Text-to-Image
Diffusers
TensorBoard
stable-diffusion-xl
stable-diffusion-xl-diffusers
lora
template:sd-lora
Instructions to use mtgentry/mason-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use mtgentry/mason-test 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("mtgentry/mason-test") prompt = "photo of sksScotty person smiling outdoors with trees in background" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
SDXL LoRA DreamBooth - mtgentry/mason-test

- Prompt
- photo of sksScotty person smiling outdoors with trees in background

- Prompt
- photo of sksScotty person smiling in front of a window with plants outside

- Prompt
- photo of sksScotty person sitting at a desk with a black case in front of him

- Prompt
- photo of sksScotty person holding a transparent phone in one hand and a Samsung S9+ box in the other

- Prompt
- photo of sksScotty person indoors with shelves and tools in the background looking serious

- Prompt
- photo of sksScotty person holding a smartphone with the home screen visible

- Prompt
- photo of sksScotty person smiling and talking with another person indoors

- Prompt
- photo of sksScotty person smiling outdoors with trees in the background

- Prompt
- photo of sksScotty person standing in a factory with machines in the background

- Prompt
- photo of sksScotty person smiling while plugging wired earphones into a smartphone

- Prompt
- photo of sksScotty person holding a camera on a flexible tripod while vlogging outdoors

- Prompt
- photo of sksScotty person wearing red Adidas track jacket taking a mirror selfie indoors

- Prompt
- photo of sksScotty person smiling while using a microscope in a lab setting

- Prompt
- photo of sksScotty person smiling in a factory wearing a white lab coat and cap

- Prompt
- photo of sksScotty person smiling outdoors wearing a black cap

- Prompt
- photo of sksScotty person smiling indoors with shelves of tools behind him

- Prompt
- photo of sksScotty person smiling and holding an open iPhone showing its components

- Prompt
- photo of sksScotty person indoors holding an open iPhone and looking at it with amused expression

- Prompt
- photo of sksScotty person indoors holding a Samsung Galaxy S9+ phone and smiling

- Prompt
- photo of sksScotty person indoors close-up resting chin on hands with neutral expression
Model description
These are mtgentry/mason-test 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
mason-test.safetensorshere 💾.- Place it on your
models/Lorafolder. - On AUTOMATIC1111, load the LoRA by adding
<lora:mason-test:1>to your prompt. On ComfyUI just load it as a regular LoRA.
- Place it on your
- Embeddings: download
mason-test_emb.safetensorshere 💾.- Place it on it on your
embeddingsfolder - Use it by adding
mason-test_embto your prompt. For example,A photo of mason-test_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('mtgentry/mason-test', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='mtgentry/mason-test', filename='mason-test_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 photo 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.
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Model tree for mtgentry/mason-test
Base model
stabilityai/stable-diffusion-xl-base-1.0