Add calibrated Base93 production-v3 export under 5 MB with decoders and visual validation

.gitattributes

@@ -338,3 +338,12 @@ experiments/final-20260929/v3_resolution_2.jpg filter=lfs diff=lfs merge=lfs -te
 experiments/final-20260929/v3_resolution_3.jpg filter=lfs diff=lfs merge=lfs -text
 experiments/final-20260929/v3_resolution_4.jpg filter=lfs diff=lfs merge=lfs -text
 reports/FINAL_PALETTE_REVIEW.jpg filter=lfs diff=lfs merge=lfs -text
+quantized/base93-v3/evaluation/comparison_01.jpg filter=lfs diff=lfs merge=lfs -text
+quantized/base93-v3/evaluation/comparison_02.jpg filter=lfs diff=lfs merge=lfs -text
+quantized/base93-v3/evaluation/comparison_03.jpg filter=lfs diff=lfs merge=lfs -text
+quantized/base93-v3/evaluation/comparison_04.jpg filter=lfs diff=lfs merge=lfs -text
+quantized/base93-v3/evaluation/comparison_05.jpg filter=lfs diff=lfs merge=lfs -text
+quantized/base93-v3/evaluation/comparison_06.jpg filter=lfs diff=lfs merge=lfs -text
+quantized/base93-v3/evaluation/comparison_07.jpg filter=lfs diff=lfs merge=lfs -text
+quantized/base93-v3/evaluation/comparison_08.jpg filter=lfs diff=lfs merge=lfs -text
+quantized/base93-v3/evaluation/comparison_09.jpg filter=lfs diff=lfs merge=lfs -text

README.md

@@ -34,6 +34,10 @@ Historical research documents have moved to `reports/history/`. See [the report
 - `app.py` and `requirements-space.txt`: the tested Gradio / ZeroGPU application.
 - `RELEASE_REPORT.md`, `QA.json` and `SHA256SUMS.json`: selection evidence, runtime checks and file hashes.
 
+## Scratch Base93 export
+
+The complete production v3 model is also available as a [Base93 text export](quantized/base93-v3/README.md), including its MobileNetV3 encoder. [weights_base93.txt](quantized/base93-v3/weights_base93.txt) is 4,946,819 bytes, or **4,949,484 bytes as a JSON string including escaping**. It uses one character for 87.94% of learned parameters and two for sensitive weights. Python and JavaScript decoders, format documentation and output-preservation evaluation are included. This is lossy storage quantisation; the FP32 checkpoint remains the standard runtime release.
+
 ## Use
 
 Download this repository, then:

SHA256SUMS.json

@@ -2,7 +2,7 @@
   "DEPLOYMENT.md": "1ecb94969482959a65e40a6fbd41c985dd3c9df1ff23ad7b13c4f22545c8782b",
   "PIPELINE_EVAL.json": "595be88704d580c01a3c4dd642409b3dc5dd25e86ca1c7eb40a696605f181a19",
   "QA.json": "b249b1d49045e31b0bf85f9e01515b91a72c4dd973963751c30d14272d262b90",
-  "README.md": "39654812ef18de704d72b364d75b3ae8fe45b21751e4b73bcca3418a936578c7",
+  "README.md": "095c7f3043d428711825105a1c29ff715c3b4e2dcd57445695ad40a3ecfb7023",
   "RELEASE_MANIFEST.json": "872eebfb322c8990b5e6ac493e4e4068b87f8daac0ddf219b8b55299514ca03c",
   "RELEASE_REPORT.md": "69677ec5e13f5c1249f59b01c31ed1b0f1588bddc216bce9167b5d31423a5574",
   "app.py": "9fbfae85bc7e9976aee57229cb0825a05b836e31177a4288b2b1efebb075e58b",
@@ -32,4 +32,4 @@
   "semantic_model.py": "b019358cbcaa214743cbd244d20eb6e59eeff0ab5264093ec0927be8529b25a2",
   "training/final_config.json": "39502272a7bbc05c1ad15e70de4e93550b53cdd9fbea399192d1b400311205dd",
   "training/train_final.py": "ba42f07649c05cfcf8d7a6cee78088a4ed0838f4e09e24227cf9fb3509b34b5b"
-}
+}

quantized/base93-v3/README.md

@@ -0,0 +1,188 @@
+# Production v3 — Base93 export
+
+This is the complete 3,994,676-parameter production v3 colouriser, including its MobileNetV3 encoder, quantised to the supplied Scratch Base93 alphabet. It uses 93-level weights for most parameters and 8,649-level weights for sensitive tensors. It is a lossy weight export; the original production checkpoint remains available.
+
+## Download and size
+
+Use **`weights_base93.txt`**. It is self-contained: weights, scales, normalization buffers, tensor names/shapes, model configuration and source identity are all inside it. `manifest.json` is an optional, easier-to-read index.
+
+| Item | Exact size/count |
+|---|---:|
+| Text file, ASCII / UTF-8 | **4,946,819 bytes** |
+| Entire file as one JSON string, including escaping and outer quotes | **4,949,484 bytes** |
+| Remaining below 5,000,000 bytes | **50,516 bytes** |
+| Learned parameters using one character | 3,512,832 (87.94%) |
+| Learned parameters using two characters | 481,844 (12.06%) |
+| Total learned parameters | **3,994,676** |
+| Lossless non-learned buffer values | 24,452 |
+| Group scale values | 66,253 |
+
+The text contains 2,637 backslash separators and 26 quotation marks; each becomes two bytes in JSON. Everything else is one byte. The byte budget includes this overhead, scales and metadata. Other Scratch project blocks/assets/variables add to the project size. Store the text as a single string; splitting every character into a JSON list adds substantial overhead.
+
+SHA256 of `weights_base93.txt`:
+
+```
+e9ef164317e7832781753585c5533c781ce134527dd71385ef490cf5cf5fe9ba
+```
+
+Source: [`User-2468/mini-unet-colorizer` at `1a9eb8af2754ad2329a24cfe50d388cb559441d0`](https://huggingface.co/User-2468/mini-unet-colorizer/tree/1a9eb8af2754ad2329a24cfe50d388cb559441d0).
+Source `model.safetensors` SHA256: `ec1f27d74533adc83f7ab3639a091fc4d8738a434dafc7d172c7873c28a9e715`.
+
+## Validation
+
+Precision allocation used 16 calibration photographs. Layer sensitivity was measured against the FP32 model's own output, followed by conditional refinement of the allocation. All candidate selection used those calibration images. No model training or cloud GPU job was required.
+
+The final file was then decoded and compared with FP32 on **64 separate COCO photographs and six historical photographs**. Both used the production pipeline: 256-pixel maximum network side, smoothing radius 8, saturation 1, original output resolution.
+
+| Evaluation set | Mean RGB absolute difference (0–255) | Mean image PSNR | Largest image mean difference |
+|---|---:|---:|---:|
+| COCO holdout, 64 images | 0.784 | 47.61 dB | 2.988 |
+| Historical, 6 images | 0.990 | 45.82 dB | 1.483 |
+| Additional 512-size check, 4 images | 0.746 | 47.71 dB | 1.644 |
+
+These are **differences from the original model**, not accuracy against unknowable original colours. Means give each image equal weight. The COCO subset was held out from quantisation calibration; no claim is made about overlap with upstream pretraining.
+
+All 70 default-size image pairs were visually reviewed in `evaluation/comparison_01.jpg` through `comparison_09.jpg`. They retain the original model's overall palette, boundaries and existing limitations. Some warmth/saturation shifts are visible: the largest measured change is the desk scene `coco_1056.jpg` (35.86 dB PSNR, 2.99/255 image MAE). Field/court images also show small shifts. This export does not fix the original model's muted colouring, warm casts or pre-existing colour bleeding.
+
+Both independent decoders reconstruct **bitwise-identical values in all 376 tensors**, with strict PyTorch loading. JSON string round-trip, deterministic re-export and alpha preservation were verified. Quantisation statistics, per-image results, input identities and decoded tensor hashes are included.
+
+## Python use
+
+From this directory:
+
+```bash
+pip install -r requirements.txt
+python base93_codec.py weights_base93.txt --output decoded_model
+python inference.py input.jpg output.png --model decoded_model --device cpu
+```
+
+Or decode directly in memory:
+
+```python
+from PIL import Image
+from base93_codec import load_model
+from inference import colorize
+
+model = load_model('weights_base93.txt', device='cpu')
+colorize(model, Image.open('input.jpg')).save('output.png')
+```
+
+The decoder restores floating-point weights for the existing architecture. The export reduces stored weight size; it does not itself provide an integer inference engine or a Scratch implementation of the neural network. Decoded weights occupy approximately 16 MB as float32, plus runtime activations and overhead.
+
+## JavaScript use
+
+`decode_base93.js` has no package dependencies and can run in Node or a browser script. Node:
+
+```javascript
+const fs = require('fs');
+const {decodeBase93} = require('./decode_base93');
+const model = decodeBase93(fs.readFileSync('weights_base93.txt', 'ascii'));
+// model.tensors[name] = {shape, kind, values}
+// Float32Array for floating tensors; BigInt array for integer buffers.
+```
+
+Run `node verify_decoders.js` to check every decoded tensor against the included Python hashes. This decoder supplies tensors, not an inference runtime.
+
+## Exact alphabet
+
+There is a **space as the first character** of the line below:
+
+```text
+ !#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[]^_`abcdefghijklmnopqrstuvwxyz{|}~
+```
+
+Indices run from 0 to 92. This is ASCII 32–126 excluding double quote (34) and backslash (92). Space is digit 0. Preserve whitespace and case; never trim, normalize case, wrap lines or add a byte-order mark. The file contains no newline.
+
+Scratch's ordinary string equality/list lookup does not distinguish uppercase and lowercase reliably for this alphabet. Reuse the case-sensitive costume-name lookup mechanism from the supplied reference project, or an equivalent verified method. A possible dedicated lookup sprite has exactly 93 costumes named `digit + "_"`, ordered by the alphabet; select that exact costume name and use costume number minus one. If using the supplied project's existing costumes, follow its existing index routine rather than assuming those costumes are alphabetically ordered.
+
+## B93Q1 format and decoding
+
+This new container uses the user's alphabet, but **its numerical mapping is symmetric and group-scaled**. The older reference notes' min/max affine decoder is not compatible with B93Q1.
+
+A single literal backslash separates fields. Every record ends with a separator, including the last record. Empty fields are meaningful and must be retained when splitting.
+
+The first five fields are:
+
+1. Literal `B93Q1`.
+2. The exact 93-character alphabet.
+3. Compact JSON model configuration.
+4. Compact JSON source provenance.
+5. Decimal tensor count (`376`).
+
+Each tensor then has seven fields:
+
+1. Tensor name.
+2. Shape as comma-separated decimal dimensions; empty means scalar.
+3. Kind: `q`, `f` or `i`.
+4. Characters per stored value.
+5. Group size.
+6. Scale payload (empty for `f` and `i`).
+7. Value payload.
+
+The manifest's offsets are zero-based character/byte offsets into the unescaped text. Add one when addressing Scratch's `letter () of ()`. JSON backslash escapes are not part of the decoded string and must not be counted in those offsets.
+
+### Learned weights (`q`)
+
+Every learned weight has exactly one or two Base93 digits. Two-digit numbers are most-significant digit first:
+
+```
+code = digit0                         # one character, 0..92
+code = digit0 * 93 + digit1           # two characters, 0..8648
+center = 46                          # one character
+center = 4324                        # two characters
+weight = (code - center) * scale
+```
+
+The digit `O` represents the central zero code; two-character zero is `OO`. Each group has its own scale, stored losslessly as described below. Python and JavaScript cast the reconstructed weight to float32. Scratch number arithmetic can use the product directly.
+
+Flatten tensors in PyTorch C order. Convolution shapes are `[out_channels, in_channels/groups, height, width]`; linear shapes are `[out_features, in_features]`.
+
+For rank two or higher, treat the first dimension as rows, and the product of all remaining dimensions as row width. For a one-dimensional tensor, use one row. **Groups restart at every row.** A final short group has no padding stored in the value payload.
+
+```
+groups_per_row = ceil(row_width / group_size)
+scale_index = row * groups_per_row + floor(column / group_size)
+```
+
+Indices here are zero-based. Read that scale at `scale_index * 5` in the scale field. Different tensors can have different group sizes; use their own field/manifest value.
+
+Encoder rule, for reproducibility: scale = group maximum absolute weight / center; all-zero groups use scale 1. Round weight/scale to nearest integer with ties to even, clamp to [-center, center], and add center.
+
+### Scales and non-learned float buffers (`f`)
+
+Each scale and each non-learned float buffer value uses **five Base93 digits carrying its IEEE754 float32 bit pattern exactly**. They are metadata/statistics, not extra learned weights. There is no hidden high-precision learned tensor.
+
+Fold five digits into the unsigned integer `u` using repeated `u = u * 93 + digit`. Reject `u > 4294967295`. Convert its bit pattern to a float32. In Scratch arithmetic:
+
+```
+s = floor(u / 2147483648)
+e = floor(u / 8388608) mod 256
+m = u mod 8388608
+
+if e = 0: value = (-1)^s * m * 2^(-149)
+otherwise: value = (-1)^s * (1 + m / 8388608) * 2^(e - 127)
+```
+
+Exponent 255 is invalid for this file. Scratch's numeric range can exactly represent all intermediate unsigned 32-bit integers. `f` records have precision 5, group size 0 and an empty scale field.
+
+### Integer buffers (`i`)
+
+These are the 46 non-learned BatchNorm batch counters. Their payload is decimal integer text (comma separated if an array), precision/group size are zero, and the scale field is empty. They are not needed for inference but are retained for a complete strict-loadable state dictionary.
+
+## Reproducing the export
+
+Download `model.safetensors` and `config.json` from the immutable source revision into `reference_fp32/`, then:
+
+```bash
+python export_base93.py --source reference_fp32 --output recreated
+```
+
+The included precision plan reproduces the exact published text file and SHA256. The script refuses a different source checkpoint. `SHA256SUMS.json` covers the release files.
+
+To repeat the evaluation, reconstruct the inputs identified by `image_manifest.json` and `nara_sources.json`, then run:
+
+```bash
+python evaluate.py --reference reference_fp32 --images images --archives archive_inputs
+```
+
+`image_manifest.json` identifies the pinned COCO parquet file and zero-based row positions within that file. Rows 1000–1015 were calibration; rows 1016–1079 were evaluation. Historical source URLs and hashes are included. Full source photographs are not bundled. See the parent model card for architecture, training provenance, intended use and licensing.

