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
File size: 7,749 Bytes
1e6baf8 29f557b 1e6baf8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 | ---
license: other
base_model: "stabilityai/stable-diffusion-3.5-medium"
tags:
- sd3
- sd3-diffusers
- text-to-image
- image-to-image
- diffusers
- simpletuner
- safe-for-work
- full
pipeline_tag: text-to-image
inference: true
widget:
- text: 'unconditional (blank prompt)'
parameters:
negative_prompt: 'blurry, cropped, ugly'
output:
url: ./assets/image_0_0.png
- text: 'minecraft skin, police officer with dark blue uniform: crisp button‐up shirt tucked into matching pants; sturdy black boots covering lower legs; black tactical gloves on hands; silver badge on left chest; face with focused expression, blue eyes under a peaked cap; short brown hair peeking at nape; utility belt with radio pouches around waist'
parameters:
negative_prompt: 'blurry, cropped, ugly'
output:
url: ./assets/image_1_0.png
- text: 'minecraft skin, fantasy dragon‐scale armor in deep red and gold: overlapping scale plates on chest and shoulders; gauntleted gloves with claw tips on hands; armored greaves on legs; sturdy boots with scale fins; face framed by horned helm opening to reveal green reptilian eyes; dark flowing hair braided into a tail at back'
parameters:
negative_prompt: 'blurry, cropped, ugly'
output:
url: ./assets/image_2_0.png
- text: 'minecraft skin, cyberpunk streetwear: black bio‐leather jacket with neon blue circuit patterns, high‐collar around neck; fingerless gloves on hands; slim cargo pants with glowing seams and knee pads; tech‐bonded boots; face with glowing augmented ocular implants and angular cheek tattoos; buzzed undercut hair in electric purple'
parameters:
negative_prompt: 'blurry, cropped, ugly'
output:
url: ./assets/image_3_0.png
- text: 'minecraft skin, samurai warrior in red and black lacquered armor: layered cuirass over kimono sleeves; wrapped hand‐guards on forearms; hakama trousers tying at calf; straw‐soled sandals on feet; face calm with golden eyes beneath a half‐mask; jet‐black topknot hairstyle'
parameters:
negative_prompt: 'blurry, cropped, ugly'
output:
url: ./assets/image_4_0.png
- text: 'minecraft skin, elven forest scout: green hooded cloak draping over slender shoulders; leather bracers on forearms; fitted tunic and leggings dyed in moss and bark tones; soft boots for silent steps; delicate hands with finger‐woven gloves; face with sharp emerald eyes and freckled cheeks; long auburn hair braided with leaf ornaments'
parameters:
negative_prompt: 'blurry, cropped, ugly'
output:
url: ./assets/image_5_0.png
- text: 'minecraft skin, steampunk mechanic: worn brown leather apron over soot‐stained shirt; fingerless leather gloves with metal knuckle plates on hands; reinforced trousers with tool pockets; heavy leather boots with brass buckles; face smudged with oil, bright hazel eyes behind round goggles; tousled sandy hair'
parameters:
negative_prompt: 'blurry, cropped, ugly'
output:
url: ./assets/image_6_0.png
- text: 'minecraft skin, high‐tech spacesuit: white and blue pressurized suit with panel lines on torso; glove‐sealed sleeves and articulated gauntlets on hands; reinforced leggings with cable conduits to boots; helmet viewport revealing calm face with hazel eyes and chin strap; short dark hair neatly cut'
parameters:
negative_prompt: 'blurry, cropped, ugly'
output:
url: ./assets/image_7_0.png
- text: 'minecraft skin, desert robes: flowing sand‐colored tunic over loose pants; wrapped cloth around forearms and calves; sand‐proof gauntlets on hands; leather sandals on feet; face partially veiled with brown scarf exposing only bright amber eyes; sun‐bleached blonde hair tied back'
parameters:
negative_prompt: 'blurry, cropped, ugly'
output:
url: ./assets/image_8_0.png
- text: 'minecraft skin, shining plate armor: breastplate embossed with crest over padded gambeson; articulated gauntlets on hands; greaves and sabatons covering legs and feet; face visible through open helm, with steely gray eyes and a cropped brown beard; short cropped hair'
parameters:
negative_prompt: 'blurry, cropped, ugly'
output:
url: ./assets/image_9_0.png
- text: 'minecraft skin, arcane mage robes: deep violet robe embroidered with glowing runes on torso and sleeves; delicate fingerless silk gloves on hands; flowing skirt over fitted leggings; soft pointed boots; face with luminous violet eyes and pale skin; long silver hair cascading over shoulders'
parameters:
negative_prompt: 'blurry, cropped, ugly'
output:
url: ./assets/image_10_0.png
---
# simpletunner-full-novaskin
This is a full rank finetune derived from [stabilityai/stable-diffusion-3.5-medium](https://huggingface.co/stabilityai/stable-diffusion-3.5-medium).
No validation prompt was used during training.
None
## Validation settings
- CFG: `5.0`
- CFG Rescale: `0.0`
- Steps: `20`
- Sampler: `FlowMatchEulerDiscreteScheduler`
- Seed: `42`
- Resolution: `64x64`
- Skip-layer guidance:
Note: The validation settings are not necessarily the same as the [training settings](#training-settings).
You can find some example images in the following gallery:
<Gallery />
The text encoder **was not** trained.
You may reuse the base model text encoder for inference.
## Training settings
- Training epochs: 5
- Training steps: 60260
- Learning rate: 1e-06
- Learning rate schedule: cosine
- Warmup steps: 200
- Max grad value: 1.0
- Effective batch size: 12
- Micro-batch size: 12
- Gradient accumulation steps: 1
- Number of GPUs: 1
- Gradient checkpointing: False
- Prediction type: flow_matching (extra parameters=['shift=3'])
- Optimizer: adamw_bf16
- Trainable parameter precision: Pure BF16
- Base model precision: `no_change`
- Caption dropout probability: 0.1%
## Datasets
### skins-64
- Repeats: 0
- Total number of images: 100000
- Total number of aspect buckets: 1
- Resolution: 0.004096 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
- Used for regularisation data: No
### skins-256
- Repeats: 1
- Total number of images: 20000
- Total number of aspect buckets: 1
- Resolution: 256 px
- Cropped: False
- Crop style: None
- Crop aspect: None
- Used for regularisation data: No
### skins-heads
- Repeats: 3
- Total number of images: 5000
- Total number of aspect buckets: 1
- Resolution: 256 px
- Cropped: False
- Crop style: None
- Crop aspect: None
- Used for regularisation data: No
## Inference
```python
import torch
from diffusers import DiffusionPipeline
model_id = 'saviski/simpletunner-full-novaskin'
pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16
prompt = "An astronaut is riding a horse through the jungles of Thailand."
negative_prompt = 'blurry, cropped, ugly'
pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu') # the pipeline is already in its target precision level
model_output = pipeline(
prompt=prompt,
negative_prompt=negative_prompt,
num_inference_steps=20,
generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(42),
width=64,
height=64,
guidance_scale=5.0,
).images[0]
model_output.save("output.png", format="PNG")
```
## Exponential Moving Average (EMA)
SimpleTuner generates a safetensors variant of the EMA weights and a pt file.
The safetensors file is intended to be used for inference, and the pt file is for continuing finetuning.
The EMA model may provide a more well-rounded result, but typically will feel undertrained compared to the full model as it is a running decayed average of the model weights.
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