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import spaces |
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import torch |
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def my_arange(*args, **kwargs): |
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return torch.arange(*args, **kwargs) |
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torch.arange = my_arange |
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from pathlib import Path |
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import gradio as gr |
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from gradio_imageslider import ImageSlider |
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from huggingface_hub import hf_hub_download |
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from PIL import Image |
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from refiners.fluxion.utils import manual_seed |
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from refiners.foundationals.latent_diffusion import Solver, solvers |
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from enhancer import ESRGANUpscaler, ESRGANUpscalerCheckpoints |
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TITLE = """ |
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<h1 align="center">Image Enhancer, implemented using refiners</h1> |
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<p> |
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<center> |
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<a style="font-size: 1.25rem;" href="https://blog.finegrain.ai/posts/reproducing-clarity-upscaler/" target="_blank">[blog post]</a> |
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<a style="font-size: 1.25rem;" href="https://github.com/finegrain-ai/refiners" target="_blank">[refiners]</a> |
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<a style="font-size: 1.25rem;" href="https://github.com/philz1337x/clarity-upscaler" target="_blank">[clarity-upscaler]</a> |
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<a style="font-size: 1.25rem;" href="https://finegrain.ai/" target="_blank">[finegrain]</a> |
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</center> |
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</p> |
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""" |
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CHECKPOINTS = ESRGANUpscalerCheckpoints( |
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unet=Path( |
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hf_hub_download( |
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repo_id="refiners/juggernaut.reborn", |
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filename="unet.safetensors", |
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revision="948510aaf4c8e8e9b32b5a7c25736422253f7b93", |
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) |
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), |
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clip_text_encoder=Path( |
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hf_hub_download( |
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repo_id="refiners/juggernaut.reborn", |
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filename="text_encoder.safetensors", |
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revision="948510aaf4c8e8e9b32b5a7c25736422253f7b93", |
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) |
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), |
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lda=Path( |
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hf_hub_download( |
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repo_id="refiners/juggernaut.reborn", |
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filename="autoencoder.safetensors", |
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revision="948510aaf4c8e8e9b32b5a7c25736422253f7b93", |
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) |
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), |
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controlnet_tile=Path( |
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hf_hub_download( |
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repo_id="refiners/controlnet.sd15.tile", |
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filename="model.safetensors", |
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revision="48ced6ff8bfa873a8976fa467c3629a240643387", |
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) |
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), |
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esrgan=Path( |
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hf_hub_download( |
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repo_id="philz1337x/upscaler", |
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filename="4x-UltraSharp.pth", |
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revision="011deacac8270114eb7d2eeff4fe6fa9a837be70", |
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) |
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), |
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negative_embedding=Path( |
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hf_hub_download( |
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repo_id="philz1337x/embeddings", |
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filename="JuggernautNegative-neg.pt", |
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revision="203caa7e9cc2bc225031a4021f6ab1ded283454a", |
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) |
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), |
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negative_embedding_key="string_to_param.*", |
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loras={ |
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"more_details": Path( |
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hf_hub_download( |
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repo_id="philz1337x/loras", |
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filename="more_details.safetensors", |
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revision="a3802c0280c0d00c2ab18d37454a8744c44e474e", |
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) |
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), |
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"sdxl_render": Path( |
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hf_hub_download( |
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repo_id="philz1337x/loras", |
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filename="SDXLrender_v2.0.safetensors", |
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revision="a3802c0280c0d00c2ab18d37454a8744c44e474e", |
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) |
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), |
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}, |
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) |
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LORA_SCALES = { |
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"more_details": 0.5, |
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"sdxl_render": 1.0, |
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} |
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DEVICE_CPU = torch.device("cpu") |
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DTYPE = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float32 |
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enhancer = ESRGANUpscaler(checkpoints=CHECKPOINTS, device=DEVICE_CPU, dtype=DTYPE) |
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DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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enhancer.to(device=DEVICE, dtype=DTYPE) |
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@spaces.GPU |
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def process( |
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input_image: Image.Image, |
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prompt: str = "masterpiece, best quality, highres", |
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negative_prompt: str = "worst quality, low quality, normal quality", |
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seed: int = 42, |
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upscale_factor: int = 2, |
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controlnet_scale: float = 0.6, |
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controlnet_decay: float = 1.0, |
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condition_scale: int = 6, |
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tile_width: int = 112, |
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tile_height: int = 144, |
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denoise_strength: float = 0.35, |
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num_inference_steps: int = 18, |
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solver: str = "DDIM", |
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) -> tuple[Image.Image, Image.Image]: |
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manual_seed(seed) |
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solver_type: type[Solver] = getattr(solvers, solver) |
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enhanced_image = enhancer.upscale( |
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image=input_image, |
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prompt=prompt, |
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negative_prompt=negative_prompt, |
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upscale_factor=upscale_factor, |
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controlnet_scale=controlnet_scale, |
