BRIA 2.3 Image-Prompt

BRIA 2.3 Image-Prompt enables the generation of high-quality images guided by an image as input, alongside (or instead of) the textual prompt. This allows for the creation of images inspired by the content or style of an existing images, which can be useful for the creation of image variations or for transferring the style or content of an image. This module uses the architecture of IP-Adapter-Plus and is trained on the foundation of BRIA 2.3 Text-to-Image.

This adapter can be used in combination with other adapters trained over our foundation model, such as ControlNet-Depth or ControlNet-Canny.

Similar to BRIA 2.3, this adapter was trained from scratch exclusively on licensed data from our data partners. Therefore, it is 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.

Image Variations (textual prompt: "high quality"):

Style Transfer (textual prompt: "capybara"):

Model Description

Developed by: BRIA AI
Model type: IP-Adapter for Latent diffusion
License: Commercial licensing terms & conditions.
Model Description: IP-Adapter for BRIA 2.3 Text-to-Image model. The model generates images guided by an image prompt.
Resources for more information: BRIA AI

Bria AI licenses the foundation model on which this model was trained, with full legal liability coverage. Our dataset does not contain copyrighted materials, such as fictional characters, logos, trademarks, public figures, harmful content, or privacy-infringing content. For more information, please visit our website.

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Interested in BRIA 2.3? Purchase is required to license and access BRIA 2.3, ensuring royalty management with our data partners and full liability coverage for commercial use.

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Code example using Diffusers

pip install diffusers

from diffusers import AutoPipelineForText2Image
from diffusers.utils import load_image
import torch

pipeline = AutoPipelineForText2Image.from_pretrained("briaai/BRIA-2.3", torch_dtype=torch.float16, force_zeros_for_empty_prompt=False).to("cuda")
pipeline.load_ip_adapter("briaai/Image-Prompt", subfolder='models', weight_name="ip_adapter_bria.bin")

Create variations of the input image

pipeline.set_ip_adapter_scale(1.0)
image = load_image("examples/example1.jpg")
generator = torch.Generator(device="cpu").manual_seed(0)
images = pipeline(
    prompt="high quality",
    ip_adapter_image=image.resize((224, 224)),
    num_inference_steps=25,
    generator=generator,
    height=1024, width=1024,
    guidance_scale=7
).images
images[0]

Use both image and textual prompt as inputs

textual_prompt = "Paris, high quality"
pipeline.set_ip_adapter_scale(0.7)
image = load_image("examples/example2.jpg")
generator = torch.Generator(device="cpu").manual_seed(0)
images = pipeline(
    prompt=textual_prompt,
    ip_adapter_image=image.resize((224, 224)),
    num_inference_steps=25,
    generator=generator,
    height=1024, width=1024,
    guidance_scale=7
).images
images[0]

Some tips for using our text-to-image model at inference:

You must set pipe.force_zeros_for_empty_prompt = False
For image variations, you can try setting an empty prompt. Also, you can add a negative prompt.
We support multiple aspect ratios, yet resolution should overall consists approximately 1024*1024=1M pixels, for example: (1024,1024), (1280, 768), (1344, 768), (832, 1216), (1152, 832), (1216, 832), (960,1088)
Change the scale of the ip-adapter by using the "set_ip_adapter_scale()" method (range 0-1). The higher the scale, the closer the output will be to the input image.
Resize the input image into a square, otherwise the CLIP image embedder will perform center-crop.

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Image-Prompt

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