app_local.py

import gradio as gr
import numpy as np
import random
import torch
import spaces
from PIL import Image
from diffusers import QwenImageEditPipeline
from diffusers.utils import is_xformers_available
import os
import re
import gc
import json  # Added json import
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig

#############################
os.environ.setdefault('GRADIO_ANALYTICS_ENABLED', 'False')
os.environ.setdefault('HF_HUB_DISABLE_TELEMETRY', '1')

# Model configuration
REWRITER_MODEL = "Qwen/Qwen1.5-7B-Chat"  # Upgraded to 7B for better JSON handling
dtype = torch.bfloat16
device = "cuda" if torch.cuda.is_available() else "cpu"

# Preload enhancement model at startup
print("🔄 Loading prompt enhancement model...")
rewriter_tokenizer = AutoTokenizer.from_pretrained(REWRITER_MODEL)

# Quantization configuration
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_use_double_quant=True
)

rewriter_model = AutoModelForCausalLM.from_pretrained(
    REWRITER_MODEL,
    torch_dtype=dtype,
    device_map="auto",
    quantization_config=bnb_config,
    max_memory={0: "48GiB"},  # Reserve adequate memory
)
print("✅ Enhancement model loaded and ready!")

SYSTEM_PROMPT_EDIT = '''
# Edit Instruction Rewriter
You are a professional edit instruction rewriter. Your task is to generate a precise, concise, and visually achievable instruction based on the user's intent and the input image.
## 1. General Principles
- Keep the rewritten instruction **concise** and clear.
- Avoid contradictions, vagueness, or unachievable instructions.
- Maintain the core logic of the original instruction; only enhance clarity and feasibility.
- Ensure new added elements or modifications align with the image's original context and art style.
## 2. Task Types
### Add, Delete, Replace:
- When the input is detailed, only refine grammar and clarity.
- For vague instructions, infer minimal but sufficient details.
- For replacement, use the format: `"Replace X with Y"`.
### Text Editing (e.g., text replacement):
- Enclose text content in quotes, e.g., `Replace "abc" with "xyz"`.
- Preserving the original structure and language—**do not translate** or alter style.
### Human Editing (e.g., change a person’s face/hair):
- Preserve core visual identity (gender, ethnic features).
- Describe expressions in subtle and natural terms.
- Maintain key clothing or styling details unless explicitly replaced.
### Style Transformation:
- If a style is specified, e.g., `Disco style`, rewrite it to encapsulate the essential visual traits.
- Use a fixed template for **coloring/restoration**:  
  `"Restore old photograph, remove scratches, reduce noise, enhance details, high resolution, realistic, natural skin tones, clear facial features, no distortion, vintage photo restoration"`  
  if applicable.
## 4. Output Format
Please provide the rewritten instruction in a clean `json` format as:
{
  "Rewritten": "..."
}
'''

def extract_json_response(model_output: str) -> str:
    """Extract rewritten instruction from potentially messy JSON output"""
    # New: Remove code block markers first
    model_output = re.sub(r'```(?:json)?\s*', '', model_output)
    
    try:
        # Try to find the JSON portion in the output
        start_idx = model_output.find('{')
        end_idx = model_output.rfind('}')
        if start_idx == -1 or end_idx == -1:
            return None
        
        # Expand to the full object including outer braces
        end_idx += 1  # Include the closing brace
        
        json_str = model_output[start_idx:end_idx]
        
        # Improved quote handling for values
        json_str = re.sub(r'(\w+)\s*:', r'"\1":', json_str)  # Quote keys
        json_str = re.sub(r':\s*([^"\s{[]+)', r': "\1"', json_str)  # Quote unquoted string values
        
        # Parse JSON
        data = json.loads(json_str)
        
        # Extract rewritten prompt from possible key variations
        possible_keys = [
            "Rewritten", "rewritten", "Rewrited", "rewrited", "Rewrittent",
            "Output", "output", "Enhanced", "enhanced"
        ]
        for key in possible_keys:
            if key in data:
                return data[key].strip()
        
        # Try nested path
        if "Response" in data and "Rewritten" in data["Response"]:
            return data["Response"]["Rewritten"].strip()
        
        # Handle nested JSON objects (additional protection)
        if isinstance(data, dict):
            for value in data.values():
                if isinstance(value, dict) and "Rewritten" in value:
                    return value["Rewritten"].strip()
            
        # Try to find any string value that looks like an instruction
        str_values = [v for v in data.values() if isinstance(v, str) and 10 < len(v) < 500]
        if str_values:
            return str_values[0].strip()
                
    except Exception as e:
        print(f"JSON parse error: {str(e)}")
    
    return None

def polish_prompt(original_prompt: str) -> str:
    """Enhanced prompt rewriting using original system prompt with JSON handling"""
    load_rewriter()
    
