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.gitignore

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+*.pyc

README.md

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----
-title: Qwen
-emoji: 🔥
-colorFrom: gray
-colorTo: green
-sdk: gradio
-sdk_version: 6.1.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Qwen Image Edit Outpaint
+emoji: 🌖
+colorFrom: pink
+colorTo: gray
+sdk: gradio
+sdk_version: 5.43.1
+app_file: app.py
+pinned: false
+short_description: 'outpaint images with Qwen Image Edit '
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -1,742 +1,742 @@
-import gradio as gr
-import numpy as np
-import random
-import torch
-import spaces
-import os
-import json
-import time
-
-from PIL import Image, ImageDraw
-import torch 
-import math
-
-from optimization import optimize_pipeline_
-from qwenimage.pipeline_qwen_image_edit import QwenImageEditPipeline
-from qwenimage.transformer_qwenimage import QwenImageTransformer2DModel
-from qwenimage.qwen_fa3_processor import QwenDoubleStreamAttnProcessorFA3
-
-from huggingface_hub import InferenceClient
-import math
-
-# --- Prompt Enhancement using Hugging Face InferenceClient ---
-def polish_prompt_hf(original_prompt, system_prompt):
-    """
-    Rewrites the prompt using a Hugging Face InferenceClient.
-    """
-    # Ensure HF_TOKEN is set
-    api_key = os.environ.get("HF_TOKEN")
-    if not api_key:
-        print("Warning: HF_TOKEN not set. Falling back to original prompt.")
-        return original_prompt
-
-    try:
-        # Initialize the client
-        client = InferenceClient(
-            provider="cerebras",
-            api_key=api_key,
-        )
-
-        # Format the messages for the chat completions API
-        messages = [
-            {"role": "system", "content": system_prompt},
-            {"role": "user", "content": original_prompt}
-        ]
-
-        # Call the API
-        completion = client.chat.completions.create(
-            model="Qwen/Qwen3-235B-A22B-Instruct-2507",
-            messages=messages,
-        )
-        
-        # Parse the response
-        result = completion.choices[0].message.content
-        
-        # Try to extract JSON if present
-        if '{"Rewritten"' in result:
-            try:
-                # Clean up the response
-                result = result.replace('```json', '').replace('```', '')
-                result_json = json.loads(result)
-                polished_prompt = result_json.get('Rewritten', result)
-            except:
-                polished_prompt = result
-        else:
-            polished_prompt = result
-            
-        polished_prompt = polished_prompt.strip().replace("\n", " ")
-        return polished_prompt
-        
-    except Exception as e:
-        print(f"Error during API call to Hugging Face: {e}")
-        # Fallback to original prompt if enhancement fails
-        return original_prompt
-
-
-def polish_prompt(prompt, img):
-    """
-    Main function to polish prompts for image editing using HF inference.
-    """
-    SYSTEM_PROMPT = '''
-# Edit Instruction Rewriter
-You are a professional edit instruction rewriter. Your task is to generate a precise, concise, and visually achievable professional-level edit instruction based on the user-provided instruction and the image to be edited.  
-
-Please strictly follow the rewriting rules below:
-
-## 1. General Principles
-- Keep the rewritten prompt **concise**. Avoid overly long sentences and reduce unnecessary descriptive language.  
-- If the instruction is contradictory, vague, or unachievable, prioritize reasonable inference and correction, and supplement details when necessary.  
-- Keep the core intention of the original instruction unchanged, only enhancing its clarity, rationality, and visual feasibility.  
-- All added objects or modifications must align with the logic and style of the edited input image's overall scene.  
-
-## 2. Task Type Handling Rules
-### 1. Add, Delete, Replace Tasks
-- If the instruction is clear (already includes task type, target entity, position, quantity, attributes), preserve the original intent and only refine the grammar.  
-- If the description is vague, supplement with minimal but sufficient details (category, color, size, orientation, position, etc.). For example:  
-    > Original: "Add an animal"  
-    > Rewritten: "Add a light-gray cat in the bottom-right corner, sitting and facing the camera"  
-- Remove meaningless instructions: e.g., "Add 0 objects" should be ignored or flagged as invalid.  
-- For replacement tasks, specify "Replace Y with X" and briefly describe the key visual features of X.  
-
-### 2. Text Editing Tasks
-- All text content must be enclosed in English double quotes " ". Do not translate or alter the original language of the text, and do not change the capitalization.  
-- **For text replacement tasks, always use the fixed template:**
-    - Replace "xx" to "yy".  
-    - Replace the xx bounding box to "yy".  
-- If the user does not specify text content, infer and add concise text based on the instruction and the input image's context. For example:  
-    > Original: "Add a line of text" (poster)  
-    > Rewritten: "Add text "LIMITED EDITION" at the top center with slight shadow"  
-- Specify text position, color, and layout in a concise way.  
-
-### 3. Human Editing Tasks
-- Maintain the person's core visual consistency (ethnicity, gender, age, hairstyle, expression, outfit, etc.).  
-- If modifying appearance (e.g., clothes, hairstyle), ensure the new element is consistent with the original style.  
-- **For expression changes, they must be natural and subtle, never exaggerated.**  
-- If deletion is not specifically emphasized, the most important subject in the original image (e.g., a person, an animal) should be preserved.
-    - For background change tasks, emphasize maintaining subject consistency at first.  
-- Example:  
-    > Original: "Change the person's hat"  
-    > Rewritten: "Replace the man's hat with a dark brown beret; keep smile, short hair, and gray jacket unchanged"  
-
-### 4. Style Transformation or Enhancement Tasks
-- If a style is specified, describe it concisely with key visual traits. For example:  
-    > Original: "Disco style"  
-    > Rewritten: "1970s disco: flashing lights, disco ball, mirrored walls, colorful tones"  
-- If the instruction says "use reference style" or "keep current style," analyze the input image, extract main features (color, composition, texture, lighting, art style), and integrate them concisely.  
-- **For coloring tasks, including restoring old photos, always use the fixed template:** "Restore old photograph, remove scratches, reduce noise, enhance details, high resolution, realistic, natural skin tones, clear facial features, no distortion, vintage photo restoration"  
-- If there are other changes, place the style description at the end.
-
-## 3. Rationality and Logic Checks
-- Resolve contradictory instructions: e.g., "Remove all trees but keep all trees" should be logically corrected.  
-- Add missing key information: if position is unspecified, choose a reasonable area based on composition (near subject, empty space, center/edges).  
-
-# Output Format
-Return only the rewritten instruction text directly, without JSON formatting or any other wrapper.
-'''
-    
-    # Note: We're not actually using the image in the HF version, 
-    # but keeping the interface consistent
-    full_prompt = f"{SYSTEM_PROMPT}\n\nUser Input: {prompt}\n\nRewritten Prompt:"
-    
-    return polish_prompt_hf(full_prompt, SYSTEM_PROMPT)
-
-# --- Outpainting Functions ---
-def can_expand(source_width, source_height, target_width, target_height, alignment):
-    """Checks if the image can be expanded based on the alignment."""
-    if alignment in ("Left", "Right") and source_width >= target_width:
-        return False
-    if alignment in ("Top", "Bottom") and source_height >= target_height:
-        return False
-    return True
-
-def prepare_image_and_mask(image, width, height, overlap_percentage, resize_option, custom_resize_percentage, alignment, overlap_left, overlap_right, overlap_top, overlap_bottom):
-    """Prepares the image with white margins and creates a mask for outpainting."""
