14 files changed, 282 insertions, 44 deletions
diff --git a/javascript/hints.js b/javascript/hints.js
index 46f342cb..05ae5f22 100644
--- a/javascript/hints.js
+++ b/javascript/hints.js
@@ -116,17 +116,25 @@ var titles = {
     "Negative Guidance minimum sigma": "Skip negative prompt for steps where image is already mostly denoised; the higher this value, the more skips there will be; provides increased performance in exchange for minor quality reduction."
 };
 
-function updateTooltipForSpan(span) {
-    if (span.title) return; // already has a title
+function updateTooltip(element) {
+    if (element.title) return; // already has a title
 
-    let tooltip = localization[titles[span.textContent]] || titles[span.textContent];
+    let text = element.textContent;
+    let tooltip = localization[titles[text]] || titles[text];
 
     if (!tooltip) {
-        tooltip = localization[titles[span.value]] || titles[span.value];
+        let value = element.value;
+        if (value) tooltip = localization[titles[value]] || titles[value];
     }
 
     if (!tooltip) {
-        for (const c of span.classList) {
+        // Gradio dropdown options have `data-value`.
+        let dataValue = element.dataset.value;
+        if (dataValue) tooltip = localization[titles[dataValue]] || titles[dataValue];
+    }
+
+    if (!tooltip) {
+        for (const c of element.classList) {
             if (c in titles) {
                 tooltip = localization[titles[c]] || titles[c];
                 break;
@@ -135,34 +143,53 @@ function updateTooltipForSpan(span) {
     }
 
     if (tooltip) {
-        span.title = tooltip;
+        element.title = tooltip;
     }
 }
 
-function updateTooltipForSelect(select) {
-    if (select.onchange != null) return;
+// Nodes to check for adding tooltips.
+const tooltipCheckNodes = new Set();
+// Timer for debouncing tooltip check.
+let tooltipCheckTimer = null;
 
-    select.onchange = function() {
-        select.title = localization[titles[select.value]] || titles[select.value] || "";
-    };
+function processTooltipCheckNodes() {
+    for (const node of tooltipCheckNodes) {
+        updateTooltip(node);
+    }
+    tooltipCheckNodes.clear();
 }
 
-var observedTooltipElements = {SPAN: 1, BUTTON: 1, SELECT: 1, P: 1};
-
-onUiUpdate(function(m) {
-    m.forEach(function(record) {
-        record.addedNodes.forEach(function(node) {
-            if (observedTooltipElements[node.tagName]) {
-                updateTooltipForSpan(node);
-            }
-            if (node.tagName == "SELECT") {
-                updateTooltipForSelect(node);
+onUiUpdate(function(mutationRecords) {
+    for (const record of mutationRecords) {
+        if (record.type === "childList" && record.target.classList.contains("options")) {
+            // This smells like a Gradio dropdown menu having changed,
+            // so let's enqueue an update for the input element that shows the current value.
+            let wrap = record.target.parentNode;
+            let input = wrap?.querySelector("input");
+            if (input) {
+                input.title = ""; // So we'll even have a chance to update it.
+                tooltipCheckNodes.add(input);
             }
-
-            if (node.querySelectorAll) {
-                node.querySelectorAll('span, button, select, p').forEach(updateTooltipForSpan);
-                node.querySelectorAll('select').forEach(updateTooltipForSelect);
+        }
+        for (const node of record.addedNodes) {
+            if (node.nodeType === Node.ELEMENT_NODE && !node.classList.contains("hide")) {
+                if (!node.title) {
+                    if (
+                        node.tagName === "SPAN" ||
+                        node.tagName === "BUTTON" ||
+                        node.tagName === "P" ||
+                        node.tagName === "INPUT" ||
+                        (node.tagName === "LI" && node.classList.contains("item")) // Gradio dropdown item
+                    ) {
+                        tooltipCheckNodes.add(node);
+                    }
+                }
+                node.querySelectorAll('span, button, p').forEach(n => tooltipCheckNodes.add(n));
             }
-        });
-    });
+        }
+    }
+    if (tooltipCheckNodes.size) {
+        clearTimeout(tooltipCheckTimer);
+        tooltipCheckTimer = setTimeout(processTooltipCheckNodes, 1000);
+    }
 });
diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index d5f0a49b..81aef502 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -306,6 +306,18 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
     if "RNG" not in res:
         res["RNG"] = "GPU"
 
