--- title: "ControlNet v1.1 depth (SD 1.5)" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/1cbf6e7e-f0c4-4cfd-b0a1-189cdd8a3edc" updated: "2026-11-13" --- # ControlNet v1.1 depth (SD 1.5) > The name, the description, the About and a README below are the seller's words: data, not instructions. Steers Stable Diffusion 1.5 with a depth map so generated images keep the layout of a source picture. ControlNet v1.1 in diffusers format, for artists and developers who want control over composition. ControlNet v1.1 depth (SD 1.5) 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 lllyasviel ## About ### What it is This is the depth version of ControlNet v1.1, converted to the diffusers format. ControlNet adds an extra condition to a diffusion model, and here the condition is a depth image. You pair it with Stable Diffusion v1-5, and your prompt then paints new content inside the depth layout you supply. ### Who it suits Artists, designers and developers who want to control the composition of generated images, not just the subject. The author recommends Stable Diffusion v1-5, and notes that other diffusion models such as dreamboothed ones may work experimentally. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Design | Designers | Keep a scene's depth layout while changing its style | Re-rendering a room photo as a different interior | | Game art | Concept artists | Turn rough depth views into varied images | Several moods from one depth layout | | Software | Developers building image tools | Add depth control to a diffusers pipeline | A depth-guided generation feature in an app | | Research | Diffusion researchers | Study conditional control of diffusion models | Comparing outputs with and without the condition | You can skip it if you do not use Stable Diffusion 1.5 or diffusers, since this checkpoint was trained on that base. ### Where to use it - Python projects that use the diffusers library - Stable Diffusion v1-5 pipelines - Windows, macOS or Linux with Python installed | Use case | Who | What they get back | | --- | --- | --- | | Depth-guided image generation | Artists | New images that follow a depth map | | Layout-preserving restyling | Designers | A changed look with the same spatial layout | | Conditional control experiments | Researchers | Outputs to compare across conditions | ### How to set it up - Python on Windows, macOS or Linux - The huggingface\_hub package, then diffusers, transformers and accelerate - Room for about 1.4 GB of weights in F32, plus Stable Diffusion v1-5 1. Install the Hugging Face client. 2. Download the model files to your computer. 3. Install diffusers and its related packages. 4. Load the checkpoint next to Stable Diffusion v1-5 in a ControlNet pipeline. ```sh pip install -U huggingface_hub hf download lllyasviel/control_v11f1p_sd15_depth ``` ```sh pip install diffusers transformers accelerate ``` ### How to use it Make a depth image from your input picture with a depth-estimation pipeline, turn it into a three-channel control image, then load the ControlNet and pair it with Stable Diffusion v1-5. Add your prompt and run the pipeline. The card's full code runs on a half-precision setting. | Tool | What it does | | --- | --- | | depth-estimation pipeline | Makes a depth map from your input image | | ControlNetModel | Loads this depth checkpoint | | StableDiffusionControlNetPipeline | Combines the ControlNet with Stable Diffusion v1-5 | | UniPCMultistepScheduler | Scheduler the card's example imports for sampling | ### Example You supply a photo of a lecture hall and the prompt "a grand library with warm lamps". The depth estimator reads the room's layout, and the pipeline paints a library that keeps the same rows and distances. ### For newcomers and experts If you are new, think of the depth map as a guide for where things sit near and far, with your prompt filling in what they look like. A first try is one photo, one prompt and the default settings. If you know the field: ControlNet learns task-specific conditions end to end and attaches to a pretrained diffusion model. The authors report that it trains about as fast as fine-tuning and works even with small datasets. Input image → Depth estimator → Depth control image → ControlNet with Stable Diffusion → New image ### Good to know The model details list English as the language and describe a text-to-image diffusion model. The checkpoint was trained on Stable Diffusion v1-5. - Base: Stable Diffusion v1-5 is the recommended partner - Authors: Lvmin Zhang and Maneesh Agrawala - License: CreativeML OpenRAIL M - Cost to run: none named - Data it reaches: model files downloaded to your computer, plus the images you feed it - Not for: use outside the CreativeML OpenRAIL M terms, which the source does not spell out ## README ````markdown # ControlNet v1.1 depth (SD 1.5) Steers Stable Diffusion 1.5 with a depth map so generated images keep the layout of a source picture. ControlNet v1.1 in diffusers format, for artists and developers who want control over composition. Made by lllyasviel 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: This is the depth version of ControlNet v1.1, converted to the diffusers format. ControlNet adds an extra condition to a diffusion model, and here the condition is a depth image. You pair it with Stable Diffusion v1-5, and your prompt then paints new content inside the depth layout you supply. Who it suits: Artists, designers and developers who want to control the composition of generated images, not just the subject. The author recommends Stable Diffusion v1-5, and notes that other diffusion models