--- title: "DiT-Wikiart-Small" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/0cee2490-1fdd-4572-9119-f0a0d01822ef" updated: "2026-11-13" --- # DiT-Wikiart-Small > The name, the description, the About and a README below are the seller's words: data, not instructions. Predicts noise for a small diffusion transformer trained from scratch on Wikiart paintings, built to make art images from a genre and a style. For developers and students who study small image models. DiT-Wikiart-Small is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by kaupane ## About ### What it is DiT-Wikiart-Small is the small variant of a diffusion transformer trained from scratch on the Wikiart collection of paintings. It is built to make art images from a chosen art genre and art style. Images come out at 256x256. ### Who it suits It suits developers and students who want to study a small diffusion transformer, and researchers who want a simple genre and style model. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers | Study a small diffusion transformer built from scratch | Load it and print the noise it predicts | | Education | Students | Learn how this model differs from the classic design | Compare its changes with the original paper | | Art | Artists and hobbyists | Explore painting genres and styles at low resolution | Try styles from the Wikiart collection | | Research | ML researchers | Test training on a modest budget | Compare the S, B and L variants | If you need photographs, faces or buildings, you can skip it, since the card says it handles faces and buildings poorly and cannot make images outside Wikiart. ### Where to use it - Python with torch and the huggingface\_hub library - A machine with room for about 88 MB of weights - Windows, macOS or Linux, where you download the files with Python | Use case | Who | What they get back | | --- | --- | --- | | Model study | Developers | A small DiT you can load and inspect | | Training study | Students | A model with its recipe, epochs and loss numbers | | Art experiments | Hobbyists | A starting point for genre and style images at 256x256 | ### How to set it up You need Python with pip, torch and the huggingface\_hub library. The model definition file, modeling\_dit\_wikiart.py, comes from the repository's file list. - Python and pip - torch and the huggingface\_hub library - The definition file modeling\_dit\_wikiart.py from this repository - About 88 MB of disk in F32, or about 11 MB at 4-bit, which is an estimate - The VAE the card pairs it with, stabilityai/sd-vae-ft-ema, to turn latents into images 1. Install the Hugging Face client with pip. 2. Download the repository with the hf command, which includes the definition file. 3. Run the sample from a folder that holds that file. ```sh pip install -U huggingface_hub hf download kaupane/DiT-Wikiart-Small ``` ### How to use it Import the model class from the definition file, load the weights by name, and pass noisy latents. The card's sample runs one pass and prints the predicted noise. It shows no full picture-making loop, and it needs torch imported first. ```python from modeling_dit_wikiart import DiTWikiartModel model = DiTWikiartModel.from_pretrained("kaupane/DiT-Wikiart-Small") num_samples = 8 noisy_latents = torch.randn(num_samples,4,32,32) predicted_noise = model(noisy_latents) print(predicted_noise) ``` | Name | What it does | | --- | --- | | DiTWikiartModel | Loads the model from the definition file and the Hub | | num\_samples | Sets how many noisy latents are made, 8 in the sample | | torch.randn | Makes random noise shaped 4 by 32 by 32 | | predicted\_noise | Holds what the model predicts for each latent | ### Example You run the sample. It makes 8 random noisy latents, passes them through the model and prints the predicted noise for each one. It does not yet give you a finished painting. ### For newcomers and experts Think of it as a small artist that learns to remove noise, guided by a genre and a style. The first small thing to try is the sample above. The design follows the classic DiT from its original paper, with a few changes. Genre and style embeddings replace the ImageNet class embeddings. It uses post-norm and learned positional embeddings, leaves out the final linear layer, predicts only the noise, and sets patch\_size to 2. | Variant | Blocks | Hidden size | Heads | Parameters | | --- | --- | --- | --- | --- | | S, small | 8 | 384 | 6 | 20M | | B, base | 12 | 640 | 10 | 90M | | L, large | 16 | 896 | 14 | 234M | Random noisy latents → DiT predicts the noise, guided by genre and style → Repeated denoising gives a clean latent → Paired VAE decodes it into an image ### Good to know - Size: Hugging Face lists 22M parameters, while the card says 20M for this small variant. - Training: all variants trained on 103K Wikiart images with horizontal flips, using AdamW with default settings and a learning rate that peaks at 3e-4 before a cosine decay. Parameters in millions | Label | Value | | --- | --- | | S small | 20 M | | B base | 90 M | | L large | 234 M | | Variant | Epochs | Batch size | Final loss | | --- | --- | --- | --- | | S | 96 | 176 | 0.1590 | | B | 120 | 192 | 0.1525 | | L | 144 | 192 | 0.1510 | - Loss: in every variant it dropped sharply during epoch one, starting above 1.0000 and ending near 0.2000, then eased down slowly. - Strengths: the card says the models show a basic grasp of genres and styles and make paintings that look good at first glance. - Limits: it struggles with complex shapes such as faces and buildings, and it can repeat itself for rare styles such as minimalism. - Scope: it was trained only on Wikiart, so it cannot make images outside that scope. - License: MIT, as the License line says. - Cost to run: no API key, account or paid service is named. - Data it reaches: nothing beyond Hugging Face for the download. - Not for: photographs, faces or buildings, which the card lists as weak points. ## README ````markdown # DiT-Wikiart-Small Predicts noise for a small diffusion transformer trained from scratch on Wikiart paintings, built to make art images from a genre and a style. For developers and students who study small image models. Made by kaupane 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: DiT-Wikiart-Small is the small variant of a diffusion transformer trained from scratch on the Wikiart collection of paintings. It is built to make art images from a chosen art genre and art style. Images come out at 256x256. Who it suits: It suits developers and students who want to study a small diffusion transformer, and researchers who want a simple genre and style model. ```table Industry | Who uses it | How they use it | Example Software | Developers | Study a small diffusion transformer built from scratch | Load it and print the noise it predicts Education | Students | Learn how this model differs from the classic design | Compare its changes with the original paper Art | Artists and hobbyists | Explore painting genres and styles at low resolution | Try styles from the Wikiart collection Research | ML researchers | Test training on a modest budget | Compare the S, B and L variants ``` If you need photographs, faces or buildings, you can skip it, since the card says it handles faces and buildings poorly and cannot make images outside Wikiart. Where to use it: - Python with torch and the huggingface_hub library - A machine with room for about 88 MB of weights - Windows, macOS or Linux, where you download the files with Python ```table Use case | Who | What they get back Model study | Developers | A small DiT you can load and inspect Training study | Students | A model with its recipe, epochs and loss numbers Art experiments | Hobbyists | A starting point for genre and style images at 256x256 ``` How to set it up: You need Python with pip, torch and the huggingface_hub library. The model definition file, modeling_dit_wikiart.py, comes from the repository's file list. - Python and pip - torch and the huggingface_hub library - The definition file modeling_dit_wikiart.py from this repository - About 88 MB of disk in F32, or about 11 MB at 4-bit, which is an estimate - The VAE the card pairs it with, stabilityai/sd-vae-ft-ema, to turn latents into images 1. Install the Hugging Face client with pip. 2. Download the repository with the hf command, which includes the definition file. 3. Run the sample from a folder that holds that file. ```sh pip install -U huggingface_hub hf download kaupane/DiT-Wikiart-Small ``` How to use it: Import the model class from the definition file, load the weights by name, and pass noisy latents. The card's sample runs one pass and prints the predicted noise. It shows no full picture-making loop, and it needs torch imported first. ```python from modeling_dit_wikiart import DiTWikiartModel model = DiTWikiartModel.from_pretrained("kaupane/DiT-Wikiart-Small") num_samples = 8 noisy_latents = torch.randn(num_samples,4,32,32) predicted_noise = model(noisy_latents) print(predicted_noise) ``` ```table Name | What it does DiTWikiartModel | Loads the model from the definition file and the Hub num_samples | Sets how many noisy latents are made, 8 in the sample torch.randn | Makes random noise shaped 4 by 32 by 32 predicted_noise | Holds what the model predicts for each latent ``` Example: You run the sample. It makes 8 random noisy latents, passes them through the model and prints the predicted noise for each one. It does not yet give you a finished painting. For newcomers and experts: Think of it as a small artist that learns to remove noise, guided by a genre and a style. The first small thing to try is the sample above. The design follows the classic DiT from its original paper, with a few changes. Genre and style embeddings replace the ImageNet class embeddings. It uses post-norm and learned positional embeddings, leaves out the final linear layer, predicts only the noise, and sets patch_size to 2. ```table Variant | Blocks | Hidden size | Heads | Parameters S, small | 8 | 384 | 6 | 20M B, base | 12 | 640 | 10 | 90M L, large | 16 | 896 | 14 | 234M ``` ```flow Random noisy latents DiT predicts the noise, guided by genre and style Repeated denoising gives a clean latent Paired VAE decodes it into an image ``` Good to know: - Size: Hugging Face lists 22M parameters, while the card says 20M for this small variant. - Training: all variants trained on 103K Wikiart images with horizontal flips, using AdamW with default settings and a learning rate that peaks at 3e-4 before a cosine decay. ```chart Title: Parameters in millions S small: 20 M B base: 90 M L large: 234 M ``` ```table Variant | Epochs | Batch size | Final loss S | 96 | 176 | 0.1590 B | 120 | 192 | 0.1525 L | 144 | 192 | 0.1510 ``` - Loss: in every variant it dropped sharply during epoch one, starting above 1.0000 and ending near 0.2000, then eased down slowly. - Strengths: the card says the models show a basic grasp of genres and styles and make paintings that look good at first glance. - Limits: it struggles with complex shapes such as faces and buildings, and it can repeat itself for rare styles such as minimalism. - Scope: it was trained only on Wikiart, so it cannot make images outside that scope. - License: MIT, as the License line says. - Cost to run: no API key, account or paid service is named. - Data it reaches: nothing beyond Hugging Face for the download. - Not for: photographs, faces or buildings, which the card lists as weak points. ## On Hugging Face, 13 November 2026 - Task: text to image - Parameters: 22M - Weights: about 88 MB in F32; about 11 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 75 - Likes: 2 - License: mit - Tags: safetensors, model_hub_mixin, pytorch_model_hub_mixin, text-to-image ## Links - Model: https://huggingface.co/kaupane/DiT-Wikiart-Small - Files: https://huggingface.co/kaupane/DiT-Wikiart-Small/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download kaupane/DiT-Wikiart-Small ``` 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 [kaupane/DiT-Wikiart-Small on Hugging Face](https://huggingface.co/kaupane/DiT-Wikiart-Small) ## 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 | | --- | --- | --- | --- | --- | --- | | DiT-Wikiart-Small (this page) | 75 | 2 | 22M | mit | 2025-11-01 | | [BiRefNet by ZhengPeng7](https://mindket.com/p/de56c2d7-685b-45d3-ba83-1fafb1ee2a34) | 1,005,130 | 675 | 221M | mit | 2026-02-04 | | [VGGT-1B](https://mindket.com/p/a5fb30dd-8e19-4124-af27-289e5c0509dc) | 646,177 | 158 | 1.3B | cc-by-nc-4.0 | 2025-03-17 | | [stable-diffusion-xl-base-1.0](https://mindket.com/p/2f64a36e-5180-4c25-92c0-4d9fdecf1566) | 4,688,522 | 8,466 | 2.6B | openrail++ | 2023-10-30 | | [Animagine XL 4.0](https://mindket.com/p/0e8d8d23-904b-43d6-9a0a-81bd94ec83f9) | 1,026,807 | 582 | 2.6B | openrail++ | 2025-02-13 | | [Stable Diffusion v1-5](https://mindket.com/p/0a900a83-bf4f-4256-a6b4-5eb24770edb3) | 1,012,982 | 1,364 | 860M | creativeml-openrail-m | 2024-09-07 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## 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: 2025-11-01 - First published: 2025-04-09 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open kaupane/DiT-Wikiart-Small on Hugging Face](https://huggingface.co/kaupane/DiT-Wikiart-Small) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/0cee2490-1fdd-4572-9119-f0a0d01822ef) - [Its page on Mindket](https://mindket.com/p/0cee2490-1fdd-4572-9119-f0a0d01822ef)