feat(samplers): group sampler dropdown into labeled sections

Reorder samplers_data_diffusers into recognizable solver-family groups (Euler, DPM/DPM++, UniPC/DEIS, Heun/KDPM2, ER-SDE, Classic, Distilled, Misc), each ending with its FlowMatch variants, and Res4Lyf as a fenced experimental section, so the dropdown is scannable.

Dividers are SamplerData sentinels with U+2500 names: create_sampler keeps the current scheduler when one is selected, get_sampler_name falls back to Default, set_samplers and validate_sampler_name exclude them, and a visible_samplers() helper drops them from the xyz axes, detailer, and folder pickers. The main and refine dropdowns render them as section labels. No sampler is removed or renamed, so saved infotexts, styles, and API calls keep resolving.

modules/api/helpers.py

@@ -16,6 +16,8 @@ def register_upload_store(getter_fn):
 
 
 def validate_sampler_name(name):
+    if sd_samplers.is_separator(name):  # dropdown divider, not a selectable sampler
+        raise HTTPException(status_code=404, detail="Sampler not found")
     config = sd_samplers.all_samplers_map.get(name, None)
     if config is not None:
         return name

modules/postprocess/yolo.py

@@ -612,7 +612,7 @@ class YoloRestorer(Detailer):
             if tab == 'extras': # fold the standalone sampler settings into the detailer accordion; values applied per-job in make_processing, never global opts
                 from modules import sd_samplers
                 sd_samplers.set_samplers()
-                sampler_choices = [s.name for s in sd_samplers.samplers if s.name != 'Same as primary']
+                sampler_choices = [s.name for s in sd_samplers.visible_samplers() if s.name != 'Same as primary']
                 with gr.Accordion('Sampler', open=False, elem_id=f"{tab}_detailer_sampler_accordion", elem_classes=["small-accordion"]):
                     with gr.Row():
                         d_sampler = gr.Dropdown(label='Sampling method', choices=sampler_choices, value='Default', elem_id=f"{tab}_detailer_sampler")

modules/processing_helpers.py

@@ -162,6 +162,8 @@ def get_sampler_name(sampler_index: int | None = None, img: bool = False) -> str
     else:
         sampler_name = "Default"
         log.warning(f'Sampler not found: index={sampler_index} available={[s.name for s in sd_samplers.samplers]} fallback={sampler_name}')
+    if sd_samplers.is_separator(sampler_name):  # divider row selected, treat as Default
+        sampler_name = "Default"
     if img and sampler_name == "PLMS":
         sampler_name = "Default"
         log.warning(f'Sampler not compatible: name=PLMS fallback={sampler_name}')

modules/sd_samplers.py

@@ -13,6 +13,15 @@ samplers_map = {}
 loaded_config = None
 
 
+def is_separator(name) -> bool:
+    return isinstance(name, str) and name.startswith('─')  # U+2500 box-drawing; dropdown divider rows
+
+
+def visible_samplers(img: bool = False):
+    pool = samplers_for_img2img if img else samplers
+    return [s for s in pool if not is_separator(s.name)]
+
+
 def find_sampler(name:str):
     if name is None or name == 'None':
         return all_samplers_map.get("Default", None)
@@ -66,7 +75,7 @@ def restore_default(model, requested="Default"):
 
 
 def create_sampler(name, model, scheduler_overrides=None):
-    if name is None or name == 'None':
+    if name is None or name == 'None' or is_separator(name):  # separator = dropdown divider, keep current scheduler
         return model.scheduler if model is not None else None
 
     # create default scheduler if it doesnt exist
@@ -172,6 +181,8 @@ def set_samplers():
     samplers_for_img2img = samplers
     samplers_map.clear()
     for sampler in all_samplers:
+        if is_separator(sampler.name):
+            continue
         samplers_map[sampler.name.lower()] = sampler.name
         for alias in sampler.aliases:
             samplers_map[alias.lower()] = sampler.name

modules/sd_samplers_diffusers.py

@@ -238,14 +238,14 @@ config.update({
 samplers_data_diffusers = [
     SamplerData('Default', None, [], {}),
 
