import gradio as gr import numpy as np import random import torch import spaces import os import json import time from PIL import Image, ImageDraw import torch import math from optimization import optimize_pipeline_ from qwenimage.pipeline_qwen_image_edit import QwenImageEditPipeline from qwenimage.transformer_qwenimage import QwenImageTransformer2DModel from qwenimage.qwen_fa3_processor import QwenDoubleStreamAttnProcessorFA3 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) # --- Outpainting Functions --- def can_expand(source_width, source_height, target_width, target_height, alignment): """Checks if the image can be expanded based on the alignment.""" if alignment in ("Left", "Right") and source_width >= target_width: return False if alignment in ("Top", "Bottom") and source_height >= target_height: return False return True def prepare_image_and_mask(image, width, height, overlap_percentage, resize_option, custom_resize_percentage, alignment, overlap_left, overlap_right, overlap_top, overlap_bottom): """Prepares the image with white margins and creates a mask for outpainting.""" target_size = (width, height) # Calculate the scaling factor to fit the image within the target size scale_factor = min(target_size[0] / image.width, target_size[1] / image.height) new_width = int(image.width * scale_factor) new_height = int(image.height * scale_factor) # Resize the source image to fit within target size source = image.resize((new_width, new_height), Image.LANCZOS) # Apply resize option using percentages if resize_option == "Full": resize_percentage = 100 elif resize_option == "50%": resize_percentage = 50 elif resize_option == "33%": resize_percentage = 33 elif resize_option == "25%": resize_percentage = 25 else: # Custom resize_percentage = custom_resize_percentage # Calculate new dimensions based on percentage resize_factor = resize_percentage / 100 new_width = int(source.width * resize_factor) new_height = int(source.height * resize_factor) # Ensure minimum size of 64 pixels new_width = max(new_width, 64) new_height = max(new_height, 64) # Resize the image source = source.resize((new_width, new_height), Image.LANCZOS) # Calculate the overlap in pixels based on the percentage overlap_x = int(new_width * (overlap_percentage / 100)) overlap_y = int(new_height * (overlap_percentage / 100)) # Ensure minimum overlap of 1 pixel overlap_x = max(overlap_x, 1) overlap_y = max(overlap_y, 1) # Calculate margins based on alignment if alignment == "Middle": margin_x = (target_size[0] - new_width) // 2 margin_y = (target_size[1] - new_height) // 2 elif alignment == "Left": margin_x = 0 margin_y = (target_size[1] - new_height) // 2 elif alignment == "Right": margin_x = target_size[0] - new_width margin_y = (target_size[1] - new_height) // 2 elif alignment == "Top": margin_x = (target_size[0] - new_width) // 2 margin_y = 0 elif alignment == "Bottom": margin_x = (target_size[0] - new_width) // 2 margin_y = target_size[1] - new_height # Adjust margins to eliminate gaps margin_x = max(0, min(margin_x, target_size[0] - new_width)) margin_y = max(0, min(margin_y, target_size[1] - new_height)) # Create a new background image with white margins and paste the resized source image background = Image.new('RGB', target_size, (255, 255, 255)) background.paste(source, (margin_x, margin_y)) # Create the mask mask = Image.new('L', target_size, 255) mask_draw = ImageDraw.Draw(mask) # Calculate overlap areas white_gaps_patch = 2 left_overlap = margin_x + overlap_x if overlap_left else margin_x + white_gaps_patch right_overlap = margin_x + new_width - overlap_x if overlap_right else margin_x + new_width - white_gaps_patch top_overlap = margin_y + overlap_y if overlap_top else margin_y + white_gaps_patch bottom_overlap = margin_y + new_height - overlap_y if overlap_bottom else margin_y + new_height - white_gaps_patch if alignment == "Left": left_overlap = margin_x + overlap_x if overlap_left else margin_x elif alignment == "Right": right_overlap = margin_x + new_width - overlap_x if overlap_right else margin_x + new_width elif alignment == "Top": top_overlap = margin_y + overlap_y if overlap_top else margin_y elif alignment == "Bottom": bottom_overlap = margin_y + new_height - overlap_y if overlap_bottom else margin_y + new_height # Draw the mask mask_draw.rectangle([ (left_overlap, top_overlap), (right_overlap, bottom_overlap) ], fill=0) return background, mask def preview_image_and_mask(image, width, height, overlap_percentage, resize_option, custom_resize_percentage, alignment, overlap_left, overlap_right, overlap_top, overlap_bottom): """Creates a preview showing the mask overlay.""" background, mask = prepare_image_and_mask(image, width, height, overlap_percentage, resize_option, custom_resize_percentage, alignment, overlap_left, overlap_right, overlap_top, overlap_bottom) # Create a preview image