Update app.py

app.py

@@ -13,11 +13,126 @@ from qwenimage.pipeline_qwenimage_edit_inpaint import QwenImageEditInpaintPipeli
 from qwenimage.transformer_qwenimage import QwenImageTransformer2DModel
 from qwenimage.qwen_fa3_processor import QwenDoubleStreamAttnProcessorFA3
 import math
+from huggingface_hub import InferenceClient
 
 from PIL import Image
 
 # Set environment variable for parallel loading
-os.environ["HF_ENABLE_PARALLEL_LOADING"] = "YES"
+# 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,
+        )
+        
+        # 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)
+
 
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 2048
@@ -56,19 +171,31 @@ pipe.fuse_lora()
 pipe.transformer.set_attn_processor(QwenDoubleStreamAttnProcessorFA3())
 
 
-
 # dummy_mask  = load_image("https://github.com/Trgtuan10/Image_storage/blob/main/mask_cat.png?raw=true")
 
 # # --- Ahead-of-time compilation ---
 # optimize_pipeline_(pipe, image=Image.new("RGB", (1328, 1328)), prompt="prompt", mask_image=dummy_mask)
 
 @spaces.GPU(duration=120)
-def infer(edit_images, prompt, negative_prompt="", seed=42, randomize_seed=False, strength=1.0, num_inference_steps=35, true_cfg_scale=4.0, progress=gr.Progress(track_tqdm=True)):
+def infer(edit_images, 
+          prompt, 
+          negative_prompt="", 
+          seed=42, 
+          randomize_seed=False, 
+          strength=1.0, 
+          num_inference_steps=8, 
+          true_cfg_scale=1.0,
+          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(
@@ -164,6 +291,7 @@ with gr.Blocks(css=css) as demo:
             
             randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
             
+            
             with gr.Row():
                 strength = gr.Slider(
                     label="Strength",
@@ -190,11 +318,15 @@ with gr.Blocks(css=css) as demo:
                     step=1,
                     value=8,
                 )
+            rewrite_prompt = gr.Checkbox(
+                label="Enhance prompt (using HF Inference)", 
+                value=True
+            )
 
     gr.on(
         triggers=[run_button.click, prompt.submit],
         fn = infer,
-        inputs = [edit_image, prompt, negative_prompt, seed, randomize_seed, strength, num_inference_steps, true_cfg_scale],
+        inputs = [edit_image, prompt, negative_prompt, seed, randomize_seed, strength, num_inference_steps, true_cfg_scale, rewrite_prompt],
         outputs = [result, seed]
     )