README.md

---
license: creativeml-openrail-m
base_model: "terminusresearch/pixart-900m-1024"
tags:
  - stable-diffusion
  - stable-diffusion-diffusers
  - text-to-image
  - diffusers
  - full

inference: true

---

# pixart-900m-1024-ft

This is a full rank finetune derived from [terminusresearch/pixart-900m-1024](https://huggingface.co/terminusresearch/pixart-900m-1024).



The main validation prompt used during training was:

```
a cute anime character named toast holding a sign that says SOON, sitting next to a red square on her left side, and a transparent sphere on her right side
```

## Validation settings
- CFG: `6.5`
- CFG Rescale: `0.7`
- Steps: `30`
- Sampler: `euler-a`
- Seed: `42`
- Resolutions: `1024x1024,1152x960,896x1152`

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: 0
- Training steps: 500
- Learning rate: 4e-07
- Effective batch size: 512
  - Micro-batch size: 32
  - Gradient accumulation steps: 2
  - Number of GPUs: 8
- Prediction type: epsilon
- Rescaled betas zero SNR: False
- Optimizer: AdamW, stochastic bf16
- Precision: Pure BF16
- Xformers: Not used


## Datasets

### photo-concept-bucket
- Repeats: 0
- Total number of images: ~559104
- 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-900m-1024-ft"
prompt = "a cute anime character named toast holding a sign that says SOON, sitting next to a red square on her left side, and a transparent sphere on her right side"
negative_prompt = "malformed, disgusting, overexposed, washed-out"

pipeline = DiffusionPipeline.from_pretrained(model_id)
pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu')
image = pipeline(
    prompt=prompt,
    negative_prompt='',
    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=6.5,
    guidance_rescale=0.7,
).images[0]
image.save("output.png", format="PNG")
```