quantized/base93-v3/SHA256SUMS.json

@@ -0,0 +1,30 @@
+{
+  "README.md": "4429db2319c60f14755dec54e75f6639cf7519382a39f81c2b807d3611fffe09",
+  "base93_codec.py": "94fe46fdf3ef8b4de08d827f11b74523302006811c1ccce4108c077fd3461c4d",
+  "calibration.json": "8f8c36e84213af8ebd1bafffd01d778db794919eee5ec63d141d0cee8dda7d15",
+  "config.json": "eaeb49b39f9116bfed0d954e81852dc93476298752787dba88a63c0eec97ccec",
+  "decode_base93.js": "1e32a0b933b537a532a8b8208961409ceff89e5025f65b912bb954dbdd44a3be",
+  "decoded_tensor_hashes.json": "cd6990dc7dc13d2fcf8b7f6fe3b84b711f9476eb7df349b967f189d4012a6d94",
+  "decoder_verification.json": "7296de6d2cd2cee6638fcd7baeba958b5a029c9671fbd37d0abc2c8fd4ad311a",
+  "evaluate.py": "49f36056d5eef2b56a3dec68bb48e33c188fe5c8b0d503591ce998b8cb4694a8",
+  "evaluation/comparison_01.jpg": "70b0b3fb40480d78209d64f49dd071ee8ac462eabda19631bc1d4764751d5e99",
+  "evaluation/comparison_02.jpg": "652c6ba4cdc316629560540843e69f096d2322cbcfc33a3d3ea359581e95fd75",
+  "evaluation/comparison_03.jpg": "febd0388b105ea2dd9a8a67141f49395dd1747aa3eb50d9f5a204e9d2aa8b962",
+  "evaluation/comparison_04.jpg": "138cea89e388ab9e61911b3ad4ea9a48a4c5c92bf5332c274d5aa6f04178e726",
+  "evaluation/comparison_05.jpg": "317ca03858dac621207bf27e0b5654e09742d9f55fdec254e3a833b8f7eaef78",
+  "evaluation/comparison_06.jpg": "39635897df1ae9510df4134453d8426171216bdb524e816ee153a9604e7b90d7",
+  "evaluation/comparison_07.jpg": "776610302d5100e0c0a3257ede57fe4b3282df72f33c5b707e5cc1893a485b10",
+  "evaluation/comparison_08.jpg": "9175a154e983bfe237c113d63c1251f48a1496da82d84dfd346834d01588e4a8",
+  "evaluation/comparison_09.jpg": "6fa6d60a4c89af0d6fc961580f7fe822e7472a6d1898ef9387b4eb8fb5b3fdd4",
+  "evaluation.json": "34e04e511d9fe0fe078a4df9f5255a71b9615ba4851b49238388d4981a62e1cc",
+  "export_base93.py": "087509aae648a84884b8c5311cbd4ae260dbab25ec87bbed303af81a6401c14f",
+  "image_manifest.json": "da415c18cf91fd355a4a9df2672114a2da1eaae009e71cfaeab5a78ad1ce0e9d",
+  "inference.py": "0ac1f382205b42cef0afaa9173017fa660e6859194a65a61b9b9052c437cd476",
+  "manifest.json": "e61f34d64192335a56be492c36dacf5b0593a125872d622db33528c19d51b5ef",
+  "nara_sources.json": "d4551fb5857161ecd72be4f315dc9e643e33700827e7fcf51e90049a6ef0f6ba",
+  "precision_plan.json": "8dab370f2fe7077faf90d4dc7ecd30e241b485a5c762837be4ab1c95d173a03a",
+  "requirements.txt": "c855eb70020dc89f71fd38e44a0be2cfb80f2fe3d442d5ba45c91398c336f83f",
+  "semantic_model.py": "b019358cbcaa214743cbd244d20eb6e59eeff0ab5264093ec0927be8529b25a2",
+  "verify_decoders.js": "e30655fa26627ce046a8efff5802c896d7ffd55d031cd3c2050b5bf04a1ec0dd",
+  "weights_base93.txt": "e9ef164317e7832781753585c5533c781ce134527dd71385ef490cf5cf5fe9ba"
+}

quantized/base93-v3/base93_codec.py

@@ -0,0 +1,149 @@
+"""B93Q1 self-contained, JSON-safe mixed-precision model storage.
+
+93-level and 8649-level symmetric weight quantisation. Buffers and quantisation
+scales are lossless IEEE float32 carried as five Base93 digits. The alphabet is
+the user's exact Scratch alphabet. This is weight storage, not an integer-only
+inference engine.
+"""
+from pathlib import Path
+import json
+import numpy as np
+
+ALPHABET=''.join(chr(i) for i in range(32,127) if i not in (34,92))
+LUT=np.frombuffer(ALPHABET.encode('ascii'),dtype=np.uint8)
+INV=np.full(128,-1,dtype=np.int16)
+INV[LUT]=np.arange(93,dtype=np.int16)
+SEP='\\'
+MAGIC='B93Q1'
+
+def encode_uint(values,digits):
+    values=np.asarray(values,dtype=np.uint64).reshape(-1).copy()
+    if np.any(values>=93**digits):raise ValueError('Integer does not fit')
+    out=np.empty((len(values),digits),dtype=np.uint8)
+    for i in range(digits-1,-1,-1):
+        out[:,i]=LUT[values%93];values//=93
+    return out.tobytes().decode('ascii')
+
+def decode_uint(text,digits):
+    if digits<1 or len(text)%digits:raise ValueError('Invalid digit count')
+    chars=np.frombuffer(text.encode('ascii'),dtype=np.uint8)
+    if np.any(chars>=128):raise ValueError('Non-ASCII digit')
+    codes=INV[chars]
+    if np.any(codes<0):raise ValueError('Invalid Base93 digit')
+    codes=codes.reshape(-1,digits).astype(np.uint64)
+    values=np.zeros(len(codes),dtype=np.uint64)
+    for i in range(digits):values=values*93+codes[:,i]
+    return values
+
+def pack_f32(values):
+    a=np.asarray(values,dtype='<f4').reshape(-1)
+    if not np.isfinite(a).all():raise ValueError('Non-finite float')
+    return encode_uint(a.view('<u4'),5)
+
+def unpack_f32(text):
+    bits=decode_uint(text,5)
+    if np.any(bits>0xffffffff):raise ValueError('Invalid float32 bits')
+    a=bits.astype('<u4').view('<f4')
+    if not np.isfinite(a).all():raise ValueError('Non-finite float')
+    return a
+
+def row_shape(shape):
+    return (shape[0],int(np.prod(shape[1:]))) if len(shape)>=2 else (1,int(np.prod(shape)))
+
+def quantize(values,digits,group_size):
+    a=np.asarray(values,dtype=np.float32)
+    if digits not in (1,2):raise ValueError('Weight precision must be 1 or 2')
+    rows,width=row_shape(a.shape);groups=(width+group_size-1)//group_size
+    padded=np.zeros((rows,groups*group_size),dtype=np.float32)
+    padded[:,:width]=a.reshape(rows,width)
+    blocks=padded.reshape(rows,groups,group_size)
+    qmax=(93**digits-1)//2
+    scales=np.max(np.abs(blocks),axis=-1)/qmax
+    scales=np.where(scales>0,scales,1).astype(np.float32)
+    q=np.rint(blocks/scales[...,None]).clip(-qmax,qmax).astype(np.int32)
+    codes=(q.reshape(rows,-1)[:,:width]+qmax).astype(np.uint16).reshape(-1)
+    restored=(q*scales[...,None]).reshape(rows,-1)[:,:width].reshape(a.shape).astype(np.float32)
+    return restored,scales.reshape(-1),codes
+
+def dequantize(codes,scales,shape,digits,group_size):
+    rows,width=row_shape(shape);groups=(width+group_size-1)//group_size
+    if len(codes)!=rows*width or len(scales)!=rows*groups:raise ValueError('Payload shape mismatch')
+    scales=np.asarray(scales).reshape(rows,groups)
+    per_value=scales[:,np.arange(width)//group_size]
+    qmax=(93**digits-1)//2
+    signed=np.asarray(codes,dtype=np.int32).reshape(rows,width)-qmax
+    return (signed*per_value).astype(np.float32).reshape(shape)
+
+def encode_state(state,parameter_names,config,provenance,plan):
+    fields=[MAGIC,ALPHABET,json.dumps(config,separators=(',',':'),ensure_ascii=True),
+            json.dumps(provenance,separators=(',',':'),ensure_ascii=True),str(len(state))]
+    records=[];position=sum(len(f)+1 for f in fields)
+    for name,t in state.items():
+        a=t.detach().cpu().numpy() if hasattr(t,'detach') else np.asarray(t)
+        shape=list(a.shape);start=position
+        if name in parameter_names:
+            digits,group_size=plan[name]
+            restored,scales,codes=quantize(a,digits,group_size)
+            kind='q';scale_text=pack_f32(scales);payload=encode_uint(codes,digits)
+        elif a.dtype.kind=='f':
+            kind='f';digits=5;group_size=0;scale_text='';payload=pack_f32(a)
+        elif a.dtype.kind in 'iu':
+            kind='i';digits=0;group_size=0;scale_text='';payload=','.join(map(str,a.reshape(-1)))
+        else:raise ValueError('Unsupported dtype '+str(a.dtype))
+        head=[name,','.join(map(str,shape)),kind,str(digits),str(group_size)]
+        scale_start=position+sum(len(f)+1 for f in head)
+        payload_start=scale_start+len(scale_text)+1
+        record=head+[scale_text,payload]
+        if any(SEP in x for x in record):raise ValueError('Separator inside field')
+        fields+=record;position+=sum(len(f)+1 for f in record)
+        records.append({'name':name,'shape':shape,'kind':kind,'chars_per_value':digits,
+            'group_size':group_size,'num_values':int(a.size),'scale_offset':scale_start,
+            'scale_count':len(scale_text)//5,'payload_offset':payload_start,
+            'payload_length':len(payload),'record_offset':start})
+    text=SEP.join(fields)+SEP
+    manifest={'format':MAGIC,'alphabet':ALPHABET,'offset_base':0,'config':config,
+        'provenance':provenance,'text_bytes':len(text.encode('ascii')),
+        'json_string_bytes':len(json.dumps(text,ensure_ascii=True).encode('ascii')),
+        'tensors':records}
+    return text,manifest
+
+def decode_state(text):
+    fields=text.split(SEP)
+    if len(fields)<6 or fields[0]!=MAGIC or fields[1]!=ALPHABET or fields[-1]!='':
+        raise ValueError('Invalid B93Q1 header or terminator')
+    config=json.loads(fields[2]);provenance=json.loads(fields[3]);n=int(fields[4])
+    if len(fields)!=6+n*7:raise ValueError('Incorrect field count')
+    result={}
+    for i in range(n):
+        name,shape,kind,digits,group_size,scale_text,payload=fields[5+i*7:12+i*7]
+        if name in result:raise ValueError('Duplicate tensor')
+        shape=tuple(int(x) for x in shape.split(',')) if shape else ()
+        digits=int(digits);group_size=int(group_size)
+        if kind=='q':
+            if digits not in (1,2) or group_size<1:raise ValueError('Invalid quantisation')
+            a=dequantize(decode_uint(payload,digits),unpack_f32(scale_text),shape,digits,group_size)
+        elif kind=='f':a=unpack_f32(payload).reshape(shape)
+        elif kind=='i':a=np.array([int(x) for x in payload.split(',')],dtype=np.int64).reshape(shape)
+        else:raise ValueError('Unknown tensor encoding')
+        result[name]=a
+    return result,config,provenance
+
+def load_model(path,device='cpu'):
+    import torch
+    from semantic_model import SemanticColorizer
+    state,config,_=decode_state(Path(path).read_text(encoding='ascii'))
+    model=SemanticColorizer(**{k:config[k] for k in ('head','width','queries')})
+    model.load_state_dict({k:torch.from_numpy(v.copy()) for k,v in state.items()},strict=True)
+    return model.to(device).eval()
+
+if __name__=='__main__':
+    import argparse
+    p=argparse.ArgumentParser();p.add_argument('text');p.add_argument('--output',default='decoded_model')
+    args=p.parse_args()
+    from safetensors.torch import save_file
+    import torch
+    arrays,config,provenance=decode_state(Path(args.text).read_text(encoding='ascii'))
+    dest=Path(args.output);dest.mkdir(exist_ok=True,parents=True)
+    save_file({k:torch.from_numpy(v.copy()) for k,v in arrays.items()},str(dest/'model.safetensors'))
+    (dest/'config.json').write_text(json.dumps(config,indent=2))
+    print('Decoded',len(arrays),'tensors to',dest)