-    SamplerData('UniPC', lambda model: DiffusionSampler('UniPC', UniPCMultistepScheduler, model), [], {}),
-    SamplerData('DDIM', lambda model: DiffusionSampler('DDIM', DDIMScheduler, model), [], {}),
+    SamplerData('── Euler ──', None, [], {}),
     SamplerData('Euler', lambda model: DiffusionSampler('Euler', EulerDiscreteScheduler, model), [], {}),
     SamplerData('Euler a', lambda model: DiffusionSampler('Euler a', EulerAncestralDiscreteScheduler, model), [], {}),
     SamplerData('Euler SGM', lambda model: DiffusionSampler('Euler SGM', EulerDiscreteScheduler, model), [], {}),
     SamplerData('Euler EDM', lambda model: DiffusionSampler('Euler EDM', EDMEulerScheduler, model), [], {}),
     SamplerData('Euler FlowMatch', lambda model: DiffusionSampler('Euler FlowMatch', FlowMatchEulerDiscreteScheduler, model), [], {}),
 
+    SamplerData('── DPM / DPM++ ──', None, [], {}),
     SamplerData('DPM++', lambda model: DiffusionSampler('DPM++', DPMSolverMultistepScheduler, model), [], {}),
     SamplerData('DPM++ 2M', lambda model: DiffusionSampler('DPM++ 2M', DPMSolverMultistepScheduler, model), [], {}),
     SamplerData('DPM++ 3M', lambda model: DiffusionSampler('DPM++ 3M', DPMSolverMultistepScheduler, model), [], {}),
@@ -255,12 +255,9 @@ samplers_data_diffusers = [
     SamplerData('DPM++ 2M EDM', lambda model: DiffusionSampler('DPM++ 2M EDM', EDMDPMSolverMultistepScheduler, model), [], {}),
     SamplerData('DPM++ Cosine', lambda model: DiffusionSampler('DPM++ Cosine', CosineDPMSolverMultistepScheduler, model), [], {}),
     SamplerData('DPM SDE', lambda model: DiffusionSampler('DPM SDE', DPMSolverSDEScheduler, model), [], {}),
-
     SamplerData('DPM++ Inverse', lambda model: DiffusionSampler('DPM++ Inverse', DPMSolverMultistepInverseScheduler, model), [], {}),
     SamplerData('DPM++ 2M Inverse', lambda model: DiffusionSampler('DPM++ 2M Inverse', DPMSolverMultistepInverseScheduler, model), [], {}),
     SamplerData('DPM++ 3M Inverse', lambda model: DiffusionSampler('DPM++ 3M Inverse', DPMSolverMultistepInverseScheduler, model), [], {}),
-
-    SamplerData('UniPC FlowMatch', lambda model: DiffusionSampler('UniPC FlowMatch', FlowUniPCMultistepScheduler, model), [], {}),
     SamplerData('DPM2 FlowMatch', lambda model: DiffusionSampler('DPM2 FlowMatch', FlowMatchDPMSolverMultistepScheduler, model), [], {}),
     SamplerData('DPM2a FlowMatch', lambda model: DiffusionSampler('DPM2a FlowMatch', FlowMatchDPMSolverMultistepScheduler, model), [], {}),
     SamplerData('DPM2++ 2M FlowMatch', lambda model: DiffusionSampler('DPM2++ 2M FlowMatch', FlowMatchDPMSolverMultistepScheduler, model), [], {}),
@@ -269,53 +266,75 @@ samplers_data_diffusers = [
     SamplerData('DPM2++ 2M SDE FlowMatch', lambda model: DiffusionSampler('DPM2++ 2M SDE FlowMatch', FlowMatchDPMSolverMultistepScheduler, model), [], {}),
     SamplerData('DPM2++ 3M SDE FlowMatch', lambda model: DiffusionSampler('DPM2++ 3M SDE FlowMatch', FlowMatchDPMSolverMultistepScheduler, model), [], {}),
 
