# 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 import logging from optimization import optimize_pipeline_ # Hugging Face from huggingface_hub import HfApi, upload_file import uuid from datetime import datetime from queue import Queue from threading import Thread import time # ----------------------------- # Constants # ----------------------------- 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) HF_MODEL = os.environ.get("HF_UPLOAD_REPO", "rahul7star/VideoExplain") 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" # ----------------------------- # Initialize Pipeline # ----------------------------- 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') # Clean GPU for _ in range(3): gc.collect() torch.cuda.synchronize() torch.cuda.empty_cache() # Optimize pipeline optimize_pipeline_(pipe, image=Image.new('RGB', (LANDSCAPE_WIDTH, LANDSCAPE_HEIGHT)), prompt='prompt', height=LANDSCAPE_HEIGHT, width=LANDSCAPE_WIDTH, num_frames=MAX_FRAMES_MODEL, ) # ----------------------------- # Upload Queue for Lazy Upload # ----------------------------- upload_queue = Queue() def upload_worker(): while True: try: video_path, summary_text = upload_queue.get() logging.info(f"⏳ Uploading video in background: {video_path}") upscale_and_upload_4k(video_path, summary_text) logging.info(f"✅ Background upload finished: {video_path}") except Exception as e: logging.error(f"Upload failed: {e}") time.sleep(3) # Delay to avoid HF rate limits upload_queue.task_done() Thread(target=upload_worker, daemon=True).start() # ----------------------------- # Helper Functions # ----------------------------- 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, steps, negative_prompt, duration_seconds, guidance_scale, guidance_scale_2, seed, randomize_seed, progress): return int(steps) * 15 # ----------------------------- # Upscale + HF Upload Function # ----------------------------- import subprocess def upscale_and_upload_4k(input_video_path: str, summary_text: str) -> str: logging.info(f"Upscaling video to 4K for upload: {input_video_path}") # Temporary file for upscaled video with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as tmp_upscaled: upscaled_path = tmp_upscaled.name cmd = [ "ffmpeg", "-i", input_video_path, "-vf", "scale=3840:2160:flags=lanczos", "-c:v", "libx264", "-crf", "18", "-preset", "slow", "-y", upscaled_path, ] try: subprocess.run(cmd, check=True, capture_output=True) logging.info(f"✅ Upscaled video created at: {upscaled_path}") except subprocess.CalledProcessError as e: logging.error(f"FFmpeg failed:\n{e.stderr.decode()}") raise # HF Upload folder today_str = datetime.now().strftime("%Y-%m-%d") unique_subfolder = f"upload_{uuid.uuid4().hex[:8]}" hf_folder = f"{today_str}-WAN-I2V/{unique_subfolder}" # Upload video video_filename = os.path.basename(input_video_path) video_hf_path = f"{hf_folder}/{video_filename}" upload_file( path_or_fileobj=upscaled_path, path_in_repo=video_hf_path, repo_id=HF_MODEL, repo_type="model", token=os.environ.get("HUGGINGFACE_HUB_TOKEN"), ) # Upload summary summary_file = tempfile.NamedTemporaryFile(delete=False, suffix=".txt").name with open(summary_file, "w", encoding="utf-8") as f: f.write(summary_text) summary_hf_path = f"{hf_folder}/summary.txt" upload_file( path_or_fileobj=summary_file, path_in_repo=summary_hf_path, repo_id=HF_MODEL, repo_type="model", token=os.environ.get("HUGGINGFACE_HUB_TOKEN"), ) logging.info(f"✅ Uploaded summary to HF: {summary_hf_path}") # Cleanup os.remove(upscaled_path) os.remove(summary_file) return hf_folder # ----------------------------- # Video Generation Function # ----------------------------- @spaces.GPU(duration=get_duration) def generate_video( input_image, prompt, steps = 4, negative_prompt=default_negative_prompt, duration_seconds = MAX_DURATION, guidance_scale = 1, guidance_scale_2 = 1, seed = 42, randomize_seed = False, progress=gr.Progress(track_tqdm=True), ): 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) # Queue upload in background upload_queue.put((video_path, prompt)) logging.info(f"Video queued for background upload: {video_path}") return video_path, current_seed # ----------------------------- # Gradio UI # ----------------------------- with gr.Blocks() as demo: gr.Markdown("# Fast 4 steps Wan 2.2 I2V (14B) with Lightning LoRA") gr.Markdown("run Wan 2.2 in just 4-8 steps, with Lightning LoRA, fp8 quantization & AoT compilation") with gr.Row(): with gr.Column(): input_image_component = gr.Image(type="pil", label="Input Image (auto-resized)") 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)") 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=6, 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, steps_slider, negative_prompt_input, duration_seconds_input, guidance_scale_input, guidance_scale_2_input, seed_input, randomize_seed_checkbox ] generate_button.click(fn=generate_video, inputs=ui_inputs, outputs=[video_output, seed_input]) if __name__ == "__main__": demo.queue().launch(mcp_server=True)