import gradio as gr import numpy as np import spaces import torch import random import os # from diffusers import QwenImageEditInpaintPipeline from optimization import optimize_pipeline_ from diffusers.utils import load_image from diffusers import FlowMatchEulerDiscreteScheduler from qwenimage.pipeline_qwenimage_edit_inpaint import QwenImageEditInpaintPipeline from qwenimage.transformer_qwenimage import QwenImageTransformer2DModel from qwenimage.qwen_fa3_processor import QwenDoubleStreamAttnProcessorFA3 import math from huggingface_hub import InferenceClient from PIL import Image # Set environment variable for parallel loading # os.environ["HF_ENABLE_PARALLEL_LOADING"] = "YES" # --- 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) MAX_SEED = np.iinfo(np.int32).max MAX_IMAGE_SIZE = 2048 # --- Helper functions for reuse feature --- def clear_result(): """Clears the result image.""" return gr.update(value=None) def use_output_as_input(output_image): """Sets the generated output as the new input image.""" if output_image is not None: return gr.update(value=output_image[1]) return gr.update() # Initialize Qwen Image Edit pipeline # 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) pipe = QwenImageEditInpaintPipeline.from_pretrained("Qwen/Qwen-Image-Edit", scheduler=scheduler, torch_dtype=torch.bfloat16).to("cuda") pipe.load_lora_weights( "lightx2v/Qwen-Image-Lightning", weight_name="Qwen-Image-Lightning-8steps-V1.1.safetensors" ) pipe.fuse_lora() # pipe.transformer.__class__ = QwenImageTransformer2DModel pipe.transformer.set_attn_processor(QwenDoubleStreamAttnProcessorFA3()) # dummy_mask = load_image("https://github.com/Trgtuan10/Image_storage/blob/main/mask_cat.png?raw=true") # # --- Ahead-of-time compilation --- # optimize_pipeline_(pipe, image=Image.new("RGB", (1328, 1328)), prompt="prompt", mask_image=dummy_mask) @spaces.GPU(duration=120) def infer(edit_images, prompt, negative_prompt="", seed=42, randomize_seed=False, strength=1.0, num_inference_steps=8, true_cfg_scale=1.0, rewrite_prompt=True, progress=gr.Progress(track_tqdm=True)): image = edit_images["background"] mask = edit_images["layers"][0] if randomize_seed: seed = random.randint(0, MAX_SEED) if rewrite_prompt: prompt = polish_prompt(prompt, image) print(f"Rewritten Prompt: {prompt}") # Generate image using Qwen pipeline result_image = pipe( prompt=prompt, negative_prompt=negative_prompt, image=image, mask_image=mask, strength=strength, num_inference_steps=num_inference_steps, true_cfg_scale=true_cfg_scale, generator=torch.Generator(device="cuda").manual_seed(seed) ).images[0] return [image,result_image], seed examples = [ "change the hat to red", "make the background a beautiful sunset", "replace the object with a flower vase", ] 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("""
Qwen-Image Edit Logo

Inpaint

""") gr.Markdown(""" Inpaint images with Qwen Image Edit. [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 with FA3 for accelerated 8-step 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(): edit_image = gr.ImageEditor( label='Upload and draw mask for inpainting', type='pil', sources=["upload", "webcam"], image_mode='RGB', layers=False, brush=gr.Brush(colors=["#FFFFFF"], color_mode="fixed"), height=600 ) prompt = gr.Text( label="Prompt", show_label=False, max_lines=1, placeholder="Enter your prompt (e.g., 'change the hat to red')", container=False, ) negative_prompt = gr.Text( label="Negative Prompt", show_label=True, max_lines=1, placeholder="Enter what you don't want (optional)", container=False, value="", visible=False ) run_button = gr.Button("Run") with gr.Column(): result = gr.ImageSlider(label="Result", show_label=False, interactive=False) use_as_input_button = gr.Button("🔄 Use as Input Image", visible=False, variant="secondary") with gr.Accordion("Advanced Settings", open=False): seed = gr.Slider( label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=42, ) randomize_seed = gr.Checkbox(label="Randomize seed", value=True) with gr.Row(): strength = gr.Slider( label="Strength", minimum=0.0, maximum=1.0, step=0.1, value=1.0, info="Controls how much the inpainted region should change" ) true_cfg_scale = gr.Slider( label="True CFG Scale", minimum=1.0, maximum=10.0, step=0.5, value=1.0, info="Classifier-free guidance scale" ) num_inference_steps = gr.Slider( label="Number of inference steps", minimum=1, maximum=50, step=1, value=8, ) rewrite_prompt = gr.Checkbox( label="Enhance prompt (using HF Inference)", value=True ) # Event handlers for reuse functionality use_as_input_button.click( fn=use_output_as_input, inputs=[result], outputs=[edit_image], show_api=False ) # Main generation pipeline with result clearing and button visibility gr.on( triggers=[run_button.click, prompt.submit], fn=clear_result, inputs=None, outputs=result, show_api=False ).then( fn = infer, inputs = [edit_image, prompt, negative_prompt, seed, randomize_seed, strength, num_inference_steps, true_cfg_scale, rewrite_prompt], outputs = [result, seed] ).then( fn=lambda: gr.update(visible=True), inputs=None, outputs=use_as_input_button, show_api=False ) demo.launch()