diff --git a/CHANGELOG.md b/CHANGELOG.md
index 295d26c8c..596b1ec45 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,3 +1,8 @@
+## 1.9.4
+
+### Bug Fixes:
+* pin setuptools version to fix the startup error ([#15882](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15882))
+
## 1.9.3
### Bug Fixes:
diff --git a/configs/alt-diffusion-inference.yaml b/configs/alt-diffusion-inference.yaml
index cfbee72d7..4944ab5c8 100644
--- a/configs/alt-diffusion-inference.yaml
+++ b/configs/alt-diffusion-inference.yaml
@@ -40,7 +40,7 @@ model:
use_spatial_transformer: True
transformer_depth: 1
context_dim: 768
- use_checkpoint: True
+ use_checkpoint: False
legacy: False
first_stage_config:
diff --git a/configs/alt-diffusion-m18-inference.yaml b/configs/alt-diffusion-m18-inference.yaml
index 41a031d55..c60dca8c7 100644
--- a/configs/alt-diffusion-m18-inference.yaml
+++ b/configs/alt-diffusion-m18-inference.yaml
@@ -41,7 +41,7 @@ model:
use_linear_in_transformer: True
transformer_depth: 1
context_dim: 1024
- use_checkpoint: True
+ use_checkpoint: False
legacy: False
first_stage_config:
diff --git a/configs/instruct-pix2pix.yaml b/configs/instruct-pix2pix.yaml
index 4e896879d..564e50ae2 100644
--- a/configs/instruct-pix2pix.yaml
+++ b/configs/instruct-pix2pix.yaml
@@ -45,7 +45,7 @@ model:
use_spatial_transformer: True
transformer_depth: 1
context_dim: 768
- use_checkpoint: True
+ use_checkpoint: False
legacy: False
first_stage_config:
diff --git a/configs/sd_xl_inpaint.yaml b/configs/sd_xl_inpaint.yaml
index 3bad37218..f40f45e33 100644
--- a/configs/sd_xl_inpaint.yaml
+++ b/configs/sd_xl_inpaint.yaml
@@ -21,7 +21,7 @@ model:
params:
adm_in_channels: 2816
num_classes: sequential
- use_checkpoint: True
+ use_checkpoint: False
in_channels: 9
out_channels: 4
model_channels: 320
diff --git a/configs/v1-inference.yaml b/configs/v1-inference.yaml
index d4effe569..25c4d9ed0 100644
--- a/configs/v1-inference.yaml
+++ b/configs/v1-inference.yaml
@@ -40,7 +40,7 @@ model:
use_spatial_transformer: True
transformer_depth: 1
context_dim: 768
- use_checkpoint: True
+ use_checkpoint: False
legacy: False
first_stage_config:
diff --git a/configs/v1-inpainting-inference.yaml b/configs/v1-inpainting-inference.yaml
index f9eec37d2..68c199f99 100644
--- a/configs/v1-inpainting-inference.yaml
+++ b/configs/v1-inpainting-inference.yaml
@@ -40,7 +40,7 @@ model:
use_spatial_transformer: True
transformer_depth: 1
context_dim: 768
- use_checkpoint: True
+ use_checkpoint: False
legacy: False
first_stage_config:
diff --git a/extensions-builtin/Lora/networks.py b/extensions-builtin/Lora/networks.py
index 42b14dc23..8869d2c82 100644
--- a/extensions-builtin/Lora/networks.py
+++ b/extensions-builtin/Lora/networks.py
@@ -143,6 +143,14 @@ def assign_network_names_to_compvis_modules(sd_model):
sd_model.network_layer_mapping = network_layer_mapping
+class BundledTIHash(str):
+ def __init__(self, hash_str):
+ self.hash = hash_str
+
+ def __str__(self):
+ return self.hash if shared.opts.lora_bundled_ti_to_infotext else ''
+
+
def load_network(name, network_on_disk):
net = network.Network(name, network_on_disk)
net.mtime = os.path.getmtime(network_on_disk.filename)
@@ -229,6 +237,7 @@ def load_network(name, network_on_disk):
for emb_name, data in bundle_embeddings.items():
embedding = textual_inversion.create_embedding_from_data(data, emb_name, filename=network_on_disk.filename + "/" + emb_name)
embedding.loaded = None
+ embedding.shorthash = BundledTIHash(name)
embeddings[emb_name] = embedding
net.bundle_embeddings = embeddings
@@ -260,6 +269,16 @@ def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=No
loaded_networks.clear()
+ unavailable_networks = []
+ for name in names:
+ if name.lower() in forbidden_network_aliases and available_networks.get(name) is None:
+ unavailable_networks.append(name)
+ elif available_network_aliases.get(name) is None:
+ unavailable_networks.append(name)
+
+ if unavailable_networks:
+ update_available_networks_by_names(unavailable_networks)
+
networks_on_disk = [available_networks.get(name, None) if name.lower() in forbidden_network_aliases else available_network_aliases.get(name, None) for name in names]
if any(x is None for x in networks_on_disk):
list_available_networks()
@@ -378,13 +397,18 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
self.network_weights_backup = weights_backup
bias_backup = getattr(self, "network_bias_backup", None)
- if bias_backup is None:
+ if bias_backup is None and wanted_names != ():
if isinstance(self, torch.nn.MultiheadAttention) and self.out_proj.bias is not None:
bias_backup = self.out_proj.bias.to(devices.cpu, copy=True)
elif getattr(self, 'bias', None) is not None:
bias_backup = self.bias.to(devices.cpu, copy=True)
else:
bias_backup = None
+
+ # Unlike weight which always has value, some modules don't have bias.
+ # Only report if bias is not None and current bias are not unchanged.
+ if bias_backup is not None and current_names != ():
+ raise RuntimeError("no backup bias found and current bias are not unchanged")
self.network_bias_backup = bias_backup
if current_names != wanted_names:
@@ -566,22 +590,16 @@ def network_MultiheadAttention_load_state_dict(self, *args, **kwargs):
return originals.MultiheadAttention_load_state_dict(self, *args, **kwargs)
-def list_available_networks():
- available_networks.clear()
- available_network_aliases.clear()
- forbidden_network_aliases.clear()
- available_network_hash_lookup.clear()
- forbidden_network_aliases.update({"none": 1, "Addams": 1})
-
- os.makedirs(shared.cmd_opts.lora_dir, exist_ok=True)
-
+def process_network_files(names: list[str] | None = None):
candidates = list(shared.walk_files(shared.cmd_opts.lora_dir, allowed_extensions=[".pt", ".ckpt", ".safetensors"]))
candidates += list(shared.walk_files(shared.cmd_opts.lyco_dir_backcompat, allowed_extensions=[".pt", ".ckpt", ".safetensors"]))
for filename in candidates:
if os.path.isdir(filename):
continue
-
name = os.path.splitext(os.path.basename(filename))[0]
+ # if names is provided, only load networks with names in the list
+ if names and name not in names:
+ continue
try:
entry = network.NetworkOnDisk(name, filename)
except OSError: # should catch FileNotFoundError and PermissionError etc.
