Roll back to before we wasted our money with Zero
Browse files
app.py
CHANGED
@@ -10,9 +10,6 @@ import os
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from PIL import Image
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import spaces
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print(f"Is CUDA available: {torch.cuda.is_available()}")
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print(f"CUDA device: {torch.cuda.get_device_name(torch.cuda.current_device())}")
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from diffusers import (
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StableDiffusionPipeline,
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StableDiffusionControlNetImg2ImgPipeline,
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@@ -41,7 +38,7 @@ pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
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safety_checker=None,
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torch_dtype=torch.float16,
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).to("cuda")
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-
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# pipe.controlnet = torch.nn.DataParallel(pipe.controlnet)
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# pipe.unet = torch.nn.DataParallel(pipe.unet)
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@@ -83,7 +80,7 @@ SAMPLER_MAP = {
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"DEIS": lambda config: DEISMultistepScheduler.from_config(config),
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}
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@spaces.GPU(
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def inference(
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qr_code_content: str,
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prompt: str,
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@@ -327,4 +324,3 @@ model: https://huggingface.co/DionTimmer/controlnet_qrcode-control_v1p_sd15
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os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
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blocks.queue(max_size=20,api_open=False)
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blocks.launch(share=bool(os.environ.get("SHARE", False)), show_api=False)
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-
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from PIL import Image
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import spaces
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from diffusers import (
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StableDiffusionPipeline,
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StableDiffusionControlNetImg2ImgPipeline,
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safety_checker=None,
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torch_dtype=torch.float16,
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).to("cuda")
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+
pipe.enable_xformers_memory_efficient_attention()
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# pipe.controlnet = torch.nn.DataParallel(pipe.controlnet)
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# pipe.unet = torch.nn.DataParallel(pipe.unet)
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"DEIS": lambda config: DEISMultistepScheduler.from_config(config),
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}
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@spaces.GPU()
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def inference(
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qr_code_content: str,
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prompt: str,
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os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
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blocks.queue(max_size=20,api_open=False)
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blocks.launch(share=bool(os.environ.get("SHARE", False)), show_api=False)
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