title	emoji	colorFrom	colorTo	sdk	sdk_version	app_file	pinned	model	theme	header	license	short_description
INSTANT WALLPAPER	🌅	blue	pink	gradio	4.37.2	app.py	true	For better results -- SG161222/RealVisXL_V4.0 / others	bethecloud/storj_theme	mini	creativeml-openrail-m	Wallpaper | Desktop (16:9) | Mobile (9:16)

DREAM WALLPAPER 🌅

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

Spaces: https://huggingface.co/spaces/prithivMLmods/Dream-Wallpaper Clone by:

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

git clone https://huggingface.co/spaces/prithivMLmods/Dream-Wallpaper

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

GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/spaces/prithivMLmods/Dream-Wallpaper

Sample Images

How it works

Libraries Overview

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')

[email protected]
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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