Merge pull request #15333 from AUTOMATIC1111/scheduler_selection

Scheduler selection in main UI

modules/img2img.py

@@ -146,7 +146,7 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=Fal
     return batch_results
 
 
-def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_name: str, mask_blur: int, mask_alpha: float, inpainting_fill: int, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, selected_scale_tab: int, height: int, width: int, scale_by: float, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, img2img_batch_use_png_info: bool, img2img_batch_png_info_props: list, img2img_batch_png_info_dir: str, request: gr.Request, *args):
+def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, mask_blur: int, mask_alpha: float, inpainting_fill: int, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, selected_scale_tab: int, height: int, width: int, scale_by: float, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, img2img_batch_use_png_info: bool, img2img_batch_png_info_props: list, img2img_batch_png_info_dir: str, request: gr.Request, *args):
     override_settings = create_override_settings_dict(override_settings_texts)
 
     is_batch = mode == 5
@@ -193,10 +193,8 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
         prompt=prompt,
         negative_prompt=negative_prompt,
         styles=prompt_styles,
-        sampler_name=sampler_name,
         batch_size=batch_size,
         n_iter=n_iter,
-        steps=steps,
         cfg_scale=cfg_scale,
         width=width,
         height=height,

modules/processing.py

@@ -152,6 +152,7 @@ class StableDiffusionProcessing:
     seed_resize_from_w: int = -1
     seed_enable_extras: bool = True
     sampler_name: str = None
+    scheduler: str = None
     batch_size: int = 1
     n_iter: int = 1
     steps: int = 50
@@ -721,6 +722,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
     generation_params = {
         "Steps": p.steps,
         "Sampler": p.sampler_name,
+        "Schedule type": p.scheduler,
         "CFG scale": p.cfg_scale,
         "Image CFG scale": getattr(p, 'image_cfg_scale', None),
         "Seed": p.all_seeds[0] if use_main_prompt else all_seeds[index],

modules/processing_scripts/sampler.py

@@ -0,0 +1,79 @@
+import gradio as gr
+import functools
+
+from modules import scripts, sd_samplers, sd_schedulers, shared
+from modules.infotext_utils import PasteField
+from modules.ui_components import FormRow, FormGroup
+
+
+def get_sampler_from_infotext(d: dict):
+    return get_sampler_and_scheduler(d.get("Sampler"), d.get("Schedule type"))[0]
+
+
+def get_scheduler_from_infotext(d: dict):
+    return get_sampler_and_scheduler(d.get("Sampler"), d.get("Schedule type"))[1]
+
+
+@functools.cache
+def get_sampler_and_scheduler(sampler_name, scheduler_name):
+    default_sampler = sd_samplers.samplers[0]
+    found_scheduler = sd_schedulers.schedulers_map.get(scheduler_name, sd_schedulers.schedulers[0])
+
+    name = sampler_name or default_sampler.name
+
+    for scheduler in sd_schedulers.schedulers:
+        name_options = [scheduler.label, scheduler.name, *(scheduler.aliases or [])]
+
+        for name_option in name_options:
+            if name.endswith(" " + name_option):
+                found_scheduler = scheduler
+                name = name[0:-(len(name_option) + 1)]
+                break
+
+    sampler = sd_samplers.all_samplers_map.get(name, default_sampler)
+
+    # revert back to Automatic if it's the default scheduler for the selected sampler
+    if sampler.options.get('scheduler', None) == found_scheduler.name:
+        found_scheduler = sd_schedulers.schedulers[0]
+
+    return sampler.name, found_scheduler.label
+
+
+class ScriptSampler(scripts.ScriptBuiltinUI):
+    section = "sampler"
+
+    def __init__(self):
+        self.steps = None
+        self.sampler_name = None
+        self.scheduler = None
+
+    def title(self):
+        return "Sampler"
+
+    def ui(self, is_img2img):
+        sampler_names = [x.name for x in sd_samplers.visible_samplers()]
+        scheduler_names = [x.label for x in sd_schedulers.schedulers]
+
+        if shared.opts.samplers_in_dropdown:
+            with FormRow(elem_id=f"sampler_selection_{self.tabname}"):
+                self.sampler_name = gr.Dropdown(label='Sampling method', elem_id=f"{self.tabname}_sampling", choices=sampler_names, value=sampler_names[0])
+                self.scheduler = gr.Dropdown(label='Schedule type', elem_id=f"{self.tabname}_scheduler", choices=scheduler_names, value=scheduler_names[0])
+                self.steps = gr.Slider(minimum=1, maximum=150, step=1, elem_id=f"{self.tabname}_steps", label="Sampling steps", value=20)
+        else:
+            with FormGroup(elem_id=f"sampler_selection_{self.tabname}"):
+                self.steps = gr.Slider(minimum=1, maximum=150, step=1, elem_id=f"{self.tabname}_steps", label="Sampling steps", value=20)
+                self.sampler_name = gr.Radio(label='Sampling method', elem_id=f"{self.tabname}_sampling", choices=sampler_names, value=sampler_names[0])
+                self.scheduler = gr.Dropdown(label='Schedule type', elem_id=f"{self.tabname}_scheduler", choices=scheduler_names, value=scheduler_names[0])
+
+        self.infotext_fields = [
+            PasteField(self.steps, "Steps", api="steps"),
+            PasteField(self.sampler_name, get_sampler_from_infotext, api="sampler_name"),
+            PasteField(self.scheduler, get_scheduler_from_infotext, api="scheduler"),
+        ]
+
+        return self.steps, self.sampler_name, self.scheduler
+
+    def setup(self, p, steps, sampler_name, scheduler):
+        p.steps = steps
+        p.sampler_name = sampler_name
+        p.scheduler = scheduler

