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# Diffusers' ControlNet Implementation Subjective Evaluation
# https://github.com/takuma104/diffusers/tree/controlnet
import einops
import numpy as np
import torch
import sys
from diffusers import StableDiffusionControlNetPipeline
from PIL import Image
test_prompt = "best quality, extremely detailed"
test_negative_prompt = "lowres, bad anatomy, worst quality, low quality"
def generate_image(seed, control):
latent = torch.randn((1,4,64,64), device="cpu", generator=torch.Generator(device="cpu").manual_seed(seed)).cuda()
image = pipe(
prompt=test_prompt,
negative_prompt=test_negative_prompt,
guidance_scale=9.0,
num_inference_steps=20,
latents=latent,
#generator=torch.Generator(device="cuda").manual_seed(seed),
image=control,
).images[0]
return image
if __name__ == '__main__':
model_name = sys.argv[1]
control_image_folder = '../huggingface/controlnet_dev/gen_compare/control_images/converted/'
output_image_folder = '../huggingface/controlnet_dev/gen_compare/output_images/diffusers/'
model_id = f'../huggingface/control_sd15_{model_name}'
pipe = StableDiffusionControlNetPipeline.from_pretrained(model_id).to("cuda")
pipe.enable_attention_slicing(1)
image_types = {'bird', 'human', 'room', 'vermeer'}
for image_type in image_types:
control_image = Image.open(f'{control_image_folder}control_{image_type}_{model_name}.png')
control = np.array(control_image)[:,:,::-1].copy()
control = torch.from_numpy(control).float().cuda() / 255.0
control = torch.stack([control for _ in range(1)], dim=0)
control = einops.rearrange(control, 'b h w c -> b c h w').clone()
for seed in range(4):
image = generate_image(seed=seed, control=control)
image.save(f'{output_image_folder}output_{image_type}_{model_name}_{seed}.png') |