flux-dev-controlnet
Maintainer: xlabs-ai - Last updated 12/8/2024
Model overview
flux-dev-controlnet is an AI model developed by XLabs-AI that uses ComfyUI to generate images with the FLUX.1-dev model and XLabs' controlnet models. This model provides canny, depth, and soft edge controlnets that can be used to guide the image generation process. It builds upon similar models like flux-controlnet-canny-v3, flux-controlnet-canny, and flux-controlnet-depth-v3 that offer specific controlnet capabilities for the FLUX.1-dev model.
Model inputs and outputs
The flux-dev-controlnet model takes a variety of inputs to control the image generation process, including a prompt, a control image, and various parameters to adjust the controlnet strength, guidance scale, and output quality. The model outputs one or more generated images in the specified format (e.g., WEBP).
Inputs
- Seed: Set a seed for reproducibility.
- Steps: The number of steps to use during image generation, up to 50.
- Prompt: The text prompt to guide the image generation.
- Lora URL: An optional LoRA model to use, specified as a URL.
- Control Type: The type of controlnet to use, such as canny, depth, or soft edge.
- Control Image: The image to use as the controlnet input.
- Lora Strength: The strength of the LoRA model to apply.
- Output Format: The format of the output images, such as WEBP.
- Guidance Scale: The guidance scale to use during image generation.
- Output Quality: The quality of the output images, from 0 to 100.
- Negative Prompt: Things to avoid in the generated image.
- Control Strength: The strength of the controlnet, which varies depending on the type.
- Depth Preprocessor: The preprocessor to use with the depth controlnet.
- Soft Edge Preprocessor: The preprocessor to use with the soft edge controlnet.
- Image to Image Strength: The strength of the image-to-image control.
- Return Preprocessed Image: Whether to return the preprocessed control image.
Outputs
- One or more generated images in the specified output format.
Capabilities
The flux-dev-controlnet model is capable of generating high-quality, realistic images by leveraging the FLUX.1-dev model and various controlnet techniques. The canny, depth, and soft edge controlnets can be used to guide the generation process and produce images with specific visual characteristics, such as defined edges, depth information, or soft transitions.
What can I use it for?
You can use the flux-dev-controlnet model to create a wide range of images, from photorealistic scenes to stylized and abstract compositions. The controlnet capabilities make it well-suited for tasks like product visualization, architectural design, and character creation. The model could be useful for individuals and companies working on visual content creation, design, and digital art.
Things to try
To get the most out of the flux-dev-controlnet model, you can experiment with different control types, preprocessors, and parameter settings. Try using the canny controlnet to generate images with clear edges, the depth controlnet to create scenes with a strong sense of depth, or the soft edge controlnet to produce images with softer, more organic transitions. Additionally, you can explore the use of LoRA models to fine-tune the generation process for specific styles or subjects.
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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