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Find files, editable templates and browser test targets by what you need to make or test. The directory below is cut by format; the two collections under it cut the same library by subject and by workflow.

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A av m5: depth map

A depth map for the published plate a-av-m5.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 95.2% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

A av m5: surface normal map

A tangent-space surface normal map for the published plate a-av-m5.png, 1024x1024 - the same size as the plate, so the two compare pixel for pixel with no resample in between. RGB encodes the XYZ surface direction remapped from -1..1 into 0..255. Decoded back to vectors this file measures a mean length of 0.9965, which is the number to check your own decode against: a reader that transposes the channels or inverts the remap still produces a plausible-looking image, and lights the surface the wrong way.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: PBR material testing

A av m6: canny edge map

A canny edge map for the published plate a-av-m6.png, 1024x1024. Canny edge detection at low threshold 0.1 and high 0.3, run at the plate's own resolution so the edges land on the same pixels as the photograph they came from. Lit coverage measures 3.2% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Edge detection

A av m6: dense pose map

A dense pose map for the published plate a-av-m6.png, 512x512. DensePose body-part segmentation, which labels regions of every person it finds rather than returning a skeleton, so background figures are labelled as well as the subject. Lit coverage measures 100.0% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Segmentation · 512 × 512 px
Use case: Pose estimation

A av m6: depth map

A depth map for the published plate a-av-m6.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 94.7% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

A av m6: surface normal map

A tangent-space surface normal map for the published plate a-av-m6.png, 1024x1024 - the same size as the plate, so the two compare pixel for pixel with no resample in between. RGB encodes the XYZ surface direction remapped from -1..1 into 0..255. Decoded back to vectors this file measures a mean length of 0.996, which is the number to check your own decode against: a reader that transposes the channels or inverts the remap still produces a plausible-looking image, and lights the surface the wrong way.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: PBR material testing

A av nb1: canny edge map

A canny edge map for the published plate a-av-nb1.png, 1024x1024. Canny edge detection at low threshold 0.1 and high 0.3, run at the plate's own resolution so the edges land on the same pixels as the photograph they came from. Lit coverage measures 3.8% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Edge detection

A av nb1: clean plate (ground truth)

A clean 512x512 crop of head and shoulders portrait of a young androgynous person with short pink hair, plain lilac background, taken from the published plate a-av-nb1.png before anything was added to it. This is the ANSWER KEY for its group: the object in the source file was composited onto this image, so this is exactly what was behind it. Nothing else in the group came from a second tool's guess.

File: PNG · Inpaint Cuts · 512 × 512 px
Use case: Inpainting· Conversion set

A av nb1: dense pose map

A dense pose map for the published plate a-av-nb1.png, 512x512. DensePose body-part segmentation, which labels regions of every person it finds rather than returning a skeleton, so background figures are labelled as well as the subject. Lit coverage measures 100.0% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Segmentation · 512 × 512 px
Use case: Pose estimation

A av nb1: depth map

A depth map for the published plate a-av-nb1.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 87.4% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

A av nb1: object composited in

The same view of head and shoulders portrait of a young androgynous person with short pink hair, plain lilac background, with a foreign object composited over 5.08% of the frame as one long thin region, where growing the mask by 12 pixels more than doubles the area it covers. This is the file an object-removal tool is given. Outside the mask it is byte-identical to the clean plate beside it, so any difference a tool leaves there is damage it did rather than content it was handed.

File: PNG · Inpaint Cuts · 512 × 512 px
Use case: Inpainting· Conversion set

A av nb1: object removed

Head and shoulders portrait of a young androgynous person with short pink hair, plain lilac background, with the object taken back out by Stable Diffusion 1.5 inpainting and the gap reconstructed from the surrounding context alone - the masked latents are erased before sampling, so the model never saw what it was painting over. Inside the mask it differs from the source by 51.809/255 and from the ground-truth plate by 7.091/255; the second number is NOT expected to be small, because an inpainter invents plausible content rather than recovering what was there. Beyond a 16-pixel ring around the mask the frame changes by only 2.995/255, which is the full-frame VAE round trip and not an edit.

File: PNG · Inpaint Cuts · 512 × 512 px
Use case: Inpainting· Conversion set

A av nb1: reconstructed mesh (GLB)

A single-image 3D reconstruction of head and shoulders portrait of a young androgynous person with short pink hair, written as binary glTF, geometry addressed through an accessor table. Its twin in this group is the same mesh written as the other format, from ONE reconstruction rather than two runs - so the pair tests whether a reader returns the same geometry from two containers with nothing in common. Both hold 33,734 vertices and 67,464 triangles; those counts were read from the GLB's accessor table and by counting lines in the OBJ, by separate code, and a disagreement would have stopped this shipping. It is closed: every edge belongs to exactly two triangles, so there are no holes and no non-manifold edges. The bounding box measures 0.147 x 1.0309 x 1.0116 units.

