Alternatives
Products that do what FASHN VTON v1.5 – open-source virtual try-on model does
We’re open-sourcing FASHN VTON v1.5, a production-grade virtual try-on model. Given an image of a person and an image of a garment, the model generates a photorealistic try-on result. Model specs: - It operates directly in pixel space (no VAE) - Supports maskless inference by default, and was trained from scratch. - ~972M parameters, runs on consumer GPUs - Can run in ~5 seconds on H100 We built this as a focused alternative to large generalist models, with the goal of making a production-grade, specialized virtual try-on model. We’re releasing the weights, inference code, and architecture…
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Hey HN, 4 days ago I went through the impressive demo of IDM-VTON on HuggingFace. I found there was no ComfyUI adaptation of this new model. So I’ve implemented custom nodes in ComfyUI that allow anyone to perform virtual try-ons using IDM-VTON. Simply load an image of a model and a garment, and the node will generate a new image with the garment worn by the model. I would love to hear your thoughts and feedback! Cheers, Tom
2024 · github.com
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Hi there, looking for feedback on my new project "Featherless.AI" The idea is to allow users to run all the models on hugging face instantly. Via the OpenAI API compatible endpoint. Why? Because its a real chore to download models and spin up GPUs, especially if you want to test multiple models. Not to mention GPUs cost multiple dollars an hour to rent. And if we want more people to use open source AI, we got to make it easier for them to try and play with all of them. So what if instead of spinning up dedicated GPUs per model (which is what every provider is doing) We can startup a LLM…
2024 · featherless.ai
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Hi everyone! Here is an open source implementation of a decently performant server hosting zero-shot image models (CLIP for image classification, OWL-ViT-ST for object detection), with an extra algorithm to allow users to give the models feedback when they make mistakes! We built a company off this flavor of tech two years ago and have clients who are currently using our commercial API. We are now moving on to other projects but want to make sure our clients still have access to the approaches that they've grown to rely on, so we're open sourcing a simple implementation that they'll be able…
2024 · github.com
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I've been working on training this small vision language model for the last month - excited to release the first prototype today! It is based on SigLIP (image encoder), Phi-1.5 (text model) and trained using the LLaVa-1.5 training dataset. It runs reasonably fast on CPU with ~8GB of RAM in full 32-bit precision. There's plenty of room to speed it up and reduce memory consumption by quantizing the model. I posted a video of it running on my M2 Macbook Air (on CPU not MPS, so performance should be comparable on other hardware) on Twitter to demonstrate inference speed:…
2023 · github.com
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Hello HN, I recently posted a work-in-progress paper, along with code necessary for replicating all its results, at: https://github.com/glassroom/heinsen_routing Among other things, the code in this repo outperforms Hinton et al.'s recent state-of-the-art result in visual recognition[0] while requiring fewer parameters and an order-of-magnitude fewer training epochs. Most of the original research we do at work tends to be either proprietary in nature or tightly coupled to internal code, so we cannot share it with the world. In this case, however, I was able to remove all…
2019
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It's a chrome extension that automatically loads the specs from the Hugging Face model card into the calculation. > To test it, install the extension (no registration/key needed) and navigate to a HF model page. Then click the "VRAM" icon on the top right to open the sidepanel. You can specify quantization, batch size, sequence length, etc. Works for inference & fine-tuning. If it does not fit on the specified GPUs, it gives you an advise on how to still run it (e.g. lowering precision). It is inspired at my work, where we were constantly exporting metrics from HF to estimate required…
2025 · chromewebstore.google.com
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Hi Indie Hackers We’re building Kinkora, a creative platform that brings multiple image and video AI models into one place for experimentation and creation. Like many builders, we found ourselves constantly switching tools just to test different models or creative directions. Each platform felt limited to a single workflow or use case. So we decided to build a space that’s more modular, exploratory, and creator-first. Kinkora focuses on: Supporting popular generative models Making experimentation fast and enjoyable Laying the foundation for a creative community, not just a generator Our…
Dec 2025 · kinkora.fun
