update reference data and enable preview for ernie and nucleus

Signed-off-by: Vladimir Mandic <mandic00@live.com>

.github/skills/reference-catalog/SKILL.md

@@ -31,6 +31,7 @@ Use this skill to audit and update SD.Next model reference catalogs with minimal
 - Preserve existing field names and conventions used by neighboring entries.
 - Prefer deterministic normalization (stable key order, consistent value style).
 - Do not overwrite real thumbnails with placeholders.
+- For `size` backfill, use `cli/hf-info.py` as the primary source of truth.
 
 ## Validation Checklist
 
@@ -62,13 +63,23 @@ Use this skill to audit and update SD.Next model reference catalogs with minimal
 - Validate field value formats (e.g. size in GB, date format).
 - Ensure that all fields are consistent and not null, empty or contain zero values.  
 
+7. Size backfill checks (`size: 0`)
+- Enumerate all entries with `"size": 0` across `data/reference*.json`.
+- For each Hugging Face repo-style path (`owner/name`), run `cli/hf-info.py`.
+- Parse `data.size` from tool output when present (format is MB string, e.g. `"23933.4MB"`).
+- Convert MB to GB using deterministic rounding: `gb = round(mb / 1024, 2)`.
+- Update only the `size` field for resolvable records; do not modify unrelated fields.
+- If `cli/hf-info.py` returns `ok: false`, missing `data.size`, or non-repo paths, leave `size` unchanged and report as unresolved.
+- Do not invent fallback sizes unless explicitly requested.
+
 ## Safe Edit Workflow
 
 1. Identify target entries and category intent.
 2. Audit only relevant catalog files first.
-3. Propose minimal edits (or apply when asked).
-4. Re-validate JSON and duplicate checks.
-5. Summarize exact changed records and rationale.
+3. Run size backfill using `cli/hf-info.py`.
+4. Propose minimal edits (or apply when asked).
+5. Re-validate JSON and duplicate checks.
+6. Summarize exact changed records and rationale.
 
 ## Common Failure Modes To Prevent
 
@@ -77,6 +88,9 @@ Use this skill to audit and update SD.Next model reference catalogs with minimal
 - Breaking JSON structure while editing by hand
 - Inconsistent key naming across similar entries
 - Creating placeholder thumbnail over an existing asset
+- Running `cli/hf-info.py` with the wrong Python environment/interpreter
+- Treating `subfolder` variants as unsupported when the repo path itself is valid
+- Writing guessed `size` values when `cli/hf-info.py` returns no size
 
 ## Output Contract
 
@@ -87,4 +101,5 @@ When using this skill, provide:
 - Exact records changed (before/after summary)
 - Duplicate/conflict report across catalogs
 - Thumbnail sync result for `models/Reference`
+- `size: 0` backfill report: total candidates, updated count, unresolved count, unresolved reasons
 - Residual risks or follow-up items

CHANGELOG.md

@@ -71,6 +71,7 @@
   - wrap `hf-download` methods
   - additional *typing* and *typechecks*, thanks @awsr
   - refactor `hash-cache` management, thanks @awsr
+  - validate all `reference` jsons and backfill all fields  
 - **Fixes**
   - Prohibit `python==3.14` unless `--experimental`
   - UI CSS fixes, thanks @awsr
@@ -92,7 +93,6 @@
   - patch `z-image` for fp16 compatibility, thanks @resonantsky
   - patch `unipc` for timesteps device placement, thanks @resonantsky
   - `civitai` search and base-model discovery improvements
-  - validate all `reference` jsons
   - ui log formatting  
 
 ## Update for 2026-04-01

data/reference-distilled.json

@@ -62,7 +62,7 @@
     "desc": "ERNIE-Image-Turbo is a distilled ERNIE-Image variant optimized for fast generation with fewer denoising steps.",
     "skip": true,
     "extras": "sampler: Default, cfg_scale: 1.0, steps: 8",
-    "size": 0,
+    "size": 23.37,
     "tags": "distilled",
     "date": "2026 April"
   },
@@ -155,7 +155,7 @@
     "extras": "",
     "tags": "distilled",
     "skip": true,
-    "size": 0,
+    "size": 51.93,
     "date": "2025 August"
   },
 

