Post
1109
Just created a Gradio space for playing with the new OAI realtime voice API!
freddyaboulton/openai-realtime-voice
freddyaboulton/openai-realtime-voice
Gradio Client Demos
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freddyaboulton
posted
an
update
8 days ago
freddyaboulton
posted
an
update
8 days ago
Post
460
Gemini can talk 🗣️
Check out the new multimodal API from Google on @akhaliq 's anychat or my space. It's very fast and smart 🍓
https://huggingface.co./spaces/freddyaboulton/gemini-voicehttps://huggingface.co./spaces/akhaliq/anychat
Check out the new multimodal API from Google on @akhaliq 's anychat or my space. It's very fast and smart 🍓
https://huggingface.co./spaces/freddyaboulton/gemini-voicehttps://huggingface.co./spaces/akhaliq/anychat
- 1 reply
freddyaboulton
posted
an
update
13 days ago
Post
1829
Version 0.0.21 of gradio-pdf now properly loads chinese characters!
freddyaboulton
posted
an
update
14 days ago
Post
1512
Hello Llama 3.2! 🗣️🦙
Build a Siri-like coding assistant that responds to "Hello Llama" in 100 lines of python! All with Gradio, webRTC 😎
freddyaboulton/hey-llama-code-editor
Build a Siri-like coding assistant that responds to "Hello Llama" in 100 lines of python! All with Gradio, webRTC 😎
freddyaboulton/hey-llama-code-editor
freddyaboulton
posted
an
update
15 days ago
Post
1050
Just created a cookbook of real time audio/video spaces created using Gradio and WebRTC ⚡️
Use this and the [docs](https://freddyaboulton.github.io/gradio-webrtc/) to get started building the next gen of AI apps!
freddyaboulton/gradio-webrtc-cookbook-6758ba7745aeca7b1be7de0f
Use this and the [docs](https://freddyaboulton.github.io/gradio-webrtc/) to get started building the next gen of AI apps!
freddyaboulton/gradio-webrtc-cookbook-6758ba7745aeca7b1be7de0f
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- 2 replies
Post
4608
👋 Hi Gradio community,
I'm excited to share that Gradio 5 will launch in October with improvements across security, performance, SEO, design (see the screenshot for Gradio 4 vs. Gradio 5), and user experience, making Gradio a mature framework for web-based ML applications.
Gradio 5 is currently in beta, so if you'd like to try it out early, please refer to the instructions below:
---------- Installation -------------
Gradio 5 depends on Python 3.10 or higher, so if you are running Gradio locally, please ensure that you have Python 3.10 or higher, or download it here: https://www.python.org/downloads/
* Locally: If you are running gradio locally, simply install the release candidate with
* Spaces: If you would like to update an existing gradio Space to use Gradio 5, you can simply update the
In most cases, that’s all you have to do to run Gradio 5.0. If you start your Gradio application, you should see your Gradio app running, with a fresh new UI.
-----------------------------
Fore more information, please see: https://github.com/gradio-app/gradio/issues/9463
I'm excited to share that Gradio 5 will launch in October with improvements across security, performance, SEO, design (see the screenshot for Gradio 4 vs. Gradio 5), and user experience, making Gradio a mature framework for web-based ML applications.
Gradio 5 is currently in beta, so if you'd like to try it out early, please refer to the instructions below:
---------- Installation -------------
Gradio 5 depends on Python 3.10 or higher, so if you are running Gradio locally, please ensure that you have Python 3.10 or higher, or download it here: https://www.python.org/downloads/
* Locally: If you are running gradio locally, simply install the release candidate with
pip install gradio --pre* Spaces: If you would like to update an existing gradio Space to use Gradio 5, you can simply update the
sdk_version to be 5.0.0b3 in the README.md file on Spaces.In most cases, that’s all you have to do to run Gradio 5.0. If you start your Gradio application, you should see your Gradio app running, with a fresh new UI.