@@ -597,6 +615,22 @@ def list_available_networks():
available_network_aliases[entry.alias] = entry
+def update_available_networks_by_names(names: list[str]):
+ process_network_files(names)
+
+
+def list_available_networks():
+ available_networks.clear()
+ available_network_aliases.clear()
+ forbidden_network_aliases.clear()
+ available_network_hash_lookup.clear()
+ forbidden_network_aliases.update({"none": 1, "Addams": 1})
+
+ os.makedirs(shared.cmd_opts.lora_dir, exist_ok=True)
+
+ process_network_files()
+
+
re_network_name = re.compile(r"(.*)\s*\([0-9a-fA-F]+\)")
diff --git a/extensions-builtin/Lora/scripts/lora_script.py b/extensions-builtin/Lora/scripts/lora_script.py
index 1518f7e5c..d3ea369ae 100644
--- a/extensions-builtin/Lora/scripts/lora_script.py
+++ b/extensions-builtin/Lora/scripts/lora_script.py
@@ -36,6 +36,7 @@ shared.options_templates.update(shared.options_section(('extra_networks', "Extra
"sd_lora": shared.OptionInfo("None", "Add network to prompt", gr.Dropdown, lambda: {"choices": ["None", *networks.available_networks]}, refresh=networks.list_available_networks),
"lora_preferred_name": shared.OptionInfo("Alias from file", "When adding to prompt, refer to Lora by", gr.Radio, {"choices": ["Alias from file", "Filename"]}),
"lora_add_hashes_to_infotext": shared.OptionInfo(True, "Add Lora hashes to infotext"),
+ "lora_bundled_ti_to_infotext": shared.OptionInfo(True, "Add Lora name as TI hashes for bundled Textual Inversion").info('"Add Textual Inversion hashes to infotext" needs to be enabled'),
"lora_show_all": shared.OptionInfo(False, "Always show all networks on the Lora page").info("otherwise, those detected as for incompatible version of Stable Diffusion will be hidden"),
"lora_hide_unknown_for_versions": shared.OptionInfo([], "Hide networks of unknown versions for model versions", gr.CheckboxGroup, {"choices": ["SD1", "SD2", "SDXL"]}),
"lora_in_memory_limit": shared.OptionInfo(0, "Number of Lora networks to keep cached in memory", gr.Number, {"precision": 0}),
diff --git a/extensions-builtin/Lora/ui_extra_networks_lora.py b/extensions-builtin/Lora/ui_extra_networks_lora.py
index b627f7dc2..3e34d69dc 100644
--- a/extensions-builtin/Lora/ui_extra_networks_lora.py
+++ b/extensions-builtin/Lora/ui_extra_networks_lora.py
@@ -60,7 +60,7 @@ class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage):
else:
sd_version = lora_on_disk.sd_version
- if shared.opts.lora_show_all or not enable_filter:
+ if shared.opts.lora_show_all or not enable_filter or not shared.sd_model:
pass
elif sd_version == network.SdVersion.Unknown:
model_version = network.SdVersion.SDXL if shared.sd_model.is_sdxl else network.SdVersion.SD2 if shared.sd_model.is_sd2 else network.SdVersion.SD1
diff --git a/extensions-builtin/hypertile/scripts/hypertile_script.py b/extensions-builtin/hypertile/scripts/hypertile_script.py
index 395d584b6..59e7f9907 100644
--- a/extensions-builtin/hypertile/scripts/hypertile_script.py
+++ b/extensions-builtin/hypertile/scripts/hypertile_script.py
@@ -1,6 +1,5 @@
import hypertile
from modules import scripts, script_callbacks, shared
-from scripts.hypertile_xyz import add_axis_options
class ScriptHypertile(scripts.Script):
@@ -93,7 +92,6 @@ def on_ui_settings():
"hypertile_max_depth_unet": shared.OptionInfo(3, "Hypertile U-Net max depth", gr.Slider, {"minimum": 0, "maximum": 3, "step": 1}, infotext="Hypertile U-Net max depth").info("larger = more neural network layers affected; minor effect on performance"),
"hypertile_max_tile_unet": shared.OptionInfo(256, "Hypertile U-Net max tile size", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}, infotext="Hypertile U-Net max tile size").info("larger = worse performance"),
"hypertile_swap_size_unet": shared.OptionInfo(3, "Hypertile U-Net swap size", gr.Slider, {"minimum": 0, "maximum": 64, "step": 1}, infotext="Hypertile U-Net swap size"),
-
"hypertile_enable_vae": shared.OptionInfo(False, "Enable Hypertile VAE", infotext="Hypertile VAE").info("minimal change in the generated picture"),
"hypertile_max_depth_vae": shared.OptionInfo(3, "Hypertile VAE max depth", gr.Slider, {"minimum": 0, "maximum": 3, "step": 1}, infotext="Hypertile VAE max depth"),
"hypertile_max_tile_vae": shared.OptionInfo(128, "Hypertile VAE max tile size", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}, infotext="Hypertile VAE max tile size"),
@@ -105,5 +103,20 @@ def on_ui_settings():
shared.opts.add_option(name, opt)
+def add_axis_options():
+ xyz_grid = [x for x in scripts.scripts_data if x.script_class.__module__ == "xyz_grid.py"][0].module
+ xyz_grid.axis_options.extend([
+ xyz_grid.AxisOption("[Hypertile] Unet First pass Enabled", str, xyz_grid.apply_override('hypertile_enable_unet', boolean=True), choices=xyz_grid.boolean_choice(reverse=True)),
+ xyz_grid.AxisOption("[Hypertile] Unet Second pass Enabled", str, xyz_grid.apply_override('hypertile_enable_unet_secondpass', boolean=True), choices=xyz_grid.boolean_choice(reverse=True)),
+ xyz_grid.AxisOption("[Hypertile] Unet Max Depth", int, xyz_grid.apply_override("hypertile_max_depth_unet"), confirm=xyz_grid.confirm_range(0, 3, '[Hypertile] Unet Max Depth'), choices=lambda: [str(x) for x in range(4)]),
+ xyz_grid.AxisOption("[Hypertile] Unet Max Tile Size", int, xyz_grid.apply_override("hypertile_max_tile_unet"), confirm=xyz_grid.confirm_range(0, 512, '[Hypertile] Unet Max Tile Size')),
+ xyz_grid.AxisOption("[Hypertile] Unet Swap Size", int, xyz_grid.apply_override("hypertile_swap_size_unet"), confirm=xyz_grid.confirm_range(0, 64, '[Hypertile] Unet Swap Size')),
+ xyz_grid.AxisOption("[Hypertile] VAE Enabled", str, xyz_grid.apply_override('hypertile_enable_vae', boolean=True), choices=xyz_grid.boolean_choice(reverse=True)),
+ xyz_grid.AxisOption("[Hypertile] VAE Max Depth", int, xyz_grid.apply_override("hypertile_max_depth_vae"), confirm=xyz_grid.confirm_range(0, 3, '[Hypertile] VAE Max Depth'), choices=lambda: [str(x) for x in range(4)]),
+ xyz_grid.AxisOption("[Hypertile] VAE Max Tile Size", int, xyz_grid.apply_override("hypertile_max_tile_vae"), confirm=xyz_grid.confirm_range(0, 512, '[Hypertile] VAE Max Tile Size')),
+ xyz_grid.AxisOption("[Hypertile] VAE Swap Size", int, xyz_grid.apply_override("hypertile_swap_size_vae"), confirm=xyz_grid.confirm_range(0, 64, '[Hypertile] VAE Swap Size')),
+ ])
+
+
script_callbacks.on_ui_settings(on_ui_settings)
script_callbacks.on_before_ui(add_axis_options)
diff --git a/extensions-builtin/hypertile/scripts/hypertile_xyz.py b/extensions-builtin/hypertile/scripts/hypertile_xyz.py
deleted file mode 100644
index 9e96ae3c5..000000000
--- a/extensions-builtin/hypertile/scripts/hypertile_xyz.py
+++ /dev/null
@@ -1,51 +0,0 @@
-from modules import scripts
-from modules.shared import opts
-
-xyz_grid = [x for x in scripts.scripts_data if x.script_class.__module__ == "xyz_grid.py"][0].module
-
-def int_applier(value_name:str, min_range:int = -1, max_range:int = -1):
- """
- Returns a function that applies the given value to the given value_name in opts.data.