quantized/base93-v3/calibration.json

@@ -0,0 +1,345 @@
+{
+  "winner": {
+    "name": "compact_scales_refine8",
+    "mse": 0.8620080910623074,
+    "ab_mae": 0.6051067784428596,
+    "json_string_bytes": 4949484,
+    "upgrade": [
+      "encoder.9.block.0.0.weight",
+      1,
+      32
+    ],
+    "calibration_subset_gain": 0.02464677393436432
+  },
+  "candidates": [
+    {
+      "name": "group32",
+      "group_size": 32,
+      "json_string_bytes": 4828067,
+      "mse": 18.104391634464264,
+      "ab_mae": 3.1036613807082176
+    },
+    {
+      "name": "group64",
+      "group_size": 64,
+      "json_string_bytes": 4526797,
+      "mse": 23.284358993172646,
+      "ab_mae": 3.433950934559107
+    },
+    {
+      "name": "group128",
+      "group_size": 128,
+      "json_string_bytes": 4380453,
+      "mse": 26.695782251656055,
+      "ab_mae": 3.64544490352273
+    },
+    {
+      "name": "group256",
+      "group_size": 256,
+      "json_string_bytes": 4308573,
+      "mse": 23.869282491505146,
+      "ab_mae": 3.38033676892519
+    },
+    {
+      "name": "group64_improvement_per_byte",
+      "group_size": 64,
+      "json_string_bytes": 4934229,
+      "promoted_tensors": [
+        "encoder.1.block.0.0.weight",
+        "encoder.0.0.weight",
+        "encoder.2.block.1.0.weight",
+        "encoder.2.block.0.0.weight",
+        "encoder.2.block.2.0.weight",
+        "encoder.3.block.0.0.weight",
+        "encoder.4.block.0.0.weight",
+        "encoder.5.block.1.0.weight",
+        "encoder.9.block.2.0.weight",
+        "encoder.7.block.1.0.weight",
+        "encoder.8.block.2.0.weight",
+        "encoder.7.block.2.0.weight",
+        "encoder.4.block.1.0.weight",
+        "encoder.12.block.1.0.weight",
+        "encoder.6.block.2.fc2.weight",
+        "encoder.6.block.1.0.weight",
+        "encoder.5.block.2.fc2.weight",
+        "encoder.10.block.0.0.weight",
+        "encoder.15.block.1.0.weight",
+        "lateral.0.weight",
+        "encoder.6.block.0.0.weight",
+        "encoder.8.block.0.0.weight",
+        "encoder.12.block.3.0.weight",
+        "encoder.16.0.weight",
+        "encoder.5.block.2.fc1.weight",
+        "encoder.9.block.0.0.weight",
+        "query_blocks.0.self_attn.out_proj.weight",
+        "residual.weight"
+      ],
+      "mse": 2.5121002276428044,
+      "ab_mae": 1.0109938234090805
+    },
+    {
+      "name": "group64_mse_improvement",
+      "group_size": 64,
+      "json_string_bytes": 4949909,
+      "promoted_tensors": [
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+        "encoder.0.0.weight",
+        "encoder.2.block.2.0.weight",
+        "encoder.8.block.2.0.weight",
+        "encoder.7.block.2.0.weight",
+        "encoder.2.block.0.0.weight",
+        "encoder.3.block.0.0.weight",
+        "encoder.5.block.1.0.weight",
+        "encoder.2.block.1.0.weight",
+        "encoder.4.block.0.0.weight",
+        "encoder.16.0.weight",
+        "encoder.15.block.3.0.weight",
+        "encoder.15.block.1.0.weight",
+        "encoder.7.block.1.0.weight",
+        "encoder.10.block.0.0.weight",
+        "encoder.12.block.1.0.weight",
+        "encoder.6.block.2.fc2.weight",
+        "encoder.4.block.1.0.weight",
+        "encoder.6.block.1.0.weight",
+        "residual.weight"
+      ],
+      "mse": 2.493656279752031,
+      "ab_mae": 1.0097566042095423
+    },
+    {
+      "name": "group256_improvement_per_byte",
+      "group_size": 256,
+      "json_string_bytes": 4944941,
+      "promoted_tensors": [
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+        "encoder.0.0.weight",
+        "encoder.2.block.0.0.weight",
+        "encoder.2.block.2.0.weight",
+        "encoder.3.block.0.0.weight",
+        "encoder.4.block.0.0.weight",
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+        "encoder.4.block.1.0.weight",
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+        "encoder.6.block.2.fc2.weight",
+        "encoder.8.block.2.0.weight",
+        "encoder.12.block.1.0.weight",
+        "lateral.0.weight",
+        "encoder.5.block.2.fc2.weight",
+        "encoder.6.block.0.0.weight",
+        "encoder.6.block.2.fc1.weight",
+        "encoder.5.block.2.fc1.weight",
+        "encoder.15.block.1.0.weight",
+        "encoder.11.block.0.0.weight",
+        "encoder.9.block.0.0.weight",
+        "encoder.11.block.3.0.weight",
+        "encoder.12.block.3.0.weight",
+        "palette.0.weight",
+        "encoder.11.block.2.fc2.weight",
+        "encoder.14.block.2.fc2.weight",
+        "query_blocks.0.ff.2.weight"
+      ],
+      "mse": 2.986875234171748,
+      "ab_mae": 1.0296866707503796
+    },
+    {
+      "name": "group256_mse_improvement",
+      "group_size": 256,
+      "json_string_bytes": 4948653,
+      "promoted_tensors": [
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+        "encoder.5.block.2.fc2.weight",
+        "encoder.6.block.0.0.weight",
+        "lateral.0.weight"
+      ],
+      "mse": 3.097676645964384,
+      "ab_mae": 1.044976083561778
+    }
+  ],
+  "calibration_images": 16,
+  "parameter_count": 3994676,
+  "one_character_parameters": 3512832,
+  "two_character_parameters": 481844,
+  "text_bytes": 4946819,
+  "json_string_bytes": 4949484,
+  "sha256": "e9ef164317e7832781753585c5533c781ce134527dd71385ef490cf5cf5fe9ba",
+  "roundtrip_exact_to_quantized_state": true,
+  "initial_calibration": {
+    "name": "group64_mse_improvement",
+    "group_size": 64,
+    "json_string_bytes": 4949909,
+    "promoted_tensors": [
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+      "encoder.2.block.2.0.weight",
+      "encoder.8.block.2.0.weight",
+      "encoder.7.block.2.0.weight",
+      "encoder.2.block.0.0.weight",
+      "encoder.3.block.0.0.weight",
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+      "encoder.4.block.0.0.weight",
+      "encoder.16.0.weight",
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+      "encoder.10.block.0.0.weight",
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+      "encoder.6.block.2.fc2.weight",
+      "encoder.4.block.1.0.weight",
+      "encoder.6.block.1.0.weight",
+      "residual.weight"
+    ],
+    "mse": 2.493656279752031,
+    "ab_mae": 1.0097566042095423
+  },
+  "refinement_candidates": [
+    {
+      "name": "initial",
+      "mse": 2.493656279752031,
+      "ab_mae": 1.0097566042095423,
+      "json_string_bytes": 4949909
+    },
+    {
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+      "json_string_bytes": 4946379
+    },
+    {
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+      "json_string_bytes": 4948299
+    },
+    {
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+    },
+    {
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+      "upgrade": [
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+    },
+    {
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+      "json_string_bytes": 4927848,
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+    },
+    {
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+      "json_string_bytes": 4928105,
+      "upgrade": [
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+        2,
+        256
+      ],
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+    },
+    {
+      "name": "compact_scales_refine4",
+      "mse": 0.9462693016976118,
+      "ab_mae": 0.6352904969826341,
+      "json_string_bytes": 4942026,
+      "upgrade": [
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+        2,
+        256
+      ],
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+    },
+    {
+      "name": "compact_scales_refine5",
+      "mse": 0.9175981525331736,
+      "ab_mae": 0.6283372240141034,
+      "json_string_bytes": 4943226,
+      "upgrade": [
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+        1,
+        32
+      ],
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+    },
+    {
+      "name": "compact_scales_refine6",
+      "mse": 0.9215640546754003,
+      "ab_mae": 0.6188815664499998,
+      "json_string_bytes": 4944883,
+      "upgrade": [
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+        2,
+        256
+      ],
+      "calibration_subset_gain": 0.08233188092708588
+    },
+    {
+      "name": "compact_scales_refine7",
+      "mse": 0.8778455322608352,
+      "ab_mae": 0.6111623970791698,
+      "json_string_bytes": 4948564,
+      "upgrade": [
+        "encoder.5.block.2.fc1.weight",
+        2,
+        256
+      ],
+      "calibration_subset_gain": 0.040941715240478516
+    },
+    {
+      "name": "compact_scales_refine8",
+      "mse": 0.8620080910623074,
+      "ab_mae": 0.6051067784428596,
+      "json_string_bytes": 4949484,
+      "upgrade": [
+        "encoder.9.block.0.0.weight",
+        1,
+        32
+      ],
+      "calibration_subset_gain": 0.02464677393436432
+    }
+  ],
+  "initial_search_seconds": 43.03861778800001,
+  "selection_note": "All precision selection used 16 calibration inputs (8-image subset for sensitivity ranking); final candidate chosen by mean squared ab error on all 16. Holdout was opened only after final selection."
+}

