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Radamés Ajna

radames

https://observablehq.com/@radames

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reacted to cbensimon's post with 👍 2 days ago

🚀 ZeroGPU `medium` size is now available as a power-user feature Nothing too fancy for now—ZeroGPU Spaces still default to `large` (70GB VRAM)—but this paves the way for: - 💰 size-based quotas / pricing (`medium` will offer significantly more usage than `large`) - 🦣 the upcoming `xlarge` size (141GB VRAM) You can as of now control GPU size via a Space variable. Accepted values: - `auto` (future default) - `medium` - `large` (current default) The auto mode checks total CUDA tensor size during startup: - More than 30GB → `large` - Otherwise → `medium`

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reacted to cbensimon's post with 👍👀🤗🚀🔥 2 days ago

Post

5654

🚀 ZeroGPU medium size is now available as a power-user feature

Nothing too fancy for now—ZeroGPU Spaces still default to large (70GB VRAM)—but this paves the way for:
- 💰 size-based quotas / pricing (medium will offer significantly more usage than large)
- 🦣 the upcoming xlarge size (141GB VRAM)

You can as of now control GPU size via a Space variable. Accepted values:
- auto (future default)
- medium
- large (current default)

The auto mode checks total CUDA tensor size during startup:
- More than 30GB → large
- Otherwise → medium

3 replies

reacted to prithivMLmods's post with ❤️ 5 months ago

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6554

Sketchify 😉🎨

+ strangerzonehf/Flux-Sketch-Smudge-LoRA
+ strangerzonehf/Flux-Sketch-Sized-LoRA
+ strangerzonehf/Sketch-Paint

- strangerzonehf/sketch-fav-675ba869c7ceaec7e652ee1c

reacted to ucsahin's post with 🔥🚀 7 months ago

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Florence-2 has a great capability of detecting various objects in a zero-shot setting with the task prompt "<OD>". However, if you want to detect specific objects that the base model is not able to in its current form, you can easily finetune it for this particular task. Below I show how to finetune the model to detect tables in a given image, but a similar process can be applied to detect any objects. Thanks to @andito , @merve , and @SkalskiP for sharing the fix for finetuning the Florence-2 model. Please also check their great blog post at https://huggingface.co/blog/finetune-florence2.

Colab notebook: https://colab.research.google.com/drive/1Y8GVjwzBIgfmfD3ZypDX5H1JA_VG0YDL?usp=sharing
Finetuned model: ucsahin/Florence-2-large-TableDetection

6 replies

reacted to prithivMLmods's post with ❤️ 7 months ago

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New Style, New Mix, New Drop 🧤

🧨Flux LoRA DLC: prithivMLmods/FLUX-LoRA-DLC

🎆Glowing-Body: prithivMLmods/Glowing-Body-Flux-LoRA
🎆Electric-Blue: prithivMLmods/Electric-Blue-Flux-LoRA
🎆Intense-Red: prithivMLmods/Intense-Red-Flux-LoRA
🎆Clouds-Illusion: prithivMLmods/Clouds-Illusion-Flux-LoRA
🎆Digital-Yellow: prithivMLmods/Digital-Yellow-Flux-LoRA

🧨Flux LoRA Collection: prithivMLmods/flux-lora-collections-66dd5908be2206cfaa8519be

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@prithivMLmods

reacted to gokaygokay's post with 🔥 10 months ago

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I've built a space for creating prompts for FLUX

gokaygokay/FLUX-Prompt-Generator

You can create long prompts from images or simple words. Enhance your short prompts with prompt enhancer. You can configure various settings such as artform, photo type, character details, scene details, style, and artist to create tailored prompts.

And you can combine all of them with custom prompts using llms (Mixtral, Mistral, Llama 3, and Mistral-Nemo).

The UI is a bit complex, but it includes almost everything you need. Choosing random option is the most fun!

And i've created some other spaces for using FLUX models with captioners and enhancers.

- gokaygokay/FLUX.1-dev-with-Captioner

4 replies

reacted to sayakpaul's post with 🔥 10 months ago

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Flux.1-Dev like images but in fewer steps.

Merging code (very simple), inference code, merged params: sayakpaul/FLUX.1-merged

Enjoy the Monday 🤗

4 replies

reacted to sayakpaul's post with ❤️ 11 months ago

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What is your favorite part of our Diffusers integration of Stable Diffusion 3?

My personal favorite is the ability to run it on a variety of different GPUs with minimal code changes.

Learn more about them here:
https://huggingface.co/blog/sd3

reacted to sayakpaul's post with 🔥 11 months ago

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2248

Were you aware that we have a dedicated guide on different prompting mechanisms to improve the image generation quality? 🧨

Takes you through simple prompt engineering, prompt weighting, prompt enhancement using GPT-2, and more.

Check out the guide here 🦯
https://huggingface.co/docs/diffusers/main/en/using-diffusers/weighted_prompts

1 reply

reacted to merve's post with 🤯👀 11 months ago

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3616

EPFL and Apple (at @EPFL-VILAB ) just released 4M-21: single any-to-any model that can do anything from text-to-image generation to generating depth masks! 🙀
4M is a multimodal training framework introduced by Apple and EPFL.
Resulting model takes image and text and output image and text 🤩

Models: EPFL-VILAB/4m-models-660193abe3faf4b4d98a2742
Demo: EPFL-VILAB/4M
Paper: 4M-21: An Any-to-Any Vision Model for Tens of Tasks and Modalities (2406.09406)

This model consists of transformer encoder and decoder, where the key to multimodality lies in input and output data:

input and output tokens are decoded to generate bounding boxes, generated image's pixels, captions and more!

