Testaroo, Real, documented example files for testing

Commuter train at a station (JPEG q90)

The same plate encoded once at JPEG quality 90. Blocking and ringing are bounded by that single pass, so a restorer can be scored against the PNG reference in this group rather than against an opinion Plate v-train-plat.

File: JPG · Comfy Realism · 1024 × 1024 px
Use case: Media handling· Conversion set

Commuter train at a station (lossless PNG)

A synthetic photographic plate of a vehicle in context, 1024x1024, lossless PNG. This is the reference every lossy variant in this group is derived from Plate v-train-plat.

File: PNG · Comfy Realism · 1024 × 1024 px
Use case: Media handling· Conversion set

Commuter train at a station (WebP q85)

The same plate as WebP at quality 85, for format-handling coverage beside the PNG and JPEG encodings of identical pixels Plate v-train-plat.

File: WEBP · Raster Formats · 1024 × 1024 px
Use case: Media handling· Conversion set

Concrete spiral stairwell seen (JPEG q25)

The same plate encoded once at JPEG quality 25. Blocking and ringing are bounded by that single pass, so a restorer can be scored against the PNG reference in this group rather than against an opinion Plate l-stairwell.

File: JPG · JPEG Artefacts · 1024 × 1024 px
Use case: Media handling· Conversion set

Concrete spiral stairwell seen (JPEG q50)

The same plate encoded once at JPEG quality 50. Blocking and ringing are bounded by that single pass, so a restorer can be scored against the PNG reference in this group rather than against an opinion Plate l-stairwell.

File: JPG · JPEG Artefacts · 1024 × 1024 px
Use case: Media handling· Conversion set

Concrete spiral stairwell seen (JPEG q75)

The same plate encoded once at JPEG quality 75. Blocking and ringing are bounded by that single pass, so a restorer can be scored against the PNG reference in this group rather than against an opinion Plate l-stairwell.

File: JPG · JPEG Artefacts · 1024 × 1024 px
Use case: Media handling· Conversion set

Concrete spiral stairwell seen (JPEG q90)

The same plate encoded once at JPEG quality 90. Blocking and ringing are bounded by that single pass, so a restorer can be scored against the PNG reference in this group rather than against an opinion Plate l-stairwell.

File: JPG · Comfy Realism · 1024 × 1024 px
Use case: Media handling· Conversion set

Concrete spiral stairwell seen (lossless PNG)

A synthetic photographic plate of a landscape or architectural scene, 1024x1024, lossless PNG. This is the reference every lossy variant in this group is derived from Plate l-stairwell.

File: PNG · Comfy Realism · 1024 × 1024 px
Use case: Media handling· Conversion set

Concrete spiral stairwell seen (WebP q85)

The same plate as WebP at quality 85, for format-handling coverage beside the PNG and JPEG encodings of identical pixels Plate l-stairwell.

File: WEBP · Raster Formats · 1024 × 1024 px
Use case: Media handling· Conversion set

Conditioning Ground Truth: Apple (512px)

Colour ground-truth for the apple conditioning suite. Every map in this group is derived from this exact image, so a guided model's output can be scored against it.

File: PNG · Conditioning Maps · 512 × 512 px
Use case: ControlNet conditioningImage pipeline QA· Conversion set

Conditioning 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.

File: PNG · Conditioning Maps · 512 × 512 px
Use case: ControlNet conditioningImage pipeline QA· Conversion set

Conditioning Ground Truth: Fruit (512px)

Colour ground-truth for the fruit conditioning suite. Every map in this group is derived from this exact image, so a guided model's output can be scored against it.

File: PNG · Conditioning Maps · 512 × 512 px
Use case: ControlNet conditioningImage pipeline QA· Conversion set

Conditioning Ground Truth: Mug (512px)

Colour ground-truth for the mug conditioning suite. Every map in this group is derived from this exact image, so a guided model's output can be scored against it.

File: PNG · Conditioning Maps · 512 × 512 px
Use case: ControlNet conditioningImage pipeline QA· Conversion set

Conditioning Ground Truth: Plant (512px)

Colour ground-truth for the plant conditioning suite. Every map in this group is derived from this exact image, so a guided model's output can be scored against it.

File: PNG · Conditioning Maps · 512 × 512 px
Use case: ControlNet conditioningImage pipeline QA· Conversion set

Conditioning Ground Truth: Robot (512px)

Colour ground-truth for the robot conditioning suite. Every map in this group is derived from this exact image, so a guided model's output can be scored against it.

File: PNG · Conditioning Maps · 512 × 512 px
Use case: ControlNet conditioningImage pipeline QA· Conversion set

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.

File: PNG · Conditioning Maps · 512 × 512 px
Use case: ControlNet conditioningEdge detection testing+1· Conversion set

Conditioning Map: Apple Depth Map (512px)

Synthetic depth map (near = bright) for depth-conditioned generation and monocular depth-estimation regression against a known reference.

File: PNG · Conditioning Maps · 512 × 512 px
Use case: ControlNet conditioningDepth estimation testing+1· Conversion set

Conditioning Map: Apple Line Art (512px)

Clean line-art extraction (black lines on white) for line-art-conditioned generation and line-extraction quality tests.

File: PNG · Conditioning Maps · 512 × 512 px
Use case: ControlNet conditioningLine-art extraction testing+1· Conversion set

Conditioning Map: Apple Normal Map (512px)

Tangent-space surface-normal map encoded from the scene depth: a normal-conditioning input for guided generation and relighting tests.

File: PNG · Conditioning Maps · 512 × 512 px
Use case: ControlNet conditioningNormal map testing+1· Conversion set

Conditioning Map: Apple Scribble (512px)

Sparse scribble derived from the edges: the loose hand-drawn control input for scribble-guided image generation.

File: PNG · Conditioning Maps · 512 × 512 px
Use case: ControlNet conditioningScribble conditioning+1· Conversion set

Conditioning Map: Apple Segmentation Map (512px)

Semantic segmentation map (subject vs background): the label reference for segmentation-conditioned generation and mask-quality scoring.

File: PNG · Conditioning Maps · 512 × 512 px
Use case: ControlNet conditioningSemantic segmentation testing+1· Conversion set

Conditioning Map: Cat Canny Edges (512px)