easydiffusion
tortoise-tts
easydiffusion | tortoise-tts | |
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16 | 145 | |
9,116 | 11,819 | |
1.8% | - | |
9.4 | 8.0 | |
9 days ago | 4 days ago | |
JavaScript | Jupyter Notebook | |
GNU General Public License v3.0 or later | Apache License 2.0 |
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easydiffusion
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What ai do you use I need help
Stable diffusion because it's free and has tons of customization, EasyDiffusion is the simplest to install. But you should download custom models from Civit.ai because the default one is bad.
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Information for some people new to AI or intermediate levels.
A simple 1-click way to create beautiful artwork on your computer using AI. No dependencies or technical knowledge required. https://easydiffusion.github.io/
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Go is bigger than crab!
Easy Diffusion
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Dalle-3 Examples
Easydiffusion is what I use. I jumped in way past when it was just a CLU. https://easydiffusion.github.io/
- Sortie de Easy diffusion 3.0, support de SDXL !
- EasyDiffusion 3.0 released with SDXL, ControlNet, LoRA, lower RAM, and more
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Trying out SDXL without GPU
install EasyDiffusion (https://github.com/easydiffusion/easydiffusion/)
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Stability AI releases its latest image-generating model, Stable Diffusion XL 1.0
Easy Diffusion (previously cmdr2 UI) can run SDXL in 768x768 in about 7 GB of VRAM. And SDXL 512x512 in about 5 GB of VRAM.
Regular SD can run in less than 2 GB of VRAM with Easy Diffusion.
1. Installation (no dependencies, python etc): https://github.com/easydiffusion/easydiffusion#installation
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Build Personal ChatGPT Using Your Data
Easiest 1-click way to install and use Stable Diffusion on your computer."
https://github.com/easydiffusion/easydiffusion
And while Whisper is OpenAI, it is trivial to use locally and extremely usefull
https://github.com/chidiwilliams/buzz
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Ai donghua
I would install automatic1111. If you can't try easy diffusion first. Once you get advance you can install comfyUI and controlnet addon.
tortoise-tts
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ESpeak-ng: speech synthesizer with more than one hundred languages and accents
The quality also depends on the type of model. I'm not really sure what ESpeak-ng actually uses? The classical TTS approaches often use some statistical model (e.g. HMM) + some vocoder. You can get to intelligible speech pretty easily but the quality is bad (w.r.t. how natural it sounds).
There are better open source TTS models. E.g. check https://github.com/neonbjb/tortoise-tts or https://github.com/NVIDIA/tacotron2. Or here for more: https://www.reddit.com/r/MachineLearning/comments/12kjof5/d_...
- FLaNK Stack Weekly 12 February 2024
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OpenVoice: Versatile Instant Voice Cloning
I use Tortoise TTS. It's slow, a little clunky, and sometimes the output gets downright weird. But it's the best quality-oriented TTS I've found that I can run locally.
https://github.com/neonbjb/tortoise-tts
- [discussion] text to voice generation for textbooks
- DALL-E 3: Improving image generation with better captions [pdf]
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Open Source Libraries
neonbjb/tortoise-tts
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Running Tortoise-TTS - IndexError: List out of range
EDIT: It appears to be the exact same issue as this
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My Deep Learning Rig
It was primarily being used to train TTS models (see https://github.com/neonbjb/tortoise-tts), which largely fit into a single GPUs memory. So, for data parallelism, x8 PCIe isn't that much of a concern.
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PlayHT2.0: State-of-the-Art Generative Voice AI Model for Conversational Speech
Previously TortoiseTTS was associated with PlayHT in some way, although the exact connection is a bit vague [0].
From the descriptions here it sounds a lot like AudioLM / SPEAR TTS / some of Meta's recent multilingual TTS approaches, although those models are not open source, sounds like PlayHT's approach is in a similar spirit. The discussion of "mel tokens" is closer to what I would call the classic TTS pipeline in many ways... PlayHT has generally been kind of closed about what they used, would be interesting to know more.
I assume the key factor here is high quality, emotive audio with good data cleaning processes. Probably not even a lot of data, at least in the scale of "a lot" in speech, e.g. ASR (millions of hours) or TTS (hundreds to thousands). As opposed to some radically new architectural piece never before seen in the literature, there are lots of really nice tools for emotive and expressive TTS buried in recent years of publications.
Tacotron 2 is perfectly capable of this type of stuff as well, as shown by Dessa [1] a few years ago (this writeup is a nice intro to TTS concepts). With the limit largely being, at some point you haven't heard certain phonetic sounds before in a voice, and need to do something to get plausible outcomes for new voices.
[0] Discussion here https://github.com/neonbjb/tortoise-tts/issues/182#issuecomm...
[1] https://medium.com/dessa-news/realtalk-how-it-works-94c1afda...
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Comparing Tortoise and Bark for Voice Synthesis
Tortoise GitHub repo - Source code, documentation, and usage guide
What are some alternatives?
stable-diffusion
TTS - πΈπ¬ - a deep learning toolkit for Text-to-Speech, battle-tested in research and production
stable-diffusion-webui - Stable Diffusion web UI
bark - π Text-Prompted Generative Audio Model
SillyTavern - LLM Frontend for Power Users.
Real-Time-Voice-Cloning - Clone a voice in 5 seconds to generate arbitrary speech in real-time
InvokeAI - InvokeAI is a leading creative engine for Stable Diffusion models, empowering professionals, artists, and enthusiasts to generate and create visual media using the latest AI-driven technologies. The solution offers an industry leading WebUI, supports terminal use through a CLI, and serves as the foundation for multiple commercial products.
piper - A fast, local neural text to speech system
stable-diffusion-colab - Adapdet for google colab
tacotron2 - Tacotron 2 - PyTorch implementation with faster-than-realtime inference
HidamariDiffusionColab - colab for stable diffusion
larynx - End to end text to speech system using gruut and onnx