simpletuner-lora

This is a standard PEFT LoRA derived from stabilityai/stable-diffusion-3.5-medium.

The main validation prompt used during training was:

A simplistic, hand-drawn illustration of an elephant. the elephant is depicted in a walking pose, with its trunk raised slightly. the drawing is done in black ink on a white background. the elephant's posture and the positioning of its legs suggest movement. the style is minimalistic, with clean lines and a lack of intricate details. the lighting appears to be coming from the top left, casting a shadow on the right side of the elephant.

Validation settings

• CFG: 7.5
• CFG Rescale: 0.0
• Steps: 35
• Sampler: FlowMatchEulerDiscreteScheduler
• Seed: 42
• Resolution: 512x512
• Skip-layer guidance:

Note: The validation settings are not necessarily the same as the training settings.

You can find some example images in the following gallery:

Prompt

unconditional (blank prompt)

Negative Prompt

blurry, cropped, ugly

Prompt

A simplistic, hand-drawn illustration of an elephant. the elephant is depicted in a walking pose, with its trunk raised slightly. the drawing is done in black ink on a white background. the elephant's posture and the positioning of its legs suggest movement. the style is minimalistic, with clean lines and a lack of intricate details. the lighting appears to be coming from the top left, casting a shadow on the right side of the elephant.

Negative Prompt

blurry, cropped, ugly

The text encoder was not trained. You may reuse the base model text encoder for inference.

Training settings

• Training epochs: 6

• Training steps: 4000

• Learning rate: 0.0001

  • Learning rate schedule: cosine
  • Warmup steps: 100

• Max grad norm: 0.01

• Effective batch size: 16

  • Micro-batch size: 4
  • Gradient accumulation steps: 4
  • Number of GPUs: 1

• Gradient checkpointing: True

• Prediction type: flow-matching (extra parameters=['shift=3'])

• Optimizer: adamw_bf16

• Trainable parameter precision: Pure BF16

• Caption dropout probability: 10.0%

• LoRA Rank: 128

• LoRA Alpha: None

• LoRA Dropout: 0.1

• LoRA initialisation style: default

Datasets

pacs

• Repeats: 0
• Total number of images: 9980
• Total number of aspect buckets: 1
• Resolution: 1.0 megapixels
• Cropped: False
• Crop style: None
• Crop aspect: None
• Used for regularisation data: No

Inference

import torch
from diffusers import DiffusionPipeline

model_id = 'stabilityai/stable-diffusion-3.5-medium'
adapter_id = 'Cha-Imaa/simpletuner-lora'
pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16
pipeline.load_lora_weights(adapter_id)

prompt = "A simplistic, hand-drawn illustration of an elephant. the elephant is depicted in a walking pose, with its trunk raised slightly. the drawing is done in black ink on a white background. the elephant's posture and the positioning of its legs suggest movement. the style is minimalistic, with clean lines and a lack of intricate details. the lighting appears to be coming from the top left, casting a shadow on the right side of the elephant."
negative_prompt = 'blurry, cropped, ugly'

## Optional: quantise the model to save on vram.
## Note: The model was quantised during training, and so it is recommended to do the same 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
image = pipeline(
    prompt=prompt,
    negative_prompt=negative_prompt,
    num_inference_steps=35,
    generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(42),
    width=512,
    height=512,
    guidance_scale=7.5,
).images[0]
image.save("output.png", format="PNG")