modules/scripts.py

@@ -352,6 +352,9 @@ class ScriptBuiltinUI(Script):
 
         return f'{tabname}{item_id}'
 
+    def show(self, is_img2img):
+        return AlwaysVisible
+
 
 current_basedir = paths.script_path
 

modules/sd_samplers.py

@@ -1,7 +1,10 @@
-from modules import sd_samplers_kdiffusion, sd_samplers_timesteps, sd_samplers_lcm, shared
+from __future__ import annotations
+
+from modules import sd_samplers_kdiffusion, sd_samplers_timesteps, sd_samplers_lcm, shared, sd_samplers_common
 
 # imports for functions that previously were here and are used by other modules
-from modules.sd_samplers_common import samples_to_image_grid, sample_to_image  # noqa: F401
+samples_to_image_grid = sd_samplers_common.samples_to_image_grid
+sample_to_image = sd_samplers_common.sample_to_image
 
 all_samplers = [
     *sd_samplers_kdiffusion.samplers_data_k_diffusion,
@@ -10,8 +13,8 @@ all_samplers = [
 ]
 all_samplers_map = {x.name: x for x in all_samplers}
 
-samplers = []
+samplers: list[sd_samplers_common.SamplerData] = []
-samplers_for_img2img = []
+samplers_for_img2img: list[sd_samplers_common.SamplerData] = []
 samplers_map = {}
 samplers_hidden = {}
 
@@ -57,4 +60,8 @@ def visible_sampler_names():
     return [x.name for x in samplers if x.name not in samplers_hidden]
 
 
+def visible_samplers():
+    return [x for x in samplers if x.name not in samplers_hidden]
+
+
 set_samplers()

modules/sd_samplers_custom_schedulers.py

@@ -1,12 +0,0 @@
-import torch
-
-
-def sgm_uniform(n, sigma_min, sigma_max, inner_model, device):
-    start = inner_model.sigma_to_t(torch.tensor(sigma_max))
-    end = inner_model.sigma_to_t(torch.tensor(sigma_min))
-    sigs = [
-        inner_model.t_to_sigma(ts)
-        for ts in torch.linspace(start, end, n)[:-1]
-    ]
-    sigs += [0.0]
-    return torch.FloatTensor(sigs).to(device)

modules/sd_samplers_kdiffusion.py

@@ -1,41 +1,28 @@
 import torch
 import inspect
 import k_diffusion.sampling
-from modules import sd_samplers_common, sd_samplers_extra, sd_samplers_cfg_denoiser
+from modules import sd_samplers_common, sd_samplers_extra, sd_samplers_cfg_denoiser, sd_schedulers
 from modules.sd_samplers_cfg_denoiser import CFGDenoiser  # noqa: F401
-from modules.sd_samplers_custom_schedulers import sgm_uniform
 from modules.script_callbacks import ExtraNoiseParams, extra_noise_callback
 
