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.