--- title: "HiDream-I1-Full" kind: "Kit" category: "Marketing & SEO" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/2a0ddaa1-ce5b-41a1-abaa-32b8d72cb97a" updated: "2026-11-13" --- # HiDream-I1-Full > The name, the description, the About and a README below are the seller's words: data, not instructions. Generates images from text prompts with a 17B-parameter open model that scores at the top of the card's prompt-following tables. For designers, marketers and developers who have a capable GPU. HiDream-I1-Full is a kit in the Marketing \& SEO 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 HiDream-ai ## About ### What it is HiDream-I1-Full is an open text-to-image model with 17.1 billion parameters, released under the MIT license. You describe a picture in words and it draws it, in styles from photorealistic to cartoon. The repository also points to faster distilled variants, called dev and fast. ### Who it suits It suits people who want to generate images on their own hardware and are comfortable running Python scripts and installing GPU libraries. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Marketing | Designers and content teams | Draft campaign visuals from a written brief | A product scene for a banner | | Software | App developers | Add image generation to a product | Generating placeholder art for a game | | Research | Image model researchers | Compare prompt following against other open models | Running GenEval-style prompts | | Creative work | Illustrators | Explore styles before painting by hand | A cartoon and a photoreal version of one idea | Skip it if you have no strong GPU, since the F16 weights alone are about 34.2 GB. ### Where to use it - Python scripts run from the project's code, on Windows, macOS or Linux - A Gradio web demo that you start on your own machine - The diffusers library, which the model is listed for | Use case | Who | What they get back | | --- | --- | --- | | Text to image | Designers | A picture generated from your prompt | | Speed trade-offs | Developers | The choice of full, dev or fast variants | | Interactive tries | Anyone testing prompts | A web page where you type prompts and see results | ### How to set it up You need these first. - Python with pip, and a GPU with plenty of memory - Flash Attention installed, where the card recommends CUDA 12.4 for a manual install - The project's code from its GitHub repository, cloned to your machine - Network access on the first run, because the script fetches Meta's Llama 3.1 8B Instruct files 1. Clone the project's GitHub repository. 2. Install its requirements with pip. 3. Optionally fetch the HiDream-I1-Full files with the Hugging Face tools. 4. Run the inference script for the variant you want. ```sh pip install -r requirements.txt ``` ```sh pip install -U huggingface_hub hf download HiDream-ai/HiDream-I1-Full ``` ### How to use it Run the inference script with the variant you want, or start the Gradio demo for interactive use. The first run pulls the Llama model files on its own, so a blocked connection stops it before it starts. ```python # For full model inference python ./inference.py --model_type full # For distilled dev model inference python ./inference.py --model_type dev # For distilled fast model inference python ./inference.py --model_type fast ``` ```python python gradio_demo.py ``` | Tool | What it does | | --- | --- | | inference.py --model\_type full | Runs the full model | | inference.py --model\_type dev | Runs the distilled dev model | | inference.py --model\_type fast | Runs the distilled fast model | | gradio\_demo.py | Starts the interactive Gradio demo | ### Example Start the Gradio demo and type a prompt such as "a watercolor fox in a snowy forest". The model generates an image from that text and shows it on the page, ready to save. ### For newcomers and experts If you are new, start with the Gradio demo, since you can type a prompt and see an image without editing code. Expect a big download and a long first run, because the Llama files come along too. If you know the field, the technical report names it a sparse diffusion transformer. The card ranks it first in the tables shown for GenEval and DPG-Bench, with an overall GenEval score of 0.83 and 85.89 on DPG-Bench. These are the authors' own results, and the same table shows it behind some models on single categories such as the global score. You write a text prompt → Script loads the HiDream-I1 