data/reference-nunchaku.json

@@ -7,7 +7,7 @@
     "skip": true,
     "nunchaku": ["Model", "TE"],
     "tags": "nunchaku",
-    "size": 0,
+    "size": 32.95,
     "date": "2025 June"
   },
   "FLUX.1-Schnell Nunchaku SVDQuant": {
@@ -19,7 +19,7 @@
     "nunchaku": ["Model", "TE"],
     "tags": "nunchaku",
     "extras": "sampler: Default, cfg_scale: 1.0, steps: 4",
-    "size": 0,
+    "size": 32.93,
     "date": "2025 June"
   },
   "FLUX.1-Kontext Nunchaku SVDQuant": {
@@ -30,7 +30,7 @@
     "skip": true,
     "nunchaku": ["Model", "TE"],
     "tags": "nunchaku",
-    "size": 0,
+    "size": 32.95,
     "date": "2025 June"
   },
   "FLUX.1-Krea Nunchaku SVDQuant": {
@@ -41,7 +41,7 @@
     "skip": true,
     "nunchaku": ["Model", "TE"],
     "tags": "nunchaku",
-    "size": 0,
+    "size": 32.95,
     "date": "2025 June"
   },
   "FLUX.1-Fill Nunchaku SVDQuant": {
@@ -53,7 +53,7 @@
     "hidden": true,
     "nunchaku": ["Model", "TE"],
     "tags": "nunchaku",
-    "size": 0,
+    "size": 33.12,
     "date": "2025 June"
   },
   "FLUX.1-Depth Nunchaku SVDQuant": {
@@ -65,7 +65,7 @@
     "hidden": true,
     "nunchaku": ["Model", "TE"],
     "tags": "nunchaku",
-    "size": 0,
+    "size": 42.66,
     "date": "2025 June"
   },
   "Shuttle Jaguar Nunchaku SVDQuant": {
@@ -76,7 +76,7 @@
     "skip": true,
     "nunchaku": ["Model", "TE"],
     "tags": "nunchaku",
-    "size": 0,
+    "size": 32.93,
     "date": "2025 June"
   },
   "Qwen-Image Nunchaku SVDQuant": {
@@ -87,7 +87,7 @@
     "skip": true,
     "nunchaku": ["Model"],
     "tags": "nunchaku",
-    "size": 0,
+    "size": 56.35,
     "date": "2025 June"
   },
   "Qwen-Lightning (8-step) Nunchaku SVDQuant": {
@@ -99,7 +99,7 @@
     "nunchaku": ["Model"],
     "tags": "nunchaku",
     "extras": "steps: 8",
-    "size": 0,
+    "size": 56.35,
     "date": "2025 June"
   },
   "Qwen-Lightning (4-step) Nunchaku SVDQuant": {
@@ -111,7 +111,7 @@
     "nunchaku": ["Model"],
     "tags": "nunchaku",
     "extras": "steps: 4",
-    "size": 0,
+    "size": 56.35,
     "date": "2025 June"
   },
   "Qwen-Image-Edit Nunchaku SVDQuant": {
@@ -122,7 +122,7 @@
     "skip": true,
     "nunchaku": ["Model"],
     "tags": "nunchaku",
-    "size": 0,
+    "size": 56.35,
     "date": "2025 June"
   },
   "Qwen-Lightning-Edit (8-step) Nunchaku SVDQuant": {
@@ -134,7 +134,7 @@
     "nunchaku": ["Model"],
     "tags": "nunchaku",
     "extras": "steps: 8",
-    "size": 0,
+    "size": 56.35,
     "date": "2025 June"
   },
   "Qwen-Lightning-Edit (4-step) Nunchaku SVDQuant": {
@@ -146,7 +146,7 @@
     "nunchaku": ["Model"],
     "tags": "nunchaku",
     "extras": "steps: 4",
-    "size": 0,
+    "size": 56.35,
     "date": "2025 June"
   },
   "Qwen-Image-Edit-2509 Nunchaku SVDQuant": {
@@ -157,7 +157,7 @@
     "skip": true,
     "nunchaku": ["Model"],
     "tags": "nunchaku",
-    "size": 0,
+    "size": 56.35,
     "date": "2025 September"
   },
   "Sana 1.6B 1k Nunchaku SVDQuant": {
@@ -168,7 +168,7 @@
     "skip": true,
     "nunchaku": ["Model"],
     "tags": "nunchaku",
-    "size": 0,
+    "size": 23.3,
     "date": "2025 June"
   },
   "Z-Image-Turbo Nunchaku SVDQuant": {
@@ -180,7 +180,7 @@
     "nunchaku": ["Model"],
     "tags": "nunchaku",
     "extras": "sampler: Default, cfg_scale: 1.0, steps: 9",
-    "size": 0,
+    "size": 32.06,
     "date": "2025 June"
   },
   "SDXL Base Nunchaku SVDQuant": {
@@ -191,7 +191,7 @@
     "skip": true,
     "nunchaku": ["Model"],
     "tags": "nunchaku",
-    "size": 0,
+    "size": 33.55,
     "date": "2025 June"
   },
   "SDXL Turbo Nunchaku SVDQuant": {
@@ -203,7 +203,7 @@
     "nunchaku": ["Model"],
     "tags": "nunchaku",
     "extras": "sampler: Default, cfg_scale: 1.0, steps: 4",
-    "size": 0,
+    "size": 20.33,
     "date": "2025 June"
   }
 }

