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import torch |
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import os |
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from diffusers import DiffusionPipeline, 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 = "blur, lowres, bad anatomy, worst quality, low quality" |
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def resize_for_condition_image(input_image: Image, resolution: int): |
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input_image = input_image.convert("RGB") |
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W, H = input_image.size |
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k = float(resolution) / min(H, W) |
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H *= k |
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W *= k |
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H = int(round(H / 64.0)) * 64 |
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W = int(round(W / 64.0)) * 64 |
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img = input_image.resize((W, H), resample=Image.LANCZOS if k > 1 else Image.AREA) |
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return img |
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def generate_image(seed, prompt, negative_prompt, control, guess_mode=False): |
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latent = torch.randn( |
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(1, 4, 64, 64), |
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device="cpu", |
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generator=torch.Generator(device="cpu").manual_seed(seed), |
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).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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controlnet_conditioning_image=control, |
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strength=1.0, |
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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 = "f1e_sd15_tile" |
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original_image_folder = "./control_images/" |
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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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controlnet = ControlNetModel.from_pretrained('takuma104/control_v11', |
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subfolder='control_v11f1e_sd15_tile') |
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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 = DiffusionPipeline.from_pretrained( |
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base_model_id, |
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revision=base_model_revision, |
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custom_pipeline="stable_diffusion_controlnet_img2img", |
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controlnet=controlnet, |
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safety_checker=None, |
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).to("cuda") |
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pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config) |
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original_image_filenames = [ |
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"dog_64x64.png", |
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] |
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image_conditions = [ |
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resize_for_condition_image( |
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Image.open(f"{original_image_folder}{fn}"), |
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resolution=512, |
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) |
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for fn in original_image_filenames |
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] |
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for i, control in enumerate(image_conditions): |
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for seed in range(4): |
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image = generate_image( |
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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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) |
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image.save(f"{output_image_folder}output_{model_name}_{i}_{seed}.png") |
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