--- title: "Stable Diffusion v1-5" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/0a900a83-bf4f-4256-a6b4-5eb24770edb3" updated: "2026-11-13" --- # Stable Diffusion v1-5 > 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 latent diffusion model of 860M parameters, usable in diffusers, ComfyUI and other tools. Suits researchers and developers who want the classic open Stable Diffusion checkpoint. Stable Diffusion v1-5 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 stable-diffusion-v1-5 ## About ### What it is Stable Diffusion v1-5 turns text prompts into images, and it can modify images too. Robin Rombach and Patrick Esser developed it as a latent diffusion model. The checkpoint started from the v1-2 weights and was fine-tuned for 595 thousand steps at 512 by 512 pixels. This repository is a mirror of a deprecated one and has no tie to RunwayML. ### Who it suits Researchers, developers and artists who want the classic open Stable Diffusion checkpoint, under the CreativeML OpenRAIL M license. The card says the model is intended for research. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Research | ML researchers | Study diffusion models and their limits | Run controlled experiments on prompts | | Software | Developers | Build image generation into an app with diffusers | Generate an image from a text field | | Creative work | Artists | Make images in a local interface such as ComfyUI | Sketch ideas from a prompt | | Education | Students | Learn how text-to-image models work | Compare outputs from different prompts | Skip it if you need the newest image quality, since this is an older model, or if your project cannot accept the license terms. ### Where to use it The card lists several tools that run this checkpoint. - The Diffusers library in Python - ComfyUI - AUTOMATIC1111 - SD.Next - InvokeAI | Use case | Who | What they get back | | --- | --- | --- | | Text-to-image | Artists | An image from a written prompt | | Research runs | Researchers | Samples for studying model behavior | | Fine-tuning | Developers | A base for your own tuned checkpoint | ### How to set it up You need Python and a GPU for comfortable speed. - Python with pip - The diffusers library and torch for the Python route - About 3.4 GB of space for the weights in F32 1. Install the Hugging Face hub tools with pip. 2. Download the model files with the hf command. 3. Load them in diffusers, or point ComfyUI, AUTOMATIC1111, SD.Next or InvokeAI at the checkpoint. ```sh pip install -U huggingface_hub hf download stable-diffusion-v1-5/stable-diffusion-v1-5 ``` ### How to use it In Python, load the pipeline, move it to the GPU, and call it with a prompt. The card shows this. ```python from diffusers import StableDiffusionPipeline import torch model_id = "sd-legacy/stable-diffusion-v1-5" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe = pipe.to("cuda") ``` The card also offers two single-file checkpoints for the other tools. One holds only the EMA weights, uses less memory and suits inference. The other holds both kinds of weights and suits fine-tuning. | Tool | What it does | | --- | --- | | StableDiffusionPipeline | Loads the model and generates images in diffusers | | pipe.to cuda | Moves the pipeline to an NVIDIA GPU | | v1-5-pruned-emaonly.safetensors | EMA-only weights for inference with less VRAM | | v1-5-pruned.safetensors | EMA and non-EMA weights for fine-tuning | ### Example You pass the prompt "a watercolor painting of a lighthouse at dusk" to the pipeline. It returns an image object that you save to a png file and open. ### For newcomers and experts If you are new to image models, run the Python snippet once with a simple prompt and look at the result. Then change a few words in the prompt and see how the image shifts. If you know the field, it is a latent diffusion model with a fixed, pretrained CLIP ViT-L/14 text encoder. The card says text conditioning was dropped for 10 percent of training examples to improve classifier-free guidance sampling. Training data was laion-aesthetics v2 5+. Download the model files → Load in diffusers or a local interface → Write a text prompt → Run the diffusion steps → Save the generated image ### Good to know The model has 860 million parameters. It is a community mirror, so check which source you want to depend on. - License: CreativeML OpenRAIL M - Cost to run: none named, apart from your own hardware - Data it reaches: the model files on your computer, and nothing else the card names - Not for: shipping a product before you read the license and the research-use note ## README ````markdown # Stable Diffusion v1-5 Generates images from text prompts with a latent diffusion model of 860M parameters, usable in diffusers, ComfyUI and other tools. Suits researchers and developers who want the classic open Stable Diffusion checkpoint. Made by stable-diffusion-v1-5 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: Stable Diffusion v1-5 turns text prompts into images, and it can modify images too. Robin Rombach and Patrick Esser developed it as a latent diffusion model. The checkpoint started from the v1-2 weights and was fine-tuned for 595 thousand steps at 512 by 512 pixels. This repository is a mirror of a deprecated one and has no tie to RunwayML. Who it suits: Researchers, developers and artists who want the classic open Stable Diffusion checkpoint, under the CreativeML OpenRAIL M license. The card says the model is intended for research. ```table Industry | Who uses it | How they use it | Example Research | ML researchers | Study diffusion models and their limits | Run controlled experiments on prompts Software | Developers | Build image generation into an app with diffusers | Generate an image from a text field Creative work | Artists | Make images in a local interface such as ComfyUI | Sketch ideas from a prompt Education | Students | Learn how text-to-image models work | Compare outputs from different prompts ``` Skip it if you need the newest image quality, since this is an older model, or if your project cannot accept the license terms. Where to use it: The card lists several tools that run this checkpoint. - The Diffusers library in Python - ComfyUI - AUTOMATIC1111 - SD.Next - InvokeAI ```table Use case | Who | What they get back Text-to-image | Artists | An image from a written prompt Research runs | Researchers | Samples for studying model behavior Fine-tuning | Developers | A base for your own tuned checkpoint ``` How to set it up: You need Python and a GPU for comfortable speed. - Python with pip - The diffusers library and torch for the Python route - About 3.4 GB of space for the weights in F32 1. Install the Hugging Face hub tools with pip. 2. Download the model files with the hf command. 