Update app.py

app.py

@@ -1,304 +1,87 @@
-import gradio as gr
-import numpy as np
-import spaces
-import torch
-import random
-import os
-
-# from diffusers import QwenImageEditInpaintPipeline
-from optimization import optimize_pipeline_
-from diffusers.utils import load_image
-
-from diffusers import QwenImageControlNetModel, QwenImageControlNetInpaintPipeline
-
-import math
-from huggingface_hub import InferenceClient
-
-from PIL import Image
-
-# Set environment variable for parallel loading
-# os.environ["HF_ENABLE_PARALLEL_LOADING"] = "YES"
-
-# --- 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,
+with gr.Blocks(css=css, theme=gr.themes.Citrus()) as demo:
+    gr.HTML("<h1 style='text-align: center'>Qwen-Image with InstantX Inpainting ControlNet</style>")
+    gr.Markdown(
+        "Generate images with the [InstantX/Qwen-Image-ControlNet-Inpainting](https://huggingface.co/InstantX/Qwen-Image-ControlNet-Inpainting) that takes depth, pose and canny conditionings"
+    )
+    with gr.Row():
+        with gr.Column():
+            edit_image = gr.ImageEditor(
+                label='Upload and draw mask for inpainting',
+                type='pil',
+                sources=["upload", "webcam"],
+                image_mode='RGB',
+                layers=False,
+                brush=gr.Brush(colors=["#FFFFFF"], color_mode="fixed"),
+                height=600
+            )
+            prompt = gr.Text(
+                label="Prompt",
+                show_label=False,
+                max_lines=1,
+                placeholder="Enter your prompt (e.g., 'change the hat to red')",
+                container=False,
+            )
+            negative_prompt = gr.Text(
+                label="Negative Prompt",
+                show_label=True,
+                max_lines=1,
+                placeholder="Enter what you don't want (optional)",
+                container=False,
+                value="",
+                visible=False
+            )
+            run_button = gr.Button("Run")
+            
+        with gr.Column():
+            result = gr.ImageSlider(label="Result", show_label=False, interactive=False)
+            use_as_input_button = gr.Button("🔄 Use as Input Image", visible=False, variant="secondary")
+    
+    with gr.Accordion("Advanced Settings", open=False):
+        
+        seed = gr.Slider(
+            label="Seed",
+            minimum=0,
+            maximum=MAX_SEED,
+            step=1,
+            value=42,
         )
         
-        # Parse the response
-        result = completion.choices[0].message.content
+        randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
         
