Create app.py

app.py

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+import gradio as gr
+import numpy as np
+import random
+import torch
+import spaces
+import os
+import json
+
+from PIL import Image
+from diffusers import QwenImageEditPipeline, FlowMatchEulerDiscreteScheduler
+
+from huggingface_hub import InferenceClient
+import math
+
+# Assuming optimization.py and qwenimage/ are in the same directory
+from optimization import optimize_pipeline_
+from qwenimage.pipeline_qwen_image_edit import QwenImageEditPipeline as QwenImageEditPipelineCustom
+from qwenimage.transformer_qwen_image import QwenImageTransformer2DModel
+from qwenimage.qwen_fa3_processor import QwenDoubleStreamAttnProcessorFA3
+
+# --- prompt enhancement using hugging face inferenceclient ---
+def polish_prompt_hf(original_prompt, system_prompt):
+    """
+    Rewrites the prompt using a Hugging Face InferenceClient.
+    """
+    api_key = os.environ.get("HF_TOKEN") # Changed to HF_TOKEN as per common practice
+    if not api_key:
+        print("Warning: HF_TOKEN not set. Falling back to original prompt.")
+        return original_prompt
+
+    try:
+        client = InferenceClient(
+            provider="cerebras",
+            api_key=api_key,
+        )
+
+        messages = [
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": original_prompt}
+        ]
+
+        completion = client.chat.completions.create(
+            model="qwen/qwen3-235b-a22b-instruct-2507",
+            messages=messages,
+        )
+        
+        result = completion.choices[0].message.content
+        
+        if '{"rewritten"' in result:
+            try:
+                result = result.replace('```json', '').replace('```', '')
+                result_json = json.loads(result)
+                polished_prompt = result_json.get('rewritten', result)
+            except Exception: # Catch broader exception for JSON parsing
+                polished_prompt = result
+        else:
+            polished_prompt = result
+            
+        polished_prompt = polished_prompt.strip().replace("\n", " ")
+        return polished_prompt
+        
+    except Exception as e: # Catch broader exception for API calls
+        print(f"Error during API call to Hugging Face: {e}")
+        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.
+'''
+    
+    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"
+
+# Scheduler configuration for lightning
+scheduler_config = {
+    "base_image_seq_len": 256,
+    "base_shift": math.log(3),
+    "invert_sigmas": False, # Corrected boolean case
+    "max_image_seq_len": 8192,
+    "max_shift": math.log(3),
+    "num_train_timesteps": 1000,
+    "shift": 1.0,
+    "shift_terminal": None, # Corrected None case
+    "stochastic_sampling": False, # Corrected boolean case
+    "time_shift_type": "exponential",
+    "use_beta_sigmas": False, # Corrected boolean case
+    "use_dynamic_shifting": True, # Corrected boolean case
+    "use_exponential_sigmas": False, # Corrected boolean case
+    "use_karras_sigmas": False, # Corrected boolean case
+}
+
+# Initialize scheduler with lightning config
+scheduler = FlowMatchEulerDiscreteScheduler.from_config(scheduler_config)
+
+# Load the edit pipeline with lightning scheduler
+pipe = QwenImageEditPipelineCustom.from_pretrained( # Corrected class name
+    "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: # Catch broader exception
+    print(f"Warning: Could not load lightning LoRA weights: {e}")
+    print("Continuing with base model...")
+
+# Apply the same optimizations from the first version
+pipe.transformer.__class__ = QwenImageTransformer2DModel # Corrected class name
+pipe.transformer.set_attn_processor(QwenDoubleStreamAttnProcessorFA3()) # Corrected class name
+
+# --- Ahead-of-time compilation ---
+# It's important that the dummy image for optimization has the expected dimensions (e.g., 1024x1024)
+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 ---
+spaces.gpu(duration=60)
+def infer(
+    image,
+    prompt,
+    seed=42,
+    randomize_seed=False, # Corrected boolean case
+    true_guidance_scale=1.0,
+    num_inference_steps=8,  # Default to 8 steps for fast inference
+    rewrite_prompt=True, # Corrected boolean case
+    output_size="Original (1024x1024)", # New parameter for output size
+    progress=gr.Progress(track_tqdm=True), # Corrected class name
+):
+    """
+    Generates an edited image using the Qwen-Image-Edit pipeline with lightning acceleration,
+    and optionally resizes the output.
+    """
+    negative_prompt = " "
+    
+    if randomize_seed:
+        seed = random.randint(0, max_seed)
+
+    generator = torch.Generator(device=device).manual_seed(seed) # Corrected class name
+    
+    print(f"Original prompt: '{prompt}'")
+    print(f"Negative prompt: '{negative_prompt}'")
+    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}")
+
+    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=1
+        ).images
+        
+        output_image = images[0]
+
+        # Post-processing: Resize if a different output size is selected
+        if output_size != "Original (1024x1024)":
+            try:
+                if output_size == "Small (512x512)":
+                    target_size = (512, 512)
+                elif output_size == "Medium (768x768)":
+                    target_size = (768, 768)
+                elif output_size == "Large (1536x1536)":
+                    target_size = (1536, 1536)
+                else: # Custom size, parse it from "Custom (WxH)"
+                    width, height = map(int, output_size.split('(')[1][:-1].split('x'))
+                    target_size = (width, height)
+                
+                output_image = output_image.resize(target_size, Image.LANCZOS) # Use LANCZOS for high quality down/upscaling
+                print(f"Resized output image to: {target_size[0]}x{target_size[1]}")
+            except Exception as resize_e:
+                print(f"Warning: Could not resize image to {output_size}: {resize_e}")
+                print("Returning original size image.")
+
+        return output_image, seed
+        
+    except Exception as e:
+        print(f"Error during inference: {e}")
+        raise e
+
+# --- Examples and UI layout ---
+examples = [
+    # Example for demonstration, replace with actual image paths
+    # Ensure these paths are valid if running locally, or adjust for Hugging Face Spaces
+    [Image.new("RGB", (1024, 1024), color = 'red'), "Change the color to blue"],
+    [Image.new("RGB", (1024, 1024), color = 'green'), "Add a fluffy white cat sitting in the center"],
+]
+
+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("""
+        <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: -27px !important;margin-left: 96px">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=True, 
+                    type="pil"
+                )
+            result = gr.Image(
+                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):
+            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=4,
+                    maximum=28,
+                    step=1,
+                    value=8
+                )
+                
+            rewrite_prompt = gr.Checkbox(
+                label="Enhance Prompt (using HF Inference)", 
+                value=True
+            )
+
+            # New dropdown for output image size
+            output_size = gr.Dropdown(
+                label="Output Image Size",
+                choices=["Original (1024x1024)", "Small (512x512)", "Medium (768x768)", "Large (1536x1536)"],
+                value="Original (1024x1024)"
+            )
+
+        gr.Examples(examples=examples, inputs=[input_image, prompt], outputs=[result, seed], fn=infer, cache_examples=False) # Changed to use the new example inputs/outputs
+
+    gr.on(
+        triggers=[run_button.click, prompt.submit],
+        fn=infer,
+        inputs=[
+            input_image,
+            prompt,
+            seed,
+            randomize_seed,
+            true_guidance_scale,
+            num_inference_steps,
+            rewrite_prompt,
+            output_size, # Added output_size to inputs
+        ],
+        outputs=[result, seed],
+    )
+
+if __name__ == "__main__":
+    demo.launch()