app.py

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 os

from torchao.quantization import quantize_
from torchao.quantization import Float8DynamicActivationFloat8WeightConfig
from torchao.quantization import Int8WeightOnlyConfig

import aoti
import subprocess
import ffmpeg
import os


MODEL_ID = "Wan-AI/Wan2.2-I2V-A14B-Diffusers"

MAX_DIM = 832
MIN_DIM = 480
SQUARE_DIM = 640
MULTIPLE_OF = 16

MAX_SEED = np.iinfo(np.int32).max

FIXED_FPS = 16
MIN_FRAMES_MODEL = 8
MAX_FRAMES_MODEL = 176#80

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')


pipe.load_lora_weights(
    "Kijai/WanVideo_comfy", 
    weight_name="Lightx2v/lightx2v_I2V_14B_480p_cfg_step_distill_rank128_bf16.safetensors", 
    adapter_name="lightx2v"
)

kwargs_lora_h = {}
kwargs_lora_h["load_into_transformer"] = True
pipe.load_lora_weights(
    "kingofpersia/wan-22-nsfw-loras", 
    weight_name="iGOON_Blink_Blowjob_I2V_HIGH.safetensors", 
    adapter_name="lora_h", **kwargs_lora_h
)
kwargs_lora = {}
kwargs_lora["load_into_transformer_2"] = True
pipe.load_lora_weights(
    "Kijai/WanVideo_comfy", 
    weight_name="Lightx2v/lightx2v_I2V_14B_480p_cfg_step_distill_rank128_bf16.safetensors", 
    adapter_name="lightx2v_2", **kwargs_lora
)

kwargs_lora_l = {}
kwargs_lora_l["load_into_transformer_2"] = True
pipe.load_lora_weights(
    "kingofpersia/wan-22-nsfw-loras", 
    weight_name="iGOON_Blink_Blowjob_I2V_LOW.safetensors", 
    adapter_name="lora_l", **kwargs_lora_l
)


pipe.set_adapters(["lightx2v", "lora_h", "lightx2v_2", "lora_l"], adapter_weights=[1., 1., 1., 1.])
pipe.fuse_lora(adapter_names=["lightx2v"], lora_scale=3., components=["transformer"])
pipe.fuse_lora(adapter_names=["lora_h"], lora_scale=0.6, components=["transformer"])
pipe.fuse_lora(adapter_names=["lightx2v_2"], lora_scale=1., components=["transformer_2"])
pipe.fuse_lora(adapter_names=["lora_l"], lora_scale=1., components=["transformer_2"])
pipe.unload_lora_weights()

quantize_(pipe.text_encoder, Int8WeightOnlyConfig())
quantize_(pipe.transformer, Float8DynamicActivationFloat8WeightConfig())
quantize_(pipe.transformer_2, Float8DynamicActivationFloat8WeightConfig())

aoti.aoti_blocks_load(pipe.transformer, 'rahul7star/WanAot', variant='fp8da')
aoti.aoti_blocks_load(pipe.transformer_2, 'rahul7star/WanAot', variant='fp8da')


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:
    """
    Resizes an image to fit within the model's constraints, preserving aspect ratio as much as possible.
    """
    width, height = image.size

    # Handle square case
    if width == height:
        return image.resize((SQUARE_DIM, SQUARE_DIM), Image.LANCZOS)

    aspect_ratio = width / height

    MAX_ASPECT_RATIO = MAX_DIM / MIN_DIM
    MIN_ASPECT_RATIO = MIN_DIM / MAX_DIM

    image_to_resize = image

    if aspect_ratio > MAX_ASPECT_RATIO:
        # Very wide image -> crop width to fit 832x480 aspect ratio
        target_w, target_h = MAX_DIM, MIN_DIM
        crop_width = int(round(height * MAX_ASPECT_RATIO))
        left = (width - crop_width) // 2
        image_to_resize = image.crop((left, 0, left + crop_width, height))
    elif aspect_ratio < MIN_ASPECT_RATIO:
        # Very tall image -> crop height to fit 480x832 aspect ratio
        target_w, target_h = MIN_DIM, MAX_DIM
        crop_height = int(round(width / MIN_ASPECT_RATIO))
        top = (height - crop_height) // 2
        image_to_resize = image.crop((0, top, width, top + crop_height))
    else:
        if width > height:  # Landscape
            target_w = MAX_DIM
            target_h = int(round(target_w / aspect_ratio))
        else:  # Portrait
            target_h = MAX_DIM
            target_w = int(round(target_h * aspect_ratio))

    final_w = round(target_w / MULTIPLE_OF) * MULTIPLE_OF
    final_h = round(target_h / MULTIPLE_OF) * MULTIPLE_OF

    final_w = max(MIN_DIM, min(MAX_DIM, final_w))
    final_h = max(MIN_DIM, min(MAX_DIM, final_h))

    return image_to_resize.resize((final_w, final_h), Image.LANCZOS)


HF_MODEL = os.environ.get("HF_UPLOAD_REPO", "rahul7star/wan22-aot-image-2025-dec")


# --- CPU-only upload function ---
def upload_image_and_prompt_cpu(input_image, prompt_text) -> str:
    from datetime import datetime
    import tempfile, os, uuid, shutil
    from huggingface_hub import HfApi

    # Instantiate the HfApi class
    api = HfApi()

    today_str = datetime.now().strftime("%Y-%m-%d")
    unique_subfolder = f"Upload-Image-{uuid.uuid4().hex[:8]}"
    hf_folder = f"{today_str}/{unique_subfolder}"

    # Save image temporarily
    with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp_img:
        if isinstance(input_image, str):
            shutil.copy(input_image, tmp_img.name)
        else:
            input_image.save(tmp_img.name, format="PNG")
        tmp_img_path = tmp_img.name