-    target_size = (width, height)
-    
-    # Calculate the scaling factor to fit the image within the target size
-    scale_factor = min(target_size[0] / image.width, target_size[1] / image.height)
-    new_width = int(image.width * scale_factor)
-    new_height = int(image.height * scale_factor)
-    
-    # Resize the source image to fit within target size
-    source = image.resize((new_width, new_height), Image.LANCZOS)
-    
-    # Apply resize option using percentages
-    if resize_option == "Full":
-        resize_percentage = 100
-    elif resize_option == "50%":
-        resize_percentage = 50
-    elif resize_option == "33%":
-        resize_percentage = 33
-    elif resize_option == "25%":
-        resize_percentage = 25
-    else:  # Custom
-        resize_percentage = custom_resize_percentage
-    
-    # Calculate new dimensions based on percentage
-    resize_factor = resize_percentage / 100
-    new_width = int(source.width * resize_factor)
-    new_height = int(source.height * resize_factor)
-    
-    # Ensure minimum size of 64 pixels
-    new_width = max(new_width, 64)
-    new_height = max(new_height, 64)
-    
-    # Resize the image
-    source = source.resize((new_width, new_height), Image.LANCZOS)
-    
-    # Calculate the overlap in pixels based on the percentage
-    overlap_x = int(new_width * (overlap_percentage / 100))
-    overlap_y = int(new_height * (overlap_percentage / 100))
-    
-    # Ensure minimum overlap of 1 pixel
-    overlap_x = max(overlap_x, 1)
-    overlap_y = max(overlap_y, 1)
-    
-    # Calculate margins based on alignment
-    if alignment == "Middle":
-        margin_x = (target_size[0] - new_width) // 2
-        margin_y = (target_size[1] - new_height) // 2
-    elif alignment == "Left":
-        margin_x = 0
-        margin_y = (target_size[1] - new_height) // 2
-    elif alignment == "Right":
-        margin_x = target_size[0] - new_width
-        margin_y = (target_size[1] - new_height) // 2
-    elif alignment == "Top":
-        margin_x = (target_size[0] - new_width) // 2
-        margin_y = 0
-    elif alignment == "Bottom":
-        margin_x = (target_size[0] - new_width) // 2
-        margin_y = target_size[1] - new_height
-    
-    # Adjust margins to eliminate gaps
-    margin_x = max(0, min(margin_x, target_size[0] - new_width))
-    margin_y = max(0, min(margin_y, target_size[1] - new_height))
-    
-    # Create a new background image with white margins and paste the resized source image
-    background = Image.new('RGB', target_size, (255, 255, 255))
-    background.paste(source, (margin_x, margin_y))
-    
-    # Create the mask
-    mask = Image.new('L', target_size, 255)
-    mask_draw = ImageDraw.Draw(mask)
-    
-    # Calculate overlap areas
-    white_gaps_patch = 2
-    left_overlap = margin_x + overlap_x if overlap_left else margin_x + white_gaps_patch
-    right_overlap = margin_x + new_width - overlap_x if overlap_right else margin_x + new_width - white_gaps_patch
-    top_overlap = margin_y + overlap_y if overlap_top else margin_y + white_gaps_patch
-    bottom_overlap = margin_y + new_height - overlap_y if overlap_bottom else margin_y + new_height - white_gaps_patch
-    
-    if alignment == "Left":
-        left_overlap = margin_x + overlap_x if overlap_left else margin_x
-    elif alignment == "Right":
-        right_overlap = margin_x + new_width - overlap_x if overlap_right else margin_x + new_width
-    elif alignment == "Top":
-        top_overlap = margin_y + overlap_y if overlap_top else margin_y
-    elif alignment == "Bottom":
-        bottom_overlap = margin_y + new_height - overlap_y if overlap_bottom else margin_y + new_height
-    
-    # Draw the mask
-    mask_draw.rectangle([
-        (left_overlap, top_overlap),
-        (right_overlap, bottom_overlap)
-    ], fill=0)
-    
-    return background, mask
-
-def preview_image_and_mask(image, width, height, overlap_percentage, resize_option, custom_resize_percentage, alignment, overlap_left, overlap_right, overlap_top, overlap_bottom):
-    """Creates a preview showing the mask overlay."""
-    background, mask = prepare_image_and_mask(image, width, height, overlap_percentage, resize_option, custom_resize_percentage, alignment, overlap_left, overlap_right, overlap_top, overlap_bottom)
-    
-    # Create a preview image showing the mask
-    preview = background.copy().convert('RGBA')
-    
-    # Create a semi-transparent red overlay
-    red_overlay = Image.new('RGBA', background.size, (255, 0, 0, 64))  # Reduced alpha to 64 (25% opacity)
-    
-    # Convert black pixels in the mask to semi-transparent red
-    red_mask = Image.new('RGBA', background.size, (0, 0, 0, 0))
-    red_mask.paste(red_overlay, (0, 0), mask)
-    
-    # Overlay the red mask on the background
-    preview = Image.alpha_composite(preview, red_mask)
-    
-    return preview
-
-# --- Model Loading ---
-dtype = torch.bfloat16
-device = "cuda" if torch.cuda.is_available() else "cpu"
-pipe = QwenImageEditPipeline.from_pretrained("Qwen/Qwen-Image-Edit", torch_dtype=dtype).to(device)
-pipe.transformer.__class__ = QwenImageTransformer2DModel
-pipe.transformer.set_attn_processor(QwenDoubleStreamAttnProcessorFA3())
-
-# --- Ahead-of-time compilation ---
-optimize_pipeline_(pipe, image=Image.new("RGB", (1024, 1024)), prompt="prompt")
-
-# --- UI Constants and Helpers ---
-MAX_SEED = np.iinfo(np.int32).max
-
-def clear_result():
-    """Clears the result image."""
-    return gr.update(value=None)
-
-def update_history(new_image, history):
-    """Updates the history gallery with the new image."""
-    time.sleep(0.5)  # Small delay to ensure image is ready
-    if history is None:
-        history = []
-    if new_image is not None:
-        # Convert to list if needed (Gradio sometimes returns tuples)
-        if not isinstance(history, list):
-            history = list(history) if history else []
-        history.insert(0, new_image)
-    # Keep only the last 20 images in history
-    history = history[:20]
-    return history
-
-def use_history_as_input(evt: gr.SelectData, history):
-    """Sets the selected history image as the new input image."""
-    if history and evt.index < len(history):
-        return gr.update(value=history[evt.index][0])
-    return gr.update()
-
-def use_output_as_input(output_image):
-    """Sets the generated output as the new input image."""
-    if output_image is not None:
-        return gr.update(value=output_image)
-    return gr.update()
-
-def preload_presets(target_ratio, ui_width, ui_height):
-    """Updates the width and height sliders based on the selected aspect ratio."""
-    if target_ratio == "9:16":
-        changed_width = 720
-        changed_height = 1280
-        return changed_width, changed_height, gr.update()
-    elif target_ratio == "16:9":
-        changed_width = 1280
-        changed_height = 720
-        return changed_width, changed_height, gr.update()
-    elif target_ratio == "1:1":
-        changed_width = 1024
-        changed_height = 1024
-        return changed_width, changed_height, gr.update()
-    elif target_ratio == "Custom":
-        return ui_width, ui_height, gr.update(open=True)
-
-def select_the_right_preset(user_width, user_height):
-    if user_width == 720 and user_height == 1280:
-        return "9:16"
-    elif user_width == 1280 and user_height == 720:
-        return "16:9"
-    elif user_width == 1024 and user_height == 1024:
-        return "1:1"
-    else:
-        return "Custom"
-
-def toggle_custom_resize_slider(resize_option):
-    return gr.update(visible=(resize_option == "Custom"))
-
-# --- Main Inference Function (with outpainting preprocessing) ---
-@spaces.GPU(duration=120)
-def infer(
-    image,
-    prompt,
-    width,
-    height,
-    overlap_percentage,
-    resize_option,
-    custom_resize_percentage,
-    alignment,
-    overlap_left,
-    overlap_right,
-    overlap_top,
-    overlap_bottom,
-    seed=42,
-    randomize_seed=False,
-    true_guidance_scale=4.0,
-    num_inference_steps=50,
-    rewrite_prompt=True,
-    progress=gr.Progress(track_tqdm=True),
-):
-    """
-    Generates an outpainted image using the Qwen-Image-Edit pipeline.
