LLMs Lost in Translation: M-ALERT uncovers Cross-Linguistic Safety Gaps
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mbrack
authored
a
paper
7 days ago
felfri
authored
a
paper
7 days ago
PSaiml
authored
a
paper
7 days ago
multimodalart
posted
an
update
5 months ago
Post
20299
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
- 2 replies
mbrack
authored
a
paper
6 months ago
felfri
updated
a
Space
6 months ago
Post
2275
🚀 Excited to announce the release of our new research paper, "LLAVAGUARD: VLM-based Safeguards for Vision Dataset Curation and Safety Assessment"!
In this work, we introduce LLAVAGUARD, a family of cutting-edge Vision-Language Model (VLM) judges designed to enhance the safety and integrity of vision datasets and generative models. Our approach leverages flexible policies for assessing safety in diverse settings. This context awareness ensures robust data curation and model safeguarding alongside comprehensive safety assessments, setting a new standard for vision datasets and models. We provide three versions (7B, 13B, and 34B) and our data, see below. This achievement wouldn't have been possible without the incredible teamwork and dedication of my great colleagues @LukasHug , @PSaiml , @mbrack . 🙏 Together, we've pushed the boundaries of what’s possible at the intersection of large generative models and safety.
🔍 Dive into our paper to explore:
Innovative methodologies for dataset curation and model safeguarding.
State-of-the-art safety assessments.
Practical implications for AI development and deployment.
Find more at AIML-TUDA/llavaguard-665b42e89803408ee8ec1086 and https://ml-research.github.io/human-centered-genai/projects/llavaguard/index.html
In this work, we introduce LLAVAGUARD, a family of cutting-edge Vision-Language Model (VLM) judges designed to enhance the safety and integrity of vision datasets and generative models. Our approach leverages flexible policies for assessing safety in diverse settings. This context awareness ensures robust data curation and model safeguarding alongside comprehensive safety assessments, setting a new standard for vision datasets and models. We provide three versions (7B, 13B, and 34B) and our data, see below. This achievement wouldn't have been possible without the incredible teamwork and dedication of my great colleagues @LukasHug , @PSaiml , @mbrack . 🙏 Together, we've pushed the boundaries of what’s possible at the intersection of large generative models and safety.
🔍 Dive into our paper to explore:
Innovative methodologies for dataset curation and model safeguarding.
State-of-the-art safety assessments.
Practical implications for AI development and deployment.
Find more at AIML-TUDA/llavaguard-665b42e89803408ee8ec1086 and https://ml-research.github.io/human-centered-genai/projects/llavaguard/index.html
- 2 replies
Post
5748
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
6520
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
2008
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
8 months ago
Post
25664
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
2523
HiDiffusion SDXL now supports Image-to-Image, so I've created an "Enhance This" version using the latest ControlNet Line Art model called MistoLine. It's faster than DemoFusion
Demo: radames/Enhance-This-HiDiffusion-SDXL
Older version based on DemoFusion radames/Enhance-This-DemoFusion-SDXL
New Controlnet SDXL Controls Every Line TheMistoAI/MistoLine
HiDiffusion is compatible with diffusers and support many SD models - https://github.com/megvii-research/HiDiffusion
Demo: radames/Enhance-This-HiDiffusion-SDXL
Older version based on DemoFusion radames/Enhance-This-DemoFusion-SDXL
New Controlnet SDXL Controls Every Line TheMistoAI/MistoLine
HiDiffusion is compatible with diffusers and support many SD models - https://github.com/megvii-research/HiDiffusion
- 1 reply
Post
2447
I've built a custom component that integrates Rerun web viewer with Gradio, making it easier to share your demos as Gradio apps.
Basic snippet
More details here radames/gradio_rerun
Source https://github.com/radames/gradio-rerun-viewer
Follow Rerun here https://huggingface.co/rerun
Basic snippet
# pip install gradio_rerun gradio
import gradio as gr
from gradio_rerun import Rerun
gr.Interface(
inputs=gr.File(file_count="multiple", type="filepath"),
outputs=Rerun(height=900),
fn=lambda file_path: file_path,
).launch()More details here radames/gradio_rerun
Source https://github.com/radames/gradio-rerun-viewer
Follow Rerun here https://huggingface.co/rerun
Post
2459
ByteDance released new distillation technique Hyper-SD (
ByteDance/Hyper-SD) for efficient image generation.