- """
- def validate(value_name:str, value:str):
- value = int(value)
- # validate value
- if not min_range == -1:
- assert value >= min_range, f"Value {value} for {value_name} must be greater than or equal to {min_range}"
- if not max_range == -1:
- assert value <= max_range, f"Value {value} for {value_name} must be less than or equal to {max_range}"
- def apply_int(p, x, xs):
- validate(value_name, x)
- opts.data[value_name] = int(x)
- return apply_int
-
-def bool_applier(value_name:str):
- """
- Returns a function that applies the given value to the given value_name in opts.data.
- """
- def validate(value_name:str, value:str):
- assert value.lower() in ["true", "false"], f"Value {value} for {value_name} must be either true or false"
- def apply_bool(p, x, xs):
- validate(value_name, x)
- value_boolean = x.lower() == "true"
- opts.data[value_name] = value_boolean
- return apply_bool
-
-def add_axis_options():
- extra_axis_options = [
- xyz_grid.AxisOption("[Hypertile] Unet First pass Enabled", str, bool_applier("hypertile_enable_unet"), choices=xyz_grid.boolean_choice(reverse=True)),
- xyz_grid.AxisOption("[Hypertile] Unet Second pass Enabled", str, bool_applier("hypertile_enable_unet_secondpass"), choices=xyz_grid.boolean_choice(reverse=True)),
- xyz_grid.AxisOption("[Hypertile] Unet Max Depth", int, int_applier("hypertile_max_depth_unet", 0, 3), choices=lambda: [str(x) for x in range(4)]),
- xyz_grid.AxisOption("[Hypertile] Unet Max Tile Size", int, int_applier("hypertile_max_tile_unet", 0, 512)),
- xyz_grid.AxisOption("[Hypertile] Unet Swap Size", int, int_applier("hypertile_swap_size_unet", 0, 64)),
- xyz_grid.AxisOption("[Hypertile] VAE Enabled", str, bool_applier("hypertile_enable_vae"), choices=xyz_grid.boolean_choice(reverse=True)),
- xyz_grid.AxisOption("[Hypertile] VAE Max Depth", int, int_applier("hypertile_max_depth_vae", 0, 3), choices=lambda: [str(x) for x in range(4)]),
- xyz_grid.AxisOption("[Hypertile] VAE Max Tile Size", int, int_applier("hypertile_max_tile_vae", 0, 512)),
- xyz_grid.AxisOption("[Hypertile] VAE Swap Size", int, int_applier("hypertile_swap_size_vae", 0, 64)),
- ]
- set_a = {opt.label for opt in xyz_grid.axis_options}
- set_b = {opt.label for opt in extra_axis_options}
- if set_a.intersection(set_b):
- return
-
- xyz_grid.axis_options.extend(extra_axis_options)
diff --git a/extensions-builtin/soft-inpainting/scripts/soft_inpainting.py b/extensions-builtin/soft-inpainting/scripts/soft_inpainting.py
index f56e1e226..0e629963a 100644
--- a/extensions-builtin/soft-inpainting/scripts/soft_inpainting.py
+++ b/extensions-builtin/soft-inpainting/scripts/soft_inpainting.py
@@ -3,6 +3,7 @@ import gradio as gr
import math
from modules.ui_components import InputAccordion
import modules.scripts as scripts
+from modules.torch_utils import float64
class SoftInpaintingSettings:
@@ -79,13 +80,11 @@ def latent_blend(settings, a, b, t):
# Calculate the magnitude of the interpolated vectors. (We will remove this magnitude.)
# 64-bit operations are used here to allow large exponents.
- current_magnitude = torch.norm(image_interp, p=2, dim=1, keepdim=True).to(torch.float64).add_(0.00001)
+ current_magnitude = torch.norm(image_interp, p=2, dim=1, keepdim=True).to(float64(image_interp)).add_(0.00001)
