Total runs:
50.5K
Run Growth:
13.3K
Growth Rate:
26.40%
Sponsored by
APIMart
multimodalart / public-domain-mouse-style
Model's Last Updated: January 01 2024
text-to-image
Introduction of public-domain-mouse-style
Model Details of public-domain-mouse-style
SDXL LoRA DreamBooth - multimodalart/public-domain-mouse-style
- Prompt
- in the style of <s0><s1>
- Prompt
- in the style of <s0><s1>
- Prompt
- in the style of <s0><s1>
- Prompt
- in the style of <s0><s1>
- Prompt
- in the style of <s0><s1>
- Prompt
- in the style of <s0><s1>
- Prompt
- in the style of <s0><s1>
Model description
These are multimodalart/public-domain-mouse-style LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
Download model
Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke
-
LoRA
: download
public-domain-mouse-style.safetensorshere 💾 .-
Place it on your
models/Lorafolder. -
On AUTOMATIC1111, load the LoRA by adding
<lora:public-domain-mouse-style:1>to your prompt. On ComfyUI just load it as a regular LoRA .
-
Place it on your
-
Embeddings
: download
public-domain-mouse-style_emb.safetensorshere 💾 .-
Place it on it on your
embeddingsfolder -
Use it by adding
public-domain-mouse-style_embto your prompt. For example,in the style of public-domain-mouse-style_emb(you need both the LoRA and the embeddings as they were trained together for this LoRA)
-
Place it on it on your
Use it with the 🧨 diffusers library
from diffusers import AutoPipelineForText2Image
import torch
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('multimodalart/public-domain-mouse-style', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='multimodalart/public-domain-mouse-style', filename='public-domain-mouse-style_emb.safetensors' repo_type="model")
state_dict = load_file(embedding_path)
pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer)
pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2)
image = pipeline('in the style of <s0><s1>').images[0]
For more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers
Trigger words
To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:
to trigger concept
TOK
→ use
<s0><s1>
in your prompt
Details
All Files & versions .
The weights were trained using 🧨 diffusers Advanced Dreambooth Training Script .
LoRA for the text encoder was enabled. False.
Pivotal tuning was enabled: True.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
Runs of multimodalart public-domain-mouse-style on huggingface.co
30
Total runs
0
24-hour runs
2
3-day runs
-1
7-day runs
14
30-day runs
More Information About public-domain-mouse-style huggingface.co Model
Url of public-domain-mouse-style
public-domain-mouse-style huggingface.co Url
Provider of public-domain-mouse-style huggingface.co
Other API from multimodalart
Updated:June 05 2026
Total runs:
8.1K
Run Growth:
-661
Growth Rate:
-8.16%
Total runs:
369
Run Growth:
180
Growth Rate:
48.78%
Total runs:
241
Run Growth:
-1.9K
Growth Rate:
-763.14%
Total runs:
205
Run Growth:
-37
Growth Rate:
-18.05%
Total runs:
200
Run Growth:
40
Growth Rate:
20.00%
Total runs:
195
Run Growth:
181
Growth Rate:
92.82%
Total runs:
180
Run Growth:
47
Growth Rate:
26.11%
Total runs:
161
Run Growth:
35
Growth Rate:
21.74%
Total runs:
120
Run Growth:
0
Growth Rate:
0.00%
Total runs:
104
Run Growth:
104
Growth Rate:
100.00%
Total runs:
102
Run Growth:
102
Growth Rate:
100.00%
Total runs:
80
Run Growth:
27
Growth Rate:
33.75%
Total runs:
60
Run Growth:
31
Growth Rate:
51.67%
Total runs:
53
Run Growth:
6
Growth Rate:
11.32%
Total runs:
48
Run Growth:
19
Growth Rate:
39.58%
