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# Diffusers' ControlNet Implementation Subjective Evaluation
import torch
import os
from diffusers import DiffusionPipeline, ControlNetModel, DDIMScheduler
from PIL import Image
test_prompt = "best quality, extremely detailed"
test_negative_prompt = "blur, lowres, bad anatomy, worst quality, low quality"
def resize_for_condition_image(input_image: Image, resolution: int):
input_image = input_image.convert("RGB")
W, H = input_image.size
k = float(resolution) / min(H, W)
H *= k
W *= k
H = int(round(H / 64.0)) * 64
W = int(round(W / 64.0)) * 64
img = input_image.resize((W, H), resample=Image.LANCZOS if k > 1 else Image.AREA)
return img
def generate_image(seed, prompt, negative_prompt, control, guess_mode=False):
latent = torch.randn(
(1, 4, 64, 64),
device="cpu",
generator=torch.Generator(device="cpu").manual_seed(seed),
).cuda()
image = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
guidance_scale=4.0 if guess_mode else 9.0,
num_inference_steps=50 if guess_mode else 20,
latents=latent,
image=control,
controlnet_conditioning_image=control,
strength=1.0,
# guess_mode=guess_mode,
).images[0]
return image
if __name__ == "__main__":
model_name = "f1e_sd15_tile"
original_image_folder = "./control_images/"
control_image_folder = "./control_images/converted/"
output_image_folder = "./output_images/diffusers/"
os.makedirs(output_image_folder, exist_ok=True)
# model_id = f"lllyasviel/control_v11{model_name}"
# controlnet = ControlNetModel.from_pretrained(model_id)
controlnet = ControlNetModel.from_pretrained('takuma104/control_v11',
subfolder='control_v11f1e_sd15_tile')
if model_name == "p_sd15s2_lineart_anime":
base_model_id = "Linaqruf/anything-v3.0"
base_model_revision = None
else:
base_model_id = "runwayml/stable-diffusion-v1-5"
base_model_revision = "non-ema"
pipe = DiffusionPipeline.from_pretrained(
base_model_id,
revision=base_model_revision,
custom_pipeline="stable_diffusion_controlnet_img2img",
controlnet=controlnet,
safety_checker=None,
).to("cuda")
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
original_image_filenames = [
"dog_64x64.png",
]
image_conditions = [
resize_for_condition_image(
Image.open(f"{original_image_folder}{fn}"),
resolution=512,
)
for fn in original_image_filenames
]
for i, control in enumerate(image_conditions):
for seed in range(4):
image = generate_image(
seed=seed,
prompt=test_prompt,
negative_prompt=test_negative_prompt,
control=control,
)
image.save(f"{output_image_folder}output_{model_name}_{i}_{seed}.png")
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