Correct sampler hints

html/locale_en.json

@@ -193,10 +193,10 @@
   {"id":"","label":"Init Image Same As Control","localized":"","reload":"","hint":"Will additionally treat any image placed into the Control input window as a source for img2img type tasks, an image to modify for example."},
   {"id":"","label":"Separate Init Image","localized":"","reload":"","hint":"Creates an additional window next to Control input labeled Init input, so you can have a separate image for both Control operations and an init source."},
   {"id":"","label":"Override settings","localized":"","reload":"","hint":"If generation parameters deviate from your system settings override settings populated with those settings to override your system configuration for this workflow"},
-  {"id":"","label":"sigma method","localized":"","reload":"","hint":"Controls how noise levels (sigmas) are distributed across diffusion steps. Options:\n- default: standard linear progression\n- karras: smoother noise schedule, higher quality with fewer steps\n- beta: based on beta schedule values\n- exponential: exponential decay of noise\n- lambdas: experimental, balances signal-to-noise\n- flowmatch: tuned for flow matching models"},
-  {"id":"","label":"timestep spacing","localized":"","reload":"","hint":"Determines how timesteps are spaced across the diffusion process. Options:\n- default: evenly spaced\n- linspace: strict linear spacing\n- leading: more steps at the start (better detail preservation)\n- trailing: more steps at the end (better final refinement)"},
-  {"id":"","label":"beta schedule","localized":"","reload":"","hint":"Defines how beta (noise strength per step) grows. Options:\n- default: standard linear schedule\n- linear: evenly increases noise per step\n- scaled: scaled variance schedule\n- cosine: smoother decay, often better results with fewer steps\n- sigmoid: sharp transition, experimental"},
-  {"id":"","label":"prediction method","localized":"","reload":"","hint":"Defines what the model predicts at each step. Options:\n- default: backend default\n- epsilon: noise (most common for Stable Diffusion)\n- sample: direct denoised image prediction\n- v_prediction: velocity prediction (used in newer SDXL/Flux models, more stable)\n- flow_prediction: prediction tuned for flow-matching samplers"},
+  {"id":"","label":"sigma method","localized":"","reload":"","hint":"Controls how noise levels (sigmas) are distributed across diffusion steps. Options:\n- default: the model default\n- karras: smoother noise schedule, higher quality with fewer steps\n- beta: based on beta schedule values\n- exponential: exponential decay of noise\n- lambdas: experimental, balances signal-to-noise\n- flowmatch: tuned for flow-matching models"},
+  {"id":"","label":"timestep spacing","localized":"","reload":"","hint":"Determines how timesteps are spaced across the diffusion process. Options:\n- default: the model default\n- leading: creates evenly spaced steps\n- linspace: includes the first and last steps and evenly selects the remaining intermediate steps\n- trailing: only includes the last step and evenly selects the remaining intermediate steps starting from the end"},
+  {"id":"","label":"beta schedule","localized":"","reload":"","hint":"Defines how beta (noise strength per step) grows. Options:\n- default: the model default\n- linear: evenly decays noise per step\n- scaled: squared version of linear, used only by Stable Diffusion\n- cosine: smoother decay, often better results with fewer steps\n- sigmoid: sharp transition, experimental"},
+  {"id":"","label":"prediction method","localized":"","reload":"","hint":"Defines what the model predicts at each step. Options:\n- default: the model default\n- epsilon: noise (most common for Stable Diffusion)\n- sample: direct denoised image prediction, also called as x0 prediction\n- v_prediction: velocity prediction, used by CosXL and NoobAI VPred models\n- flow_prediction: used with newer flow-matching models like SD3 and Flux"},
   {"id":"","label":"sampler order","localized":"","reload":"","hint":"Order of solver updates in the sampler. Higher order improves stability/accuracy but increases compute cost."},
   {"id":"","label":"flow shift","localized":"","reload":"","hint":"Adjustment for flow-based samplers. Shifts noise distribution during generation, useful for fine-tuning balance between detail and consistency."},
   {"id":"","label":"resize mode","localized":"","reload":"","hint":"Defines how the input is resized or adapted in second-pass refinement:\n- none: no resizing, keep original resolution\n- fixed: force resize to target resolution (may distort)\n- crop: center-crop to fit target while keeping aspect ratio\n- fill: resize to fit and pad empty space with borders\n- outpaint: extend canvas beyond image borders\n- context aware: smart resize that blends or adapts surrounding areas"}