 from modules.shared import opts
 import modules.shared as shared
 
 samplers_k_diffusion = [
-    ('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
+    ('DPM++ 2M', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {'scheduler': 'karras'}),
-    ('DPM++ SDE Karras', 'sample_dpmpp_sde', ['k_dpmpp_sde_ka'], {'scheduler': 'karras', "second_order": True, "brownian_noise": True}),
+    ('DPM++ SDE', 'sample_dpmpp_sde', ['k_dpmpp_sde'], {'scheduler': 'karras', "second_order": True, "brownian_noise": True}),
-    ('DPM++ 2M SDE Exponential', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_exp'], {'scheduler': 'exponential', "brownian_noise": True}),
+    ('DPM++ 2M SDE', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde'], {'scheduler': 'exponential', "brownian_noise": True}),
-    ('DPM++ 2M SDE Karras', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {'scheduler': 'karras', "brownian_noise": True}),
+    ('DPM++ 2M SDE Heun', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_heun'], {'scheduler': 'exponential', "brownian_noise": True, "solver_type": "heun"}),
+    ('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {'scheduler': 'karras', "uses_ensd": True, "second_order": True}),
+    ('DPM++ 3M SDE', 'sample_dpmpp_3m_sde', ['k_dpmpp_3m_sde'], {'scheduler': 'exponential', 'discard_next_to_last_sigma': True, "brownian_noise": True}),
     ('Euler a', 'sample_euler_ancestral', ['k_euler_a', 'k_euler_ancestral'], {"uses_ensd": True}),
     ('Euler', 'sample_euler', ['k_euler'], {}),
     ('LMS', 'sample_lms', ['k_lms'], {}),
     ('Heun', 'sample_heun', ['k_heun'], {"second_order": True}),
-    ('DPM2', 'sample_dpm_2', ['k_dpm_2'], {'discard_next_to_last_sigma': True, "second_order": True}),
+    ('DPM2', 'sample_dpm_2', ['k_dpm_2'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "second_order": True}),
-    ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {'discard_next_to_last_sigma': True, "uses_ensd": True, "second_order": True}),
+    ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True, "second_order": True}),
-    ('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {"uses_ensd": True, "second_order": True}),
-    ('DPM++ 2M', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),
-    ('DPM++ SDE', 'sample_dpmpp_sde', ['k_dpmpp_sde'], {"second_order": True, "brownian_noise": True}),
-    ('DPM++ 2M SDE', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {"brownian_noise": True}),
-    ('DPM++ 2M SDE Heun', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_heun'], {"brownian_noise": True, "solver_type": "heun"}),
-    ('DPM++ 2M SDE Heun Karras', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_heun_ka'], {'scheduler': 'karras', "brownian_noise": True, "solver_type": "heun"}),
-    ('DPM++ 2M SDE Heun Exponential', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_heun_exp'], {'scheduler': 'exponential', "brownian_noise": True, "solver_type": "heun"}),
-    ('DPM++ 3M SDE', 'sample_dpmpp_3m_sde', ['k_dpmpp_3m_sde'], {'discard_next_to_last_sigma': True, "brownian_noise": True}),
-    ('DPM++ 3M SDE Karras', 'sample_dpmpp_3m_sde', ['k_dpmpp_3m_sde_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "brownian_noise": True}),
-    ('DPM++ 3M SDE Exponential', 'sample_dpmpp_3m_sde', ['k_dpmpp_3m_sde_exp'], {'scheduler': 'exponential', 'discard_next_to_last_sigma': True, "brownian_noise": True}),
     ('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {"uses_ensd": True}),
     ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {"uses_ensd": True}),
-    ('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}),
-    ('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True, "second_order": True}),
-    ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True, "second_order": True}),
-    ('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras', "uses_ensd": True, "second_order": True}),
     ('Restart', sd_samplers_extra.restart_sampler, ['restart'], {'scheduler': 'karras', "second_order": True}),
 ]
 