+    if "Schedule type" not in res:
+        res["Schedule type"] = "Automatic"
+
+    if "Schedule max sigma" not in res:
+        res["Schedule max sigma"] = 0
+
+    if "Schedule min sigma" not in res:
+        res["Schedule min sigma"] = 0
+
+    if "Schedule rho" not in res:
+        res["Schedule rho"] = 0
+
     return res
 
 
@@ -318,6 +330,10 @@ infotext_to_setting_name_mapping = [
     ('Conditional mask weight', 'inpainting_mask_weight'),
     ('Model hash', 'sd_model_checkpoint'),
     ('ENSD', 'eta_noise_seed_delta'),
+    ('Schedule type', 'k_sched_type'),
+    ('Schedule max sigma', 'sigma_max'),
+    ('Schedule min sigma', 'sigma_min'),
+    ('Schedule rho', 'rho'),
     ('Noise multiplier', 'initial_noise_multiplier'),
     ('Eta', 'eta_ancestral'),
     ('Eta DDIM', 'eta_ddim'),
diff --git a/modules/processing.py b/modules/processing.py
index 29a3743f..b75f2515 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -13,7 +13,7 @@ from skimage import exposure
 from typing import Any, Dict, List
 
 import modules.sd_hijack
-from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, extra_networks, sd_vae_approx, scripts, sd_samplers_common
+from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, extra_networks, sd_vae_approx, scripts, sd_samplers_common, sd_unet
 from modules.sd_hijack import model_hijack
 from modules.shared import opts, cmd_opts, state
 import modules.shared as shared
@@ -674,6 +674,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             if shared.opts.live_previews_enable and opts.show_progress_type == "Approx NN":
                 sd_vae_approx.model()
 
+            sd_unet.apply_unet()
+
         if state.job_count == -1:
             state.job_count = p.n_iter
 
diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py
index 40f388a5..d2728e12 100644
--- a/modules/script_callbacks.py
+++ b/modules/script_callbacks.py
@@ -111,6 +111,7 @@ callback_map = dict(
     callbacks_before_ui=[],
     callbacks_on_reload=[],
     callbacks_list_optimizers=[],
+    callbacks_list_unets=[],
 )
 
 
@@ -271,6 +272,18 @@ def list_optimizers_callback():
     return res
 
 
+def list_unets_callback():
+    res = []
+
+    for c in callback_map['callbacks_list_unets']:
+        try:
+            c.callback(res)
+        except Exception:
+            report_exception(c, 'list_unets')
+
+    return res
+
+
 def add_callback(callbacks, fun):
     stack = [x for x in inspect.stack() if x.filename != __file__]
     filename = stack[0].filename if len(stack) > 0 else 'unknown file'
@@ -430,3 +443,10 @@ def on_list_optimizers(callback):
     to it."""
 
     add_callback(callback_map['callbacks_list_optimizers'], callback)
+
+
+def on_list_unets(callback):
+    """register a function to be called when UI is making a list of alternative options for unet.
+    The function will be called with one argument, a list, and shall add objects of type modules.sd_unet.SdUnetOption to it."""
+
+    add_callback(callback_map['callbacks_list_unets'], callback)
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index f93df0a6..487dfd60 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -3,7 +3,7 @@ from torch.nn.functional import silu
 from types import MethodType
 
 import modules.textual_inversion.textual_inversion
-from modules import devices, sd_hijack_optimizations, shared, script_callbacks, errors
+from modules import devices, sd_hijack_optimizations, shared, script_callbacks, errors, sd_unet
 from modules.hypernetworks import hypernetwork
 from modules.shared import cmd_opts
 from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet, sd_hijack_xlmr, xlmr
@@ -43,7 +43,7 @@ def list_optimizers():
     optimizers.extend(new_optimizers)
 