quantized/base93-v3/config.json

@@ -0,0 +1,7 @@
+{
+  "architecture": "SemanticColorizer",
+  "head": "palette",
+  "width": 128,
+  "queries": 16,
+  "format_version": 1
+}

quantized/base93-v3/decode_base93.js

@@ -0,0 +1,57 @@
+/* B93Q1 reference decoder. Node and browser compatible; no ML runtime needed. */
+const ALPHABET = Array.from({length:95},(_,i)=>String.fromCharCode(i+32)).filter(c=>c.charCodeAt(0)!==34&&c.charCodeAt(0)!==92).join('');
+function digit(c) {
+  const i=ALPHABET.indexOf(c);
+  if(i<0)throw new Error('Invalid Base93 digit');
+  return i;
+}
+function uintAt(s,p,n) {
+  let x=0;
+  for(let i=0;i<n;i++)x=x*93+digit(s[p+i]);
+  return x;
+}
+const floatBytes=new DataView(new ArrayBuffer(4));
+function floatAt(s,p) {
+  const bits=uintAt(s,p,5);
+  if(bits>4294967295)throw new Error('Invalid float bits');
+  floatBytes.setUint32(0,bits,false);
+  const value=floatBytes.getFloat32(0,false);
+  if(!Number.isFinite(value))throw new Error('Invalid float');
+  return value;
+}
+function decodeBase93(text) {
+  const f=text.split('\\');
+  if(f[0]!=='B93Q1'||f[1]!==ALPHABET||f[f.length-1]!=='')throw new Error('Invalid header');
+  const config=JSON.parse(f[2]),provenance=JSON.parse(f[3]),n=Number(f[4]),tensors={};
+  if(f.length!==6+7*n)throw new Error('Invalid record count');
+  for(let t=0;t<n;t++) {
+    const [name,shapeText,kind,precision,groupText,scaleText,payload]=f.slice(5+7*t,12+7*t);
+    if(Object.hasOwn(tensors,name))throw new Error('Duplicate tensor');
+    const shape=shapeText===''?[]:shapeText.split(',').map(Number);
+    const count=shape.reduce((a,b)=>a*b,1),d=Number(precision),g=Number(groupText);
+    let values;
+    if(kind==='q') {
+      if(![1,2].includes(d)||g<1||payload.length!==count*d)throw new Error('Invalid quantized tensor');
+      const width=shape.length>=2?shape.slice(1).reduce((a,b)=>a*b,1):count;
+      const groups=Math.ceil(width/g),rows=count/width;
+      if(scaleText.length!==rows*groups*5)throw new Error('Invalid scales');
+      const scales=Float32Array.from({length:rows*groups},(_,i)=>floatAt(scaleText,5*i));
+      values=new Float32Array(count);
+      const center=(93**d-1)/2;
+      for(let i=0;i<count;i++) {
+        const row=Math.floor(i/width),column=i%width;
+        const si=row*groups+Math.floor(column/g);
+        values[i]=(uintAt(payload,i*d,d)-center)*scales[si];
+      }
+    } else if(kind==='f') {
+      if(payload.length!==count*5)throw new Error('Invalid float tensor');
+      values=Float32Array.from({length:count},(_,i)=>floatAt(payload,5*i));
+    } else if(kind==='i') {
+      values=payload.split(',').map(BigInt);
+      if(values.length!==count)throw new Error('Invalid integer tensor');
+    } else throw new Error('Invalid tensor kind');
+    tensors[name]={shape,kind,values};
+  }
+  return {config,provenance,tensors};
+}
+if(typeof module!=='undefined')module.exports={ALPHABET,decodeBase93};

quantized/base93-v3/decoded_tensor_hashes.json

@@ -0,0 +1,378 @@
+{
+  "queries": "8b3c544aebe22e525efd595f3658c50be9875d7cc0bba352b53499e507f939f3",
+  "rgb_mean": "d32aed199a5cd169c15313d055578913bc0b45eeffb1222dc36d113312762357",
+  "rgb_std": "6c289cec73b3e488e198b9c8e236edbbd1c22e7fce3e189dcb12b1ed87903a21",
+  "encoder.0.0.weight": "1c17881b452a641c04c9c2cd3fe2d2c2ede0988bd04151ef944ddbf018c3c119",
+  "encoder.0.1.weight": "a17ac726191cd0d2814fb21a6b1c43f195067c166dbc2b0965a23dea4a7fcdc9",
+  "encoder.0.1.bias": "e79f80706ed2362d19344c5e729e9adf767c77dd310e59f120305299939020a4",
+  "encoder.0.1.running_mean": "8f1b7aa591611866d13857968a5d6082f58bcda0dc179136c13b399dfaa0edd6",
+  "encoder.0.1.running_var": "31564e22a7defc8574e9cb0dc414d8c05340b0cfc177b2b0bf1f9a2145862683",
+  "encoder.0.1.num_batches_tracked": "2068591d4231cd5803688fc778965320f161c9d097a8caab87b95c4faca642b2",
+  "encoder.1.block.0.0.weight": "14b699aab013a35925c4ada814fdb30bb4ecb6b1461b9a7db60bb0974b0ea2dc",
+  "encoder.1.block.0.1.weight": "b49dc8a569ad886eaeb1cbae59fa8094e3697312a4275a26eae4c8492a6f9f1f",
+  "encoder.1.block.0.1.bias": "9983c9655a4f7748a840c74304e293f33ee399f289a2d4700a8d539fbba21435",
+  "encoder.1.block.0.1.running_mean": "93e9926605d53e343f6c63f924d5822c72ada2f87ad05555c071794ff960096f",
+  "encoder.1.block.0.1.running_var": "414d37d03947315a0f5956641e8b63b7a704d9267f2400ad846c16a9ba844c4e",
+  "encoder.1.block.0.1.num_batches_tracked": "2068591d4231cd5803688fc778965320f161c9d097a8caab87b95c4faca642b2",
+  "encoder.1.block.1.0.weight": "4e8afb7584b5c44b0193ddb6bc06fbb1791f4ff5ec6a8d16845e81e7715d436c",
+  "encoder.1.block.1.1.weight": "bbdff9a1e4f79dffff039cbb5bb05cbe1246d22314df8bc56fb4790bb7cbcf1f",
+  "encoder.1.block.1.1.bias": "6dfcf7ff97bb5495595525fac141616c80defd7a632a1c49e62bbd8335216d29",
+  "encoder.1.block.1.1.running_mean": "b327dc2f663b80b0518ae175e4c5ab5be229d62b10f91abf76a248eadefcd113",
+  "encoder.1.block.1.1.running_var": "0def88d83697a84e33f508f2f2dc11a27ae49fd09a367d5e807e9a06623c66e9",
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quantized/base93-v3/decoder_verification.json

@@ -0,0 +1 @@
+{"tensors":376,"python_javascript_bitwise_match":true,"json_string_bytes":4949484,"sha256":"e9ef164317e7832781753585c5533c781ce134527dd71385ef490cf5cf5fe9ba"}

quantized/base93-v3/evaluate.py

@@ -0,0 +1,62 @@
+"""Held-out output preservation check; does not select or alter quantisation."""
+import argparse,hashlib,json,math
+from pathlib import Path
+import numpy as np
+import torch
+from PIL import Image,ImageDraw,ImageOps
+from semantic_model import load_semantic
+from inference import prepare,chroma_coefficients,render
+from base93_codec import load_model,decode_state
+ROOT=Path(__file__).resolve().parent
+p=argparse.ArgumentParser();p.add_argument('--reference',required=True);p.add_argument('--images',default=str(ROOT/'images'));p.add_argument('--archives',default=str(ROOT/'archive_inputs'));args=p.parse_args()
+OUT=ROOT/'evaluation';OUT.mkdir(exist_ok=True)
+torch.set_num_threads(4)
+reference=load_semantic(Path(args.reference)).eval()
+quantized=load_model(ROOT/'weights_base93.txt')
+source=json.loads((ROOT/'image_manifest.json').read_text())
+records=[dict(r,path=Path(args.images)/r['name'],subset='COCO_holdout') for r in source['rows'] if r['split']=='validation']
+archives=json.loads((ROOT/'nara_sources.json').read_text())
+records += [dict(r,path=Path(args.archives)/r['name'],subset='historical') for r in archives]
+results=[];thumbs=[]
+@torch.inference_mode()
+def run(record,size=256):
+    im=Image.open(record['path']).convert('RGB')
+    light,alpha,x=prepare(im,size)
+    baseline_ab=reference(x);quantized_ab=quantized(x)
+    delta=baseline_ab-quantized_ab
+    baseline=render(light,alpha,chroma_coefficients(reference,x,8))
+    candidate=render(light,alpha,chroma_coefficients(quantized,x,8))
+    aa=np.asarray(baseline,dtype=np.float32);bb=np.asarray(candidate,dtype=np.float32)
+    d=aa-bb;mse=float(np.mean(d*d));mae=float(np.mean(np.abs(d)))
+    row={'name':record['name'],'subset':record['subset'],'size':size,'source_sha256':hashlib.sha256(record['path'].read_bytes()).hexdigest(),'ab_mae':delta.abs().mean().item(),'ab_rmse':delta.square().mean().sqrt().item(),'rgb_mae_255':mae,'rgb_psnr_db':10*math.log10(255**2/max(mse,1e-15)),'rgb_max_difference':float(np.max(np.abs(d)))}
+    if size==256:
+        baseline.save(OUT/(record['name']+'_fp32.png'));candidate.save(OUT/(record['name']+'_base93.png'))
+        neutral=render(light,alpha,(torch.zeros_like(baseline_ab),torch.zeros_like(baseline_ab)))
+        thumbs.append((row,[neutral,baseline,candidate]))
+    return row
+for i,record in enumerate(records):
+    row=run(record);results.append(row)
+    if (i+1)%10==0:print('EVALUATED',i+1,flush=True)
+# Resolution/alpha checks supplement the default-size held-out check.
+large=[run(r,512) for r in records[:4]]
+rgba=Image.open(records[0]['path']).convert('RGBA');arr=np.asarray(rgba).copy();arr[...,3]=np.linspace(0,255,arr.shape[1],dtype=np.uint8)[None,:];rgba=Image.fromarray(arr)
+light,alpha,x=prepare(rgba,256);output=render(light,alpha,chroma_coefficients(quantized,x,8))
+assert np.array_equal(np.asarray(output)[...,3],arr[...,3]) and output.size==rgba.size
+for page in range(math.ceil(len(thumbs)/8)):
+    sheet=Image.new('RGB',(1002,8*264+32),'#202020');draw=ImageDraw.Draw(sheet)
+    for j,label in enumerate(['Grayscale input','Production FP32','Base93 decoded']):draw.text((j*334+8,10),label,fill='white')
+    for r,(row,ims) in enumerate(thumbs[page*8:page*8+8]):
+        y=32+r*264
+        draw.text((8,y),f"{row['name']}  RGB MAE {row['rgb_mae_255']:.2f}/255; PSNR {row['rgb_psnr_db']:.1f} dB",fill='white')
+        for j,im in enumerate(ims):
+            im=ImageOps.contain(im,(330,236));sheet.paste(im,(j*334+(334-im.width)//2,y+23))
+    sheet.save(OUT/f'comparison_{page+1:02}.jpg',quality=92)
+def aggregate(rows):
+    return {'images':len(rows),**{key:float(np.mean([r[key] for r in rows])) for key in ['ab_mae','ab_rmse','rgb_mae_255','rgb_psnr_db']},'worst_rgb_mae':max(rows,key=lambda r:r['rgb_mae_255']),'lowest_psnr':min(rows,key=lambda r:r['rgb_psnr_db'])}
+report={'purpose':'Preservation of production FP32 outputs, not colourisation accuracy or ground-truth recovery. No validation input used to select quantisation.','reference_revision':'1a9eb8af2754ad2329a24cfe50d388cb559441d0','base93_sha256':hashlib.sha256((ROOT/'weights_base93.txt').read_bytes()).hexdigest(),'default_settings':{'size':256,'smoothing_radius':8,'saturation':1},'COCO_holdout':aggregate([r for r in results if r['subset']=='COCO_holdout']),'historical':aggregate([r for r in results if r['subset']=='historical']),'size512':aggregate(large),'alpha_preserved':True,'rows':results,'size512_rows':large}
+(ROOT/'evaluation.json').write_text(json.dumps(report,indent=2))
+print(json.dumps({k:v for k,v in report.items() if k not in ('rows','size512_rows')},indent=2),flush=True)
+# Hash every decoded array in little-endian form for the independent JS decoder.
+arrays,_,_=decode_state((ROOT/'weights_base93.txt').read_text())
+hashes={n:hashlib.sha256(a.astype('<f4' if a.dtype.kind=='f' else '<i8').tobytes()).hexdigest() for n,a in arrays.items()}
+(ROOT/'decoded_tensor_hashes.json').write_text(json.dumps(hashes,indent=2))

