TencentARC / t2iadapter_seg_sd14v1

huggingface.co
Total runs: 27
24-hour runs: 0
7-day runs: 4
30-day runs: 9
Model's Last Updated: 2023年7月31日
image-to-image

Introduction of t2iadapter_seg_sd14v1

Model Details of t2iadapter_seg_sd14v1

T2I Adapter - Segment

T2I Adapter is a network providing additional conditioning to stable diffusion. Each t2i checkpoint takes a different type of conditioning as input and is used with a specific base stable diffusion checkpoint.

This checkpoint provides conditioning on semantic segmentation for the stable diffusion 1.4 checkpoint.

Model Details
  • Developed by: T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models

  • Model type: Diffusion-based text-to-image generation model

  • Language(s): English

  • License: Apache 2.0

  • Resources for more information: GitHub Repository , Paper .

  • Cite as:

    @misc{ title={T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models}, author={Chong Mou, Xintao Wang, Liangbin Xie, Yanze Wu, Jian Zhang, Zhongang Qi, Ying Shan, Xiaohu Qie}, year={2023}, eprint={2302.08453}, archivePrefix={arXiv}, primaryClass={cs.CV} }

Checkpoints
Model Name Control Image Overview Control Image Example Generated Image Example
TencentARC/t2iadapter_color_sd14v1
Trained with spatial color palette
A image with 8x8 color palette.
TencentARC/t2iadapter_canny_sd14v1
Trained with canny edge detection
A monochrome image with white edges on a black background.
TencentARC/t2iadapter_sketch_sd14v1
Trained with PidiNet edge detection
A hand-drawn monochrome image with white outlines on a black background.
TencentARC/t2iadapter_depth_sd14v1
Trained with Midas depth estimation
A grayscale image with black representing deep areas and white representing shallow areas.
TencentARC/t2iadapter_openpose_sd14v1
Trained with OpenPose bone image
A OpenPose bone image.
TencentARC/t2iadapter_keypose_sd14v1
Trained with mmpose skeleton image
A mmpose skeleton image.
TencentARC/t2iadapter_seg_sd14v1
Trained with semantic segmentation
An custom segmentation protocol image.
TencentARC/t2iadapter_canny_sd15v2
TencentARC/t2iadapter_depth_sd15v2
TencentARC/t2iadapter_sketch_sd15v2
TencentARC/t2iadapter_zoedepth_sd15v1
Example
  1. Dependencies
pip install diffusers transformers
  1. Run code:
import torch
from PIL import Image
import numpy as np
from transformers import AutoImageProcessor, UperNetForSemanticSegmentation

from diffusers import (
    T2IAdapter,
    StableDiffusionAdapterPipeline
)

