flux-controlnet-canny
Maintainer: XLabs-AI
262
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
The flux-controlnet-canny model is a checkpoint with a trained ControlNet Canny model for the FLUX.1-dev model by Black Forest Labs. ControlNet is a neural network structure that can control diffusion models by adding extra conditions, in this case Canny edge detection. It can be used in combination with Stable Diffusion models.
Similar models include the sd-controlnet-canny checkpoint, which also uses Canny edge conditioning, as well as the controlnet-canny-sdxl-1.0 and controlnet-canny-sdxl-1.0 models, which use Canny conditioning with the larger Stable Diffusion XL base model.
Model inputs and outputs
Inputs
- Control image: A Canny edge image used to guide the image generation process.
- Prompt: A text description of the desired output image.
Outputs
- Generated image: An image created by the model based on the provided prompt and control image.
Capabilities
The flux-controlnet-canny model can generate high-quality images guided by Canny edge maps, allowing for precise control over the output. This can be useful for creating illustrations, concept art, and design assets where the edges and structure of the image are important.
What can I use it for?
The flux-controlnet-canny model can be used for a variety of image generation tasks, such as:
- Generating detailed illustrations and concept art
- Creating design assets and product visualizations
- Producing architectural renderings and technical diagrams
- Enhancing existing images by adding edge-based details
Things to try
One interesting thing to try with the flux-controlnet-canny model is to experiment with different types of control images. While the model was trained on Canny edge maps, you could try using other edge detection techniques or even hand-drawn sketches as the control image to see how the model responds. This could lead to unexpected and creative results.
Another idea is to try combining the flux-controlnet-canny model with other AI-powered tools, such as 3D modeling software or animation tools, to create more complex and multi-faceted projects. The ability to precisely control the edges and structure of the generated images could be a valuable asset in these types of workflows.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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flux-controlnet
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The flux-controlnet model, developed by the XLabs-AI team, is a ControlNet model fine-tuned on the FLUX.1-dev model by Black Forest Labs. It includes a Canny edge detection ControlNet checkpoint that can be used to generate images based on provided control images and text prompts. This model builds upon similar flux-dev-controlnet, flux-controlnet-canny, and flux-controlnet-canny-v3 models released by XLabs-AI. Model inputs and outputs The flux-controlnet model takes in a text prompt, a control image, and optional parameters like CFG scale and seed. It outputs a generated image based on the provided inputs. Inputs Prompt**: A text description of the desired image Image**: A control image, such as a Canny edge map, that guides the generation process CFG Scale**: The Classifier-Free Guidance Scale, which controls the influence of the text prompt Seed**: The random seed, which controls the stochastic elements of the generation process Outputs Image**: A generated image that matches the provided prompt and control image Capabilities The flux-controlnet model can generate a wide variety of images based on the provided prompt and control image. For example, it can create detailed, cinematic scenes of characters and environments using the Canny edge control image. The model is particularly skilled at generating realistic, high-quality images with a strong sense of artistic style. What can I use it for? The flux-controlnet model can be used for a variety of creative and artistic projects, such as concept art, illustrations, and even film/game asset creation. By leveraging the power of ControlNet, users can guide the generation process and create images that closely match their creative vision. Additionally, the model's capabilities could be useful for tasks like image inpainting, where the control image is used to guide the generation of missing or damaged parts of an existing image. Things to try One interesting thing to try with the flux-controlnet model is exploring the interplay between the text prompt and the control image. By varying the control image, users can see how it influences the final generated image, even with the same prompt. Experimenting with different control image types, such as depth maps or normal maps, could also yield unique and unexpected results. Additionally, users can try adjusting the CFG scale and seed to see how these parameters affect the generation process and the final output.
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