10 files changed, 175 insertions, 71 deletions
diff --git a/modules/api/api.py b/modules/api/api.py
index 54ee7cb0..89935a70 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -157,12 +157,7 @@ class Api:
         args.pop('include_init_images', None)  # this is meant to be done by "exclude": True in model, but it's for a reason that I cannot determine.
         p = StableDiffusionProcessingImg2Img(**args)
 
-        imgs = []
-        for img in init_images:
-            img = decode_base64_to_image(img)
-            imgs = [img] * p.batch_size
-
-        p.init_images = imgs
+        p.init_images = [decode_base64_to_image(x) for x in init_images]
 
         shared.state.begin()
 
diff --git a/modules/extras.py b/modules/extras.py
index bc349d5e..0ad8deec 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -62,7 +62,7 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
     # Also keep track of original file names
     imageNameArr = []
     outputs = []
-    
+
     if extras_mode == 1:
         #convert file to pillow image
         for img in image_folder:
@@ -234,7 +234,7 @@ def run_pnginfo(image):
     return '', geninfo, info
 
 
-def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_name, interp_method, multiplier, save_as_half, custom_name, checkpoint_format):
+def run_modelmerger(primary_model_name, secondary_model_name, tertiary_model_name, interp_method, multiplier, save_as_half, custom_name, checkpoint_format):
     def weighted_sum(theta0, theta1, alpha):
         return ((1 - alpha) * theta0) + (alpha * theta1)
 
@@ -246,30 +246,25 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam
 
     primary_model_info = sd_models.checkpoints_list[primary_model_name]
     secondary_model_info = sd_models.checkpoints_list[secondary_model_name]
-    teritary_model_info = sd_models.checkpoints_list.get(teritary_model_name, None)
+    tertiary_model_info = sd_models.checkpoints_list.get(tertiary_model_name, None)
     result_is_inpainting_model = False
 
-    print(f"Loading {primary_model_info.filename}...")
-    theta_0 = sd_models.read_state_dict(primary_model_info.filename, map_location='cpu')
-
-    print(f"Loading {secondary_model_info.filename}...")
-    theta_1 = sd_models.read_state_dict(secondary_model_info.filename, map_location='cpu')
-
-    if teritary_model_info is not None:
-        print(f"Loading {teritary_model_info.filename}...")
-        theta_2 = sd_models.read_state_dict(teritary_model_info.filename, map_location='cpu')
-    else:
-        theta_2 = None
-
     theta_funcs = {
         "Weighted sum": (None, weighted_sum),
         "Add difference": (get_difference, add_difference),
     }
     theta_func1, theta_func2 = theta_funcs[interp_method]
 
-    print(f"Merging...")
+    if theta_func1 and not tertiary_model_info:
+        return ["Failed: Interpolation method requires a tertiary model."] + [gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(4)]
+
+    print(f"Loading {secondary_model_info.filename}...")
+    theta_1 = sd_models.read_state_dict(secondary_model_info.filename, map_location='cpu')
 
     if theta_func1:
+        print(f"Loading {tertiary_model_info.filename}...")
+        theta_2 = sd_models.read_state_dict(tertiary_model_info.filename, map_location='cpu')
+
         for key in tqdm.tqdm(theta_1.keys()):
             if 'model' in key:
                 if key in theta_2:
@@ -277,7 +272,12 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam
                     theta_1[key] = theta_func1(theta_1[key], t2)
                 else:
                     theta_1[key] = torch.zeros_like(theta_1[key])
-    del theta_2
+        del theta_2
+
+    print(f"Loading {primary_model_info.filename}...")
+    theta_0 = sd_models.read_state_dict(primary_model_info.filename, map_location='cpu')
+
+    print("Merging...")
 
     for key in tqdm.tqdm(theta_0.keys()):
         if 'model' in key and key in theta_1:
@@ -307,6 +307,7 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam
             theta_0[key] = theta_1[key]
             if save_as_half:
                 theta_0[key] = theta_0[key].half()
+    del theta_1
 
     ckpt_dir = shared.cmd_opts.ckpt_dir or sd_models.model_path
 
@@ -332,5 +333,5 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam
 
     sd_models.list_models()
 