showing the mask preview = background.copy().convert('RGBA') # Create a semi-transparent red overlay red_overlay = Image.new('RGBA', background.size, (255, 0, 0, 64)) # Reduced alpha to 64 (25% opacity) # Convert black pixels in the mask to semi-transparent red red_mask = Image.new('RGBA', background.size, (0, 0, 0, 0)) red_mask.paste(red_overlay, (0, 0), mask) # Overlay the red mask on the background preview = Image.alpha_composite(preview, red_mask) return preview # --- 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 pipe.transformer.set_attn_processor(QwenDoubleStreamAttnProcessorFA3()) # --- 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 def clear_result(): """Clears the result image.""" return gr.update(value=None) def update_history(new_image, history): """Updates the history gallery with the new image.""" time.sleep(0.5) # Small delay to ensure image is ready if history is None: history = [] if new_image is not None: # Convert to list if needed (Gradio sometimes returns tuples) if not isinstance(history, list): history = list(history) if history else [] history.insert(0, new_image) # Keep only the last 20 images in history history = history[:20] return history def use_history_as_input(evt: gr.SelectData, history): """Sets the selected history image as the new input image.""" if history and evt.index < len(history): return gr.update(value=history[evt.index][0]) return gr.update() 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) return gr.update() def preload_presets(target_ratio, ui_width, ui_height): """Updates the width and height sliders based on the selected aspect ratio.""" if target_ratio == "9:16": changed_width = 720 changed_height = 1280 return changed_width, changed_height, gr.update() elif target_ratio == "16:9": changed_width = 1280 changed_height = 720 return changed_width, changed_height, gr.update() elif target_ratio == "1:1": changed_width = 1024 changed_height = 1024 return changed_width, changed_height, gr.update() elif target_ratio == "Custom": return ui_width, ui_height, gr.update(open=True) def select_the_right_preset(user_width, user_height): if user_width == 720 and user_height == 1280: return "9:16" elif user_width == 1280 and user_height == 720: return "16:9" elif user_width == 1024 and user_height == 1024: return "1:1" else: return "Custom" def toggle_custom_resize_slider(resize_option): return gr.update(visible=(resize_option == "Custom")) # --- Main Inference Function (with outpainting preprocessing) --- @spaces.GPU(duration=120) def infer( image, prompt, width, height, overlap_percentage, resize_option, custom_resize_percentage, alignment, overlap_left, overlap_right, overlap_top, overlap_bottom, 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 outpainted image using the Qwen-Image-Edit 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"Original 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}") # Prepare the image with white margins for outpainting outpaint_image, mask = prepare_image_and_mask( image, width, height, overlap_percentage, resize_option, custom_resize_percentage, alignment, overlap_left, overlap_right, overlap_top, overlap_bottom ) # Check if expansion is possible if not can_expand(image.width, image.height, width, height, alignment): alignment = "Middle" outpaint_image, mask = prepare_image_and_mask( image, width, height, overlap_percentage, resize_option, custom_resize_percentage, "Middle", overlap_left, overlap_right, overlap_top, overlap_bottom ) print(f"Outpaint dimensions: {outpaint_image.size}") # Generate the image with outpainting preprocessing result_image = pipe( outpaint_image, # Use the preprocessed image with white margins prompt="replace the white margins. "+ prompt, negative_prompt=negative_prompt, num_inference_steps=num_inference_steps, generator=generator, true_cfg_scale=true_guidance_scale, ).images[0] return result_image, seed # --- Examples and UI Layout --- # You can add examples here if you have sample images # examples = [ # ["path/to/example1.jpg", "extend the landscape", 1280, 720, "Middle"], # ["path/to/example2.jpg", "add more sky", 1024, 1024, "Top"], # ] 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} .preview-container { border: 1px solid #e0e0e0; border-radius: 8px; padding: 10px; margin-top: 10px; } .gallery-container { margin-top: 20px; } """ with gr.Blocks(css=css) as demo: with gr.Column(elem_id="col-container"): gr.HTML("""
Qwen-Image Edit Logo

Outpaint [Fast]

