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Update app.py
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app.py
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@@ -20,43 +20,28 @@ from RealESRGAN import RealESRGAN
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import gradio as gr
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from gradio_imageslider import ImageSlider
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USE_TORCH_COMPILE = False
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ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "0") == "1"
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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def download_file(url, folder_path, filename):
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if not os.path.exists(folder_path):
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os.makedirs(folder_path)
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file_path = os.path.join(folder_path, filename)
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if os.path.isfile(file_path):
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print(f"File already exists: {file_path}")
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else:
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response = requests.get(url, stream=True)
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if response.status_code == 200:
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with open(file_path, 'wb') as file:
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for chunk in response.iter_content(chunk_size=1024):
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file.write(chunk)
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print(f"File successfully downloaded and saved: {file_path}")
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else:
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print(f"Error downloading the file. Status code: {response.status_code}")
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def download_models():
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models = {
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"MODEL": ("
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"UPSCALER_X2": ("
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"UPSCALER_X4": ("
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"NEGATIVE_1": ("
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"NEGATIVE_2": ("
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"LORA_1": ("
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"LORA_2": ("
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"CONTROLNET": ("
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"VAE": ("
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}
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for model, (
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download_models()
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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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USE_TORCH_COMPILE = False
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ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "0") == "1"
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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def download_models():
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models = {
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"MODEL": ("dantea1118/juggernaut_reborn", "juggernaut_reborn.safetensors", "models/models/Stable-diffusion"),
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"UPSCALER_X2": ("ai-forever/Real-ESRGAN", "RealESRGAN_x2.pth", "models/upscalers/"),
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"UPSCALER_X4": ("ai-forever/Real-ESRGAN", "RealESRGAN_x4.pth", "models/upscalers/"),
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"NEGATIVE_1": ("philz1337x/embeddings", "verybadimagenegative_v1.3.pt", "models/embeddings"),
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"NEGATIVE_2": ("philz1337x/embeddings", "JuggernautNegative-neg.pt", "models/embeddings"),
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"LORA_1": ("philz1337x/loras", "SDXLrender_v2.0.safetensors", "models/Lora"),
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"LORA_2": ("philz1337x/loras", "more_details.safetensors", "models/Lora"),
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"CONTROLNET": ("lllyasviel/ControlNet-v1-1", "control_v11f1e_sd15_tile.pth", "models/ControlNet"),
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"VAE": ("stabilityai/sd-vae-ft-mse-original", "vae-ft-mse-840000-ema-pruned.safetensors", "models/VAE"),
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}
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for model, (repo_id, filename, local_dir) in models.items():
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hf_hub_download(repo_id=repo_id, filename=filename, local_dir=local_dir)
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download_models()
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