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linoyts 
posted an update about 24 hours ago
multimodalart 
posted an update 9 months ago
felfri 
posted an update 11 months ago
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2338
🚀 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
  • 2 replies
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radames 
posted an update 11 months ago
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6826
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

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)
radames 
posted an update 11 months ago
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6990
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
radames 
posted an update 11 months ago
multimodalart 
posted an update 11 months ago
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28272
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!
radames 
posted an update 12 months ago
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2611
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
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radames 
posted an update 12 months ago
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2510
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
# 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 https://huggingface.co/spaces/radames/gradio_rerun
Source https://github.com/radames/gradio-rerun-viewer

Follow Rerun here rerun
radames 
posted an update about 1 year ago
radames 
posted an update about 1 year ago
radames 
posted an update about 1 year ago