@@ -59,13 +46,7 @@ sampler_extra_params = {
 }
 
 k_diffusion_samplers_map = {x.name: x for x in samplers_data_k_diffusion}
-k_diffusion_scheduler = {
+k_diffusion_scheduler = {x.name: x.function for x in sd_schedulers.schedulers}
-    'Automatic': None,
-    'karras': k_diffusion.sampling.get_sigmas_karras,
-    'exponential': k_diffusion.sampling.get_sigmas_exponential,
-    'polyexponential': k_diffusion.sampling.get_sigmas_polyexponential,
-    'sgm_uniform' : sgm_uniform,
-}
 
 
 class CFGDenoiserKDiffusion(sd_samplers_cfg_denoiser.CFGDenoiser):
@@ -98,47 +79,41 @@ class KDiffusionSampler(sd_samplers_common.Sampler):
 
         steps += 1 if discard_next_to_last_sigma else 0
 
+        scheduler_name = p.scheduler or 'Automatic'
+        if scheduler_name == 'Automatic':
+            scheduler_name = self.config.options.get('scheduler', None)
+
+        scheduler = sd_schedulers.schedulers_map.get(scheduler_name)
+
+        m_sigma_min, m_sigma_max = self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item()
+        sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (m_sigma_min, m_sigma_max)
+
         if p.sampler_noise_scheduler_override:
             sigmas = p.sampler_noise_scheduler_override(steps)
-        elif opts.k_sched_type != "Automatic":
+        elif scheduler is None or scheduler.function is None:
-            m_sigma_min, m_sigma_max = (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
+            sigmas = self.model_wrap.get_sigmas(steps)
-            sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (m_sigma_min, m_sigma_max)
+        else:
-            sigmas_kwargs = {
+            sigmas_kwargs = {'sigma_min': sigma_min, 'sigma_max': sigma_max}
-                'sigma_min': sigma_min,
-                'sigma_max': sigma_max,
-            }
 
-            sigmas_func = k_diffusion_scheduler[opts.k_sched_type]
+            if scheduler.label != 'Automatic':
-            p.extra_generation_params["Schedule type"] = opts.k_sched_type
+                p.extra_generation_params["Schedule type"] = scheduler.label
 
-            if opts.sigma_min != m_sigma_min and opts.sigma_min != 0:
+            if opts.sigma_min != 0 and opts.sigma_min != m_sigma_min:
                 sigmas_kwargs['sigma_min'] = opts.sigma_min
                 p.extra_generation_params["Schedule min sigma"] = opts.sigma_min
-            if opts.sigma_max != m_sigma_max and opts.sigma_max != 0:
+
+            if opts.sigma_max != 0 and opts.sigma_max != m_sigma_max:
                 sigmas_kwargs['sigma_max'] = opts.sigma_max
                 p.extra_generation_params["Schedule max sigma"] = opts.sigma_max
 
-            default_rho = 1. if opts.k_sched_type == "polyexponential" else 7.
+            if scheduler.default_rho != -1 and opts.rho != 0 and opts.rho != scheduler.default_rho:
-
-            if opts.k_sched_type != 'exponential' and opts.rho != 0 and opts.rho != default_rho:
                 sigmas_kwargs['rho'] = opts.rho
                 p.extra_generation_params["Schedule rho"] = opts.rho
-            if opts.k_sched_type == 'sgm_uniform':
+
-                # Ensure the "step" will be target step + 1
+            if scheduler.need_inner_model:
-                steps += 1 if not discard_next_to_last_sigma else 0
                 sigmas_kwargs['inner_model'] = self.model_wrap
-                sigmas_kwargs.pop('rho', None)
 