    # Format as Qwen chat
    messages = [
        {"role": "system", "content": SYSTEM_PROMPT_EDIT},
        {"role": "user", "content": original_prompt}
    ]
    
    text = rewriter_tokenizer.apply_chat_template(
        messages,
        tokenize=False,
        add_generation_prompt=True
    )
    
    model_inputs = rewriter_tokenizer(text, return_tensors="pt").to(device)
    
    with torch.no_grad():
        generated_ids = rewriter_model.generate(
            **model_inputs,
            max_new_tokens=256,  # Reduced for better quality
            do_sample=True,
            temperature=0.5,  # Less creative but more focused
            top_p=0.9,
            no_repeat_ngram_size=3,
            pad_token_id=rewriter_tokenizer.eos_token_id
        )
    
    # Extract and clean response
    enhanced = rewriter_tokenizer.decode(
        generated_ids[0][model_inputs.input_ids.shape[1]:],
        skip_special_tokens=True
    ).strip()
    
    # New: Last-resort JSON content extraction
    json_str = enhanced
    if '```' in enhanced:
        parts = enhanced.split('```')
        if len(parts) >= 3:
            json_str = parts[1]  # Take content between first set of ```
    
    # Try to extract JSON content
    rewritten_prompt = extract_json_response(json_str if '```' in enhanced else enhanced)
    
    if rewritten_prompt:
        # Clean up remaining artifacts
        rewritten_prompt = re.sub(r'(Replace|Change|Add) "(.*?)"', r'\1 \2', rewritten_prompt)
        rewritten_prompt = rewritten_prompt.replace('\\"', '"').replace('\\n', ' ')
        return rewritten_prompt
    
    # Fallback cleanup if JSON extraction fails
    if '```' in enhanced:
        # Extract content from code blocks
        parts = enhanced.split('```')
        if len(parts) >= 3:
            rewritten_prompt = parts[1].strip()
        else:
            rewritten_prompt = enhanced
    else:
        rewritten_prompt = enhanced
    
    # Improved cleaning of fallback output
    rewritten_prompt = re.sub(r'.*{.*}.*', '', rewritten_prompt)
    rewritten_prompt = re.sub(r'\s\s+', ' ', rewritten_prompt).strip()
    if ': ' in rewritten_prompt:
        rewritten_prompt = rewritten_prompt.split(': ', 1)[-1].strip()
    
    return rewritten_prompt[:200]  # Ensure reasonable length

# Load main image editing pipeline
pipe = QwenImageEditPipeline.from_pretrained(
    "Qwen/Qwen-Image-Edit",
    torch_dtype=dtype
).to(device)

# Load LoRA weights for acceleration
pipe.load_lora_weights(
    "lightx2v/Qwen-Image-Lightning",
    weight_name="Qwen-Image-Lightning-8steps-V1.1.safetensors"
)
pipe.fuse_lora()

if is_xformers_available():
    pipe.enable_xformers_memory_efficient_attention()
else:
    print("xformers not available")

# def unload_rewriter():
#     """Clear enhancement model from memory"""
#     global rewriter_tokenizer, rewriter_model
#     if rewriter_model:
#         del rewriter_tokenizer, rewriter_model
#         rewriter_tokenizer = None
#         rewriter_model = None
#     torch.cuda.empty_cache()
#     gc.collect()

@spaces.GPU(duration=60)
def infer(
    image,
    prompt,
    seed=42,
    randomize_seed=False,
    true_guidance_scale=1.0,
    num_inference_steps=8,
    rewrite_prompt=False,
    num_images_per_prompt=1,
):
    """Image editing endpoint with optimized prompt handling"""
    original_prompt = prompt
    prompt_info = ""
    
    # Handle prompt rewriting
    if rewrite_prompt:
        try:
            enhanced_instruction = polish_prompt(original_prompt)
            prompt_info = (
                f"<div style='margin:10px; padding:15px; border-radius:8px; border-left:4px solid #4CAF50; background: #f5f9fe'>"
                f"<h4 style='margin-top: 0;'>🚀 Prompt Enhancement</h4>"
                f"<p><strong>Original:</strong> {original_prompt}</p>"
                f"<p><strong style='color:#2E7D32;'>Enhanced:</strong> {enhanced_instruction}</p>"
                f"</div>"
            )
            prompt = enhanced_instruction
        except Exception as e:
            gr.Warning(f"Prompt enhancement failed: {str(e)}")
            prompt_info = (
                f"<div style='margin:10px; padding:15px; border-radius:8px; border-left:4px solid #FF5252; background: #fef5f5'>"
                f"<h4 style='margin-top: 0;'>⚠️ Enhancement Not Applied</h4>"
                f"<p>Using original prompt. Error: {str(e)[:100]}</p>"
                f"</div>"
            )
    else:
        prompt_info = (
            f"<div style='margin:10px; padding:10px; border-radius:8px; background: #f8f9fa'>"
            f"<h4 style='margin-top: 0;'>📝 Original Prompt</h4>"
            f"<p>{original_prompt}</p>"
            f"</div>"
        )
    