-    """
-    # Hardcode the negative prompt as requested
-    negative_prompt = " "
-    
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-
-    # Set up the generator for reproducibility
-    generator = torch.Generator(device=device).manual_seed(seed)
-    
-    print(f"Original Prompt: '{prompt}'")
-    print(f"Negative Prompt: '{negative_prompt}'")
-    print(f"Seed: {seed}, Steps: {num_inference_steps}")
-
-    if rewrite_prompt:
-        prompt = polish_prompt(prompt, image)
-        print(f"Rewritten Prompt: {prompt}")
-
-    # Prepare the image with white margins for outpainting
-    outpaint_image, mask = prepare_image_and_mask(
-        image, width, height, overlap_percentage,
-        resize_option, custom_resize_percentage, alignment,
-        overlap_left, overlap_right, overlap_top, overlap_bottom
-    )
-    
-    # Check if expansion is possible
-    if not can_expand(image.width, image.height, width, height, alignment):
-        alignment = "Middle"
-        outpaint_image, mask = prepare_image_and_mask(
-            image, width, height, overlap_percentage,
-            resize_option, custom_resize_percentage, "Middle",
-            overlap_left, overlap_right, overlap_top, overlap_bottom
-        )
-    
-    print(f"Outpaint dimensions: {outpaint_image.size}")
-
-    # Generate the image with outpainting preprocessing
-    result_image = pipe(
-        outpaint_image,  # Use the preprocessed image with white margins
-        prompt="replace the white margins. "+ prompt,
-        negative_prompt=negative_prompt,
-        num_inference_steps=num_inference_steps,
-        generator=generator,
-        true_cfg_scale=true_guidance_scale,
-    ).images[0]
-
-    return result_image, seed
-
-# --- Examples and UI Layout ---
-# You can add examples here if you have sample images
-# examples = [
-#     ["path/to/example1.jpg", "extend the landscape", 1280, 720, "Middle"],
-#     ["path/to/example2.jpg", "add more sky", 1024, 1024, "Top"],
-# ]
-
-css = """
-#col-container {
-    margin: 0 auto;
-    max-width: 1024px;
-}
-#logo-title {
-    text-align: center;
-}
-#logo-title img {
-    width: 400px;
-}
-#edit_text{margin-top: -62px !important}
-.preview-container {
-    border: 1px solid #e0e0e0;
-    border-radius: 8px;
-    padding: 10px;
-    margin-top: 10px;
-}
-.gallery-container {
-    margin-top: 20px;
-}
-"""
-
-with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.HTML("""
-        <div id="logo-title">
-            <img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-Image/qwen_image_edit_logo.png" alt="Qwen-Image Edit Logo" width="400" style="display: block; margin: 0 auto;">
-            <h2 style="font-style: italic;color: #5b47d1;margin-top: -27px !important;margin-left: 133px;">Outpaint [Fast]</h2>
-        </div>
-        """)
-        gr.Markdown("""
-        
-        Outpaint images with Qwen Image Edit. [Learn more](https://github.com/QwenLM/Qwen-Image) about the Qwen-Image series. 
-        
-        This demo uses the [Qwen-Image-Lightning](https://huggingface.co/lightx2v/Qwen-Image-Lightning) LoRA with AoT compilation and FA3 for accelerated 8-step inference.
-        Try on [Qwen Chat](https://chat.qwen.ai/), or [download model](https://huggingface.co/Qwen/Qwen-Image-Edit) to run locally with ComfyUI or diffusers.
-        """)
-        
-        
-        with gr.Row():
-            with gr.Column():
-                input_image = gr.Image(label="Input Image", type="pil")
-                
-                prompt = gr.Text(
-                    label="Prompt",
-                    info="Describe what should appear in the extended areas",
-                    value="extend the image naturally", 
-                )
-                
-                with gr.Row():
-                    target_ratio = gr.Radio(
-                        label="Target Ratio",
-                        choices=["9:16", "16:9", "1:1", "Custom"],
-                        value="16:9",
-                        scale=2
-                    )
-                    alignment_dropdown = gr.Dropdown(
-                        choices=["Middle", "Left", "Right", "Top", "Bottom"],
-                        value="Middle",
-                        label="Alignment"
-                    )
-                
-                run_button = gr.Button("run", variant="primary")
-                
-                with gr.Accordion("Outpainting Settings", open=False) as settings_panel:
-                    with gr.Row():
-                        width_slider = gr.Slider(
-                            label="Target Width",
-                            minimum=512,
-                            maximum=2048,
-                            step=8,
-                            value=1280,
-                        )
-                        height_slider = gr.Slider(
-                            label="Target Height",
-                            minimum=512,
-                            maximum=2048,
-                            step=8,
-                            value=720,
-                        )
-                    
-                    with gr.Group():
-                        overlap_percentage = gr.Slider(
-                            label="Mask overlap (%)",
-                            minimum=1,
-                            maximum=50,
-                            value=10,
-                            step=1,
-                            info="Controls the blending area between original and new content"
-                        )
-                        
-                        with gr.Row():
-                            overlap_top = gr.Checkbox(label="Overlap Top", value=True)
-                            overlap_right = gr.Checkbox(label="Overlap Right", value=True)
-                        with gr.Row():
-                            overlap_left = gr.Checkbox(label="Overlap Left", value=True)
-                            overlap_bottom = gr.Checkbox(label="Overlap Bottom", value=True)
-                    
-                    with gr.Row():
-                        resize_option = gr.Radio(
-                            label="Resize input image",
-                            choices=["Full", "50%", "33%", "25%", "Custom"],
-                            value="Full",
-                            info="How much of the target canvas the original image should occupy"
-                        )
-                        custom_resize_percentage = gr.Slider(
-                            label="Custom resize (%)",
-                            minimum=1,
-                            maximum=100,
-                            step=1,
-                            value=50,
-                            visible=False
-                        )
-                    
-                    preview_button = gr.Button("👁️ Preview alignment and mask", variant="secondary")
-
-                with gr.Accordion("Advanced Settings", open=False):
-                    seed = gr.Slider(
-                        label="Seed",
-                        minimum=0,
-                        maximum=MAX_SEED,
-                        step=1,
-                        value=0,
-                    )
-
-                    randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-
-                    with gr.Row():
-                        true_guidance_scale = gr.Slider(
-                            label="True guidance scale",
-                            minimum=1.0,
-                            maximum=10.0,
-                            step=0.1,
-                            value=1.0
-                        )
-
-                        num_inference_steps = gr.Slider(
-                            label="Number of inference steps",
-                            minimum=1,
-                            maximum=28,
-                            step=1,
-                            value=8,
-                        )
-
-                    rewrite_prompt = gr.Checkbox(
-                        label="Enhance prompt (using HF Inference)", 
-                        value=True
-                    )
-
-            with gr.Column():
