DeepSpeech-examples
Examples of how to use or integrate DeepSpeech (by mozilla)
stable-diffusion
A latent text-to-image diffusion model (by CompVis)
| DeepSpeech-examples | stable-diffusion | |
|---|---|---|
| 2 | 382 | |
| 801 | 65,504 | |
| 0.9% | 1.1% | |
| 0.0 | 0.0 | |
| 9 months ago | 21 days ago | |
| Python | Jupyter Notebook | |
| - | GNU General Public License v3.0 or later |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
DeepSpeech-examples
Posts with mentions or reviews of DeepSpeech-examples.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-09-21.
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Whisper – open source speech recognition by OpenAI
Perhaps this could be adapted? https://github.com/mozilla/DeepSpeech-examples/blob/r0.9/mic...
- anyone know of a free online audio to text transcriber?
stable-diffusion
Posts with mentions or reviews of stable-diffusion.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-10-08.
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Go is bigger than crab!
Which is a 1-click install of Stable Diffusion with an alternative web interface. You can choose a different approach but this one is pretty simple and I am new to this stuff.
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Why & How to check Invisible Watermark
an invisible watermarking of the outputs, to help viewers identify the images as machine-generated.
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How to create an Image generating AI?
It sounds like you just want to set up Stable Diffusion to run locally. I don't think your computer's specs will be able to do it. You need a graphics card with a decent amount of VRAM. Stable diffusion is in Python as is almost every AI open source project I've seen. If you can get your hands on a system with an Nvidia RTX card with as much VRAM as possible, you're in business. I have an RTX 3060 with 12 gigs of VRAM and I can run stable diffusion and a whole variety of open source LLMs as well as other projects like face swap, Roop, tortoise TTS, sadtalker, etc...
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Two video cards...one dedicated to Stable Diffusion...the other for everything else on my PC?
Use specific GPU on multi GPU systems · Issue #87 · CompVis/stable-diffusion · GitHub
- Automatic1111 - Multiple GPUs
- Ist Google inzwischen einfach unbrauchbar?
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Why are people so against compensation for artists?
I dealt with this in one of my posts. At least SD 1.1 till 1.5 are all trained on a batch size of 2048. The version pretty much everyone uses (1.5) is first pretrained at a resolution of 256x256 for 237K steps on laion2B-en, at the end of those training steps it will have seen roughly 500M images in laion2B-en. After that it is pre-trained for 194K steps on laion-high-resolution at a resolution of 512x512, which is a subset of 170M images from laion5B. Finally it is trained for 1.110K steps on LAION aesthetic v2 5+. This is easily verified by taking a glance at the model card of SD 1.5. Though that one doesn't specify for part of the training exactly which aesthetic set was used for part of the training, for that you have to look at the CompVis github repo. Thus at the end of it all both the most recent images and the majority of images will have come from LAION aesthetic v2 5+ (seeing every image approx 4 times). Realistically a lot of the weights obtained from pretraining on 2B will have been lost, and only provided a good starting point for the weights.
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Is SDXL really open-source?
stable diffusion · CompVis/stable-diffusion@2ff270f · GitHub
- I want to ask the AI to draw me as a Pokemon anime character then draw six of Pokemon of my choice next to me. What are my best free, 15$ or under and 30$ or under choices?
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how can i create my own ai image model
Here for example --> https://github.com/CompVis/stable-diffusion
What are some alternatives?
When comparing DeepSpeech-examples and stable-diffusion you can also consider the following projects:
whisper
GFPGAN - GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration.
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
Real-ESRGAN - Real-ESRGAN aims at developing Practical Algorithms for General Image/Video Restoration.
openai-whisper-realtime - A quick experiment to achieve almost realtime transcription using Whisper.
diffusers-uncensored - Uncensored fork of diffusers
PaddleSpeech - Easy-to-use Speech Toolkit including Self-Supervised Learning model, SOTA/Streaming ASR with punctuation, Streaming TTS with text frontend, Speaker Verification System, End-to-End Speech Translation and Keyword Spotting. Won NAACL2022 Best Demo Award.
diffusers - 🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch and FLAX.
whisper - Robust Speech Recognition via Large-Scale Weak Supervision
VQGAN-CLIP - Just playing with getting VQGAN+CLIP running locally, rather than having to use colab.
py-webrtcvad - Python interface to the WebRTC Voice Activity Detector
onnx - Open standard for machine learning interoperability
DeepSpeech-examples vs whisper
stable-diffusion vs GFPGAN
DeepSpeech-examples vs transformers
stable-diffusion vs Real-ESRGAN
DeepSpeech-examples vs openai-whisper-realtime
stable-diffusion vs diffusers-uncensored
DeepSpeech-examples vs PaddleSpeech
stable-diffusion vs diffusers
DeepSpeech-examples vs whisper
stable-diffusion vs VQGAN-CLIP
DeepSpeech-examples vs py-webrtcvad
stable-diffusion vs onnx