    # Set seed for reproducibility
    seed_val = seed if not randomize_seed else random.randint(0, MAX_SEED)
    generator = torch.Generator(device=device).manual_seed(seed_val)
    
    try:
        # Generate images
        edited_images = pipe(
            image=image,
            prompt=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
        return edited_images, seed_val, prompt_info
        
    except Exception as e:
        gr.Error(f"Image generation failed: {str(e)}")
        return [], seed_val, (
            f"<div style='margin:10px; padding:15px; border-radius:8px; border-left:4px solid #dd2c00; background: #fef5f5'>"
            f"<h4 style='margin-top: 0;'>⚠️ Processing Error</h4>"
            f"<p>{str(e)[:200]}</p>"
            f"</div>"
        )

MAX_SEED = np.iinfo(np.int32).max

with gr.Blocks(title="Qwen Image Editor Fast") as demo:
    gr.Markdown("""
    <div style="text-align: center; background: linear-gradient(to right, #3a7bd5, #00d2ff); color: white; padding: 20px; border-radius: 8px;">
        <h1 style="margin-bottom: 5px;">⚡️ Qwen-Image-Edit Lightning</h1>
        <p>✨ 8-step inferencing with lightx2v's LoRA.")
        <p>📝 Local Prompt Enhancement</p>
    </div>
    """)
    
    with gr.Row(equal_height=True):
        # Input Column
        with gr.Column(scale=1):
            input_image = gr.Image(
                label="Source Image", 
                type="pil", 
                height=300
            )
            prompt = gr.Textbox(
                label="Edit Instructions", 
                placeholder="e.g. Replace the background with a beach sunset...",
                lines=2,
                max_lines=4
            )
            
            with gr.Row():
                rewrite_toggle = gr.Checkbox(
                    label="Enable Prompt Enhancement", 
                    value=True,
                    interactive=True
                )
                run_button = gr.Button(
                    "Generate Edits", 
                    variant="primary", 
                    min_width=120
                )
            
            with gr.Accordion("Advanced Parameters", open=False):
                with gr.Row():
                    seed = gr.Slider(
                        label="Seed", 
                        minimum=0, 
                        maximum=MAX_SEED, 
                        step=1, 
                        value=42
                    )
                    randomize_seed = gr.Checkbox(
                        label="Random Seed", 
                        value=True
                    )
                with gr.Row():
                    true_guidance_scale = gr.Slider(
                        label="Guidance Scale", 
                        minimum=1.0, 
                        maximum=5.0, 
                        step=0.1, 
                        value=1.0
                    )
                    num_inference_steps = gr.Slider(
                        label="Inference Steps", 
                        minimum=4, 
                        maximum=16, 
                        step=1, 
                        value=8
                    )
                num_images_per_prompt = gr.Slider(
                    label="Output Count", 
                    minimum=1, 
                    maximum=4, 
                    step=1, 
                    value=1
                )
            
        # Output Column
        with gr.Column(scale=1):
            result = gr.Gallery(
                label="Edited Images",
                columns=lambda x: min(x, 2),
                height=500,
                object_fit="cover",
                preview=True
            )
            prompt_info = gr.HTML(
                value="<div style='padding:15px; background:#f8f9fa; border-radius:8px; margin-top:15px'>"
                "Prompt details will appear after generation</div>"
            )
    
    # # Examples
    # gr.Examples(
    #     examples=[
    #         "Change the background scene to a rooftop bar at night",
    #         "Transform to pixel art style with 8-bit graphics",
    #         "Replace all text with 'Qwen AI' in futuristic font"
    #     ],
    #     inputs=[prompt],
    #     label="Sample Instructions",
    #     cache_examples=True
    # )

    # Set up processing
    inputs = [
        input_image,
        prompt,
        seed,
        randomize_seed,
        true_guidance_scale,
        num_inference_steps,
        rewrite_toggle,
        num_images_per_prompt
    ]
    
    outputs = [result, seed, prompt_info]
    
    run_button.click(
        fn=infer,
        inputs=inputs,
        outputs=outputs
    )
    
    prompt.submit(
        fn=infer,
        inputs=inputs,
        outputs=outputs
    )

demo.queue(max_size=5).launch()