ThreadWeaver: Adaptive Threading for Efficient Parallel Reasoning in Language Models
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2512.07843
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akhaliq
submitted
a
paper
to Daily Papers
about 8 hours ago
multimodalart
posted
an
update
about 2 months ago
Post
6286
Want to iterate on a Hugging Face Space with an LLM?
Now you can easily convert any HF entire repo (Model, Dataset or Space) to a text file and feed it to a language model!
multimodalart/repo2txt
Now you can easily convert any HF entire repo (Model, Dataset or Space) to a text file and feed it to a language model!
multimodalart/repo2txt
akhaliq
authored
a
paper
about 2 months ago
osanseviero
authored
a
paper
3 months ago
multimodalart
posted
an
update
6 months ago
Post
17884
Self-Forcing - a real-time video distilled model from Wan 2.1 by
@adobe
is out, and they open sourced it 🐐
I've built a live real time demo on Spaces 📹💨
multimodalart/self-forcing
I've built a live real time demo on Spaces 📹💨
multimodalart/self-forcing
-
- 6 replies
Post
12023
FramePack is hands down one of the best OS releases in video generation 🙇🏻♀️🤯
✅ fully open sourced + amazing quality + reduced memory + improved speed
but more even - its gonna facilitate *soooo* many downstream applications
like this version adapted for landscape rotation 👇https://huggingface.co/spaces/tori29umai/FramePack_rotate_landscape
✅ fully open sourced + amazing quality + reduced memory + improved speed
but more even - its gonna facilitate *soooo* many downstream applications
like this version adapted for landscape rotation 👇https://huggingface.co/spaces/tori29umai/FramePack_rotate_landscape
- 2 replies
Post
3206
We just shipped HiDream Image LoRA fine-tuning to diffusers🧨
HiDream's sota capabilities (and mit license) bring a lot of potential to explore with fine-tunes 🔥
- more upgrades and features soon!
- code, weights and config example 👇
🧶Yarn art lora: linoyts/HiDream-yarn-art-LoRA
code: https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_hidream.md
HiDream's sota capabilities (and mit license) bring a lot of potential to explore with fine-tunes 🔥
- more upgrades and features soon!
- code, weights and config example 👇
🧶Yarn art lora: linoyts/HiDream-yarn-art-LoRA
code: https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_hidream.md
- 2 replies
osanseviero
authored
a
paper
9 months ago
Post
46372
Google drops Gemini 2.0 Flash Thinking
a new experimental model that unlocks stronger reasoning capabilities and shows its thoughts. The model plans (with thoughts visible), can solve complex problems with Flash speeds, and more
now available in anychat, try it out: https://huggingface.co/spaces/akhaliq/anychat
a new experimental model that unlocks stronger reasoning capabilities and shows its thoughts. The model plans (with thoughts visible), can solve complex problems with Flash speeds, and more
now available in anychat, try it out: https://huggingface.co/spaces/akhaliq/anychat
- 5 replies
Post
45532
QwQ-32B-Preview is now available in anychat
A reasoning model that is competitive with OpenAI o1-mini and o1-preview
try it out: https://huggingface.co/spaces/akhaliq/anychat
A reasoning model that is competitive with OpenAI o1-mini and o1-preview
try it out: https://huggingface.co/spaces/akhaliq/anychat
- 1 reply
Post
5023
New model drop in anychat
allenai/Llama-3.1-Tulu-3-8B is now available
try it here: https://huggingface.co/spaces/akhaliq/anychat
allenai/Llama-3.1-Tulu-3-8B is now available
try it here: https://huggingface.co/spaces/akhaliq/anychat
Post
3790
anychat
supports chatgpt, gemini, perplexity, claude, meta llama, grok all in one app
try it out there: https://huggingface.co/spaces/akhaliq/anychat
supports chatgpt, gemini, perplexity, claude, meta llama, grok all in one app
try it out there: https://huggingface.co/spaces/akhaliq/anychat
multimodalart
posted
an
update
over 1 year ago
Post
35549
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
linoyts
updated
a
Space
over 1 year ago
Post
21188
Phased Consistency Model
Phased Consistency Model (2405.18407)
The consistency model (CM) has recently made significant progress in accelerating the generation of diffusion models. However, its application to high-resolution, text-conditioned image generation in the latent space (a.k.a., LCM) remains unsatisfactory. In this paper, we identify three key flaws in the current design of LCM. We investigate the reasons behind these limitations and propose the Phased Consistency Model (PCM), which generalizes the design space and addresses all identified limitations. Our evaluations demonstrate that PCM significantly outperforms LCM across 1--16 step generation settings. While PCM is specifically designed for multi-step refinement, it achieves even superior or comparable 1-step generation results to previously state-of-the-art specifically designed 1-step methods. Furthermore, we show that PCM's methodology is versatile and applicable to video generation, enabling us to train the state-of-the-art few-step text-to-video generator.
