---
license_name: bria-i2i
license: other
license_link: https://bria.ai/bria-2-0-huggingface-model-license-agreement/
library_name: diffusers
inference: false
tags:
- text-to-image
-  image-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 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](https://bria.ai/customer-general-terms-and-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](https://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.**


*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 1.5 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 test BRIA for free?**

You can test BRIA’s models and platform for free in three ways before making a purchase:
- Get 1,000 free [API](https://bria.ai/api/?_gl=1*1asrb8n*_ga*MTcxNDcwNjAzMi4xNzAzMjU0MjEx*_ga_WRN60H46X4*MTcwNzA4MTc0OC41NS4wLjE3MDcwODE3NDguNjAuMC4w) calls per month by registering to the Bria open platform.
- Try it out for free in our [playground](https://labs.bria.ai/)
- Experience it for free with our [demos](https://huggingface.co/briaai)


### How To Use

```python
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")
```