Update app.py

app.py

@@ -3,42 +3,201 @@ import numpy as np
 import random
 import torch
 import spaces
+import os
+import json
 
 from PIL import Image
-from diffusers import QwenImageEditPipeline
+from diffusers import QwenImageEditPipeline, FlowMatchEulerDiscreteScheduler
+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)
 
-import os
 
 # --- Model Loading ---
 dtype = torch.bfloat16
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
-# Load the model pipeline
-pipe = QwenImageEditPipeline.from_pretrained("Qwen/Qwen-Image-Edit", torch_dtype=dtype).to(device)
-pipe.load_lora_weights(
-    "lightx2v/Qwen-Image-Lightning", weight_name="Qwen-Image-Lightning-8steps-V1.1.safetensors"
-)
-pipe.fuse_lora()
+# Scheduler configuration for Lightning
+scheduler_config = {
+    "base_image_seq_len": 256,
+    "base_shift": math.log(3),
+    "invert_sigmas": False,
+    "max_image_seq_len": 8192,
+    "max_shift": math.log(3),
+    "num_train_timesteps": 1000,
+    "shift": 1.0,
+    "shift_terminal": None,
+    "stochastic_sampling": False,
+    "time_shift_type": "exponential",
+    "use_beta_sigmas": False,
+    "use_dynamic_shifting": True,
+    "use_exponential_sigmas": False,
+    "use_karras_sigmas": False,
+}
+
+# Initialize scheduler with Lightning config
+scheduler = FlowMatchEulerDiscreteScheduler.from_config(scheduler_config)
+
+# Load the edit pipeline with Lightning scheduler
+pipe = QwenImageEditPipeline.from_pretrained(
+    "Qwen/Qwen-Image-Edit", 
+    scheduler=scheduler,
+    torch_dtype=dtype
+).to(device)
+
+# Load Lightning LoRA weights for acceleration
+try:
+    pipe.load_lora_weights(
+        "lightx2v/Qwen-Image-Lightning", 
+        weight_name="Qwen-Image-Lightning-8steps-V1.1.safetensors"
+    )
+    pipe.fuse_lora()
+    print("Successfully loaded Lightning LoRA weights")
+except Exception as e:
+    print(f"Warning: Could not load Lightning LoRA weights: {e}")
+    print("Continuing with base model...")
 
 # --- UI Constants and Helpers ---
 MAX_SEED = np.iinfo(np.int32).max
 
-# --- Main Inference Function (with hardcoded negative prompt) ---
-@spaces.GPU(duration=120)
+# --- Main Inference Function ---
+@spaces.GPU(duration=60)
 def infer(
     image,
     prompt,
     seed=42,
     randomize_seed=False,
-    guidance_scale=4.0,
-    num_inference_steps=50,
+    true_guidance_scale=1.0,
+    num_inference_steps=8,  # Default to 8 steps for fast inference
+    rewrite_prompt=True,
+    num_images_per_prompt=1,
     progress=gr.Progress(track_tqdm=True),
 ):
     """
-    Generates an image using the local Qwen-Image diffusers pipeline.
+    Generates an edited image using the Qwen-Image-Edit pipeline with Lightning acceleration.
     """
-    # Hardcode the negative prompt as requested
-    negative_prompt = "text, watermark, copyright, blurry, low resolution"
+    # Hardcode the negative prompt as in the original
+    negative_prompt = " "
     
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
@@ -46,55 +205,89 @@ def infer(
     # Set up the generator for reproducibility
     generator = torch.Generator(device=device).manual_seed(seed)
     
-    print(f"Calling pipeline with prompt: '{prompt}'")
+    print(f"Original prompt: '{prompt}'")
     print(f"Negative Prompt: '{negative_prompt}'")
-    print(f"Seed: {seed}, Steps: {num_inference_steps}, Guidance: {guidance_scale}")
+    print(f"Seed: {seed}, Steps: {num_inference_steps}, Guidance: {true_guidance_scale}")
+    
+    if rewrite_prompt:
+        prompt = polish_prompt(prompt, image)
+        print(f"Rewritten Prompt: {prompt}")
 
