| title | emoji | colorFrom | colorTo | sdk | sdk_version | app_file | base_model | model | type | pinned | header | theme | get_hamster_from | license | short_description |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
HF_SPACE DEMO |
🐹 |
blue |
pink |
gradio |
4.36.1 |
app.py |
stabilityai/sdxl-turbo |
SG161222/RealVisXL_V4.0 / other models based on the conditions |
base_model, model |
true |
mini |
bethecloud/storj_theme |
creativeml-openrail-m |
Fast as Hamster | Stable Hamster | Stable Diffusion |
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
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
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The Sample results 1 & 2 from the colab space
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The original resultant image from the space // gradio.live
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🚀Working Link for the Colab :
https://colab.research.google.com/drive/1rpL-UPkVpJgj5U2WXOupV0GWbBGqJ5-p
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👇🏻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.
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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
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outcome ⭐
🚀The Resultant image generated
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