Conditioning Map — Apple Canny Edges (512px)
Binary Canny edge map extracted from the ground truth — feed as a structure-control input and score the generated result against the reference.
Specifications
- Width
- 512
- Height
- 512
- Role
- canny conditioning
- Subject
- apple
- Seed
- 1102
- Reference
- img-cond-apple-gt
What is a .png file?
PNG (Portable Network Graphics) is a raster image format using lossless DEFLATE compression. It supports full 8- or 16-bit-per-channel truecolor, palette, and greyscale modes with an optional alpha channel, but no animation. It is the standard choice for screenshots, logos, and graphics with sharp edges or transparency.
How to use this file
Use an example PNG to test image decoders, alpha-compositing, thumbnail generators, and format converters, or to verify that a pipeline preserves transparency and color depth on round-trip.
How to use this file for testing
“Conditioning Map — Apple Canny Edges (512px)” is a deterministic Novus Examples fixture for ControlNet conditioning, Edge detection testing, Image pipeline QA. Scenes shipped with the structure-control inputs a guided-generation model consumes — Canny, depth, normal, segmentation, scribble, line-art, and pose — each derived from the same colour ground truth so outputs can be scored against their source.
Documented properties for this file: 512×512 · seed 1102 · canny conditioning. Compare results against paired or grouped companions on this page when present (clean↔damaged, searchable↔scanned, or format twins) so scores stay reproducible across runs.
Download the file once, keep the path stable in CI or local scripts, and treat the spec table as the contract: dimensions, seeds, field lists, and roles are intentional. Corrupt or invalid samples are labelled as such — expect parsers to fail loudly rather than silently accept them.
For image AI or enhancement tools, run the model or filter on this file and diff against the clean or ground-truth companion in the same group when available. Keep seeds and documented damage parameters in your evaluation notes so regressions are attributable.
Code examples
<img src="apple-canny.png" alt="Example image" width="640" loading="lazy">Related files
- pngConditioning Map — Cat Canny Edges (512px)Binary Canny edge map extracted from the ground truth — feed as a structure-control input and score the generated result against the reference.
- pngConditioning Map — Fruit Canny Edges (512px)Binary Canny edge map extracted from the ground truth — feed as a structure-control input and score the generated result against the reference.
- pngConditioning Map — Mug Canny Edges (512px)Binary Canny edge map extracted from the ground truth — feed as a structure-control input and score the generated result against the reference.
- pngConditioning Map — Plant Canny Edges (512px)Binary Canny edge map extracted from the ground truth — feed as a structure-control input and score the generated result against the reference.
- pngConditioning Map — Robot Canny Edges (512px)Binary Canny edge map extracted from the ground truth — feed as a structure-control input and score the generated result against the reference.
- pngConditioning Ground Truth — Cat (512px)Colour ground-truth for the cat conditioning suite. Every map in this group is derived from this exact image, so a guided model's output can be scored against it.
Generated by generation/images_conditioning.py. Free for any use, no attribution required — license.