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---
library_name: diffusers
base_model:
- stabilityai/stable-diffusion-xl-base-1.0
- etri-vilab/koala-700m
---
# 🍰 Hybrid-sd-xl-700m for Stable Diffusion XL
[Hybrid-sd-xl-700m](https://huggingface.co/cqyan/hybrid-sd-xl-700m) is a pruned-finetuned version UNet which is based on SDXL1.0 and Koala-700M.
Compared to 2560M parameter size of SDXL Unet, our UNet is 3.2x smaller, 2.4x faster, as well as better-performed in generating more realistic photographic image, displaying more details and rich variety of backgroud. We used a few training tricks and resourceful dataset to finetune the model, thus ended up with higher image aesthetics and image authenticity. Hybrid-sd-xl-700m is useful for real-time previewing of the SDXL generation process, and you are very welcome to try it !!!!!!
<br>
**Index table**
| Model | Params (M) | UNet 1-step inference time (ms) | GPU Memory Usage (MiB) |
|----|----|----|----|
| SDXL | 2560 | 448.47 | 18431 |
| **Hybrid-sd-xl-700m**| **780 ↓** | **185.93 ↓** | **10651 ↓** |
T2I Comparison using one A100 GPU, The image order from left to right : SDXL1.0 -> Koala-700M -> Hybrid-sd-xl-700m
This repo contains `.safetensors` versions of the Hybrid-sd-xl-700m weights.
## Using in 🧨 diffusers
```python
import torch
from diffusers import StableDiffusionXLPipeline,UNet2DConditionModel
unet = UNet2DConditionModel.from_pretrained('cqyan/hybrid-sd-xl-700m')
pipe = StableDiffusionXLPipeline.from_pretrained(
'stabilityai/stable-diffusion-xl-base-1.0',
unet = unet,
torch_dtype=torch.float16)
prompt = "full body, cat dressed as a Viking, with weapon in his paws, battle coloring, glow hyper-detail, hyper-realism, cinematic"
image = pipe(prompt, num_inference_steps=25, guidance_scale=7).images[0]
image.save("cat.png")
``` |