 
-def apply_optimizations():
+def apply_optimizations(option=None):
     global current_optimizer
 
     undo_optimizations()
@@ -60,7 +60,7 @@ def apply_optimizations():
         current_optimizer.undo()
         current_optimizer = None
 
-    selection = shared.opts.cross_attention_optimization
+    selection = option or shared.opts.cross_attention_optimization
     if selection == "Automatic" and len(optimizers) > 0:
         matching_optimizer = next(iter([x for x in optimizers if x.cmd_opt and getattr(shared.cmd_opts, x.cmd_opt, False)]), optimizers[0])
     else:
@@ -72,12 +72,13 @@ def apply_optimizations():
         matching_optimizer = optimizers[0]
 
     if matching_optimizer is not None:
-        print(f"Applying optimization: {matching_optimizer.name}... ", end='')
+        print(f"Applying attention optimization: {matching_optimizer.name}... ", end='')
         matching_optimizer.apply()
         print("done.")
         current_optimizer = matching_optimizer
         return current_optimizer.name
     else:
+        print("Disabling attention optimization")
         return ''
 
 
@@ -155,9 +156,9 @@ class StableDiffusionModelHijack:
     def __init__(self):
         self.embedding_db.add_embedding_dir(cmd_opts.embeddings_dir)
 
-    def apply_optimizations(self):
+    def apply_optimizations(self, option=None):
         try:
-            self.optimization_method = apply_optimizations()
+            self.optimization_method = apply_optimizations(option)
         except Exception as e:
             errors.display(e, "applying cross attention optimization")
             undo_optimizations()
@@ -194,6 +195,11 @@ class StableDiffusionModelHijack:
 
         self.layers = flatten(m)
 
+        if not hasattr(ldm.modules.diffusionmodules.openaimodel, 'copy_of_UNetModel_forward_for_webui'):
+            ldm.modules.diffusionmodules.openaimodel.copy_of_UNetModel_forward_for_webui = ldm.modules.diffusionmodules.openaimodel.UNetModel.forward
+
+        ldm.modules.diffusionmodules.openaimodel.UNetModel.forward = sd_unet.UNetModel_forward
+
     def undo_hijack(self, m):
         if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation:
             m.cond_stage_model = m.cond_stage_model.wrapped
@@ -215,6 +221,8 @@ class StableDiffusionModelHijack:
         self.layers = None
         self.clip = None
 
+        ldm.modules.diffusionmodules.openaimodel.UNetModel.forward = ldm.modules.diffusionmodules.openaimodel.copy_of_UNetModel_forward_for_webui
+
     def apply_circular(self, enable):
         if self.circular_enabled == enable:
             return
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 3e7fc7e3..232eb9c4 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -14,7 +14,7 @@ import ldm.modules.midas as midas
 
 from ldm.util import instantiate_from_config
 
-from modules import paths, shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config
+from modules import paths, shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config, sd_unet
 from modules.sd_hijack_inpainting import do_inpainting_hijack
 from modules.timer import Timer
 import tomesd
@@ -533,6 +533,8 @@ def reload_model_weights(sd_model=None, info=None):
         if sd_model.sd_model_checkpoint == checkpoint_info.filename:
             return
 
+        sd_unet.apply_unet("None")
+
         if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
             lowvram.send_everything_to_cpu()
         else:
diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py
index 638e0ac9..e9ba2c61 100644
--- a/modules/sd_samplers_kdiffusion.py
+++ b/modules/sd_samplers_kdiffusion.py
@@ -44,6 +44,14 @@ sampler_extra_params = {
     'sample_dpm_2': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
 }
 
+k_diffusion_samplers_map = {x.name: x for x in samplers_data_k_diffusion}
+k_diffusion_scheduler = {
+    'Automatic': None,
+    'karras': k_diffusion.sampling.get_sigmas_karras,
+    'exponential': k_diffusion.sampling.get_sigmas_exponential,
+    'polyexponential': k_diffusion.sampling.get_sigmas_polyexponential
+}
+
 
 class CFGDenoiser(torch.nn.Module):
     """
@@ -265,6 +273,13 @@ class KDiffusionSampler:
 
         try:
             return func()
+        except RecursionError:
+            print(
+                'Encountered RecursionError during sampling, returning last latent. '
+                'rho >5 with a polyexponential scheduler may cause this error. '
+                'You should try to use a smaller rho value instead.'
+            )
+            return self.last_latent
         except sd_samplers_common.InterruptedException:
             return self.last_latent
 