""") gr.Markdown(""" Outpaint 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 AoT compilation and 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(): input_image = gr.Image(label="Input Image", type="pil") prompt = gr.Text( label="Prompt", info="Describe what should appear in the extended areas", value="extend the image naturally", ) with gr.Row(): target_ratio = gr.Radio( label="Target Ratio", choices=["9:16", "16:9", "1:1", "Custom"], value="16:9", scale=2 ) alignment_dropdown = gr.Dropdown( choices=["Middle", "Left", "Right", "Top", "Bottom"], value="Middle", label="Alignment" ) run_button = gr.Button("run", variant="primary") with gr.Accordion("Outpainting Settings", open=False) as settings_panel: with gr.Row(): width_slider = gr.Slider( label="Target Width", minimum=512, maximum=2048, step=8, value=1280, ) height_slider = gr.Slider( label="Target Height", minimum=512, maximum=2048, step=8, value=720, ) with gr.Group(): overlap_percentage = gr.Slider( label="Mask overlap (%)", minimum=1, maximum=50, value=10, step=1, info="Controls the blending area between original and new content" ) with gr.Row(): overlap_top = gr.Checkbox(label="Overlap Top", value=True) overlap_right = gr.Checkbox(label="Overlap Right", value=True) with gr.Row(): overlap_left = gr.Checkbox(label="Overlap Left", value=True) overlap_bottom = gr.Checkbox(label="Overlap Bottom", value=True) with gr.Row(): resize_option = gr.Radio( label="Resize input image", choices=["Full", "50%", "33%", "25%", "Custom"], value="Full", info="How much of the target canvas the original image should occupy" ) custom_resize_percentage = gr.Slider( label="Custom resize (%)", minimum=1, maximum=100, step=1, value=50, visible=False ) preview_button = gr.Button("👁️ Preview alignment and mask", variant="secondary") 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=1, maximum=28, step=1, value=8, ) rewrite_prompt = gr.Checkbox( label="Enhance prompt (using HF Inference)", value=True ) with gr.Column(): result = gr.Image(label="Result", type="pil", interactive=False) use_as_input_button = gr.Button("🔄 Use as Input Image", visible=False, variant="secondary") with gr.Column(visible=False) as preview_container: preview_image = gr.Image(label="Preview (red area will be generated)", type="pil") gr.Markdown("---") with gr.Row(): gr.Markdown("### 📜 History") clear_history_button = gr.Button("🗑️ Clear History", size="sm", variant="stop") history_gallery = gr.Gallery( label="Click any image to use as input", columns=4, rows=2, object_fit="contain", height="auto", interactive=False, show_label=True, elem_classes=["gallery-container"] ) # Event handlers use_as_input_button.click( fn=use_output_as_input, inputs=[result], outputs=[input_image], show_api=False ) history_gallery.select( fn=use_history_as_input, inputs=[history_gallery], outputs=[input_image], show_api=False ) clear_history_button.click( fn=lambda: [], inputs=None, outputs=history_gallery, show_api=False ) target_ratio.change( fn=preload_presets, inputs=[target_ratio, width_slider, height_slider], outputs=[width_slider, height_slider, settings_panel], queue=False, ) width_slider.change( fn=select_the_right_preset, inputs=[width_slider, height_slider], outputs=[target_ratio], queue=False, ) height_slider.change( fn=select_the_right_preset, inputs=[width_slider, height_slider], outputs=[target_ratio], queue=False, ) resize_option.change( fn=toggle_custom_resize_slider, inputs=[resize_option], outputs=[custom_resize_percentage], queue=False, ) preview_button.click( fn=lambda: gr.update(visible=True), inputs=None, outputs=[preview_container], queue=False, ).then( fn=preview_image_and_mask, inputs=[ input_image, width_slider, height_slider, overlap_percentage, resize_option, custom_resize_percentage, alignment_dropdown, overlap_left, overlap_right, overlap_top, overlap_bottom ], outputs=preview_image, queue=False, ) # Main generation pipeline with result clearing, history update, and button visibility run_button.click( fn=clear_result, inputs=None, outputs=result, show_api=False ).then( fn=infer, inputs=[ input_image, prompt, width_slider, height_slider, overlap_percentage, resize_option, custom_resize_percentage, alignment_dropdown, overlap_left, overlap_right, overlap_top, overlap_bottom, seed, randomize_seed, true_guidance_scale, num_inference_steps, rewrite_prompt, ], outputs=[result, seed], ).then( fn=lambda: gr.update(visible=True), inputs=None, outputs=use_as_input_button, show_api=False ).then( fn=update_history, inputs=[result, history_gallery], outputs=history_gallery, show_api=False ) # Also trigger on prompt submit prompt.submit( fn=clear_result, inputs=None, outputs=result, show_api=False ).then( fn=infer, inputs=[ input_image, prompt, width_slider, height_slider, overlap_percentage, resize_option, custom_resize_percentage, alignment_dropdown, overlap_left, overlap_right, overlap_top, overlap_bottom, seed, randomize_seed, true_guidance_scale, num_inference_steps, rewrite_prompt, ], outputs=[result, seed], ).then( fn=lambda: gr.update(visible=True), inputs=None, outputs=use_as_input_button, show_api=False ).then( fn=update_history, inputs=[result, history_gallery], outputs=history_gallery, show_api=False ) if __name__ == "__main__": demo.launch()