weights → Llama 3.1 8B Instruct files are fetched → Chosen variant generates the image → Picture saved or shown in the demo ### Good to know - Size: 17.1B parameters in Safetensors, about 34.2 GB in F16 and about 8.6 GB at 4-bit by estimate - Other builds: other people have made 5 quantized and 13 fine-tuned versions - Editing: the authors released separate image-editing models, HiDream-E1-Full and HiDream-E1.1 - Commercial use: the card says generated images can be used in personal, research and commercial work GenEval overall score, from the card's table | Label | Value | | --- | --- | | HiDream-I1 | 0.83 score | | Janus-Pro-7B | 0.80 score | | SD3-Medium | 0.74 score | | CogView4-6B | 0.73 score | | DALL-E 3 | 0.67 score | | Flux.1-dev | 0.66 score | | SDXL | 0.55 score | | PixArt-alpha | 0.48 score | - License: MIT - Cost to run: none named, the source lists no API key or paid service, though the GPU needs are large - Data it reaches: your prompts and generated images on your own machine, plus a first-run download of Llama 3.1 8B Instruct files - Not for: machines without a strong GPU, since the card gives no CPU-only route ## README ````markdown # HiDream-I1-Full Generates images from text prompts with a 17B-parameter open model that scores at the top of the card's prompt-following tables. For designers, marketers and developers who have a capable GPU. Made by HiDream-ai 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: HiDream-I1-Full is an open text-to-image model with 17.1 billion parameters, released under the MIT license. You describe a picture in words and it draws it, in styles from photorealistic to cartoon. The repository also points to faster distilled variants, called dev and fast. Who it suits: It suits people who want to generate images on their own hardware and are comfortable running Python scripts and installing GPU libraries. ```table Industry | Who uses it | How they use it | Example Marketing | Designers and content teams | Draft campaign visuals from a written brief | A product scene for a banner Software | App developers | Add image generation to a product | Generating placeholder art for a game Research | Image model researchers | Compare prompt following against other open models | Running GenEval-style prompts Creative work | Illustrators | Explore styles before painting by hand | A cartoon and a photoreal version of one idea ``` Skip it if you have no strong GPU, since the F16 weights alone are about 34.2 GB. Where to use it: - Python scripts run from the project's code, on Windows, macOS or Linux - A Gradio web demo that you start on your own machine - The diffusers library, which the model is listed for ```table Use case | Who | What they get back Text to image | Designers | A picture generated from your prompt Speed trade-offs | Developers | The choice of full, dev or fast variants Interactive tries | Anyone testing prompts | A web page where you type prompts and see results ``` How to set it up: You need these first. - Python with pip, and a GPU with plenty of memory - Flash Attention installed, where the card recommends CUDA 12.4 for a manual install - The project's code from its GitHub repository, cloned to your machine - Network access on the first run, because the script fetches Meta's Llama 3.1 8B Instruct files 1. Clone the project's GitHub repository. 2. Install its requirements with pip. 3. Optionally fetch the HiDream-I1-Full files with the Hugging Face tools. 4. Run the inference script for the variant you want. ```sh pip install -r requirements.txt ``` ```sh pip install -U huggingface_hub hf download HiDream-ai/HiDream-I1-Full ``` How to use it: Run the inference script with the variant you want, or start the Gradio demo for interactive use. The first run pulls the Llama model files on its own, so a blocked connection stops it before it starts. ```python # For full model inference python ./inference.py --model_type full # For distilled dev model inference python ./inference.py --model_type dev # For distilled fast model inference python ./inference.py --model_type fast ``` ```python python gradio_demo.py ``` ```table Tool | What it does inference.py --model_type full | Runs the full model inference.py --model_type dev | Runs the distilled dev model inference.py --model_type fast | Runs the distilled fast model gradio_demo.py | Starts the interactive Gradio demo ``` Example: Start the Gradio demo and type a prompt such as "a