Total runs:
50.5K
Run Growth:
13.3K
Growth Rate:
26.40%
Sponsored by
APIMart
multimodalart / alice-100-multiplier
Model's Last Updated: December 22 2023
text-to-image
Introduction of alice-100-multiplier
Model Details of alice-100-multiplier
SDXL LoRA DreamBooth - multimodalart/alice-100-multiplier
- Prompt
- A photo of <s0><s1> woman with a cigarette in her mouth
- Prompt
- A photo of <s0><s1> woman in a red dress and pearls
- Prompt
- A photo of <s0><s1> woman in a pink shirt
- Prompt
- A photo of <s0><s1> woman with long brown hair and green dress
- Prompt
- A photo of <s0><s1> woman in a black top and earrings
- Prompt
- A photo of <s0><s1> woman with long hair wearing a black shirt
- Prompt
- A photo of <s0><s1> woman in a red top is looking down
- Prompt
- A photo of <s0><s1> women smiling and sitting on a bench
Model description
These are multimodalart/alice-100-multiplier LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
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
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/alice-100-multiplier', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='multimodalart/alice-100-multiplier', filename="embeddings.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('A photo of <s0><s1>').images[0]
For more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers
Download model
Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke
- Download the LoRA *.safetensors here . Rename it and place it on your Lora folder.
- Download the text embeddings *.safetensors here . Rename it and place it on it on your embeddings folder.
All Files & versions .
Details
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 alice-100-multiplier on huggingface.co
35
Total runs
1
24-hour runs
5
3-day runs
9
7-day runs
22
30-day runs
More Information About alice-100-multiplier huggingface.co Model
Url of alice-100-multiplier
alice-100-multiplier huggingface.co Url
Provider of alice-100-multiplier huggingface.co
Other API from multimodalart
Updated:June 05 2026
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