community-events
v-diffusion-pytorch
community-events | v-diffusion-pytorch | |
---|---|---|
8 | 10 | |
379 | 690 | |
2.1% | - | |
7.2 | 0.0 | |
5 months ago | over 1 year ago | |
Jupyter Notebook | Python | |
- | MIT License |
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community-events
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Controlling Stable Diffusion with JAX & Diffusers using TPU v4
Best applications that will come out of this sprint will receive prizes. You can find more information here. If you want to get started, simply join huggingface.co/discord, take the role 🧨 Diffusers and head to #jax-diffusers-ideas to share your idea or join one of the teams, and fill this form: https://forms.gle/t3M7aNPuLL9V1sfa9
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JAX & Diffusers to Control Stable Diffusion (with TPUs ⚡️ )
It will start on 17th of April. To join us, you can join huggingface.co/join/discord and take the role Diffusers from #role-assignment. After this, simply fill the form provided in this guide to later get access to TPUs. https://github.com/huggingface/community-events/tree/main/jax-controlnet-sprint
- “Control Stable Diffusion” Sprint kicks off with free TPU-v4 from Google
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Free compute to train custom ControlNet by Hugging Face
Details and sign-up: https://github.com/huggingface/community-events/tree/main/jax-controlnet-sprint
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How can I create a dataset to refine Whisper AI from old videos with subtitles?
For the training, I extremely recommend checking out the Whisper Fine-Tuning Event. It has a python script to train in one command, tons of tips, even a walkthrough video.
- I am using OpenAi's whisper transcription/translation model. I am wondering if I can improve it's performance by optimizing the audio files somehow. What features of audio files should I look into to make the whisper model perform better?
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[N] Gradio Blocks + Hugging Face event is starting this week. A hackathon type event from May 17th to May 31st with prizes in which we will create interactive web demos for state-of-the-art machine learning models
We are happy to invite you to the Gradio Blocks Party - a community event in which we will create interactive demos for state-of-the-art machine learning models. Demos are powerful because they allow anyone — not just ML engineers — to try out models in the browser, give feedback on predictions, identify trustworthy models. The event will take place from May 17th to 31st. We will be organizing this event on Github and the Hugging Face discord channel. Prizes will be given at the end of the event, see the Prizes section
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Dall-E 2
If you're interested in generative models, Hugging Face is putting on an event around generative models right now called the HugGAN sprint, where they're giving away free access to compute to train models like this.
You can join it by following the steps in the guide here: https://github.com/huggingface/community-events/tree/main/hu...
There will also be talks from awesome folks at EleutherAI, Google, and Deepmind
v-diffusion-pytorch
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Leaked deck raises questions over Stability AI’s Series A pitch to investors
This is dumb.
We employed Eleuther team members as Stability AI employees/contractors and incubated them until the 501(c)3 was set up and we managed to bring in other funders too: https://techcrunch.com/2023/03/02/stability-ai-hugging-face-...
I am on the board and delighted to continue to support their work as an independent organisation for LM evaluation, alignment and interpretability which is much needed.
Indeed though our approach was handing out significant compute for no control, no equity, no IP.
Anyone who has received Stability AI grants will be able to attest to this with multiple breakthroughs as a result, for example funding https://github.com/BlinkDL/RWKV-LM, the work of https://github.com/lucidrains and others.
Similarly we funded the beta of MidJourney with a cash grant for compute without ever even floating asking for equity etc as it is a market-creating innovation.
At the time MidJourney was using cc12m_1, a model developed by one of our lead (employed) generative AI developers Katherine Crownson / RiversHaveWings (https://github.com/crowsonkb/v-diffusion-pytorch)
Our model is simply to take open innovation and create commercial variants of that (our stable series models) from scratch and on our own, plus variants of that for private data - https://twitter.com/EMostaque/status/1649152422634221593?s=2...
This means we can be hands off versus other funders and trust researchers and help them succeed, something others do not.
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[D] Is Midjourney AI more-or-less the same architecture as DALL-E 2? Can I read about the model in detail somewhere or is there anything published in this regard?
From what I've gathered by being involved early in the beta / in other discords, Midjourney was originally based on a fine-tuned version of classifier-free guided v-diffusion. The fine-tuning dataset was a manually curated set largely from LAION-2B similar to the laion-art / laion-hd. To make it so fast they were using Progressive Distillation (possibly distilling on PLMS steps rather than p/ddim?) and settings optimized to let them skip a few of the first steps like Quick CLIP-Guided Diffusion. There's a good chance they were doing some prompt augmentation as well, although I think this would be susceptible to prompt discovery attacks which I haven't seen any examples of for Midjourney.
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Tweet: "Give us a few weeks, open version in the works." regarding an open Google Imagen-like system
Source. This tweet is from a person whose organization has been publicly credited with providing compute for others in the past (example: "Thank you to stability.ai for compute to train these models!").
- Does anyone know which GAN this is?
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Dall-E 2
com/RiversHaveWings/status/1462859669454536711, 2021.
[8] Katherine Crowson. v-diffusion. https://github.com/crowsonkb/v-diffusion-pytorch, 2021.
- How do I start creating my own AI generated art?
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Advice on improving Text to Image Model (CC12M Diffusion) model at higher output dimensions?
More parameters are available as seen in this code. The fix was adapted from this.
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Colab notebook "Text to Image (CC12M Diffusion)" from RiversHaveWings was updated with significantly faster image generation speed. It generates 4 images in 4.75 minutes (not including setup time) on a Tesla K80 GPU (free-tier Colab).
I'm not sure if this Colab notebook was mentioned in this sub previously, but it's been available since January 2022. The cc12m_1_cfg model used by this Colab notebook is different than the cc12m_1 model from this December 2021 post (reference).
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Airport carpets (a genurary submission)
A few more here https://twitter.com/metasemantic/status/1486334535436488705. Samples careful constructed with a heavily modified diffusion model by @rivershavewings https://github.com/crowsonkb/v-diffusion-pytorch
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Steampunk Airships
Most of the code was from Katherine Crowson's (@RiversHaveWings) v-diffusion-pytorch library (https://github.com/crowsonkb/v-diffusion-pytorch), which is an implementation of denoising diffusion probabilistic models (https://arxiv.org/abs/2006.11239). I used the CC12M_1 CFG checkpoint.
What are some alternatives?
dalle-2-preview
dalle-mini - DALL·E Mini - Generate images from a text prompt
tensorrtx - Implementation of popular deep learning networks with TensorRT network definition API
bevy_retro - Plugin pack for making 2D games with Bevy
gpt-3 - GPT-3: Language Models are Few-Shot Learners
lm-human-preferences - Code for the paper Fine-Tuning Language Models from Human Preferences
jaxtorch - A JAX nn library
glide-text2im - GLIDE: a diffusion-based text-conditional image synthesis model