-            sigmas = sigmas_func(n=steps, **sigmas_kwargs, device=shared.device)
+            sigmas = scheduler.function(n=steps, **sigmas_kwargs, device=shared.device)
-        elif self.config is not None and self.config.options.get('scheduler', None) == 'karras':
-            sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
-
-            sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, device=shared.device)
-        elif self.config is not None and self.config.options.get('scheduler', None) == 'exponential':
-            m_sigma_min, m_sigma_max = (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
-            sigmas = k_diffusion.sampling.get_sigmas_exponential(n=steps, sigma_min=m_sigma_min, sigma_max=m_sigma_max, device=shared.device)
-        else:
-            sigmas = self.model_wrap.get_sigmas(steps)
 
         if discard_next_to_last_sigma:
             sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])

modules/sd_schedulers.py

@@ -0,0 +1,43 @@
+import dataclasses
+
+import torch
+
+import k_diffusion
+
+
+@dataclasses.dataclass
+class Scheduler:
+    name: str
+    label: str
+    function: any
+
+    default_rho: float = -1
+    need_inner_model: bool = False
+    aliases: list = None
+
+
+def uniform(n, sigma_min, sigma_max, inner_model, device):
+    return inner_model.get_sigmas(n)
+
+
+def sgm_uniform(n, sigma_min, sigma_max, inner_model, device):
+    start = inner_model.sigma_to_t(torch.tensor(sigma_max))
+    end = inner_model.sigma_to_t(torch.tensor(sigma_min))
+    sigs = [
+        inner_model.t_to_sigma(ts)
+        for ts in torch.linspace(start, end, n + 1)[:-1]
+    ]
+    sigs += [0.0]
+    return torch.FloatTensor(sigs).to(device)
+
+
+schedulers = [
+    Scheduler('automatic', 'Automatic', None),
+    Scheduler('uniform', 'Uniform', uniform, need_inner_model=True),
+    Scheduler('karras', 'Karras', k_diffusion.sampling.get_sigmas_karras, default_rho=7.0),
+    Scheduler('exponential', 'Exponential', k_diffusion.sampling.get_sigmas_exponential),
+    Scheduler('polyexponential', 'Polyexponential', k_diffusion.sampling.get_sigmas_polyexponential, default_rho=1.0),
+    Scheduler('sgm_uniform', 'SGM Uniform', sgm_uniform, need_inner_model=True, aliases=["SGMUniform"]),
+]
+
+schedulers_map = {**{x.name: x for x in schedulers}, **{x.label: x for x in schedulers}}

modules/shared_options.py

@@ -368,7 +368,6 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
     's_tmin':  OptionInfo(0.0, "sigma tmin",  gr.Slider, {"minimum": 0.0, "maximum": 10.0, "step": 0.01}, infotext='Sigma tmin').info('enable stochasticity; start value of the sigma range; only applies to Euler, Heun, and DPM2'),
     's_tmax':  OptionInfo(0.0, "sigma tmax",  gr.Slider, {"minimum": 0.0, "maximum": 999.0, "step": 0.01}, infotext='Sigma tmax').info("0 = inf; end value of the sigma range; only applies to Euler, Heun, and DPM2"),
     's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.1, "step": 0.001}, infotext='Sigma noise').info('amount of additional noise to counteract loss of detail during sampling'),
-    'k_sched_type':  OptionInfo("Automatic", "Scheduler type", gr.Dropdown, {"choices": ["Automatic", "karras", "exponential", "polyexponential", "sgm_uniform"]}, infotext='Schedule type').info("lets you override the noise schedule for k-diffusion samplers; choosing Automatic disables the three parameters below"),
     'sigma_min': OptionInfo(0.0, "sigma min", gr.Number, infotext='Schedule min sigma').info("0 = default (~0.03); minimum noise strength for k-diffusion noise scheduler"),
     'sigma_max': OptionInfo(0.0, "sigma max", gr.Number, infotext='Schedule max sigma').info("0 = default (~14.6); maximum noise strength for k-diffusion noise scheduler"),
     'rho':  OptionInfo(0.0, "rho", gr.Number, infotext='Schedule rho').info("0 = default (7 for karras, 1 for polyexponential); higher values result in a steeper noise schedule (decreases faster)"),

