FLUX.1 Dev ControlNet Canny

--- 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") ```