Here a few demos:
Official:
Hyper-SDXL-1Step-T2I ByteDance/Hyper-SDXL-1Step-T2I
Hyper-SD15-Scribble ByteDance/Hyper-SD15-Scribble
Unofficial Demos: InstantStyle + Hyper SD1.5 (not great but super fast) radames/InstantStyle-Hyper-SD
InstantStyle + Hyper SDXL radames/InstantStyle-Hyper-SDXL
Here a few demos:
Official:
Hyper-SDXL-1Step-T2I ByteDance/Hyper-SDXL-1Step-T2I
Hyper-SD15-Scribble ByteDance/Hyper-SD15-Scribble
Unofficial Demos: InstantStyle + Hyper SD1.5 (not great but super fast) radames/InstantStyle-Hyper-SD
InstantStyle + Hyper SDXL radames/InstantStyle-Hyper-SDXL
Post
2199
InstantStyle works with the 2-step SDXL-Lightning distilled model, reducing generation time from ~20s to ~9s!
In a big related update, as of today, Diffusers@main supports InstantStyle. I'm looking forward to playing with it!
https://github.com/huggingface/diffusers/pull/7668
radames/InstantStyle-SDXL-Lightning
ByteDance/SDXL-Lightning
In a big related update, as of today, Diffusers@main supports InstantStyle. I'm looking forward to playing with it!
https://github.com/huggingface/diffusers/pull/7668
radames/InstantStyle-SDXL-Lightning
ByteDance/SDXL-Lightning
PSaiml
authored
a
paper
9 months ago
Post
3855
Here's a utility component for integrating your Gradio app with Hugging Face. This custom component enables you to search for models, spaces, datasets, and users.
You can see it in action here. arcee-ai/mergekit-config-generator
And learn how to use it here radames/gradio_huggingfacehub_search
pip install gradio_huggingfacehub_searchYou can see it in action here. arcee-ai/mergekit-config-generator
And learn how to use it here radames/gradio_huggingfacehub_search
Post
2758
Following up on
@vikhyatk
's Moondream2 update and
@santiagomed
's implementation on Candle, I quickly put togheter the WASM module so that you could try running the ~1.5GB quantized model in the browser. Perhaps the next step is to rewrite it using https://github.com/huggingface/ratchet and run it even faster with WebGPU,
@FL33TW00D-HF
.
radames/Candle-Moondream-2
ps: I have a collection of all Candle WASM demos here radames/candle-wasm-examples-650898dee13ff96230ce3e1f
radames/Candle-Moondream-2
ps: I have a collection of all Candle WASM demos here radames/candle-wasm-examples-650898dee13ff96230ce3e1f
Post
3704
Testing new pix2pix-Turbo in real-time, very interesting GAN architecture that leverages SD-Turbo model. Here I'm using edge2image LoRA single-step inference 🤯
It's very interesting how ControlNet Canny quality is comparable, but in a single step. Looking forward to when they release the code: https://github.com/GaParmar/img2img-turbo/issues/1
I've been keeping a list of fast diffusion model pipelines together with this real-time websocket app. Have a look if you want to test it locally, or check out the demo here on Spaces.
radames/real-time-pix2pix-turbo
Github app:
https://github.com/radames/Real-Time-Latent-Consistency-Model/
You can also check the authors img2img sketch model here
gparmar/img2img-turbo-sketch
Refs:
One-Step Image Translation with Text-to-Image Models (2403.12036)
cc @gparmar @junyanz
It's very interesting how ControlNet Canny quality is comparable, but in a single step. Looking forward to when they release the code: https://github.com/GaParmar/img2img-turbo/issues/1
I've been keeping a list of fast diffusion model pipelines together with this real-time websocket app. Have a look if you want to test it locally, or check out the demo here on Spaces.
radames/real-time-pix2pix-turbo
Github app:
https://github.com/radames/Real-Time-Latent-Consistency-Model/
You can also check the authors img2img sketch model here
gparmar/img2img-turbo-sketch
Refs:
One-Step Image Translation with Text-to-Image Models (2403.12036)
cc @gparmar @junyanz
multimodalart
posted
an
update
10 months 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