ada_palette = np.asarray([
      [0, 0, 0],
      [120, 120, 120],
      [180, 120, 120],
      [6, 230, 230],
      [80, 50, 50],
      [4, 200, 3],
      [120, 120, 80],
      [140, 140, 140],
      [204, 5, 255],
      [230, 230, 230],
      [4, 250, 7],
      [224, 5, 255],
      [235, 255, 7],
      [150, 5, 61],
      [120, 120, 70],
      [8, 255, 51],
      [255, 6, 82],
      [143, 255, 140],
      [204, 255, 4],
      [255, 51, 7],
      [204, 70, 3],
      [0, 102, 200],
      [61, 230, 250],
      [255, 6, 51],
      [11, 102, 255],
      [255, 7, 71],
      [255, 9, 224],
      [9, 7, 230],
      [220, 220, 220],
      [255, 9, 92],
      [112, 9, 255],
      [8, 255, 214],
      [7, 255, 224],
      [255, 184, 6],
      [10, 255, 71],
      [255, 41, 10],
      [7, 255, 255],
      [224, 255, 8],
      [102, 8, 255],
      [255, 61, 6],
      [255, 194, 7],
      [255, 122, 8],
      [0, 255, 20],
      [255, 8, 41],
      [255, 5, 153],
      [6, 51, 255],
      [235, 12, 255],
      [160, 150, 20],
      [0, 163, 255],
      [140, 140, 140],
      [250, 10, 15],
      [20, 255, 0],
      [31, 255, 0],
      [255, 31, 0],
      [255, 224, 0],
      [153, 255, 0],
      [0, 0, 255],
      [255, 71, 0],
      [0, 235, 255],
      [0, 173, 255],
      [31, 0, 255],
      [11, 200, 200],
      [255, 82, 0],
      [0, 255, 245],
      [0, 61, 255],
      [0, 255, 112],
      [0, 255, 133],
      [255, 0, 0],
      [255, 163, 0],
      [255, 102, 0],
      [194, 255, 0],
      [0, 143, 255],
      [51, 255, 0],
      [0, 82, 255],
      [0, 255, 41],
      [0, 255, 173],
      [10, 0, 255],
      [173, 255, 0],
      [0, 255, 153],
      [255, 92, 0],
      [255, 0, 255],
      [255, 0, 245],
      [255, 0, 102],
      [255, 173, 0],
      [255, 0, 20],
      [255, 184, 184],
      [0, 31, 255],
      [0, 255, 61],
      [0, 71, 255],
      [255, 0, 204],
      [0, 255, 194],
      [0, 255, 82],
      [0, 10, 255],
      [0, 112, 255],
      [51, 0, 255],
      [0, 194, 255],
      [0, 122, 255],
      [0, 255, 163],
      [255, 153, 0],
      [0, 255, 10],
      [255, 112, 0],
      [143, 255, 0],
      [82, 0, 255],
      [163, 255, 0],
      [255, 235, 0],
      [8, 184, 170],
      [133, 0, 255],
      [0, 255, 92],
      [184, 0, 255],
      [255, 0, 31],
      [0, 184, 255],
      [0, 214, 255],
      [255, 0, 112],
      [92, 255, 0],
      [0, 224, 255],
      [112, 224, 255],
      [70, 184, 160],
      [163, 0, 255],
      [153, 0, 255],
      [71, 255, 0],
      [255, 0, 163],
      [255, 204, 0],
      [255, 0, 143],
      [0, 255, 235],
      [133, 255, 0],
      [255, 0, 235],
      [245, 0, 255],
      [255, 0, 122],
      [255, 245, 0],
      [10, 190, 212],
      [214, 255, 0],
      [0, 204, 255],
      [20, 0, 255],
      [255, 255, 0],
      [0, 153, 255],
      [0, 41, 255],
      [0, 255, 204],
      [41, 0, 255],
      [41, 255, 0],
      [173, 0, 255],
      [0, 245, 255],
      [71, 0, 255],
      [122, 0, 255],
      [0, 255, 184],
      [0, 92, 255],
      [184, 255, 0],
      [0, 133, 255],
      [255, 214, 0],
      [25, 194, 194],
      [102, 255, 0],
      [92, 0, 255],
  ])


image_processor = AutoImageProcessor.from_pretrained("openmmlab/upernet-convnext-small")
image_segmentor = UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-convnext-small")

checkpoint = "lllyasviel/control_v11p_sd15_seg"

image = Image.open('./images/seg_input.jpeg')

pixel_values = image_processor(image, return_tensors="pt").pixel_values
with torch.no_grad():
  outputs = image_segmentor(pixel_values)

seg = image_processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]

color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8) # height, width, 3

for label, color in enumerate(ada_palette):
    color_seg[seg == label, :] = color

color_seg = color_seg.astype(np.uint8)
control_image = Image.fromarray(color_seg)

control_image.save("./images/segment_image.png")

adapter = T2IAdapter.from_pretrained("TencentARC/t2iadapter_seg_sd14v1", torch_dtype=torch.float16)
pipe = StableDiffusionAdapterPipeline.from_pretrained(
    "CompVis/stable-diffusion-v1-4", adapter=adapter, safety_checker=None, torch_dtype=torch.float16, variant="fp16"
)

pipe.to('cuda')

generator = torch.Generator().manual_seed(0)

sketch_image_out = pipe(prompt="motorcycles driving", image=control_image, generator=generator).images[0]

sketch_image_out.save('./images/seg_image_out.png')

seg_input segment_image seg_image_out

Runs of TencentARC t2iadapter_seg_sd14v1 on huggingface.co

27
Total runs
0
24-hour runs
5
3-day runs
4
7-day runs
9
30-day runs

More Information About t2iadapter_seg_sd14v1 huggingface.co Model

More t2iadapter_seg_sd14v1 license Visit here:

https://choosealicense.com/licenses/apache-2.0

t2iadapter_seg_sd14v1 huggingface.co

t2iadapter_seg_sd14v1 huggingface.co is an AI model on huggingface.co that provides t2iadapter_seg_sd14v1's model effect (), which can be used instantly with this TencentARC t2iadapter_seg_sd14v1 model. huggingface.co supports a free trial of the t2iadapter_seg_sd14v1 model, and also provides paid use of the t2iadapter_seg_sd14v1. Support call t2iadapter_seg_sd14v1 model through api, including Node.js, Python, http.

