Instructions to use d-wang26/mk1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use d-wang26/mk1 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("d-wang26/mk1", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
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Download diffusers/examples/textual_inversion/README_sdxl.md from d-wang26/mk1: direct link, hf CLI and curl.
- Browser
- Download file 742 Bytes
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https://huggingface.co/d-wang26/mk1/resolve/main/diffusers/examples/textual_inversion/README_sdxl.md
- Command line
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hf download hf://d-wang26/mk1/diffusers/examples/textual_inversion/README_sdxl.md
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curl -L -o README_sdxl.md https://huggingface.co/d-wang26/mk1/resolve/main/diffusers/examples/textual_inversion/README_sdxl.md
742 Bytes
| ## Textual Inversion fine-tuning example for SDXL | |
| ``` | |
| export MODEL_NAME="stabilityai/stable-diffusion-xl-base-1.0" | |
| export DATA_DIR="./cat" | |
| accelerate launch textual_inversion_sdxl.py \ | |
| --pretrained_model_name_or_path=$MODEL_NAME \ | |
| --train_data_dir=$DATA_DIR \ | |
| --learnable_property="object" \ | |
| --placeholder_token="<cat-toy>" \ | |
| --initializer_token="toy" \ | |
| --mixed_precision="bf16" \ | |
| --resolution=768 \ | |
| --train_batch_size=1 \ | |
| --gradient_accumulation_steps=4 \ | |
| --max_train_steps=500 \ | |
| --learning_rate=5.0e-04 \ | |
| --scale_lr \ | |
| --lr_scheduler="constant" \ | |
| --lr_warmup_steps=0 \ | |
| --save_as_full_pipeline \ | |
| --output_dir="./textual_inversion_cat_sdxl" | |
| ``` | |
| For now, only training of the first text encoder is supported. |