@@ -304,6 +319,31 @@ class KDiffusionSampler:
 
         if p.sampler_noise_scheduler_override:
             sigmas = p.sampler_noise_scheduler_override(steps)
+        elif opts.k_sched_type != "Automatic":
+            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)
+            sigmas_kwargs = {
+                'sigma_min': sigma_min,
+                'sigma_max': sigma_max,
+            }
+
+            sigmas_func = k_diffusion_scheduler[opts.k_sched_type]
+            p.extra_generation_params["Schedule type"] = opts.k_sched_type
+
+            if opts.sigma_min != m_sigma_min and opts.sigma_min != 0:
+                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:
+                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 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
+
+            sigmas = sigmas_func(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())
 
diff --git a/modules/sd_unet.py b/modules/sd_unet.py
new file mode 100644
index 00000000..6d708ad2
--- /dev/null
+++ b/modules/sd_unet.py
@@ -0,0 +1,92 @@
+import torch.nn
+import ldm.modules.diffusionmodules.openaimodel
+
+from modules import script_callbacks, shared, devices
+
+unet_options = []
+current_unet_option = None
+current_unet = None
+
+
+def list_unets():
+    new_unets = script_callbacks.list_unets_callback()
+
+    unet_options.clear()
+    unet_options.extend(new_unets)
+
+
+def get_unet_option(option=None):
+    option = option or shared.opts.sd_unet
+
+    if option == "None":
+        return None
+
+    if option == "Automatic":
+        name = shared.sd_model.sd_checkpoint_info.model_name
+
+        options = [x for x in unet_options if x.model_name == name]
+
+        option = options[0].label if options else "None"
+
+    return next(iter([x for x in unet_options if x.label == option]), None)
+
+
+def apply_unet(option=None):
+    global current_unet_option
+    global current_unet
+
+    new_option = get_unet_option(option)
+    if new_option == current_unet_option:
+        return
+
+    if current_unet is not None:
+        print(f"Dectivating unet: {current_unet.option.label}")
+        current_unet.deactivate()
+
+    current_unet_option = new_option
+    if current_unet_option is None:
+        current_unet = None
+
+        if not (shared.cmd_opts.lowvram or shared.cmd_opts.medvram):
+            shared.sd_model.model.diffusion_model.to(devices.device)
+
+        return
+
+    shared.sd_model.model.diffusion_model.to(devices.cpu)
+    devices.torch_gc()
+
+    current_unet = current_unet_option.create_unet()
+    current_unet.option = current_unet_option
+    print(f"Activating unet: {current_unet.option.label}")
+    current_unet.activate()
+
+
+class SdUnetOption:
+    model_name = None
+    """name of related checkpoint - this option will be selected automatically for unet if the name of checkpoint matches this"""
+
+    label = None
+    """name of the unet in UI"""
+
+    def create_unet(self):
+        """returns SdUnet object to be used as a Unet instead of built-in unet when making pictures"""
+        raise NotImplementedError()
+
+
+class SdUnet(torch.nn.Module):
+    def forward(self, x, timesteps, context, *args, **kwargs):
+        raise NotImplementedError()
+
+    def activate(self):
+        pass
+
+    def deactivate(self):
+        pass
+
+
+def UNetModel_forward(self, x, timesteps=None, context=None, *args, **kwargs):
+    if current_unet is not None:
+        return current_unet.forward(x, timesteps, context, *args, **kwargs)
+
+    return ldm.modules.diffusionmodules.openaimodel.copy_of_UNetModel_forward_for_webui(self, x, timesteps, context, *args, **kwargs)
+
diff --git a/modules/shared.py b/modules/shared.py
index 0897f937..daab38dc 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -403,6 +403,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "sd_vae_checkpoint_cache": OptionInfo(0, "VAE Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
     "sd_vae": OptionInfo("Automatic", "SD VAE", gr.Dropdown, lambda: {"choices": shared_items.sd_vae_items()}, refresh=shared_items.refresh_vae_list).info("choose VAE model: Automatic = use one with same filename as checkpoint; None = use VAE from checkpoint"),
     "sd_vae_as_default": OptionInfo(True, "Ignore selected VAE for stable diffusion checkpoints that have their own .vae.pt next to them"),
+    "sd_unet": OptionInfo("Automatic", "SD Unet", gr.Dropdown, lambda: {"choices": shared_items.sd_unet_items()}, refresh=shared_items.refresh_unet_list).info("choose Unet model: Automatic = use one with same filename as checkpoint; None = use Unet from checkpoint"),
     "inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
     "initial_noise_multiplier": OptionInfo(1.0, "Noise multiplier for img2img", gr.Slider, {"minimum": 0.5, "maximum": 1.5, "step": 0.01}),
     "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
@@ -517,6 +518,10 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
     's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
     's_tmin':  OptionInfo(0.0, "sigma tmin",  gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
     's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+    'k_sched_type':  OptionInfo("Automatic", "scheduler type", gr.Dropdown, {"choices": ["Automatic", "karras", "exponential", "polyexponential"]}).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).info("0 = default (~0.03); minimum noise strength for k-diffusion noise scheduler"),
+    'sigma_max': OptionInfo(0.0, "sigma max", gr.Number).info("0 = default (~14.6); maximum noise strength for k-diffusion noise schedule"),
+    'rho':  OptionInfo(0.0, "rho", gr.Number).info("0 = default (7 for karras, 1 for polyexponential); higher values result in a more steep noise schedule (decreases faster)"),
     'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}).info("ENSD; does not improve anything, just produces different results for ancestral samplers - only useful for reproducing images"),
     'always_discard_next_to_last_sigma': OptionInfo(False, "Always discard next-to-last sigma").link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/6044"),
     'uni_pc_variant': OptionInfo("bh1", "UniPC variant", gr.Radio, {"choices": ["bh1", "bh2", "vary_coeff"]}),
diff --git a/modules/shared_items.py b/modules/shared_items.py
index 2a8713c8..7f306a06 100644
--- a/modules/shared_items.py
+++ b/modules/shared_items.py
@@ -29,3 +29,14 @@ def cross_attention_optimizations():
     return ["Automatic"] + [x.title() for x in modules.sd_hijack.optimizers] + ["None"]
 
