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("""
Qwen-Image Edit Logo

Fast, 8-steps with Lightning LoRA

""") 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()