Delete src/app.py

src/app.py

@@ -1,299 +0,0 @@
-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 = """
-<center>
-
-  <h1 style="font-size: 1.5rem; margin-bottom: 0.5rem;">
-    Image Enhancer Powered By Refiners
-  </h1>
-
-  <div style="
-    display: flex;
-    align-items: center;
-    justify-content: center;
-    gap: 0.5rem;
-    margin-bottom: 0.5rem;
-    font-size: 1.25rem;
-    flex-wrap: wrap;
-  ">
-    <a href="https://blog.finegrain.ai/posts/reproducing-clarity-upscaler/" target="_blank">[Blog Post]</a>
-    <a href="https://github.com/finegrain-ai/refiners" target="_blank">[Refiners]</a>
-    <a href="https://finegrain.ai/" target="_blank">[Finegrain]</a>
-    <a href="https://huggingface.co/spaces/finegrain/finegrain-object-eraser" target="_blank">
-        [Finegrain Object Eraser]
-    </a>
-    <a href="https://huggingface.co/spaces/finegrain/finegrain-object-cutter" target="_blank">
-        [Finegrain Object Cutter]
-    </a>
-  </div>
-
-  <p>
-    Turn low resolution images into high resolution versions with added generated details (your image will be modified).
-  </p>
-
-  <p>
-    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!
-  </p>
-
-  <a href="https://github.com/finegrain-ai/refiners" target="_blank">
-    <img src="https://img.shields.io/github/stars/finegrain-ai/refiners?style=social" />
-  </a>
-
-</center>
-"""
-
-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)