t2iadapter_seg_sd14v1 huggingface.co Url

https://huggingface.co/TencentARC/t2iadapter_seg_sd14v1

TencentARC t2iadapter_seg_sd14v1 online free

t2iadapter_seg_sd14v1 huggingface.co is an online trial and call api platform, which integrates t2iadapter_seg_sd14v1's modeling effects, including api services, and provides a free online trial of t2iadapter_seg_sd14v1, you can try t2iadapter_seg_sd14v1 online for free by clicking the link below.

TencentARC t2iadapter_seg_sd14v1 online free url in huggingface.co:

https://huggingface.co/TencentARC/t2iadapter_seg_sd14v1

t2iadapter_seg_sd14v1 install

t2iadapter_seg_sd14v1 is an open source model from GitHub that offers a free installation service, and any user can find t2iadapter_seg_sd14v1 on GitHub to install. At the same time, huggingface.co provides the effect of t2iadapter_seg_sd14v1 install, users can directly use t2iadapter_seg_sd14v1 installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

t2iadapter_seg_sd14v1 install url in huggingface.co:

https://huggingface.co/TencentARC/t2iadapter_seg_sd14v1

Url of t2iadapter_seg_sd14v1

t2iadapter_seg_sd14v1 huggingface.co Url

Provider of t2iadapter_seg_sd14v1 huggingface.co

TencentARC
ORGANIZATIONS

Other API from TencentARC

huggingface.co

Total runs: 66.3K
Run Growth: -691
Growth Rate: -1.04%
Updated: 2024年4月11日
huggingface.co

Create photos, paintings and avatars for anyone in any style within seconds.

Total runs: 35.0K
Run Growth: -43.4K
Growth Rate: -124.12%
Updated: 2024年7月22日
huggingface.co

Total runs: 140
Run Growth: -78
Growth Rate: -55.71%
Updated: 2024年12月16日
huggingface.co

Total runs: 114
Run Growth: 22
Growth Rate: 19.30%
Updated: 2024年11月29日
huggingface.co

Total runs: 19
Run Growth: 11
Growth Rate: 57.89%
Updated: 2024年12月10日
huggingface.co

Total runs: 16
Run Growth: -3
Growth Rate: -17.65%
Updated: 2024年8月26日
huggingface.co

Total runs: 5
Run Growth: -1
Growth Rate: -20.00%
Updated: 2024年12月30日
huggingface.co

Total runs: 5
Run Growth: -2
Growth Rate: -40.00%
Updated: 2024年12月30日
huggingface.co

Total runs: 4
Run Growth: -6
Growth Rate: -150.00%
Updated: 2024年12月30日
huggingface.co

Total runs: 0
Run Growth: 0
Growth Rate: 0.00%
Updated: 2024年6月29日
huggingface.co

Total runs: 0
Run Growth: 0
Growth Rate: 0.00%
Updated: 2025年1月12日
huggingface.co

Total runs: 0
Run Growth: 0
Growth Rate: 0.00%
Updated: 2023年8月20日
huggingface.co

Total runs: 0
Run Growth: 0
Growth Rate: 0.00%
Updated: 2024年12月16日
huggingface.co

Total runs: 0
Run Growth: 0
Growth Rate: 0.00%
Updated: 2023年8月28日
huggingface.co

Total runs: 0
Run Growth: 0
Growth Rate: 0.00%
Updated: 2024年12月20日
huggingface.co

Total runs: 0
Run Growth: 0
Growth Rate: 0.00%
Updated: 2024年8月13日
huggingface.co

Total runs: 0
Run Growth: 0
Growth Rate: 0.00%
Updated: 2024年12月17日
huggingface.co

Total runs: 0
Run Growth: 0
Growth Rate: 0.00%
Updated: 2024年4月11日
huggingface.co

Total runs: 0
Run Growth: 0
Growth Rate: 0.00%
Updated: 2022年10月8日
huggingface.co

Total runs: 0
Run Growth: 0
Growth Rate: 0.00%
Updated: 2024年1月20日
huggingface.co

Total runs: 0
Run Growth: 0
Growth Rate: 0.00%
Updated: 2024年7月22日