Add parameter-count metadata for verification

Adds config.json with a full parameter breakdown (total, per-layer,
text-encoder/VAE = 0) and embeds the same info directly in the
model.safetensors header metadata, so total parameter count can be
verified by reading either file without running any code.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

README.md

@@ -51,6 +51,8 @@ python convert_to_safetensors.py --model model.png --out model.safetensors
 
 Re-run this after training if you retrain into a new `model.png` — `model.safetensors` doesn't update itself.
 
+Parameter count is verifiable two ways without running any code: `config.json` (`total_parameters: 202752`, full per-layer breakdown) and the safetensors file's own header metadata (`total_parameters`, `param_breakdown`, `has_bias`, `text_encoder_parameters`, `vae_parameters` — all 0 except the MLP itself).
+
 ---
 
 ## 🧪 Dataset vs Outputs
@@ -71,6 +73,7 @@ Re-run this after training if you retrain into a new `model.png` — `model.safe
 ```text
 model.png                   ← THE MODEL (64×3200 px)
 model.safetensors           ← same weights, standard format (generated, see below)
+config.json                 ← architecture + parameter-count metadata
 main.py                     ← inference, loads model.png
 INFERENCE.py                ← inference, loads model.safetensors
 convert_to_safetensors.py   ← model.png -> model.safetensors

config.json

@@ -0,0 +1,21 @@
+{
+  "model_type": "pixelmodel",
+  "architecture": "3-layer MLP (char-embed -> tanh -> tanh -> sigmoid)",
+  "weights_file": "model.safetensors",
+  "total_parameters": 202752,
+  "parameter_breakdown": {
+    "text_encoder_parameters": 0,
+    "generative_backbone_parameters": 202752,
+    "vae_parameters": 0
+  },
+  "layers": {
+    "W1": { "shape": [64, 32], "role": "prompt_embedding -> hidden", "parameters": 2048 },
+    "W2": { "shape": [64, 64], "role": "hidden -> hidden", "parameters": 4096 },
+    "W3": { "shape": [3072, 64], "role": "hidden -> output (32x32x3 flattened)", "parameters": 196608 }
+  },
+  "has_bias": false,
+  "prompt_dim": 32,
+  "hidden_dim": 64,
+  "output_resolution": "32x32",
+  "output_channels": 3
+}

convert_to_safetensors.py

@@ -20,22 +20,29 @@ from model import load_model, pixels_to_weights
 def convert(model_path: str, out_path: str):
     pixels = load_model(model_path)
     W1, W2, W3 = pixels_to_weights(pixels)
+    W1, W2, W3 = W1.contiguous(), W2.contiguous(), W3.contiguous()
+
+    total = W1.numel() + W2.numel() + W3.numel()
 
     save_file(
-        {
-            "W1": W1.contiguous(),
-            "W2": W2.contiguous(),
-            "W3": W3.contiguous(),
-        },
+        {"W1": W1, "W2": W2, "W3": W3},
         out_path,
         metadata={
             "format": "pt",
             "source": model_path,
             "architecture": "PixelModel 3-layer MLP (char-embed -> tanh -> tanh -> sigmoid)",
+            "total_parameters": str(total),
+            "param_breakdown": (
+                f"W1(prompt->hidden)={W1.numel()}, "
+                f"W2(hidden->hidden)={W2.numel()}, "
+                f"W3(hidden->output)={W3.numel()}"
+            ),
+            "has_bias": "false",
+            "text_encoder_parameters": "0",
+            "vae_parameters": "0",
         },
     )
 
-    total = W1.numel() + W2.numel() + W3.numel()
     print(f"Wrote {out_path}  ({total:,} parameters: W1={tuple(W1.shape)}, W2={tuple(W2.shape)}, W3={tuple(W3.shape)})")
 
 

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:8c365a7f71955241d343da88796e7c221f88b040e93006e9f1a46ecf5b36f10a
-size 811344
+oid sha256:f0e88c9e0b4fa233565ef1598b52b88d3e8853bf5ced0e507a3d319cce59d5d4
+size 811544