app.py

# 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')
import logging
# 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_

import ffmpeg
import tempfile
import os


MODEL_ID = "Wan-AI/Wan2.2-T2V-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)

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_t2v = "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"

from huggingface_hub import HfApi, upload_file
import os
import uuid

import os
import uuid
import logging
from datetime import datetime
from huggingface_hub import HfApi, upload_file
import tempfile
import random
import logging
from datetime import datetime
import uuid
import numpy as np
import torch
import ffmpeg

HF_MODEL = os.environ.get("HF_UPLOAD_REPO", "rahul7star/VideoExplain")
def upload_to_hf(video_path: str, summary_text: str):
    api = HfApi()
    # Create date-based folder
    today_str = datetime.now().strftime("%Y-%m-%d")
    unique_subfolder = f"WANT2V-EXP-upload_{uuid.uuid4().hex[:8]}"
    hf_folder = f"{today_str}/{unique_subfolder}"
    logging.info(f"Uploading to HF folder: {hf_folder}")

    # Upload video
    video_filename = os.path.basename(video_path)
    video_hf_path = f"{hf_folder}/{video_filename}"
    upload_file(
        path_or_fileobj=video_path,
        path_in_repo=video_hf_path,
        repo_id=HF_MODEL,
        repo_type="model",
        token=os.environ.get("HUGGINGFACE_HUB_TOKEN"),
    )
    logging.info(f"✅ Uploaded video: {video_hf_path}")

    # Upload summary
    summary_file = os.path.join(tempfile.gettempdir(), "summary.txt")
    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: {summary_hf_path}")

    return hf_folder


def save_video_ffmpeg(frames: list, video_path: str, fps: int = FIXED_FPS):
    h, w, c = frames[0].shape
    process = (
        ffmpeg
        .input(
            'pipe:', format='rawvideo', pix_fmt='rgb24',
            s=f'{w}x{h}', framerate=fps
        )
        .output(
            video_path,
            pix_fmt='yuv420p',
            vcodec='libx264',
            crf=18,
            preset='slow'
        )
        .overwrite_output()
        .run_async(pipe_stdin=True)
    )
    for frame in frames:
        process.stdin.write(frame.astype(np.uint8).tobytes())
    process.stdin.close()
    process.wait()
    logging.info(f"✅ Video saved to {video_path}")



def upload_to_hf0(video_path, summary_text):
    api = HfApi()
    
    # Create a date-based folder (YYYY-MM-DD)
    today_str = datetime.now().strftime("%Y-%m-%d")
    date_folder = today_str
    
    # Generate a unique subfolder for this upload
    unique_subfolder = f"WANT2V-EXP-upload_{uuid.uuid4().hex[:8]}"
    hf_folder = f"{date_folder}/{unique_subfolder}"
    logging.info(f"Uploading files to HF folder: {hf_folder} in repo {HF_MODEL}")

    # Upload video
    video_filename = os.path.basename(video_path)
    video_hf_path = f"{hf_folder}/{video_filename}"
    upload_file(
        path_or_fileobj=video_path,
        path_in_repo=video_hf_path,
        repo_id=HF_MODEL,
        repo_type="model",
        token=os.environ.get("HUGGINGFACE_HUB_TOKEN"),
    )
    logging.info(f"✅ Uploaded video to HF: {video_hf_path}")

    # Upload summary.txt
    summary_file = "/tmp/summary.txt"
    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}")

    return hf_folder

    
def get_duration(
    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(
    prompt,
    negative_prompt=default_negative_prompt,
    duration_seconds=MAX_DURATION,
    guidance_scale=1,
    guidance_scale_2=3,
    steps=4,
    seed=42,
    randomize_seed=False,
    progress=gr.Progress(track_tqdm=True),
):
    print("Prompt:", prompt)

    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)

    # Generate frames
    output_frames_list = pipe(
        prompt=prompt,
        negative_prompt=negative_prompt,
        height=LANDSCAPE_HEIGHT,
        width=LANDSCAPE_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]

  
  
    video_path = os.path.join(tempfile.gettempdir(), f"video_{current_seed}.mp4")
    save_video_ffmpeg(output_frames_list, video_path, fps=FIXED_FPS)

   
    hf_folder = upload_to_hf(video_path, summary_text=prompt)
    logging.info(f"✅ Uploaded folder: {hf_folder}")

    return video_path, current_seed


with gr.Blocks() as demo:
    gr.Markdown("# Fast 4 steps Wan 2.2 T2V (14B) with Lightning LoRA")
    gr.Markdown("run Wan 2.2 in just 4-8 steps, with [Wan 2.2 Lightning LoRA](https://huggingface.co/Kijai/WanVideo_comfy/tree/main/Wan22-Lightning), fp8 quantization & AoT compilation - compatible with 🧨 diffusers and ZeroGPU⚡️")
    with gr.Row():
        with gr.Column():
            prompt_input = gr.Textbox(label="Prompt", value=default_prompt_t2v)
            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=3, 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 = [
        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=[ 
            [
                "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.",
            ],
            [
                "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage.",
            ],
            [
                "A cinematic shot of a boat sailing on a calm sea at sunset.",
            ],
            [
                "Drone footage flying over a futuristic city with flying cars.",
            ],
        ],
        inputs=[prompt_input], outputs=[video_output, seed_input], fn=generate_video, cache_examples="lazy"
    )

if __name__ == "__main__":
    demo.queue().launch(mcp_server=True)