data/reference.json

@@ -472,7 +472,7 @@
     "desc": "X-Omni: Reinforcement learning makes discrete autoregressive image generative models great again",
     "preview": "X-Omni--X-Omni-SFT.jpg",
     "skip": true,
-    "size": 0,
+    "size": 23.8,
     "date": "2024 September",
     "experimental": true
   },
@@ -577,7 +577,7 @@
     "preview": "hunyuanvideo-community--HunyuanImage-2.1-Diffusers.jpg",
     "extras": "",
     "skip": true,
-    "size": 0,
+    "size": 51.88,
     "date": "2025 August"
   },
   "Tencent HunyuanImage 2.1 Refiner": {
@@ -586,7 +586,7 @@
     "preview": "hunyuanvideo-community--HunyuanImage-2.1-Diffusers.jpg",
     "extras": "",
     "skip": true,
-    "size": 0,
+    "size": 48.01,
     "date": "2025 August"
   },
   "Tencent HunyuanDiT 1.2": {

javascript/logMonitor.js

@@ -31,7 +31,8 @@ async function logMonitor() {
       if (l.level === 'WARNING') logWarnings++;
       if (l.level === 'ERROR') logErrors++;
       const module = `<td style="color: var(--neutral-400)">${l.module}</td>`;
-      const facility = l.facility !== 'sd' ? `<td>${l.facility}</td>` : '<td></td>';
+      const facilityText = l.facility.length > 20 ? `${l.facility.substring(0, 20)}...` : l.facility;
+      const facility = l.facility !== 'sd' ? `<td>${facilityText}</td>` : '<td></td>';
       row.innerHTML = `<td>${dateToStr(l.created)}</td>${level}${facility}${module}<td>${htmlEscape(l.msg)}</td>`;
       logMonitorEl.appendChild(row);
     } catch (e) {

modules/modeldata.py

@@ -80,6 +80,8 @@ def get_model_type(pipe):
         model_type = 'pixartsigma'
     elif "PixArtAlpha" in name:
         model_type = 'pixartalpha'
+    elif 'FIBO' in name:
+        model_type = 'fibo'
     elif "Bria" in name:
         model_type = 'bria'
     elif 'Kolors' in name:

modules/sd_hijack_hfhub.py

@@ -1,4 +1,5 @@
 import os
+import time
 from modules.logger import log
 
 
@@ -14,11 +15,13 @@ def http_get_hijack(*args, **kwargs):
     jobid = state.begin('Download')
     fn = kwargs.get("displayed_filename", None)
     size = kwargs.get("expected_size", None)
-    if fn and not fn.endswith(".json"):
+    if fn and not fn.endswith(".json") and size is not None and size > 10240:
         log.debug(f'Download start: type=http fn="{fn}" size={size}')
     debug(f'Download start: type=http args={args} kwargs={kwargs}')
+    t0 = time.time()
     res = orig_http_get(*args, **kwargs)
-    debug(f'Download end: type=http res={res}')
+    t1 = time.time()
+    debug(f'Download end: type=http res={res} time={t1-t0:.2f} perf={size/(t1-t0)/1024/1024:.2f} MB/s')
     state.end(jobid)
     return res
 

modules/vae/sd_vae_taesd.py

@@ -69,12 +69,12 @@ def get_model(model_type = 'decoder', variant = None):
     elif model_cls in {'f1', 'h1', 'zimage', 'lumina2', 'chroma', 'longcat', 'omnigen2', 'flite', 'ovis', 'kandinsky5', 'glmimage', 'cogview3', 'cogview4'}:
         model_cls = 'f1'
         variant = 'TAE FLUX.1'
-    elif model_cls == 'f2':
+    elif model_cls in {'f2', 'ernieimage'}:
         model_cls = 'f2'
         variant = 'TAE FLUX.2'
-    elif model_cls == 'sd3':
+    elif model_cls in {'sd3'}:
         variant = 'TAE SD3'
-    elif model_cls in {'wanai', 'qwen', 'chrono', 'cosmos'}:
+    elif model_cls in {'wanai', 'qwen', 'chrono', 'cosmos', 'nucleusimage', 'fibo'}:
         variant = variant or 'TAE WanVideo'
     elif model_cls not in supported:
         warn_once(f'cls={shared.sd_model.__class__.__name__} type={shared.sd_model_type} unsuppported', variant=variant)