quantized/base93-v3/evaluation.json

@@ -0,0 +1,916 @@
+{
+  "purpose": "Preservation of production FP32 outputs, not colourisation accuracy or ground-truth recovery. No validation input used to select quantisation.",
+  "reference_revision": "1a9eb8af2754ad2329a24cfe50d388cb559441d0",
+  "base93_sha256": "e9ef164317e7832781753585c5533c781ce134527dd71385ef490cf5cf5fe9ba",
+  "default_settings": {
+    "size": 256,
+    "smoothing_radius": 8,
+    "saturation": 1
+  },
+  "COCO_holdout": {
+    "images": 64,
+    "ab_mae": 0.5811994269024581,
+    "ab_rmse": 0.7796662729233503,
+    "rgb_mae_255": 0.7840837354306132,
+    "rgb_psnr_db": 47.61341975277594,
+    "worst_rgb_mae": {
+      "name": "coco_1056.jpg",
+      "subset": "COCO_holdout",
+      "size": 256,
+      "source_sha256": "72e91c9eea461ca5656b3f8df0ffb7d03c6d151d1778295f0534e501853a0f0c",
+      "ab_mae": 2.1831459999084473,
+      "ab_rmse": 2.8584671020507812,
+      "rgb_mae_255": 2.98801589012146,
+      "rgb_psnr_db": 35.85841242699141,
+      "rgb_max_difference": 18.0
+    },
+    "lowest_psnr": {
+      "name": "coco_1056.jpg",
+      "subset": "COCO_holdout",
+      "size": 256,
+      "source_sha256": "72e91c9eea461ca5656b3f8df0ffb7d03c6d151d1778295f0534e501853a0f0c",
+      "ab_mae": 2.1831459999084473,
+      "ab_rmse": 2.8584671020507812,
+      "rgb_mae_255": 2.98801589012146,
+      "rgb_psnr_db": 35.85841242699141,
+      "rgb_max_difference": 18.0
+    }
+  },
+  "historical": {
+    "images": 6,
+    "ab_mae": 0.6835264513889948,
+    "ab_rmse": 0.9132436762253443,
+    "rgb_mae_255": 0.9896730780601501,
+    "rgb_psnr_db": 45.81755162620851,
+    "worst_rgb_mae": {
+      "name": "nara_46.jpg",
+      "subset": "historical",
+      "size": 256,
+      "source_sha256": "e7f2881c420c51c1855eb64219dc8c31e9a112976a3c81bfe0bcacb690d6b6e8",
+      "ab_mae": 0.8899980783462524,
+      "ab_rmse": 1.3845140933990479,
+      "rgb_mae_255": 1.4832963943481445,
+      "rgb_psnr_db": 41.02733433669738,
+      "rgb_max_difference": 10.0
+    },
+    "lowest_psnr": {
+      "name": "nara_46.jpg",
+      "subset": "historical",
+      "size": 256,
+      "source_sha256": "e7f2881c420c51c1855eb64219dc8c31e9a112976a3c81bfe0bcacb690d6b6e8",
+      "ab_mae": 0.8899980783462524,
+      "ab_rmse": 1.3845140933990479,
+      "rgb_mae_255": 1.4832963943481445,
+      "rgb_psnr_db": 41.02733433669738,
+      "rgb_max_difference": 10.0
+    }
+  },
+  "size512": {
+    "images": 4,
+    "ab_mae": 0.5359255634248257,
+    "ab_rmse": 0.8267999440431595,
+    "rgb_mae_255": 0.7455227300524712,
+    "rgb_psnr_db": 47.71224340335761,
+    "worst_rgb_mae": {
+      "name": "coco_1019.jpg",
+      "subset": "COCO_holdout",
+      "size": 512,
+      "source_sha256": "70d31beda1c0e3ac96190f69cdae7d39f630a29f4ec337a953ddb4964906ff26",
+      "ab_mae": 1.1465343236923218,
+      "ab_rmse": 1.8872750997543335,
+      "rgb_mae_255": 1.644206166267395,
+      "rgb_psnr_db": 39.03467060849164,
+      "rgb_max_difference": 18.0
+    },
+    "lowest_psnr": {
+      "name": "coco_1019.jpg",
+      "subset": "COCO_holdout",
+      "size": 512,
+      "source_sha256": "70d31beda1c0e3ac96190f69cdae7d39f630a29f4ec337a953ddb4964906ff26",
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+      "name": "coco_1057.jpg",
+      "subset": "COCO_holdout",
+      "size": 256,
+      "source_sha256": "e101bdf508bf0a74adb38301dc97cb0612fa95a84e2a660eb97749b31edec18a",
+      "ab_mae": 0.7829222679138184,
+      "ab_rmse": 1.1600793600082397,
+      "rgb_mae_255": 1.1945466995239258,
+      "rgb_psnr_db": 42.77879854575258,
+      "rgb_max_difference": 9.0
+    },
+    {
+      "name": "coco_1058.jpg",
+      "subset": "COCO_holdout",
+      "size": 256,
+      "source_sha256": "2818f1c1caaf4583919d4a6723350b588f272e06295eb3bedd83623ccad833e5",
+      "ab_mae": 0.8312724232673645,
+      "ab_rmse": 1.1431342363357544,
+      "rgb_mae_255": 0.9047533869743347,
+      "rgb_psnr_db": 44.11219256345834,
+      "rgb_max_difference": 15.0
+    },
+    {
+      "name": "coco_1059.jpg",
+      "subset": "COCO_holdout",
+      "size": 256,
+      "source_sha256": "9551e30c4b72c2c8034424d1da07d5ca23a6f8f27462c2402561e2d83a039069",
+      "ab_mae": 0.7320425510406494,
+      "ab_rmse": 0.9694391489028931,
+      "rgb_mae_255": 1.043619155883789,
+      "rgb_psnr_db": 44.66711942796674,
+      "rgb_max_difference": 6.0
+    },
+    {
+      "name": "coco_1060.jpg",
+      "subset": "COCO_holdout",
+      "size": 256,
+      "source_sha256": "c7953700dc8004aacbff68cb8b2c33e88a29b0ee21110bc66b06bcefae8ca636",
+      "ab_mae": 0.32508137822151184,
+      "ab_rmse": 0.4312102794647217,
+      "rgb_mae_255": 0.351641982793808,
+      "rgb_psnr_db": 52.206696967775315,
+      "rgb_max_difference": 3.0
+    },
+    {
+      "name": "coco_1061.jpg",
+      "subset": "COCO_holdout",
+      "size": 256,
+      "source_sha256": "05435cdebe50a959e357ddd3eb9621559934b8761d3526e57fd9170658ce3d93",
+      "ab_mae": 0.32836607098579407,
+      "ab_rmse": 0.4034212529659271,
+      "rgb_mae_255": 0.3910970091819763,
+      "rgb_psnr_db": 51.83733385774279,
+      "rgb_max_difference": 3.0
+    },
+    {
+      "name": "coco_1062.jpg",
+      "subset": "COCO_holdout",
+      "size": 256,
+      "source_sha256": "e0f6ceccdafeb5578deb7876cbfaa0a19f44a19f580b7123c865a941b6e9dcee",
+      "ab_mae": 0.3735934793949127,
+      "ab_rmse": 0.46995747089385986,
+      "rgb_mae_255": 0.6125932931900024,
+      "rgb_psnr_db": 48.44860969118368,
+      "rgb_max_difference": 6.0
+    },
+    {
+      "name": "coco_1063.jpg",
+      "subset": "COCO_holdout",
+      "size": 256,
+      "source_sha256": "63f866d3b8b97d1087131c18b9f80662ccf10bf864e1a20d64ff733ac731e2e1",
+      "ab_mae": 0.26975545287132263,
+      "ab_rmse": 0.3394005298614502,
+      "rgb_mae_255": 0.3309558928012848,
+      "rgb_psnr_db": 52.70306774507186,
+      "rgb_max_difference": 5.0
+    },
+    {
+      "name": "coco_1064.jpg",
+      "subset": "COCO_holdout",
+      "size": 256,
+      "source_sha256": "bbd1f8687deeaa813f2b57509dabb1e87f7ba532f06c8e79868cc7f1453d0bc1",
+      "ab_mae": 0.8908953070640564,
+      "ab_rmse": 1.110667109489441,
+      "rgb_mae_255": 1.2832040786743164,
+      "rgb_psnr_db": 43.30378890377353,
+      "rgb_max_difference": 5.0
+    },
+    {
+      "name": "coco_1065.jpg",
+      "subset": "COCO_holdout",
+      "size": 256,
+      "source_sha256": "4434c715aa868745ed8d4506dfd812da5e0e6fdf0fba8250c7bded4cd15337b8",
+      "ab_mae": 0.14240413904190063,
+      "ab_rmse": 0.1986604779958725,
+      "rgb_mae_255": 0.18697550892829895,
+      "rgb_psnr_db": 55.411653443308296,
+      "rgb_max_difference": 2.0
+    },
+    {
+      "name": "coco_1066.jpg",
+      "subset": "COCO_holdout",
+      "size": 256,
+      "source_sha256": "14794d8f9a4d7241f55e3dc15794a8063b83bbc0ae95f4b787aca5891a48bc3b",
+      "ab_mae": 0.4939364194869995,
+      "ab_rmse": 0.6235232353210449,
+      "rgb_mae_255": 0.7928873896598816,
+      "rgb_psnr_db": 46.438275796567254,
+      "rgb_max_difference": 5.0
+    },
+    {
+      "name": "coco_1067.jpg",
+      "subset": "COCO_holdout",
+      "size": 256,
+      "source_sha256": "f6f2620707df79c606e0c1cfab462860ece49bee7768f0fb2dbdfac7bdf8346e",
+      "ab_mae": 0.1879967451095581,
+      "ab_rmse": 0.29431673884391785,
+      "rgb_mae_255": 0.22250759601593018,
+      "rgb_psnr_db": 54.23877738675412,
+      "rgb_max_difference": 3.0
+    },
+    {
+      "name": "coco_1068.jpg",
+      "subset": "COCO_holdout",
+      "size": 256,
+      "source_sha256": "fa32398a8441c2a27f5047bc2ad2f9144127f10f2c6663b22f0701679e8e97c3",
+      "ab_mae": 0.30755534768104553,
+      "ab_rmse": 0.4232846796512604,
+      "rgb_mae_255": 0.414189875125885,
+      "rgb_psnr_db": 51.057234237954326,
+      "rgb_max_difference": 4.0
+    },
+    {
+      "name": "coco_1069.jpg",
+      "subset": "COCO_holdout",
+      "size": 256,
