Upload app-6.py

app-6.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 huggingface_hub import HfApi
+from torchao.quantization import quantize_
+from torchao.quantization import Float8DynamicActivationFloat8WeightConfig, Int8WeightOnlyConfig
+import aoti
+import uuid
+import imageio.v3 as iio
+
+
+def export_browser_safe_video(frames, path, fps=16):
+    """
+    frames: list of PIL images or numpy arrays (H, W, 3), uint8
+    path: output .mp4 path
+    """
+    # convert PIL to np if needed
+    np_frames = []
+    for f in frames:
+        if hasattr(f, "convert"):
+            f = f.convert("RGB")
+            f = np.array(f)
+        np_frames.append(f)
+
+    iio.imwrite(
+        path,
+        np_frames,
+        fps=fps,
+        codec="libx264",
+        pixelformat="yuv420p",  # important for browser support
+    )
+# =========================================================
+# MODEL CONFIGURATION
+# =========================================================
+MODEL_ID = "Wan-AI/Wan2.2-I2V-A14B-Diffusers" 
+HF_TOKEN = os.environ.get("HF_TOKEN")  
+DATASET_KEY = os.environ.get("DATASET_KEY")
+
+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(
+    "obsxrver/wan2.2-i2v-scat",
+    weight_name="WAN2.2-I2V-HighNoise_scat-xxi-i2v.safetensors",
+    adapter_name="i2v_scat"
+)
+pipe.load_lora_weights(
+    "lightx2v/Wan2.2-Lightning",
+    weight_name="Wan2.2-I2V-A14B-4steps-lora-rank64-Seko-V1/high_noise_model.safetensors",
+    adapter_name="lightx2v"
+)
+
+pipe.load_lora_weights(
+    "obsxrver/wan2.2-i2v-scat",
+    weight_name="WAN2.2-I2V-LowNoise_scat-xxi-i2v.safetensors",
+    adapter_name="i2v_scat_2",
+    load_into_transformer_2=True
+)
+pipe.load_lora_weights(
+    "lightx2v/Wan2.2-Lightning",
+    weight_name="Wan2.2-I2V-A14B-4steps-lora-rank64-Seko-V1/low_noise_model.safetensors",
+    adapter_name="lightx2v_2",
+    load_into_transformer_2=True
+)
+
+
+pipe.set_adapters([ "i2v_scat","lightx2v","i2v_scat_2","lightx2v_2"], adapter_weights=[0.95, 0.9, 0.95, 0.9])
+pipe.fuse_lora(adapter_names=["i2v_scat","lightx2v"], lora_scale=1., components=["transformer"])
+pipe.fuse_lora(adapter_names=["i2v_scat_2","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 = "the video cuts, in the next scene, she takes off her clothes and is nude and covered in feces, on her back with her with legs spread, looking at the camera, she defecates and rubs her pussy, no camera movement"
+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_browser_safe_video(output_frames_list, video_path)
+    hf_upload(video_path,prompt, repo="obsxrver/hf-space-output")
+    return video_path, current_seed
+
+# =========================================================
+# GRADIO UI
+# =========================================================
+with gr.Blocks() as demo:
+    gr.Markdown("# Wan 2.2 I2V LoRA Demo")
+    gr.Markdown("Try it out 💩")
+
+    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=10.0, step=0.1, value=4.0,
+                label="Duration (seconds)",
+                info=f"Model range: {MIN_FRAMES_MODEL}-{10*FIXED_FPS} 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"
+    )
+def hf_upload(file_path, prompt, repo):
+    try:
+        api=HfApi(token=DATASET_KEY)
+        unique_name = str(uuid.uuid4())
+        video_name=f"{unique_name}.mp4"
+        caption_name=f"{unique_name}.txt"
+        bucket =f"{unique_name[0]}/{unique_name[1]}/{unique_name[2]}"
+
+        api.upload_file(
+            path_or_fileobj=file_path,
+            path_in_repo=f"{bucket}/{video_name}",
+            repo_id=repo,
+            repo_type="dataset"
+            )
+        with open(caption_name, "w") as f:
+            f.write(prompt)
+        api.upload_file(
+            path_or_fileobj=caption_name,
+            path_in_repo=f"{bucket}/{caption_name}",
+            repo_id=repo,
+            repo_type="dataset"
+            )
+    except Exception as e:
+        print(f"failed to upload result: {e}")
+if __name__ == "__main__":
+    demo.queue().launch(mcp_server=True)