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multimodalart
published
a
Space
11 days ago
multimodalart
updated
a
Space
11 days ago
pcuenq
authored
a
paper
2 months ago
multimodalart
posted
an
update
11 months ago
Post
34683
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
- 3 replies
multimodalart
posted
an
update
about 1 year ago
Post
28372
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
7636
OpenELM in Core ML
Apple recently released a set of efficient LLMs in sizes varying between 270M and 3B parameters. Their quality, according to benchmarks, is similar to OLMo models of comparable size, but they required half the pre-training tokens because they use layer-wise scaling, where the number of attention heads increases in deeper layers.
I converted these models to Core ML, for use on Apple Silicon, using this script: https://gist.github.com/pcuenca/23cd08443460bc90854e2a6f0f575084. The converted models were uploaded to this community in the Hub for anyone that wants to integrate inside their apps: corenet-community/openelm-core-ml-6630c6b19268a5d878cfd194
The conversion was done with the following parameters:
- Precision: float32.
- Sequence length: fixed to 128.
With swift-transformers (https://github.com/huggingface/swift-transformers), I'm getting about 56 tok/s with the 270M on my M1 Max, and 6.5 with the largest 3B model. These speeds could be improved by converting to
I'm also looking at optimizing inference using an experimental kv cache in swift-transformers. It's a bit tricky because the layers have varying number of attention heads, but I'm curious to see how much this feature can accelerate performance in this model family :)
Regarding the instruct fine-tuned models, I don't know the chat template that was used. The models use the Llama 2 tokenizer, but the Llama 2 chat template, or the default Alignment Handbook one that was used to train, are not recognized. Any ideas on this welcome!
Apple recently released a set of efficient LLMs in sizes varying between 270M and 3B parameters. Their quality, according to benchmarks, is similar to OLMo models of comparable size, but they required half the pre-training tokens because they use layer-wise scaling, where the number of attention heads increases in deeper layers.
I converted these models to Core ML, for use on Apple Silicon, using this script: https://gist.github.com/pcuenca/23cd08443460bc90854e2a6f0f575084. The converted models were uploaded to this community in the Hub for anyone that wants to integrate inside their apps: corenet-community/openelm-core-ml-6630c6b19268a5d878cfd194
The conversion was done with the following parameters:
- Precision: float32.
- Sequence length: fixed to 128.
With swift-transformers (https://github.com/huggingface/swift-transformers), I'm getting about 56 tok/s with the 270M on my M1 Max, and 6.5 with the largest 3B model. These speeds could be improved by converting to
float16. However, there's some precision loss somewhere and generation doesn't work in float16 mode yet. I'm looking into this and will keep you posted! Or take a look at this issue if you'd like to help: https://github.com/huggingface/swift-transformers/issues/95I'm also looking at optimizing inference using an experimental kv cache in swift-transformers. It's a bit tricky because the layers have varying number of attention heads, but I'm curious to see how much this feature can accelerate performance in this model family :)
Regarding the instruct fine-tuned models, I don't know the chat template that was used. The models use the Llama 2 tokenizer, but the Llama 2 chat template, or the default Alignment Handbook one that was used to train, are not recognized. Any ideas on this welcome!
-
- 4 replies
multimodalart
posted
an
update
over 1 year 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
over 1 year 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
multimodalart
posted
an
update
over 1 year ago
Post
It seems February started with a fully open source AI renaissance 🌟
Models released with fully open dataset, training code, weights ✅
LLM - allenai/olmo-suite-65aeaae8fe5b6b2122b46778 🧠
Embedding - nomic-ai/nomic-embed-text-v1 📚 (sota!)
And it's literally February 1st - can't wait to see what else the community will bring 👀
Models released with fully open dataset, training code, weights ✅
LLM - allenai/olmo-suite-65aeaae8fe5b6b2122b46778 🧠
Embedding - nomic-ai/nomic-embed-text-v1 📚 (sota!)
And it's literally February 1st - can't wait to see what else the community will bring 👀
multimodalart
authored
2
papers
over 1 year ago
multimodalart
authored
a
paper
almost 2 years ago