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Flux.1 Depth AI Image Generator

Edit images while preserving spatial relationships.

Flux Depth

Upload images and describe variant images

Upload an image or provide a URL to the image.

Image file is required

Describe the prompt for the image transformation

Random Seed, useful for generating the same style image

Flux Depth Result

Your generated variant images will appear here

Result Time30s - 1min

Variant images will appear here

Flux Depth Example

Example image 1

Original Image 1

Example image 1

Flux Depth Result 1

Example image 2

Original Image 2

Example image 2

Flux Depth Result 2

Flux Depth FAQ

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What is FLUX Depth?
FLUX Depth is a model that uses depth detection to maintain precise control during image transformations. By preserving the original image’s structure through depth maps, it allows for text-guided edits while keeping the core composition intact, making it particularly effective for retexturing images.
How does FLUX Depth work?
FLUX Depth utilizes depth detection to maintain the structural integrity of an image during transformations. By using depth maps and text prompts, it enables text-guided edits while preserving the core composition of the original image.
What are the key features of FLUX Depth?
Key features include structural conditioning through depth maps, integration with FLUX.1 Pro Ultra for high-quality outputs, and the ability to perform text-guided image transformations while maintaining structural fidelity.
What makes FLUX Depth unique?
FLUX Depth stands out for its ability to provide state-of-the-art structural guidance in image transformations, ensuring high fidelity to the original image structure. It supports complex workflows and precise image restyling.
What are the typical use cases for FLUX Depth?
Typical use cases include retexturing images, performing precise image edits guided by text prompts, and maintaining structural integrity during image transformations.
What is the difference between FLUX Depth and FLUX Canny?
FLUX Depth uses depth detection for structural conditioning, while FLUX Canny uses canny edge detection. Both models maintain the original image's structure during transformations, but FLUX Depth is particularly effective for depth-aware tasks, whereas FLUX Canny excels in edge-based transformations.