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---
license: creativeml-openrail-m
base_model: "PixArt-alpha/PixArt-Sigma-XL-2-1024-MS"
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- simpletuner
- full
inference: true
widget:
- text: 'unconditional (blank prompt)'
parameters:
negative_prompt: 'blurry, cropped, ugly'
output:
url: ./assets/image_0_0.png
- text: 'Digital art of a topless anthro male wolf wearing a sun hat and blue banana-patterned swimming trunks'
parameters:
negative_prompt: 'blurry, cropped, ugly'
output:
url: ./assets/image_1_0.png
---
# pixart-sigma-test
This is a full rank finetune derived from [PixArt-alpha/PixArt-Sigma-XL-2-1024-MS](https://huggingface.co/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](#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: 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
```python
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")
```
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