--- license: other license_name: bria-2.3 license_link: https://bria.ai/bria-huggingface-model-license-agreement/ inference: false tags: - text-to-image - controlnet model - legal liability - commercial use extra_gated_description: BRIA 2.3 ControlNet-Background Generation requires access to BRIA 2.3 Text-to-Image model extra_gated_heading: "Fill in this form to get access" extra_gated_fields: Name: type: text Company/Org name: type: text Org Type (Early/Growth Startup, Enterprise, Academy): type: text Role: type: text Country: type: text Email: type: text By submitting this form, I agree to BRIA’s Privacy policy and Terms & conditions, see links below: type: checkbox --- # BRIA-2.3-ControlNet-Background-Generation, Model Card BRIA 2.3 ControlNet-Background Generation, trained on the foundation of [BRIA 2.3 Text-to-Image](https://huggingface.co/briaai/BRIA-2.3), enables the generation of high-quality images guided by a textual prompt and the extracted background mask estimation from an input image. This allows for the creation of different background variations of an image, all sharing the same foreground. [BRIA 2.3](https://huggingface.co/briaai/BRIA-2.3) was trained from scratch exclusively on licensed data from our esteemed data partners. Therefore, they are safe for commercial use and provide full legal liability coverage for copyright and privacy infringement, as well as harmful content mitigation. That is, our dataset does not contain copyrighted materials, such as fictional characters, logos, trademarks, public figures, harmful content, or privacy-infringing content. Join our [Discord community](https://discord.gg/Nxe9YW9zHS) for more information, tutorials, tools, and to connect with other users! ![examples](bg_img.png) ### Model Description - **Developed by:** BRIA AI - **Model type:** [ControlNet](https://huggingface.co/docs/diffusers/using-diffusers/controlnet) for Latent diffusion - **License:** [bria-2.3](https://bria.ai/bria-huggingface-model-license-agreement/) - **Model Description:** ControlNet Background-Generation for BRIA 2.3 Text-to-Image model. The model generates images guided by text and the background mask. - **Resources for more information:** [BRIA AI](https://bria.ai/) ### Get Access BRIA 2.3 ControlNet-Background Generation requires access to BRIA 2.3 Text-to-Image. For more information, [click here](https://huggingface.co/briaai/BRIA-2.3). ## Usage Installation Install huggingface_hub and login if need to - \ https://huggingface.co/docs/huggingface_hub/en/guides/cli#getting-started \ https://huggingface.co/docs/huggingface_hub/en/quick-start#authentication Download and install BRIA-2.3-ControlNet-BG-Gen ```bash pip install -qr https://huggingface.co/briaai/BRIA-2.3-ControlNet-BG-Gen/resolve/main/requirements.txt ``` ```bash torch torchvision pillow numpy scikit-image Diffusers==0.26.2 transformers>=4.39.1 ``` ```bash huggingface-cli download briaai/BRIA-2.3-ControlNet-BG-Gen --include replace_bg/* --local-dir . --quiet ``` Run Inpainting script ```python import torch from diffusers import ( AutoencoderKL, EulerAncestralDiscreteScheduler, ) from diffusers.utils import load_image from replace_bg.model.pipeline_controlnet_sd_xl import StableDiffusionXLControlNetPipeline from replace_bg.model.controlnet import ControlNetModel from replace_bg.utilities import resize_image, remove_bg_from_image, paste_fg_over_image, get_control_image_tensor controlnet = ControlNetModel.from_pretrained("briaai/BRIA-2.3-ControlNet-BG-Gen", torch_dtype=torch.float16) vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16) pipe = StableDiffusionXLControlNetPipeline.from_pretrained("briaai/BRIA-2.3", controlnet=controlnet, torch_dtype=torch.float16, vae=vae).to('cuda:0') pipe.scheduler = EulerAncestralDiscreteScheduler( beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000, steps_offset=1 ) image_path = "https://farm5.staticflickr.com/4007/4322154488_997e69e4cf_z.jpg" image = load_image(image_path) image = resize_image(image) mask = remove_bg_from_image(image_path) control_tensor = get_control_image_tensor(pipe.vae, image, mask) prompt = "in a zoo" negative_prompt = "Logo,Watermark,Text,Ugly,Bad proportions,Bad quality,Out of frame,Mutation" generator = torch.Generator(device="cuda:0").manual_seed(0) gen_img = pipe( negative_prompt=negative_prompt, prompt=prompt, controlnet_conditioning_scale=1.0, num_inference_steps=50, image = control_tensor, generator=generator ).images[0] result_image = paste_fg_over_image(gen_img, image, mask) ```