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https://github.com/CompVis/stable-diffusion.git
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correct merg
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commit
f3f60fccc3
@ -239,9 +239,7 @@ def main():
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if opt.fixed_code:
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if opt.fixed_code:
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start_code = torch.randn([opt.n_samples, opt.C, opt.H // opt.f, opt.W // opt.f], device=device)
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start_code = torch.randn([opt.n_samples, opt.C, opt.H // opt.f, opt.W // opt.f], device=device)
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print("start code", start_code.abs().sum())
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precision_scope = autocast if opt.precision=="autocast" else nullcontext
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precision_scope = autocast if opt.precision=="autocast" else nullcontext
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precision_scope = nullcontext
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with torch.no_grad():
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with torch.no_grad():
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with precision_scope("cuda"):
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with precision_scope("cuda"):
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with model.ema_scope():
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with model.ema_scope():
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@ -297,6 +295,13 @@ def main():
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Image.fromarray(grid.astype(np.uint8)).save(os.path.join(outpath, f'grid-{grid_count:04}.png'))
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Image.fromarray(grid.astype(np.uint8)).save(os.path.join(outpath, f'grid-{grid_count:04}.png'))
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grid_count += 1
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grid_count += 1
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image = x_samples_ddim.cpu().permute(0, 2, 3, 1).numpy()
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# run safety checker
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safety_checker_input = pipe.feature_extractor(numpy_to_pil(image), return_tensors="pt")
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image, has_nsfw_concept = pipe.safety_checker(images=image, clip_input=safety_checker_input.pixel_values)
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toc = time.time()
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print(f"Your samples are ready and waiting for you here: \n{outpath} \n"
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print(f"Your samples are ready and waiting for you here: \n{outpath} \n"
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f" \nEnjoy.")
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f" \nEnjoy.")
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