Upload app_lora.py

app_lora.py

@@ -0,0 +1,374 @@
+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)
+
+
+HF_MODEL = os.environ.get("HF_UPLOAD_REPO", "rahul7star/wan22-aot-image")
+def upload_image_and_prompt(input_image, prompt_text) -> str:
+    """
+    Upload an image and a prompt text to Hugging Face Hub in a date-based folder.
+    
+    Args:
+        input_image (PIL.Image.Image or path-like): The image to upload.
+        prompt_text (str): Text prompt or summary associated with the image.
+    Returns:
+        str: Hugging Face folder path where the image and prompt were uploaded.
+    """
+    import tempfile
+    import os
+    import uuid
+    from datetime import datetime
+    from huggingface_hub import upload_file
+
+    # Create a date-based folder on HF
+    today_str = datetime.now().strftime("%Y-%m-%d")
+    unique_subfolder = f"Upload-Image-{uuid.uuid4().hex[:8]}"
+    hf_folder = f"{today_str}/{unique_subfolder}"
+
+    # Save the image temporarily
+    with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp_img:
+        if isinstance(input_image, str):
+            # If path provided, just copy
+            import shutil
+            shutil.copy(input_image, tmp_img.name)
+        else:
+            # PIL.Image.Image
+            input_image.save(tmp_img.name, format="PNG")
+        tmp_img_path = tmp_img.name
+
+    # Upload image
+    image_filename = "input_image.png"
+    image_hf_path = f"{hf_folder}/{image_filename}"
+    upload_file(
+        path_or_fileobj=tmp_img_path,
+        path_in_repo=image_hf_path,
+        repo_id=HF_MODEL,
+        repo_type="model",
+        token=os.environ.get("HUGGINGFACE_HUB_TOKEN"),
+    )
+
+    # Upload prompt as summary.txt
+    summary_file = tempfile.NamedTemporaryFile(delete=False, suffix=".txt").name
+    with open(summary_file, "w", encoding="utf-8") as f:
+        f.write(prompt_text)
+    summary_hf_path = f"{hf_folder}/summary.txt"
+    upload_file(
+        path_or_fileobj=summary_file,
+        path_in_repo=summary_hf_path,
+        repo_id=HF_MODEL,
+        repo_type="model",
+        token=os.environ.get("HUGGINGFACE_HUB_TOKEN"),
+    )
+
+    # Cleanup
+    os.remove(tmp_img_path)
+    os.remove(summary_file)
+
+    return hf_folder
+
+
+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
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Wan22 AOT")
+    #gr.Markdown("run Wan 2.2 in just 4-8 steps, with [Lightning LoRA](https://huggingface.co/Kijai/WanVideo_comfy/tree/main/Wan22-Lightning), 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")
+            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"Clamped to model's {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, interactive=True)
+                randomize_seed_checkbox = gr.Checkbox(label="Randomize seed", value=True, interactive=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, interactive=False)
+  
+    #upload_image_and_prompt(input_image_component, prompt_input)
+    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])
+
+if __name__ == "__main__":
+    demo.queue().launch(mcp_server=True)