Text-to-Image
Diffusers
TensorBoard
stable-diffusion-xl
stable-diffusion-xl-diffusers
lora
template:sd-lora
Instructions to use kevinc21ox/becky123 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use kevinc21ox/becky123 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("kevinc21ox/becky123") prompt = "A <s0><s1> of a 19 year old blonde woman. She is 5 feet 4 inches tall. 36 DD breast size. Large but perfect butt. a girl with long hair smiling in a photo" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
SDXL LoRA DreamBooth - kevinc21ox/becky123

- Prompt
- A <s0><s1> of a 19 year old blonde woman. She is 5 feet 4 inches tall. 36 DD breast size. Large but perfect butt. a girl with long hair smiling in a photo

- Prompt
- A <s0><s1> of a 19 year old blonde woman. She is 5 feet 4 inches tall. 36 DD breast size. Large but perfect butt. a girl with long blonde hair is sitting on a chair

- Prompt
- A <s0><s1> of a 19 year old blonde woman. She is 5 feet 4 inches tall. 36 DD breast size. Large but perfect butt. a woman with a bun in her hair

- Prompt
- A <s0><s1> of a 19 year old blonde woman. She is 5 feet 4 inches tall. 36 DD breast size. Large but perfect butt. a woman with a big smile and a big hair

- Prompt
- A <s0><s1> of a 19 year old blonde woman. She is 5 feet 4 inches tall. 36 DD breast size. Large but perfect butt. a woman with a ponytail and a blue shirt

- Prompt
- A <s0><s1> of a 19 year old blonde woman. She is 5 feet 4 inches tall. 36 DD breast size. Large but perfect butt. a woman with a ponytail and a white shirt

- Prompt
- A <s0><s1> of a 19 year old blonde woman. She is 5 feet 4 inches tall. 36 DD breast size. Large but perfect butt. a woman with a ponytail

- Prompt
- A <s0><s1> of a 19 year old blonde woman. She is 5 feet 4 inches tall. 36 DD breast size. Large but perfect butt. a woman with a ponytail

- Prompt
- A <s0><s1> of a 19 year old blonde woman. She is 5 feet 4 inches tall. 36 DD breast size. Large but perfect butt. a young woman with long hair

- Prompt
- A <s0><s1> of a 19 year old blonde woman. She is 5 feet 4 inches tall. 36 DD breast size. Large but perfect butt. a girl with long hair

- Prompt
- A <s0><s1> of a 19 year old blonde woman. She is 5 feet 4 inches tall. 36 DD breast size. Large but perfect butt. a young man and woman in an orange shirt
Model description
These are kevinc21ox/becky123 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
becky123.safetensorshere 💾.- Place it on your
models/Lorafolder. - On AUTOMATIC1111, load the LoRA by adding
<lora:becky123:1>to your prompt. On ComfyUI just load it as a regular LoRA.
- Place it on your
- Embeddings: download
becky123_emb.safetensorshere 💾.- Place it on it on your
embeddingsfolder - Use it by adding
becky123_embto your prompt. For example,A becky123_emb of a 19 year old blonde woman. She is 5 feet 4 inches tall. 36 DD breast size. Large but perfect butt.(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('kevinc21ox/becky123', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='kevinc21ox/becky123', filename='becky123_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('A <s0><s1> of a 19 year old blonde woman. She is 5 feet 4 inches tall. 36 DD breast size. Large but perfect butt.').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.
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Model tree for kevinc21ox/becky123
Base model
stabilityai/stable-diffusion-xl-base-1.0