-    SamplerData('Heun', lambda model: DiffusionSampler('Heun', HeunDiscreteScheduler, model), [], {}),
-    SamplerData('Heun FlowMatch', lambda model: DiffusionSampler('Heun FlowMatch', FlowMatchHeunDiscreteScheduler, model), [], {}),
-    SamplerData('Flash FlowMatch', lambda model: DiffusionSampler('Flash FlowMatch', FlashFlowMatchEulerDiscreteScheduler, model), [], {}),
-
+    SamplerData('── UniPC / DEIS ──', None, [], {}),
+    SamplerData('UniPC', lambda model: DiffusionSampler('UniPC', UniPCMultistepScheduler, model), [], {}),
+    SamplerData('UniPC FlowMatch', lambda model: DiffusionSampler('UniPC FlowMatch', FlowUniPCMultistepScheduler, model), [], {}),
     SamplerData('DEIS', lambda model: DiffusionSampler('DEIS', DEISMultistepScheduler, model), [], {}),
     SamplerData('SA Solver', lambda model: DiffusionSampler('SA Solver', SASolverScheduler, model), [], {}),
     SamplerData('DC Solver', lambda model: DiffusionSampler('DC Solver', DCSolverMultistepScheduler, model), [], {}),
 
-    SamplerData('DDPM', lambda model: DiffusionSampler('DDPM', DDPMScheduler, model), [], {}),
-    SamplerData('DDPM Parallel', lambda model: DiffusionSampler('DDPM Parallel', DDPMParallelScheduler, model), [], {}),
-    SamplerData('DDIM Parallel', lambda model: DiffusionSampler('DDIM Parallel', DDIMParallelScheduler, model), [], {}),
-    SamplerData('PNDM', lambda model: DiffusionSampler('PNDM', PNDMScheduler, model), [], {}),
-    SamplerData('IPNDM', lambda model: DiffusionSampler('IPNDM', IPNDMScheduler, model), [], {}),
-    SamplerData('LMSD', lambda model: DiffusionSampler('LMSD', LMSDiscreteScheduler, model), [], {}),
+    SamplerData('── Heun / KDPM2 ──', None, [], {}),
+    SamplerData('Heun', lambda model: DiffusionSampler('Heun', HeunDiscreteScheduler, model), [], {}),
+    SamplerData('Heun FlowMatch', lambda model: DiffusionSampler('Heun FlowMatch', FlowMatchHeunDiscreteScheduler, model), [], {}),
     SamplerData('KDPM2', lambda model: DiffusionSampler('KDPM2', KDPM2DiscreteScheduler, model), [], {}),
     SamplerData('KDPM2 a', lambda model: DiffusionSampler('KDPM2 a', KDPM2AncestralDiscreteScheduler, model), [], {}),
-    SamplerData('CMSI', lambda model: DiffusionSampler('CMSI', CMStochasticIterativeScheduler, model), [], {}),
 