-    print(f"Checkpoint saved.")
+    print("Checkpoint saved.")
     return ["Checkpoint saved to " + output_modelname] + [gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(4)]
diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index e8d5250a..565e342d 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -77,6 +77,7 @@ def integrate_settings_paste_fields(component_dict):
         'inpainting_mask_weight': 'Conditional mask weight',
         'sd_model_checkpoint': 'Model hash',
         'eta_noise_seed_delta': 'ENSD',
+        'initial_noise_multiplier': 'Noise multiplier',
     }
     settings_paste_fields = [
         (component_dict[k], lambda d, k=k, v=v: ui.apply_setting(k, d.get(v, None)))
diff --git a/modules/processing.py b/modules/processing.py
index 3d2c4dc9..81400d14 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -21,7 +21,10 @@ import modules.face_restoration
 import modules.images as images
 import modules.styles
 import logging
+from ldm.data.util import AddMiDaS
+from ldm.models.diffusion.ddpm import LatentDepth2ImageDiffusion
 
+from einops import repeat, rearrange
 
 # some of those options should not be changed at all because they would break the model, so I removed them from options.
 opt_C = 4
@@ -150,11 +153,26 @@ class StableDiffusionProcessing():
 
         return image_conditioning
 
-    def img2img_image_conditioning(self, source_image, latent_image, image_mask = None):
-        if self.sampler.conditioning_key not in {'hybrid', 'concat'}:
-            # Dummy zero conditioning if we're not using inpainting model.
-            return latent_image.new_zeros(latent_image.shape[0], 5, 1, 1)
+    def depth2img_image_conditioning(self, source_image):
+        # Use the AddMiDaS helper to Format our source image to suit the MiDaS model
+        transformer = AddMiDaS(model_type="dpt_hybrid")
+        transformed = transformer({"jpg": rearrange(source_image[0], "c h w -> h w c")})
+        midas_in = torch.from_numpy(transformed["midas_in"][None, ...]).to(device=shared.device)
+        midas_in = repeat(midas_in, "1 ... -> n ...", n=self.batch_size)
+
+        conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(source_image))
+        conditioning = torch.nn.functional.interpolate(
+            self.sd_model.depth_model(midas_in),
+            size=conditioning_image.shape[2:],
+            mode="bicubic",
+            align_corners=False,
+        )
+
+        (depth_min, depth_max) = torch.aminmax(conditioning)
+        conditioning = 2. * (conditioning - depth_min) / (depth_max - depth_min) - 1.
+        return conditioning
 
+    def inpainting_image_conditioning(self, source_image, latent_image, image_mask = None):
         self.is_using_inpainting_conditioning = True
 
         # Handle the different mask inputs
@@ -191,6 +209,18 @@ class StableDiffusionProcessing():
 
         return image_conditioning
 
+    def img2img_image_conditioning(self, source_image, latent_image, image_mask=None):
+        # HACK: Using introspection as the Depth2Image model doesn't appear to uniquely
+        # identify itself with a field common to all models. The conditioning_key is also hybrid.
+        if isinstance(self.sd_model, LatentDepth2ImageDiffusion):
+            return self.depth2img_image_conditioning(source_image)
+
+        if self.sampler.conditioning_key in {'hybrid', 'concat'}:
+            return self.inpainting_image_conditioning(source_image, latent_image, image_mask=image_mask)
+
+        # Dummy zero conditioning if we're not using inpainting or depth model.
+        return latent_image.new_zeros(latent_image.shape[0], 5, 1, 1)
+
     def init(self, all_prompts, all_seeds, all_subseeds):
         pass
 
@@ -734,7 +764,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
     sampler = None
 
-    def __init__(self, init_images: list=None, resize_mode: int=0, denoising_strength: float=0.75, mask: Any=None, mask_blur: int=4, inpainting_fill: int=0, inpaint_full_res: bool=True, inpaint_full_res_padding: int=0, inpainting_mask_invert: int=0, **kwargs):
+    def __init__(self, init_images: list = None, resize_mode: int = 0, denoising_strength: float = 0.75, mask: Any = None, mask_blur: int = 4, inpainting_fill: int = 0, inpaint_full_res: bool = True, inpaint_full_res_padding: int = 0, inpainting_mask_invert: int = 0, initial_noise_multiplier: float = None, **kwargs):
         super().__init__(**kwargs)
 
         self.init_images = init_images
@@ -749,6 +779,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
         self.inpaint_full_res = inpaint_full_res
         self.inpaint_full_res_padding = inpaint_full_res_padding
         self.inpainting_mask_invert = inpainting_mask_invert
+        self.initial_noise_multiplier = opts.initial_noise_multiplier if initial_noise_multiplier is None else initial_noise_multiplier
         self.mask = None
         self.nmask = None
         self.image_conditioning = None
@@ -862,6 +893,10 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
         x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
 
