0206

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

The main validation prompt used during training was:

A dark-element wolf in pixel art style, featuring a sleek body in deep black with dark purple tones and subtle midnight blue accents. Sharp, angular patterns resembling tendrils of darkness adorn its fur. The wolf’s glowing yellow eyes radiate a menacing and mysterious aura, and its tail is surrounded by faint mist-like effects. Small shadowy tendrils and pixelated wisps enhance its connection to the dark element. The plain white background keeps the focus on its enigmatic and powerful design.

Validation settings

CFG: 5.0
CFG Rescale: 0.0
Steps: 20
Sampler: FlowMatchEulerDiscreteScheduler
Seed: 42
Resolution: 1024x1024
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 dark-element wolf in pixel art style, featuring a sleek body in deep black with dark purple tones and subtle midnight blue accents. Sharp, angular patterns resembling tendrils of darkness adorn its fur. The wolf’s glowing yellow eyes radiate a menacing and mysterious aura, and its tail is surrounded by faint mist-like effects. Small shadowy tendrils and pixelated wisps enhance its connection to the dark element. The plain white background keeps the focus on its enigmatic and powerful design.

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: 18
Training steps: 10000
Learning rate: 8e-05
- Learning rate schedule: polynomial
- Warmup steps: 100
Max grad norm: 2.0
Effective batch size: 1
- Micro-batch size: 1
- Gradient accumulation steps: 1
- 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: 5.0%
LoRA Rank: 64
LoRA Alpha: None
LoRA Dropout: 0.1
LoRA initialisation style: default

Datasets

dataset-1024

Repeats: 10
Total number of images: 24
Total number of aspect buckets: 1
Resolution: 1.048576 megapixels
Cropped: False
Crop style: None
Crop aspect: None
Used for regularisation data: No

dataset-crop-1024

Repeats: 10
Total number of images: 24
Total number of aspect buckets: 1
Resolution: 1.048576 megapixels
Cropped: True
Crop style: center
Crop aspect: square
Used for regularisation data: No

Inference

import torch
from diffusers import DiffusionPipeline

model_id = 'stabilityai/stable-diffusion-3.5-large'
adapter_id = 'badul13/0206'
pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16
pipeline.load_lora_weights(adapter_id)

prompt = "A dark-element wolf in pixel art style, featuring a sleek body in deep black with dark purple tones and subtle midnight blue accents. Sharp, angular patterns resembling tendrils of darkness adorn its fur. The wolf’s glowing yellow eyes radiate a menacing and mysterious aura, and its tail is surrounded by faint mist-like effects. Small shadowy tendrils and pixelated wisps enhance its connection to the dark element. The plain white background keeps the focus on its enigmatic and powerful design."
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=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=1024,
    height=1024,
    guidance_scale=5.0,
).images[0]
image.save("output.png", format="PNG")

badul13
/

0206