+      "source_sha256": "6eda0abfc23d8612800893b44c5a1fe8a4ceb0c43cfc86a3c0deac9a0681c53e",
+      "ab_mae": 0.2967095375061035,
+      "ab_rmse": 0.3827492594718933,
+      "rgb_mae_255": 0.36820080876350403,
+      "rgb_psnr_db": 51.99739590776551,
+      "rgb_max_difference": 11.0
+    },
+    {
+      "name": "coco_1070.jpg",
+      "subset": "COCO_holdout",
+      "size": 256,
+      "source_sha256": "9e5b2e1a12413fa4365e34a459369de6b05b3fe03dea5fe094bd579692413104",
+      "ab_mae": 0.4261647164821625,
+      "ab_rmse": 0.5248377323150635,
+      "rgb_mae_255": 0.652355432510376,
+      "rgb_psnr_db": 48.546061720769956,
+      "rgb_max_difference": 4.0
+    },
+    {
+      "name": "coco_1071.jpg",
+      "subset": "COCO_holdout",
+      "size": 256,
+      "source_sha256": "8d3945f3c30be9a21aed6e9808f39e74a46bb20198f7bd29e24f4279e716af1e",
+      "ab_mae": 0.8692493438720703,
+      "ab_rmse": 1.1637614965438843,
+      "rgb_mae_255": 1.2661339044570923,
+      "rgb_psnr_db": 43.01175399581257,
+      "rgb_max_difference": 6.0
+    },
+    {
+      "name": "coco_1072.jpg",
+      "subset": "COCO_holdout",
+      "size": 256,
+      "source_sha256": "4f3aa179879b53163772b1866e1790e98f36ddc384ee8dace37ec468b604dcba",
+      "ab_mae": 0.5333219170570374,
+      "ab_rmse": 0.6932693719863892,
+      "rgb_mae_255": 0.7168706655502319,
+      "rgb_psnr_db": 47.639591421682475,
+      "rgb_max_difference": 5.0
+    },
+    {
+      "name": "coco_1073.jpg",
+      "subset": "COCO_holdout",
+      "size": 256,
+      "source_sha256": "d35dac662f828ab1b671f9acafaea3f1cc1992e231811806119fe3a063896471",
+      "ab_mae": 0.4666561186313629,
+      "ab_rmse": 0.6421653032302856,
+      "rgb_mae_255": 0.562593400478363,
+      "rgb_psnr_db": 49.05380682816582,
+      "rgb_max_difference": 5.0
+    },
+    {
+      "name": "coco_1074.jpg",
+      "subset": "COCO_holdout",
+      "size": 256,
+      "source_sha256": "a02be69d9dbaf2ebb1e21950d1a0c54238ea6b479b8200c46fd3330de5863236",
+      "ab_mae": 0.2920343577861786,
+      "ab_rmse": 0.37993666529655457,
+      "rgb_mae_255": 0.34981879591941833,
+      "rgb_psnr_db": 51.98822061326059,
+      "rgb_max_difference": 9.0
+    },
+    {
+      "name": "coco_1075.jpg",
+      "subset": "COCO_holdout",
+      "size": 256,
+      "source_sha256": "0d3dd7d85cfcc4cf0ea1d814197ca3c4e419a1ce968b269f5d06fabe0b367f58",
+      "ab_mae": 0.8748445510864258,
+      "ab_rmse": 0.9437631368637085,
+      "rgb_mae_255": 1.1631814241409302,
+      "rgb_psnr_db": 45.176254120891016,
+      "rgb_max_difference": 5.0
+    },
+    {
+      "name": "coco_1076.jpg",
+      "subset": "COCO_holdout",
+      "size": 256,
+      "source_sha256": "9160e969a838a9f2005aac2db2c3e0677879daea9dcacecc96aad010b0d6a42c",
+      "ab_mae": 0.49375951290130615,
+      "ab_rmse": 0.73063725233078,
+      "rgb_mae_255": 0.7047297954559326,
+      "rgb_psnr_db": 47.20944685554747,
+      "rgb_max_difference": 6.0
+    },
+    {
+      "name": "coco_1077.jpg",
+      "subset": "COCO_holdout",
+      "size": 256,
+      "source_sha256": "f3dc76875460b05e6d348a4867e3e44bad554f819f2e4cf25b9b66615d72302c",
+      "ab_mae": 1.1439827680587769,
+      "ab_rmse": 1.5291945934295654,
+      "rgb_mae_255": 1.6052864789962769,
+      "rgb_psnr_db": 40.935771729125044,
+      "rgb_max_difference": 7.0
+    },
+    {
+      "name": "coco_1078.jpg",
+      "subset": "COCO_holdout",
+      "size": 256,
+      "source_sha256": "6774fb650081bd03fedb03b1c26b85de477afa0086cbbf157b2fde2dc03d6628",
+      "ab_mae": 0.25251901149749756,
+      "ab_rmse": 0.3399495482444763,
+      "rgb_mae_255": 0.3604215681552887,
+      "rgb_psnr_db": 52.183212399780075,
+      "rgb_max_difference": 4.0
+    },
+    {
+      "name": "coco_1079.jpg",
+      "subset": "COCO_holdout",
+      "size": 256,
+      "source_sha256": "7d0e069b095e8611362263baac6905e447882dd04e128fd83e85307b733fb753",
+      "ab_mae": 0.8126828670501709,
+      "ab_rmse": 1.1655176877975464,
+      "rgb_mae_255": 1.1020296812057495,
+      "rgb_psnr_db": 43.153547009938094,
+      "rgb_max_difference": 7.0
+    },
+    {
+      "name": "nara_42.jpg",
+      "subset": "historical",
+      "size": 256,
+      "source_sha256": "a73186b25853d95a8e1785d1497923c1052747e8b045c5eac4c05dade60b2e87",
+      "ab_mae": 0.460681289434433,
+      "ab_rmse": 0.6142368316650391,
+      "rgb_mae_255": 0.6225173473358154,
+      "rgb_psnr_db": 48.45927421455151,
+      "rgb_max_difference": 4.0
+    },
+    {
+      "name": "nara_43.jpg",
+      "subset": "historical",
+      "size": 256,
+      "source_sha256": "f22dc07c4bdd0f3dac4b65ff0548395dc9aa88d8169a714fc40524249233b977",
+      "ab_mae": 0.991566002368927,
+      "ab_rmse": 1.2025388479232788,
+      "rgb_mae_255": 1.261067509651184,
+      "rgb_psnr_db": 43.85090309746551,
+      "rgb_max_difference": 9.0
+    },
+    {
+      "name": "nara_44.jpg",
+      "subset": "historical",
+      "size": 256,
+      "source_sha256": "c5e9a3fa57a6a0789e4b1529910e0294d98f245b24d205a1caccd08a5a3069b2",
+      "ab_mae": 0.30323904752731323,
+      "ab_rmse": 0.4101198613643646,
+      "rgb_mae_255": 0.34567946195602417,
+      "rgb_psnr_db": 52.027383772273545,
+      "rgb_max_difference": 3.0
+    },
+    {
+      "name": "nara_45.jpg",
+      "subset": "historical",
+      "size": 256,
+      "source_sha256": "3a3e3900cdb1384ca028a0a8843dcbcf499ffdd34832bc6b2e3f477d520403ae",
+      "ab_mae": 0.9602954983711243,
+      "ab_rmse": 1.226324200630188,
+      "rgb_mae_255": 1.480059266090393,
+      "rgb_psnr_db": 42.0335371241524,
+      "rgb_max_difference": 7.0
+    },
+    {
+      "name": "nara_46.jpg",
+      "subset": "historical",
+      "size": 256,
+      "source_sha256": "e7f2881c420c51c1855eb64219dc8c31e9a112976a3c81bfe0bcacb690d6b6e8",
+      "ab_mae": 0.8899980783462524,
+      "ab_rmse": 1.3845140933990479,
+      "rgb_mae_255": 1.4832963943481445,
+      "rgb_psnr_db": 41.02733433669738,
+      "rgb_max_difference": 10.0
+    },
+    {
+      "name": "nara_49.jpg",
+      "subset": "historical",
+      "size": 256,
+      "source_sha256": "6d46bcc386aabf2dc090b2a05baf2e3ead04bc1274c38d87a60e1806ecfb4bab",
+      "ab_mae": 0.4953787922859192,
+      "ab_rmse": 0.6417282223701477,
+      "rgb_mae_255": 0.7454184889793396,
+      "rgb_psnr_db": 47.50687721211074,
+      "rgb_max_difference": 4.0
+    }
+  ],
+  "size512_rows": [
+    {
+      "name": "coco_1016.jpg",
+      "subset": "COCO_holdout",
+      "size": 512,
+      "source_sha256": "e041ce705b88df703e66213dfed5ee67894a1849c2c1bafcf49ba586d93aad19",
+      "ab_mae": 0.21910063922405243,
+      "ab_rmse": 0.30491358041763306,
+      "rgb_mae_255": 0.3387152850627899,
+      "rgb_psnr_db": 52.250933058951674,
+      "rgb_max_difference": 3.0
+    },
+    {
+      "name": "coco_1017.jpg",
+      "subset": "COCO_holdout",
+      "size": 512,
+      "source_sha256": "62514a521cb0e8893f31ac15ceae6cf8c7e0014becc56297f816a4a92534ef10",
+      "ab_mae": 0.3096548020839691,
+      "ab_rmse": 0.45300573110580444,
+      "rgb_mae_255": 0.38620829582214355,
+      "rgb_psnr_db": 51.290298501126614,
+      "rgb_max_difference": 12.0
+    },
+    {
+      "name": "coco_1018.jpg",
+      "subset": "COCO_holdout",
+      "size": 512,
+      "source_sha256": "b73523841e0dd790e28cfb7b74fa2081ac156e012f56aa6a63ddcb3a9545a8a1",
+      "ab_mae": 0.46841248869895935,
+      "ab_rmse": 0.6620053648948669,
+      "rgb_mae_255": 0.6129611730575562,
+      "rgb_psnr_db": 48.27307144486052,
+      "rgb_max_difference": 6.0
+    },
+    {
+      "name": "coco_1019.jpg",
+      "subset": "COCO_holdout",
+      "size": 512,
+      "source_sha256": "70d31beda1c0e3ac96190f69cdae7d39f630a29f4ec337a953ddb4964906ff26",
+      "ab_mae": 1.1465343236923218,
+      "ab_rmse": 1.8872750997543335,
+      "rgb_mae_255": 1.644206166267395,
+      "rgb_psnr_db": 39.03467060849164,
+      "rgb_max_difference": 18.0
+    }
+  ]
+}

