init update

app.py

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+import gradio as gr
+import numpy as np
+import random
+import torch
+import spaces
+
+from PIL import Image
+from diffusers import QwenImageEditPlusPipeline
+
+import os
+import base64
+import json
+
+SYSTEM_PROMPT = '''
+# Edit Prompt Enhancer
+You are a professional edit prompt enhancer. Your task is to generate a direct and specific edit prompt based on the user-provided instruction and the image input conditions.  
+
+Please strictly follow the enhancing rules below:
+
+## 1. General Principles
+- Keep the enhanced prompt **direct and specific**.  
+- 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 `" "`. Keep the original language of the text, and keep the capitalization.  
+- Both adding new text and replacing existing text are text replacement tasks, For example:  
+    - Replace "xx" to "yy"  
+    - Replace the mask / bounding box to "yy"  
+    - Replace the visual object to "yy"  
+- Specify text position, color, and layout only if user has required.  
+- If font is specified, keep the original language of the font.  
+
+### 3. Human (ID) Editing Tasks
+- Emphasize maintaining 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 / beauty / make up changes, they must be natural and subtle, never exaggerated.**  
+- 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 Conversion or Enhancement Tasks
+- If a style is specified, describe it concisely using key visual features. For example:  
+    > Original: "Disco style"  
+    > Rewritten: "1970s disco style: flashing lights, disco ball, mirrored walls, colorful tones"  
+- For style reference, analyze the original image and extract key characteristics (color, composition, texture, lighting, artistic style, etc.), integrating them into the instruction.  
+- **Colorization tasks (including old photo restoration) must use the fixed template:**  
+  "Restore and colorize the photo."  
+- Clearly specify the object to be modified. For example:  
+    > Original: Modify the subject in Picture 1 to match the style of Picture 2.  
+    > Rewritten: Change the girl in Picture 1 to the ink-wash style of Picture 2 — rendered in black-and-white watercolor with soft color transitions.
+
+- If there are other changes, place the style description at the end.
+
+### 5. Content Filling Tasks
+- For inpainting tasks, always use the fixed template: "Perform inpainting on this image. The original caption is: ".
+- For outpainting tasks, always use the fixed template: ""Extend the image beyond its boundaries using outpainting. The original caption is: ".
+
+### 6. Multi-Image Tasks
+- Rewritten prompts must clearly point out which image’s element is being modified. For example:  
+    > Original: "Replace the subject of picture 1 with the subject of picture 2"  
+    > Rewritten: "Replace the girl of picture 1 with the boy of picture 2, keeping picture 2’s background unchanged"  
+- For stylization tasks, describe the reference image’s style in the rewritten prompt, while preserving the visual content of the source image.  
+
+## 3. Rationale and Logic Checks
+- Resolve contradictory instructions: e.g., "Remove all trees but keep all trees" should be logically corrected.  
+- Add missing key information: e.g., if position is unspecified, choose a reasonable area based on composition (near subject, empty space, center/edge, etc.).  
+
+# Output Format Example
+```json
+{
+   "Rewritten": "..."
+}
+'''
+
+def polish_prompt(prompt, img):
+    prompt = f"{SYSTEM_PROMPT}\n\nUser Input: {prompt}\n\nRewritten Prompt:"
+    success=False
+    while not success:
+        try:
+            result = api(prompt, [img])
+            # print(f"Result: {result}")
+            # print(f"Polished Prompt: {polished_prompt}")
+            if isinstance(result, str):
+                result = result.replace('```json','')
+                result = result.replace('```','')
+                result = json.loads(result)
+            else:
+                result = json.loads(result)
+
+            polished_prompt = result['Rewritten']
+            polished_prompt = polished_prompt.strip()
+            polished_prompt = polished_prompt.replace("\n", " ")
+            success = True
+        except Exception as e:
+            print(f"[Warning] Error during API call: {e}")
+    return polished_prompt
+
+
+def encode_image(pil_image):
+    import io
+    buffered = io.BytesIO()
+    pil_image.save(buffered, format="PNG")
+    return base64.b64encode(buffered.getvalue()).decode("utf-8")
+
+
+
+
+def api(prompt, img_list, model="qwen-vl-max-latest", kwargs={}):
+    import dashscope
+    api_key = os.environ.get('DASH_API_KEY')
+    if not api_key:
+        raise EnvironmentError("DASH_API_KEY is not set")
+    assert model in ["qwen-vl-max-latest"], f"Not implemented model {model}"
+    sys_promot = "you are a helpful assistant, you should provide useful answers to users."
