FLUX.1-dev-ControlNet-Union-Pro

This repository contains a unified ControlNet for FLUX.1-dev model jointly released by researchers from InstantX Team and Shakker Labs.

Model Cards

• This checkpoint is a Pro version of FLUX.1-dev-Controlnet-Union trained with more steps and datasets.
• This model supports 7 control modes, including canny (0), tile (1), depth (2), blur (3), pose (4), gray (5), low quality (6).
• The recommended controlnet_conditioning_scale is 0.3-0.8.
• This model can be jointly used with other ControlNets.

Showcases

Inference

Please install diffusers from the source, as the PR has not been included in currently released version yet.

Multi-Controls Inference

import torch
from diffusers.utils import load_image

from diffusers import FluxControlNetPipeline, FluxControlNetModel
from diffusers.models import FluxMultiControlNetModel

base_model = 'black-forest-labs/FLUX.1-dev'
controlnet_model_union = 'Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro'

controlnet_union = FluxControlNetModel.from_pretrained(controlnet_model_union, torch_dtype=torch.bfloat16)
controlnet = FluxMultiControlNetModel([controlnet_union]) # we always recommend loading via FluxMultiControlNetModel

pipe = FluxControlNetPipeline.from_pretrained(base_model, controlnet=controlnet, torch_dtype=torch.bfloat16)
pipe.to("cuda")

prompt = 'A bohemian-style female travel blogger with sun-kissed skin and messy beach waves.'
control_image_depth = load_image("https://huggingface.co./Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro/resolve/main/assets/depth.jpg")
control_mode_depth = 2

control_image_canny = load_image("https://huggingface.co./Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro/resolve/main/assets/canny.jpg")
control_mode_canny = 0

width, height = control_image_depth.size

image = pipe(
    prompt, 
    control_image=[control_image_depth, control_image_canny],
    control_mode=[control_mode_depth, control_mode_canny],
    width=width,
    height=height,
    controlnet_conditioning_scale=[0.2, 0.4],
    num_inference_steps=24, 
    guidance_scale=3.5,
    generator=torch.manual_seed(42),
).images[0]

We also support loading multiple ControlNets as before, you can load as

from diffusers import FluxControlNetModel
from diffusers.models import FluxMultiControlNetModel

controlnet_model_union = 'Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro'
controlnet_union = FluxControlNetModel.from_pretrained(controlnet_model_union, torch_dtype=torch.bfloat16)

controlnet_model_depth = 'Shakker-Labs/FLUX.1-dev-Controlnet-Depth'
controlnet_depth = FluxControlNetModel.from_pretrained(controlnet_model_depth, torch_dtype=torch.bfloat16)

controlnet = FluxMultiControlNetModel([controlnet_union, controlnet_depth])

# set mode to None for other ControlNets
control_mode=[2, None]

Resources

• InstantX/FLUX.1-dev-Controlnet-Canny
• Shakker-Labs/FLUX.1-dev-ControlNet-Depth
• Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro

Acknowledgements

This project is trained by InstantX Team and sponsored by Shakker AI. The original idea is inspired by xinsir/controlnet-union-sdxl-1.0. All copyright reserved.