This model also learnt to generate canny maps, SAM edges and other things for steerable text-to-image generation 🖼️

The authors only added image-to-all capabilities for the demo, but you can try to use this model for text-to-image generation as well ☺️

reacted to merve's post with ❤️🔥 11 months ago

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I love Depth Anything V2 😍
It’s Depth Anything, but scaled with both larger teacher model and a gigantic dataset!

Here's a small TLDR of paper with a lot of findings, experiments and more.
I have also created a collection that has the models, the dataset, the demo and CoreML converted model 😚 merve/depth-anything-v2-release-6671902e798cd404513ffbf5

The authors have analyzed Marigold, a diffusion based model against Depth Anything and found out what’s up with using synthetic images vs real images for MDE:

🔖 Real data has a lot of label noise, inaccurate depth maps (caused by depth sensors missing transparent objects etc) and there are many details overlooked

🔖 Synthetic data have more precise and detailed depth labels and they are truly ground-truth, but there’s a distribution shift between real and synthetic images, and they have restricted scene coverage

The authors train different image encoders only on synthetic images and find out unless the encoder is very large the model can’t generalize well (but large models generalize inherently anyway) 🧐
But they still fail encountering real images that have wide distribution in labels (e.g. diverse instances of objects) 🥲

Depth Anything v2 framework is to..

🦖 Train a teacher model based on DINOv2-G based on 595K synthetic images
🏷️ Label 62M real images using teacher model
🦕 Train a student model using the real images labelled by teacher
Result: 10x faster and more accurate than Marigold!

The authors also construct a new benchmark called DA-2K that is less noisy, highly detailed and more diverse!

reacted to m-ric's post with 👍 11 months ago

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💰 𝗚𝗲𝘁 𝘁𝗵𝗲 𝗽𝗿𝗶𝗰𝗲 𝗼𝗳 𝗮𝗻𝘆 𝗟𝗟𝗠 𝗔𝗣𝗜 𝗿𝗲𝗾𝘂𝗲𝘀𝘁 ⇒ 𝘁𝗼𝗸𝗲𝗻𝗰𝗼𝘀𝘁

I've just found out about 𝙰𝚐𝚎𝚗𝚝𝙾𝚙𝚜-𝙰𝙸/𝚝𝚘𝚔𝚎𝚗𝚌𝚘𝚜𝚝 (https://github.com/AgentOps-AI/tokencost).
𝗧𝗵𝗶𝘀 𝗹𝗶𝗯𝗿𝗮𝗿𝘆 𝗴𝗶𝘃𝗲𝘀 𝘆𝗼𝘂 𝘁𝗵𝗲 𝗽𝗿𝗶𝗰𝗲 𝗼𝗳 𝘆𝗼𝘂𝗿 𝗰𝗮𝗹𝗹𝘀 𝘁𝗼 𝗮𝗻𝘆 𝗟𝗟𝗠 𝗔𝗣𝗜: OpenAI, Anthropic, Mistral, AWS or Databricks...

For any model, you can use as input either string prompts or messages, and get as outputs either the price or token count.

Congrats to the AgentOps-AI team: this will be very useful when trying to get a ballpark estimate of a project's price, to compare APIs, or for precise monitoring of usage!

✨ Daily reminder: 𝗿𝘂𝗻𝗻𝗶𝗻𝗴 𝗮𝗻 𝗔𝟭𝟬𝟬 𝗰𝗼𝘀𝘁𝘀 𝘆𝗼𝘂 𝗲𝘅𝗮𝗰𝘁𝗹𝘆 $𝟬.𝟬𝟬/𝗵𝗼𝘂𝗿 (or 0.00€ in current exchange rates) on a HF space with ZeroGPU!
Learn more on ZeroGPU 👉 https://www.datacenterdynamics.com/en/news/hugging-face-launches-zerogpu-project-to-democratize-ai-gives-away-10-million-worth-of-compute/

5 replies

reacted to flozi00's post with ❤️ 11 months ago

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🌟 Progress in the German FineWeb edu reproduction 🌟

We're delighted to share the launch of our new Data Quality Classification Model, designed specifically for evaluating educational content in German. This tool uses advanced machine learning techniques to assess texts across all educational levels, from primary school to university.

🔍 Inspired by Huggingface's fine web edu dataset, we've worked hard to refine data classification methods ensuring educators and learners access top-quality resources.
We're excited about the future as we continue improving our models and expanding our datasets.

Access the model here: pL-Community/GermanEduScorer-Qwen2-1.5b

🙏 A huge thank you to David and Daryoush from Vago Solutions; Björn and Jan from Ellamind / DiscoResearch for their expert insights throughout this project. Your support has been crucial.
This project was made possible by the support of PrimeLine AI.

2 replies

replied to dvilasuero's post 12 months ago

Congrats and welcome to the team!