Merge diffusion-train-precision: grad norm chart + review fixes

# Conflicts:
#	studio/backend/core/training/diffusion_dit_trainer.py
#	studio/backend/core/training/diffusion_lora_trainer.py
#	studio/backend/core/training/diffusion_training_service.py
#	studio/backend/models/training.py
#	studio/frontend/src/features/images/api.ts
#	studio/frontend/src/features/images/train/diffusion-charts.tsx
#	studio/frontend/src/features/images/train/diffusion-train-panel.tsx

studio/backend/core/inference/diffusion_eager_patches.py

@@ -181,6 +181,11 @@ def install_compile_safe_patches() -> int:
     for cls, new_fn in _specs():
         if cls is None:
             continue
+        # torch < 2.4 has no F.rms_norm: leave diffusers' original RMSNorm.forward in
+        # place rather than installing a patch whose fast path would AttributeError.
+        if cls is _RMSNorm and not hasattr(F, "rms_norm"):
+            logger.info("eager-patch: skipping RMSNorm (this torch has no F.rms_norm)")
+            continue
         # Capture the live original BEFORE patching so the RMSNorm fast path can fall back
         # to it for the uncommon (NPU / bias / fp32-weight / tuple-dim) cases.
         if cls is _RMSNorm:

studio/backend/core/training/diffusion_train_common.py

@@ -482,9 +482,10 @@ def discover_image_caption_pairs(
             if sidecar.is_file():
                 caption = sidecar.read_text(encoding = "utf-8").strip()
                 break
-        # 2. metadata row keyed by file name (basename or the name as written).
+        # 2. metadata row keyed by file name (basename or the relative path; as_posix so a
+        #    Windows backslash path still matches the jsonl's forward-slash keys).
         if caption is None:
-            caption = meta_caption.get(img.name) or meta_caption.get(str(img.relative_to(root)))
+            caption = meta_caption.get(img.name) or meta_caption.get(img.relative_to(root).as_posix())
         # 3. dreambooth instance prompt.
         if caption is None and instance_prompt:
             caption = instance_prompt

studio/backend/routes/training.py

@@ -1549,7 +1549,15 @@ def _image_record(
                 caption = None
             break
     if caption is None:
+        # Basename first, then the relative path as written in the jsonl (as_posix so a
+        # Windows backslash path still matches forward-slash keys) -- the same lookup
+        # order discover_image_caption_pairs uses.
         meta = meta_captions.get(image_path.name)
+        if meta is None:
+            try:
+                meta = meta_captions.get(image_path.relative_to(folder).as_posix())
+            except ValueError:
+                meta = None
         if meta is not None:
             caption = meta
             source = "metadata"

studio/frontend/src/features/images/images-page.tsx

@@ -481,7 +481,7 @@ function AdvancedSelect({
   return (
     <div className="flex flex-col gap-1">
       <div className="flex items-center justify-between gap-2">
-        <span className="flex items-center gap-1 text-xs font-medium text-muted-foreground">
+        <span className="flex shrink-0 items-center gap-1 whitespace-nowrap text-xs font-medium text-muted-foreground">
           {label}
           {hint && <InfoHint>{hint}</InfoHint>}
         </span>
@@ -1872,7 +1872,7 @@ export function ImagesPage({ active = true }: { active?: boolean }) {
       {!status?.loaded || status.model_kind === "gguf" ? (
         <AdvancedSelect
           label="GGUF compute"
-          desc="Off runs the GGUF as-is. INT8/FP8/FP4 dequantise the transformer onto low-precision tensor cores for a faster step, at the cost of a larger download and more VRAM."
+          desc="Off runs the GGUF as-is. INT8/FP8/FP4 instead download the base model's bf16 transformer and quantise it directly onto low-precision tensor cores (the GGUF is not requantised): a faster step, at the cost of a larger download and more VRAM."
           hint="Optional speed-up for GGUF models. Off runs the GGUF as-is. FP8/INT8/FP4 instead load the FULL base model and quantise its transformer onto low-precision tensor cores: faster per step, but a larger download and more VRAM, and it falls back to the GGUF if it can't fit. Needs CUDA."
           value={transformerQuant}
           onValueChange={(v) => setTransformerQuant(v as typeof transformerQuant)}

studio/frontend/src/features/images/train/diffusion-charts.tsx

@@ -106,7 +106,6 @@ export function DiffusionCharts({
     () => buildYDomain(gradData.map((p) => p.displayGradNorm)),
     [gradData],
   );
-
   const avgRaw =
     lossItems.length > 0
       ? +(lossItems.reduce((s, p) => s + p.loss, 0) / lossItems.length).toFixed(4)