app.py

import os
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 torchao.quantization import quantize_
from torchao.quantization import Float8DynamicActivationFloat8WeightConfig, Int8WeightOnlyConfig
import aoti

# =========================================================
# MODEL CONFIGURATION
# =========================================================
MODEL_ID = "Wan-AI/Wan2.2-I2V-A14B-Diffusers"
HF_TOKEN = os.environ.get("HF_TOKEN")

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 = 7720

MIN_DURATION = round(MIN_FRAMES_MODEL / FIXED_FPS, 1)
MAX_DURATION = round(MAX_FRAMES_MODEL / FIXED_FPS, 1)

# =========================================================
# LOAD PIPELINE
# =========================================================
print("Loading pipeline...")
pipe = WanImageToVideoPipeline.from_pretrained(
    MODEL_ID,
    transformer=WanTransformer3DModel.from_pretrained(
        MODEL_ID,
        subfolder="transformer",
        torch_dtype=torch.bfloat16,
        device_map="cuda",
        token=HF_TOKEN
    ),
    transformer_2=WanTransformer3DModel.from_pretrained(
        MODEL_ID,
        subfolder="transformer_2",
        torch_dtype=torch.bfloat16,
        device_map="cuda",
        token=HF_TOKEN
    ),
    torch_dtype=torch.bfloat16,
).to("cuda")

# =========================================================
# LOAD LORA ADAPTERS
# =========================================================
print("Loading LoRA adapters...")
pipe.load_lora_weights(
    "Kijai/WanVideo_comfy",
    weight_name="Lightx2v/lightx2v_I2V_14B_480p_cfg_step_distill_rank128_bf16.safetensors",
    adapter_name="lightx2v"
)
pipe.load_lora_weights(
    "Kijai/WanVideo_comfy",
    weight_name="Lightx2v/lightx2v_I2V_14B_480p_cfg_step_distill_rank128_bf16.safetensors",
    adapter_name="lightx2v_2",
    load_into_transformer_2=True
)

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

# =========================================================
# QUANTIZATION & AOT OPTIMIZATION
# =========================================================
print("Applying quantization...")
quantize_(pipe.text_encoder, Int8WeightOnlyConfig())
quantize_(pipe.transformer, Float8DynamicActivationFloat8WeightConfig())
quantize_(pipe.transformer_2, Float8DynamicActivationFloat8WeightConfig())

print("Loading AOTI blocks...")
aoti.aoti_blocks_load(pipe.transformer, 'zerogpu-aoti/Wan2', variant='fp8da')
aoti.aoti_blocks_load(pipe.transformer_2, 'zerogpu-aoti/Wan2', variant='fp8da')

# =========================================================
# DEFAULT PROMPTS
# =========================================================
default_prompt_i2v = "Make this image come alive with dynamic, cinematic human motion. Create smooth, natural, lifelike animation with fluid transitions, expressive body movement, realistic physics, and elegant camera flow. Deliver a polished, high-quality motion style that feels immersive, artistic, and visually captivating."
default_negative_prompt = (
    "low quality, worst quality, motion artifacts, unstable motion, jitter, frame jitter, wobbling limbs, motion distortion, inconsistent movement, robotic movement, animation-like motion, awkward transitions, incorrect body mechanics, unnatural posing, off-balance poses, broken motion paths, frozen frames, duplicated frames, frame skipping, warped motion, stretching artifacts bad anatomy, incorrect proportions, deformed body, twisted torso, broken joints, dislocated limbs, distorted neck, unnatural spine curvature, malformed hands, extra fingers, missing fingers, fused fingers, distorted legs, extra limbs, collapsed feet, floating feet, foot sliding, foot jitter, backward walking, unnatural gait blurry details, long exposure blur, ghosting, shadow trails, smearing, washed-out colors, overexposure, underexposure, excessive contrast, blown highlights, poorly rendered clothing, fabric glitches, texture warping, clothing merging with body, incorrect cloth physics ugly background, cluttered scene, crowded background, random objects, unwanted text, subtitles, logos, graffiti, grain, noise, static artifacts, compression noise, jpeg artifacts, image-like stillness, painting-like look, cartoon texture, low-resolution textures"
)

# =========================================================
# IMAGE RESIZING LOGIC
# =========================================================
def resize_image(image: Image.Image) -> Image.Image:
    width, height = image.size
    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:
        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:
        crop_height = int(round(width / MIN_ASPECT_RATIO))
        top = (height - crop_height) // 2
        image_to_resize = image.crop((0, top, width, top + crop_height))

    if width > height:
        target_w = MAX_DIM
        target_h = int(round(target_w / aspect_ratio))
    else:
        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)

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

def get_duration(
    input_image, prompt, steps, negative_prompt,
    duration_seconds, guidance_scale, guidance_scale_2,
    seed, randomize_seed, progress,
):
    # --- CRITICAL FIX: Handle NoneType for input_image ---
    if input_image is None:
        return 120 # Return default duration if image is missing to prevent crash
    # -----------------------------------------------------

    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

# =========================================================
# MAIN 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 = get_num_frames(duration_seconds)
    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

# =========================================================
# GRADIO UI
# =========================================================
with gr.Blocks(theme=gr.themes.Soft()) as demo:
    
    # --- ADVERTISEMENT BANNER FOR DREAM HUB PRO ---
    gr.HTML("""
    <div style="background: linear-gradient(90deg, #4f46e5, #9333ea); color: white; padding: 15px; border-radius: 10px; text-align: center; margin-bottom: 20px; box-shadow: 0 4px 15px rgba(0,0,0,0.1);">
        <div style="display: flex; align-items: center; justify-content: center; gap: 20px; flex-wrap: wrap;">
            <div style="text-align: left;">
                <h3 style="margin: 0; font-weight: bold; font-size: 18px;">✨ New: Dream Hub Pro (All-in-One)</h3>
                <p style="margin: 5px 0 0 0; opacity: 0.9; font-size: 14px;">Access all your pro tools (Wan2.1, Qwen, Audio, Video Enhance) in one place!</p>
            </div>
            <a href="https://huggingface.co/spaces/dream2589632147/Dream-Hub-Pro" target="_blank" style="text-decoration: none;">
                <button style="background-color: white; color: #4f46e5; border: none; padding: 10px 25px; border-radius: 25px; font-weight: bold; cursor: pointer; transition: all 0.2s; font-size: 15px; box-shadow: 0 2px 5px rgba(0,0,0,0.2);">
                    🚀 Open Hub Pro Now
                </button>
            </a>
        </div>
    </div>
    """)
    # ---------------------------------------------

    gr.Markdown("# 🚀 Dream Wan 2.2 Faster Pro (14B) — Ultra Fast I2V with Lightning LoRA")
    gr.Markdown("Optimized FP8 quantized pipeline with AoT blocks & 4-step fast inference ⚡")

    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"Model range: {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)
                randomize_seed_checkbox = gr.Checkbox(label="Randomize seed", value=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)")
                guidance_scale_2_input = gr.Slider(minimum=0.0, maximum=10.0, step=0.5, value=1, label="Guidance Scale 2 (low noise)")

            generate_button = gr.Button("🎬 Generate Video", variant="primary")

        with gr.Column():
            video_output = gr.Video(label="Generated Video", autoplay=True)

    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])

    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.",
                4,
            ],
        ],
        inputs=[input_image_component, prompt_input, steps_slider],
        outputs=[video_output, seed_input],
        fn=generate_video,
        cache_examples=False # Disabled caching to prevent startup errors
    )

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