-    SamplerData('VDM Solver', lambda model: DiffusionSampler('VDM Solver', VDMScheduler, model), [], {}),
-    SamplerData('BDIA DDIM', lambda model: DiffusionSampler('BDIA DDIM', BDIA_DDIMScheduler, model), [], {}),
+    SamplerData('── ER-SDE ──', None, [], {}),
     SamplerData('ER-SDE', lambda model: DiffusionSampler('ER-SDE', ERSDEScheduler, model), [], {}),
     SamplerData('ER-SDE 2M', lambda model: DiffusionSampler('ER-SDE 2M', ERSDEScheduler, model), [], {}),
     SamplerData('ER-SDE 3M', lambda model: DiffusionSampler('ER-SDE 3M', ERSDEScheduler, model), [], {}),
     SamplerData('ER-SDE FlowMatch', lambda model: DiffusionSampler('ER-SDE FlowMatch', ERSDEScheduler, model), [], {}),
     SamplerData('ER-SDE 2M FlowMatch', lambda model: DiffusionSampler('ER-SDE 2M FlowMatch', ERSDEScheduler, model), [], {}),
     SamplerData('ER-SDE 3M FlowMatch', lambda model: DiffusionSampler('ER-SDE 3M FlowMatch', ERSDEScheduler, model), [], {}),
+
+    SamplerData('── Classic ──', None, [], {}),
+    SamplerData('DDIM', lambda model: DiffusionSampler('DDIM', DDIMScheduler, model), [], {}),
+    SamplerData('DDIM Parallel', lambda model: DiffusionSampler('DDIM Parallel', DDIMParallelScheduler, model), [], {}),
+    SamplerData('DDPM', lambda model: DiffusionSampler('DDPM', DDPMScheduler, model), [], {}),
+    SamplerData('DDPM Parallel', lambda model: DiffusionSampler('DDPM Parallel', DDPMParallelScheduler, model), [], {}),
+    SamplerData('PNDM', lambda model: DiffusionSampler('PNDM', PNDMScheduler, model), [], {}),
+    SamplerData('IPNDM', lambda model: DiffusionSampler('IPNDM', IPNDMScheduler, model), [], {}),
+    SamplerData('LMSD', lambda model: DiffusionSampler('LMSD', LMSDiscreteScheduler, model), [], {}),
+
+    SamplerData('── Distilled ──', None, [], {}),
     SamplerData('LCM', lambda model: DiffusionSampler('LCM', LCMScheduler, model), [], {}),
     SamplerData('LCM FlowMatch', lambda model: DiffusionSampler('LCM FlowMatch', FlowMatchLCMScheduler, model), [], {}),
     SamplerData('TCD', lambda model: DiffusionSampler('TCD', TCDScheduler, model), [], {}),
     SamplerData('TDD', lambda model: DiffusionSampler('TDD', TDDScheduler, model), [], {}),
     SamplerData('PeRFlow', lambda model: DiffusionSampler('PeRFlow', PeRFlowScheduler, model), [], {}),
     SamplerData('UFOGen', lambda model: DiffusionSampler('UFOGen', UFOGenScheduler, model), [], {}),
+    SamplerData('CMSI', lambda model: DiffusionSampler('CMSI', CMStochasticIterativeScheduler, model), [], {}),
+    SamplerData('Flash FlowMatch', lambda model: DiffusionSampler('Flash FlowMatch', FlashFlowMatchEulerDiscreteScheduler, model), [], {}),
+
+    SamplerData('── Misc / Video ──', None, [], {}),
+    SamplerData('VDM Solver', lambda model: DiffusionSampler('VDM Solver', VDMScheduler, model), [], {}),
+    SamplerData('BDIA DDIM', lambda model: DiffusionSampler('BDIA DDIM', BDIA_DDIMScheduler, model), [], {}),
     SamplerData('CogX DDIM', lambda model: DiffusionSampler('CogX DDIM', CogVideoXDDIMScheduler, model), [], {}),
 