-----------------------------
Fore more information, please see: https://github.com/gradio-app/gradio/issues/9463
- 2 replies
multimodalart
posted
an
update
5 months ago
Post
19890
New feature 🔥
Image models and LoRAs now have little previews 🤏
If you don't know where to start to find them, I invite you to browse cool LoRAs in the profile of some amazing fine-tuners: @artificialguybr , @alvdansen , @DoctorDiffusion , @e-n-v-y , @KappaNeuro @ostris
Image models and LoRAs now have little previews 🤏
If you don't know where to start to find them, I invite you to browse cool LoRAs in the profile of some amazing fine-tuners: @artificialguybr , @alvdansen , @DoctorDiffusion , @e-n-v-y , @KappaNeuro @ostris
- 2 replies
freddyaboulton
posted
an
update
6 months ago
Post
1503
@dwancin
Can you please reset your toggle component's space? It's stuck for some reason. Happy to help
dwancin/gradio_toggle
dwancin/gradio_toggle
Post
4089
𝗣𝗿𝗼𝘁𝗼𝘁𝘆𝗽𝗶𝗻𝗴 holds an important place in machine learning. But it has traditionally been quite difficult to go from prototype code to production-ready APIs
We're working on making that a lot easier with 𝗚𝗿𝗮𝗱𝗶𝗼 and will unveil something new on June 6th: https://www.youtube.com/watch?v=44vi31hehw4&ab_channel=HuggingFace
We're working on making that a lot easier with 𝗚𝗿𝗮𝗱𝗶𝗼 and will unveil something new on June 6th: https://www.youtube.com/watch?v=44vi31hehw4&ab_channel=HuggingFace
- 2 replies
multimodalart
posted
an
update
8 months ago
Post
25263
The first open Stable Diffusion 3-like architecture model is JUST out 💣 - but it is not SD3! 🤔
It is Tencent-Hunyuan/HunyuanDiT by Tencent, a 1.5B parameter DiT (diffusion transformer) text-to-image model 🖼️✨, trained with multi-lingual CLIP + multi-lingual T5 text-encoders for english 🤝 chinese understanding
Try it out by yourself here ▶️ https://huggingface.co./spaces/multimodalart/HunyuanDiT
(a bit too slow as the model is chunky and the research code isn't super optimized for inference speed yet)
In the paper they claim to be SOTA open source based on human preference evaluation!
It is Tencent-Hunyuan/HunyuanDiT by Tencent, a 1.5B parameter DiT (diffusion transformer) text-to-image model 🖼️✨, trained with multi-lingual CLIP + multi-lingual T5 text-encoders for english 🤝 chinese understanding
Try it out by yourself here ▶️ https://huggingface.co./spaces/multimodalart/HunyuanDiT
(a bit too slow as the model is chunky and the research code isn't super optimized for inference speed yet)
In the paper they claim to be SOTA open source based on human preference evaluation!
Post
3619
Open Models vs. Closed APIs for Software Engineers
-----------------------------------------------------------------------
If you're an ML researcher / scientist, you probably don't need much convincing to use open models instead of closed APIs -- open models give you reproducibility and let you deeply investigate the model's behavior.
But what if you are a software engineer building products on top of LLMs? I'd argue that open models are a much better option even if you are using them as APIs. For at least 3 reasons:
1) The most obvious reason is reliability of your product. Relying on a closed API means that your product has a single point-of-failure. On the other hand, there are at least 7 different API providers that offer Llama3 70B already. As well as libraries that abstract on top of these API providers so that you can make a single request that goes to different API providers depending on availability / latency.
2) Another benefit is eventual consistency going local. If your product takes off, it will be more economical and lower latency to have a dedicated inference endpoint running on your VPC than to call external APIs. If you've started with an open-source model, you can always deploy the same model locally. You don't need to modify prompts or change any surrounding logic to get consistent behavior. Minimize your technical debt from the beginning.
3) Finally, open models give you much more flexibility. Even if you keep using APIs, you might want to tradeoff latency vs. cost, or use APIs that support batches of inputs, etc. Because different API providers have different infrastructure, you can use the API provider that makes the most sense for your product -- or you can even use multiple API providers for different users (free vs. paid) or different parts of your product (priority features vs. nice-to-haves)
-----------------------------------------------------------------------
If you're an ML researcher / scientist, you probably don't need much convincing to use open models instead of closed APIs -- open models give you reproducibility and let you deeply investigate the model's behavior.
But what if you are a software engineer building products on top of LLMs? I'd argue that open models are a much better option even if you are using them as APIs. For at least 3 reasons:
1) The most obvious reason is reliability of your product. Relying on a closed API means that your product has a single point-of-failure. On the other hand, there are at least 7 different API providers that offer Llama3 70B already. As well as libraries that abstract on top of these API providers so that you can make a single request that goes to different API providers depending on availability / latency.
2) Another benefit is eventual consistency going local. If your product takes off, it will be more economical and lower latency to have a dedicated inference endpoint running on your VPC than to call external APIs. If you've started with an open-source model, you can always deploy the same model locally. You don't need to modify prompts or change any surrounding logic to get consistent behavior. Minimize your technical debt from the beginning.