+        if self.initial_noise_multiplier != 1.0:
+            self.extra_generation_params["Noise multiplier"] = self.initial_noise_multiplier
+            x *= self.initial_noise_multiplier
+
         samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning, image_conditioning=self.image_conditioning)
 
         if self.mask is not None:
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 95a17093..690a9ec2 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -1,17 +1,11 @@
-import math
-import os
-import sys
-import traceback
 import torch
-import numpy as np
-from torch import einsum
 from torch.nn.functional import silu
 
 import modules.textual_inversion.textual_inversion
-from modules import prompt_parser, devices, sd_hijack_optimizations, shared, sd_hijack_checkpoint
+from modules import devices, sd_hijack_optimizations, shared, sd_hijack_checkpoint
 from modules.hypernetworks import hypernetwork
-from modules.shared import opts, device, cmd_opts
-from modules import sd_hijack_clip, sd_hijack_open_clip
+from modules.shared import cmd_opts
+from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet
 
 from modules.sd_hijack_optimizations import invokeAI_mps_available
 
@@ -35,10 +29,12 @@ ldm.modules.attention.BasicTransformerBlock.ATTENTION_MODES["softmax-xformers"]
 ldm.modules.attention.print = lambda *args: None
 ldm.modules.diffusionmodules.model.print = lambda *args: None
 
+
 def apply_optimizations():
     undo_optimizations()
 
     ldm.modules.diffusionmodules.model.nonlinearity = silu
+    ldm.modules.diffusionmodules.openaimodel.th = sd_hijack_unet.th
 
     if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (9, 0)):
         print("Applying xformers cross attention optimization.")
diff --git a/modules/sd_hijack_unet.py b/modules/sd_hijack_unet.py
new file mode 100644
index 00000000..1b9d7757
--- /dev/null
+++ b/modules/sd_hijack_unet.py
@@ -0,0 +1,30 @@
+import torch
+
+
+class TorchHijackForUnet:
+    """
+    This is torch, but with cat that resizes tensors to appropriate dimensions if they do not match;
+    this makes it possible to create pictures with dimensions that are muliples of 8 rather than 64
+    """
+
+    def __getattr__(self, item):
+        if item == 'cat':
+            return self.cat
+
+        if hasattr(torch, item):
+            return getattr(torch, item)
+
+        raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, item))
+
+    def cat(self, tensors, *args, **kwargs):
+        if len(tensors) == 2:
+            a, b = tensors
+            if a.shape[-2:] != b.shape[-2:]:
+                a = torch.nn.functional.interpolate(a, b.shape[-2:], mode="nearest")
+
+            tensors = (a, b)
+
+        return torch.cat(tensors, *args, **kwargs)
+
+
+th = TorchHijackForUnet()
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 283cf1cd..5b37f3fe 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -7,6 +7,9 @@ import torch
 import re
 import safetensors.torch
 from omegaconf import OmegaConf
+from os import mkdir
+from urllib import request
+import ldm.modules.midas as midas
 
 from ldm.util import instantiate_from_config
 
@@ -36,6 +39,7 @@ def setup_model():
         os.makedirs(model_path)
 
     list_models()
+    enable_midas_autodownload()
 
 
 def checkpoint_tiles(): 
@@ -223,10 +227,54 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
     model.sd_model_checkpoint = checkpoint_file
     model.sd_checkpoint_info = checkpoint_info
 
+    sd_vae.delete_base_vae()
+    sd_vae.clear_loaded_vae()
     vae_file = sd_vae.resolve_vae(checkpoint_file, vae_file=vae_file)
     sd_vae.load_vae(model, vae_file)
 