-                result = gr.Image(label="Result", type="pil", interactive=False)
-                
-                use_as_input_button = gr.Button("🔄 Use as Input Image", visible=False, variant="secondary")
-                
-                with gr.Column(visible=False) as preview_container:
-                    preview_image = gr.Image(label="Preview (red area will be generated)", type="pil")
-                
-                gr.Markdown("---")
-                
-                with gr.Row():
-                    gr.Markdown("### 📜 History")
-                    clear_history_button = gr.Button("🗑️ Clear History", size="sm", variant="stop")
-                
-                history_gallery = gr.Gallery(
-                    label="Click any image to use as input", 
-                    columns=4, 
-                    rows=2,
-                    object_fit="contain", 
-                    height="auto",
-                    interactive=False,
-                    show_label=True,
-                    elem_classes=["gallery-container"]
-                )
-
-    # Event handlers
-    use_as_input_button.click(
-        fn=use_output_as_input,
-        inputs=[result],
-        outputs=[input_image],
-        show_api=False
-    )
-    
-    history_gallery.select(
-        fn=use_history_as_input,
-        inputs=[history_gallery],
-        outputs=[input_image],
-        show_api=False
-    )
-    
-    clear_history_button.click(
-        fn=lambda: [],
-        inputs=None,
-        outputs=history_gallery,
-        show_api=False
-    )
-    
-    target_ratio.change(
-        fn=preload_presets,
-        inputs=[target_ratio, width_slider, height_slider],
-        outputs=[width_slider, height_slider, settings_panel],
-        queue=False,
-    )
-    
-    width_slider.change(
-        fn=select_the_right_preset,
-        inputs=[width_slider, height_slider],
-        outputs=[target_ratio],
-        queue=False,
-    )
-    
-    height_slider.change(
-        fn=select_the_right_preset,
-        inputs=[width_slider, height_slider],
-        outputs=[target_ratio],
-        queue=False,
-    )
-    
-    resize_option.change(
-        fn=toggle_custom_resize_slider,
-        inputs=[resize_option],
-        outputs=[custom_resize_percentage],
-        queue=False,
-    )
-    
-    preview_button.click(
-        fn=lambda: gr.update(visible=True),
-        inputs=None,
-        outputs=[preview_container],
-        queue=False,
-    ).then(
-        fn=preview_image_and_mask,
-        inputs=[
-            input_image, width_slider, height_slider, overlap_percentage,
-            resize_option, custom_resize_percentage, alignment_dropdown,
-            overlap_left, overlap_right, overlap_top, overlap_bottom
-        ],
-        outputs=preview_image,
-        queue=False,
-    )
-
-    # Main generation pipeline with result clearing, history update, and button visibility
-    run_button.click(
-        fn=clear_result,
-        inputs=None,
-        outputs=result,
-        show_api=False
-    ).then(
-        fn=infer,
-        inputs=[
-            input_image,
-            prompt,
-            width_slider,
-            height_slider,
-            overlap_percentage,
-            resize_option,
-            custom_resize_percentage,
-            alignment_dropdown,
-            overlap_left,
-            overlap_right,
-            overlap_top,
-            overlap_bottom,
-            seed,
-            randomize_seed,
-            true_guidance_scale,
-            num_inference_steps,
-            rewrite_prompt,
-        ],
-        outputs=[result, seed],
-    ).then(
-        fn=lambda: gr.update(visible=True),
-        inputs=None,
-        outputs=use_as_input_button,
-        show_api=False
-    ).then(
-        fn=update_history,
-        inputs=[result, history_gallery],
-        outputs=history_gallery,
-        show_api=False
-    )
-    
-    # Also trigger on prompt submit
-    prompt.submit(
-        fn=clear_result,
-        inputs=None,
-        outputs=result,
-        show_api=False
-    ).then(
-        fn=infer,
-        inputs=[
-            input_image,
-            prompt,
-            width_slider,
-            height_slider,
-            overlap_percentage,
-            resize_option,
-            custom_resize_percentage,
-            alignment_dropdown,
-            overlap_left,
-            overlap_right,
-            overlap_top,
-            overlap_bottom,
-            seed,
-            randomize_seed,
-            true_guidance_scale,
-            num_inference_steps,
-            rewrite_prompt,
-        ],
-        outputs=[result, seed],
-    ).then(
-        fn=lambda: gr.update(visible=True),
-        inputs=None,
-        outputs=use_as_input_button,
-        show_api=False
-    ).then(
-        fn=update_history,
-        inputs=[result, history_gallery],
-        outputs=history_gallery,
-        show_api=False
-    )
-
-if __name__ == "__main__":
+import gradio as gr
+import numpy as np
+import random
+import torch
+import spaces
+import os
+import json
+import time
+
+from PIL import Image, ImageDraw
+import torch 
+import math
+
+from optimization import optimize_pipeline_
+from qwenimage.pipeline_qwen_image_edit import QwenImageEditPipeline
+from qwenimage.transformer_qwenimage import QwenImageTransformer2DModel
+from qwenimage.qwen_fa3_processor import QwenDoubleStreamAttnProcessorFA3
+
+from huggingface_hub import InferenceClient
+import math
+
+# --- Prompt Enhancement using Hugging Face InferenceClient ---
+def polish_prompt_hf(original_prompt, system_prompt):
+    """
+    Rewrites the prompt using a Hugging Face InferenceClient.
+    """
+    # Ensure HF_TOKEN is set
+    api_key = os.environ.get("HF_TOKEN")
+    if not api_key:
+        print("Warning: HF_TOKEN not set. Falling back to original prompt.")
+        return original_prompt
+
+    try:
+        # Initialize the client
+        client = InferenceClient(
+            provider="cerebras",
+            api_key=api_key,
+        )
+
+        # Format the messages for the chat completions API
+        messages = [
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": original_prompt}
+        ]
+
+        # Call the API
+        completion = client.chat.completions.create(
+            model="Qwen/Qwen3-235B-A22B-Instruct-2507",
+            messages=messages,
+        )
+        
+        # Parse the response
+        result = completion.choices[0].message.content
+        
+        # Try to extract JSON if present
+        if '{"Rewritten"' in result:
+            try:
+                # Clean up the response
+                result = result.replace('```json', '').replace('```', '')
+                result_json = json.loads(result)
+                polished_prompt = result_json.get('Rewritten', result)
+            except:
+                polished_prompt = result
+        else:
+            polished_prompt = result
+            
+        polished_prompt = polished_prompt.strip().replace("\n", " ")
+        return polished_prompt
+        
+    except Exception as e:
+        print(f"Error during API call to Hugging Face: {e}")
+        # Fallback to original prompt if enhancement fails
+        return original_prompt
+
+
+def polish_prompt(prompt, img):
+    """
+    Main function to polish prompts for image editing using HF inference.
+    """
+    SYSTEM_PROMPT = '''
+# Edit Instruction Rewriter
+You are a professional edit instruction rewriter. Your task is to generate a precise, concise, and visually achievable professional-level edit instruction based on the user-provided instruction and the image to be edited.  
+
+Please strictly follow the rewriting rules below:
+
+## 1. General Principles
+- Keep the rewritten prompt **concise**. Avoid overly long sentences and reduce unnecessary descriptive language.  
+- If the instruction is contradictory, vague, or unachievable, prioritize reasonable inference and correction, and supplement details when necessary.  
+- Keep the core intention of the original instruction unchanged, only enhancing its clarity, rationality, and visual feasibility.  