modules/txt2img.py

@@ -11,7 +11,7 @@ from PIL import Image
 import gradio as gr
 
 
-def txt2img_create_processing(id_task: str, request: gr.Request, prompt: str, negative_prompt: str, prompt_styles, steps: int, sampler_name: str, n_iter: int, batch_size: int, cfg_scale: float, height: int, width: int, enable_hr: bool, denoising_strength: float, hr_scale: float, hr_upscaler: str, hr_second_pass_steps: int, hr_resize_x: int, hr_resize_y: int, hr_checkpoint_name: str, hr_sampler_name: str, hr_prompt: str, hr_negative_prompt, override_settings_texts, *args, force_enable_hr=False):
+def txt2img_create_processing(id_task: str, request: gr.Request, prompt: str, negative_prompt: str, prompt_styles, n_iter: int, batch_size: int, cfg_scale: float, height: int, width: int, enable_hr: bool, denoising_strength: float, hr_scale: float, hr_upscaler: str, hr_second_pass_steps: int, hr_resize_x: int, hr_resize_y: int, hr_checkpoint_name: str, hr_sampler_name: str, hr_prompt: str, hr_negative_prompt, override_settings_texts, *args, force_enable_hr=False):
     override_settings = create_override_settings_dict(override_settings_texts)
 
     if force_enable_hr:
@@ -24,10 +24,8 @@ def txt2img_create_processing(id_task: str, request: gr.Request, prompt: str, ne
         prompt=prompt,
         styles=prompt_styles,
         negative_prompt=negative_prompt,
-        sampler_name=sampler_name,
         batch_size=batch_size,
         n_iter=n_iter,
-        steps=steps,
         cfg_scale=cfg_scale,
         width=width,
         height=height,

modules/ui.py

@@ -12,7 +12,7 @@ import numpy as np
 from PIL import Image, PngImagePlugin  # noqa: F401
 from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call
 
-from modules import gradio_extensons  # noqa: F401
+from modules import gradio_extensons, sd_schedulers  # noqa: F401
 from modules import sd_hijack, sd_models, script_callbacks, ui_extensions, deepbooru, extra_networks, ui_common, ui_postprocessing, progress, ui_loadsave, shared_items, ui_settings, timer, sysinfo, ui_checkpoint_merger, scripts, sd_samplers, processing, ui_extra_networks, ui_toprow, launch_utils
 from modules.ui_components import FormRow, FormGroup, ToolButton, FormHTML, InputAccordion, ResizeHandleRow
 from modules.paths import script_path
@@ -229,19 +229,6 @@ def create_output_panel(tabname, outdir, toprow=None):
     return ui_common.create_output_panel(tabname, outdir, toprow)
 
 
-def create_sampler_and_steps_selection(choices, tabname):
-    if opts.samplers_in_dropdown:
-        with FormRow(elem_id=f"sampler_selection_{tabname}"):
-            sampler_name = gr.Dropdown(label='Sampling method', elem_id=f"{tabname}_sampling", choices=choices, value=choices[0])
-            steps = gr.Slider(minimum=1, maximum=150, step=1, elem_id=f"{tabname}_steps", label="Sampling steps", value=20)
-    else:
-        with FormGroup(elem_id=f"sampler_selection_{tabname}"):
-            steps = gr.Slider(minimum=1, maximum=150, step=1, elem_id=f"{tabname}_steps", label="Sampling steps", value=20)
-            sampler_name = gr.Radio(label='Sampling method', elem_id=f"{tabname}_sampling", choices=choices, value=choices[0])
-
-    return steps, sampler_name
-
-
 def ordered_ui_categories():
     user_order = {x.strip(): i * 2 + 1 for i, x in enumerate(shared.opts.ui_reorder_list)}
 
@@ -295,9 +282,6 @@ def create_ui():
                     if category == "prompt":
                         toprow.create_inline_toprow_prompts()
 
-                    if category == "sampler":
-                        steps, sampler_name = create_sampler_and_steps_selection(sd_samplers.visible_sampler_names(), "txt2img")
-
                     elif category == "dimensions":
                         with FormRow():
                             with gr.Column(elem_id="txt2img_column_size", scale=4):
@@ -396,8 +380,6 @@ def create_ui():
                 toprow.prompt,
                 toprow.negative_prompt,
                 toprow.ui_styles.dropdown,
-                steps,
-                sampler_name,
                 batch_count,
                 batch_size,
                 cfg_scale,
@@ -461,8 +443,6 @@ def create_ui():
             txt2img_paste_fields = [
                 PasteField(toprow.prompt, "Prompt", api="prompt"),
                 PasteField(toprow.negative_prompt, "Negative prompt", api="negative_prompt"),
-                PasteField(steps, "Steps", api="steps"),
-                PasteField(sampler_name, "Sampler", api="sampler_name"),
                 PasteField(cfg_scale, "CFG scale", api="cfg_scale"),
                 PasteField(width, "Size-1", api="width"),
                 PasteField(height, "Size-2", api="height"),
@@ -488,11 +468,13 @@ def create_ui():
                 paste_button=toprow.paste, tabname="txt2img", source_text_component=toprow.prompt, source_image_component=None,
             ))
 
