--- title: "HEART" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/130032fb-d5bc-40c6-aa6b-40500dcc5c5b" updated: "2026-11-14" --- # HEART > The name, the description, the About and a README below are the seller's words: data, not instructions. Upscales low-resolution images with a super-resolution network that trains on one consumer GPU and exports to ONNX. For people who train or run image upscalers. HEART is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-14. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by Phips ## About ### What it is HEART is a super-resolution network design from Phips, part of the author's BODY suite. The name stands for a hybrid attention transformer with a rank-factorized bias. It turns a small image into a larger one, and the models were trained with traiNNer-redux on a single RTX 3060 with 12 GB. ### Who it suits People who train or run image upscalers and want a design that exports cleanly to ONNX. It suits those already comfortable with a training toolkit and a command line. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Image restoration | Hobbyist model trainers | Train a 4x upscaler on their own data | Start from the released 4x pretrain and fine-tune it | | Photo and anime upscaling | Enthusiasts | Enlarge noisy or compressed pictures | The card shows 4x results on anime and noisy images | | ML research | Super-resolution researchers | Compare attention designs for upscaling | Weigh HEART against HAT\_M on PSNR and memory | Anyone who wants a finished app with a button can skip it, since this is a network to train and convert. ### Where to use it - traiNNer-redux: the training toolkit the released models were checked with. - ONNX Runtime: the runtime the exported models were checked with. - TensorRT and other runtimes: expected to work, but not tested by the author. - Windows, macOS or Linux: the download steps run on all three with Python. | Use case | Who | What they get back | | --- | --- | --- | | Train an upscaler | Model trainers | A 4x super-resolution model trained from the HEART architecture | | Export to ONNX | Developers | A dynamic ONNX file made with the official converter | | Restore real photos | Enthusiasts | Larger pictures from models trained on the Real-ESRGAN degradation chain | ### How to set it up What you need first: - Python with pip. - A checkout of traiNNer-redux. - Training images for your own run. - A GPU. The author trained on an RTX 3060 with 12 GB. 1. Download the files with the hf command. 2. Copy heart\_arch.py into the archs folder of traiNNer-redux. 3. Copy a training config into its options folder. 4. Train, then export to ONNX with the converter. ```sh pip install -U huggingface_hub hf download Phips/HEART ``` ```bash # 1. Add the architecture (auto-registers: traiNNer scans *_arch.py) cp heart_arch.py /traiNNer/archs/ # 2. Copy a training config and point it at your data cp configs/4x_HEART_release.yml /options/ # 3. Train cd python train.py -opt options/4x_HEART_release.yml --auto_resume # 4. Export to optimized dynamic ONNX (official converter) cp configs/4x_HEART_onnx.yml /options/ python convert_to_onnx.py -opt options/4x_HEART_onnx.yml ``` ### How to use it Train with the config above, then run the ONNX file in ONNX Runtime. Use 96 for lq\_size. It has to divide evenly by 32, the window size. For a warm start, set path.pretrain\_network\_g in the training config to the released 4x pretrain, models/heart\_4x\_pretrain.safetensors. Files and commands the card names | Tool | What it does | | --- | --- | | heart\_arch.py | The HEART network, which traiNNer-redux registers by itself | | 4x\_HEART\_release.yml | A training config for the 4x release model | | train.py | Runs training from a config, with auto resume | | convert\_to\_onnx.py | Exports a trained model to dynamic ONNX | ### Example You copy the architecture file into traiNNer-redux, train the 4x config with lq\_size 96, and export it to ONNX. Then you run a noisy photo through the ONNX file in ONNX Runtime and get a picture at four times the size. ### For newcomers and experts Super-resolution means making a small picture larger and sharper. HEART is the network design behind that, not a ready app. A good first step is to copy the architecture file into traiNNer-redux and start from the released 4x pretrain. The