Text-to-Image
Diffusers
Safetensors
English
Text-to-Image
ControlNet
Diffusers
Flux.1-dev
image-generation
Stable Diffusion
Instructions to use Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro-2.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro-2.0 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro-2.0", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Recommended strength and guidance end when chaining multiple controlnets
#7
by jingno123 - opened
Hi, thanks for the recommended strength and guidance, it's very helpful. Wondering if there's any recommendation when chaining multiple controlnets together? Like if I use depth then canny, should the guidance end be 0.8 and half for depth and half for canny?
Thanks!
oh I saw in the Multi-Inference example code. It should be the strength adds up to about 0.7, but both guidance end will be 0.8?
For Canny, do you have a recommended thresholds?