+- All added objects or modifications must align with the logic and style of the edited input image's overall scene.  
+
+## 2. Task Type Handling Rules
+### 1. Add, Delete, Replace Tasks
+- If the instruction is clear (already includes task type, target entity, position, quantity, attributes), preserve the original intent and only refine the grammar.  
+- If the description is vague, supplement with minimal but sufficient details (category, color, size, orientation, position, etc.). For example:  
+    > Original: "Add an animal"  
+    > Rewritten: "Add a light-gray cat in the bottom-right corner, sitting and facing the camera"  
+- Remove meaningless instructions: e.g., "Add 0 objects" should be ignored or flagged as invalid.  
+- For replacement tasks, specify "Replace Y with X" and briefly describe the key visual features of X.  
+
+### 2. Text Editing Tasks
+- All text content must be enclosed in English double quotes " ". Do not translate or alter the original language of the text, and do not change the capitalization.  
+- **For text replacement tasks, always use the fixed template:**
+    - Replace "xx" to "yy".  
+    - Replace the xx bounding box to "yy".  
+- If the user does not specify text content, infer and add concise text based on the instruction and the input image's context. For example:  
+    > Original: "Add a line of text" (poster)  
+    > Rewritten: "Add text "LIMITED EDITION" at the top center with slight shadow"  
+- Specify text position, color, and layout in a concise way.  
+
+### 3. Human Editing Tasks
+- Maintain the person's core visual consistency (ethnicity, gender, age, hairstyle, expression, outfit, etc.).  
+- If modifying appearance (e.g., clothes, hairstyle), ensure the new element is consistent with the original style.  
+- **For expression changes, they must be natural and subtle, never exaggerated.**  
+- If deletion is not specifically emphasized, the most important subject in the original image (e.g., a person, an animal) should be preserved.
+    - For background change tasks, emphasize maintaining subject consistency at first.  
+- Example:  
+    > Original: "Change the person's hat"  
+    > Rewritten: "Replace the man's hat with a dark brown beret; keep smile, short hair, and gray jacket unchanged"  
+
+### 4. Style Transformation or Enhancement Tasks
+- If a style is specified, describe it concisely with key visual traits. For example:  
+    > Original: "Disco style"  
+    > Rewritten: "1970s disco: flashing lights, disco ball, mirrored walls, colorful tones"  
+- If the instruction says "use reference style" or "keep current style," analyze the input image, extract main features (color, composition, texture, lighting, art style), and integrate them concisely.  
+- **For coloring tasks, including restoring old photos, always use the fixed template:** "Restore old photograph, remove scratches, reduce noise, enhance details, high resolution, realistic, natural skin tones, clear facial features, no distortion, vintage photo restoration"  
+- If there are other changes, place the style description at the end.
+
+## 3. Rationality and Logic Checks
+- Resolve contradictory instructions: e.g., "Remove all trees but keep all trees" should be logically corrected.  
+- Add missing key information: if position is unspecified, choose a reasonable area based on composition (near subject, empty space, center/edges).  
+
+# Output Format
+Return only the rewritten instruction text directly, without JSON formatting or any other wrapper.
+'''
+    
+    # Note: We're not actually using the image in the HF version, 
+    # but keeping the interface consistent
+    full_prompt = f"{SYSTEM_PROMPT}\n\nUser Input: {prompt}\n\nRewritten Prompt:"
+    
+    return polish_prompt_hf(full_prompt, SYSTEM_PROMPT)
+
+# --- Outpainting Functions ---
+def can_expand(source_width, source_height, target_width, target_height, alignment):
+    """Checks if the image can be expanded based on the alignment."""
+    if alignment in ("Left", "Right") and source_width >= target_width:
+        return False
+    if alignment in ("Top", "Bottom") and source_height >= target_height:
+        return False
+    return True
+
+def prepare_image_and_mask(image, width, height, overlap_percentage, resize_option, custom_resize_percentage, alignment, overlap_left, overlap_right, overlap_top, overlap_bottom):
+    """Prepares the image with white margins and creates a mask for outpainting."""
+    target_size = (width, height)
+    
+    # Calculate the scaling factor to fit the image within the target size
+    scale_factor = min(target_size[0] / image.width, target_size[1] / image.height)
+    new_width = int(image.width * scale_factor)
+    new_height = int(image.height * scale_factor)
+    
+    # Resize the source image to fit within target size
+    source = image.resize((new_width, new_height), Image.LANCZOS)
+    
+    # Apply resize option using percentages
+    if resize_option == "Full":
+        resize_percentage = 100
+    elif resize_option == "50%":
+        resize_percentage = 50
+    elif resize_option == "33%":
+        resize_percentage = 33
+    elif resize_option == "25%":
+        resize_percentage = 25
+    else:  # Custom
+        resize_percentage = custom_resize_percentage
+    
+    # Calculate new dimensions based on percentage
+    resize_factor = resize_percentage / 100
+    new_width = int(source.width * resize_factor)
+    new_height = int(source.height * resize_factor)
+    
+    # Ensure minimum size of 64 pixels
+    new_width = max(new_width, 64)
+    new_height = max(new_height, 64)
+    
+    # Resize the image
+    source = source.resize((new_width, new_height), Image.LANCZOS)
+    
+    # Calculate the overlap in pixels based on the percentage
+    overlap_x = int(new_width * (overlap_percentage / 100))
+    overlap_y = int(new_height * (overlap_percentage / 100))
+    
+    # Ensure minimum overlap of 1 pixel
+    overlap_x = max(overlap_x, 1)
+    overlap_y = max(overlap_y, 1)
+    
+    # Calculate margins based on alignment
+    if alignment == "Middle":
+        margin_x = (target_size[0] - new_width) // 2
+        margin_y = (target_size[1] - new_height) // 2
+    elif alignment == "Left":
+        margin_x = 0
+        margin_y = (target_size[1] - new_height) // 2
+    elif alignment == "Right":
+        margin_x = target_size[0] - new_width
+        margin_y = (target_size[1] - new_height) // 2
+    elif alignment == "Top":
+        margin_x = (target_size[0] - new_width) // 2
+        margin_y = 0
+    elif alignment == "Bottom":
+        margin_x = (target_size[0] - new_width) // 2
+        margin_y = target_size[1] - new_height
+    
+    # Adjust margins to eliminate gaps
+    margin_x = max(0, min(margin_x, target_size[0] - new_width))
+    margin_y = max(0, min(margin_y, target_size[1] - new_height))
+    
+    # Create a new background image with white margins and paste the resized source image
+    background = Image.new('RGB', target_size, (255, 255, 255))
+    background.paste(source, (margin_x, margin_y))
+    
+    # Create the mask
+    mask = Image.new('L', target_size, 255)
+    mask_draw = ImageDraw.Draw(mask)
+    
+    # Calculate overlap areas
+    white_gaps_patch = 2
+    left_overlap = margin_x + overlap_x if overlap_left else margin_x + white_gaps_patch
+    right_overlap = margin_x + new_width - overlap_x if overlap_right else margin_x + new_width - white_gaps_patch
+    top_overlap = margin_y + overlap_y if overlap_top else margin_y + white_gaps_patch
+    bottom_overlap = margin_y + new_height - overlap_y if overlap_bottom else margin_y + new_height - white_gaps_patch
+    
+    if alignment == "Left":
+        left_overlap = margin_x + overlap_x if overlap_left else margin_x
+    elif alignment == "Right":
+        right_overlap = margin_x + new_width - overlap_x if overlap_right else margin_x + new_width
+    elif alignment == "Top":
+        top_overlap = margin_y + overlap_y if overlap_top else margin_y
+    elif alignment == "Bottom":
+        bottom_overlap = margin_y + new_height - overlap_y if overlap_bottom else margin_y + new_height
+    
+    # Draw the mask
+    mask_draw.rectangle([
+        (left_overlap, top_overlap),
+        (right_overlap, bottom_overlap)
+    ], fill=0)
+    
+    return background, mask
+
+def preview_image_and_mask(image, width, height, overlap_percentage, resize_option, custom_resize_percentage, alignment, overlap_left, overlap_right, overlap_top, overlap_bottom):
+    """Creates a preview showing the mask overlay."""