# Interpolate the powered magnitudes, then un-power them (bring them back to a power of 1).
- a_magnitude = torch.norm(a, p=2, dim=1, keepdim=True).to(torch.float64).pow_(
- settings.inpaint_detail_preservation) * one_minus_t3
- b_magnitude = torch.norm(b, p=2, dim=1, keepdim=True).to(torch.float64).pow_(
- settings.inpaint_detail_preservation) * t3
+ a_magnitude = torch.norm(a, p=2, dim=1, keepdim=True).to(float64(a)).pow_(settings.inpaint_detail_preservation) * one_minus_t3
+ b_magnitude = torch.norm(b, p=2, dim=1, keepdim=True).to(float64(b)).pow_(settings.inpaint_detail_preservation) * t3
desired_magnitude = a_magnitude
desired_magnitude.add_(b_magnitude).pow_(1 / settings.inpaint_detail_preservation)
del a_magnitude, b_magnitude, t3, one_minus_t3
diff --git a/javascript/contextMenus.js b/javascript/contextMenus.js
index ccae242f2..e01fd67e8 100644
--- a/javascript/contextMenus.js
+++ b/javascript/contextMenus.js
@@ -8,9 +8,6 @@ var contextMenuInit = function() {
};
function showContextMenu(event, element, menuEntries) {
- let posx = event.clientX + document.body.scrollLeft + document.documentElement.scrollLeft;
- let posy = event.clientY + document.body.scrollTop + document.documentElement.scrollTop;
-
let oldMenu = gradioApp().querySelector('#context-menu');
if (oldMenu) {
oldMenu.remove();
@@ -23,10 +20,8 @@ var contextMenuInit = function() {
contextMenu.style.background = baseStyle.background;
contextMenu.style.color = baseStyle.color;
contextMenu.style.fontFamily = baseStyle.fontFamily;
- contextMenu.style.top = posy + 'px';
- contextMenu.style.left = posx + 'px';
-
-
+ contextMenu.style.top = event.pageY + 'px';
+ contextMenu.style.left = event.pageX + 'px';
const contextMenuList = document.createElement('ul');
contextMenuList.className = 'context-menu-items';
@@ -43,21 +38,6 @@ var contextMenuInit = function() {
});
gradioApp().appendChild(contextMenu);
-
- let menuWidth = contextMenu.offsetWidth + 4;
- let menuHeight = contextMenu.offsetHeight + 4;
-
- let windowWidth = window.innerWidth;
- let windowHeight = window.innerHeight;
-
- if ((windowWidth - posx) < menuWidth) {
- contextMenu.style.left = windowWidth - menuWidth + "px";
- }
-
- if ((windowHeight - posy) < menuHeight) {
- contextMenu.style.top = windowHeight - menuHeight + "px";
- }
-
}
function appendContextMenuOption(targetElementSelector, entryName, entryFunction) {
@@ -107,16 +87,23 @@ var contextMenuInit = function() {
oldMenu.remove();
}
});
- gradioApp().addEventListener("contextmenu", function(e) {
- let oldMenu = gradioApp().querySelector('#context-menu');
- if (oldMenu) {
- oldMenu.remove();
- }
- menuSpecs.forEach(function(v, k) {
- if (e.composedPath()[0].matches(k)) {
- showContextMenu(e, e.composedPath()[0], v);
- e.preventDefault();
+ ['contextmenu', 'touchstart'].forEach((eventType) => {
+ gradioApp().addEventListener(eventType, function(e) {
+ let ev = e;
+ if (eventType.startsWith('touch')) {
+ if (e.touches.length !== 2) return;
+ ev = e.touches[0];
}
+ let oldMenu = gradioApp().querySelector('#context-menu');
+ if (oldMenu) {
+ oldMenu.remove();
+ }
+ menuSpecs.forEach(function(v, k) {
+ if (e.composedPath()[0].matches(k)) {
+ showContextMenu(ev, e.composedPath()[0], v);
+ e.preventDefault();
+ }
+ });
});
});
eventListenerApplied = true;
diff --git a/javascript/dragdrop.js b/javascript/dragdrop.js
index 0c0183564..882562d73 100644
--- a/javascript/dragdrop.js
+++ b/javascript/dragdrop.js
@@ -56,6 +56,15 @@ function eventHasFiles(e) {
return false;
}
+function isURL(url) {
+ try {
+ const _ = new URL(url);
+ return true;
+ } catch {
+ return false;
+ }
+}
+
function dragDropTargetIsPrompt(target) {
if (target?.placeholder && target?.placeholder.indexOf("Prompt") >= 0) return true;
if (target?.parentNode?.parentNode?.className?.indexOf("prompt") > 0) return true;
@@ -77,7 +86,7 @@ window.document.addEventListener('dragover', e => {
window.document.addEventListener('drop', async e => {
const target = e.composedPath()[0];
const url = e.dataTransfer.getData('text/uri-list') || e.dataTransfer.getData('text/plain');
- if (!eventHasFiles(e) && !url) return;
+ if (!eventHasFiles(e) && !isURL(url)) return;
if (dragDropTargetIsPrompt(target)) {
e.stopPropagation();
diff --git a/javascript/imageviewer.js b/javascript/imageviewer.js
index d4d4f016d..a3f08ad16 100644
--- a/javascript/imageviewer.js
+++ b/javascript/imageviewer.js
@@ -51,14 +51,7 @@ function modalImageSwitch(offset) {
var galleryButtons = all_gallery_buttons();
if (galleryButtons.length > 1) {
- var currentButton = selected_gallery_button();
-
- var result = -1;
- galleryButtons.forEach(function(v, i) {
- if (v == currentButton) {
- result = i;
- }
- });
+ var result = selected_gallery_index();
if (result != -1) {
var nextButton = galleryButtons[negmod((result + offset), galleryButtons.length)];
diff --git a/javascript/ui.js b/javascript/ui.js
index e0f5feebd..16faacebb 100644
--- a/javascript/ui.js
+++ b/javascript/ui.js
@@ -337,8 +337,8 @@ onOptionsChanged(function() {
let txt2img_textarea, img2img_textarea = undefined;
function restart_reload() {
+ document.body.style.backgroundColor = "var(--background-fill-primary)";
document.body.innerHTML = '
Reloading...
';
-
var requestPing = function() {
requestGet("./internal/ping", {}, function(data) {
location.reload();
diff --git a/modules/api/api.py b/modules/api/api.py
index f468c3852..d8e54529b 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -438,15 +438,19 @@ class Api:
self.apply_infotext(txt2imgreq, "txt2img", script_runner=script_runner, mentioned_script_args=infotext_script_args)
selectable_scripts, selectable_script_idx = self.get_selectable_script(txt2imgreq.script_name, script_runner)
+ sampler, scheduler = sd_samplers.get_sampler_and_scheduler(txt2imgreq.sampler_name or txt2imgreq.sampler_index, txt2imgreq.scheduler)
populate = txt2imgreq.copy(update={ # Override __init__ params
- "sampler_name": validate_sampler_name(txt2imgreq.sampler_name or txt2imgreq.sampler_index),
+ "sampler_name": validate_sampler_name(sampler),
"do_not_save_samples": not txt2imgreq.save_images,
"do_not_save_grid": not txt2imgreq.save_images,
})
if populate.sampler_name:
populate.sampler_index = None # prevent a warning later on
+ if not populate.scheduler and scheduler != "Automatic":
+ populate.scheduler = scheduler
+
args = vars(populate)
args.pop('script_name', None)
args.pop('script_args', None) # will refeed them to the pipeline directly after initializing them
@@ -502,9 +506,10 @@ class Api:
self.apply_infotext(img2imgreq, "img2img", script_runner=script_runner, mentioned_script_args=infotext_script_args)
selectable_scripts, selectable_script_idx = self.get_selectable_script(img2imgreq.script_name, script_runner)
+ sampler, scheduler = sd_samplers.get_sampler_and_scheduler(img2imgreq.sampler_name or img2imgreq.sampler_index, img2imgreq.scheduler)
populate = img2imgreq.copy(update={ # Override __init__ params
- "sampler_name": validate_sampler_name(img2imgreq.sampler_name or img2imgreq.sampler_index),
+ "sampler_name": validate_sampler_name(sampler),
"do_not_save_samples": not img2imgreq.save_images,
"do_not_save_grid": not img2imgreq.save_images,
"mask": mask,
@@ -512,6 +517,9 @@ class Api:
if populate.sampler_name:
populate.sampler_index = None # prevent a warning later on
+ if not populate.scheduler and scheduler != "Automatic":
+ populate.scheduler = scheduler
+
args = vars(populate)
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.
