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tanmaypatil123/krea-2-controlnet

Analysis updated 2026-05-18

21PythonAudience · researcherComplexity · 5/5Setup · hard

TLDR

A LoRA add-on for the Krea-2 AI image generator that uses a photo's depth map to keep the same 3D structure and composition while changing style and content via a text prompt.

Mindmap

mindmap
  root((Krea-2 ControlNet-LoRA))
    What it does
      Depth guided generation
      Keeps 3D structure
      Style and content change
    Tech stack
      Python
      PyTorch
      LoRA
      Depth Anything V2
    Use cases
      Structure preserving edits
      Depth only generation
      Custom control training
    Audience
      AI researchers
      Image generation developers

Code map

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What do people build with it?

USE CASE 1

Generate a new image from a photo that keeps the same layout and perspective but shows a different subject or style.

USE CASE 2

Test how much visual structure a depth map alone provides by generating images without any text prompt.

USE CASE 3

Train a custom ControlNet-LoRA for a different kind of visual guide, such as edge outlines, using the included trainer code.

USE CASE 4

Adjust how strictly a generated image follows the original photo's structure using the control strength setting.

What is it built with?

PythonPyTorchLoRADepth-Anything-V2Hugging Face

How does it compare?

tanmaypatil123/krea-2-controlnet0whitedev/detranspiler2951461586/mulerun-pool
Stars212121
LanguagePythonPythonPython
Setup difficultyhardhardmoderate
Complexity5/54/53/5
Audienceresearcherdeveloperdeveloper

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · hard Time to first run · 1h+

Requires downloading an 862MB checkpoint and a GPU capable of running the Krea-2 diffusion model.

In plain English

Krea-2 Depth ControlNet-LoRA is a tool that adds depth-guided control to an AI image generation model called Krea-2. Give it a photo and a text description, and it will create a brand new image that keeps the same three-dimensional structure and composition as the original photo, while changing the content and style to match whatever you described. For example, feeding in a photo of a robot and asking for a tiger produces a new image shaped like the original but rendered as a tiger. Under the hood, the tool first extracts a depth map from your input image using a separate model called Depth-Anything-V2, which figures out how near or far each part of the image is from the camera. That depth information then guides the image generator so the new picture follows the same layout and perspective as the original. This is done with a small add-on file called a LoRA, a lightweight adjustment layered onto the existing Krea-2 model rather than a full retrained model, which keeps the download size to under a gigabyte. The project works with both the full Krea-2-Raw model and the faster eight-step Krea-2-Turbo version. It reports strong consistency between the depth of the input and the depth of the generated output, meaning the 3D shape is preserved closely. Users can also skip the text prompt entirely and let the depth map alone drive the output, which is useful for testing how much structure the depth control provides on its own. To use it, you install the code and download a shared checkpoint file from Hugging Face, then run a command line script pointing at your photo and prompt, with options to adjust step count, guidance strength, and how tightly the output follows the original structure. The project also includes code for training your own version of this control system on other kinds of visual guides beyond depth, such as edge outlines. It works best with photos or 3D renders that have real depth variation, since flat illustrations do not give it much to work with. The model weights are released under a separate community license from Krea, not a standard open-source license.

Copy-paste prompts

Prompt 1
Show me how to run inference.py with a photo and a prompt to generate a depth-guided image using the Turbo base model.
Prompt 2
Explain how to download the depth-control-lora.safetensors checkpoint from Hugging Face and set up this project.
Prompt 3
Walk me through training my own ControlNet-LoRA for canny edge control using the trainer scripts in this repo.
Prompt 4
How do I use the Python API in pipeline.py to load the LoRA and generate an image from a photo and text prompt?

Frequently asked questions

What is krea-2-controlnet?

A LoRA add-on for the Krea-2 AI image generator that uses a photo's depth map to keep the same 3D structure and composition while changing style and content via a text prompt.

What language is krea-2-controlnet written in?

Mainly Python. The stack also includes Python, PyTorch, LoRA.

How hard is krea-2-controlnet to set up?

Setup difficulty is rated hard, with roughly 1h+ to a first successful run.

Who is krea-2-controlnet for?

Mainly researcher.

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