sd35m-sfwbooru-lokr
This is a standard PEFT LoRA derived from stabilityai/stable-diffusion-3.5-medium.
The main validation prompt used during training was:
A photo-realistic image of a cat
Validation settings
- CFG:
3.2 - CFG Rescale:
0.0 - Steps:
30 - Sampler:
FlowMatchEulerDiscreteScheduler - Seed:
42 - Resolution:
1024x1024 - Skip-layer guidance: skip_guidance_layers=[7, 8, 9],
Note: The validation settings are not necessarily the same as the training settings.
You can find some example images in the following gallery:
The text encoder was not trained. You may reuse the base model text encoder for inference.
Training settings
-
Training epochs: 4
-
Training steps: 264250
-
Learning rate: 1.0
- Learning rate schedule: cosine
- Warmup steps: 500000
-
Max grad value: 0.0
-
Effective batch size: 6
- Micro-batch size: 6
- Gradient accumulation steps: 1
- Number of GPUs: 1
-
Gradient checkpointing: True
-
Prediction type: flow-matching (extra parameters=['shift=3'])
-
Optimizer: prodigyd0=1e-8,eps=1e-8
-
Trainable parameter precision: Pure BF16
-
Base model precision:
no_change -
Caption dropout probability: 10.0%
-
LoRA Rank: 16
-
LoRA Alpha: None
-
LoRA Dropout: 0.1
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LoRA initialisation style: default
Datasets
sfwbooru-crop
- Repeats: 0
- Total number of images: 363920
- Total number of aspect buckets: 1
- Resolution: 1.048576 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: square
- Used for regularisation data: No
Inference
import torch
from diffusers import DiffusionPipeline
model_id = 'stabilityai/stable-diffusion-3.5-medium'
adapter_id = 'bghira/sd35m-sfwbooru-lokr'
pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16
pipeline.load_lora_weights(adapter_id)
prompt = "A photo-realistic image of a cat"
negative_prompt = 'blurry, cropped, ugly'
## Optional: quantise the model to save on vram.
## Note: The model was not quantised during training, so it is not necessary to quantise it during inference time.
#from optimum.quanto import quantize, freeze, qint8
#quantize(pipeline.transformer, weights=qint8)
#freeze(pipeline.transformer)
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=30,
generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(42),
width=1024,
height=1024,
guidance_scale=3.2,
skip_guidance_layers=[7, 8, 9],
).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.