args.pop('script_name', None)
diff --git a/modules/cmd_args.py b/modules/cmd_args.py
index 016a33d10..61ec1866b 100644
--- a/modules/cmd_args.py
+++ b/modules/cmd_args.py
@@ -20,6 +20,7 @@ parser.add_argument("--dump-sysinfo", action='store_true', help="launch.py argum
parser.add_argument("--loglevel", type=str, help="log level; one of: CRITICAL, ERROR, WARNING, INFO, DEBUG", default=None)
parser.add_argument("--do-not-download-clip", action='store_true', help="do not download CLIP model even if it's not included in the checkpoint")
parser.add_argument("--data-dir", type=normalized_filepath, default=os.path.dirname(os.path.dirname(os.path.realpath(__file__))), help="base path where all user data is stored")
+parser.add_argument("--models-dir", type=normalized_filepath, default=None, help="base path where models are stored; overrides --data-dir")
parser.add_argument("--config", type=normalized_filepath, default=sd_default_config, help="path to config which constructs model",)
parser.add_argument("--ckpt", type=normalized_filepath, default=sd_model_file, help="path to checkpoint of stable diffusion model; if specified, this checkpoint will be added to the list of checkpoints and loaded",)
parser.add_argument("--ckpt-dir", type=normalized_filepath, default=None, help="Path to directory with stable diffusion checkpoints")
@@ -41,7 +42,7 @@ parser.add_argument("--lowvram", action='store_true', help="enable stable diffus
parser.add_argument("--lowram", action='store_true', help="load stable diffusion checkpoint weights to VRAM instead of RAM")
parser.add_argument("--always-batch-cond-uncond", action='store_true', help="does not do anything")
parser.add_argument("--unload-gfpgan", action='store_true', help="does not do anything.")
-parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast")
+parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "half", "autocast"], default="autocast")
parser.add_argument("--upcast-sampling", action='store_true', help="upcast sampling. No effect with --no-half. Usually produces similar results to --no-half with better performance while using less memory.")
parser.add_argument("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site")
parser.add_argument("--ngrok", type=str, help="ngrok authtoken, alternative to gradio --share", default=None)
diff --git a/modules/devices.py b/modules/devices.py
index e4f671ac6..7de34ac51 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -114,6 +114,9 @@ errors.run(enable_tf32, "Enabling TF32")
cpu: torch.device = torch.device("cpu")
fp8: bool = False
+# Force fp16 for all models in inference. No casting during inference.
+# This flag is controlled by "--precision half" command line arg.
+force_fp16: bool = False
device: torch.device = None
device_interrogate: torch.device = None
device_gfpgan: torch.device = None
@@ -127,6 +130,8 @@ unet_needs_upcast = False
def cond_cast_unet(input):
+ if force_fp16:
+ return input.to(torch.float16)
return input.to(dtype_unet) if unet_needs_upcast else input
@@ -206,6 +211,11 @@ def autocast(disable=False):
if disable:
return contextlib.nullcontext()
+ if force_fp16:
+ # No casting during inference if force_fp16 is enabled.
+ # All tensor dtype conversion happens before inference.
+ return contextlib.nullcontext()
+
if fp8 and device==cpu:
return torch.autocast("cpu", dtype=torch.bfloat16, enabled=True)
@@ -269,3 +279,17 @@ def first_time_calculation():
x = torch.zeros((1, 1, 3, 3)).to(device, dtype)
conv2d = torch.nn.Conv2d(1, 1, (3, 3)).to(device, dtype)
conv2d(x)
+
+
+def force_model_fp16():
+ """
+ ldm and sgm has modules.diffusionmodules.util.GroupNorm32.forward, which
+ force conversion of input to float32. If force_fp16 is enabled, we need to
+ prevent this casting.
+ """
+ assert force_fp16
+ import sgm.modules.diffusionmodules.util as sgm_util
+ import ldm.modules.diffusionmodules.util as ldm_util
+ sgm_util.GroupNorm32 = torch.nn.GroupNorm
+ ldm_util.GroupNorm32 = torch.nn.GroupNorm
+ print("ldm/sgm GroupNorm32 replaced with normal torch.nn.GroupNorm due to `--precision half`.")
diff --git a/modules/extensions.py b/modules/extensions.py
index 5ad934b4d..24de766eb 100644
--- a/modules/extensions.py
+++ b/modules/extensions.py
@@ -191,8 +191,9 @@ class Extension:
def check_updates(self):
repo = Repo(self.path)
+ branch_name = f'{repo.remote().name}/{self.branch}'
for fetch in repo.remote().fetch(dry_run=True):
- if self.branch and fetch.name != f'{repo.remote().name}/{self.branch}':
+ if self.branch and fetch.name != branch_name:
continue
if fetch.flags != fetch.HEAD_UPTODATE:
self.can_update = True
@@ -200,7 +201,7 @@ class Extension:
return
try:
- origin = repo.rev_parse('origin')
+ origin = repo.rev_parse(branch_name)
if repo.head.commit != origin:
self.can_update = True
self.status = "behind HEAD"
@@ -213,8 +214,10 @@ class Extension:
self.can_update = False
self.status = "latest"
- def fetch_and_reset_hard(self, commit='origin'):
+ def fetch_and_reset_hard(self, commit=None):
repo = Repo(self.path)
+ if commit is None:
+ commit = f'{repo.remote().name}/{self.branch}'