quantized/base93-v3/export_base93.py

@@ -0,0 +1,17 @@
+"""Reproduce the released Base93 file from its immutable FP32 source and plan."""
+import argparse,hashlib,json
+from pathlib import Path
+from semantic_model import load_semantic
+from base93_codec import encode_state
+ROOT=Path(__file__).resolve().parent
+p=argparse.ArgumentParser();p.add_argument('--source',required=True,help='Directory containing production v3 model.safetensors and config.json');p.add_argument('--output',default=str(ROOT));args=p.parse_args()
+source=Path(args.source);out=Path(args.output);out.mkdir(parents=True,exist_ok=True)
+provenance={'repo':'User-2468/mini-unet-colorizer','revision':'1a9eb8af2754ad2329a24cfe50d388cb559441d0','sha256':hashlib.sha256((source/'model.safetensors').read_bytes()).hexdigest()}
+if provenance['sha256']!='ec1f27d74533adc83f7ab3639a091fc4d8738a434dafc7d172c7873c28a9e715':raise ValueError('Source is not the selected production v3 checkpoint')
+model=load_semantic(source)
+plan=json.loads((ROOT/'precision_plan.json').read_text())
+text,manifest=encode_state(model.state_dict(),set(dict(model.named_parameters())),model.config,provenance,plan)
+assert manifest['json_string_bytes']<5_000_000
+(out/'weights_base93.txt').write_bytes(text.encode('ascii'))
+(out/'manifest.json').write_text(json.dumps(manifest,indent=2))
+print(json.dumps({'text_bytes':manifest['text_bytes'],'json_string_bytes':manifest['json_string_bytes'],'sha256':hashlib.sha256(text.encode('ascii')).hexdigest()}))

quantized/base93-v3/image_manifest.json

@@ -0,0 +1,489 @@
+{
+  "dataset": "detection-datasets/coco",
+  "revision": "26ddc382fe75dfc2a0655b5977e296ea10efebce",
+  "file": "default/val/0001.parquet",
+  "config": "default",
+  "source_split": "val",
+  "rows": [
+    {
+      "name": "coco_1000.jpg",
+      "row": 1000,
+      "sha256": "733a103198563276d4f70b5517fd5aba3ce53f4d9dc6d3684d5f8b3413619520",
+      "split": "calibration"
+    },
+    {
+      "name": "coco_1001.jpg",
+      "row": 1001,
+      "sha256": "73d1d5fff4154d5d221f8a7826d64a93c13556068d5826047749e7b1179d6678",
+      "split": "calibration"
+    },
+    {
+      "name": "coco_1002.jpg",
+      "row": 1002,
+      "sha256": "bb7a986f21b54cb4d9f35cdb753e29eb5a029f60ce21260b6103a5a2e3d63a3f",
+      "split": "calibration"
+    },
+    {
+      "name": "coco_1003.jpg",
+      "row": 1003,
+      "sha256": "04a0a063f7f2348a1d18b705903d22f12ef242b5076f6a2580a3242bbb3a8d2a",
+      "split": "calibration"
+    },
+    {
+      "name": "coco_1004.jpg",
+      "row": 1004,
+      "sha256": "4b09a3f5a827a10c3217929a528cc6eef65e9b6db0881da21dbad0dea2eef1cc",
+      "split": "calibration"
+    },
+    {
+      "name": "coco_1005.jpg",
+      "row": 1005,
+      "sha256": "7a22cbb635f1a89081df046549ad066422ca9cee3270b57f66627cd7a0d096c6",
+      "split": "calibration"
+    },
+    {
+      "name": "coco_1006.jpg",
+      "row": 1006,
+      "sha256": "e040b3d434af83c78dab765ebcca1719430fd1194f8d938b2f0294393a5d9f59",
+      "split": "calibration"
+    },
+    {
+      "name": "coco_1007.jpg",
+      "row": 1007,
+      "sha256": "9224c110ba73a25ff895f7e67347be76abd7df7acc588c91539c4a452d6f1788",
+      "split": "calibration"
+    },
+    {
+      "name": "coco_1008.jpg",
+      "row": 1008,
+      "sha256": "0518c35b7359ef5fb2d882dd2c61d95898f413a93307dcd240fe010b3ed771b4",
+      "split": "calibration"
+    },
+    {
+      "name": "coco_1009.jpg",
+      "row": 1009,
+      "sha256": "dd3bddaa96d4d0beb4d8770a77dd76cbd9507f771e9d3c301ef84199ab8c083c",
+      "split": "calibration"
+    },
+    {
+      "name": "coco_1010.jpg",
+      "row": 1010,
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+    {
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+      "sha256": "f3dc76875460b05e6d348a4867e3e44bad554f819f2e4cf25b9b66615d72302c",
+      "split": "validation"
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+    {
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+      "row": 1078,
+      "sha256": "6774fb650081bd03fedb03b1c26b85de477afa0086cbbf157b2fde2dc03d6628",
+      "split": "validation"
+    },
+    {
+      "name": "coco_1079.jpg",
+      "row": 1079,
+      "sha256": "7d0e069b095e8611362263baac6905e447882dd04e128fd83e85307b733fb753",
+      "split": "validation"
+    }
+  ]
+}

quantized/base93-v3/inference.py

@@ -0,0 +1,111 @@
+"""Aspect-preserving colourisation with a compact semantic model.
+
+Input and output are PIL images. No teacher, critic or second learned model is
+loaded. Original lightness and alpha are retained; out-of-gamut chroma is reduced.
+"""
+import json
+import math
+from pathlib import Path
+import numpy as np
+import torch
+import torch.nn.functional as F
+from PIL import Image, ImageOps
+from skimage.color import rgb2lab
+from semantic_model import load_semantic
+
+MAX_PIXELS = 12_000_000
+
+def load_colorizer(path, device='cpu'):
+    model = load_semantic(path, device)
+    count = sum(p.numel() for p in model.parameters())
+    if count >= 4_000_000:
+        raise ValueError('Model exceeds the four-million-parameter limit')
+    return model
+
+def box_mean(x, r):
+    return F.avg_pool2d(x, 2*r+1, 1, r, count_include_pad=False)
+
+def prepare(image, size=256):
+    if not isinstance(image, Image.Image):
+        raise TypeError('Expected a PIL image')
+    if not 128 <= int(size) <= 512:
+        raise ValueError('Input size must be between 128 and 512')
+    image = ImageOps.exif_transpose(image)
+    if image.width * image.height > MAX_PIXELS:
+        raise ValueError('Please resize the image to at most 12 megapixels')
+    alpha = np.asarray(image.getchannel('A')).copy() if 'A' in image.getbands() else None
+    rgb = np.asarray(image.convert('RGB'), dtype=np.float32) / 255
+    light = rgb2lab(rgb)[..., 0].astype(np.float32)
+    scale = min(int(size)/max(image.size), 1.)
+    shape = (max(8, round(image.height*scale)), max(8, round(image.width*scale)))
+    x = torch.from_numpy(light)[None,None]/50-1
+    small = F.interpolate(x, size=shape, mode='bilinear', align_corners=False, antialias=True)
+    return light, alpha, small
+
+@torch.inference_mode()
+def chroma_coefficients(model, small, radius=8):
+    if radius not in (0,4,8,12,16):
+        raise ValueError('Unsupported smoothing radius')
+    device = next(model.parameters()).device
+    L = small.to(device)
+    ab = model(L).float()
+    if not torch.isfinite(ab).all():
+        raise RuntimeError('Model returned non-finite colours')
+    if radius == 0:
+        return torch.zeros_like(ab).cpu(), ab.cpu()
+    guide = (L.float()+1)/2
+    mi, mp = box_mean(guide,radius), box_mean(ab,radius)
+    var = (box_mean(guide*guide,radius)-mi*mi).clamp_min(0)
+    cov = box_mean(guide*ab,radius)-mi*mp
+    a = cov/(var+.001)
+    b = mp-a*mi
+    # Coefficients are upsampled, then evaluated against original-resolution L.
+    return box_mean(a,radius).cpu(), box_mean(b,radius).cpu()
+
+def _linear_rgb(light, ab):
+    fy = (light+16)/116
+    f = np.stack([fy+ab[...,0]/500,fy,fy-ab[...,1]/200],axis=-1)
+    xyz = np.where(f>6/29,f**3,(f-4/29)*(3*(6/29)**2))
+    xyz *= np.array([.95047,1.,1.08883],np.float32)
+    matrix = np.array([[3.24048134,-1.53715152,-.49853633],[-.96925495,1.87599,.04155593],[.05564664,-.20404134,1.05731107]],np.float32)
+    return xyz @ matrix.T
+
+def render(light, alpha, coefficients, saturation=1.):
+    saturation = float(saturation)
+    if not math.isfinite(saturation) or not 0 <= saturation <= 1.5:
+        raise ValueError('Colour strength must be between 0 and 1.5')
+    a,b = [F.interpolate(v.float(),size=light.shape,mode='bilinear',align_corners=False)[0].permute(1,2,0).numpy() for v in coefficients]
+    ab = (a*(light[...,None]/100)+b)*saturation
+    # Binary-search chroma compression retains Lab hue and lightness.
+    linear = _linear_rgb(light,ab)
+    invalid = ((linear < -1e-5)|(linear > 1+1e-5)).any(-1)
+    if invalid.any():
+        L = light[invalid]; colors=ab[invalid];lo=np.zeros(len(L),np.float32);hi=np.ones(len(L),np.float32)
+        for _ in range(9):
+            mid=(lo+hi)/2; candidate=_linear_rgb(L,colors*mid[:,None])
+            valid=((candidate>=-1e-5)&(candidate<=1+1e-5)).all(-1)
+            lo=np.where(valid,mid,lo);hi=np.where(valid,hi,mid)
+        ab[invalid]=colors*lo[:,None]
+        linear[invalid]=_linear_rgb(L,ab[invalid])
+    linear=np.clip(linear,0,1)
+    rgb=np.where(linear<=.0031308,12.92*linear,1.055*np.power(linear,1/2.4)-.055)
+    pixels=np.uint8(np.clip(np.rint(rgb*255),0,255))
+    if alpha is not None: pixels=np.concatenate([pixels,alpha[...,None]],axis=-1)
+    return Image.fromarray(pixels)
+
+def colorize(model, image, size=256, radius=8, saturation=1.):
+    light,alpha,small=prepare(image,size)
+    return render(light,alpha,chroma_coefficients(model,small,radius),saturation)
+
+def main():
+    import argparse
+    parser=argparse.ArgumentParser(description='Compact photo colouriser')
+    parser.add_argument('input');parser.add_argument('output')
+    parser.add_argument('--model',default='.');parser.add_argument('--device',default='cpu')
+    parser.add_argument('--size',type=int,default=256);parser.add_argument('--saturation',type=float,default=1.)
+    args=parser.parse_args()
+    model=load_colorizer(args.model,args.device)
+    with Image.open(args.input) as image:
+        colorize(model,image,args.size,saturation=args.saturation).save(args.output)
+
+if __name__=='__main__':main()

quantized/base93-v3/nara_sources.json

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+[
+  {
+    "name": "nara_42.jpg",
+    "title": "Power station",
+    "source": "https://www.archives.gov/exhibits/picturing_the_century/greatdep/greatdep_img42.html",
+    "image_url": "https://www.archives.gov/exhibits/picturing_the_century/images/greatdep_042_v71.jpg",
+    "sha256": "a73186b25853d95a8e1785d1497923c1052747e8b045c5eac4c05dade60b2e87",
+    "bytes": 47847
+  },
+  {
+    "name": "nara_43.jpg",
+    "title": "Boxing team",
+    "source": "https://www.archives.gov/exhibits/picturing_the_century/greatdep/greatdep_img43.html",
+    "image_url": "https://www.archives.gov/exhibits/picturing_the_century/images/greatdep_043_v69.jpg",
+    "sha256": "f22dc07c4bdd0f3dac4b65ff0548395dc9aa88d8169a714fc40524249233b977",
+    "bytes": 40864
+  },
+  {
+    "name": "nara_44.jpg",
+    "title": "Street corner",
+    "source": "https://www.archives.gov/exhibits/picturing_the_century/greatdep/greatdep_img44.html",
+    "image_url": "https://www.archives.gov/exhibits/picturing_the_century/images/greatdep_044_v73.jpg",
+    "sha256": "c5e9a3fa57a6a0789e4b1529910e0294d98f245b24d205a1caccd08a5a3069b2",
+    "bytes": 68857
+  },
+  {
+    "name": "nara_45.jpg",
+    "title": "Children and sugar beets",
+    "source": "https://www.archives.gov/exhibits/picturing_the_century/greatdep/greatdep_img45.html",
+    "image_url": "https://www.archives.gov/exhibits/picturing_the_century/images/greatdep_045_v75.jpg",
+    "sha256": "3a3e3900cdb1384ca028a0a8843dcbcf499ffdd34832bc6b2e3f477d520403ae",
+    "bytes": 66706
+  },
+  {
+    "name": "nara_46.jpg",
+    "title": "Abandoned house",
+    "source": "https://www.archives.gov/exhibits/picturing_the_century/greatdep/greatdep_img46.html",
+    "image_url": "https://www.archives.gov/exhibits/picturing_the_century/images/greatdep_046_v76.jpg",
+    "sha256": "e7f2881c420c51c1855eb64219dc8c31e9a112976a3c81bfe0bcacb690d6b6e8",
+    "bytes": 74972
+  },
+  {
+    "name": "nara_49.jpg",
+    "title": "Man in his home",
+    "source": "https://www.archives.gov/exhibits/picturing_the_century/greatdep/greatdep_img49.html",
+    "image_url": "https://www.archives.gov/exhibits/picturing_the_century/images/greatdep_049_v72.jpg",
+    "sha256": "6d46bcc386aabf2dc090b2a05baf2e3ead04bc1274c38d87a60e1806ecfb4bab",
+    "bytes": 54941
+  }
+]

