Instructions to use tzvc/b3d0ef12-11d6-43df-8a96-ebcb5ca71ea1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use tzvc/b3d0ef12-11d6-43df-8a96-ebcb5ca71ea1 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("tzvc/b3d0ef12-11d6-43df-8a96-ebcb5ca71ea1", dtype=torch.bfloat16, device_map="cuda") prompt = "me" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
training params
{
"pretrained_model_name_or_path": "runwayml/stable-diffusion-v1-5",
"instance_data_dir": "./b3d0ef12-11d6-43df-8a96-ebcb5ca71ea1/instance_data",
"class_data_dir": "./class_data/person",
"output_dir": "./b3d0ef12-11d6-43df-8a96-ebcb5ca71ea1/",
"train_text_encoder": true,
"with_prior_preservation": true,
"prior_loss_weight": 1.0,
"instance_prompt": "me",
"class_prompt": "person",
"resolution": 512,
"train_batch_size": 1,
"gradient_accumulation_steps": 1,
"gradient_checkpointing": true,
"use_8bit_adam": true,
"learning_rate": 1e-06,
"lr_scheduler": "polynomial",
"lr_warmup_steps": 0,
"num_class_images": 500,
"max_train_steps": 1050,
"mixed_precision": "fp16"
}
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