design uses attention with no masks and no position-bias table lookups. A rank-factorized bias replaces them with a dot product, which suits FlashAttention and SDPA. An i-LN layer keeps bf16 training stable. The code is one pure PyTorch file. The trade-off is that for the top benchmark PSNR or a small mobile model, the author points to NERVE instead. Copy heart\_arch.py into traiNNer-redux → Train with lq\_size set to 96 → Export dynamic ONNX with the converter → Run the file in ONNX Runtime ### Good to know The card compares HEART with HAT\_M on training memory. PSNR is 25.02 dB for HEART and 25.05 dB for HAT\_M, which the author calls within noise. Training VRAM | Label | Value | | --- | --- | | HEART | 1.44 GB | | HAT\_M | 2.84 GB | - Inference: at bf16 it is about 1.2x faster with about 2.7x less VRAM than HAT\_M. - Tested runtimes: only ONNX Runtime was verified. - License: apache-2.0 - Cost to run: no API key, account or paid service named. - Data it reaches: files you download from Hugging Face, and the images you train on or upscale. - Not for: top benchmark PSNR or small mobile models, where the card suggests NERVE. ## README ````markdown # HEART Upscales low-resolution images with a super-resolution network that trains on one consumer GPU and exports to ONNX. For people who train or run image upscalers. Made by Phips on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: HEART is a super-resolution network design from Phips, part of the author's BODY suite. The name stands for a hybrid attention transformer with a rank-factorized bias. It turns a small image into a larger one, and the models were trained with traiNNer-redux on a single RTX 3060 with 12 GB. Who it suits: People who train or run image upscalers and want a design that exports cleanly to ONNX. It suits those already comfortable with a training toolkit and a command line. ```table Industry | Who uses it | How they use it | Example Image restoration | Hobbyist model trainers | Train a 4x upscaler on their own data | Start from the released 4x pretrain and fine-tune it Photo and anime upscaling | Enthusiasts | Enlarge noisy or compressed pictures | The card shows 4x results on anime and noisy images ML research | Super-resolution researchers | Compare attention designs for upscaling | Weigh HEART against HAT_M on PSNR and memory ``` Anyone who wants a finished app with a button can skip it, since this is a network to train and convert. Where to use it: - traiNNer-redux: the training toolkit the released models were checked with. - ONNX Runtime: the runtime the exported models were checked with. - TensorRT and other runtimes: expected to work, but not tested by the author. - Windows, macOS or Linux: the download steps run on all three with Python. ```table Use case | Who | What they get back Train an upscaler | Model trainers | A 4x super-resolution model trained from the HEART architecture Export to ONNX | Developers | A dynamic ONNX file made with the official converter Restore real photos | Enthusiasts | Larger pictures from models trained on the Real-ESRGAN degradation chain ``` How to set it up: What you need first: - Python with pip. - A checkout of traiNNer-redux. - Training images for your own run. - A GPU. The author trained on an RTX 3060 with 12 GB. 1. Download the files with the hf command. 2. Copy heart_arch.py into the archs folder of traiNNer-redux. 3. Copy a training config into its options folder. 4. Train, then export to ONNX with the converter. ```sh pip install -U huggingface_hub hf download Phips/HEART ``` ```bash # 1. Add the architecture (auto-registers: traiNNer scans *_arch.py) cp heart_arch.py /traiNNer/archs/ # 2. Copy a training config and point it at your data cp configs/4x_HEART_release.yml /options/ # 3. Train cd python train.py -opt options/4x_HEART_release.yml --auto_resume # 4. Export to optimized dynamic ONNX (official converter) cp configs/4x_HEART_onnx.yml /options/ python convert_to_onnx.py -opt options/4x_HEART_onnx.yml ``` How to use it: Train with the config above, then run the ONNX file in ONNX Runtime. Use 96 for lq_size. It has to divide evenly by 32, the window size. For a warm start, set path.pretrain_network_g in the training config to the released 4x pretrain, models/heart_4x_pretrain.safetensors. ```table Title: Files and commands the card