3) Finally, open models give you much more flexibility. Even if you keep using APIs, you might want to tradeoff latency vs. cost, or use APIs that support batches of inputs, etc. Because different API providers have different infrastructure, you can use the API provider that makes the most sense for your product -- or you can even use multiple API providers for different users (free vs. paid) or different parts of your product (priority features vs. nice-to-haves)
freddyaboulton
posted
an
update
9 months ago
Post
3659
We just released gradio version 4.26.0 ! We *highly* recommend you upgrade your apps to this version to bring in these nice changes:
🎥 Introducing the API recorder. Any gradio app running 4.26.0 and above will have an "API Recorder" that will record your interactions with the app and auto-generate the corresponding python or js code needed to recreate those actions programmatically. It's very neat!
📝 Enhanced markdown rendering in gr.Chatbot
🐢 Fix for slow load times on spaces as well as the UI locking up on rapid generations
See the full changelog of goodies here: https://www.gradio.app/changelog#4-26-0
🎥 Introducing the API recorder. Any gradio app running 4.26.0 and above will have an "API Recorder" that will record your interactions with the app and auto-generate the corresponding python or js code needed to recreate those actions programmatically. It's very neat!
📝 Enhanced markdown rendering in gr.Chatbot
🐢 Fix for slow load times on spaces as well as the UI locking up on rapid generations
See the full changelog of goodies here: https://www.gradio.app/changelog#4-26-0
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- 1 reply
Post
3310
Introducing the Gradio API Recorder 🪄
Every Gradio app now includes an API recorder that lets you reconstruct your interaction in a Gradio app as code using the Python or JS clients! Our goal is to make Gradio the easiest way to build ML APIs, not just UIs 🔥
Every Gradio app now includes an API recorder that lets you reconstruct your interaction in a Gradio app as code using the Python or JS clients! Our goal is to make Gradio the easiest way to build ML APIs, not just UIs 🔥
- 5 replies
freddyaboulton
posted
an
update
9 months ago
Post
2431
Gradio 4.25.0 is out with some nice improvements and bug fixes!
🧹 Automatic deletion of gr.State variables stored in the server. Never run out of RAM again. Also adds an unload event you can run when a user closes their browser tab.
😴 Lazy example caching. You can set cache_examples="lazy" to cache examples when they're first requested as opposed to before the server launches. This can cut down the server's start-up time drastically.
🔊 Fixes a bug with streaming audio outputs
🤖 Improvements to gr.ChatInterface like pasting images directly from the clipboard.
See the rest of the changelog here: https://www.gradio.app/changelog#4-25-0
🧹 Automatic deletion of gr.State variables stored in the server. Never run out of RAM again. Also adds an unload event you can run when a user closes their browser tab.
😴 Lazy example caching. You can set cache_examples="lazy" to cache examples when they're first requested as opposed to before the server launches. This can cut down the server's start-up time drastically.
🔊 Fixes a bug with streaming audio outputs
🤖 Improvements to gr.ChatInterface like pasting images directly from the clipboard.
See the rest of the changelog here: https://www.gradio.app/changelog#4-25-0
freddyaboulton
posted
an
update
9 months ago
Post
1690
Tips for saving disk space with Gradio 💾
Try these out with gradio 4.22.0 ! Code snippet attached.
1. Set delete_cache. The delete_cache parameter will periodically delete files from gradio's cache that are older than a given age. Setting it will also delete all files created by that app when the app shuts down. It is a tuple of two ints, (frequency, age) expressed in seconds. So delete_cache=(3600, 3600), will delete files older than an hour every hour.
2. Use static files. Static files are not copied to the cache and are instead served directly to users of your app. This is useful for components displaying a lot of content that won't change, like a gallery with hundreds of images.
3. Set format="jpeg" for images and galleries. JPEGs take up less disk space than PNGs. This can also speed up the speed of your prediction function as they will be written to the cache faster.
Try these out with gradio 4.22.0 ! Code snippet attached.
1. Set delete_cache. The delete_cache parameter will periodically delete files from gradio's cache that are older than a given age. Setting it will also delete all files created by that app when the app shuts down. It is a tuple of two ints, (frequency, age) expressed in seconds. So delete_cache=(3600, 3600), will delete files older than an hour every hour.
2. Use static files. Static files are not copied to the cache and are instead served directly to users of your app. This is useful for components displaying a lot of content that won't change, like a gallery with hundreds of images.
