license: other
license_name: bria-2.3
license_link: https://bria.ai/bria-huggingface-model-license-agreement/
inference: false
tags:
- text-to-image
- controlnet model
- legal liability
- commercial use
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BRIA 2.3 ControlNet Depth Model Card
BRIA 2.3 ControlNet-Depth, trained on the foundation of BRIA 2.3 Text-to-Image, enables the generation of high-quality images guided by a textual prompt and the extracted monocular depth estimation from an input image. This allows for the creation of different variations of an image, all sharing the same geometry.
BRIA 2.3 was trained from scratch exclusively on licensed data from our esteemed data partners. Therefore, they are 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.
Model Description
Developed by: BRIA AI
Model type: ControlNet for Latent diffusion
License: bria-2.3
Model Description: ControlNet Depth for BRIA 2.3 Text-to-Image model. The model generates images guided by text and the monocular depth estimation of the conditioned image.
Resources for more information: BRIA AI
Get Access
BRIA 2.3 ControlNet-Depth requires access to BRIA 2.3 Text-to-Image. For more information, click here.
Code example using Diffusers
pip install diffusers
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline
import torch
from transformers import DPTFeatureExtractor, DPTForDepthEstimation
depth_estimator = DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas").to("cuda")
feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-hybrid-midas")
def get_depth_map(image):
image = feature_extractor(images=image, return_tensors="pt").pixel_values.to("cuda")
with torch.no_grad(), torch.autocast("cuda"):
depth_map = depth_estimator(image).predicted_depth
image = transforms.functional.center_crop(image, min(image.shape[-2:]))
depth_map = torch.nn.functional.interpolate(
depth_map.unsqueeze(1),
size=(1024, 1024),
mode="bicubic",
align_corners=False,
)
depth_min = torch.amin(depth_map, dim=[1, 2, 3], keepdim=True)
depth_max = torch.amax(depth_map, dim=[1, 2, 3], keepdim=True)
depth_map = (depth_map - depth_min) / (depth_max - depth_min)
image = torch.cat([depth_map] * 3, dim=1)
image = image.permute(0, 2, 3, 1).cpu().numpy()[0]
image = Image.fromarray((image * 255.0).clip(0, 255).astype(np.uint8))
return image
controlnet = ControlNetModel.from_pretrained(
"briaai/BRIA-2.3-ControlNet-Depth",
torch_dtype=torch.float16
)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"briaai/BRIA-2.3",
controlnet=controlnet,
torch_dtype=torch.float16,
)
pipe.to("cuda")
prompt = "A portrait of a Beautiful and playful ethereal singer, golden designs, highly detailed, blurry background"
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"
# Calculate Depth image
input_image = cv2.imread('pics/singer.png')
depth_image = get_depth_map(input_image)
image = pipe(prompt=prompt, negative_prompt=negative_prompt, image=depth_image, controlnet_conditioning_scale=1.0, height=1024, width=1024).images[0]