    # Upload image using HfApi instance
    api.upload_file(
        path_or_fileobj=tmp_img_path,
        path_in_repo=f"{hf_folder}/input_image.png",
        repo_id=HF_MODEL,
        repo_type="model",
        token=os.environ.get("HUGGINGFACE_HUB_TOKEN")
    )

    # Save prompt as summary.txt
    summary_file = tempfile.NamedTemporaryFile(delete=False, suffix=".txt").name
    with open(summary_file, "w", encoding="utf-8") as f:
        f.write(prompt_text)

    api.upload_file(
        path_or_fileobj=summary_file,
        path_in_repo=f"{hf_folder}/summary.txt",
        repo_id=HF_MODEL,
        repo_type="model",
        token=os.environ.get("HUGGINGFACE_HUB_TOKEN")
    )

    # Cleanup
    os.remove(tmp_img_path)
    os.remove(summary_file)

    return hf_folder

def get_num_frames(duration_seconds: float):
    return 1 + int(np.clip(
        int(round(duration_seconds * FIXED_FPS)),
        MIN_FRAMES_MODEL,
        MAX_FRAMES_MODEL,
    ))


# --- Wrapper to upload image/prompt on CPU before GPU generation ---
def generate_video_with_upload(input_image, prompt, steps=4, negative_prompt=default_negative_prompt,
                               duration_seconds=2, guidance_scale=1, guidance_scale_2=1,
                               seed=44, randomize_seed=False):
    # Upload on CPU (hidden, no UI)
    try:
        upload_image_and_prompt_cpu(input_image, prompt)
    except Exception as e:
        print("Upload failed:", e)

    # Proceed with GPU video generation
    return generate_video(input_image, prompt, steps,
                          negative_prompt, duration_seconds,
                          guidance_scale, guidance_scale_2, seed, randomize_seed)

# def get_duration(
#     input_image,
#     prompt,
#     steps,
#     negative_prompt,
#     duration_seconds,
#     guidance_scale,
#     guidance_scale_2,
#     seed,
#     randomize_seed,
#     progress,
# ):
#     BASE_FRAMES_HEIGHT_WIDTH = 81 * 832 * 624
#     BASE_STEP_DURATION = 15
#     width, height = resize_image(input_image).size
#     frames = get_num_frames(duration_seconds)
#     factor = frames * width * height / BASE_FRAMES_HEIGHT_WIDTH
#     step_duration = BASE_STEP_DURATION * factor ** 1.5
#     return 10 + int(steps) * step_duration



@spaces.GPU(duration=120)
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),
):
    """
    Generate a video from an input image using the Wan 2.2 14B I2V model with Lightning 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 Lightning LoRA
    for fast generation in 4-8 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.
        steps (int, optional): Number of inference steps. More steps = higher quality but slower.
            Defaults to 4. Range: 1-30.
        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.
        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:
        - 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 = get_num_frames(duration_seconds)
    current_seed = random.randint(0, MAX_SEED) if randomize_seed else int(seed)
    resized_image = resize_image(input_image)
    print("pompt is")
    print(prompt)
    if "child" in prompt.lower():
        print("Found 'child' in prompt. Exiting loop.")
        return

    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)

    if check_ffmpeg():
        try:
            # Создаем временный файл для видео с звуком
            with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as audio_tmpfile:
                video_with_audio_path = audio_tmpfile.name
            
            # Команда ffmpeg для добавления тихого аудио
            cmd = [
                'ffmpeg',
                '-f', 'lavfi', 
                '-i', 'anullsrc=channel_layout=stereo:sample_rate=44100',
                '-i', video_path,
                '-c:v', 'copy',
                '-c:a', 'aac',
                '-shortest',
                '-y',
                video_with_audio_path
            ]
            
            # Запускаем ffmpeg
            subprocess.run(cmd, capture_output=True, check=True)

            # Создаем временный файл для заблюренного видео
            with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as blur_tmpfile:
                blurred_video_path = blur_tmpfile.name
            
            # Команда ffmpeg для создания гауссова размытия
            cmd_blur = [
                'ffmpeg',
                '-i', video_with_audio_path,  
                '-vf', 'gblur=sigma=40',       
                '-c:a', 'copy',               
                '-y',
                blurred_video_path
            ]
            
            # Запускаем ffmpeg для создания блюра
            subprocess.run(cmd_blur, capture_output=True, check=True)
            
            # Удаляем исходный файл без звука
            os.unlink(video_path)
            
            return video_with_audio_path, blurred_video_path, current_seed
            
        except Exception as e:
            print(f"Error adding audio: {e}")
            # В случае ошибки возвращаем видео без звука
            return video_path, video_path, current_seed
    else:
        print("FFmpeg not available, returning video without audio")
        return video_path, video_path, current_seed

with gr.Blocks() as demo:
    gr.Markdown("# Wan22 AOT")
    #gr.Markdown("run Wan 2.2 in just 4-8 steps, with [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():
            input_image_component = gr.Image(type="pil", label="Input Image")
            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=3.5, 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=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_1 = gr.Video(label="Generated Video", autoplay=True, interactive=False)
            video_output_2 = gr.Video(label="Generated Video", autoplay=True, interactive=False)
  
    #upload_image_and_prompt(input_image_component, prompt_input)
    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_with_upload, inputs=ui_inputs, outputs=[video_output_1, video_output_2, seed_input])

def check_ffmpeg():
    try:
        subprocess.run(['ffmpeg', '-version'], capture_output=True, check=True)
        return True
    except (subprocess.CalledProcessError, FileNotFoundError):
        return False

if __name__ == "__main__":
    demo.queue().launch(ssl_verify=False)