, ): return steps * 15 @spaces.GPU(duration=get_duration) def generate_image( prompt, negative_prompt=default_negative_prompt, guidance_scale = 3.5, guidance_scale_2 = 4, steps = 27, seed = 42, randomize_seed = False, progress=gr.Progress(track_tqdm=True), ): """ Generate a video from an input image using the Wan 2.2 14B I2V model with Phantom LoRA. This function takes an input image and generates a video animation based on the provided prompt and parameters. It uses an FP8 qunatized Wan 2.2 14B Image-to-Video model in with Phantom LoRA for fast generation in 6-8 steps. Args: prompt (str): Text prompt describing the desired animation or motion. negative_prompt (str, optional): Negative prompt to avoid unwanted elements. Defaults to default_negative_prompt (contains unwanted visual artifacts). guidance_scale (float, optional): Controls adherence to the prompt. Higher values = more adherence. Defaults to 1.0. Range: 0.0-20.0. guidance_scale_2 (float, optional): Controls adherence to the prompt. Higher values = more adherence. Defaults to 1.0. Range: 0.0-20.0. steps (int, optional): Number of inference steps. More steps = higher quality but slower. Defaults to 4. Range: 1-30. seed (int, optional): Random seed for reproducible results. Defaults to 42. Range: 0 to MAX_SEED (2147483647). randomize_seed (bool, optional): Whether to use a random seed instead of the provided seed. Defaults to False. progress (gr.Progress, optional): Gradio progress tracker. Defaults to gr.Progress(track_tqdm=True). Returns: tuple: A tuple containing: - video_path (str): Path to the generated video file (.mp4) - current_seed (int): The seed used for generation (useful when randomize_seed=True) Raises: gr.Error: If input_image is None (no image uploaded). Note: - The function automatically resizes the input image to the target dimensions - Frame count is calculated as duration_seconds * FIXED_FPS (24) - Output dimensions are adjusted to be multiples of MOD_VALUE (32) - The function uses GPU acceleration via the @spaces.GPU decorator - Generation time varies based on steps and duration (see get_duration function) """ current_seed = random.randint(0, MAX_SEED) if randomize_seed else int(seed) out_img = pipe( prompt=prompt, negative_prompt=negative_prompt, height=1024, width=1024, num_frames=1, guidance_scale=float(guidance_scale), guidance_scale_2=float(guidance_scale_2), num_inference_steps=int(steps), output_type="pil", generator=torch.Generator(device="cuda").manual_seed(current_seed), ).frames[0][0] return out_img, current_seed with gr.Blocks() as demo: gr.Markdown("# Wan 2.2 T2I (14B)") #gr.Markdown("run Wan 2.2 in just 6-8 steps, with [FusionX Phantom LoRA by DeeJayT](https://huggingface.co/vrgamedevgirl84/Wan14BT2VFusioniX/tree/main/FusionX_LoRa), compatible with 🧨 diffusers") with gr.Row(): with gr.Column(): prompt_input = gr.Textbox(label="Prompt", value=default_prompt_i2v) #duration_seconds_input = gr.Slider(minimum=MIN_DURATION, maximum=MAX_DURATION, step=0.1, value=MAX_DURATION, label="Duration (seconds)", info=f"Clamped to model's {MIN_FRAMES_MODEL}-{MAX_FRAMES_MODEL} frames at {FIXED_FPS}fps.") with gr.Accordion("Advanced Settings", open=False): negative_prompt_input = gr.Textbox(label="Negative Prompt", value=default_negative_prompt, lines=3) seed_input = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=42, interactive=True) randomize_seed_checkbox = gr.Checkbox(label="Randomize seed", value=True, interactive=True) steps_slider = gr.Slider(minimum=1, maximum=30, step=1, value=27, label="Inference Steps") guidance_scale_input = gr.Slider(minimum=0.0, maximum=10.0, step=0.5, value=3.5, label="Guidance Scale - high noise stage") guidance_scale_2_input = gr.Slider(minimum=0.0, maximum=10.0, step=0.5, value=4, label="Guidance Scale 2 - low noise stage") generate_button = gr.Button("Generate Image", variant="primary") with gr.Column(): img_output = gr.Image(label="Generated Image", interactive=False) ui_inputs = [ prompt_input, negative_prompt_input, guidance_scale_input, guidance_scale_2_input, steps_slider, seed_input, randomize_seed_checkbox ] generate_button.click(fn=generate_image, inputs=ui_inputs, outputs=[img_output, seed_input]) gr.Examples( examples=[ [ "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage." ], ], inputs=[prompt_input], outputs=[img_output, seed_input], fn=generate_image, cache_examples="lazy" ) if __name__ == "__main__": demo.queue().launch(mcp_server=True)