+    background, mask = prepare_image_and_mask(image, width, height, overlap_percentage, resize_option, custom_resize_percentage, alignment, overlap_left, overlap_right, overlap_top, overlap_bottom)
+    
+    # Create a preview image showing the mask
+    preview = background.copy().convert('RGBA')
+    
+    # Create a semi-transparent red overlay
+    red_overlay = Image.new('RGBA', background.size, (255, 0, 0, 64))  # Reduced alpha to 64 (25% opacity)
+    
+    # Convert black pixels in the mask to semi-transparent red
+    red_mask = Image.new('RGBA', background.size, (0, 0, 0, 0))
+    red_mask.paste(red_overlay, (0, 0), mask)
+    
+    # Overlay the red mask on the background
+    preview = Image.alpha_composite(preview, red_mask)
+    
+    return preview
+
+# --- Model Loading ---
+dtype = torch.bfloat16
+device = "cuda" if torch.cuda.is_available() else "cpu"
+pipe = QwenImageEditPipeline.from_pretrained("Qwen/Qwen-Image-Edit", torch_dtype=dtype).to(device)
+pipe.transformer.__class__ = QwenImageTransformer2DModel
+pipe.transformer.set_attn_processor(QwenDoubleStreamAttnProcessorFA3())
+
+# --- Ahead-of-time compilation ---
+optimize_pipeline_(pipe, image=Image.new("RGB", (1024, 1024)), prompt="prompt")
+
+# --- UI Constants and Helpers ---
+MAX_SEED = np.iinfo(np.int32).max
+
+def clear_result():
+    """Clears the result image."""
+    return gr.update(value=None)
+
+def update_history(new_image, history):
+    """Updates the history gallery with the new image."""
+    time.sleep(0.5)  # Small delay to ensure image is ready
+    if history is None:
+        history = []
+    if new_image is not None:
+        # Convert to list if needed (Gradio sometimes returns tuples)
+        if not isinstance(history, list):
+            history = list(history) if history else []
+        history.insert(0, new_image)
+    # Keep only the last 20 images in history
+    history = history[:20]
+    return history
+
+def use_history_as_input(evt: gr.SelectData, history):
+    """Sets the selected history image as the new input image."""
+    if history and evt.index < len(history):
+        return gr.update(value=history[evt.index][0])
+    return gr.update()
+
+def use_output_as_input(output_image):
+    """Sets the generated output as the new input image."""
+    if output_image is not None:
+        return gr.update(value=output_image)
+    return gr.update()
+
+def preload_presets(target_ratio, ui_width, ui_height):
+    """Updates the width and height sliders based on the selected aspect ratio."""
+    if target_ratio == "9:16":
+        changed_width = 720
+        changed_height = 1280
+        return changed_width, changed_height, gr.update()
+    elif target_ratio == "16:9":
+        changed_width = 1280
+        changed_height = 720
+        return changed_width, changed_height, gr.update()
+    elif target_ratio == "1:1":
+        changed_width = 1024
+        changed_height = 1024
+        return changed_width, changed_height, gr.update()
+    elif target_ratio == "Custom":
+        return ui_width, ui_height, gr.update(open=True)
+
+def select_the_right_preset(user_width, user_height):
+    if user_width == 720 and user_height == 1280:
+        return "9:16"
+    elif user_width == 1280 and user_height == 720:
+        return "16:9"
+    elif user_width == 1024 and user_height == 1024:
+        return "1:1"
+    else:
+        return "Custom"
+
+def toggle_custom_resize_slider(resize_option):
+    return gr.update(visible=(resize_option == "Custom"))
+
+# --- Main Inference Function (with outpainting preprocessing) ---
+@spaces.GPU(duration=120)
+def infer(
+    image,
+    prompt,
+    width,
+    height,
+    overlap_percentage,
+    resize_option,
+    custom_resize_percentage,
+    alignment,
+    overlap_left,
+    overlap_right,
+    overlap_top,
+    overlap_bottom,
+    seed=42,
+    randomize_seed=False,
+    true_guidance_scale=4.0,
+    num_inference_steps=50,
+    rewrite_prompt=True,
+    progress=gr.Progress(track_tqdm=True),
+):
+    """
+    Generates an outpainted image using the Qwen-Image-Edit pipeline.
+    """
+    # Hardcode the negative prompt as requested
+    negative_prompt = " "
+    
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+
+    # Set up the generator for reproducibility
+    generator = torch.Generator(device=device).manual_seed(seed)
+    
+    print(f"Original Prompt: '{prompt}'")
+    print(f"Negative Prompt: '{negative_prompt}'")
+    print(f"Seed: {seed}, Steps: {num_inference_steps}")
+
+    if rewrite_prompt:
+        prompt = polish_prompt(prompt, image)
+        print(f"Rewritten Prompt: {prompt}")
+
+    # Prepare the image with white margins for outpainting
+    outpaint_image, mask = prepare_image_and_mask(
+        image, width, height, overlap_percentage,
+        resize_option, custom_resize_percentage, alignment,
+        overlap_left, overlap_right, overlap_top, overlap_bottom
+    )
+    
+    # Check if expansion is possible
+    if not can_expand(image.width, image.height, width, height, alignment):
+        alignment = "Middle"
+        outpaint_image, mask = prepare_image_and_mask(
+            image, width, height, overlap_percentage,
+            resize_option, custom_resize_percentage, "Middle",
+            overlap_left, overlap_right, overlap_top, overlap_bottom
+        )
+    
+    print(f"Outpaint dimensions: {outpaint_image.size}")
+
+    # Generate the image with outpainting preprocessing
+    result_image = pipe(
+        outpaint_image,  # Use the preprocessed image with white margins
+        prompt="replace the white margins. "+ prompt,
+        negative_prompt=negative_prompt,
+        num_inference_steps=num_inference_steps,
+        generator=generator,
+        true_cfg_scale=true_guidance_scale,
+    ).images[0]
+
+    return result_image, seed
+
+# --- Examples and UI Layout ---
+# You can add examples here if you have sample images
+# examples = [
+#     ["path/to/example1.jpg", "extend the landscape", 1280, 720, "Middle"],
+#     ["path/to/example2.jpg", "add more sky", 1024, 1024, "Top"],
+# ]
+
+css = """
+#col-container {
+    margin: 0 auto;
+    max-width: 1024px;
+}
+#logo-title {
+    text-align: center;
+}
+#logo-title img {
+    width: 400px;
+}
+#edit_text{margin-top: -62px !important}
+.preview-container {
+    border: 1px solid #e0e0e0;
+    border-radius: 8px;
+    padding: 10px;
+    margin-top: 10px;
+}
+.gallery-container {
+    margin-top: 20px;
+}
+"""
+
+with gr.Blocks(css=css) as demo:
+    with gr.Column(elem_id="col-container"):
+        gr.HTML("""
+        <div id="logo-title">
+            <img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-Image/qwen_image_edit_logo.png" alt="Qwen-Image Edit Logo" width="400" style="display: block; margin: 0 auto;">
+            <h2 style="font-style: italic;color: #5b47d1;margin-top: -27px !important;margin-left: 133px;">Outpaint [Fast]</h2>
+        </div>
+        """)
+        gr.Markdown("""
+        
+        Outpaint images with Qwen Image Edit. [Learn more](https://github.com/QwenLM/Qwen-Image) about the Qwen-Image series. 