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controlnet_scale_decay=controlnet_decay, |
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condition_scale=condition_scale, |
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tile_size=(tile_height, tile_width), |
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denoise_strength=denoise_strength, |
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num_inference_steps=num_inference_steps, |
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loras_scale=LORA_SCALES, |
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solver_type=solver_type, |
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) |
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return (input_image, enhanced_image) |
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with gr.Blocks() as demo: |
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gr.HTML(TITLE) |
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with gr.Row(): |
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with gr.Column(): |
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input_image = gr.Image(type="pil", label="Input Image") |
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run_button = gr.ClearButton(components=None, value="Enhance Image") |
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with gr.Column(): |
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output_slider = ImageSlider(label="Before / After") |
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run_button.add(output_slider) |
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with gr.Accordion("Advanced Options", open=False): |
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prompt = gr.Textbox( |
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label="Prompt", |
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placeholder="masterpiece, best quality, highres", |
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) |
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negative_prompt = gr.Textbox( |
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label="Negative Prompt", |
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placeholder="worst quality, low quality, normal quality", |
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) |
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seed = gr.Slider( |
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minimum=0, |
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maximum=10_000, |
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value=42, |
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step=1, |
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label="Seed", |
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) |
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upscale_factor = gr.Slider( |
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minimum=1, |
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maximum=4, |
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value=2, |
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step=0.2, |
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label="Upscale Factor", |
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) |
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controlnet_scale = gr.Slider( |
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minimum=0, |
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maximum=1.5, |
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value=0.6, |
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step=0.1, |
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label="ControlNet Scale", |
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) |
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controlnet_decay = gr.Slider( |
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minimum=0.5, |
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maximum=1, |
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value=1.0, |
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step=0.025, |
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label="ControlNet Scale Decay", |
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) |
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condition_scale = gr.Slider( |
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minimum=2, |
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maximum=20, |
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value=6, |
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step=1, |
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label="Condition Scale", |
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) |
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tile_width = gr.Slider( |
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minimum=64, |
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maximum=200, |
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value=112, |
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step=1, |
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label="Latent Tile Width", |
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) |
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tile_height = gr.Slider( |
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minimum=64, |
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maximum=200, |
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value=144, |
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step=1, |
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label="Latent Tile Height", |
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) |
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denoise_strength = gr.Slider( |
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minimum=0, |
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maximum=1, |
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value=0.35, |
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step=0.1, |
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label="Denoise Strength", |
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) |
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num_inference_steps = gr.Slider( |
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minimum=1, |
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maximum=30, |
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value=18, |
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step=1, |
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label="Number of Inference Steps", |
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) |
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solver = gr.Radio( |
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choices=["DDIM", "DPMSolver"], |
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value="DDIM", |
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label="Solver", |
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) |
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run_button.click( |
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fn=process, |
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inputs=[ |
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input_image, |
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prompt, |
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negative_prompt, |
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seed, |
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upscale_factor, |
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controlnet_scale, |
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controlnet_decay, |
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condition_scale, |
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tile_width, |
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tile_height, |
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denoise_strength, |
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num_inference_steps, |
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solver, |
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], |
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outputs=output_slider, |
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) |
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gr.Examples( |
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examples=[ |
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"examples/kara-eads-L7EwHkq1B2s-unsplash.jpg", |
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"examples/clarity_bird.webp", |
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"examples/edgar-infocus-gJH8AqpiSEU-unsplash.jpg", |
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"examples/jeremy-wallace-_XjW3oN8UOE-unsplash.jpg", |
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"examples/karina-vorozheeva-rW-I87aPY5Y-unsplash.jpg", |
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"examples/karographix-photography-hIaOPjYCEj4-unsplash.jpg", |
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"examples/melissa-walker-horn-gtDYwUIr9Vg-unsplash.jpg", |
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"examples/ryoji-iwata-X53e51WfjlE-unsplash.jpg", |
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"examples/tadeusz-lakota-jggQZkITXng-unsplash.jpg", |
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], |
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inputs=[input_image], |
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outputs=output_slider, |
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fn=process, |
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cache_examples="lazy", |
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run_on_click=False, |
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) |
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demo.launch(share=False) |
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