# Fix: `error: Your local changes to the following files would be overwritten by merge`,
# because WSL2 Docker set 755 file permissions instead of 644, this results to the error.
repo.git.fetch(all=True)
diff --git a/modules/images.py b/modules/images.py
index c0ff8a630..9c73a0323 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -653,7 +653,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
# WebP and JPG formats have maximum dimension limits of 16383 and 65535 respectively. switch to PNG which has a much higher limit
if (image.height > 65535 or image.width > 65535) and extension.lower() in ("jpg", "jpeg") or (image.height > 16383 or image.width > 16383) and extension.lower() == "webp":
print('Image dimensions too large; saving as PNG')
- extension = ".png"
+ extension = "png"
if save_to_dirs is None:
save_to_dirs = (grid and opts.grid_save_to_dirs) or (not grid and opts.save_to_dirs and not no_prompt)
@@ -789,7 +789,10 @@ def read_info_from_image(image: Image.Image) -> tuple[str | None, dict]:
if exif_comment:
geninfo = exif_comment
elif "comment" in items: # for gif
- geninfo = items["comment"].decode('utf8', errors="ignore")
+ if isinstance(items["comment"], bytes):
+ geninfo = items["comment"].decode('utf8', errors="ignore")
+ else:
+ geninfo = items["comment"]
for field in IGNORED_INFO_KEYS:
items.pop(field, None)
diff --git a/modules/img2img.py b/modules/img2img.py
index a1d042c21..24f869f5c 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -17,11 +17,14 @@ from modules.ui import plaintext_to_html
import modules.scripts
-def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=False, scale_by=1.0, use_png_info=False, png_info_props=None, png_info_dir=None):
+def process_batch(p, input, output_dir, inpaint_mask_dir, args, to_scale=False, scale_by=1.0, use_png_info=False, png_info_props=None, png_info_dir=None):
output_dir = output_dir.strip()
processing.fix_seed(p)
- batch_images = list(shared.walk_files(input_dir, allowed_extensions=(".png", ".jpg", ".jpeg", ".webp", ".tif", ".tiff")))
+ if isinstance(input, str):
+ batch_images = list(shared.walk_files(input, allowed_extensions=(".png", ".jpg", ".jpeg", ".webp", ".tif", ".tiff")))
+ else:
+ batch_images = [os.path.abspath(x.name) for x in input]
is_inpaint_batch = False
if inpaint_mask_dir:
@@ -146,7 +149,7 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=Fal
return batch_results
-def img2img(id_task: str, request: gr.Request, 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, *args):
+def img2img(id_task: str, request: gr.Request, 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, img2img_batch_source_type: str, img2img_batch_upload: list, *args):
override_settings = create_override_settings_dict(override_settings_texts)
is_batch = mode == 5
@@ -221,8 +224,15 @@ def img2img(id_task: str, request: gr.Request, mode: int, prompt: str, negative_
with closing(p):
if is_batch:
- assert not shared.cmd_opts.hide_ui_dir_config, "Launched with --hide-ui-dir-config, batch img2img disabled"
- processed = process_batch(p, img2img_batch_input_dir, img2img_batch_output_dir, img2img_batch_inpaint_mask_dir, args, to_scale=selected_scale_tab == 1, scale_by=scale_by, use_png_info=img2img_batch_use_png_info, png_info_props=img2img_batch_png_info_props, png_info_dir=img2img_batch_png_info_dir)
+ if img2img_batch_source_type == "upload":
+ assert isinstance(img2img_batch_upload, list) and img2img_batch_upload
+ output_dir = ""
+ inpaint_mask_dir = ""
+ png_info_dir = img2img_batch_png_info_dir if not shared.cmd_opts.hide_ui_dir_config else ""
+ processed = process_batch(p, img2img_batch_upload, output_dir, inpaint_mask_dir, args, to_scale=selected_scale_tab == 1, scale_by=scale_by, use_png_info=img2img_batch_use_png_info, png_info_props=img2img_batch_png_info_props, png_info_dir=png_info_dir)
+ else: # "from dir"
+ assert not shared.cmd_opts.hide_ui_dir_config, "Launched with --hide-ui-dir-config, batch img2img disabled"
+ processed = process_batch(p, img2img_batch_input_dir, img2img_batch_output_dir, img2img_batch_inpaint_mask_dir, args, to_scale=selected_scale_tab == 1, scale_by=scale_by, use_png_info=img2img_batch_use_png_info, png_info_props=img2img_batch_png_info_props, png_info_dir=img2img_batch_png_info_dir)
if processed is None:
processed = Processed(p, [], p.seed, "")
diff --git a/modules/launch_utils.py b/modules/launch_utils.py
index 5812b0e58..e22da4ec6 100644
--- a/modules/launch_utils.py
+++ b/modules/launch_utils.py
@@ -76,7 +76,7 @@ def git_tag():
except Exception:
try:
- changelog_md = os.path.join(os.path.dirname(os.path.dirname(__file__)), "CHANGELOG.md")
+ changelog_md = os.path.join(script_path, "CHANGELOG.md")
with open(changelog_md, "r", encoding="utf-8") as file:
line = next((line.strip() for line in file if line.strip()), "")
line = line.replace("## ", "")
@@ -231,7 +231,7 @@ def run_extension_installer(extension_dir):
try:
env = os.environ.copy()
- env['PYTHONPATH'] = f"{os.path.abspath('.')}{os.pathsep}{env.get('PYTHONPATH', '')}"
+ env['PYTHONPATH'] = f"{script_path}{os.pathsep}{env.get('PYTHONPATH', '')}"
stdout = run(f'"{python}" "{path_installer}"', errdesc=f"Error running install.py for extension {extension_dir}", custom_env=env).strip()
if stdout:
diff --git a/modules/modelloader.py b/modules/modelloader.py
index 115415c8e..5421e59b0 100644
--- a/modules/modelloader.py
+++ b/modules/modelloader.py
@@ -23,6 +23,7 @@ def load_file_from_url(
model_dir: str,
progress: bool = True,
file_name: str | None = None,
+ hash_prefix: str | None = None,
) -> str:
"""Download a file from `url` into `model_dir`, using the file present if possible.
@@ -36,11 +37,11 @@ def load_file_from_url(
if not os.path.exists(cached_file):
print(f'Downloading: "{url}" to {cached_file}\n')
from torch.hub import download_url_to_file
- download_url_to_file(url, cached_file, progress=progress)
+ download_url_to_file(url, cached_file, progress=progress, hash_prefix=hash_prefix)
return cached_file
-def load_models(model_path: str, model_url: str = None, command_path: str = None, ext_filter=None, download_name=None, ext_blacklist=None) -> list:
+def load_models(model_path: str, model_url: str = None, command_path: str = None, ext_filter=None, download_name=None, ext_blacklist=None, hash_prefix=None) -> list:
"""
A one-and done loader to try finding the desired models in specified directories.
@@ -49,6 +50,7 @@ def load_models(model_path: str, model_url: str = None, command_path: str = None
@param model_path: The location to store/find models in.
@param command_path: A command-line argument to search for models in first.
