title	emoji	colorFrom	colorTo	sdk	sdk_version	app_file	base_model	model	type	pinned	header	theme	get_hamster_from	license	short_description
STABLE HAMSTER	🐹	blue	pink	gradio	4.36.1	app.py	stabilityai/sdxl-turbo	any	base_model, model	true	mini	bethecloud/storj_theme	https://prithivhamster.vercel.app/	creativeml-openrail-m	Fast as Hamster | Stable Hamster | Stable Diffusion

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

Spaces: https://huggingface.co/spaces/prithivMLmods/STABLE-HAMSTER

Take Clone :

# Make sure you have git-lfs installed (https://git-lfs.com)
git lfs install

git clone https://huggingface.co/spaces/prithivMLmods/STABLE-HAMSTER

# If you want to clone without large files - just their pointers

GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/spaces/prithivMLmods/STABLE-HAMSTER

Sample Images

How it works

Libraries Overview_req

Library	Library	Library
diffusers	pipeline	sentencepiece
torch	transformers	spaces
gdown	accelerate	peft
torchvision	safetensors	gradio
pillow

Compatibility

ZeroGPU Spaces should mostly be compatible with any PyTorch-based GPU Space. Compatibility with high level HF libraries like transformers or diffusers is slightly more guaranteed That said, ZeroGPU Spaces are not as broadly compatible as classical GPU Spaces and you might still encounter unexpected bugs

Also, for now, ZeroGPU Spaces only works with the Gradio SDK

Supported versions:

Gradio: 4+
PyTorch: All versions from 2.0.0 to 2.2.0
Python: 3.10.13

Usage

In order to make your Space work with ZeroGPU you need to decorate the Python functions that actually require a GPU with @spaces.GPU During the time when a decorated function is invoked, the Space will be attributed a GPU, and it will release it upon completion of the function. Here is a practical example :

+import spaces
from diffusers import DiffusionPipeline

pipe = DiffusionPipeline.from_pretrained(...)
pipe.to('cuda')

+@spaces.GPU
def generate(prompt):
    return pipe(prompt).images

gr.Interface(
    fn=generate,
    inputs=gr.Text(),
    outputs=gr.Gallery(),
).launch()

We first import spaces (importing it first might prevent some issues but is not mandatory) Then we decorate the generate function by adding a @spaces.GPU line before its definition

Duration

If you expect your GPU function to take more than 60s then you need to specify a duration param in the decorator like:

@spaces.GPU(duration=120)
def generate(prompt):
   return pipe(prompt).images

It will set the maximum duration of your function call to 120s.

You can also specify a duration if you know that your function will take far less than the 60s default.

The lower the duration, the higher priority your Space visitors will have in the queue

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