Text-to-Image
Diffusers
Safetensors
StableDiffusion3Pipeline
sd3
sd3-diffusers
image-to-image
simpletuner
safe-for-work
full
Instructions to use saviski/simpletunner-full-novaskin with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use saviski/simpletunner-full-novaskin with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("saviski/simpletunner-full-novaskin", dtype=torch.bfloat16, device_map="cuda") prompt = "unconditional (blank prompt)" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Trained for 5 epochs and 60260 steps.
Browse filesTrained with datasets ['text-embed-cache', 'skins-64', 'skins-256', 'skins-heads']
Learning rate 1e-06, batch size 12, and 1 gradient accumulation steps.
Trained with None prediction type and rescaled_betas_zero_snr=False
Using 'trailing' timestep spacing.
Base model: stabilityai/stable-diffusion-3.5-medium
VAE: None
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{
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"_class_name": "SD3Transformer2DModel",
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"_diffusers_version": "0.34.0.dev0",
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"_name_or_path": "/workspace/models/skins/checkpoint-
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"attention_head_dim": 64,
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"caption_projection_dim": 1536,
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"dual_attention_layers": [
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