+        
+        This demo uses the [Qwen-Image-Lightning](https://huggingface.co/lightx2v/Qwen-Image-Lightning) LoRA with AoT compilation and FA3 for accelerated 8-step inference.
+        Try on [Qwen Chat](https://chat.qwen.ai/), or [download model](https://huggingface.co/Qwen/Qwen-Image-Edit) to run locally with ComfyUI or diffusers.
+        """)
+        
+        
+        with gr.Row():
+            with gr.Column():
+                input_image = gr.Image(label="Input Image", type="pil")
+                
+                prompt = gr.Text(
+                    label="Prompt",
+                    info="Describe what should appear in the extended areas",
+                    value="extend the image naturally", 
+                )
+                
+                with gr.Row():
+                    target_ratio = gr.Radio(
+                        label="Target Ratio",
+                        choices=["9:16", "16:9", "1:1", "Custom"],
+                        value="16:9",
+                        scale=2
+                    )
+                    alignment_dropdown = gr.Dropdown(
+                        choices=["Middle", "Left", "Right", "Top", "Bottom"],
+                        value="Middle",
+                        label="Alignment"
+                    )
+                
+                run_button = gr.Button("run", variant="primary")
+                
+                with gr.Accordion("Outpainting Settings", open=False) as settings_panel:
+                    with gr.Row():
+                        width_slider = gr.Slider(
+                            label="Target Width",
+                            minimum=512,
+                            maximum=2048,
+                            step=8,
+                            value=1280,
+                        )
+                        height_slider = gr.Slider(
+                            label="Target Height",
+                            minimum=512,
+                            maximum=2048,
+                            step=8,
+                            value=720,
+                        )
+                    
+                    with gr.Group():
+                        overlap_percentage = gr.Slider(
+                            label="Mask overlap (%)",
+                            minimum=1,
+                            maximum=50,
+                            value=10,
+                            step=1,
+                            info="Controls the blending area between original and new content"
+                        )
+                        
+                        with gr.Row():
+                            overlap_top = gr.Checkbox(label="Overlap Top", value=True)
+                            overlap_right = gr.Checkbox(label="Overlap Right", value=True)
+                        with gr.Row():
+                            overlap_left = gr.Checkbox(label="Overlap Left", value=True)
+                            overlap_bottom = gr.Checkbox(label="Overlap Bottom", value=True)
+                    
+                    with gr.Row():
+                        resize_option = gr.Radio(
+                            label="Resize input image",
+                            choices=["Full", "50%", "33%", "25%", "Custom"],
+                            value="Full",
+                            info="How much of the target canvas the original image should occupy"
+                        )
+                        custom_resize_percentage = gr.Slider(
+                            label="Custom resize (%)",
+                            minimum=1,
+                            maximum=100,
+                            step=1,
+                            value=50,
+                            visible=False
+                        )
+                    
+                    preview_button = gr.Button("👁️ Preview alignment and mask", variant="secondary")
+
+                with gr.Accordion("Advanced Settings", open=False):
+                    seed = gr.Slider(
+                        label="Seed",
+                        minimum=0,
+                        maximum=MAX_SEED,
+                        step=1,
+                        value=0,
+                    )
+
+                    randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+
+                    with gr.Row():
+                        true_guidance_scale = gr.Slider(
+                            label="True guidance scale",
+                            minimum=1.0,
+                            maximum=10.0,
+                            step=0.1,
+                            value=1.0
+                        )
+
+                        num_inference_steps = gr.Slider(
+                            label="Number of inference steps",
+                            minimum=1,
+                            maximum=28,
+                            step=1,
+                            value=8,
+                        )
+
+                    rewrite_prompt = gr.Checkbox(
+                        label="Enhance prompt (using HF Inference)", 
+                        value=True
+                    )
+
+            with gr.Column():
+                result = gr.Image(label="Result", type="pil", interactive=False)
+                
+                use_as_input_button = gr.Button("🔄 Use as Input Image", visible=False, variant="secondary")
+                
+                with gr.Column(visible=False) as preview_container:
+                    preview_image = gr.Image(label="Preview (red area will be generated)", type="pil")
+                
+                gr.Markdown("---")
+                
+                with gr.Row():
+                    gr.Markdown("### 📜 History")
+                    clear_history_button = gr.Button("🗑️ Clear History", size="sm", variant="stop")
+                
+                history_gallery = gr.Gallery(
+                    label="Click any image to use as input", 
+                    columns=4, 
+                    rows=2,
+                    object_fit="contain", 
+                    height="auto",
+                    interactive=False,
+                    show_label=True,
+                    elem_classes=["gallery-container"]
+                )
+
+    # Event handlers
+    use_as_input_button.click(
+        fn=use_output_as_input,
+        inputs=[result],
+        outputs=[input_image],
+        show_api=False
+    )
+    
+    history_gallery.select(
+        fn=use_history_as_input,
+        inputs=[history_gallery],
+        outputs=[input_image],
+        show_api=False
+    )
+    
+    clear_history_button.click(
+        fn=lambda: [],
+        inputs=None,
+        outputs=history_gallery,
+        show_api=False
+    )
+    
+    target_ratio.change(
+        fn=preload_presets,
+        inputs=[target_ratio, width_slider, height_slider],
+        outputs=[width_slider, height_slider, settings_panel],
+        queue=False,
+    )
+    
+    width_slider.change(
+        fn=select_the_right_preset,
+        inputs=[width_slider, height_slider],
+        outputs=[target_ratio],
+        queue=False,
+    )
+    
+    height_slider.change(
+        fn=select_the_right_preset,
+        inputs=[width_slider, height_slider],
+        outputs=[target_ratio],
+        queue=False,
+    )
+    
+    resize_option.change(
+        fn=toggle_custom_resize_slider,
+        inputs=[resize_option],
+        outputs=[custom_resize_percentage],
+        queue=False,
+    )