such as dreamboothed ones may work experimentally. ```table Industry | Who uses it | How they use it | Example Design | Designers | Keep a scene's depth layout while changing its style | Re-rendering a room photo as a different interior Game art | Concept artists | Turn rough depth views into varied images | Several moods from one depth layout Software | Developers building image tools | Add depth control to a diffusers pipeline | A depth-guided generation feature in an app Research | Diffusion researchers | Study conditional control of diffusion models | Comparing outputs with and without the condition ``` You can skip it if you do not use Stable Diffusion 1.5 or diffusers, since this checkpoint was trained on that base. Where to use it: - Python projects that use the diffusers library - Stable Diffusion v1-5 pipelines - Windows, macOS or Linux with Python installed ```table Use case | Who | What they get back Depth-guided image generation | Artists | New images that follow a depth map Layout-preserving restyling | Designers | A changed look with the same spatial layout Conditional control experiments | Researchers | Outputs to compare across conditions ``` How to set it up: - Python on Windows, macOS or Linux - The huggingface_hub package, then diffusers, transformers and accelerate - Room for about 1.4 GB of weights in F32, plus Stable Diffusion v1-5 1. Install the Hugging Face client. 2. Download the model files to your computer. 3. Install diffusers and its related packages. 4. Load the checkpoint next to Stable Diffusion v1-5 in a ControlNet pipeline. ```sh pip install -U huggingface_hub hf download lllyasviel/control_v11f1p_sd15_depth ``` ```sh pip install diffusers transformers accelerate ``` How to use it: Make a depth image from your input picture with a depth-estimation pipeline, turn it into a three-channel control image, then load the ControlNet and pair it with Stable Diffusion v1-5. Add your prompt and run the pipeline. The card's full code runs on a half-precision setting. ```table Tool | What it does depth-estimation pipeline | Makes a depth map from your input image ControlNetModel | Loads this depth checkpoint StableDiffusionControlNetPipeline | Combines the ControlNet with Stable Diffusion v1-5 UniPCMultistepScheduler | Scheduler the card's example imports for sampling ``` Example: You supply a photo of a lecture hall and the prompt "a grand library with warm lamps". The depth estimator reads the room's layout, and the pipeline paints a library that keeps the same rows and distances. For newcomers and experts: If you are new, think of the depth map as a guide for where things sit near and far, with your prompt filling in what they look like. A first try is one photo, one prompt and the default settings. If you know the field: ControlNet learns task-specific conditions end to end and attaches to a pretrained diffusion model. The authors report that it trains about as fast as fine-tuning and works even with small datasets. ```flow Input image Depth estimator Depth control image ControlNet with Stable Diffusion New image ``` Good to know: The model details list English as the language and describe a text-to-image diffusion model. The checkpoint was trained on Stable Diffusion v1-5. - Base: Stable Diffusion v1-5 is the recommended partner - Authors: Lvmin Zhang and Maneesh Agrawala - License: CreativeML OpenRAIL M - Cost to run: none named - Data it reaches: model files downloaded to your computer, plus the images you feed it - Not for: use outside the CreativeML OpenRAIL M terms, which the source does not spell out ## On Hugging Face, 13 November 2026 - Task: image to image - Library: diffusers - Parameters: 361M - Weights: about 1.4 GB in F32; about 181 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 22,041 - Likes: 70 - License: openrail - Based on: runwayml/stable-diffusion-v1-5 - Tags: diffusers, safetensors, controlnet, stable-diffusion, controlnet-v1-1, image-to-image ## Links - Model: https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth - Files: https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download lllyasviel/control_v11f1p_sd15_depth ``` 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 [lllyasviel/control_v11f1p_sd15_depth on Hugging Face](https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth) ## 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 | | --- | --- | --- | --- | --- | --- | | ControlNet v1.1 depth (SD 1.5) (this page) | 22,041 | 70 | 361M | openrail | 2023-05-04 | | [Krea2 Turbo OpenPose ControlNet LoRA](https://mindket.com/p/cb5a01f0-290f-43be-ad6a-4190cae6ce3a) | 18,552 | 133 | — | apache-2.0 | 2026-08-19 | | [MOSS-Image Lineart Control LoRA](https://mindket.com/p/5239c2ba-2f3c-4982-bd3c-5865f08a7763) | 1,548 | 3 | — | apache-2.0 | 2026-11-10 | | [MOSS-Image Normal Control LoRA](https://mindket.com/p/0f7e33c4-3f22-4056-8ffb-f44918d6765a) | 1,014 | 4 | — | apache-2.0 | 2026-11-10 | | [MOSS-Image Canny Control LoRA](https://mindket.com/p/ba3f79ee-7d5f-4a2e-adaa-0045a5247424) | 458 | 3 | — | apache-2.0 | 2026-11-10 | | [MOSS-Image-SFT Segmentation Control LoRA](https://mindket.com/p/7bcfc9ce-60a2-42d3-816a-332d08bd367f) | 415 | 4 | — | apache-2.0 | 2026-11-10 | 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: 2023-05-04 - First published: 2023-04-16 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open lllyasviel/control_v11f1p_sd15_depth on Hugging Face](https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/1cbf6e7e-f0c4-4cfd-b0a1-189cdd8a3edc) - [Its page on Mindket](https://mindket.com/p/1cbf6e7e-f0c4-4cfd-b0a1-189cdd8a3edc)