import gradio as gr import numpy as np import spaces import torch import random from PIL import Image #from kontext_pipeline import FluxKontextPipeline from diffusers import FluxKontextPipeline from diffusers.utils import load_image # Load Kontext model MAX_SEED = np.iinfo(np.int32).max pipe = FluxKontextPipeline.from_pretrained("black-forest-labs/FLUX.1-Kontext-dev", torch_dtype=torch.bfloat16).to("cuda") def concatenate_images(images, direction="horizontal"): """ Concatenate multiple PIL images either horizontally or vertically. Args: images: List of PIL Images direction: "horizontal" or "vertical" Returns: PIL Image: Concatenated image """ if not images: return None # Filter out None images valid_images = [img for img in images if img is not None] if not valid_images: return None if len(valid_images) == 1: return valid_images[0].convert("RGB") # Convert all images to RGB valid_images = [img.convert("RGB") for img in valid_images] if direction == "horizontal": # Calculate total width and max height total_width = sum(img.width for img in valid_images) max_height = max(img.height for img in valid_images) # Create new image concatenated = Image.new('RGB', (total_width, max_height), (255, 255, 255)) # Paste images x_offset = 0 for img in valid_images: # Center image vertically if heights differ y_offset = (max_height - img.height) // 2 concatenated.paste(img, (x_offset, y_offset)) x_offset += img.width else: # vertical # Calculate max width and total height max_width = max(img.width for img in valid_images) total_height = sum(img.height for img in valid_images) # Create new image concatenated = Image.new('RGB', (max_width, total_height), (255, 255, 255)) # Paste images y_offset = 0 for img in valid_images: # Center image horizontally if widths differ x_offset = (max_width - img.width) // 2 concatenated.paste(img, (x_offset, y_offset)) y_offset += img.height return concatenated @spaces.GPU def infer(input_images, prompt, seed=42, randomize_seed=False, guidance_scale=2.5, progress=gr.Progress(track_tqdm=True)): if randomize_seed: seed = random.randint(0, MAX_SEED) # Handle input_images - it could be a single image or a list of images if input_images is None: raise gr.Error("Please upload at least one image.") # If it's a single image (not a list), convert to list if not isinstance(input_images, list): input_images = [input_images] # Filter out None images valid_images = [img[0] for img in input_images if img is not None] if not valid_images: raise gr.Error("Please upload at least one valid image.") # Concatenate images horizontally concatenated_image = concatenate_images(valid_images, "horizontal") if concatenated_image is None: raise gr.Error("Failed to process the input images.") # original_width, original_height = concatenated_image.size # if original_width >= original_height: # new_width = 1024 # new_height = int(original_height * (new_width / original_width)) # new_height = round(new_height / 64) * 64 # else: # new_height = 1024 # new_width = int(original_width * (new_height / original_height)) # new_width = round(new_width / 64) * 64 #concatenated_image_resized = concatenated_image.resize((new_width, new_height), Image.LANCZOS) final_prompt = f"From the provided reference images, create a unified, cohesive image such that {prompt}. Maintain the identity and characteristics of each subject while adjusting their proportions, scale, and positioning to create a harmonious, naturally balanced composition. Blend and integrate all elements seamlessly with consistent lighting, perspective, and style.the final result should look like a single naturally captured scene where all subjects are properly sized and positioned relative to each other, not assembled from multiple sources." image = pipe( image=concatenated_image, prompt=final_prompt, guidance_scale=guidance_scale, # width=new_width, # height=new_height, generator=torch.Generator().manual_seed(seed), ).images[0] return image, seed, gr.update(visible=True) css=""" #col-container { margin: 0 auto; max-width: 960px; } """ with gr.Blocks(css=css) as demo: with gr.Column(elem_id="col-container"): gr.Markdown(f"""# FLUX.1 Kontext [dev] - Multi-Image Flux Kontext with multiple image input support - compose a new image with elements from multiple images using Kontext [dev] """) with gr.Row(): with gr.Column(): input_images = gr.Gallery( label="Upload image(s) for editing", show_label=True, elem_id="gallery_input", columns=3, rows=2, object_fit="contain", height="auto", file_types=['image'], type='pil' ) with gr.Row(): prompt = gr.Text( label="Prompt", show_label=False, max_lines=1, placeholder="Enter your prompt for editing (e.g., 'Remove glasses', 'Add a hat')", container=False, ) run_button = gr.Button("Run", scale=0) 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) guidance_scale = gr.Slider( label="Guidance Scale", minimum=1, maximum=10, step=0.1, value=2.5, ) with gr.Column(): result = gr.Image(label="Result", show_label=False, interactive=False) reuse_button = gr.Button("Reuse this image", visible=False) gr.on( triggers=[run_button.click, prompt.submit], fn = infer, inputs = [input_images, prompt, seed, randomize_seed, guidance_scale], outputs = [result, seed, reuse_button] ) reuse_button.click( fn = lambda image: [image] if image is not None else [], # Convert single image to list for gallery inputs = [result], outputs = [input_images] ) demo.launch()