+    SamplerData('──── Res4Lyf ────', None, [], {}),
+
+    SamplerData('── ABNorsett ──', None, [], {}),
     SamplerData('ABNorsett 2M', lambda model: DiffusionSampler('ABNorsett 2M', ABNorsettScheduler, model), [], {}),
     SamplerData('ABNorsett 3M', lambda model: DiffusionSampler('ABNorsett 3M', ABNorsettScheduler, model), [], {}),
     SamplerData('ABNorsett 4M', lambda model: DiffusionSampler('ABNorsett 4M', ABNorsettScheduler, model), [], {}),
+
+    SamplerData('── Lawson ──', None, [], {}),
     SamplerData('Lawson 2S A', lambda model: DiffusionSampler('Lawson 2S A', LawsonScheduler, model), [], {}),
     SamplerData('Lawson 2S B', lambda model: DiffusionSampler('Lawson 2S B', LawsonScheduler, model), [], {}),
     SamplerData('Lawson 4S', lambda model: DiffusionSampler('Lawson 4S', LawsonScheduler, model), [], {}),
+
+    SamplerData('── ETD-RK ──', None, [], {}),
     SamplerData('ETD-RK 2S', lambda model: DiffusionSampler('ETD-RK 2S', ETDRKScheduler, model), [], {}),
     SamplerData('ETD-RK 3S A', lambda model: DiffusionSampler('ETD-RK 3S A', ETDRKScheduler, model), [], {}),
     SamplerData('ETD-RK 3S B', lambda model: DiffusionSampler('ETD-RK 3S B', ETDRKScheduler, model), [], {}),
     SamplerData('ETD-RK 4S A', lambda model: DiffusionSampler('ETD-RK 4S A', ETDRKScheduler, model), [], {}),
     SamplerData('ETD-RK 4S B', lambda model: DiffusionSampler('ETD-RK 4S B', ETDRKScheduler, model), [], {}),
+
+    SamplerData('── PEC ──', None, [], {}),
     SamplerData('PEC 423', lambda model: DiffusionSampler('PEC 423', PECScheduler, model), [], {}),
     SamplerData('PEC 433', lambda model: DiffusionSampler('PEC 433', PECScheduler, model), [], {}),
+
+    SamplerData('── RES ──', None, [], {}),
     SamplerData('RES-Unified 2S', lambda model: DiffusionSampler('RES-Unified 2S', RESUnifiedScheduler, model), [], {}),
     SamplerData('RES-Unified 3S', lambda model: DiffusionSampler('RES-Unified 3S', RESUnifiedScheduler, model), [], {}),
     SamplerData('RES-Unified 2M', lambda model: DiffusionSampler('RES-Unified 2M', RESUnifiedScheduler, model), [], {}),
@@ -326,25 +345,44 @@ samplers_data_diffusers = [
     SamplerData('RES-Multistep 3M', lambda model: DiffusionSampler('RES-Multistep 3M', RESMultistepScheduler, model), [], {}),
     SamplerData('RES-SDE 2S', lambda model: DiffusionSampler('RES-SDE 2S', RESSinglestepSDEScheduler, model), [], {}),
     SamplerData('RES-SDE 3S', lambda model: DiffusionSampler('RES-SDE 3S', RESSinglestepSDEScheduler, model), [], {}),
+
+    SamplerData('── DEIS (Res4Lyf) ──', None, [], {}),
     SamplerData('DEIS-Multistep', lambda model: DiffusionSampler('DEIS-Multistep', RESDEISMultistepScheduler, model), [], {}),
     SamplerData('DEIS-Unified 1S', lambda model: DiffusionSampler('DEIS-Unified 1S', RESUnifiedScheduler, model), [], {}),
     SamplerData('DEIS-Unified 2M', lambda model: DiffusionSampler('DEIS-Unified 2M', RESUnifiedScheduler, model), [], {}),
+
+    SamplerData('── Sigma profiles ──', None, [], {}),
     SamplerData('Sigmoid Sigma', lambda model: DiffusionSampler('Sigmoid Sigma', CommonSigmaScheduler, model), [], {}),
     SamplerData('Sine Sigma', lambda model: DiffusionSampler('Sine Sigma', CommonSigmaScheduler, model), [], {}),
     SamplerData('Easing Sigma', lambda model: DiffusionSampler('Easing Sigma', CommonSigmaScheduler, model), [], {}),
     SamplerData('Arcsine Sigma', lambda model: DiffusionSampler('Arcsine Sigma', CommonSigmaScheduler, model), [], {}),
     SamplerData('Smoothstep Sigma', lambda model: DiffusionSampler('Smoothstep Sigma', CommonSigmaScheduler, model), [], {}),
-    SamplerData('Langevin Dynamics', lambda model: DiffusionSampler('Langevin Dynamics', LangevinDynamicsScheduler, model), [], {}),
