README.md

---
license: creativeml-openrail-m
base_model: "ptx0/pixart-900m-1024-ft-large"
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: 'unconditional (blank prompt)'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_1_1.png
- text: 'unconditional (blank prompt)'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_2_2.png
---

# pixart-900m-1024-ft

This is a full rank finetune derived from [ptx0/pixart-900m-1024-ft-large](https://huggingface.co/ptx0/pixart-900m-1024-ft-large).



The main validation prompt used during training was:

```
ethnographic photography of teddy bear at a picnic holding a sign that reads SOON
```

## Validation settings
- CFG: `7.5`
- CFG Rescale: `0.0`
- Steps: `30`
- Sampler: `euler`
- Seed: `42`
- Resolutions: `1024x1024,1344x768,916x1152`

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: 1
- Training steps: 28000
- Learning rate: 1e-06
- Effective batch size: 192
  - Micro-batch size: 24
  - Gradient accumulation steps: 1
  - 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: ~564672
- Total number of aspect buckets: 34
- Resolution: 1.0 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
### moviecollection
- Repeats: 15
- Total number of images: ~768
- Total number of aspect buckets: 7
- Resolution: 1.0 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: random
### experimental
- Repeats: 0
- Total number of images: ~1728
- Total number of aspect buckets: 7
- Resolution: 1.0 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: random
### ethnic
- Repeats: 0
- Total number of images: ~1152
- Total number of aspect buckets: 6
- Resolution: 1.0 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: random
### sports
- Repeats: 0
- Total number of images: ~576
- Total number of aspect buckets: 1
- Resolution: 1.0 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: square
### architecture
- Repeats: 0
- Total number of images: ~4224
- Total number of aspect buckets: 1
- Resolution: 1.0 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: square
### shutterstock
- Repeats: 0
- Total number of images: ~14016
- Total number of aspect buckets: 3
- Resolution: 1.0 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: random
### cinemamix-1mp
- Repeats: 0
- Total number of images: ~7296
- Total number of aspect buckets: 3
- Resolution: 1.0 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: random
### nsfw-1024
- Repeats: 0
- Total number of images: ~10368
- Total number of aspect buckets: 3
- Resolution: 1.0 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: random
### anatomy
- Repeats: 5
- Total number of images: ~15168
- Total number of aspect buckets: 3
- Resolution: 1.0 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: random
### bg20k-1024
- Repeats: 0
- Total number of images: ~89088
- Total number of aspect buckets: 3
- Resolution: 1.0 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: random
### yoga
- Repeats: 0
- Total number of images: ~2880
- Total number of aspect buckets: 3
- Resolution: 1.0 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: random
### photo-aesthetics
- Repeats: 0
- Total number of images: ~28608
- Total number of aspect buckets: 16
- Resolution: 1.0 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: random
### text-1mp
- Repeats: 125
- Total number of images: ~12864
- Total number of aspect buckets: 3
- Resolution: 1.0 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: random
### movieposters
- Repeats: 10
- Total number of images: ~192
- Total number of aspect buckets: 1
- Resolution: 1.0 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: square
### normalnudes
- Repeats: 10
- Total number of images: ~384
- Total number of aspect buckets: 8
- Resolution: 1.0 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: random
### pixel-art
- Repeats: 0
- Total number of images: ~384
- Total number of aspect buckets: 11
- Resolution: 1.0 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: random
### signs
- Repeats: 0
- Total number of images: ~384
- Total number of aspect buckets: 1
- Resolution: 1.0 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: square
### midjourney-v6-520k-raw
- Repeats: 0
- Total number of images: ~513792
- Total number of aspect buckets: 58
- Resolution: 1.0 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
### sfwbooru
- Repeats: 0
- Total number of images: ~271488
- Total number of aspect buckets: 73
- Resolution: 1.0 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
### nijijourney-v6-520k-raw
- Repeats: 0
- Total number of images: ~516288
- Total number of aspect buckets: 48
- Resolution: 1.0 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
### dalle3
- Repeats: 0
- Total number of images: ~1119168
- Total number of aspect buckets: 31
- Resolution: 1.0 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None


## Inference


```python
import torch
from diffusers import DiffusionPipeline




model_id = 'pixart-900m-1024-ft'
prompt = 'ethnographic photography of teddy bear at a picnic holding a sign that reads SOON'
negative_prompt = 'blurry, cropped, ugly'
pipeline = DiffusionPipeline.from_pretrained(model_id)
pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu')

prompt = "ethnographic photography of teddy bear at a picnic holding a sign that reads SOON"
negative_prompt = "blurry, cropped, ugly"

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='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")
```