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---
license: creativeml-openrail-m
base_model: SG161222/Realistic_Vision_V4.0
datasets:
- recastai/LAION-art-EN-improved-captions
tags:
- bksdm
- bksdm-ttiny
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
# Text-to-image Distillation
This pipeline was distilled from **SG161222/Realistic_Vision_V4.0** on a Subset of **recastai/LAION-art-EN-improved-captions** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['Portrait of a pretty girl']:
This Pipeline is based upon [the paper](https://arxiv.org/pdf/2305.15798.pdf). Training Code can be found [here](https://github.com/segmind/BKSDM).
## Pipeline usage
You can use the pipeline like so:
```python
from diffusers import DiffusionPipeline
import torch
pipeline = DiffusionPipeline.from_pretrained("segmind/tiny-sd", torch_dtype=torch.float16)
#Load LoRA finetune
pipeline.load_lora_weights("segmind/tiny_lora_mxtun3_style", weight_name="sd15_tiny_mxtun3_style_lora.safetensors")
prompt = "Portrait of a pretty girl"
image = pipeline(prompt).images[0]
image.save("my_image.png")
```
## Training info
These are the key hyperparameters used during training:
* Steps: 125000
* Learning rate: 1e-4
* Batch size: 32
* Gradient accumulation steps: 4
* Image resolution: 512
* Mixed-precision: fp16
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