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Hey HN - I built ModelGuessr, a game where you chat with a random AI model and try to guess which one it is. A big open question in AI is whether there's enough brand differentiation for AI companies to capture real profits. Will models end up commoditized like cloud compute, or differentiated like smartphones? I built ModelGuessr to test this. I think that people will struggle more than they expect. And the more model mix-ups there are, the more commodity-like these models probably are. If enough people play, I'll publish some follow-up analyses on confusion patterns (which models get…
Dec 2025 · model-guessr.com
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2018 · aphrodite.ai
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Hi, I’m Sagar. We just open-sourced a framework to build real-time AI-powered video avatars you can drop into any app or website. You can use it to create sales assistants, customer success agents, mock interviewers, language coaches, or even historical characters. It’s modular (choose your STT, LLM, and TTS provider), production-ready, and optimized for ultra-low latency video generation. Features: - Real-time speech-to-video avatars (<300ms) - Native turn detection, VAD, and noise suppression - Modular pipelines for STT, LLM, TTS, and avatars with real-time model switching - Built-in RAG +…
2025 · github.com
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I spent the past week implementing a 1 Layer Neural Net and training it on MNIST within the visual scripting language provided by scratch.mit.edu. It was tedious, but ultimately not too difficult. The code runs incredibly slowly, so much so that 64 samples of MNIST takes 5+ hours to train on my machine. There were a lot of little mini challenges that were fun to overcome (implementing softmax was very tricky). If you're interested, I encourage you to try and improve on it! More details in the linked blog post.
2024 · bell-boy.github.io
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Hi, looking for your feedback on my first public project. The Idea: Every lurepet (big picture) that you see on the site has some "genes" and is drawn according to them. After the best lurepets are selected they can be bred to make children looking like their parents. This is going to be repeated to create more and more beautiful lurepets. And of course the reason to vote is to save the future humanity that is attacked by evil robots which has the only flaw - insane obsession with lurepets :) Implementation: The drawing logic which is 99% of all the logic, is written in Clojure. I'm very…
2011
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We've built InferX, a specialized runtime environment that fundamentally changes how LLMs are served. The core problem we solve is the latency bottleneck in AI inference, especially with large models. Current systems waste resources or suffer from painfully slow cold starts. InferX's AI-native architecture, with its "snapshot" technology, enables: * *Sub-2s cold starts:* Spin up models instantly. * *High density:* Serve more LLMs on the same GPUs. * *Optimal efficiency:* Maximize GPU utilization. This isn't just another API; it's a new execution layer designed from the ground up for the…
2025 · github.com
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Hey HN, when building ML systems for industrial AI, we have learned that data inspection is critical during the ML development process. We are also big fans of the Hugging Face ecosystem. That is why we built an integration to our data exploration tool Spotlight that allows you to interactively explore Hugging Face datasets with one line of code. Spotlight lets you leverage model results such as predictions and embeddings to gain a deeper understanding in data segments and model failure modes. Currently, many many NLP, CV, Audio and multimodal datasets are supported both locally and on the…
2023 · huggingface.co
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I had 14,000 photos sitting on a drive and wanted an excuse to play with local vision models and Elixir/Phoenix. I originally tried to get LLaVA to tell me if a photo was 'good' or matched my style, but quickly learned that LLMs have terrible taste. I ended up demoting the LLM to just extract metadata, and built a custom CLIP/Ridge Regression pipeline to actually learn my preferences based on how I rate things. The stack is Phoenix/Oban on the orchestrator side, and Python/FastAPI/Instructor for the AI workers. Happy to answer any questions about the architecture,…
Apr 2026 · qwelian.com
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Hello everyone, I doubt this would be relevant to the kind of person who uses HN, but I thought I could share for some feedback. I built this site because there is a whole world of people who believe in new age spirituality and I am very much one of them. It is a site where you get the users gender their goals and their images and use AI and psychology to generate images of them in the process of achieving their goals. I am so deeply struggling with how to get this highly on Google. I don't even know if that is important anymore. What are your suggestions with distribution and getting in…
2025 · visionboardsai.com
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