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import torch
from diffusers import StableDiffusionXLControlNetPipeline, ControlNetModel, AutoencoderKL, UniPCMultistepScheduler
pipe = None
def init():
global pipe
print("Initializing depth ControlNet...")
depth_controlnet = ControlNetModel.from_pretrained(
"diffusers/controlnet-depth-sdxl-1.0",
use_safetensors=True,
torch_dtype=torch.float16
).to("cuda")
print("Initializing autoencoder...")
vae = AutoencoderKL.from_pretrained(
"madebyollin/sdxl-vae-fp16-fix",
torch_dtype=torch.float16,
).to("cuda")
print("Initializing SDXL pipeline...")
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=[depth_controlnet],
vae=vae,
variant="fp16",
use_safetensors=True,
torch_dtype=torch.float16
# low_cpu_mem_usage=True
).to("cuda")
pipe.enable_model_cpu_offload()
# speed up diffusion process with faster scheduler and memory optimization
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
# remove following line if xformers is not installed
pipe.enable_xformers_memory_efficient_attention()
def run_pipeline(image, positive_prompt, negative_prompt, seed):
if seed == -1:
print("Using random seed")
generator = None
else:
print("Using seed:", seed)
generator = torch.manual_seed(seed)
images = pipe(
prompt=positive_prompt,
negative_prompt=negative_prompt,
num_inference_steps=30,
num_images_per_prompt=4,
controlnet_conditioning_scale=0.65,
guidance_scale=10.0,
generator=generator,
image=image
).images
return images
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