diffusers safetensors stable-diffusion stable-diffusion-diffusers text-to-image simpletuner full base_model:toilaluan/turbox base_model:finetune:toilaluan/turbox license:creativeml-openrail-m autotrain_compatible endpoints_compatible diffusers:StableDiffusionXLPipeline region:us
---
base_model: toilaluan/turbox
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- simpletuner
- full
inference: true
---

# full-training

This is a full rank finetune derived from [toilaluan/turbox](https://huggingface.co/toilaluan/turbox).



The main validation prompt used during training was:

```
ethnographic photography of teddy bear at a picnic
```

## 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).




<Gallery />

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


## Training settings

- Training epochs: 4
- Training steps: 45
- Learning rate: 8e-07
- Effective batch size: 40
  - Micro-batch size: 10
  - Gradient accumulation steps: 4
  - Number of GPUs: 1
- Prediction type: epsilon
- Rescaled betas zero SNR: False
- Optimizer: AdamW, stochastic bf16
- Precision: Pure BF16
- Xformers: Not used


## Datasets

### xxx123
- Repeats: 0
- Total number of images: 360
- Total number of aspect buckets: 1
- Resolution: 1.0 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None


## Inference


```python
import torch
from diffusers import DiffusionPipeline

model_id = 'full-training'
pipeline = DiffusionPipeline.from_pretrained(model_id)

prompt = "ethnographic photography of teddy bear at a picnic"
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")
```