 
+def enable_midas_autodownload():
+    """
+    Gives the ldm.modules.midas.api.load_model function automatic downloading.
+
+    When the 512-depth-ema model, and other future models like it, is loaded,
+    it calls midas.api.load_model to load the associated midas depth model.
+    This function applies a wrapper to download the model to the correct
+    location automatically.
+    """
+
+    midas_path = os.path.join(models_path, 'midas')
+
+    # stable-diffusion-stability-ai hard-codes the midas model path to
+    # a location that differs from where other scripts using this model look.
+    # HACK: Overriding the path here.
+    for k, v in midas.api.ISL_PATHS.items():
+        file_name = os.path.basename(v)
+        midas.api.ISL_PATHS[k] = os.path.join(midas_path, file_name)
+
+    midas_urls = {
+        "dpt_large": "https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt",
+        "dpt_hybrid": "https://github.com/intel-isl/DPT/releases/download/1_0/dpt_hybrid-midas-501f0c75.pt",
+        "midas_v21": "https://github.com/AlexeyAB/MiDaS/releases/download/midas_dpt/midas_v21-f6b98070.pt",
+        "midas_v21_small": "https://github.com/AlexeyAB/MiDaS/releases/download/midas_dpt/midas_v21_small-70d6b9c8.pt",
+    }
+
+    midas.api.load_model_inner = midas.api.load_model
+
+    def load_model_wrapper(model_type):
+        path = midas.api.ISL_PATHS[model_type]
+        if not os.path.exists(path):
+            if not os.path.exists(midas_path):
+                mkdir(midas_path)
+    
+            print(f"Downloading midas model weights for {model_type} to {path}")
+            request.urlretrieve(midas_urls[model_type], path)
+            print(f"{model_type} downloaded")
+
+        return midas.api.load_model_inner(model_type)
+
+    midas.api.load_model = load_model_wrapper
+
 def load_model(checkpoint_info=None):
     from modules import lowvram, sd_hijack
     checkpoint_info = checkpoint_info or select_checkpoint()
diff --git a/modules/sd_vae.py b/modules/sd_vae.py
index 9c120975..25638a83 100644
--- a/modules/sd_vae.py
+++ b/modules/sd_vae.py
@@ -4,6 +4,7 @@ from collections import namedtuple
 from modules import shared, devices, script_callbacks
 from modules.paths import models_path
 import glob
+from copy import deepcopy
 
 
 model_dir = "Stable-diffusion"
@@ -15,7 +16,7 @@ vae_path = os.path.abspath(os.path.join(models_path, vae_dir))
 vae_ignore_keys = {"model_ema.decay", "model_ema.num_updates"}
 
 
-default_vae_dict = {"auto": "auto", "None": "None"}
+default_vae_dict = {"auto": "auto", "None": None, None: None}
 default_vae_list = ["auto", "None"]
 
 
@@ -39,7 +40,8 @@ def get_base_vae(model):
 def store_base_vae(model):
     global base_vae, checkpoint_info
     if checkpoint_info != model.sd_checkpoint_info:
-        base_vae = model.first_stage_model.state_dict().copy()
+        assert not loaded_vae_file, "Trying to store non-base VAE!"
+        base_vae = deepcopy(model.first_stage_model.state_dict())
         checkpoint_info = model.sd_checkpoint_info
 
 
@@ -50,9 +52,11 @@ def delete_base_vae():
 
 
 def restore_base_vae(model):
-    global base_vae, checkpoint_info
+    global loaded_vae_file
     if base_vae is not None and checkpoint_info == model.sd_checkpoint_info:
-        load_vae_dict(model, base_vae)
+        print("Restoring base VAE")
+        _load_vae_dict(model, base_vae)
+        loaded_vae_file = None
     delete_base_vae()
 
 
@@ -148,9 +152,10 @@ def load_vae(model, vae_file=None):
     if vae_file:
         assert os.path.isfile(vae_file), f"VAE file doesn't exist: {vae_file}"
         print(f"Loading VAE weights from: {vae_file}")
+        store_base_vae(model)
         vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location)
         vae_dict_1 = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss" and k not in vae_ignore_keys}
-        load_vae_dict(model, vae_dict_1)
+        _load_vae_dict(model, vae_dict_1)
 
         # If vae used is not in dict, update it
         # It will be removed on refresh though
@@ -158,30 +163,22 @@ def load_vae(model, vae_file=None):
         if vae_opt not in vae_dict:
             vae_dict[vae_opt] = vae_file
             vae_list.append(vae_opt)
+    elif loaded_vae_file:
+        restore_base_vae(model)
 
     loaded_vae_file = vae_file
 
-    """
-    # Save current VAE to VAE settings, maybe? will it work?
-    if save_settings:
-        if vae_file is None:
-            vae_opt = "None"
-
-        # shared.opts.sd_vae = vae_opt
-    """
-
     first_load = False
 
