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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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5-Slide Pitch Deck Template (PPTX)

A five-slide pitch-deck skeleton: title, problem, solution, market and traction, and the ask.

File: PPTX · Pitch Deck · 5 slides
Use case: Templates

5.1 Surround Channel Identification (WAV, 6ch)

A six-channel 5.1 surround WAV that plays a 1-second tone in each channel in turn, in the standard WAV order (FL, FR, C, LFE, SL, SR). A fixture for testing multichannel decoding, downmixing, and channel-order handling.

File: WAV · Channels · 44100 Hz
Use case: Audio analysis

50-Page PDF (text-light)

A 50-page, text-light PDF, for testing page-count handling, pagination, and large-document navigation without a large file.

File: PDF · PDF · 50 pages
Use case: PDF editor testing

50% Grey Card

A flat 50% grey card (rgb 128,128,128), a reference for white balance and exposure testing.

File: PNG · Greyscale Set · 256 × 256 px
Use case: Greyscale testingImage pipeline QA

60 fps Clip (MP4)

A 60 frames-per-second clip with a smoothly sweeping marker: double the usual frame rate. A fixture for testing high-frame-rate playback, frame-rate detection, and fps conversion (60→30 decimation).

File: MP4 · Formats · 480x270
Use case: Conversion testing

7z - LZMA2 Archive

A 7-Zip archive using LZMA2 compression, containing a small documented file tree. For testing 7z extraction and conversion.

File: 7Z · Sevenzip · 4 members
Use case: Conversion testing

7z - Password Protected

A password-protected 7z archive (AES-256, encrypted header). The password is “novus-example”, printed here on purpose so you can test encrypted-archive extraction. Contains only harmless sample text.

File: 7Z · Sevenzip · 3 members
Use case: Conversion testing

8-bit Greyscale Twin (PNG)

8-bit PNG twin of the linear 16-bit greyscale ramp. Compare banding after truncation.

File: PNG · Hdr Linear · 256 × 256 px
Use case: Linear 16-bit / HDR-ishConversion testing· Conversion set

A av f3: canny edge map

A canny edge map for the published plate a-av-f3.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 f3: clean plate (ground truth)

A clean 512x512 crop of head and shoulders portrait of a black woman with locs, plain terracotta studio background, taken from the published plate a-av-f3.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 f3: dense pose map

A dense pose map for the published plate a-av-f3.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 f3: depth map

A depth map for the published plate a-av-f3.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 98.1% 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 f3: object composited in

The same view of head and shoulders portrait of a black woman with locs, plain terracotta studio background, with a foreign object composited over 6.15% of the frame as a single organic region, neither a rectangle nor an ellipse. 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 f3: object removed

Head and shoulders portrait of a black woman with locs, plain terracotta studio 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 38.409/255 and from the ground-truth plate by 5.719/255; 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 3.157/255, 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 f3: removal mask

The exact footprint of the object sitting over head and shoulders portrait of a black woman with locs, plain terracotta studio background, as an 8-bit mask covering 6.15% of the frame as a single organic region, neither a rectangle nor an ellipse. 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 f3: surface normal map

A tangent-space surface normal map for the published plate a-av-f3.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 0.9962, 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 f4: canny edge map

A canny edge map for the published plate a-av-f4.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 4.4% 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

A av f4: dense pose map

A dense pose map for the published plate a-av-f4.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 f4: depth map

A depth map for the published plate a-av-f4.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 99.7% 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 f4: expanded canvas

The same view of head and shoulders portrait of an older white woman with short grey hair, plain cream background, expanded to 768x512 - 128 pixels added to the left and right, the symmetric case - so 33.3% of this frame is invented canvas. The original sits at pixels 128,0 to 640,512. Compare it INSET by 40 pixels: over that core it differs from the original by only 5.231/255, the VAE round trip, but over the whole rectangle by 6.293/255, 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 · 768 × 512 px
Use case: Inpainting· Conversion set

A av f4: frame before expansion

A 512x512 view of head and shoulders portrait of an older white woman with short grey hair, plain cream background - the frame an outpainting tool is given, cropped from the published plate nss-a-av-f4_00001_.png. Its companion expands it to 768x512 with 128 pixels added to the left and right, the symmetric case, 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

A av f4: outpaint boundary record

Where the original view of head and shoulders portrait of an older white woman with short grey hair, plain cream background landed inside the expanded frame, as JSON: the rectangle 128,0 to 640,512 in a 768x512 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

A av f4: surface normal map

A tangent-space surface normal map for the published plate a-av-f4.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 0.9961, 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 f5: canny edge map

A canny edge map for the published plate a-av-f5.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 1.2% 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