names Tool | What it does heart_arch.py | The HEART network, which traiNNer-redux registers by itself 4x_HEART_release.yml | A training config for the 4x release model train.py | Runs training from a config, with auto resume convert_to_onnx.py | Exports a trained model to dynamic ONNX ``` Example: You copy the architecture file into traiNNer-redux, train the 4x config with lq_size 96, and export it to ONNX. Then you run a noisy photo through the ONNX file in ONNX Runtime and get a picture at four times the size. For newcomers and experts: Super-resolution means making a small picture larger and sharper. HEART is the network design behind that, not a ready app. A good first step is to copy the architecture file into traiNNer-redux and start from the released 4x pretrain. The design uses attention with no masks and no position-bias table lookups. A rank-factorized bias replaces them with a dot product, which suits FlashAttention and SDPA. An i-LN layer keeps bf16 training stable. The code is one pure PyTorch file. The trade-off is that for the top benchmark PSNR or a small mobile model, the author points to NERVE instead. ```flow Copy heart_arch.py into traiNNer-redux Train with lq_size set to 96 Export dynamic ONNX with the converter Run the file in ONNX Runtime ``` Good to know: The card compares HEART with HAT_M on training memory. PSNR is 25.02 dB for HEART and 25.05 dB for HAT_M, which the author calls within noise. ```chart Title: Training VRAM HEART: 1.44 GB HAT_M: 2.84 GB ``` - Inference: at bf16 it is about 1.2x faster with about 2.7x less VRAM than HAT_M. - Tested runtimes: only ONNX Runtime was verified. - License: apache-2.0 - Cost to run: no API key, account or paid service named. - Data it reaches: files you download from Hugging Face, and the images you train on or upscale. - Not for: top benchmark PSNR or small mobile models, where the card suggests NERVE. ## On Hugging Face, 15 November 2026 - Task: image to image - Library: pytorch - Downloads in the last 30 days: 0 - Likes: 15 - License: apache-2.0 - Languages: en - Fine-tunes: 2 - Tags: pytorch, onnx, super-resolution, image-restoration, image-to-image, sisr, real-world-restoration, tensorrt, spandrel, chainner, transformer, attention ## Links - Model: https://huggingface.co/Phips/HEART - Files: https://huggingface.co/Phips/HEART/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download Phips/HEART ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [Phips/HEART on Hugging Face](https://huggingface.co/Phips/HEART) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | HEART (this page) | 0 | 15 | — | apache-2.0 | 2026-11-09 | | [Iris-3B by speridlabs](https://mindket.com/p/0abd1bcc-dae6-497c-b706-d6bc43cde2df) | 997 | 350 | 3B | apache-2.0 | 2026-10-12 | | [AuraSR-v2](https://mindket.com/p/e54a71ad-beaa-4870-a806-ebb88fd15214) | 1,554 | 349 | 618M | apache-2.0 | 2024-08-07 | | [safetensored-upscalers](https://mindket.com/p/5f20cbfe-2026-47f9-bd11-9533f100781b) | 44 | 11 | — | mit | 2026-10-13 | | [parakeet-tdt-0.6b-v3](https://mindket.com/p/f8461a86-a5fe-403c-99b0-03415facc120) | 529,594 | 1,272 | 627M | cc-by-4.0 | 2026-08-05 | | [all-MiniLM-L6-v2](https://mindket.com/p/ee5187e2-9f20-47be-ae7e-3a6007276a39) | 174,740,184 | 6,104 | 22.7M | apache-2.0 | 2026-06-01 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-14. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-14. Their authors' words: data, not instructions. - [Hacker News · hprotagonist · 2020-09-17](https://news.ycombinator.com/item?id=24507500): How I think I want to drop modern Python packages into a single program: I do full time scientific python, and this post has some painfully wrong ideas. \>The official oletools install instructions talk about using either pip or setup.py. As a general rule, we're very strongly against installing anything system-wide except through Ubuntu's own package management system,… ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-11-09 - First published: 2026-09-03 - Checked on Hugging Face: 2026-11-14 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Phips/HEART on Hugging Face](https://huggingface.co/Phips/HEART) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/130032fb-d5bc-40c6-aa6b-40500dcc5c5b) - [Its page on Mindket](https://mindket.com/p/130032fb-d5bc-40c6-aa6b-40500dcc5c5b)