BRIA 1.5 Inpainting: The Ultimate Image-to-Image Inpainting Model with Full Legal Liability for Enterprises
Trained exclusively on the largest multi-source commercial-grade licensed dataset, BRIA 1.5 Inpainting guarantees best quality while safe for commercial use. BRIA's models were trained from scratch exclusively on licensed data from our esteemed data partners, including Getty Images, Alamy (part of PA Media Group), Envato, boutique niche agencies, independent photographers and artists. BRIA 1.5 Inpaintingis safe for commercial use and provides full legal liability coverage for copyright and privacy infrigement and harmful content mitigation. In addition, having been trained on commercial-grade data, BRIA models set new standards of safety, diversity, equity and inclusion. Our dataset does not represent copyrighted materials, such as fictional characters, logos or trademarks, public figures, harmful content or privacy infringing content. Generation of these concepts using our models is impractical.
Model Description
- Developed by: BRIA AI
- Model type: Latent diffusion image-to-image model
- License: bria-1.5 inpainting Licensing terms & conditions.
- Purchase is required to license and access the model.
- Model Description: BRIA 1.5 Inpainting is a image-to-image model 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
Get Access to the source code and pre-trained model
Interested in BRIA 1.5 Inpainting? Our Image-to-Image Model is available for purchase.
Purchasing access to BRIA 1.5 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 StableDiffusionInpaintPipeline, 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((512, 512))
mask_image = download_image(mask_url).resize((512, 512))
unet = UNet2DConditionModel.from_pretrained(
"briaai/BRIA-1.5-Inpainting",
torch_dtype=torch.float16,
)
scheduler = DDIMScheduler(
beta_end=0.012,
beta_schedule="scaled_linear",
beta_start=0.00085,
num_train_timesteps=1000,
clip_sample=False,
set_alpha_to_one=False,
steps_offset=1,
trained_betas=None,
rescale_betas_zero_snr=True,prediction_type='v_prediction',timestep_spacing="trailing"
)
pipe = StableDiffusionInpaintPipeline.from_pretrained(
"briaai/BRIA-1.4-Inpainting",
unet=unet,
scheduler=scheduler,
torch_dtype=torch.float16,
)
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=7.5,guidance_rescale=0.7).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,guidance_rescale=0.7).images[0]
image.save("./a_park_bench.png")
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