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+  ],
+  "query_blocks.0.cross_attn.out_proj.bias": [
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+  ],
+  "query_blocks.0.norms.0.weight": [
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+  ],
+  "query_blocks.0.norms.0.bias": [
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+  ],
+  "query_blocks.0.norms.1.weight": [
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+  "query_blocks.0.norms.2.weight": [
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+  "query_blocks.0.ff.2.weight": [
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+  "query_blocks.0.ff.2.bias": [
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+  ],
+  "query_blocks.1.self_attn.in_proj_weight": [
+    1,
+    64
+  ],
+  "query_blocks.1.self_attn.in_proj_bias": [
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+  ],
+  "query_blocks.1.self_attn.out_proj.weight": [
+    1,
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+  ],
+  "query_blocks.1.self_attn.out_proj.bias": [
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+  ],
+  "query_blocks.1.cross_attn.in_proj_weight": [
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+  ],
+  "query_blocks.1.cross_attn.in_proj_bias": [
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+  ],
+  "query_blocks.1.cross_attn.out_proj.weight": [
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+  ],
+  "query_blocks.1.cross_attn.out_proj.bias": [
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+  ],
+  "query_blocks.1.norms.0.weight": [
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+  "query_blocks.1.norms.1.bias": [
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+  "query_blocks.1.norms.2.weight": [
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+  "query_blocks.1.norms.2.bias": [
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+  "query_blocks.1.ff.0.weight": [
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+  "query_blocks.1.ff.0.bias": [
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+  ],
+  "memory_norm.weight": [
+    2,
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+  ],
+  "memory_norm.bias": [
+    2,
+    256
+  ],
+  "query_norm.weight": [
+    2,
+    256
+  ],
+  "query_norm.bias": [
+    2,
+    256
+  ],
+  "pixel.weight": [
+    1,
+    64
+  ],
+  "pixel.bias": [
+    2,
+    256
+  ],
+  "palette.0.weight": [
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+  ],
+  "palette.0.bias": [
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+  "palette.2.weight": [
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+  ],
+  "palette.2.bias": [
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+  "residual.weight": [
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+  "residual.bias": [
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+  ]
+}

quantized/base93-v3/requirements.txt

@@ -0,0 +1,7 @@
+torch==2.8.0
+torchvision==0.23.0
+numpy==1.26.4
+Pillow==11.1.0
+scikit-image==0.25.2
+safetensors==0.5.3
+huggingface-hub>=1.0,<2

quantized/base93-v3/semantic_model.py

@@ -0,0 +1,94 @@
+"""Compact pretrained semantic colorizer; dense and shared-palette variants."""
+import json
+from pathlib import Path
+import torch
+from torch import nn
+import torch.nn.functional as F
+from torchvision.models import mobilenet_v3_large, MobileNet_V3_Large_Weights
+from safetensors.torch import save_file, load_file
+
+class QueryBlock(nn.Module):
+    def __init__(self,d=96):
+        super().__init__()
+        self.self_attn=nn.MultiheadAttention(d,4,batch_first=True,dropout=0)
+        self.cross_attn=nn.MultiheadAttention(d,4,batch_first=True,dropout=0)
+        self.norms=nn.ModuleList([nn.LayerNorm(d) for _ in range(3)])
+        self.ff=nn.Sequential(nn.Linear(d,2*d),nn.GELU(),nn.Linear(2*d,d))
+    def forward(self,q,memory):
+        x=self.norms[0](q);q=q+self.self_attn(x,x,x,need_weights=False)[0]
+        x=self.norms[1](q);q=q+self.cross_attn(x,memory,memory,need_weights=False)[0]
+        return q+self.ff(self.norms[2](q))
+
+def refine(d):
+    return nn.Sequential(nn.Conv2d(d,d,3,padding=1,bias=False),nn.GroupNorm(8,d),nn.SiLU())
+
+class SemanticColorizer(nn.Module):
+    def __init__(self,head='palette',pretrained=False,width=128,queries=16):
+        super().__init__()
+        if head not in ['palette','dense']:raise ValueError(head)
+        self.config={'architecture':'SemanticColorizer','head':head,'width':width,'queries':queries,'format_version':1}
+        self.encoder=mobilenet_v3_large(weights=MobileNet_V3_Large_Weights.IMAGENET1K_V2 if pretrained else None,progress=False).features
+        self.lateral=nn.ModuleList([nn.Conv2d(c,width,1) for c in [24,40,112,960]])
+        self.refine=nn.ModuleList([refine(width) for _ in range(3)])
+        self.register_buffer('rgb_mean',torch.tensor([.485,.456,.406]).view(1,3,1,1))
+        self.register_buffer('rgb_std',torch.tensor([.229,.224,.225]).view(1,3,1,1))
+        if head=='palette':
+            self.queries=nn.Parameter(torch.randn(queries,width)*.2)
+            self.query_blocks=nn.ModuleList([QueryBlock(width) for _ in range(2)])
+            self.memory_norm=nn.LayerNorm(width)
+            self.query_norm=nn.LayerNorm(width)
+            self.pixel=nn.Conv2d(width,width,1)
+            self.palette=nn.Sequential(nn.Linear(width,width),nn.GELU(),nn.Linear(width,2))
+            self.residual=nn.Conv2d(width,2,1)
+            nn.init.normal_(self.palette[-1].weight,std=.01);nn.init.zeros_(self.palette[-1].bias)
+            nn.init.zeros_(self.residual.weight);nn.init.zeros_(self.residual.bias)
+        else:
+            self.dense=nn.Sequential(refine(width),nn.Conv2d(width,2,1))
+            nn.init.normal_(self.dense[-1].weight,std=.01);nn.init.zeros_(self.dense[-1].bias)
+        count=sum(p.numel() for p in self.parameters())
+        if count>=4_000_000:raise ValueError(f'Parameter budget exceeded: {count}')
+
+    @staticmethod
+    def neutral_rgb(L):
+        light=(L.float()*50+50).clamp(0,100)
+        y=torch.where(light>8,((light+16)/116)**3,light/903.296296)
+        g=torch.where(y<=.0031308,12.92*y,1.055*y.clamp_min(1e-8).pow(1/2.4)-.055)
+        return g.expand(-1,3,-1,-1)
+
+    def forward(self,L):
+        h,w=L.shape[-2:]
+        x=F.pad(self.neutral_rgb(L),(0,(-w)%32,0,(-h)%32),mode='replicate')
+        x=(x-self.rgb_mean)/self.rgb_std
+        features=[]
+        for i,layer in enumerate(self.encoder):
+            x=layer(x)
+            if i in [3,6,12,16]:features.append(x)
+        projected=[layer(f) for layer,f in zip(self.lateral,features)]
+        x=projected[-1]
+        for i in range(2,-1,-1):
+            x=self.refine[2-i](F.interpolate(x,size=projected[i].shape[-2:],mode='bilinear',align_corners=False)+projected[i])
+        if self.config['head']=='palette':
+            memory=torch.cat([F.adaptive_avg_pool2d(f,(8,8)).flatten(2).transpose(1,2) for f in projected[1:]],1)
+            memory=self.memory_norm(memory)
+            q=self.queries[None].expand(L.shape[0],-1,-1)
+            for block in self.query_blocks:q=block(q,memory)
+            q=self.query_norm(q)
+            palette=80*torch.tanh(self.palette(q))
+            masks=torch.einsum('bqd,bdhw->bqhw',q,self.pixel(x))/(self.config['width']**.5)
+            weights=F.softmax(masks.float(),dim=1)
+            ab=torch.einsum('bqhw,bqc->bchw',weights,palette.float())+2*torch.tanh(self.residual(x).float())
+        else:ab=80*torch.tanh(self.dense(x).float())
+        return F.interpolate(ab,size=(x.shape[-2]*4,x.shape[-1]*4),mode='bilinear',align_corners=False)[...,:h,:w]
+    def decode(self,z,temperature=.38):return z.float()
+
+def save_semantic(model,path):
+    path=Path(path);path.mkdir(parents=True,exist_ok=True)
+    save_file({k:v.detach().cpu().contiguous() for k,v in model.state_dict().items()},str(path/'model.safetensors'))
+    (path/'config.json').write_text(json.dumps(model.config,indent=2))
+    (path/'README.md').write_text('# Experimental semantic colorizer\n\nHead: '+model.config['head']+'. Parameters: '+str(sum(p.numel() for p in model.parameters()))+'.\n\nNot approved for production. Requires semantic_model.py; incompatible with the old U-Net loader. Input is Lab lightness normalized to [-1,1]; output is Lab ab. See the run protocol, provenance, selection and visual comparisons. Predictions are plausible colors, not recovered historical truth.\n')
+
+def load_semantic(path,device='cpu'):
+    path=Path(path);cfg=json.loads((path/'config.json').read_text())
+    model=SemanticColorizer(**{k:cfg[k] for k in ['head','width','queries']})
+    model.load_state_dict(load_file(str(path/'model.safetensors')),strict=True)
+    return model.to(device).eval()

quantized/base93-v3/verify_decoders.js

@@ -0,0 +1,18 @@
+// Independent whole-file decoder verification against Python's little-endian hashes.
+const fs=require('fs'),crypto=require('crypto'),assert=require('assert');
+const {decodeBase93}=require('./decode_base93');
+const text=fs.readFileSync(process.argv[2]||__dirname+'/weights_base93.txt','ascii');
+const expected=JSON.parse(fs.readFileSync(__dirname+'/decoded_tensor_hashes.json','utf8'));
+assert.strictEqual(JSON.parse(JSON.stringify(text)),text);
+assert(Buffer.byteLength(JSON.stringify(text),'utf8')<5000000);
+const {tensors}=decodeBase93(text);
+assert.strictEqual(Object.keys(tensors).length,Object.keys(expected).length);
+for(const [name,t] of Object.entries(tensors)) {
+  const bytes=Buffer.alloc(t.values.length*(t.kind==='i'?8:4));
+  for(let i=0;i<t.values.length;i++) {
+    if(t.kind==='i')bytes.writeBigInt64LE(t.values[i],8*i);
+    else bytes.writeFloatLE(t.values[i],4*i);
+  }
+  assert.strictEqual(crypto.createHash('sha256').update(bytes).digest('hex'),expected[name],name);
+}
+console.log(JSON.stringify({tensors:Object.keys(tensors).length,python_javascript_bitwise_match:true,json_string_bytes:Buffer.byteLength(JSON.stringify(text)),sha256:crypto.createHash('sha256').update(text,'ascii').digest('hex')}));

quantized/mini-unet-colorizer-v3-base93.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e822614417cdfee019ea2f328a00cb6715f3edb62abddbb3b7e7953b1128ac74
+size 9148179