https://github.com/prithivsakthiur/stable-wallpapers

Demo space for generating, Desktop / Mobile Wallpapers. 16:9 / 9:16 #Dream Wallpaper by Stable Wallpaper [ stable diffusion xl ]
https://github.com/prithivsakthiur/stable-wallpapers

diffusion-models gradio huggingface huggingface-transformers lightning models stable-diffusion texttoimage transformer wallpapers

Last synced: 24 days ago
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Demo space for generating, Desktop / Mobile Wallpapers. 16:9 / 9:16 #Dream Wallpaper by Stable Wallpaper [ stable diffusion xl ]

• Host: GitHub
• URL: https://github.com/prithivsakthiur/stable-wallpapers
• Owner: PRITHIVSAKTHIUR
• Created: 2024-07-02T08:31:59.000Z (6 months ago)
• Default Branch: main
• Last Pushed: 2024-07-06T12:21:18.000Z (6 months ago)
• Last Synced: 2024-07-06T13:37:47.575Z (6 months ago)
• Topics: diffusion-models, gradio, huggingface, huggingface-transformers, lightning, models, stable-diffusion, texttoimage, transformer, wallpapers
• Language: Python
• Homepage: https://huggingface.co/spaces/prithivMLmods/Dream-Wallpaper
• Size: 31.5 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
---

title: INSTANT WALLPAPER

emoji: 🌅

colorFrom: blue

colorTo: pink

sdk: gradio

sdk_version: 4.37.2

app_file: app.py

pinned: true

model: For better results -- SG161222/RealVisXL_V4.0 / others

theme: bethecloud/storj_theme

header: mini

license: creativeml-openrail-m

short_description: Wallpaper | Desktop (16:9) | Mobile (9:16)

---

## DREAM WALLPAPER 🌅

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

![alt text](files/33.png)

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 

| ![Image 1](assets/1.png) | ![Image 2](assets/2.png) |

|-------------------------|-------------------------|

| ![Image 3](assets/3.png) | ![Image 4](assets/4.png) |

## How it works

![alt text](assets/rr.gif)

## Libraries Overview

| Library        | Library        | Library        |

|----------------|----------------|----------------|

| `diffusers`    | `pipeline`     | `sentencepiece`|

| `torch`        | `transformers` | `spaces`       |

| `gdown`        | `accelerate`   | `peft`         |

| `torchvision`  | `safetensors`  | `gradio`       |

| `pillow`       |                |                |

![alt text](assets/ty.gif)

## 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

.

.

.