from pathlib import Path import gradio as gr import pillow_heif import spaces import torch from gradio_imageslider import ImageSlider from huggingface_hub import hf_hub_download from PIL import Image from refiners.fluxion.utils import manual_seed from refiners.foundationals.latent_diffusion import Solver, solvers from enhancer import ESRGANUpscaler, ESRGANUpscalerCheckpoints pillow_heif.register_heif_opener() pillow_heif.register_avif_opener() TITLE = """
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Image Enhancer Powered By Refiners

Turn low resolution images into high resolution versions with added generated details (your image will be modified).

This space is powered by Refiners, our open source micro-framework for simple foundation model adaptation. If you enjoyed it, please consider starring Refiners on GitHub!

""" CHECKPOINTS = ESRGANUpscalerCheckpoints( unet=Path( hf_hub_download( repo_id="refiners/juggernaut.reborn.sd1_5.unet", filename="model.safetensors", revision="347d14c3c782c4959cc4d1bb1e336d19f7dda4d2", ) ), clip_text_encoder=Path( hf_hub_download( repo_id="refiners/juggernaut.reborn.sd1_5.text_encoder", filename="model.safetensors", revision="744ad6a5c0437ec02ad826df9f6ede102bb27481", ) ), lda=Path( hf_hub_download( repo_id="refiners/juggernaut.reborn.sd1_5.autoencoder", filename="model.safetensors", revision="3c1aae3fc3e03e4a2b7e0fa42b62ebb64f1a4c19", ) ), controlnet_tile=Path( hf_hub_download( repo_id="refiners/controlnet.sd1_5.tile", filename="model.safetensors", revision="48ced6ff8bfa873a8976fa467c3629a240643387", ) ), esrgan=Path( hf_hub_download( repo_id="philz1337x/upscaler", filename="4x-UltraSharp.pth", revision="011deacac8270114eb7d2eeff4fe6fa9a837be70", ) ), negative_embedding=Path( hf_hub_download( repo_id="philz1337x/embeddings", filename="JuggernautNegative-neg.pt", revision="203caa7e9cc2bc225031a4021f6ab1ded283454a", ) ), negative_embedding_key="string_to_param.*", loras={ "more_details": Path( hf_hub_download( repo_id="philz1337x/loras", filename="more_details.safetensors", revision="a3802c0280c0d00c2ab18d37454a8744c44e474e", ) ), "sdxl_render": Path( hf_hub_download( repo_id="philz1337x/loras", filename="SDXLrender_v2.0.safetensors", revision="a3802c0280c0d00c2ab18d37454a8744c44e474e", ) ), }, ) # initialize the enhancer, on the cpu DEVICE_CPU = torch.device("cpu") DTYPE = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float32 enhancer = ESRGANUpscaler(checkpoints=CHECKPOINTS, device=DEVICE_CPU, dtype=DTYPE) # "move" the enhancer to the gpu, this is handled by Zero GPU DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu") enhancer.to(device=DEVICE, dtype=DTYPE) @spaces.GPU def process( input_image: Image.Image, prompt: str = "masterpiece, best quality, highres", negative_prompt: str = "worst quality, low quality, normal quality", seed: int = 42, upscale_factor: int = 2, controlnet_scale: float = 0.6, controlnet_decay: float = 1.0, condition_scale: int = 6, tile_width: int = 112, tile_height: int = 144, denoise_strength: float = 0.35, num_inference_steps: int = 18, solver: str = "DDIM", ) -> tuple[Image.Image, Image.Image]: manual_seed(seed) solver_type: type[Solver] = getattr(solvers, solver) enhanced_image = enhancer.upscale( image=input_image, prompt=prompt, negative_prompt=negative_prompt, upscale_factor=upscale_factor, controlnet_scale=controlnet_scale, controlnet_scale_decay=controlnet_decay, condition_scale=condition_scale, tile_size=(tile_height, tile_width), denoise_strength=denoise_strength, num_inference_steps=num_inference_steps, loras_scale={"more_details": 0.5, "sdxl_render": 1.0}, solver_type=solver_type, ) return (input_image, enhanced_image) with gr.Blocks() as demo: gr.HTML(TITLE) with gr.Row(): with gr.Column(): input_image = gr.Image(type="pil", label="Input Image") run_button = gr.ClearButton(components=None, value="Enhance Image") with gr.Column(): output_slider = ImageSlider(label="Before / After") run_button.add(output_slider) with gr.Accordion("Advanced Options", open=False): prompt = gr.Textbox( label="Prompt", placeholder="masterpiece, best quality, highres", ) negative_prompt = gr.Textbox( label="Negative Prompt", placeholder="worst quality, low quality, normal quality", ) seed = gr.Slider( minimum=0, maximum=10_000, value=42, step=1, label="Seed", ) upscale_factor = gr.Slider( minimum=1, maximum=4, value=2, step=0.2, label="Upscale Factor", ) controlnet_scale = gr.Slider( minimum=0, maximum=1.5, value=0.6, step=0.1, label="ControlNet Scale", ) controlnet_decay = gr.Slider( minimum=0.5, maximum=1, value=1.0, step=0.025, label="ControlNet Scale Decay", ) condition_scale = gr.Slider( minimum=2, maximum=20, value=6, step=1, label="Condition Scale", ) tile_width = gr.Slider( minimum=64, maximum=200, value=112, step=1, label="Latent Tile Width", ) tile_height = gr.Slider( minimum=64, maximum=200, value=144, step=1, label="Latent Tile Height", ) denoise_strength = gr.Slider( minimum=0, maximum=1, value=0.35, step=0.1, label="Denoise Strength", ) num_inference_steps = gr.Slider( minimum=1, maximum=30, value=18, step=1, label="Number of Inference Steps", ) solver = gr.Radio( choices=["DDIM", "DPMSolver"], value="DDIM", label="Solver", ) run_button.click( fn=process, inputs=[ input_image, prompt, negative_prompt, seed, upscale_factor, controlnet_scale, controlnet_decay, condition_scale, tile_width, tile_height, denoise_strength, num_inference_steps, solver, ], outputs=output_slider, ) gr.Examples( examples=[ "examples/kara-eads-L7EwHkq1B2s-unsplash.jpg", "examples/clarity_bird.webp", "examples/edgar-infocus-gJH8AqpiSEU-unsplash.jpg", "examples/jeremy-wallace-_XjW3oN8UOE-unsplash.jpg", "examples/karina-vorozheeva-rW-I87aPY5Y-unsplash.jpg", "examples/karographix-photography-hIaOPjYCEj4-unsplash.jpg", "examples/melissa-walker-horn-gtDYwUIr9Vg-unsplash.jpg", "examples/ryoji-iwata-X53e51WfjlE-unsplash.jpg", "examples/tadeusz-lakota-jggQZkITXng-unsplash.jpg", ], inputs=[input_image], outputs=output_slider, fn=process, cache_examples="lazy", run_on_click=False, ) demo.launch(share=False)