import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("XLabs-AI/flux-controlnet-canny", dtype=torch.bfloat16, device_map="cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]
This repository provides a checkpoint with trained ControlNet Canny model for FLUX.1-dev model by Black Forest Labs
ComfyUI
See our github for comfy ui workflows.
Training details
XLabs AI team is happy to publish fune-tuning Flux scripts, including:
- LoRA 🔥
- ControlNet 🔥
See our github for train script and train configs.
Training dataset
Dataset has the following format for the training process:
├── images/
│ ├── 1.png
│ ├── 1.json
│ ├── 2.png
│ ├── 2.json
│ ├── ...
A .json file contains "caption" field with a text prompt.
Inference
To test our checkpoints, use commands presented below.
python3 demo_controlnet_inference.py \
--checkpoint controlnet.safetensors \
--control_image "input_image.jpg" \
--prompt "a handsome viking man with white hair, cinematic, MM full HD"
python3 demo_controlnet_inference.py \
--checkpoint controlnet.safetensors \
--control_image "input_image.jpg" \
--prompt "a dark evil mysterius house with ghosts, cinematic, MM full HD"
License
controlnet.safetensors falls under the FLUX.1 [dev] Non-Commercial License
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Model tree for XLabs-AI/flux-controlnet-canny
Base model
black-forest-labs/FLUX.1-dev