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---
license: creativeml-openrail-m
base_model: "terminusresearch/pixart-900m-1024-ft-v0.6"
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- simpletuner
- full
inference: true
---
# pixart-900m-1024-ft-v0.7-stage2
This is a full rank finetune derived from [terminusresearch/pixart-900m-1024-ft-v0.6](https://huggingface.co/terminusresearch/pixart-900m-1024-ft-v0.6).
The main validation prompt used during training was:
```
a cute anime character named toast, holding a sign that reads SOON
```
## Validation settings
- CFG: `4.0`
- CFG Rescale: `0.7`
- Steps: `30`
- Sampler: `None`
- Seed: `420420420`
- Resolution: `1024x1024`
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: 9
- Training steps: 16500
- Learning rate: 1e-06
- Effective batch size: 16
- Micro-batch size: 16
- Gradient accumulation steps: 1
- Number of GPUs: 1
- Prediction type: epsilon
- Rescaled betas zero SNR: False
- Optimizer: AdamW, stochastic bf16
- Precision: Pure BF16
- Xformers: Enabled
## Datasets
### celebrities
- Repeats: 4
- Total number of images: 208
- Total number of aspect buckets: 2
- Resolution: 1.0 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
### movieposters
- Repeats: 25
- Total number of images: 192
- Total number of aspect buckets: 2
- Resolution: 1.0 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
### normalnudes
- Repeats: 5
- Total number of images: 992
- Total number of aspect buckets: 1
- Resolution: 1.0 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
### moviecollection
- Repeats: 0
- Total number of images: 1728
- Total number of aspect buckets: 4
- Resolution: 1.0 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
### experimental
- Repeats: 0
- Total number of images: 2816
- Total number of aspect buckets: 3
- Resolution: 1.0 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: random
### ethnic
- Repeats: 0
- Total number of images: 1808
- Total number of aspect buckets: 3
- Resolution: 1.0 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: random
### gay
- Repeats: 0
- Total number of images: 768
- Total number of aspect buckets: 2
- Resolution: 1.0 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
### cinemamix-1mp
- Repeats: 0
- Total number of images: 7376
- Total number of aspect buckets: 4
- Resolution: 1.0 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
### nsfw-1024
- Repeats: 0
- Total number of images: 2224
- Total number of aspect buckets: 3
- Resolution: 1.0 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: random
## Inference
```python
import torch
from diffusers import DiffusionPipeline
model_id = 'pixart-900m-1024-ft-v0.7-stage2'
pipeline = DiffusionPipeline.from_pretrained(model_id)
prompt = "a cute anime character named toast, holding a sign that reads SOON"
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=4.0,
guidance_rescale=0.7,
).images[0]
image.save("output.png", format="PNG")
```
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