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import torch
from diffusers.utils import load_image
from diffusers import FluxControlNetPipeline, FluxControlNetModel
# Clear unnecessary memory
torch.cuda.empty_cache()
# Set the environment variable to handle memory fragmentation
import os
os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
base_model = 'black-forest-labs/FLUX.1-dev'
controlnet_model = 'InstantX/FLUX.1-dev-Controlnet-Union'
# Use a smaller precision if possible
controlnet = FluxControlNetModel.from_pretrained(controlnet_model, torch_dtype=torch.float16)
pipe = FluxControlNetPipeline.from_pretrained(base_model, controlnet=controlnet, torch_dtype=torch.float16)
pipe.to("cuda")
control_image_canny = load_image("https://huggingface.co/InstantX/FLUX.1-dev-Controlnet-Union-alpha/resolve/main/images/canny.jpg")
controlnet_conditioning_scale = 0.5
control_mode = 0
# Ensure that image is loaded correctly
width, height = control_image_canny.size
prompt = 'A bohemian-style female travel blogger with sun-kissed skin and messy beach waves.'
image = pipe(
prompt,
control_image=control_image_canny,
control_mode=control_mode,
width=width,
height=height,
controlnet_conditioning_scale=controlnet_conditioning_scale,
num_inference_steps=24,
guidance_scale=3.5,
).images[0]
image.save("image.jpg")
# Empty cache after the operation to free up memory
torch.cuda.empty_cache()
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