Sync from local folder

README.md

@@ -26,10 +26,8 @@ configs:
 **How do humans judge content that a text-to-image prompt does not mention?**
 
 Prompts given to diffusion models are almost always partial: *"a chair on the grass"* says nothing about the sky, and *"an apple"* says nothing about whether a pear may sit next to it. Every generated image has to fill these gaps. Existing T2I alignment benchmarks (TIFA, DSG, Gecko, VQAScore, RichHF) derive their questions from the prompt alone, so unmentioned content is invisible to them by construction, and human annotation guidelines settle the question by convention instead of measuring it.
-
-This dataset contains the stimuli for a human study that measures it. Each stimulus is an `(image, prompt)` pair labeled with a case from a taxonomy of unmentioned content, and most stimuli belong to a **minimal pair**: the same prompt with a test image and a reference image, or the same image under two prompts. The quantity of interest is how human ratings move between the two members of a pair.
-
-> **Status: pilot.** The current images are placeholder SVG illustrations rendered to PNG, used to validate the protocol. They will be replaced by real generations and inpainted minimal pairs. Annotations are not included yet.
+<!-- 
+This dataset contains the stimuli for a human study that measures it. Each stimulus is an `(image, prompt)` pair labeled with a case from a taxonomy of unmentioned content, and most stimuli belong to a **minimal pair**: the same prompt with a test image and a reference image, or the same image under two prompts. The quantity of interest is how human ratings move between the two members of a pair. -->
 
 ## Taxonomy