-    # Generate the image
-    image = pipe(
-        image,
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        num_inference_steps=num_inference_steps,
-        generator=generator,
-        true_cfg_scale=1.0,
-        guidance_scale=guidance_scale  # Use a fixed default for distilled guidance
-    ).images[0]
+    # Generate the edited image
+    try:
+        images = pipe(
+            image,
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            num_inference_steps=num_inference_steps,
+            generator=generator,
+            true_cfg_scale=true_guidance_scale,
+            num_images_per_prompt=num_images_per_prompt
+        ).images
+    except Exception as e:
+        print(f"Error during inference: {e}")
+        raise e
 
-    return image, seed
+    return images, seed
 
 # --- Examples and UI Layout ---
-examples = []
+examples = [
+    # You can add example pairs of [image_path, prompt] here
+    # ["path/to/image1.jpg", "Replace the background with a beach scene"],
+    # ["path/to/image2.jpg", "Add a red hat to the person"],
+]
 
 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) as demo:
     with gr.Column(elem_id="col-container"):
-        gr.HTML('<img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-Image/qwen_image_logo.png" alt="Qwen-Image Logo" width="400" style="display: block; margin: 0 auto;">')
-        gr.HTML('<h1 style="text-align: center;margin-left: 80px;color: #5b47d1;font-style: italic;">Edit Fast</h1>', elem_id="edit_text")
-        gr.Markdown("[Learn more](https://github.com/QwenLM/Qwen-Image) about the Qwen-Image series. 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.")
+        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: -33px !important;margin-left: 133px;">Fast, 8-steps with Lightning LoRA</h2>
+        </div>
+        """)
+        gr.Markdown("""
+        [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 for accelerated 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", show_label=False, type="pil")
-                prompt = gr.Text(
-                    label="Prompt",
-                    show_label=False,
-                    placeholder="describe the edit instruction",
-                    container=False,
+                input_image = gr.Image(
+                    label="Input Image", 
+                    show_label=True, 
+                    type="pil"
                 )
-                run_button = gr.Button("Edit!", variant="primary")
-
-            result = gr.Image(label="Result", show_label=False, type="pil")
+            result = gr.Gallery(
+                label="Result", 
+                show_label=True, 
+                type="pil"
+            )
+            
+        with gr.Row():
+            prompt = gr.Text(
+                label="Edit Instruction",
+                show_label=False,
+                placeholder="Describe the edit instruction (e.g., 'Replace the background with a sunset', 'Add a red hat', 'Remove the person')",
+                container=False,
+            )
+            run_button = gr.Button("Edit!", variant="primary")
 
         with gr.Accordion("Advanced Settings", open=False):
-            # Negative prompt UI element is removed here
-
             seed = gr.Slider(
                 label="Seed",
                 minimum=0,
@@ -106,23 +299,38 @@ with gr.Blocks(css=css) as demo:
             randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
 
             with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
+                true_guidance_scale = gr.Slider(
+                    label="True guidance scale",
+                    minimum=1.0,
                     maximum=10.0,
                     step=0.1,
-                    value=4.0,
+                    value=1.0
                 )
 
                 num_inference_steps = gr.Slider(
                     label="Number of inference steps",
+                    minimum=4,
+                    maximum=28,
+                    step=1,
+                    value=8
+                )
+                
+            with gr.Row():
+                num_images_per_prompt = gr.Slider(
+                    label="Number of images per prompt",
                     minimum=1,
-                    maximum=50,
+                    maximum=4,
                     step=1,
-                    value=8,
+                    value=1,
+                    visible=False
+                )
+                
+                rewrite_prompt = gr.Checkbox(
+                    label="Enhance prompt (using HF Inference)", 
+                    value=True
                 )
 
-        # gr.Examples(examples=examples, inputs=[prompt], outputs=[result, seed], fn=infer, cache_examples=False)
+        # gr.Examples(examples=examples, inputs=[input_image, prompt], outputs=[result, seed], fn=infer, cache_examples=False)
 
     gr.on(
         triggers=[run_button.click, prompt.submit],
@@ -130,11 +338,12 @@ with gr.Blocks(css=css) as demo:
         inputs=[
             input_image,
             prompt,
-            # negative_prompt is no longer an input from the UI
             seed,
             randomize_seed,
-            guidance_scale,
+            true_guidance_scale,
             num_inference_steps,
+            rewrite_prompt,
+            num_images_per_prompt,
         ],
         outputs=[result, seed],
     )