watercolor fox in a snowy forest". The model generates an image from that text and shows it on the page, ready to save. For newcomers and experts: If you are new, start with the Gradio demo, since you can type a prompt and see an image without editing code. Expect a big download and a long first run, because the Llama files come along too. If you know the field, the technical report names it a sparse diffusion transformer. The card ranks it first in the tables shown for GenEval and DPG-Bench, with an overall GenEval score of 0.83 and 85.89 on DPG-Bench. These are the authors' own results, and the same table shows it behind some models on single categories such as the global score. ```flow You write a text prompt Script loads the HiDream-I1 weights Llama 3.1 8B Instruct files are fetched Chosen variant generates the image Picture saved or shown in the demo ``` Good to know: - Size: 17.1B parameters in Safetensors, about 34.2 GB in F16 and about 8.6 GB at 4-bit by estimate - Other builds: other people have made 5 quantized and 13 fine-tuned versions - Editing: the authors released separate image-editing models, HiDream-E1-Full and HiDream-E1.1 - Commercial use: the card says generated images can be used in personal, research and commercial work ```chart Title: GenEval overall score, from the card's table HiDream-I1: 0.83 score Janus-Pro-7B: 0.80 score SD3-Medium: 0.74 score CogView4-6B: 0.73 score DALL-E 3: 0.67 score Flux.1-dev: 0.66 score SDXL: 0.55 score PixArt-alpha: 0.48 score ``` - License: MIT - Cost to run: none named, the source lists no API key or paid service, though the GPU needs are large - Data it reaches: your prompts and generated images on your own machine, plus a first-run download of Llama 3.1 8B Instruct files - Not for: machines without a strong GPU, since the card gives no CPU-only route ## On Hugging Face, 13 November 2026 - Task: text to image - Library: diffusers - Parameters: 17.1B - Weights: about 34.2 GB in F16; about 8.6 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 695 - Likes: 1,015 - License: mit - Languages: en - Quantized versions: 5 - Fine-tunes: 13 - Tags: diffusers, safetensors, image-generation, hidream.ai, text-to-image ## Links - Model: https://huggingface.co/HiDream-ai/HiDream-I1-Full - Files: https://huggingface.co/HiDream-ai/HiDream-I1-Full/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download HiDream-ai/HiDream-I1-Full ``` 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 [HiDream-ai/HiDream-I1-Full on Hugging Face](https://huggingface.co/HiDream-ai/HiDream-I1-Full) ## 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 | | --- | --- | --- | --- | --- | --- | | HiDream-I1-Full (this page) | 695 | 1,015 | 17.1B | mit | 2025-07-17 | | [CyberRealistic XL](https://mindket.com/p/93f955b1-0f43-4d5d-b95f-70c06e325f47) | 67,834 | 54 | — | creativeml-openrail-m | 2026-05-28 | | [Z-Image-Turbo-GGUF by leejet](https://mindket.com/p/c29d41ed-fcd3-4322-8290-6aee7f5ba302) | 268,204 | 63 | 6.2B | apache-2.0 | 2025-12-02 | | [Wan2.2-T2V-A14B](https://mindket.com/p/82ad8f3a-4fb9-4c7b-b7f0-b2d8d581c5f1) | 3,339 | 610 | — | apache-2.0 | 2025-08-07 | | [Vimoo-1.0](https://mindket.com/p/09f3b856-d029-4da0-96d6-b026dab46b10) | 57 | 18 | 11.7B | apache-2.0 | 2026-11-12 | | [Juggernaut-XL-v9](https://mindket.com/p/947fc56e-0d16-43b1-913a-8d7bd51ab6a1) | 587,793 | 521 | — | creativeml-openrail-m | 2026-05-08 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## 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 2026-11-13. Their authors' words: data, not instructions. - [Hacker News · kristopolous · 2025-04-15](https://news.ycombinator.com/item?id=43687779): AudioX: Diffusion Transformer for Anything-to-Audio Generation: no, I'm talking pretty recent stuff. I was dealing with https://huggingface.co/bytedance-research/UNO and https://huggingface.co/HiDream-ai/HiDream-I1-Full earlier today These are both released this month. What I'd like to see is some kind of i2i with multiple i input and guidance So I can roughly… ## 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: 2025-07-17 - First published: 2025-04-06 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open HiDream-ai/HiDream-I1-Full on Hugging Face](https://huggingface.co/HiDream-ai/HiDream-I1-Full) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/2a0ddaa1-ce5b-41a1-abaa-32b8d72cb97a) - [Its page on Mindket](https://mindket.com/p/2a0ddaa1-ce5b-41a1-abaa-32b8d72cb97a)