Datasets:

cocolabs
/

vybbench

@@ -15,6 +15,7 @@ pretty_name: "VybBench 1.0: Design Fidelity Benchmark"
 ---
 # VybBench 1.0
 **Almost isn't good enough.** VybBench measures how well models maintain
 design fidelity by comparing how effectively they generate specific
 fonts and colors, and perform non-destructive edits.
@@ -22,7 +23,7 @@ fonts and colors, and perform non-destructive edits.
 Our dataset consists of 145 fonts, 100 colors, and 200 edits. We have each model
 perform each task three times and score each generation individually.
-We compare top image generation models:
 ChatGPT Image (gpt-image-2.5-sunburst), Meta Muse, and Recraft v4.1. We also report
 the results for [Vyb 1.0](https://cocolabs.ai/vyb).
@@ -33,15 +34,15 @@ methodology and rationale are in
 ## Results
 ### Font fidelity
 We sample 145 [Google Fonts](https://fonts.google.com). For every font
 we choose a word to generate. We generate that word programmatically
 as well as through model calls. We compute the IoU of the ink between
-the ground truth generation and each model generation. The higher the
 score the better. As each model is allowed
 to scale and place the text individually, we compute the noise floor by
 measuring the IoU of multiple ground truth generations with the same font.
 | Model | Mean | Worst 5% of cases |
 |---|---|---|
 | **Vyb 1.0** | **0.90** | **0.78** |
@@ -56,12 +57,12 @@ measuring the IoU of multiple ground truth generations with the same font.
 output. Only Vyb generates an output that is close to the ground truth.*
 ### Color fidelity
 To measure color fidelity, we pick 100 different colors. We ask each model to generate
 a rectangle in the specified color, given as a hex code. We then calculate the
 per-channel error for each of R, G, and B (out of 255) and report the maximum across
 channels.
 | Model | Mean | Worst 5% of cases |
 |---|---|---|
 | **Vyb 1.0** | **0.0** | **0.0** |
@@ -76,7 +77,7 @@ Here we see that only Vyb reproduces every requested color exactly.
 We provide every model the same starting design and the same edit prompt.
 For Vyb, the design is provided in the layered SVG format it accepts. For
 the leading image generation models, the SVG is rendered and provided in
-the PNG format they accept.
 For each model generation, we measure the fraction of pixels in the image
 that were changed and have an LLM judge score whether the edit was performed
@@ -92,7 +93,6 @@ rate (higher is better).
 | Muse (`meta/muse-image`) | 91.9% | 99.6% | 98.0% |
 | Recraft (`v4.1`) | 98.9% | 100% | 51.2% |
 ![Edit example of changing text color](figures/bench-edit.png)
 *An example of an edit where the request is to change the color

 ---
 # VybBench 1.0
 **Almost isn't good enough.** VybBench measures how well models maintain
 design fidelity by comparing how effectively they generate specific
 fonts and colors, and perform non-destructive edits.
 Our dataset consists of 145 fonts, 100 colors, and 200 edits. We have each model
 perform each task three times and score each generation individually.
+We compare top image generation models:
 ChatGPT Image (gpt-image-2.5-sunburst), Meta Muse, and Recraft v4.1. We also report
 the results for [Vyb 1.0](https://cocolabs.ai/vyb).
 ## Results
 ### Font fidelity
 We sample 145 [Google Fonts](https://fonts.google.com). For every font
 we choose a word to generate. We generate that word programmatically
 as well as through model calls. We compute the IoU of the ink between
+the ground truth generation and each model generation. The higher the
 score the better. As each model is allowed
 to scale and place the text individually, we compute the noise floor by
 measuring the IoU of multiple ground truth generations with the same font.
 | Model | Mean | Worst 5% of cases |
 |---|---|---|
 | **Vyb 1.0** | **0.90** | **0.78** |
 output. Only Vyb generates an output that is close to the ground truth.*
 ### Color fidelity
 To measure color fidelity, we pick 100 different colors. We ask each model to generate
 a rectangle in the specified color, given as a hex code. We then calculate the
 per-channel error for each of R, G, and B (out of 255) and report the maximum across
 channels.
 | Model | Mean | Worst 5% of cases |
 |---|---|---|
 | **Vyb 1.0** | **0.0** | **0.0** |
 We provide every model the same starting design and the same edit prompt.
 For Vyb, the design is provided in the layered SVG format it accepts. For
 the leading image generation models, the SVG is rendered and provided in
+the PNG format they accept.
 For each model generation, we measure the fraction of pixels in the image
 that were changed and have an LLM judge score whether the edit was performed
 | Muse (`meta/muse-image`) | 91.9% | 99.6% | 98.0% |
 | Recraft (`v4.1`) | 98.9% | 100% | 51.2% |
 ![Edit example of changing text color](figures/bench-edit.png)
 *An example of an edit where the request is to change the color