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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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import os |
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import yaml |
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from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, DDIMScheduler |
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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, prompt, negative_prompt, control, guess_mode=False): |
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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=prompt, |
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negative_prompt=negative_prompt, |
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guidance_scale=4.0 if guess_mode else 9.0, |
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num_inference_steps=50 if guess_mode else 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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def control_images(control_image_folder, model_name): |
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with open('./control_images.yaml', 'r') as f: |
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d = yaml.safe_load(f) |
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filenames = d[model_name] |
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return [Image.open(f'{control_image_folder}/{fn}').convert("RGB") for fn in filenames] |
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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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os.makedirs(output_image_folder, exist_ok=True) |
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model_id = 'takuma104/control_v11' |
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subfolder = f'control_v11{model_name}' |
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controlnet = ControlNetModel.from_pretrained(model_id, subfolder=subfolder) |
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if model_name == 'p_sd15s2_lineart_anime': |
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base_model_id = 'Linaqruf/anything-v3.0' |
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base_model_revision = None |
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else: |
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base_model_id = "runwayml/stable-diffusion-v1-5" |
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base_model_revision = 'non-ema' |
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pipe = StableDiffusionControlNetPipeline.from_pretrained(base_model_id, |
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revision=base_model_revision, |
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controlnet=controlnet, |
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safety_checker=None).to("cuda") |
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pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config) |
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for i, control_image in enumerate(control_images(control_image_folder, model_name)): |
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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, |
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prompt=test_prompt, |
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negative_prompt=test_negative_prompt, |
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control=control) |
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image.save(f'{output_image_folder}output_{model_name}_{i}_{seed}.png') |
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