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cqyan/
hybrid-sd-xl-700m

Name: hybrid-sd-xl-700m
Availability: InStock

diffusers

safetensors

etri-vilab/koala-700m

finetune

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🍰 Hybrid-sd-xl-700m for Stable Diffusion XL

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 !!!!!!

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")