Total runs:
66.3K
Run Growth:
-691
Growth Rate:
-1.04%
TencentARC / t2iadapter_depth_sd14v1
Model's Last Updated: July 31 2023
image-to-image
Introduction of t2iadapter_depth_sd14v1
Model Details of t2iadapter_depth_sd14v1
T2I Adapter - Depth
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 depth 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
- Dependencies
pip install diffusers transformers controlnet_aux
- Run code:
from controlnet_aux import MidasDetector
from PIL import Image
from diffusers import T2IAdapter, StableDiffusionAdapterPipeline
import torch
midas = MidasDetector.from_pretrained("lllyasviel/Annotators")
image = Image.open('./images/depth_input.png')
image = midas(image)
image.save('./images/depth.png')
adapter = T2IAdapter.from_pretrained("TencentARC/t2iadapter_depth_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(1)
openpose_out = pipe(prompt="storm trooper giving a speech", image=image, generator=generator).images[0]
openpose_out.save('./images/depth_output.png')
Runs of TencentARC t2iadapter_depth_sd14v1 on huggingface.co
57
Total runs
0
24-hour runs
2
3-day runs
42
7-day runs
35
30-day runs
More Information About t2iadapter_depth_sd14v1 huggingface.co Model
Url of t2iadapter_depth_sd14v1
t2iadapter_depth_sd14v1 huggingface.co Url
Provider of t2iadapter_depth_sd14v1 huggingface.co
Other API from TencentARC
Updated: April 11 2024
Create photos, paintings and avatars for anyone in any style within seconds.
Total runs:
35.0K
Run Growth:
-43.4K
Growth Rate:
-124.12%
Total runs:
30.5K
Run Growth:
6.6K
Growth Rate:
21.61%
Total runs:
9.8K
Run Growth:
134
Growth Rate:
1.37%
Total runs:
6.8K
Run Growth:
1.1K
Growth Rate:
16.29%
Total runs:
6.7K
Run Growth:
363
Growth Rate:
5.55%
Total runs:
5.9K
Run Growth:
1.0K
Growth Rate:
17.41%
Total runs:
5.3K
Run Growth:
1.4K
Growth Rate:
27.11%
Total runs:
4.3K
Run Growth:
38
Growth Rate:
0.89%
Total runs:
2.6K
Run Growth:
327
Growth Rate:
12.57%
Total runs:
2.4K
Run Growth:
57
Growth Rate:
2.32%
Total runs:
2.4K
Run Growth:
152
Growth Rate:
6.31%
Total runs:
2.3K
Run Growth:
962
Growth Rate:
42.17%
Total runs:
2.2K
Run Growth:
743
Growth Rate:
34.30%
Total runs:
2.0K
Run Growth:
210
Growth Rate:
10.38%
Total runs:
220
Run Growth:
-181
Growth Rate:
-82.27%
Total runs:
160
Run Growth:
44
Growth Rate:
26.04%
Total runs:
153
Run Growth:
-542
Growth Rate:
-354.25%
Total runs:
140
Run Growth:
-78
Growth Rate:
-55.71%
Total runs:
51
Run Growth:
-18
Growth Rate:
-35.29%
Total runs:
50
Run Growth:
29
Growth Rate:
60.42%
Total runs:
41
Run Growth:
-103
Growth Rate:
-264.10%
Total runs:
39
Run Growth:
12
Growth Rate:
30.77%
Total runs:
33
Run Growth:
-78
Growth Rate:
-243.75%
Total runs:
27
Run Growth:
9
Growth Rate:
40.91%
Total runs:
24
Run Growth:
3
Growth Rate:
12.50%
Total runs:
19
Run Growth:
3
Growth Rate:
15.79%
Total runs:
18
Run Growth:
9
Growth Rate:
50.00%
Total runs:
12
Run Growth:
2
Growth Rate:
16.67%
Total runs:
12
Run Growth:
2
Growth Rate:
16.67%
Total runs:
12
Run Growth:
2
Growth Rate:
16.67%
Total runs:
9
Run Growth:
2
Growth Rate:
22.22%
Total runs:
7
Run Growth:
-5
Growth Rate:
-71.43%
Total runs:
6
Run Growth:
-2
Growth Rate:
-33.33%
Total runs:
5
Run Growth:
-2
Growth Rate:
-40.00%
Total runs:
5
Run Growth:
-6
Growth Rate:
-120.00%
Total runs:
4
Run Growth:
4
Growth Rate:
100.00%
Total runs:
4
Run Growth:
4
Growth Rate:
100.00%
Total runs:
0
Run Growth:
0
Growth Rate:
0.00%