-        # 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)
-
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 2048
-
-# --- Helper functions for reuse feature ---
-def clear_result():
-    """Clears the result image."""
-    return gr.update(value=None)
-
-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[1])
-    return gr.update()
-
-
-base_model = "Qwen/Qwen-Image"
-controlnet_model = "InstantX/Qwen-Image-ControlNet-Inpainting"
-
-controlnet = QwenImageControlNetModel.from_pretrained(controlnet_model, torch_dtype=torch.bfloat16)
-
-pipe = QwenImageControlNetInpaintPipeline.from_pretrained(
-    base_model, controlnet=controlnet, torch_dtype=torch.bfloat16
-)
-pipe.to("cuda")
-
-
-@spaces.GPU(duration=120)
-def infer(edit_images, 
-          prompt, 
-          negative_prompt=" ", 
-          seed=42, 
-          randomize_seed=False, 
-          strength=1.0, 
-          num_inference_steps=30, 
-          true_cfg_scale=4.0,
-          rewrite_prompt=True,
-          progress=gr.Progress(track_tqdm=True)):
-    
-    image = edit_images["background"]
-    mask = edit_images["layers"][0]
-    
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-
-    if rewrite_prompt:
-        prompt = polish_prompt(prompt, image)
-        print(f"Rewritten Prompt: {prompt}")
-    
-    # Generate image using Qwen pipeline
-    result_image = pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        control_image=image,
-        control_mask=mask_image,
-        controlnet_conditioning_scale=strength,
-        num_inference_steps=num_inference_steps,
-        true_cfg_scale=true_cfg_scale,
-        generator=torch.Generator(device="cuda").manual_seed(seed)
-    ).images[0]
-    
-    return [image,result_image], seed
-    
-examples = [
-    "change the hat to red",
-    "make the background a beautiful sunset",
-    "replace the object with a flower vase",
-]
-
-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}
-"""
-
-
-with gr.Blocks(css=css, theme=gr.themes.Citrus()) as demo:
-        gr.HTML("<h1 style='text-align: center'>Qwen-Image with InstantX Inpainting ControlNet</style>")
-        gr.Markdown(
-            "Generate images with the [InstantX/Qwen-Image-ControlNet-Inpainting](https://huggingface.co/InstantX/Qwen-Image-ControlNet-Inpainting) that takes depth, pose and canny conditionings"
-        )
         with gr.Row():
-            with gr.Column():
-                edit_image = gr.ImageEditor(
-                    label='Upload and draw mask for inpainting',
-                    type='pil',
-                    sources=["upload", "webcam"],
-                    image_mode='RGB',
-                    layers=False,
-                    brush=gr.Brush(colors=["#FFFFFF"], color_mode="fixed"),
-                    height=600
-                )
-                prompt = gr.Text(
-                    label="Prompt",
-                    show_label=False,
-                    max_lines=1,
-                    placeholder="Enter your prompt (e.g., 'change the hat to red')",
-                    container=False,
-                )
-                negative_prompt = gr.Text(
-                    label="Negative Prompt",
-                    show_label=True,
-                    max_lines=1,
-                    placeholder="Enter what you don't want (optional)",
-                    container=False,
-                    value="",
-                    visible=False
-                )
-                run_button = gr.Button("Run")
-                
-            with gr.Column():
-                result = gr.ImageSlider(label="Result", show_label=False, interactive=False)
-                use_as_input_button = gr.Button("🔄 Use as Input Image", visible=False, variant="secondary")
-        
-        with gr.Accordion("Advanced Settings", open=False):
+            strength = gr.Slider(
+                label="Conditioning Scale",
+                minimum=0.0,
+                maximum=1.0,
+                step=0.1,
+                value=1.0,
+                info="Controls how much the inpainted region should change"
+            )
             
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
+            true_cfg_scale = gr.Slider(
+                label="True CFG Scale",
+                minimum=1.0,
+                maximum=10.0,
+                step=0.5,
+                value=4.0,
+                info="Classifier-free guidance scale"
+            )
+
+            num_inference_steps = gr.Slider(
+                label="Number of inference steps",
+                minimum=1,
+                maximum=50,
                 step=1,
-                value=42,
+                value=30,
             )
             
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-            
-            
-            with gr.Row():
-                strength = gr.Slider(
-                    label="Conditioning Scale",
-                    minimum=0.0,
-                    maximum=1.0,
-                    step=0.1,
-                    value=1.0,
-                    info="Controls how much the inpainted region should change"
-                )
-                
-                true_cfg_scale = gr.Slider(
-                    label="True CFG Scale",
-                    minimum=1.0,
-                    maximum=10.0,
-                    step=0.5,
-                    value=4.0,
-                    info="Classifier-free guidance scale"
-                )
-  
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=30,
-                )
-            rewrite_prompt = gr.Checkbox(
-                label="Enhance prompt (using HF Inference)", 
-                value=True
-            )
+        rewrite_prompt = gr.Checkbox(
+            label="Enhance prompt (using HF Inference)", 
+            value=True
+        )
 
-    # Event handlers for reuse functionality
+    # Event handlers for reuse functionality (MUST be inside gr.Blocks context with 4 spaces)
     use_as_input_button.click(
         fn=use_output_as_input,
         inputs=[result],
@@ -314,9 +97,9 @@ with gr.Blocks(css=css, theme=gr.themes.Citrus()) as demo:
         outputs=result,
         show_api=False
     ).then(
-        fn = infer,
-        inputs = [edit_image, prompt, negative_prompt, seed, randomize_seed, strength, num_inference_steps, true_cfg_scale, rewrite_prompt],
-        outputs = [result, seed]
+        fn=infer,
+        inputs=[edit_image, prompt, negative_prompt, seed, randomize_seed, strength, num_inference_steps, true_cfg_scale, rewrite_prompt],
+        outputs=[result, seed]
     ).then(
         fn=lambda: gr.update(visible=True),
         inputs=None,