pixart-sigma-test

This is a full rank finetune derived from PixArt-alpha/PixArt-Sigma-XL-2-1024-MS.

The main validation prompt used during training was:

Digital art of a topless anthro male wolf wearing a sun hat and blue banana-patterned swimming trunks

Validation settings

CFG: 7.5
CFG Rescale: 0.0
Steps: 30
Sampler: None
Seed: 42
Resolution: 1024

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
Digital art of a topless anthro male wolf wearing a sun hat and blue banana-patterned swimming trunks

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: 200
Learning rate: 0.0001
Effective batch size: 160
- Micro-batch size: 2
- Gradient accumulation steps: 40
- Number of GPUs: 2
Prediction type: epsilon
Rescaled betas zero SNR: False
Optimizer: AdamW, stochastic bf16
Precision: Pure BF16
Xformers: Enabled

Datasets

4o-training-images-thinned

Repeats: 0
Total number of images: ~4960
Total number of aspect buckets: 1
Resolution: 1.0 megapixels
Cropped: True
Crop style: center
Crop aspect: square

Inference

import torch
from diffusers import DiffusionPipeline

model_id = 'pixart-sigma-test'
pipeline = DiffusionPipeline.from_pretrained(model_id)

prompt = "Digital art of a topless anthro male wolf wearing a sun hat and blue banana-patterned swimming trunks"
negative_prompt = "blurry, cropped, ugly"

pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu')
image = pipeline(
    prompt=prompt,
    negative_prompt='blurry, cropped, ugly',
    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(1641421826),
    width=1152,
    height=768,
    guidance_scale=7.5,
    guidance_rescale=0.0,
).images[0]
image.save("output.png", format="PNG")