--- license_name: bria-2.3 license: other license_link: https://bria.ai/bria-huggingface-model-license-agreement/ library_name: diffusers inference: false tags: - text-to-image - legal liability - commercial use extra_gated_description: Model weights from BRIA AI can be obtained with the purchase of a commercial license. Fill in the form below and we reach out to you. extra_gated_heading: "Fill in this form to request a commercial license for the model" extra_gated_fields: Name: text Company/Org name: text Org Type (Early/Growth Startup, Enterprise, Academy): text Role: text Country: text Email: text By submitting this form, I agree to BRIA’s Privacy policy and Terms & conditions, see links below: checkbox --- # BRIA 2.3 ControlNet Inpainting Fast Trained exclusively on the largest multi-source commercial-grade licensed dataset, BRIA 2.3 inpainting guarantees best quality while safe for commercial use. The model provides full legal liability coverage for copyright and privacy infrigement and harmful content mitigation, as our dataset does not represent copyrighted materials, such as fictional characters, logos or trademarks, public figures, harmful content or privacy infringing content. BRIA 2.3 is an inpainting model designed to fill masked regions in images based on user-provided textual prompts. The model can be applied in different scenarios, including object removal, replacement, addition, and modification within an image, while also possessing the capability to expand the image. # What's New BRIA 2.3 ControlNet Inpainting can be applied on top of BRIA 2.3 Text-to-Image and therefore enable to use [Fast-LORA](https://huggingface.co/briaai/BRIA-2.3-FAST-LORA). This results in extremely fast inpainting model, requires only 1.6s using A10 GPU. ### Model Description - **Developed by:** BRIA AI - **Model type:** Latent diffusion image-to-image model - **License:** [bria-2.3 inpainting Licensing terms & conditions](https://bria.ai/bria-huggingface-model-license-agreement/). - Purchase is required to license and access the model. - **Model Description:** BRIA 2.3 inpainting was trained exclusively on a professional-grade, licensed dataset. It is designed for commercial use and includes full legal liability coverage. - **Resources for more information:** [BRIA AI](https://bria.ai/) ### Get Access to the source code and pre-trained model Interested in BRIA 2.3 inpainting? Our Model is available for purchase. **Purchasing access to BRIA 2.3 inpainting ensures royalty management and full liability for commercial use.** *Are you a startup or a student?* We encourage you to apply for our specialized Academia and [Startup Programs](https://pages.bria.ai/the-visual-generative-ai-platform-for-builders-startups-plan?_gl=1*cqrl81*_ga*MTIxMDI2NzI5OC4xNjk5NTQ3MDAz*_ga_WRN60H46X4*MTcwOTM5OTMzNC4yNzguMC4xNzA5Mzk5MzM0LjYwLjAuMA..) to gain access. These programs are designed to support emerging businesses and academic pursuits with our cutting-edge technology. **Contact us today to unlock the potential of BRIA 2.3 inpainting!** By submitting the form above, you agree to BRIA’s [Privacy policy](https://bria.ai/privacy-policy/) and [Terms & conditions](https://bria.ai/terms-and-conditions/). ### How To Use ```python from diffusers import ( AutoencoderKL, LCMScheduler, ) from pipeline_controlnet_sd_xl import StableDiffusionXLControlNetPipeline from controlnet import ControlNetModel, ControlNetConditioningEmbedding import torch import numpy as np from PIL import Image import requests import PIL from io import BytesIO from torchvision import transforms import pandas as pd import os def resize_image_to_retain_ratio(image): pixel_number = 1024*1024 granularity_val = 8 ratio = image.size[0] / image.size[1] width = int((pixel_number * ratio) ** 0.5) width = width - (width % granularity_val) height = int(pixel_number / width) height = height - (height % granularity_val) image = image.resize((width, height)) return image def download_image(url): response = requests.get(url) return PIL.Image.open(BytesIO(response.content)).convert("RGB") def get_masked_image(image, image_mask, width, height): image_mask = image_mask # inpaint area is white image_mask = image_mask.resize((width, height)) # object to remove is white (1) image_mask_pil = image_mask image = np.array(image.convert("RGB")).astype(np.float32) / 255.0 image_mask = np.array(image_mask_pil.convert("L")).astype(np.float32) / 255.0 assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size" masked_image_to_present = image.copy() masked_image_to_present[image_mask > 0.5] = (0.5,0.5,0.5) # set as masked pixel image[image_mask > 0.5] = 0.5 # set as masked pixel - s.t. will be grey image = Image.fromarray((image * 255.0).astype(np.uint8)) masked_image_to_present = Image.fromarray((masked_image_to_present * 255.0).astype(np.uint8)) return image, image_mask_pil, masked_image_to_present image_transforms = transforms.Compose( [ transforms.ToTensor(), ] ) default_negative_prompt = "Logo,Watermark,Text,Ugly,Morbid,Extra fingers,Poorly drawn hands,Mutation,Blurry,Extra limbs,Gross proportions,Missing arms,Mutated hands,Long neck,Duplicate,Mutilated,Mutilated hands,Poorly drawn face,Deformed,Bad anatomy,Cloned face,Malformed limbs,Missing legs,Too many fingers" img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png" mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png" init_image = download_image(img_url).resize((1024, 1024)) mask_image = download_image(mask_url).resize((1024, 1024)) init_image = resize_image_to_retain_ratio(init_image) width, height = init_image.size mask_image = mask_image.convert("L").resize(init_image.size) width, height = init_image.size # Load, init model controlnet = ControlNetModel().from_pretrained("briaai/BRIA-2.3-ControlNet-Inpainting", 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.to(dtype=torch.float16), torch_dtype=torch.float16, vae=vae) #force_zeros_for_empty_prompt=False, # vae=vae) pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config) pipe.load_lora_weights("briaai/BRIA-2.3-FAST-LORA") pipe.fuse_lora() pipe = pipe.to(device="cuda") # pipe.enable_xformers_memory_efficient_attention() generator = torch.Generator(device="cuda").manual_seed(123456) vae = pipe.vae masked_image, image_mask, masked_image_to_present = get_masked_image(init_image, mask_image, width, height) masked_image_tensor = image_transforms(masked_image) masked_image_tensor = (masked_image_tensor - 0.5) / 0.5 masked_image_tensor = masked_image_tensor.unsqueeze(0).to(device="cuda") control_latents = vae.encode( masked_image_tensor[:, :3, :, :].to(vae.dtype) ).latent_dist.sample() control_latents = control_latents * vae.config.scaling_factor image_mask = np.array(image_mask)[:,:] mask_tensor = torch.tensor(image_mask, dtype=torch.float32)[None, ...] # binarize the mask mask_tensor = torch.where(mask_tensor > 128.0, 255.0, 0) mask_tensor = mask_tensor / 255.0 mask_tensor = mask_tensor.to(device="cuda") mask_resized = torch.nn.functional.interpolate(mask_tensor[None, ...], size=(control_latents.shape[2], control_latents.shape[3]), mode='nearest') masked_image = torch.cat([control_latents, mask_resized], dim=1) prompt = "" gen_img = pipe(negative_prompt=default_negative_prompt, prompt=prompt, controlnet_conditioning_scale=1.0, num_inference_steps=12, height=height, width=width, image = masked_image, # control image init_image = init_image, mask_image = mask_tensor, guidance_scale = 1.2, generator=generator).images[0] ```