app1.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"')
from huggingface_hub import HfApi, upload_file
import uuid
import subprocess
import tempfile
import logging
import shutil
from datetime import datetime

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 numpy as np
from PIL import Image
import random
import gc
from optimization import optimize_pipeline_
from huggingface_hub import hf_hub_download


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

LORA_REPO_ID = "rahul7star/wan2.2Lora"
LORA_SETS = {
    "NF": {
        "high_noise": {"file": "NSFW-22-H-e8.safetensors", "adapter_name": "nf_high"},
        "low_noise": {"file": "NSFW-22-L-e8.safetensors", "adapter_name": "nf_low"}
    },
    "BP": {
        "high_noise": {"file": "Wan2.2_BP-v1-HighNoise-I2V_T2V.safetensors", "adapter_name": "bp_high"},
        "low_noise": {"file": "Wan2.2_BP-v1-LowNoise-I2V_T2V.safetensors", "adapter_name": "bp_low"}
    },
    "Py-v1": {
        "high_noise": {"file": "wan2.2_i2v_highnoise_pov_missionary_v1.0.safetensors", "adapter_name": "py_high"},
        "low_noise": {"file": "wan2.2_i2v_lownoise_pov_missionary_v1.0.safetensors", "adapter_name": "py_low"}
    }
}

LANDSCAPE_WIDTH = 832
LANDSCAPE_HEIGHT = 576
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)


# ---------------- 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')

# Optimize once for AoT
optimize_pipeline_(
    pipe,
    image=Image.new('RGB', (LANDSCAPE_WIDTH, LANDSCAPE_HEIGHT)),
    prompt='prompt',
    height=LANDSCAPE_HEIGHT,
    width=LANDSCAPE_WIDTH,
    num_frames=MAX_FRAMES_MODEL,
)

# ---------------- Load LoRA Weights -----------------
for name, lora_set in LORA_SETS.items():
    print(f"--- LoRA 集合: {name} ---")

    high_noise_config = lora_set["high_noise"]
    print(f"High Noise: {high_noise_config['file']}...")
    pipe.load_lora_weights(
        LORA_REPO_ID,
        weight_name=high_noise_config['file'],
        adapter_name=high_noise_config['adapter_name']
    )
    print("High Noise LoRA 加载完成。")

    low_noise_config = lora_set["low_noise"]
    print(f"Low Noise: {low_noise_config['file']}...")
    pipe.load_lora_weights(
        LORA_REPO_ID,
        weight_name=low_noise_config['file'],
        adapter_name=low_noise_config['adapter_name']
    )
    print("Low Noise LoRA 加载完成。")

# Fuse once globally
try:
    pipe.fuse_lora()
    print("✅ 全局 Fuse LoRA 成功")
except Exception as e:
    print(f"⚠️ Fuse LoRA 失败: {e}")

# Clean GPU
for i in range(3):
    gc.collect()
    torch.cuda.synchronize()
    torch.cuda.empty_cache()

# ---------------- Defaults -----------------
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"
)


# ---------------- Utils -----------------
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,
    selected_loras,
    progress,
):
    return int(steps) * 15


# ---------------- LoRA Switcher -----------------
class LoraSwitcher:
    def __init__(self, selected_lora_names, switch_step):
        self.switched = False
        self.high_noise_adapters = []
        self.low_noise_adapters = []
        self.switch_step = switch_step

        if selected_lora_names:
            for name in selected_lora_names:
                if name in LORA_SETS:
                    self.high_noise_adapters.append(LORA_SETS[name]["high_noise"]["adapter_name"])
                    self.low_noise_adapters.append(LORA_SETS[name]["low_noise"]["adapter_name"])

    def __call__(self, pipe, step_index, timestep, callback_kwargs):
        if step_index == 0:
            self.switched = False
            if self.high_noise_adapters:
                print(f"激活 High Noise LoRA: {self.high_noise_adapters}")
                pipe.set_adapters(self.high_noise_adapters, adapter_weights=[1.0]*len(self.high_noise_adapters))
                try:
                    pipe.fuse_lora()
                    print("Fuse High Noise LoRA ✅")
                except Exception as e:
                    print(f"Fuse High Noise LoRA 失败: {e}")
            elif pipe.get_active_adapters():
                active = pipe.get_active_adapters()
                print(f"禁用残留的 LoRA: {active}")
                pipe.set_adapters(active, adapter_weights=[0.0]*len(active))

        if self.low_noise_adapters and step_index >= self.switch_step and not self.switched:
            print(f"在第 {step_index} 步切换到 Low Noise LoRA: {self.low_noise_adapters}")
            pipe.set_adapters(self.low_noise_adapters, adapter_weights=[1.0]*len(self.low_noise_adapters))
            try:
                pipe.fuse_lora()
                print("Fuse Low Noise LoRA ✅")
            except Exception as e:
                print(f"Fuse Low Noise LoRA 失败: {e}")
            self.switched = True

        return callback_kwargs


# ---------------- Main Generation -----------------
@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,
    selected_loras=[],
    progress=gr.Progress(track_tqdm=True),
):
    if input_image is None:
        raise gr.Error("Please upload an input image.")
    print("Prompt is:", prompt)

    # Reset fused LoRA before new run
    try:
        pipe.unfuse_lora()
        print("🔄 Reset unfuse_lora before generation")
    except Exception:
        pass

    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)

    num_inference_steps = int(steps)
    switch_step = num_inference_steps // 2

    lora_switcher_callback = LoraSwitcher(selected_loras, switch_step)

    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=num_inference_steps,
        generator=torch.Generator(device="cuda").manual_seed(current_seed),
        callback_on_step_end=lora_switcher_callback,
    ).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


# ---------------- 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 - compatible with 🧨 diffusers and ZeroGPU⚡️")

    with gr.Row():
        with gr.Column():
            input_image_component = gr.Image(type="pil", label="Input Image (auto-resized)", interactive=True)
            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)")
            lora_selection_checkbox = gr.CheckboxGroup(choices=list(LORA_SETS.keys()), label="选择要应用的 LoRA (可多选)")

            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, lora_selection_checkbox
    ]
    generate_button.click(fn=generate_video, inputs=ui_inputs, outputs=[video_output, seed_input])

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