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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
tags:
- Text-to-Image
- ControlNet
- Diffusers
- Stable Diffusion
base_model: black-forest-labs/FLUX.1-dev
---
# FLUX.1-dev Controlnet
We have completed the training of the first version.
The training was conducted with a total pixel count of `1024*1024` at multi-scale.
We trained for 30k steps using a batch size of 8*8.
<img src="./images/image_demo.jpg" width = "800" />
<img src="./images/image_demo_weight.png" width = "800" />
# Diffusers version
Please ensure that you have installed the latest version of [Diffusers](https://github.com/huggingface/diffusers).
# Demo
```python
import torch
from diffusers.utils import load_image
from diffusers.pipelines.flux.pipeline_flux_controlnet import FluxControlNetPipeline
from diffusers.models.controlnet_flux import FluxControlNetModel
base_model = 'black-forest-labs/FLUX.1-dev'
controlnet_model = 'InstantX/FLUX.1-dev-Controlnet-Canny'
controlnet = FluxControlNetModel.from_pretrained(controlnet_model, torch_dtype=torch.bfloat16)
pipe = FluxControlNetPipeline.from_pretrained(base_model, controlnet=controlnet, torch_dtype=torch.bfloat16)
pipe.to("cuda")
control_image = load_image("https://huggingface.co/InstantX/FLUX.1-dev-Controlnet-Canny/resolve/main/canny.jpg")
prompt = "A girl in city, 25 years old, cool, futuristic"
image = pipe(
prompt,
control_image=control_image,
controlnet_conditioning_scale=0.6,
num_inference_steps=28,
guidance_scale=3.5,
).images[0]
image.save("image.jpg")
```
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