+    
+    preview_button.click(
+        fn=lambda: gr.update(visible=True),
+        inputs=None,
+        outputs=[preview_container],
+        queue=False,
+    ).then(
+        fn=preview_image_and_mask,
+        inputs=[
+            input_image, width_slider, height_slider, overlap_percentage,
+            resize_option, custom_resize_percentage, alignment_dropdown,
+            overlap_left, overlap_right, overlap_top, overlap_bottom
+        ],
+        outputs=preview_image,
+        queue=False,
+    )
+
+    # Main generation pipeline with result clearing, history update, and button visibility
+    run_button.click(
+        fn=clear_result,
+        inputs=None,
+        outputs=result,
+        show_api=False
+    ).then(
+        fn=infer,
+        inputs=[
+            input_image,
+            prompt,
+            width_slider,
+            height_slider,
+            overlap_percentage,
+            resize_option,
+            custom_resize_percentage,
+            alignment_dropdown,
+            overlap_left,
+            overlap_right,
+            overlap_top,
+            overlap_bottom,
+            seed,
+            randomize_seed,
+            true_guidance_scale,
+            num_inference_steps,
+            rewrite_prompt,
+        ],
+        outputs=[result, seed],
+    ).then(
+        fn=lambda: gr.update(visible=True),
+        inputs=None,
+        outputs=use_as_input_button,
+        show_api=False
+    ).then(
+        fn=update_history,
+        inputs=[result, history_gallery],
+        outputs=history_gallery,
+        show_api=False
+    )
+    
+    # Also trigger on prompt submit
+    prompt.submit(
+        fn=clear_result,
+        inputs=None,
+        outputs=result,
+        show_api=False
+    ).then(
+        fn=infer,
+        inputs=[
+            input_image,
+            prompt,
+            width_slider,
+            height_slider,
+            overlap_percentage,
+            resize_option,
+            custom_resize_percentage,
+            alignment_dropdown,
+            overlap_left,
+            overlap_right,
+            overlap_top,
+            overlap_bottom,
+            seed,
+            randomize_seed,
+            true_guidance_scale,
+            num_inference_steps,
+            rewrite_prompt,
+        ],
+        outputs=[result, seed],
+    ).then(
+        fn=lambda: gr.update(visible=True),
+        inputs=None,
+        outputs=use_as_input_button,
+        show_api=False
+    ).then(
+        fn=update_history,
+        inputs=[result, history_gallery],
+        outputs=history_gallery,
+        show_api=False
+    )
+
+if __name__ == "__main__":
     demo.launch()

optimization.py

@@ -1,77 +1,77 @@
-"""
-"""
-
-from typing import Any
-from typing import Callable
-from typing import ParamSpec
-from torchao.quantization import quantize_
-from torchao.quantization import Float8DynamicActivationFloat8WeightConfig
-import spaces
-import torch
-from torch.utils._pytree import tree_map
-
-
-P = ParamSpec('P')
-
-
-TRANSFORMER_IMAGE_SEQ_LENGTH_DIM = torch.export.Dim('image_seq_length')
-TRANSFORMER_TEXT_SEQ_LENGTH_DIM = torch.export.Dim('text_seq_length')
-
-TRANSFORMER_DYNAMIC_SHAPES = {
-    'hidden_states': {
-        1: TRANSFORMER_IMAGE_SEQ_LENGTH_DIM,
-    },
-    'encoder_hidden_states': {
-        1: TRANSFORMER_TEXT_SEQ_LENGTH_DIM,
-    },
-    'encoder_hidden_states_mask': {
-        1: TRANSFORMER_TEXT_SEQ_LENGTH_DIM,
-    },
-    'image_rotary_emb': ({
-        0: TRANSFORMER_IMAGE_SEQ_LENGTH_DIM,
-    }, {
-        0: TRANSFORMER_TEXT_SEQ_LENGTH_DIM,
-    }),
-}
-
-
-INDUCTOR_CONFIGS = {
-    'conv_1x1_as_mm': True,
-    'epilogue_fusion': False,
-    'coordinate_descent_tuning': True,
-    'coordinate_descent_check_all_directions': True,
-    'max_autotune': True,
-    'triton.cudagraphs': True,
-}
-
-
-def optimize_pipeline_(pipeline: Callable[P, Any], *args: P.args, **kwargs: P.kwargs):
-
-    @spaces.GPU(duration=1500)
-    def compile_transformer():
-        
-        pipeline.load_lora_weights(
-        "lightx2v/Qwen-Image-Lightning", 
-        weight_name="Qwen-Image-Lightning-8steps-V1.1.safetensors"
-        )
-        pipeline.fuse_lora()
-        pipeline.unload_lora_weights()
-
-        with spaces.aoti_capture(pipeline.transformer) as call:
-            pipeline(*args, **kwargs)
-
-        dynamic_shapes = tree_map(lambda t: None, call.kwargs)
-        dynamic_shapes |= TRANSFORMER_DYNAMIC_SHAPES
-
-        # quantize_(pipeline.transformer, Float8DynamicActivationFloat8WeightConfig())
-        
-        exported = torch.export.export(
-            mod=pipeline.transformer,
-            args=call.args,
-            kwargs=call.kwargs,
-            dynamic_shapes=dynamic_shapes,
-        )
-
-        return spaces.aoti_compile(exported, INDUCTOR_CONFIGS)
-
-    spaces.aoti_apply(compile_transformer(), pipeline.transformer)
+"""
+"""
+
+from typing import Any
+from typing import Callable
+from typing import ParamSpec
+from torchao.quantization import quantize_
+from torchao.quantization import Float8DynamicActivationFloat8WeightConfig
+import spaces
+import torch
+from torch.utils._pytree import tree_map
+
+
+P = ParamSpec('P')
+
+
+TRANSFORMER_IMAGE_SEQ_LENGTH_DIM = torch.export.Dim('image_seq_length')
+TRANSFORMER_TEXT_SEQ_LENGTH_DIM = torch.export.Dim('text_seq_length')
+
+TRANSFORMER_DYNAMIC_SHAPES = {
+    'hidden_states': {
+        1: TRANSFORMER_IMAGE_SEQ_LENGTH_DIM,
+    },
+    'encoder_hidden_states': {
+        1: TRANSFORMER_TEXT_SEQ_LENGTH_DIM,
+    },
+    'encoder_hidden_states_mask': {
+        1: TRANSFORMER_TEXT_SEQ_LENGTH_DIM,
+    },
+    'image_rotary_emb': ({
+        0: TRANSFORMER_IMAGE_SEQ_LENGTH_DIM,
+    }, {
+        0: TRANSFORMER_TEXT_SEQ_LENGTH_DIM,
+    }),
+}
+
+
+INDUCTOR_CONFIGS = {
+    'conv_1x1_as_mm': True,
+    'epilogue_fusion': False,
+    'coordinate_descent_tuning': True,
+    'coordinate_descent_check_all_directions': True,
+    'max_autotune': True,
+    'triton.cudagraphs': True,
+}
+
+
+def optimize_pipeline_(pipeline: Callable[P, Any], *args: P.args, **kwargs: P.kwargs):
+
+    @spaces.GPU(duration=1500)
+    def compile_transformer():
+        
+        pipeline.load_lora_weights(
+        "lightx2v/Qwen-Image-Lightning", 
+        weight_name="Qwen-Image-Lightning-8steps-V1.1.safetensors"
+        )
+        pipeline.fuse_lora()
+        pipeline.unload_lora_weights()
+
+        with spaces.aoti_capture(pipeline.transformer) as call:
+            pipeline(*args, **kwargs)
+
+        dynamic_shapes = tree_map(lambda t: None, call.kwargs)
+        dynamic_shapes |= TRANSFORMER_DYNAMIC_SHAPES
+
+        # quantize_(pipeline.transformer, Float8DynamicActivationFloat8WeightConfig())
+        
+        exported = torch.export.export(
+            mod=pipeline.transformer,
+            args=call.args,
+            kwargs=call.kwargs,
+            dynamic_shapes=dynamic_shapes,
+        )
+
+        return spaces.aoti_compile(exported, INDUCTOR_CONFIGS)
+
+    spaces.aoti_apply(compile_transformer(), pipeline.transformer)

requirements.txt

@@ -1,10 +1,10 @@
-git+https://github.com/huggingface/diffusers.git@qwenimage-lru-cache-bypass
-kernels
-torchao==0.11.0
-transformers
-accelerate
-safetensors
-sentencepiece
-dashscope
-torchvision
+git+https://github.com/huggingface/diffusers.git@qwenimage-lru-cache-bypass
+kernels
+torchao==0.11.0
+transformers
+accelerate
+safetensors
+sentencepiece
+dashscope
+torchvision
 peft