 
 # don't call this from outside
-def load_vae_dict(model, vae_dict_1=None):
-    if vae_dict_1:
-        store_base_vae(model)
-        model.first_stage_model.load_state_dict(vae_dict_1)
-    else:
-        restore_base_vae()
+def _load_vae_dict(model, vae_dict_1):
+    model.first_stage_model.load_state_dict(vae_dict_1)
     model.first_stage_model.to(devices.dtype_vae)
 
+def clear_loaded_vae():
+    global loaded_vae_file
+    loaded_vae_file = None
 
 def reload_vae_weights(sd_model=None, vae_file="auto"):
     from modules import lowvram, devices, sd_hijack
diff --git a/modules/shared.py b/modules/shared.py
index ab9012af..44922c91 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -359,6 +359,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
     "sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}),
     "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."),
     "img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."),
     "enable_quantization": OptionInfo(False, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply."),
diff --git a/modules/ui.py b/modules/ui.py
index b2b8de90..38096aad 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -302,8 +302,8 @@ def create_seed_inputs():
 
     with gr.Row(visible=False) as seed_extra_row_2:
         seed_extras.append(seed_extra_row_2)
-        seed_resize_from_w = gr.Slider(minimum=0, maximum=2048, step=64, label="Resize seed from width", value=0)
-        seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=64, label="Resize seed from height", value=0)
+        seed_resize_from_w = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from width", value=0)
+        seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from height", value=0)
 
     random_seed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[seed])
     random_subseed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[subseed])
@@ -635,8 +635,8 @@ def create_ui():
                 sampler_index = gr.Radio(label='Sampling method', elem_id="txt2img_sampling", choices=[x.name for x in samplers], value=samplers[0].name, type="index")
 
                 with gr.Group():
-                    width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
-                    height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
+                    width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512)
+                    height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512)
 
                 with gr.Row():
                     restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1)
@@ -644,8 +644,8 @@ def create_ui():
                     enable_hr = gr.Checkbox(label='Highres. fix', value=False)
 
                 with gr.Row(visible=False) as hr_options:
-                    firstphase_width = gr.Slider(minimum=0, maximum=1024, step=64, label="Firstpass width", value=0)
-                    firstphase_height = gr.Slider(minimum=0, maximum=1024, step=64, label="Firstpass height", value=0)
+                    firstphase_width = gr.Slider(minimum=0, maximum=1024, step=8, label="Firstpass width", value=0)
+                    firstphase_height = gr.Slider(minimum=0, maximum=1024, step=8, label="Firstpass height", value=0)
                     denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7)
 
                 with gr.Row(equal_height=True):
@@ -835,8 +835,8 @@ def create_ui():
                 sampler_index = gr.Radio(label='Sampling method', choices=[x.name for x in samplers_for_img2img], value=samplers_for_img2img[0].name, type="index")
 
                 with gr.Group():
-                    width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512, elem_id="img2img_width")
-                    height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512, elem_id="img2img_height")
+                    width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="img2img_width")
+                    height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="img2img_height")
 
                 with gr.Row():
                     restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1)
@@ -1171,8 +1171,8 @@ def create_ui():
                 with gr.Tab(label="Preprocess images"):
                     process_src = gr.Textbox(label='Source directory')
                     process_dst = gr.Textbox(label='Destination directory')
-                    process_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
-                    process_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
+                    process_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512)
+                    process_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512)
                     preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"])
 
                     with gr.Row():
@@ -1230,8 +1230,8 @@ def create_ui():
                     dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
                     log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
                     template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"))
-                    training_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
-                    training_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
+                    training_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512)
+                    training_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512)
                     steps = gr.Number(label='Max steps', value=100000, precision=0)
                     create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
                     save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
@@ -1450,7 +1450,7 @@ def create_ui():
             opts.save(shared.config_filename)
         except RuntimeError:
             return opts.dumpjson(), f'{len(changed)} settings changed without save: {", ".join(changed)}.'
-        return opts.dumpjson(), f'{len(changed)} settings changed: {", ".join(changed)}.'
+        return opts.dumpjson(), f'{len(changed)} settings changed{": " if len(changed) > 0 else ""}{", ".join(changed)}.'
 
     def run_settings_single(value, key):
         if not opts.same_type(value, opts.data_labels[key].default):