25 lines
571 B
Python
25 lines
571 B
Python
import torch
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import PIL.Image
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from diffusers import DiffusionPipeline
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generator = torch.Generator()
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generator = generator.manual_seed(0)
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model_id = "fusing/glide-base"
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# load model and scheduler
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pipeline = DiffusionPipeline.from_pretrained(model_id)
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# run inference (text-conditioned denoising + upscaling)
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img = pipeline("a crayon drawing of a corgi", generator)
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# process image to PIL
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img = img.squeeze(0)
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img = ((img + 1) * 127.5).round().clamp(0, 255).to(torch.uint8).cpu().numpy()
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image_pil = PIL.Image.fromarray(img)
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# save image
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image_pil.save("test.png")
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