docs(i18n): flag offload always/never lists as needing model reload

They also take effect on a watermark change, not only on model reload,
but the loophole is not worth documenting in the hint.

ui/locale/locale_en.json

@@ -942,8 +942,8 @@
     {"id":"","label":"Model load model direct to GPU","localized":"","hint":"","ui":"settings_sd"},
     {"id":"","label":"Model offload mode","localized":"","hint":"Controls how model components move between VRAM and system RAM to fit larger models on less VRAM.<br>- <b>none</b>: keeps everything on the GPU; fastest, but only works if the whole model fits in VRAM<br>- <b>balanced</b>: the recommended default; offloads only when VRAM use crosses a threshold, so it suits almost any GPU (tuned by the watermarks below)<br>- <b>group</b>: offloads groups of layers via diffusers group offloading; an alternative middle ground with optional stream prefetch<br>- <b>model</b>: offloads whole components such as the VAE or text encoder when idle; a more compatible fallback when balanced or group are unsupported, with smaller savings<br>- <b>sequential</b>: offloads layer by layer; the most memory saving but slowest, for when even balanced runs out of memory<br><br>Command-line flags override the automatic choice:<br>- <code>--lowvram</code>: forces <b>sequential</b><br>- <code>--medvram</code>: forces <b>balanced</b> with low watermark <b>0</b><br><br>With no flag, <b>balanced</b> is the automatic default on any GPU, with watermarks set by GPU memory (low / high):<br>- 12 GB or less: <b>0</b> / <b>0.6</b><br>- 12-24 GB: <b>0.2</b> / <b>0.6</b><br>- 24 GB or more: <b>0.2</b> / <b>0.8</b><br>(or <b>none</b> if no GPU is detected)","reload":"model","ui":"settings_offload"},
     {"id":"","label":"Model types not to offload","localized":"","hint":"Model architectures to skip when offloading, separated by spaces or commas.<br>Useful for model types that misbehave when offloaded.<br><br>Applies only to <b>balanced</b> offload.<br><br>Default is empty.","ui":"settings_offload"},
-    {"id":"","label":"Modules to always offload","localized":"","hint":"Module names that are always offloaded in <b>balanced</b> mode, separated by spaces, commas, or semicolons, regardless of the watermarks.<br><br>Applies only to <b>balanced</b> offload.<br><br>Default by GPU memory: the large text encoders (<i>T5</i>, <i>UMT5</i>) are added at roughly 4-12 GB and at 24 GB or more; empty otherwise.","ui":"settings_offload"},
-    {"id":"","label":"Modules to never offload","localized":"","hint":"Module names that are never offloaded in <b>balanced</b> mode, separated by spaces, commas, or semicolons, keeping them resident in VRAM.<br><br>Applies only to <b>balanced</b> offload.<br><br>Default by GPU memory: the CLIP text encoders and the VAE are kept resident at 24 GB or more; empty otherwise.","ui":"settings_offload"},
+    {"id":"","label":"Modules to always offload","localized":"","hint":"Module names that are always offloaded in <b>balanced</b> mode, separated by spaces, commas, or semicolons, regardless of the watermarks.<br><br>Applies only to <b>balanced</b> offload.<br><br>Default by GPU memory: the large text encoders (<i>T5</i>, <i>UMT5</i>) are added at roughly 4-12 GB and at 24 GB or more; empty otherwise.","reload":"model","ui":"settings_offload"},
+    {"id":"","label":"Modules to never offload","localized":"","hint":"Module names that are never offloaded in <b>balanced</b> mode, separated by spaces, commas, or semicolons, keeping them resident in VRAM.<br><br>Applies only to <b>balanced</b> offload.<br><br>Default by GPU memory: the CLIP text encoders and the VAE are kept resident at 24 GB or more; empty otherwise.","reload":"model","ui":"settings_offload"},
     {"id":"","label":"Model types not to quantize","localized":"","hint":"","ui":"settings_quantization"},
     {"id":"","label":"Modules to not convert","localized":"","hint":"Names of modules to leave unquantized (kept in original precision), separated by spaces, commas, or semicolons.<br>Useful for layers that are sensitive to quantization, such as gate or projection layers. Example: <code>proj_out, x_embedder</code>.<br><br>Some models already exclude sensitive modules by default; entries here extend that built-in list rather than replacing it.<br><br>Default is empty.","reload":"model","ui":"settings_quantization"},
     {"id":"","label":"Modules dtype dict","localized":"","hint":"Advanced: JSON mapping a quantization type to a list of module names, to quantize specific modules at a different type than the global <b><i>Quantization type</i></b>. Example: <code>{\"uint4\": [\"proj_out\"]}</code>.<br><br>Some models already assign certain modules a specific type by default; entries here merge with those built-in mappings rather than replacing them.<br><br>Default is empty.","reload":"model","ui":"settings_quantization"},