Deploy Gradio app with multiple files

aoti.py

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+"""
+"""
+
+from typing import cast
+
+import torch
+from huggingface_hub import hf_hub_download
+from spaces.zero.torch.aoti import ZeroGPUCompiledModel
+from spaces.zero.torch.aoti import ZeroGPUWeights
+from torch._functorch._aot_autograd.subclass_parametrization import unwrap_tensor_subclass_parameters
+
+
+def _shallow_clone_module(module: torch.nn.Module) -> torch.nn.Module:
+    clone = object.__new__(module.__class__)
+    clone.__dict__ = module.__dict__.copy()
+    clone._parameters = module._parameters.copy()
+    clone._buffers = module._buffers.copy()
+    clone._modules = {k: _shallow_clone_module(v) for k, v in module._modules.items() if v is not None}
+    return clone
+
+
+def aoti_blocks_load(module: torch.nn.Module, repo_id: str, variant: str | None = None):
+    repeated_blocks = cast(list[str], module._repeated_blocks)
+    aoti_files = {name: hf_hub_download(
+        repo_id=repo_id,
+        filename='package.pt2',
+        subfolder=name if variant is None else f'{name}.{variant}',
+    ) for name in repeated_blocks}
+    for block_name, aoti_file in aoti_files.items():
+        for block in module.modules():
+            if block.__class__.__name__ == block_name:
+                block_ = _shallow_clone_module(block)
+                unwrap_tensor_subclass_parameters(block_)
+                weights = ZeroGPUWeights(block_.state_dict())
+                block.forward = ZeroGPUCompiledModel(aoti_file, weights)

app.py

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+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"  # المسار الجديد للنموذج
+HF_TOKEN = os.environ.get("HF_TOKEN")  # ضع توكن Hugging Face هنا إذا كان النموذج خاصًا
+
+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
+# =========================================================
+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
+# =========================================================
+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
+# =========================================================
+quantize_(pipe.text_encoder, Int8WeightOnlyConfig())
+quantize_(pipe.transformer, Float8DynamicActivationFloat8WeightConfig())
+quantize_(pipe.transformer_2, Float8DynamicActivationFloat8WeightConfig())
+
+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, 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"
+)
+
+# =========================================================
+# 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,
+):
+    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() as demo:
+    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="lazy"
+    )
+
+if __name__ == "__main__":
+    demo.queue().launch(mcp_server=True)

requirements.txt

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+git+https://github.com/linoytsaban/diffusers.git@wan22-loras
+transformers
+accelerate
+safetensors
+sentencepiece
+peft
+ftfy
+imageio-ffmpeg
+opencv-python
+torchao==0.11.0