File: GLB · Mesh · 67,464 triangles
Use case: Mesh processing QA· Paired fixture

A av nb1: reconstructed mesh (OBJ)

A single-image 3D reconstruction of head and shoulders portrait of a young androgynous person with short pink hair, written as Wavefront text, one `v` line per vertex and one `f` line per face. Its twin in this group is the same mesh written as the other format, from ONE reconstruction rather than two runs - so the pair tests whether a reader returns the same geometry from two containers with nothing in common. Both hold 33,734 vertices and 67,464 triangles; those counts were read from the GLB's accessor table and by counting lines in the OBJ, by separate code, and a disagreement would have stopped this shipping. It is closed: every edge belongs to exactly two triangles, so there are no holes and no non-manifold edges. The bounding box measures 0.147 x 1.0309 x 1.0116 units.

File: OBJ · Mesh · 67,464 triangles
Use case: Mesh processing QA· Paired fixture

A av nb1: removal mask

The exact footprint of the object sitting over head and shoulders portrait of a young androgynous person with short pink hair, plain lilac background, as an 8-bit mask covering 5.08% of the frame as one long thin region, where growing the mask by 12 pixels more than doubles the area it covers. It holds only the values 0 and 255. The footprint is what DREW the object, so it is ground truth by construction rather than a segmentation of it. Hard-edged on purpose: a feathered edge has no exact footprint, and the exactness is the point of shipping it.

File: PNG · Inpaint Cuts · 512 × 512 px
Use case: Inpainting· Conversion set

A av nb1: surface normal map

A tangent-space surface normal map for the published plate a-av-nb1.png, 1024x1024 - the same size as the plate, so the two compare pixel for pixel with no resample in between. RGB encodes the XYZ surface direction remapped from -1..1 into 0..255. Decoded back to vectors this file measures a mean length of 0.9957, which is the number to check your own decode against: a reader that transposes the channels or inverts the remap still produces a plausible-looking image, and lights the surface the wrong way.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: PBR material testing

png

49.6 KB

A avatar f1: canny edge map

A canny edge map for the published plate a-avatar-f1.png, 1024x1024. Canny edge detection at low threshold 0.1 and high 0.3, run at the plate's own resolution so the edges land on the same pixels as the photograph they came from. Lit coverage measures 3.4% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Edge detection

png

4.8 KB

A avatar f1: dense pose map

A dense pose map for the published plate a-avatar-f1.png, 512x512. DensePose body-part segmentation, which labels regions of every person it finds rather than returning a skeleton, so background figures are labelled as well as the subject. Lit coverage measures 100.0% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Segmentation · 512 × 512 px
Use case: Pose estimation

png

179.2 KB

A avatar f1: depth map

A depth map for the published plate a-avatar-f1.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 97.0% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

png

481 KB

A avatar f1: expanded canvas

The same view of head and shoulders portrait of a smiling white woman against a plain blue studio background, expanded to 512x704 - 192 pixels added below alone, the one case where the origin does NOT move - so 27.3% of this frame is invented canvas. The original sits at pixels 0,0 to 512,512. Compare it INSET by 40 pixels: over that core it differs from the original by only 4.251/255, the VAE round trip, but over the whole rectangle by 4.956/255, because the pad deliberately feathers the original's outer edge into the new area. A test that expects the whole rectangle untouched fails on a correct tool.

File: PNG · Outpaint Borders · 512 × 704 px
Use case: Inpainting· Conversion set

png

393.9 KB

A avatar f1: frame before expansion

A 512x512 view of head and shoulders portrait of a smiling white woman against a plain blue studio background - the frame an outpainting tool is given, cropped from the published plate nss-a-avatar-f1_00001_.png. Its companion expands it to 512x704 with 192 pixels added below alone, the one case where the origin does NOT move, and the boundary record in this group states the exact rectangle this image occupies inside that frame - so where it ended up can be checked rather than eyeballed.

File: PNG · Outpaint Borders · 512 × 512 px
Use case: Inpainting· Conversion set

json

826 B

A avatar f1: outpaint boundary record

Where the original view of head and shoulders portrait of a smiling white woman against a plain blue studio background landed inside the expanded frame, as JSON: the rectangle 0,0 to 512,512 in a 512x704 canvas, the padding that produced it, and the 40-pixel feather that means the comparison must be inset. Authored BEFORE the expansion ran, so it is ground truth rather than a rectangle recovered afterwards by matching the original against the result - which is what a consumer would otherwise have to do, and which cannot distinguish a correct offset from a plausible one.

File: JSON · Outpaint Borders
Use case: Inpainting· Conversion set

png

465.2 KB

A avatar f1: surface normal map

A tangent-space surface normal map for the published plate a-avatar-f1.png, 1024x1024 - the same size as the plate, so the two compare pixel for pixel with no resample in between. RGB encodes the XYZ surface direction remapped from -1..1 into 0..255. Decoded back to vectors this file measures a mean length of 0.9957, which is the number to check your own decode against: a reader that transposes the channels or inverts the remap still produces a plausible-looking image, and lights the surface the wrong way.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: PBR material testing

png

34.4 KB

A avatar f2: canny edge map

A canny edge map for the published plate a-avatar-f2.png, 1024x1024. Canny edge detection at low threshold 0.1 and high 0.3, run at the plate's own resolution so the edges land on the same pixels as the photograph they came from. Lit coverage measures 2.5% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Edge detection