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import einops |
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import numpy as np |
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import torch |
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import sys |
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from diffusers import StableDiffusionControlNetPipeline |
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from PIL import Image |
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test_prompt = "best quality, extremely detailed" |
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test_negative_prompt = "lowres, bad anatomy, worst quality, low quality" |
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def generate_image(seed, control): |
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latent = torch.randn((1,4,64,64), device="cpu", generator=torch.Generator(device="cpu").manual_seed(seed)).cuda() |
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image = pipe( |
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prompt=test_prompt, |
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negative_prompt=test_negative_prompt, |
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guidance_scale=9.0, |
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num_inference_steps=20, |
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latents=latent, |
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image=control, |
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).images[0] |
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return image |
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if __name__ == '__main__': |
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model_name = sys.argv[1] |
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control_image_folder = './control_images/converted/' |
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output_image_folder = './output_images/diffusers/' |
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model_id = f'../../control_sd15_{model_name}' |
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pipe = StableDiffusionControlNetPipeline.from_pretrained(model_id).to("cuda") |
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image_types = {'bird', 'human', 'room', 'vermeer'} |
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for image_type in image_types: |
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control_image = Image.open(f'{control_image_folder}control_{image_type}_{model_name}.png') |
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control = np.array(control_image)[:,:,::-1].copy() |
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control = torch.from_numpy(control).float().cuda() / 255.0 |
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control = torch.stack([control for _ in range(1)], dim=0) |
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control = einops.rearrange(control, 'b h w c -> b c h w').clone() |
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for seed in range(4): |
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image = generate_image(seed=seed, control=control) |
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image.save(f'{output_image_folder}output_{image_type}_{model_name}_{seed}.png') |
