metadata

license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
  - en
library_name: diffusers
pipeline_tag: text-to-image
tags:
  - Text-to-Image
  - ControlNet
  - Diffusers
  - Flux.1-dev
  - image-generation
  - Stable Diffusion
base_model: black-forest-labs/FLUX.1-dev

FLUX.1-dev-ControlNet-Union-Pro-2.0

This repository contains an unified ControlNet for FLUX.1-dev model released by Shakker Labs. We provide an online demo.

Keynotes

In comparison with Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro,

Remove mode embedding, has smaller model size.
Improve on canny and pose, better control and aesthetics.
Add support for soft edge. Remove support for tile.

Model Cards

This ControlNet consists of 6 double blocks and 0 single block. Mode embedding is removed.
We train the model from scratch for 300k steps using a dataset of 20M high-quality general and human images. We train at 512x512 resolution in BFloat16, batch size = 128, learning rate = 2e-5, the guidance is uniformly sampled from [1, 7]. We set the text drop ratio to 0.20.
This model supports multiple control modes, including canny, soft edge, depth, pose, gray. You can use it just as a normal ControlNet.
This model can be jointly used with other ControlNets.

Showcases

Inference

import torch
from diffusers.utils import load_image
from diffusers import FluxControlNetPipeline, FluxControlNetModel

base_model = 'black-forest-labs/FLUX.1-dev'
controlnet_model_union = 'Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro-2.0'

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

# replace with other conds
control_image = load_image("./conds/canny.png")
width, height = control_image.size

prompt = "A young girl stands gracefully at the edge of a serene beach, her long, flowing hair gently tousled by the sea breeze. She wears a soft, pastel-colored dress that complements the tranquil blues and greens of the coastal scenery. The golden hues of the setting sun cast a warm glow on her face, highlighting her serene expression. The background features a vast, azure ocean with gentle waves lapping at the shore, surrounded by distant cliffs and a clear, cloudless sky. The composition emphasizes the girl's serene presence amidst the natural beauty, with a balanced blend of warm and cool tones."

image = pipe(
    prompt, 
    control_image=control_image,
    width=width,
    height=height,
    controlnet_conditioning_scale=0.7,
    control_guidance_end=0.8,
    num_inference_steps=30, 
    guidance_scale=3.5,
    generator=torch.Generator(device="cuda").manual_seed(42),
).images[0]

Recommended Parameters

You can adjust controlnet_conditioning_scale and control_guidance_end for stronger control and better detail preservation. For better stability, we suggest to use multi-conditions.

Canny: use cv2.Canny, controlnet_conditioning_scale=0.7, control_guidance_end=0.8.
Soft Edge: use AnylineDetector, controlnet_conditioning_scale=0.7, control_guidance_end=0.8.
Depth: use depth-anything, controlnet_conditioning_scale=0.8, control_guidance_end=0.8.
Pose: use DWPose, controlnet_conditioning_scale=0.9, control_guidance_end=0.65.
Gray: use cv2.cvtColor, controlnet_conditioning_scale=0.9, control_guidance_end=0.8.

Resources

Acknowledgements

This model is developed by Shakker Labs. The original idea is inspired by xinsir/controlnet-union-sdxl-1.0. All copyright reserved.