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BRIA 2.3 Inpainting: The Ultimate Inpainting Model with Full Legal Liability for Enterprises

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 Inpainting underwent training with a 'zero-SNR' noise scheduling, minimizing bias towards initial noise and enhancing fidelity to the input image (excluding masked regions). This enhancement boosts performance in tasks demanding high fidelity to the original image, such as image expansion (outpainting) and object removal.

Model Description

  • Developed by: BRIA AI
  • Model type: Latent diffusion image-to-image model
  • License: bria-2.3 inpainting Licensing terms & conditions.
  • 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

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Purchasing access to BRIA 2.3 inpainting ensures royalty management and full liability for commercial use.

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How To Use

import PIL
import requests
import torch
from io import BytesIO
from diffusers import StableDiffusionXLInpaintPipeline, DDIMScheduler, UNet2DConditionModel

def download_image(url):
    response = requests.get(url)
    return PIL.Image.open(BytesIO(response.content)).convert("RGB")

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))

unet = UNet2DConditionModel.from_pretrained(
    "briaai/BRIA-2.3-Inpainting",
    subfolder="unet",
    torch_dtype=torch.float16,
)

scheduler = DDIMScheduler.from_pretrained("briaai/BRIA-2.3", subfolder="scheduler", 
                                                        rescale_betas_zero_snr=True,prediction_type='v_prediction',timestep_spacing="trailing",clip_sample=False)

pipe = StableDiffusionXLInpaintPipeline.from_pretrained(
    "briaai/BRIA-2.3",
    unet=unet,
    scheduler=scheduler,
    torch_dtype=torch.float16,
    force_zeros_for_empty_prompt=False
)
pipe = pipe.to("cuda")


prompt = "A ginger cat sitting"
generator = torch.Generator(device='cuda:0').manual_seed(123456)
image = pipe(prompt=prompt, image=init_image, mask_image=mask_image,generator=generator,guidance_scale=5,strength=1).images[0]
image.save("./ginger_cat_on_park_bench.png")

prompt = "A park bench"
generator = torch.Generator(device='cuda:0').manual_seed(123456)
image = pipe(prompt=prompt, image=init_image, mask_image=mask_image,generator=generator,guidance_scale=5,strength=1).images[0]
image.save("./a_park_bench.png")
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