README.md

---
license: creativeml-openrail-m
base_model: "ptx0/terminus-xl-velocity-v2"
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: 'two young girls in a classroom setting appearing surprised or concerned'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_1_0.png
---

# smoldit-base-test

This is a full rank finetune derived from [ptx0/terminus-xl-velocity-v2](https://huggingface.co/ptx0/terminus-xl-velocity-v2).



The main validation prompt used during training was:

```
two young girls in a classroom setting appearing surprised or concerned
```

## Validation settings
- CFG: `4.0`
- CFG Rescale: `0.7`
- Steps: `30`
- Sampler: `ddpm`
- Seed: `420420420`
- Resolution: `256`

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: 360
- Training steps: 167000
- Learning rate: 1e-05
- Effective batch size: 16
  - Micro-batch size: 16
  - Gradient accumulation steps: 1
  - Number of GPUs: 1
- Prediction type: v_prediction
- Rescaled betas zero SNR: True
- Optimizer: AdamW, stochastic bf16
- Precision: Pure BF16
- Xformers: Enabled


## Datasets

### cinemamix-1mp
- Repeats: 0
- Total number of images: 7408
- Total number of aspect buckets: 1
- Resolution: 256 px
- Cropped: True
- Crop style: center
- Crop aspect: square


## Inference


```python
import torch
from diffusers import DiffusionPipeline

model_id = 'smoldit-base-test'
pipeline = DiffusionPipeline.from_pretrained(model_id)

prompt = "two young girls in a classroom setting appearing surprised or concerned"
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")
```