+
+    SamplerData('── Riemannian Flow ──', None, [], {}),
     SamplerData('Euclidean Flow', lambda model: DiffusionSampler('Euclidean Flow', RiemannianFlowScheduler, model), [], {}),
     SamplerData('Hyperbolic Flow', lambda model: DiffusionSampler('Hyperbolic Flow', RiemannianFlowScheduler, model), [], {}),
     SamplerData('Spherical Flow', lambda model: DiffusionSampler('Spherical Flow', RiemannianFlowScheduler, model), [], {}),
     SamplerData('Lorentzian Flow', lambda model: DiffusionSampler('Lorentzian Flow', RiemannianFlowScheduler, model), [], {}),
+
+    SamplerData('── Langevin ──', None, [], {}),
+    SamplerData('Langevin Dynamics', lambda model: DiffusionSampler('Langevin Dynamics', LangevinDynamicsScheduler, model), [], {}),
+
+    SamplerData('── Linear-RK ──', None, [], {}),
     SamplerData('Linear-RK 2', lambda model: DiffusionSampler('Linear-RK 2', LinearRKScheduler, model), [], {}),
     SamplerData('Linear-RK 3', lambda model: DiffusionSampler('Linear-RK 3', LinearRKScheduler, model), [], {}),
     SamplerData('Linear-RK 4', lambda model: DiffusionSampler('Linear-RK 4', LinearRKScheduler, model), [], {}),
     SamplerData('Linear-RK Euler', lambda model: DiffusionSampler('Linear-RK Euler', LinearRKScheduler, model), [], {}),
     SamplerData('Linear-RK Heun', lambda model: DiffusionSampler('Linear-RK Heun', LinearRKScheduler, model), [], {}),
     SamplerData('Linear-RK Ralston', lambda model: DiffusionSampler('Linear-RK Ralston', LinearRKScheduler, model), [], {}),
+
+    SamplerData('── Explicit RK ──', None, [], {}),
+    SamplerData('Specialized-RK 3S', lambda model: DiffusionSampler('Specialized-RK 3S', SpecializedRKScheduler, model), [], {}),
+    SamplerData('Specialized-RK 4S', lambda model: DiffusionSampler('Specialized-RK 4S', SpecializedRKScheduler, model), [], {}),
+    SamplerData('Runge-Kutta 4/4', lambda model: DiffusionSampler('Runge-Kutta 4/4', RungeKutta44Scheduler, model), [], {}),
+    SamplerData('Runge-Kutta 5/7', lambda model: DiffusionSampler('Runge-Kutta 5/7', RungeKutta57Scheduler, model), [], {}),
+    SamplerData('Runge-Kutta 6/7', lambda model: DiffusionSampler('Runge-Kutta 6/7', RungeKutta67Scheduler, model), [], {}),
+
+    SamplerData('── Implicit RK ──', None, [], {}),
     SamplerData('Lobatto 2', lambda model: DiffusionSampler('Lobatto 2', LobattoScheduler, model), [], {}),
     SamplerData('Lobatto 3', lambda model: DiffusionSampler('Lobatto 3', LobattoScheduler, model), [], {}),
     SamplerData('Lobatto 4', lambda model: DiffusionSampler('Lobatto 4', LobattoScheduler, model), [], {}),
@@ -354,12 +392,6 @@ samplers_data_diffusers = [
     SamplerData('Gauss-Legendre 2S', lambda model: DiffusionSampler('Gauss-Legendre 2S', GaussLegendreScheduler, model), [], {}),
     SamplerData('Gauss-Legendre 3S', lambda model: DiffusionSampler('Gauss-Legendre 3S', GaussLegendreScheduler, model), [], {}),
     SamplerData('Gauss-Legendre 4S', lambda model: DiffusionSampler('Gauss-Legendre 4S', GaussLegendreScheduler, model), [], {}),
-    SamplerData('Specialized-RK 3S', lambda model: DiffusionSampler('Specialized-RK 3S', SpecializedRKScheduler, model), [], {}),
-    SamplerData('Specialized-RK 4S', lambda model: DiffusionSampler('Specialized-RK 4S', SpecializedRKScheduler, model), [], {}),
-    SamplerData('Runge-Kutta 4/4', lambda model: DiffusionSampler('Runge-Kutta 4/4', RungeKutta44Scheduler, model), [], {}),
-    SamplerData('Runge-Kutta 5/7', lambda model: DiffusionSampler('Runge-Kutta 5/7', RungeKutta57Scheduler, model), [], {}),
-    SamplerData('Runge-Kutta 6/7', lambda model: DiffusionSampler('Runge-Kutta 6/7', RungeKutta67Scheduler, model), [], {}),
-
     SamplerData('Same as primary', None, [], {}),
 ]
 

