https://github.com/prithivsakthiur/how-to-run-huggingface-spaces-on-local-machine-demo
Running Hugging Face Spaces on a local machine / colab T4 GPU involves several steps. Hugging Face Spaces is a platform to host machine learning demos and applications using Streamlit, Gradio, or other frameworks.
https://github.com/prithivsakthiur/how-to-run-huggingface-spaces-on-local-machine-demo
demo huggingface spaces
Last synced: 13 days ago
JSON representation
Running Hugging Face Spaces on a local machine / colab T4 GPU involves several steps. Hugging Face Spaces is a platform to host machine learning demos and applications using Streamlit, Gradio, or other frameworks.
- Host: GitHub
- URL: https://github.com/prithivsakthiur/how-to-run-huggingface-spaces-on-local-machine-demo
- Owner: PRITHIVSAKTHIUR
- Created: 2024-06-17T11:55:35.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-07-17T13:59:46.000Z (12 months ago)
- Last Synced: 2025-06-05T08:12:22.592Z (about 1 month ago)
- Topics: demo, huggingface, spaces
- Language: Jupyter Notebook
- Homepage: https://huggingface.co/posts/prithivMLmods/426564568525559
- Size: 8.91 MB
- Stars: 20
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
---
title: HF_SPACE DEMO
emoji: 🐹
colorFrom: blue
colorTo: pink
sdk: gradio
sdk_version: 4.36.1
app_file: app.py
base_model: stabilityai/sdxl-turbo
model: SG161222/RealVisXL_V4.0 / other models based on the conditions
type: base_model, model
pinned: true
header: mini
theme: bethecloud/storj_theme
get_hamster_from: https://prithivhamster.vercel.app/
license: creativeml-openrail-m
short_description: Fast as Hamster | Stable Hamster | Stable Diffusion
---
## How to run hf spaces on local cpu (ex.intel i5 / amd ryzen 7) or by google colab with T4 gpu ❓
# Before getting into the demo, let's first understand how Hugging Face access tokens are passed from the settings on your profile ⭐
You can see the hf token there : 👇🏻 in your profile
https://huggingface.co/settings/tokens
Pass the access to Login locally to Hugging face
Here we used T4 GPU Instead of Nvidia A100, where as you can access the A100 in Colab if you are a premium user. T4 is free for certain amount of computation & although it's not as powerful as the A100 or V100. Since A100 supports HCP() - Acc
## 1. Running in T4 GPU, Google Colab Space : Hardware accelerator
Choose the run-as-gpu.ipynb file from the repo & take it on to the colab notebooks
In Colab Choose the T4 GPU as a Runtime Hardware ✅ as Google Compute Engine !!
Run the modules one by one : first the requirements, sencond the hf_access_token -- Login successful!, third the main code block. After the components of the model which you have linked with the model id will be loaded.
👇🏻👇🏻After Successfully running the code the live.server for gradio will give a link like this ...
https://7770379da2bab84efe.gradio.live/
🚀Progress
After loading to the gradio.live, the gradio interface like this.. & enter the prompt and process it
|  | |
|---------------------------|--------------------------|
The Sample results 1 & 2 from the colab space
|  | |
|---------------------------|--------------------------|
The original resultant image from the space // gradio.live
|  | |
|---------------------------|--------------------------|
🚀Working Link for the Colab :
https://colab.research.google.com/drive/1rpL-UPkVpJgj5U2WXOupV0GWbBGqJ5-p
.
.
-----------------------------------------------------------------------------------------------------------------------------------------------------------------
## 2. Running on CPU, Local System : Hardware accelerator [ 0 ]
👇🏻Same Hugging_Face Login procedure for this method also !!
You can see the hf token there : 👇🏻 in your profile
https://huggingface.co/settings/tokens
Pass the access to Login locally to Hugging face
Choose the run-as-cpu.py file from the repo & take it on to the local code editor ( eg. vs.code )
Statisfy all the requirement.txt ; pip install -r requirements.txt
Run all the requirements
accelerate
diffusers
invisible_watermark
torch
transformers
xformers
🚀Run the run-as-cpu.py by ( python run-as-cpu.py )
✅ After the successful -run you will see the components loading to the local code editor
===== Application Startup at 2024-06-17 16:51:58 =====
The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.
0it [00:00, ?it/s]
0it [00:00, ?it/s]
/usr/local/lib/python3.10/site-packages/diffusers/models/transformers/transformer_2d.py:34: FutureWarning: `Transformer2DModelOutput` is deprecated and will be removed in version 1.0.0. Importing `Transformer2DModelOutput` from `diffusers.models.transformer_2d` is deprecated and this will be removed in a future version. Please use `from diffusers.models.modeling_outputs import Transformer2DModelOutput`, instead.
deprecate("Transformer2DModelOutput", "1.0.0", deprecation_message)
Loading pipeline components...: 0%| | 0/7 [00:00, ?it/s]
Loading pipeline components...: 100%|██████████| 7/7 [00:00<00:00, 9.83it/s]
Running on local URL: http://0.0.0.0:7860
To create a public link, set `share=True` in `launch()`.
IMPORTANT: You are using gradio version 4.26.0, however version 4.29.0 is available, please upgrade.
--------
0%| | 0/2 [00:00, ?it/s]
50%|█████ | 1/2 [00:15<00:15, 15.39s/it]
100%|██████████| 2/2 [00:29<00:00, 14.82s/it]
100%|██████████| 2/2 [00:29<00:00, 14.91s/it]
0%| | 0/2 [00:00, ?it/s]
50%|█████ | 1/2 [00:14<00:14, 14.12s/it]
100%|██████████| 2/2 [00:29<00:00, 14.98s/it]
100%|██████████| 2/2 [00:29<00:00, 14.85s/it]
0%| | 0/2 [00:00, ?it/s]
50%|█████ | 1/2 [00:13<00:13, 14.00s/it]
100%|██████████| 2/2 [00:29<00:00, 14.82s/it]
100%|██████████| 2/2 [00:29<00:00, 14.70s/it]
0%| | 0/2 [00:00, ?it/s]
50%|█████ | 1/2 [00:20<00:20, 20.08s/it]
100%|██████████| 2/2 [00:40<00:00, 20.57s/it]
100%|██████████| 2/2 [00:40<00:00, 20.49s/it]
0%| | 0/2 [00:00, ?it/s]
50%|█████ | 1/2 [00:21<00:21, 21.21s/it]
100%|██████████| 2/2 [00:43<00:00, 21.67s/it]
100%|██████████| 2/2 [00:43<00:00, 21.60s/it]
After that you will see it launched on the ip address ( http://127.0.0.1:7861 ) to run it locally.
And you can launch the gradio interface in public link on your local hardware ..
Enter the prompt & process it on your local CPU
|  | |
|---------------------------|--------------------------|
outcome ⭐
🚀The Resultant image generated
|  | |
|---------------------------|--------------------------|
----------------------------------------------------------------------------------
.
.
.