@param ext_filter: An optional list of filename extensions to filter by
+ @param hash_prefix: the expected sha256 of the model_url
@return: A list of paths containing the desired model(s)
"""
output = []
@@ -78,7 +80,7 @@ def load_models(model_path: str, model_url: str = None, command_path: str = None
if model_url is not None and len(output) == 0:
if download_name is not None:
- output.append(load_file_from_url(model_url, model_dir=places[0], file_name=download_name))
+ output.append(load_file_from_url(model_url, model_dir=places[0], file_name=download_name, hash_prefix=hash_prefix))
else:
output.append(model_url)
diff --git a/modules/paths_internal.py b/modules/paths_internal.py
index cf9da45ab..67521f5cd 100644
--- a/modules/paths_internal.py
+++ b/modules/paths_internal.py
@@ -24,11 +24,12 @@ default_sd_model_file = sd_model_file
# Parse the --data-dir flag first so we can use it as a base for our other argument default values
parser_pre = argparse.ArgumentParser(add_help=False)
parser_pre.add_argument("--data-dir", type=str, default=os.path.dirname(modules_path), help="base path where all user data is stored", )
+parser_pre.add_argument("--models-dir", type=str, default=None, help="base path where models are stored; overrides --data-dir", )
cmd_opts_pre = parser_pre.parse_known_args()[0]
data_path = cmd_opts_pre.data_dir
-models_path = os.path.join(data_path, "models")
+models_path = cmd_opts_pre.models_dir if cmd_opts_pre.models_dir else os.path.join(data_path, "models")
extensions_dir = os.path.join(data_path, "extensions")
extensions_builtin_dir = os.path.join(script_path, "extensions-builtin")
config_states_dir = os.path.join(script_path, "config_states")
diff --git a/modules/postprocessing.py b/modules/postprocessing.py
index 812cbccae..8ec122b7c 100644
--- a/modules/postprocessing.py
+++ b/modules/postprocessing.py
@@ -62,11 +62,13 @@ def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir,
else:
image_data = image_placeholder
+ image_data = image_data if image_data.mode in ("RGBA", "RGB") else image_data.convert("RGB")
+
parameters, existing_pnginfo = images.read_info_from_image(image_data)
if parameters:
existing_pnginfo["parameters"] = parameters
- initial_pp = scripts_postprocessing.PostprocessedImage(image_data if image_data.mode in ("RGBA", "RGB") else image_data.convert("RGB"))
+ initial_pp = scripts_postprocessing.PostprocessedImage(image_data)
scripts.scripts_postproc.run(initial_pp, args)
diff --git a/modules/processing.py b/modules/processing.py
index 76557dd7f..0ff6a45c0 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -115,20 +115,17 @@ def txt2img_image_conditioning(sd_model, x, width, height):
return x.new_zeros(x.shape[0], 2*sd_model.noise_augmentor.time_embed.dim, dtype=x.dtype, device=x.device)
else:
- sd = sd_model.model.state_dict()
- diffusion_model_input = sd.get('diffusion_model.input_blocks.0.0.weight', None)
- if diffusion_model_input is not None:
- if diffusion_model_input.shape[1] == 9:
- # The "masked-image" in this case will just be all 0.5 since the entire image is masked.
- image_conditioning = torch.ones(x.shape[0], 3, height, width, device=x.device) * 0.5
- image_conditioning = images_tensor_to_samples(image_conditioning,
- approximation_indexes.get(opts.sd_vae_encode_method))
+ if getattr(sd_model.model, "is_sdxl_inpaint", False):
+ # The "masked-image" in this case will just be all 0.5 since the entire image is masked.
+ image_conditioning = torch.ones(x.shape[0], 3, height, width, device=x.device) * 0.5
+ image_conditioning = images_tensor_to_samples(image_conditioning,
+ approximation_indexes.get(opts.sd_vae_encode_method))
- # Add the fake full 1s mask to the first dimension.
- image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
- image_conditioning = image_conditioning.to(x.dtype)
+ # Add the fake full 1s mask to the first dimension.
+ image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
+ image_conditioning = image_conditioning.to(x.dtype)
- return image_conditioning
+ return image_conditioning
# Dummy zero conditioning if we're not using inpainting or unclip models.
# Still takes up a bit of memory, but no encoder call.
@@ -238,11 +235,6 @@ class StableDiffusionProcessing:
self.styles = []
self.sampler_noise_scheduler_override = None
- self.s_min_uncond = self.s_min_uncond if self.s_min_uncond is not None else opts.s_min_uncond
- self.s_churn = self.s_churn if self.s_churn is not None else opts.s_churn
- self.s_tmin = self.s_tmin if self.s_tmin is not None else opts.s_tmin
- self.s_tmax = (self.s_tmax if self.s_tmax is not None else opts.s_tmax) or float('inf')
- self.s_noise = self.s_noise if self.s_noise is not None else opts.s_noise
self.extra_generation_params = self.extra_generation_params or {}
self.override_settings = self.override_settings or {}
@@ -259,6 +251,13 @@ class StableDiffusionProcessing:
self.cached_uc = StableDiffusionProcessing.cached_uc
self.cached_c = StableDiffusionProcessing.cached_c
+ def fill_fields_from_opts(self):
+ self.s_min_uncond = self.s_min_uncond if self.s_min_uncond is not None else opts.s_min_uncond
+ self.s_churn = self.s_churn if self.s_churn is not None else opts.s_churn
+ self.s_tmin = self.s_tmin if self.s_tmin is not None else opts.s_tmin
+ self.s_tmax = (self.s_tmax if self.s_tmax is not None else opts.s_tmax) or float('inf')
+ self.s_noise = self.s_noise if self.s_noise is not None else opts.s_noise
+
@property
def sd_model(self):
return shared.sd_model
@@ -390,11 +389,8 @@ class StableDiffusionProcessing:
if self.sampler.conditioning_key == "crossattn-adm":
return self.unclip_image_conditioning(source_image)
- sd = self.sampler.model_wrap.inner_model.model.state_dict()
- diffusion_model_input = sd.get('diffusion_model.input_blocks.0.0.weight', None)
- if diffusion_model_input is not None:
- if diffusion_model_input.shape[1] == 9:
- return self.inpainting_image_conditioning(source_image, latent_image, image_mask=image_mask)
+ if getattr(self.sampler.model_wrap.inner_model.model, "is_sdxl_inpaint", False):
+ 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)
@@ -569,7 +565,7 @@ class Processed:
self.all_negative_prompts = all_negative_prompts or p.all_negative_prompts or [self.negative_prompt]
self.all_seeds = all_seeds or p.all_seeds or [self.seed]
self.all_subseeds = all_subseeds or p.all_subseeds or [self.subseed]
- self.infotexts = infotexts or [info]
+ self.infotexts = infotexts or [info] * len(images_list)
self.version = program_version()
def js(self):
@@ -794,7 +790,6 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
"Token merging ratio hr": None if not enable_hr or token_merging_ratio_hr == 0 else token_merging_ratio_hr,
"Init image hash": getattr(p, 'init_img_hash', None),
"RNG": opts.randn_source if opts.randn_source != "GPU" else None,
- "NGMS": None if p.s_min_uncond == 0 else p.s_min_uncond,
"Tiling": "True" if p.tiling else None,
