Hermes 3 Technical Report
Paper
•
2408.11857
•
Published
•
56
Lilac AI
company
Verified
AI & ML interests
Search, structure and analyze your unstructured data with AI
teknium
authored
2
papers
4 months ago
emozilla
authored
a
paper
4 months ago
emozilla
authored
a
paper
about 1 year ago
emozilla
authored
a
paper
over 1 year ago
Post
7892
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
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
2166
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
smilkov
updated
a
Space
over 1 year ago
Post
2657
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
2541
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 https://huggingface.co/spaces/radames/gradio_rerun
Source https://github.com/radames/gradio-rerun-viewer
Follow Rerun here
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 https://huggingface.co/spaces/radames/gradio_rerun
Source https://github.com/radames/gradio-rerun-viewer
Follow Rerun here
Post
2480
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
2214
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
Post
3874
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
2809
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
3904
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
nsthorat-lilac
updated
a
Space
almost 2 years ago
nsthorat-lilac
updated
3
datasets
almost 2 years ago