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Update main.py
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main.py
CHANGED
@@ -67,9 +67,13 @@ SECRET_TOKEN = os.getenv("SECRET_TOKEN", "default_secret")
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if is_gpu:
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# Uncomment the following line if you want to enable CUDA launch blocking
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os.environ["CUDA_LAUNCH_BLOCKING"] = "1"
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else:
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# Uncomment the following line if you want to use CPU instead of GPU
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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# Get the current directory
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@@ -82,17 +86,17 @@ cache_path = os.path.join(current_dir, "cache")
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if not SSD_1B:
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unet = UNet2DConditionModel.from_pretrained(
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"latent-consistency/lcm-sdxl",
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torch_dtype=
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variant=
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cache_dir=cache_path,
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)
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pipe = DiffusionPipeline.from_pretrained(
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# "stabilityai/stable-diffusion-xl-base-1.0",
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"stabilityai/sdxl-turbo",
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unet=unet,
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torch_dtype=
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variant=
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cache_dir=cache_path,
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)
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if is_gpu:
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# Uncomment the following line if you want to enable CUDA launch blocking
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os.environ["CUDA_LAUNCH_BLOCKING"] = "1"
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torch_dtype=torch.float16
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variant="fp16"
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else:
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# Uncomment the following line if you want to use CPU instead of GPU
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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torch_dtype=torch.float32
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variant="fp32"
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# Get the current directory
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if not SSD_1B:
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unet = UNet2DConditionModel.from_pretrained(
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"latent-consistency/lcm-sdxl",
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torch_dtype=torch_dtype,
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variant=variant,
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cache_dir=cache_path,
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)
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pipe = DiffusionPipeline.from_pretrained(
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# "stabilityai/stable-diffusion-xl-base-1.0",
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"stabilityai/sdxl-turbo",
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unet=unet,
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torch_dtype=torch_dtype,
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variant=variant,
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cache_dir=cache_path,
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)
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