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chenfengx authored a paper 14 days ago

Maximizing Alignment with Minimal Feedback: Efficiently Learning Rewards for Visuomotor Robot Policy Alignment

chenfengx authored a paper 4 months ago

Law of Vision Representation in MLLMs

chenfengx authored a paper 6 months ago

Immiscible Diffusion: Accelerating Diffusion Training with Noise Assignment

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Shizuku-AI's activity

chenfengx

authored a paper 14 days ago

Maximizing Alignment with Minimal Feedback: Efficiently Learning Rewards for Visuomotor Robot Policy Alignment

Paper • 2412.04835 • Published 19 days ago • 2

chenfengx

authored a paper 4 months ago

Law of Vision Representation in MLLMs

Paper • 2408.16357 • Published Aug 29 • 92

chenfengx

authored a paper 6 months ago

Immiscible Diffusion: Accelerating Diffusion Training with Noise Assignment

Paper • 2406.12303 • Published Jun 18 • 4

chenfengx

authored a paper 7 months ago

Looking Backward: Streaming Video-to-Video Translation with Feature Banks

Paper • 2405.15757 • Published May 24 • 14

radames

posted an update 7 months ago

Post

5695

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 7 months ago

Post

6468

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 7 months ago

Post

2005

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

9 replies

radames

posted an update 8 months ago

Post

2521

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

1 reply

radames

posted an update 8 months ago

Post

2446

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 radames/gradio_rerun
Source https://github.com/radames/gradio-rerun-viewer

Follow Rerun here https://huggingface.co/rerun

radames

posted an update 8 months ago

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

radames

posted an update 8 months ago

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

radames

posted an update 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.

pip install gradio_huggingfacehub_search

You can see it in action here. arcee-ai/mergekit-config-generator

And learn how to use it here radames/gradio_huggingfacehub_search

radames

posted an update 9 months ago

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

posted an update 9 months ago

Post

3701

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