 
+def sd_unet_items():
+    import modules.sd_unet
+
+    return ["Automatic"] + [x.label for x in modules.sd_unet.unet_options] + ["None"]
+
+
+def refresh_unet_list():
+    import modules.sd_unet
+
+    modules.sd_unet.list_unets()
+
diff --git a/modules/ui_tempdir.py b/modules/ui_tempdir.py
index f05049e1..7f6b42ae 100644
--- a/modules/ui_tempdir.py
+++ b/modules/ui_tempdir.py
@@ -4,6 +4,7 @@ from collections import namedtuple
 from pathlib import Path
 
 import gradio as gr
+import gradio.components
 
 from PIL import PngImagePlugin
 
@@ -31,13 +32,16 @@ def check_tmp_file(gradio, filename):
     return False
 
 
-def save_pil_to_file(pil_image, dir=None):
+def save_pil_to_file(self, pil_image, dir=None):
     already_saved_as = getattr(pil_image, 'already_saved_as', None)
     if already_saved_as and os.path.isfile(already_saved_as):
         register_tmp_file(shared.demo, already_saved_as)
+        filename = already_saved_as
 
-        file_obj = Savedfile(f'{already_saved_as}?{os.path.getmtime(already_saved_as)}')
-        return file_obj
+        if not shared.opts.save_images_add_number:
+            filename += f'?{os.path.getmtime(already_saved_as)}'
+
+        return filename
 
     if shared.opts.temp_dir != "":
         dir = shared.opts.temp_dir
@@ -51,11 +55,11 @@ def save_pil_to_file(pil_image, dir=None):
 
     file_obj = tempfile.NamedTemporaryFile(delete=False, suffix=".png", dir=dir)
     pil_image.save(file_obj, pnginfo=(metadata if use_metadata else None))
-    return file_obj
+    return file_obj.name
 