scripts/i2i_folder.py

@@ -68,7 +68,7 @@ class I2IFolderScript(scripts_manager.Script):
         with gr.Row():
             sampler_override = gr.Dropdown(
                 label="Sampler (empty = use panel)",
-                choices=[""] + [s.name for s in sd_samplers.samplers_for_img2img],
+                choices=[""] + [s.name for s in sd_samplers.visible_samplers(img=True)],
                 value="",
                 elem_id=self.elem_id("sampler_override"),
             )

scripts/xyz/xyz_grid_classes.py

@@ -218,8 +218,8 @@ axis_options = [
     AxisOption("[Process] Model args", str, apply_task_args),
     AxisOption("[Process] Processing args", str, apply_processing),
     AxisOption("[Process] Server options", str, apply_options),
-    AxisOptionTxt2Img("[Sampler] Name", str, apply_sampler, fmt=format_value_add_label, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers]),
-    AxisOptionImg2Img("[Sampler] Name", str, apply_sampler, fmt=format_value_add_label, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers_for_img2img]),
+    AxisOptionTxt2Img("[Sampler] Name", str, apply_sampler, fmt=format_value_add_label, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.visible_samplers()]),
+    AxisOptionImg2Img("[Sampler] Name", str, apply_sampler, fmt=format_value_add_label, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.visible_samplers(img=True)]),
     AxisOption("[Sampler] Sigma method", str, apply_setting("schedulers_sigma"), choices=lambda: ['default', 'karras', 'betas', 'exponential', 'lambdas', 'flowmatch']),
     AxisOption("[Sampler] Sigma adjust", float, apply_setting("schedulers_sigma_adjust")),
     AxisOption("[Sampler] Timestep spacing", str, apply_setting("schedulers_timestep_spacing"), choices=lambda: ['default', 'linspace', 'leading', 'trailing']),
@@ -239,7 +239,7 @@ axis_options = [
     AxisOption("[Guidance] Rescale", float, apply_field("cfg_rescale")),
     AxisOption("[Guidance] Modular name", str, apply_guidance, choices=lambda: ['Default', 'CFG', 'Auto', 'Zero', 'PAG', 'APG', 'SLG', 'SEG', 'TCFG', 'FDG']),
     AxisOption("[Refine] Upscaler", str, apply_field("hr_upscaler"), cost=0.3, choices=lambda: [x.name for x in shared.sd_upscalers]),
-    AxisOption("[Refine] Sampler", str, apply_hr_sampler_name, fmt=format_value_add_label, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers]),
+    AxisOption("[Refine] Sampler", str, apply_hr_sampler_name, fmt=format_value_add_label, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.visible_samplers()]),
     AxisOption("[Refine] Denoising strength", float, apply_field("denoising_strength")),
     AxisOption("[Refine] Hires steps", int, apply_field("hr_second_pass_steps")),
     AxisOption("[Refine] Refiner start", float, apply_field("refiner_start")),