Update README.md

README.md

@@ -1,34 +1,65 @@
----
-dataset_info:
-  features:
-  - name: image
-    dtype: image
-  - name: prompt
-    dtype: string
-  - name: hash
-    dtype: string
-  - name: generated_by
-    dtype: string
-  - name: quantity
-    dtype: string
-  - name: plain_background
-    dtype: string
-  - name: has_neighbors
-    dtype: string
-  - name: style
-    sequence: string
-  - name: size
-    sequence:
-      sequence: string
-  splits:
-  - name: train
-    num_bytes: 586975377.483
-    num_examples: 2039
-  download_size: 586703624
-  dataset_size: 586975377.483
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
----
+---
+dataset_info:
+  features:
+  - name: image
+    dtype: image
+  - name: prompt
+    dtype: string
+  - name: hash
+    dtype: string
+  - name: generated_by
+    dtype: string
+  - name: quantity
+    dtype: string
+  - name: plain_background
+    dtype: string
+  - name: has_neighbors
+    dtype: string
+  - name: style
+    sequence: string
+  - name: size
+    sequence:
+      sequence: string
+  splits:
+  - name: train
+    num_bytes: 586975377.483
+    num_examples: 2039
+  download_size: 586703624
+  dataset_size: 586975377.483
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+license: mit
+task_categories:
+- text-to-image
+- unconditional-image-generation
+language:
+- en
+tags:
+- art
+pretty_name: Housey Home v2
+---
+
+# Housey House v2 - Like v1 never happened
+
+I was in the process of producing a fully synthetic dataset for ungrounded image generation using an unconventional combination of layers. As such, I needed a dataset of highly similar objects with 'themes'. In order to produce `log(x, y)` combinations of options in the final model. This is that dataset.
+
+The initial ( 07/15/2024 ) release includes ~2k unique houses, each processed using a VQA, [ybelkada/blip-vqa-base](https://huggingface.co/Salesforce/blip-vqa-base), to be precise.
+
+```
+# This code procedurally generates a simple description based upon the input lists of string selections. Feel free to expand or use as MIT.
+word_salad = [
+        ["cartoon", "happy", "goofy", "virant", "whymsical"],
+        ["realistic", "ultra hd", "best quality", "high quality", "masterpiece"],
+        ["horrifying", "nightmare", "wicked", "evil", "dark", "creepy", "scary"]
+    ]
+    sizes = [
+        ["large mansion", "mansion", "manor", "estate", "enournmous house", "palace"],
+        ["medium house", "home", "residence", "big house", "large house"],
+        ["tiny shack", "cottage", "small house", "miniature house", "dinimutive house", "shack"]
+    ]
+    post_prompt = ", ".join(chosen_salad := random.choice(word_salad)) + ", " + ", ".join(chosen_sizes := random.choice(sizes))
+    description = "A " + chosen_sizes[0].split(" ")[0] + ", " + chosen_salad[0] + " " + chosen_sizes[0].split(" ")[1]
+```