app.py

import gradio as gr
import numpy as np
import random
import torch
import spaces

from PIL import Image
import torch 
import math

from optimization import optimize_pipeline_
from qwenimage.pipeline_qwen_image_edit import QwenImageEditPipeline
from qwenimage.transformer_qwenimage import QwenImageTransformer2DModel

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)


# --- 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

# --- 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

# --- Main Inference Function (with hardcoded negative prompt) ---
@spaces.GPU(duration=120)
def infer(
    image,
    prompt,
    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 image using the local Qwen-Image diffusers 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"Calling pipeline with 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}")

    # Generate the image
    image = pipe(
        image,
        prompt=prompt,
        negative_prompt=negative_prompt,
        num_inference_steps=num_inference_steps,
        generator=generator,
        true_cfg_scale=true_guidance_scale,
    ).images[0]

    return image, seed

# --- Examples and UI Layout ---
examples = []

css = """
#col-container {
    margin: 0 auto;
    max-width: 1024px;
}
#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</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.")
        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,
                )
                run_button = gr.Button("Edit!", variant="primary")

            result = gr.Image(label="Result", show_label=False, type="pil")

        with gr.Accordion("Advanced Settings", open=False):
            # Negative prompt UI element is removed here

            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=50,
                    step=1,
                    value=50,
                )

            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.on(
        triggers=[run_button.click, prompt.submit],
        fn=infer,
        inputs=[
            input_image,
            prompt,
            # negative_prompt is no longer an input from the UI
            seed,
            randomize_seed,
            true_guidance_scale,
            num_inference_steps,
            rewrite_prompt,
        ],
        outputs=[result, seed],
    )

if __name__ == "__main__":
    demo.launch()