app_lora_latent.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


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 = 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')

# 加载并融合你的LoRA模型
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 = {}
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
)



# 新增：加载你提供的high noise LoRA

pipe.load_lora_weights(
    "rahul7star/wan2.2Lora",
    weight_name="DR34ML4Y_I2V_14B_HIGH.safetensors",
    adapter_name="high_noise_lora",
    token=os.environ.get("HF_TOKEN")
)
# 新增：加载你提供的low noise LoRA
pipe.load_lora_weights(
    "rahul7star/wan2.2Lora",
    weight_name="DR34ML4Y_I2V_14B_LOW.safetensors",
    adapter_name="low_noise_lora",
    token=os.environ.get("HF_TOKEN"),
    load_into_transformer_2=True
)


## thi s still gpood 
# pipe.load_lora_weights(
#     "rahul7star/wan2.2Lora",
#     weight_name="wan2.2_i2v_highnoise_pov_missionary_v1.0.safetensors",
#     adapter_name="high_noise_lora",
#     token=os.environ.get("HF_TOKEN")
# )
# # 新增：加载你提供的low noise LoRA
# pipe.load_lora_weights(
#     "rahul7star/wan2.2Lora",
#     weight_name="wan2.2_i2v_lownoise_pov_missionary_v1.0.safetensors",
#     adapter_name="low_noise_lora",
#     token=os.environ.get("HF_TOKEN"),
#     load_into_transformer_2=True
# )

pipe.set_adapters(["lightx2v", "lightx2v_2", "high_noise_lora", "low_noise_lora"], adapter_weights=[1., 1., 1., 1.])
# 修改了lora_scale
pipe.fuse_lora(adapter_names=["lightx2v", "high_noise_lora"], lora_scales=[3.0, 3.0], components=["transformer"])
# 修改了lora_scale
pipe.fuse_lora(adapter_names=["lightx2v_2", "low_noise_lora"], lora_scales=[1.0, 1.0], 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)


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,
):
    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=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),
):
    """
    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)

    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


import io
from PIL import Image

import gradio as gr
import torch
import tempfile
import random
from PIL import Image

# --- Your existing functions ---
# resize_image, get_num_frames, export_to_video, pipe, get_duration, MAX_SEED, etc.

@spaces.GPU(duration=get_duration)

def generate_video_with_latent_preview(
    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)

    # Run inference step by step
    output = 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),
        output_type="np",   # request numpy frames
    )

    frames = output.frames[0]   # shape: (N, H, W, 3)

    pil_frames = []
    for i, f in enumerate(frames):
        # --- convert float frame safely ---
        if f.dtype != np.uint8:
            f = np.clip(f * 255, 0, 255).astype(np.uint8)
        frame_pil = Image.fromarray(f)
        pil_frames.append(frame_pil)

        # Create a temporary GIF (latent preview so far)
        with tempfile.NamedTemporaryFile(suffix=".gif", delete=False) as tmp_gif:
            gif_path = tmp_gif.name
        imageio.mimsave(gif_path, pil_frames, fps=4)

        # Yield partial preview while generating
        yield gif_path, None   # update preview box, keep video empty for now

    # --- Final video export ---
    with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as tmpfile:
        video_path = tmpfile.name
    export_to_video(pil_frames, video_path, fps=FIXED_FPS)

    yield None, (video_path, current_seed)


# --- Gradio UI ---
with gr.Blocks() as demo:
    gr.Markdown("# wan22-AOT")
    gr.Markdown("Upload an image, set parameters, and generate video + latent preview.")

    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)")

            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 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)
            latent_preview_output = gr.Image(label="Latent Frame Preview (GIF)", type="filepath")

    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_latent_preview,
        inputs=ui_inputs,
        outputs=[video_output, latent_preview_output, seed_input]
    )
    gr.Examples(
        examples=[
            [
                "wan_i2v_input.JPG",  # image
                "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.",  # prompt
                4,  # steps
                default_negative_prompt,  # negative_prompt
                2,  # duration_seconds
                1.0,  # guidance_scale
                1.0,  # guidance_scale_2
                42,   # seed
                True  # randomize_seed
            ],
            [
                "wan22_input_2.jpg",
                "A sleek lunar vehicle glides into view from left to right, kicking up moon dust as astronauts in white spacesuits hop aboard with characteristic lunar bouncing movements. In the distant background, a VTOL craft descends straight down and lands silently on the surface. Throughout the entire scene, ethereal aurora borealis ribbons dance across the star-filled sky, casting shimmering curtains of green, blue, and purple light that bathe the lunar landscape in an otherworldly, magical glow.",
                4,
                default_negative_prompt,
                2,
                1.0,
                1.0,
                123,
                False
            ],
            [
                "kill_bill.jpeg",
                "Uma Thurman's character, Beatrix Kiddo, holds her razor-sharp katana blade steady in the cinematic lighting. Suddenly, the polished steel begins to soften and distort, like heated metal starting to lose its structural integrity. The blade's perfect edge slowly warps and droops, molten steel beginning to flow downward in silvery rivulets while maintaining its metallic sheen. The transformation starts subtly at first - a slight bend in the blade - then accelerates as the metal becomes increasingly fluid. The camera holds steady on her face as her piercing eyes gradually narrow, not with lethal focus, but with confusion and growing alarm as she watches her weapon dissolve before her eyes. Her breathing quickens slightly as she witnesses this impossible transformation. The melting intensifies, the katana's perfect form becoming increasingly abstract, dripping like liquid mercury from her grip. Molten droplets fall to the ground with soft metallic impacts. Her expression shifts from calm readiness to bewilderment and concern as her legendary instrument of vengeance literally liquefies in her hands, leaving her defenseless and disoriented.",
                6,
                default_negative_prompt,
                4.0,
                1.0,
                1.0,
                99,
                True
            ],
        ],
        inputs=ui_inputs,  # must match your input widgets exactly
        outputs=[video_output, latent_preview_output, seed_input],  # show both outputs
        fn=generate_video_with_latent_preview,  # run immediately
        cache_examples="lazy"
    )








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