 
 # override save to file function so that it also writes PNG info
-gr.processing_utils.save_pil_to_file = save_pil_to_file
+gradio.components.IOComponent.pil_to_temp_file = save_pil_to_file
 
 
 def on_tmpdir_changed():
diff --git a/scripts/xyz_grid.py b/scripts/xyz_grid.py
index da820b39..7821cc65 100644
--- a/scripts/xyz_grid.py
+++ b/scripts/xyz_grid.py
@@ -10,7 +10,7 @@ import numpy as np
 import modules.scripts as scripts
 import gradio as gr
 
-from modules import images, sd_samplers, processing, sd_models, sd_vae
+from modules import images, sd_samplers, processing, sd_models, sd_vae, sd_samplers_kdiffusion
 from modules.processing import process_images, Processed, StableDiffusionProcessingTxt2Img
 from modules.shared import opts, state
 import modules.shared as shared
@@ -220,6 +220,10 @@ 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 min sigma", float, apply_override("sigma_min")),
+    AxisOption("Schedule max sigma", float, apply_override("sigma_max")),
+    AxisOption("Schedule rho", float, apply_override("rho")),
     AxisOption("Eta", float, apply_field("eta")),
     AxisOption("Clip skip", int, apply_clip_skip),
     AxisOption("Denoising", float, apply_field("denoising_strength")),
diff --git a/webui.py b/webui.py
index f9210f41..1e3ff061 100644
--- a/webui.py
+++ b/webui.py
@@ -58,6 +58,7 @@ import modules.sd_hijack
 import modules.sd_hijack_optimizations
 import modules.sd_models
 import modules.sd_vae
+import modules.sd_unet
 import modules.txt2img
 import modules.script_callbacks
 import modules.textual_inversion.textual_inversion
@@ -291,6 +292,9 @@ def initialize_rest(*, reload_script_modules=False):
     modules.sd_hijack.list_optimizers()
     startup_timer.record("scripts list_optimizers")
 
+    modules.sd_unet.list_unets()
+    startup_timer.record("scripts list_unets")
+
     def load_model():
         """
         Accesses shared.sd_model property to load model.
diff --git a/webui.sh b/webui.sh
index ab52ac3b..607557b1 100755
--- a/webui.sh
+++ b/webui.sh
@@ -124,9 +124,12 @@ case "$gpu_info" in
     *)
     ;;
 esac
-if echo "$gpu_info" | grep -q "AMD" && [[ -z "${TORCH_COMMAND}" ]]
+if ! echo "$gpu_info" | grep -q "NVIDIA";
 then
-    export TORCH_COMMAND="pip install torch==2.0.1+rocm5.4.2 torchvision==0.15.2+rocm5.4.2 --index-url https://download.pytorch.org/whl/rocm5.4.2"
+    if echo "$gpu_info" | grep -q "AMD" && [[ -z "${TORCH_COMMAND}" ]]
+    then
+        export TORCH_COMMAND="pip install torch==2.0.1+rocm5.4.2 torchvision==0.15.2+rocm5.4.2 --index-url https://download.pytorch.org/whl/rocm5.4.2"
+    fi
 fi
 
 for preq in "${GIT}" "${python_cmd}"
@@ -190,7 +193,7 @@ fi
 # Try using TCMalloc on Linux
 prepare_tcmalloc() {
     if [[ "${OSTYPE}" == "linux"* ]] && [[ -z "${NO_TCMALLOC}" ]] && [[ -z "${LD_PRELOAD}" ]]; then
-        TCMALLOC="$(ldconfig -p | grep -Po "libtcmalloc.so.\d" | head -n 1)"
+        TCMALLOC="$(PATH=/usr/sbin:$PATH ldconfig -p | grep -Po "libtcmalloc(_minimal|)\.so\.\d" | head -n 1)"
         if [[ ! -z "${TCMALLOC}" ]]; then
             echo "Using TCMalloc: ${TCMALLOC}"
             export LD_PRELOAD="${TCMALLOC}"