3. Load them in diffusers, or point ComfyUI, AUTOMATIC1111, SD.Next or InvokeAI at the checkpoint. ```sh pip install -U huggingface_hub hf download stable-diffusion-v1-5/stable-diffusion-v1-5 ``` How to use it: In Python, load the pipeline, move it to the GPU, and call it with a prompt. The card shows this. ```python from diffusers import StableDiffusionPipeline import torch model_id = "sd-legacy/stable-diffusion-v1-5" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe = pipe.to("cuda") ``` The card also offers two single-file checkpoints for the other tools. One holds only the EMA weights, uses less memory and suits inference. The other holds both kinds of weights and suits fine-tuning. ```table Tool | What it does StableDiffusionPipeline | Loads the model and generates images in diffusers pipe.to cuda | Moves the pipeline to an NVIDIA GPU v1-5-pruned-emaonly.safetensors | EMA-only weights for inference with less VRAM v1-5-pruned.safetensors | EMA and non-EMA weights for fine-tuning ``` Example: You pass the prompt "a watercolor painting of a lighthouse at dusk" to the pipeline. It returns an image object that you save to a png file and open. For newcomers and experts: If you are new to image models, run the Python snippet once with a simple prompt and look at the result. Then change a few words in the prompt and see how the image shifts. If you know the field, it is a latent diffusion model with a fixed, pretrained CLIP ViT-L/14 text encoder. The card says text conditioning was dropped for 10 percent of training examples to improve classifier-free guidance sampling. Training data was laion-aesthetics v2 5+. ```flow Download the model files Load in diffusers or a local interface Write a text prompt Run the diffusion steps Save the generated image ``` Good to know: The model has 860 million parameters. It is a community mirror, so check which source you want to depend on. - License: CreativeML OpenRAIL M - Cost to run: none named, apart from your own hardware - Data it reaches: the model files on your computer, and nothing else the card names - Not for: shipping a product before you read the license and the research-use note ## On Hugging Face, 13 November 2026 - Task: text to image - Library: diffusers - Parameters: 860M - Weights: about 3.4 GB in F32; about 430 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 1,012,982 - Likes: 1,364 - License: creativeml-openrail-m - Quantized versions: 17 - Fine-tunes: 421 - Tags: diffusers, safetensors, stable-diffusion, stable-diffusion-diffusers, text-to-image ## Links - Model: https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5 - Files: https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download stable-diffusion-v1-5/stable-diffusion-v1-5 ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [stable-diffusion-v1-5/stable-diffusion-v1-5 on Hugging Face](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) ## 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 | | --- | --- | --- | --- | --- | --- | | Stable Diffusion v1-5 (this page) | 1,012,982 | 1,364 | 860M | creativeml-openrail-m | 2024-09-07 | | [stable-diffusion-inpainting](https://mindket.com/p/d1ed5194-378d-44f3-83be-64f18cd0f8ed) | 248,486 | 122 | — | creativeml-openrail-m | 2024-09-06 | | [sd-vae-ft-mse-original](https://mindket.com/p/31e345bc-554a-44e3-98f7-f62c25c6d9bd) | 6 | 1,447 | — | mit | 2022-12-19 | | [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 | | [Juggernaut XI v11](https://mindket.com/p/fbae0253-df88-44e8-9891-2d9a7ac5c8c7) | 28,120 | 169 | — | cc-by-nc-nd-4.0 | 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 · jacobwg · 2023-07-18](https://news.ycombinator.com/item?id=36774560): Show HN: depot.ai – easily embed ML / AI models in your Dockerfile: Hey HN\! We're excited to launch https://depot.ai today - depot.ai is an open-source Docker registry that makes it easy to embed public models from Hugging Face in your Docker images using a \`COPY\`: \# Copy all files from the model repo to the current WORKDIR COPY --link… - [Hacker News · vhold · 2023-09-13](https://news.ycombinator.com/item?id=37499122): Stable Audio: Fast Timing-Conditioned Latent Audio Diffusion: Stable Diffusion 1 contains a model OpenAI released. The CLIP encoder that was trained on text/image pairs at OpenAI. https://huggingface.co/runwayml/stable-diffusion-v1-5 https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/m... Uploaded to Hugging Face Jan 2021… - [Hacker News · perfopt · 2023-01-22](https://news.ycombinator.com/item?id=34476187): Show HN: New AI edits images based on text instructions: I keep seeing this even when the prompt is unchanged Downloading https://huggingface.co/runwayml/stable-diffusion-v1-5/resolv... from huggingface Loading model /home/hrishi/.cache/huggingface/hub/models--runwayml--stable-diffusion-v1-5/snapshots/889b629140e71758e1e0006e355c331a5744b4bf/v1-5-pruned-… - [Hacker News · ComodoHacker · 2022-10-21](https://news.ycombinator.com/item?id=33287571): Why we chose not to release Stable Diffusion 1.5 as quickly: More context: https://old.reddit.com/r/StableDiffusion/comments/y99yb1/a\_s... ============================= RunwayML, who co-authored the StableDiffusion paper and funded CompVis together with StabilityAI, have unilaterally released the newest model of StableDiffusion, version 1.5. It seems that… ## 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: 2024-09-07 - First published: 2024-08-30 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open stable-diffusion-v1-5/stable-diffusion-v1-5 on Hugging Face](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/0a900a83-bf4f-4256-a6b4-5eb24770edb3) - [Its page on Mindket](https://mindket.com/p/0a900a83-bf4f-4256-a6b4-5eb24770edb3)