import os import gradio as gr import numpy as np import spaces import torch import random from PIL import Image from diffusers import FluxKontextPipeline from diffusers.utils import load_image from huggingface_hub import hf_hub_download from aura_sr import AuraSR from gradio_imageslider import ImageSlider device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # --- # Device and CUDA Setup Check --- print("CUDA_VISIBLE_DEVICES=", os.environ.get("CUDA_VISIBLE_DEVICES")) print("torch.__version__ =", torch.__version__) print("torch.version.cuda =", torch.version.cuda) print("cuda available:", torch.cuda.is_available()) print("cuda device count:", torch.cuda.device_count()) if torch.cuda.is_available(): print("current device:", torch.cuda.current_device()) print("device name:", torch.cuda.get_device_name(torch.cuda.current_device())) print("Using device:", device) # --- Main Model Initialization --- MAX_SEED = np.iinfo(np.int32).max pipe = FluxKontextPipeline.from_pretrained("black-forest-labs/FLUX.1-Kontext-dev", torch_dtype=torch.bfloat16).to("cuda") # --- Load All Adapters --- pipe.load_lora_weights("prithivMLmods/PhotoCleanser-i2i", weight_name="PhotoCleanser-i2i.safetensors", adapter_name="cleanser") pipe.load_lora_weights("prithivMLmods/Photo-Restore-i2i", weight_name="Photo-Restore-i2i.safetensors", adapter_name="restorer") pipe.load_lora_weights("prithivMLmods/Polaroid-Warm-i2i", weight_name="Polaroid-Warm-i2i.safetensors", adapter_name="polaroid") pipe.load_lora_weights("prithivMLmods/Monochrome-Pencil", weight_name="Monochrome-Pencil-i2i.safetensors", adapter_name="pencil") # Add the new LZO adapter pipe.load_lora_weights("prithivMLmods/LZO-1-Preview", weight_name="LZO-1-Preview.safetensors", adapter_name="lzo") # --- Upscaler Model Initialization --- aura_sr = AuraSR.from_pretrained("fal/AuraSR-v2") @spaces.GPU def infer(input_image, prompt, lora_adapter, upscale_image, seed=42, randomize_seed=False, guidance_scale=2.5, steps=28, progress=gr.Progress(track_tqdm=True)): """ Perform image editing and optional upscaling, returning a pair for the ImageSlider. Returns: tuple: A 3-tuple containing: - (PIL.Image.Image, PIL.Image.Image): A tuple of the (original, generated) images for the slider. - int: The seed used for generation. - gr.update: A Gradio update to make the reuse button visible. """ if not input_image: raise gr.Error("Please upload an image for editing.") if lora_adapter == "PhotoCleanser": pipe.set_adapters(["cleanser"], adapter_weights=[1.0]) elif lora_adapter == "PhotoRestorer": pipe.set_adapters(["restorer"], adapter_weights=[1.0]) elif lora_adapter == "PolaroidWarm": pipe.set_adapters(["polaroid"], adapter_weights=[1.0]) elif lora_adapter == "MonochromePencil": pipe.set_adapters(["pencil"], adapter_weights=[1.0]) # Add the new LZO adapter condition elif lora_adapter == "LZO-Zoom": pipe.set_adapters(["lzo"], adapter_weights=[1.0]) if randomize_seed: seed = random.randint(0, MAX_SEED) original_image = input_image.copy().convert("RGB") image = pipe( image=original_image, prompt=prompt, guidance_scale=guidance_scale, width = original_image.size[0], height = original_image.size[1], num_inference_steps=steps, generator=torch.Generator().manual_seed(seed), ).images[0] if upscale_image: progress(0.8, desc="Upscaling image...") image = aura_sr.upscale_4x(image) return (original_image, image), seed, gr.Button(visible=True) @spaces.GPU def infer_example(input_image, prompt, lora_adapter): """ Wrapper function for gr.Examples to call the main infer logic for the slider. """ (original_image, generated_image), seed, _ = infer(input_image, prompt, lora_adapter, upscale_image=False) return (original_image, generated_image), seed css=""" #col-container { margin: 0 auto; max-width: 960px; } .submit-btn { background-color: #2980b9 !important; color: white !important; } .submit-btn:hover { background-color: #3498db !important; } """ with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo: with gr.Column(elem_id="col-container"): gr.Markdown(f"""# **[Photo-Mate-i2i](https://huggingface.co/collections/prithivMLmods/i2i-kontext-exp-68ce573b5c0623476b636ec7)** Image manipulation with FLUX.1 Kontext adapters. [How to Use](https://huggingface.co/spaces/prithivMLmods/Photo-Mate-i2i/discussions/2)""") with gr.Row(): with gr.Column(): input_image = gr.Image(label="Image [PIL]", type="pil", height="300") with gr.Row(): prompt = gr.Text( label="Edit 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", elem_classes="submit-btn", 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, ) steps = gr.Slider( label="Steps", minimum=1, maximum=30, value=28, step=1 ) with gr.Column(): # Replace the single image result with the ImageSlider output_slider = ImageSlider(label="Before / After", show_label=False, interactive=False) reuse_button = gr.Button("Reuse this image", visible=False) with gr.Row(): lora_adapter = gr.Dropdown( label="Chosen LoRA", choices=["PhotoCleanser", "PhotoRestorer", "PolaroidWarm", "MonochromePencil", "LZO-Zoom"], value="PhotoCleanser" ) #AuraSR Upscale with gr.Row(): upscale_checkbox = gr.Checkbox(label="Upscale the final image", value=False) gr.Examples( examples=[ ["photocleanser/2.png", "[photo content], remove the cat from the image while preserving the background and remaining elements, maintaining realism and original details.", "PhotoCleanser"], ["photocleanser/1.png", "[photo content], remove the football from the image while preserving the background and remaining elements, maintaining realism and original details.", "PhotoCleanser"], ["photorestore/1.png", "[photo content], restore and enhance the image by repairing any damage, scratches, or fading. Colorize the photo naturally while preserving authentic textures and details, maintaining a realistic and historically accurate look.", "PhotoRestorer"], # Add the new LZO example ["lzo/1.jpg", "[photo content], zoom in on the specified [face close-up], enhancing resolution and detail while preserving sharpness, realism, and original context. Maintain natural proportions and background continuity around the zoomed area.", "LZO-Zoom"], ["photorestore/2.png", "[photo content], restore and enhance the image by repairing any damage, scratches, or fading. Colorize the photo naturally while preserving authentic textures and details, maintaining a realistic and historically accurate look.", "PhotoRestorer"], ["polaroid/1.png", "[photo content], in the style of a vintage Polaroid, with warm, faded tones, and a white border.", "PolaroidWarm"], ["pencil/1.png", "[photo content], replicate the image as a pencil illustration, black and white, with sketch-like detailing.", "MonochromePencil"], ], inputs=[input_image, prompt, lora_adapter], # The output now targets the ImageSlider component outputs=[output_slider, seed], fn=infer_example, cache_examples="lazy", label="Examples" ) gr.on( triggers=[run_button.click, prompt.submit], fn=infer, inputs=[input_image, prompt, lora_adapter, upscale_checkbox, seed, randomize_seed, guidance_scale, steps], # The output now targets the ImageSlider component outputs=[output_slider, seed, reuse_button] ) # Update the reuse function to handle the ImageSlider output reuse_button.click( # The slider outputs a tuple of images; we want the second one (the generated result) fn=lambda images: images[1] if isinstance(images, (list, tuple)) and len(images) > 1 else images, inputs=[output_slider], outputs=[input_image] ) demo.launch(mcp_server=True, ssr_mode=False, show_error=True)