3. Set format="jpeg" for images and galleries. JPEGs take up less disk space than PNGs. This can also speed up the speed of your prediction function as they will be written to the cache faster.
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- 3 replies
multimodalart
posted
an
update
10 months ago
Post
The Stable Diffusion 3 research paper broken down, including some overlooked details! 📝
Model
📏 2 base model variants mentioned: 2B and 8B sizes
📐 New architecture in all abstraction levels:
- 🔽 UNet; ⬆️ Multimodal Diffusion Transformer, bye cross attention 👋
- 🆕 Rectified flows for the diffusion process
- 🧩 Still a Latent Diffusion Model
📄 3 text-encoders: 2 CLIPs, one T5-XXL; plug-and-play: removing the larger one maintains competitiveness
🗃️ Dataset was deduplicated with SSCD which helped with memorization (no more details about the dataset tho)
Variants
🔁 A DPO fine-tuned model showed great improvement in prompt understanding and aesthetics
✏️ An Instruct Edit 2B model was trained, and learned how to do text-replacement
Results
✅ State of the art in automated evals for composition and prompt understanding
✅ Best win rate in human preference evaluation for prompt understanding, aesthetics and typography (missing some details on how many participants and the design of the experiment)
Paper: https://stabilityai-public-packages.s3.us-west-2.amazonaws.com/Stable+Diffusion+3+Paper.pdf
Model
📏 2 base model variants mentioned: 2B and 8B sizes
📐 New architecture in all abstraction levels:
- 🔽 UNet; ⬆️ Multimodal Diffusion Transformer, bye cross attention 👋
- 🆕 Rectified flows for the diffusion process
- 🧩 Still a Latent Diffusion Model
📄 3 text-encoders: 2 CLIPs, one T5-XXL; plug-and-play: removing the larger one maintains competitiveness
🗃️ Dataset was deduplicated with SSCD which helped with memorization (no more details about the dataset tho)
Variants
🔁 A DPO fine-tuned model showed great improvement in prompt understanding and aesthetics
✏️ An Instruct Edit 2B model was trained, and learned how to do text-replacement
Results
✅ State of the art in automated evals for composition and prompt understanding
✅ Best win rate in human preference evaluation for prompt understanding, aesthetics and typography (missing some details on how many participants and the design of the experiment)
Paper: https://stabilityai-public-packages.s3.us-west-2.amazonaws.com/Stable+Diffusion+3+Paper.pdf
- 3 replies
multimodalart
posted
an
update
11 months ago
Post
⚔️ The TIGERLab's Text2Image arena is here! ⚔️
TIGER-Lab/GenAI-Arena
Like https://huggingface.co./spaces/lmsys/chatbot-arena-leaderboard for LLMs: you prompt, two images emerge, vote for the best one 🏆
With enough votes this will lead to an Elo-based leaderboard for text-to-image models, go vote! 🗳️
TIGER-Lab/GenAI-Arena
TIGER-Lab/GenAI-Arena
Like https://huggingface.co./spaces/lmsys/chatbot-arena-leaderboard for LLMs: you prompt, two images emerge, vote for the best one 🏆
With enough votes this will lead to an Elo-based leaderboard for text-to-image models, go vote! 🗳️
TIGER-Lab/GenAI-Arena
Post
Necessity is the mother of invention, and of Gradio components.
Sometimes we realize that we need a Gradio component to build a cool application and demo, so we just build it. For example, we just added a new gr.ParamViewer component because we needed it to display information about Python & JavaScript functions in our documentation.
Of course, our users should be able able to do the same thing for their machine learning applications, so that's why Gradio lets you build custom components, and publish them to the world 🔥
Sometimes we realize that we need a Gradio component to build a cool application and demo, so we just build it. For example, we just added a new gr.ParamViewer component because we needed it to display information about Python & JavaScript functions in our documentation.
Of course, our users should be able able to do the same thing for their machine learning applications, so that's why Gradio lets you build custom components, and publish them to the world 🔥
Post
Lots of cool Gradio custom components, but is the most generally useful one I've seen so far: insert a Modal into any Gradio app by using the
modal component!from gradio_modal import Modal
with gr.Blocks() as demo:
gr.Markdown("### Main Page")
gr.Textbox("lorem ipsum " * 1000, lines=10)
with Modal(visible=True) as modal:
gr.Markdown("# License Agreement")Post
Just out: new custom Gradio component specifically designed for code completion models 🔥
- 1 reply