# 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.pipelines.wan.pipeline_wan_i2v import WanImageToVideoPipeline 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-I2V-A14B-Diffusers" LANDSCAPE_WIDTH = 832 LANDSCAPE_HEIGHT = 480 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) pipe = WanImageToVideoPipeline.from_pretrained(MODEL_ID, transformer=WanTransformer3DModel.from_pretrained('cbensimon/Wan2.2-I2V-A14B-bf16-Diffusers', subfolder='transformer', torch_dtype=torch.bfloat16, device_map='cuda', ), transformer_2=WanTransformer3DModel.from_pretrained('cbensimon/Wan2.2-I2V-A14B-bf16-Diffusers', subfolder='transformer_2', torch_dtype=torch.bfloat16, device_map='cuda', ), torch_dtype=torch.bfloat16, ).to('cuda') # load, fuse, unload before compilation # pipe.load_lora_weights( # "vrgamedevgirl84/Wan14BT2VFusioniX", # weight_name="FusionX_LoRa/Phantom_Wan_14B_FusionX_LoRA.safetensors", # adapter_name="phantom" # ) # pipe.set_adapters(["phantom"], adapter_weights=[0.95]) # pipe.fuse_lora(adapter_names=["phantom"], lora_scale=1.0) # pipe.unload_lora_weights() # pipe.load_lora_weights( # "vrgamedevgirl84/Wan14BT2VFusioniX", # weight_name="FusionX_LoRa/Phantom_Wan_14B_FusionX_LoRA.safetensors", # adapter_name="phantom" # ) # kwargs = {} # kwargs["load_into_transformer_2"] = True # pipe.load_lora_weights( # "vrgamedevgirl84/Wan14BT2VFusioniX", # weight_name="FusionX_LoRa/Phantom_Wan_14B_FusionX_LoRA.safetensors", # adapter_name="phantom_2", **kwargs # ) # pipe.set_adapters(["phantom", "phantom_2"], adapter_weights=[1., 1.]) # pipe.fuse_lora(adapter_names=["phantom"], lora_scale=3., components=["transformer"]) # pipe.fuse_lora(adapter_names=["phantom_2"], lora_scale=1., components=["transformer_2"]) # pipe.unload_lora_weights() for i in range(3): gc.collect() torch.cuda.synchronize() torch.cuda.empty_cache() optimize_pipeline_(pipe, image=Image.new('RGB', (LANDSCAPE_WIDTH, LANDSCAPE_HEIGHT)), prompt='prompt', height=LANDSCAPE_HEIGHT, width=LANDSCAPE_WIDTH, num_frames=MAX_FRAMES_MODEL, ) default_prompt_i2v = "make this image come alive, cinematic motion, smooth animation" 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 resize_image(image: Image.Image) -> Image.Image: if image.height > image.width: transposed = image.transpose(Image.Transpose.ROTATE_90) resized = resize_image_landscape(transposed) return resized.transpose(Image.Transpose.ROTATE_270) return resize_image_landscape(image) def resize_image_landscape(image: Image.Image) -> Image.Image: target_aspect = LANDSCAPE_WIDTH / LANDSCAPE_HEIGHT width, height = image.size in_aspect = width / height if in_aspect > target_aspect: new_width = round(height * target_aspect) left = (width - new_width) // 2 image = image.crop((left, 0, left + new_width, height)) else: new_height = round(width / target_aspect) top = (height - new_height) // 2 image = image.crop((0, top, width, top + new_height)) return image.resize((LANDSCAPE_WIDTH, LANDSCAPE_HEIGHT), Image.LANCZOS) def get_duration( input_image, prompt, negative_prompt, duration_seconds, guidance_scale, guidance_scale_2, steps, seed, randomize_seed, progress, ): return steps * 15 @spaces.GPU(duration=get_duration) def generate_video( input_image, prompt, negative_prompt=default_negative_prompt, duration_seconds = MAX_DURATION, guidance_scale = 1, guidance_scale_2 = 3, steps = 6, 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: input_image (PIL.Image): The input image to animate. Will be resized to target dimensions. 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). duration_seconds (float, optional): Duration of the generated video in seconds. Defaults to 2. Clamped between MIN_FRAMES_MODEL/FIXED_FPS and MAX_FRAMES_MODEL/FIXED_FPS. 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) """ if input_image is None: raise gr.Error("Please upload an input image.") num_frames = np.clip(int(round(duration_seconds * FIXED_FPS)), MIN_FRAMES_MODEL, MAX_FRAMES_MODEL) current_seed = random.randint(0, MAX_SEED) if randomize_seed else int(seed) resized_image = resize_image(input_image) output_frames_list = pipe( image=resized_image, prompt=prompt, negative_prompt=negative_prompt, height=resized_image.height, width=resized_image.width, num_frames=num_frames, guidance_scale=float(guidance_scale), guidance_scale_2=float(guidance_scale_2), num_inference_steps=int(steps), generator=torch.Generator(device="cuda").manual_seed(current_seed), ).frames[0] with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as tmpfile: video_path = tmpfile.name export_to_video(output_frames_list, video_path, fps=FIXED_FPS) return video_path, current_seed with gr.Blocks() as demo: gr.Markdown("# Fast 6 steps Wan 2.2 I2V (14B) with Phantom LoRA") 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(): input_image_component = gr.Image(type="pil", label="Input Image (auto-resized to target H/W)") 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=4, label="Inference Steps") guidance_scale_input = gr.Slider(minimum=0.0, maximum=10.0, step=0.5, value=1, label="Guidance Scale - high noise stage") guidance_scale_2_input = gr.Slider(minimum=0.0, maximum=10.0, step=0.5, value=1, label="Guidance Scale 2 - low noise stage") generate_button = gr.Button("Generate Video", variant="primary") with gr.Column(): video_output = gr.Video(label="Generated Video", autoplay=True, interactive=False) ui_inputs = [ input_image_component, prompt_input, negative_prompt_input, duration_seconds_input, guidance_scale_input, guidance_scale_2_input, steps_slider, seed_input, randomize_seed_checkbox ] generate_button.click(fn=generate_video, inputs=ui_inputs, outputs=[video_output, seed_input]) gr.Examples( examples=[ [ "wan_i2v_input.JPG", "POV selfie video, white cat with sunglasses standing on surfboard, relaxed smile, tropical beach behind (clear water, green hills, blue sky with clouds). Surfboard tips, cat falls into ocean, camera plunges underwater with bubbles and sunlight beams. Brief underwater view of cat’s face, then cat resurfaces, still filming selfie, playful summer vacation mood.", ], ], inputs=[input_image_component, prompt_input], outputs=[video_output, seed_input], fn=generate_video, cache_examples="lazy" ) if __name__ == "__main__": demo.queue().launch(mcp_server=True)