Total runs:
48
Run Growth:
11
Growth Rate:
22.92%
Total runs:
47
Run Growth:
18
Growth Rate:
38.30%
Total runs:
46
Run Growth:
46
Growth Rate:
100.00%
Total runs:
41
Run Growth:
18
Growth Rate:
43.90%
Total runs:
38
Run Growth:
24
Growth Rate:
63.16%
Total runs:
37
Run Growth:
13
Growth Rate:
35.14%
Total runs:
37
Run Growth:
18
Growth Rate:
48.65%
Total runs:
35
Run Growth:
22
Growth Rate:
62.86%
Total runs:
33
Run Growth:
24
Growth Rate:
72.73%
Total runs:
32
Run Growth:
14
Growth Rate:
43.75%
Total runs:
31
Run Growth:
23
Growth Rate:
74.19%
Total runs:
30
Run Growth:
13
Growth Rate:
43.33%
Total runs:
30
Run Growth:
22
Growth Rate:
73.33%
Total runs:
30
Run Growth:
-66
Growth Rate:
-220.00%
Total runs:
29
Run Growth:
-6
Growth Rate:
-20.69%
Total runs:
29
Run Growth:
29
Growth Rate:
100.00%
Total runs:
29
Run Growth:
29
Growth Rate:
100.00%
Total runs:
29
Run Growth:
15
Growth Rate:
51.72%
Total runs:
27
Run Growth:
15
Growth Rate:
55.56%
Total runs:
26
Run Growth:
3
Growth Rate:
11.54%
Total runs:
24
Run Growth:
-14
Growth Rate:
-58.33%
Total runs:
25
Run Growth:
10
Growth Rate:
43.48%
Total runs:
23
Run Growth:
9
Growth Rate:
39.13%
Total runs:
23
Run Growth:
11
Growth Rate:
47.83%
Total runs:
23
Run Growth:
-1
Growth Rate:
-4.35%
Total runs:
21
Run Growth:
8
Growth Rate:
38.10%
Total runs:
20
Run Growth:
9
Growth Rate:
45.00%
Total runs:
20
Run Growth:
6
Growth Rate:
30.00%
Total runs:
19
Run Growth:
11
Growth Rate:
57.89%
Total runs:
18
Run Growth:
11
Growth Rate:
61.11%
Total runs:
17
Run Growth:
3
Growth Rate:
17.65%
Total runs:
16
Run Growth:
-1
Growth Rate:
-6.25%
Total runs:
16
Run Growth:
10
Growth Rate:
62.50%
Total runs:
15
Run Growth:
-3
Growth Rate:
-20.00%
Total runs:
14
Run Growth:
3
Growth Rate:
21.43%
Total runs:
14
Run Growth:
0
Growth Rate:
0.00%
Total runs:
13
Run Growth:
-13
Growth Rate:
-100.00%
Total runs:
13
Run Growth:
-8
Growth Rate:
-61.54%
Total runs:
13
Run Growth:
0
Growth Rate:
0.00%
Total runs:
13
Run Growth:
-2
Growth Rate:
-15.38%
Total runs:
13
Run Growth:
-7
Growth Rate:
-53.85%
Total runs:
11
Run Growth:
-8
Growth Rate:
-72.73%
Total runs:
11
Run Growth:
0
Growth Rate:
0.00%
Total runs:
11
Run Growth:
-13
Growth Rate:
-118.18%
Total runs:
11
Run Growth:
-9
Growth Rate:
-81.82%
Total runs:
10
Run Growth:
-13
Growth Rate:
-130.00%
Total runs:
10
Run Growth:
-7
Growth Rate:
-70.00%
Total runs:
9
Run Growth:
0
Growth Rate:
0.00%
Total runs:
9
Run Growth:
-3
Growth Rate:
-33.33%
Total runs:
8
Run Growth:
-17
Growth Rate:
-212.50%
Total runs:
8
Run Growth:
2
Growth Rate:
25.00%
Total runs:
8
Run Growth:
2
Growth Rate:
25.00%
Total runs:
8
Run Growth:
-19
Growth Rate:
-237.50%
Total runs:
8
Run Growth:
-13
Growth Rate:
-162.50%
Total runs:
8
Run Growth:
-13
Growth Rate:
-162.50%
Total runs:
8
Run Growth:
-9
Growth Rate:
-112.50%
Total runs:
7
Run Growth:
-1
Growth Rate:
-14.29%
Total runs:
7
Run Growth:
-18
Growth Rate:
-257.14%
Total runs:
7
Run Growth:
-15
Growth Rate:
-214.29%
Total runs:
6
Run Growth:
-15
Growth Rate:
-250.00%
Total runs:
6
Run Growth:
-10
Growth Rate:
-166.67%
Total runs:
5
Run Growth:
-11
Growth Rate:
-220.00%
Total runs:
5
Run Growth:
-13
Growth Rate:
-260.00%
Total runs:
5
Run Growth:
-14
Growth Rate:
-280.00%
Total runs:
5
Run Growth:
-16
Growth Rate:
-320.00%
Total runs:
5
Run Growth:
-14
Growth Rate:
-280.00%
Total runs:
5
Run Growth:
-15
Growth Rate:
-300.00%
Total runs:
5
Run Growth:
-11
Growth Rate:
-220.00%
Total runs:
5
Run Growth:
-11
Growth Rate:
-220.00%
Total runs:
4
Run Growth:
-19
Growth Rate:
-475.00%