+            steps = scripts.scripts_txt2img.script('Sampler').steps
+
             txt2img_preview_params = [
                 toprow.prompt,
                 toprow.negative_prompt,
                 steps,
-                sampler_name,
+                scripts.scripts_txt2img.script('Sampler').sampler_name,
                 cfg_scale,
                 scripts.scripts_txt2img.script('Seed').seed,
                 width,
@@ -623,9 +605,6 @@ def create_ui():
                         with FormRow():
                             resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", choices=["Just resize", "Crop and resize", "Resize and fill", "Just resize (latent upscale)"], type="index", value="Just resize")
 
-                    if category == "sampler":
-                        steps, sampler_name = create_sampler_and_steps_selection(sd_samplers.visible_sampler_names(), "img2img")
-
                     elif category == "dimensions":
                         with FormRow():
                             with gr.Column(elem_id="img2img_column_size", scale=4):
@@ -754,8 +733,6 @@ def create_ui():
                     inpaint_color_sketch_orig,
                     init_img_inpaint,
                     init_mask_inpaint,
-                    steps,
-                    sampler_name,
                     mask_blur,
                     mask_alpha,
                     inpainting_fill,
@@ -840,6 +817,8 @@ def create_ui():
                 **interrogate_args,
             )
 
+            steps = scripts.scripts_img2img.script('Sampler').steps
+
             toprow.ui_styles.dropdown.change(fn=wrap_queued_call(update_token_counter), inputs=[toprow.prompt, steps, toprow.ui_styles.dropdown], outputs=[toprow.token_counter])
             toprow.ui_styles.dropdown.change(fn=wrap_queued_call(update_negative_prompt_token_counter), inputs=[toprow.negative_prompt, steps, toprow.ui_styles.dropdown], outputs=[toprow.negative_token_counter])
             toprow.token_button.click(fn=update_token_counter, inputs=[toprow.prompt, steps, toprow.ui_styles.dropdown], outputs=[toprow.token_counter])
@@ -848,8 +827,6 @@ def create_ui():
             img2img_paste_fields = [
                 (toprow.prompt, "Prompt"),
                 (toprow.negative_prompt, "Negative prompt"),
-                (steps, "Steps"),
-                (sampler_name, "Sampler"),
                 (cfg_scale, "CFG scale"),
                 (image_cfg_scale, "Image CFG scale"),
                 (width, "Size-1"),

scripts/xyz_grid.py

@@ -11,7 +11,7 @@ import numpy as np
 import modules.scripts as scripts
 import gradio as gr
 
-from modules import images, sd_samplers, processing, sd_models, sd_vae, sd_samplers_kdiffusion, errors
+from modules import images, sd_samplers, processing, sd_models, sd_vae, sd_schedulers, errors
 from modules.processing import process_images, Processed, StableDiffusionProcessingTxt2Img
 from modules.shared import opts, state
 import modules.shared as shared
@@ -248,7 +248,7 @@ axis_options = [
     AxisOption("Sigma min", float, apply_field("s_tmin")),
     AxisOption("Sigma max", float, apply_field("s_tmax")),
     AxisOption("Sigma noise", float, apply_field("s_noise")),
-    AxisOption("Schedule type", str, apply_override("k_sched_type"), choices=lambda: list(sd_samplers_kdiffusion.k_diffusion_scheduler)),
+    AxisOption("Schedule type", str, apply_field("scheduler"), choices=lambda: [x.label for x in sd_schedulers.schedulers]),
     AxisOption("Schedule min sigma", float, apply_override("sigma_min")),
     AxisOption("Schedule max sigma", float, apply_override("sigma_max")),
     AxisOption("Schedule rho", float, apply_override("rho")),