+    messages = [
+        {"role": "system", "content": sys_promot},
+        {"role": "user", "content": []}]
+    for img in img_list:
+        messages[1]["content"].append(
+            {"image": f"data:image/png;base64,{encode_image(img)}"})
+    messages[1]["content"].append({"text": f"{prompt}"})
+
+    response_format = kwargs.get('response_format', None)
+
+    response = dashscope.MultiModalConversation.call(
+        api_key=api_key,
+        model=model, # For example, use qwen-plus here. You can change the model name as needed. Model list: https://help.aliyun.com/zh/model-studio/getting-started/models
+        messages=messages,
+        result_format='message',
+        response_format=response_format,
+        )
+
+    if response.status_code == 200:
+        return response.output.choices[0].message.content[0]['text']
+    else:
+        raise Exception(f'Failed to post: {response}')
+
+# --- Model Loading ---
+dtype = torch.bfloat16
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# Load the model pipeline
+pipe = QwenImageEditPlusPipeline.from_pretrained("Qwen/Qwen-Image-Edit-2511", torch_dtype=dtype).to(device)
+
+# --- UI Constants and Helpers ---
+MAX_SEED = np.iinfo(np.int32).max
+
+# --- Main Inference Function (with hardcoded negative prompt) ---
+@spaces.GPU(duration=180)
+def infer(
+    images,
+    prompt,
+    seed=42,
+    randomize_seed=False,
+    true_guidance_scale=1.0,
+    num_inference_steps=50,
+    height=None,
+    width=None,
+    rewrite_prompt=True,
+    num_images_per_prompt=1,
+    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)
+    
+    # Load input images into PIL Images
+    pil_images = []
+    if images is not None:
+        for item in images:
+            try:
+                if isinstance(item[0], Image.Image):
+                    pil_images.append(item[0].convert("RGB"))
+                elif isinstance(item[0], str):
+                    pil_images.append(Image.open(item[0]).convert("RGB"))
+                elif hasattr(item, "name"):
+                    pil_images.append(Image.open(item.name).convert("RGB"))
+            except Exception:
+                continue
+
+    if height==256 and width==256:
+        height, width = None, None
+    print(f"Calling pipeline with prompt: '{prompt}'")
+    print(f"Negative Prompt: '{negative_prompt}'")
+    print(f"Seed: {seed}, Steps: {num_inference_steps}, Guidance: {true_guidance_scale}, Size: {width}x{height}")
+    if rewrite_prompt and len(pil_images) > 0:
+        prompt = polish_prompt(prompt, pil_images[0])
+        print(f"Rewritten Prompt: {prompt}")
+    
+
+    # Generate the image
+    image = pipe(
+        image=pil_images if len(pil_images) > 0 else None,
+        prompt=prompt,
+        height=height,
+        width=width,
+        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
+
+    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_edit_logo.png" alt="Qwen-Image Logo" width="400" style="display: block; margin: 0 auto;">')
+        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_images = gr.Gallery(label="Input Images", show_label=False, type="pil", interactive=True)
+
+            # result = gr.Image(label="Result", show_label=False, type="pil")
+            result = gr.Gallery(label="Result", show_label=False, type="pil")
+        with gr.Row():
+            prompt = gr.Text(
+                    label="Prompt",
+                    show_label=False,
+                    placeholder="describe the edit instruction",
+                    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,
+                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=4.0
+                )
+
+                num_inference_steps = gr.Slider(
+                    label="Number of inference steps",
+                    minimum=1,
+                    maximum=50,
+                    step=1,
+                    value=40,
+                )
+                
+                height = gr.Slider(
+                    label="Height",
+                    minimum=256,
+                    maximum=2048,
+                    step=8,
+                    value=None,
+                )
+                
+                width = gr.Slider(
+                    label="Width",
+                    minimum=256,
+                    maximum=2048,
+                    step=8,
+                    value=None,
+                )
+                
+                
+                rewrite_prompt = gr.Checkbox(label="Rewrite prompt", 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_images,
+            prompt,
+            seed,
+            randomize_seed,
+            true_guidance_scale,
+            num_inference_steps,
+            height,
+            width,
+            rewrite_prompt,
+        ],
+        outputs=[result, seed],
+    )
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+git+https://github.com/huggingface/diffusers.git
+transformers
+accelerate
+safetensors
+sentencepiece
+dashscope
+kernels
+torchvision