Phased Consistency Model (2405.18407)
The consistency model (CM) has recently made significant progress in accelerating the generation of diffusion models. However, its application to high-resolution, text-conditioned image generation in the latent space (a.k.a., LCM) remains unsatisfactory. In this paper, we identify three key flaws in the current design of LCM. We investigate the reasons behind these limitations and propose the Phased Consistency Model (PCM), which generalizes the design space and addresses all identified limitations. Our evaluations demonstrate that PCM significantly outperforms LCM across 1--16 step generation settings. While PCM is specifically designed for multi-step refinement, it achieves even superior or comparable 1-step generation results to previously state-of-the-art specifically designed 1-step methods. Furthermore, we show that PCM's methodology is versatile and applicable to video generation, enabling us to train the state-of-the-art few-step text-to-video generator.
Post
7863
Thanks to
@OzzyGT
for pushing the new Anyline preprocessor to https://github.com/huggingface/controlnet_aux. Now you can use the
TheMistoAI/MistoLine ControlNet with Diffusers completely.
Here's a demo for you: radames/MistoLine-ControlNet-demo
Super resolution version: radames/Enhance-This-HiDiffusion-SDXL
Here's a demo for you: radames/MistoLine-ControlNet-demo
Super resolution version: radames/Enhance-This-HiDiffusion-SDXL
from controlnet_aux import AnylineDetector
anyline = AnylineDetector.from_pretrained(
"TheMistoAI/MistoLine", filename="MTEED.pth", subfolder="Anyline"
).to("cuda")
source = Image.open("source.png")
result = anyline(source, detect_resolution=1280)Post
21381
Chameleon
Mixed-Modal Early-Fusion Foundation Models
Chameleon: Mixed-Modal Early-Fusion Foundation Models (2405.09818)
We present Chameleon, a family of early-fusion token-based mixed-modal models capable of understanding and generating images and text in any arbitrary sequence. We outline a stable training approach from inception, an alignment recipe, and an architectural parameterization tailored for the early-fusion, token-based, mixed-modal setting. The models are evaluated on a comprehensive range of tasks, including visual question answering, image captioning, text generation, image generation, and long-form mixed modal generation. Chameleon demonstrates broad and general capabilities, including state-of-the-art performance in image captioning tasks, outperforms Llama-2 in text-only tasks while being competitive with models such as Mixtral 8x7B and Gemini-Pro, and performs non-trivial image generation, all in a single model. It also matches or exceeds the performance of much larger models, including Gemini Pro and GPT-4V, according to human judgments on a new long-form mixed-modal generation evaluation, where either the prompt or outputs contain mixed sequences of both images and text. Chameleon marks a significant step forward in a unified modeling of full multimodal documents.
Mixed-Modal Early-Fusion Foundation Models
Chameleon: Mixed-Modal Early-Fusion Foundation Models (2405.09818)
We present Chameleon, a family of early-fusion token-based mixed-modal models capable of understanding and generating images and text in any arbitrary sequence. We outline a stable training approach from inception, an alignment recipe, and an architectural parameterization tailored for the early-fusion, token-based, mixed-modal setting. The models are evaluated on a comprehensive range of tasks, including visual question answering, image captioning, text generation, image generation, and long-form mixed modal generation. Chameleon demonstrates broad and general capabilities, including state-of-the-art performance in image captioning tasks, outperforms Llama-2 in text-only tasks while being competitive with models such as Mixtral 8x7B and Gemini-Pro, and performs non-trivial image generation, all in a single model. It also matches or exceeds the performance of much larger models, including Gemini Pro and GPT-4V, according to human judgments on a new long-form mixed-modal generation evaluation, where either the prompt or outputs contain mixed sequences of both images and text. Chameleon marks a significant step forward in a unified modeling of full multimodal documents.
Post
7052
At Google I/O 2024, we're collaborating with the Google Visual Blocks team (https://visualblocks.withgoogle.com) to release custom Hugging Face nodes. Visual Blocks for ML is a browser-based tool that allows users to create machine learning pipelines using a visual interface. We're launching nodes with Transformers.js, running models on the browser, as well as server-side nodes running Transformers pipeline tasks and LLMs using our hosted inference. With
@Xenova
@JasonMayes
You can learn more about it here https://huggingface.co/blog/radames/hugging-face-google-visual-blocks
Source-code for the custom nodes:
https://github.com/huggingface/visual-blocks-custom-components
You can learn more about it here https://huggingface.co/blog/radames/hugging-face-google-visual-blocks
Source-code for the custom nodes:
https://github.com/huggingface/visual-blocks-custom-components
Post
2165
AI-town now runs on Hugging Face Spaces with our API for LLMs and embeddings, including the open-source Convex backend, all in one container. Easy to duplicate and config on your own
Demo: radames/ai-town
Instructions: https://github.com/radames/ai-town-huggingface
Demo: radames/ai-town
Instructions: https://github.com/radames/ai-town-huggingface
- 9 replies
multimodalart
posted
an
update
over 1 year ago
Post
28583
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!