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import torch |
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import numpy as np |
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from diffusers import StableDiffusionXLPipeline |
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path = "hf-internal-testing/tiny-stable-diffusion-xl-pipe" |
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pipe = StableDiffusionXLPipeline.from_pretrained(path) |
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pipe.unet.set_default_attn_processor() |
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prompt = "An astronaut riding a green horse on Mars" |
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steps = 3 |
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batch_size, height, width, ch = 1, 32, 32, 4 |
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num_elems = batch_size * height * width * ch |
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latents = (torch.arange(num_elems) / num_elems)[:, None, None, None].reshape(batch_size, ch, width, height) |
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print("latents", latents.abs().sum()) |
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image = pipe(prompt, latents=latents, num_inference_steps=3, output_type="np", guidance_scale=7.5).images[0] |
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print(np.abs(image).sum()) |
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