**p.extra_generation_params,
"Version": program_version() if opts.add_version_to_infotext else None,
@@ -842,6 +837,9 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
sd_models.apply_token_merging(p.sd_model, p.get_token_merging_ratio())
+ # backwards compatibility, fix sampler and scheduler if invalid
+ sd_samplers.fix_p_invalid_sampler_and_scheduler(p)
+
res = process_images_inner(p)
finally:
@@ -890,6 +888,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
modules.sd_hijack.model_hijack.apply_circular(p.tiling)
modules.sd_hijack.model_hijack.clear_comments()
+ p.fill_fields_from_opts()
p.setup_prompts()
if isinstance(seed, list):
diff --git a/modules/safe.py b/modules/safe.py
index ee8789268..af019ffd9 100644
--- a/modules/safe.py
+++ b/modules/safe.py
@@ -64,8 +64,8 @@ class RestrictedUnpickler(pickle.Unpickler):
raise Exception(f"global '{module}/{name}' is forbidden")
-# Regular expression that accepts 'dirname/version', 'dirname/data.pkl', and 'dirname/data/'
-allowed_zip_names_re = re.compile(r"^([^/]+)/((data/\d+)|byteorder|(\.data\/serialization_id)|version|(data\.pkl))$")
+# Regular expression that accepts 'dirname/version', 'dirname/byteorder', 'dirname/data.pkl', '.data/serialization_id', and 'dirname/data/'
+allowed_zip_names_re = re.compile(r"^([^/]+)/((data/\d+)|version|byteorder|.data/serialization_id|(data\.pkl))$")
data_pkl_re = re.compile(r"^([^/]+)/data\.pkl$")
def check_zip_filenames(filename, names):
diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index 7f9e328d0..0269f1f5b 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -486,7 +486,8 @@ def xformers_attention_forward(self, x, context=None, mask=None, **kwargs):
k_in = self.to_k(context_k)
v_in = self.to_v(context_v)
- q, k, v = (rearrange(t, 'b n (h d) -> b n h d', h=h) for t in (q_in, k_in, v_in))
+ q, k, v = (t.reshape(t.shape[0], t.shape[1], h, -1) for t in (q_in, k_in, v_in))
+
del q_in, k_in, v_in
dtype = q.dtype
@@ -497,7 +498,8 @@ def xformers_attention_forward(self, x, context=None, mask=None, **kwargs):
out = out.to(dtype)
- out = rearrange(out, 'b n h d -> b n (h d)', h=h)
+ b, n, h, d = out.shape
+ out = out.reshape(b, n, h * d)
return self.to_out(out)
diff --git a/modules/sd_hijack_unet.py b/modules/sd_hijack_unet.py
index 2101f1a04..b4f03b138 100644
--- a/modules/sd_hijack_unet.py
+++ b/modules/sd_hijack_unet.py
@@ -1,5 +1,7 @@
import torch
from packaging import version
+from einops import repeat
+import math
from modules import devices
from modules.sd_hijack_utils import CondFunc
@@ -36,7 +38,7 @@ th = TorchHijackForUnet()
# Below are monkey patches to enable upcasting a float16 UNet for float32 sampling
def apply_model(orig_func, self, x_noisy, t, cond, **kwargs):
-
+ """Always make sure inputs to unet are in correct dtype."""
if isinstance(cond, dict):
for y in cond.keys():
if isinstance(cond[y], list):
@@ -45,7 +47,59 @@ def apply_model(orig_func, self, x_noisy, t, cond, **kwargs):
cond[y] = cond[y].to(devices.dtype_unet) if isinstance(cond[y], torch.Tensor) else cond[y]
with devices.autocast():
- return orig_func(self, x_noisy.to(devices.dtype_unet), t.to(devices.dtype_unet), cond, **kwargs).float()
+ result = orig_func(self, x_noisy.to(devices.dtype_unet), t.to(devices.dtype_unet), cond, **kwargs)
+ if devices.unet_needs_upcast:
+ return result.float()
+ else:
+ return result
+
+
+# Monkey patch to create timestep embed tensor on device, avoiding a block.
+def timestep_embedding(_, timesteps, dim, max_period=10000, repeat_only=False):
+ """
+ Create sinusoidal timestep embeddings.
+ :param timesteps: a 1-D Tensor of N indices, one per batch element.
+ These may be fractional.
+ :param dim: the dimension of the output.
+ :param max_period: controls the minimum frequency of the embeddings.
+ :return: an [N x dim] Tensor of positional embeddings.
+ """
+ if not repeat_only:
+ half = dim // 2
+ freqs = torch.exp(
+ -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32, device=timesteps.device) / half
+ )
+ args = timesteps[:, None].float() * freqs[None]
+ embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
+ if dim % 2:
+ embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
+ else:
+ embedding = repeat(timesteps, 'b -> b d', d=dim)
+ return embedding
+
+
+# Monkey patch to SpatialTransformer removing unnecessary contiguous calls.
+# Prevents a lot of unnecessary aten::copy_ calls
+def spatial_transformer_forward(_, self, x: torch.Tensor, context=None):
+ # note: if no context is given, cross-attention defaults to self-attention
+ if not isinstance(context, list):
+ context = [context]
+ b, c, h, w = x.shape
+ x_in = x
+ x = self.norm(x)
+ if not self.use_linear:
+ x = self.proj_in(x)
+ x = x.permute(0, 2, 3, 1).reshape(b, h * w, c)
+ if self.use_linear:
+ x = self.proj_in(x)
+ for i, block in enumerate(self.transformer_blocks):
+ x = block(x, context=context[i])
+ if self.use_linear:
+ x = self.proj_out(x)
+ x = x.view(b, h, w, c).permute(0, 3, 1, 2)
+ if not self.use_linear:
+ x = self.proj_out(x)
+ return x + x_in
class GELUHijack(torch.nn.GELU, torch.nn.Module):
@@ -64,12 +118,15 @@ def hijack_ddpm_edit():
if not ddpm_edit_hijack:
CondFunc('modules.models.diffusion.ddpm_edit.LatentDiffusion.decode_first_stage', first_stage_sub, first_stage_cond)
CondFunc('modules.models.diffusion.ddpm_edit.LatentDiffusion.encode_first_stage', first_stage_sub, first_stage_cond)
- ddpm_edit_hijack = CondFunc('modules.models.diffusion.ddpm_edit.LatentDiffusion.apply_model', apply_model, unet_needs_upcast)
+ ddpm_edit_hijack = CondFunc('modules.models.diffusion.ddpm_edit.LatentDiffusion.apply_model', apply_model)
unet_needs_upcast = lambda *args, **kwargs: devices.unet_needs_upcast
CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.apply_model', apply_model, unet_needs_upcast)
+CondFunc('ldm.modules.diffusionmodules.openaimodel.timestep_embedding', timestep_embedding)
+CondFunc('ldm.modules.attention.SpatialTransformer.forward', spatial_transformer_forward)
CondFunc('ldm.modules.diffusionmodules.openaimodel.timestep_embedding', lambda orig_func, timesteps, *args, **kwargs: orig_func(timesteps, *args, **kwargs).to(torch.float32 if timesteps.dtype == torch.int64 else devices.dtype_unet), unet_needs_upcast)
+
if version.parse(torch.__version__) <= version.parse("1.13.2") or torch.cuda.is_available():
CondFunc('ldm.modules.diffusionmodules.util.GroupNorm32.forward', lambda orig_func, self, *args, **kwargs: orig_func(self.float(), *args, **kwargs), unet_needs_upcast)
CondFunc('ldm.modules.attention.GEGLU.forward', lambda orig_func, self, x: orig_func(self.float(), x.float()).to(devices.dtype_unet), unet_needs_upcast)
@@ -81,5 +138,17 @@ CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.decode_first_stage', first_s
CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.encode_first_stage', first_stage_sub, first_stage_cond)
CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.get_first_stage_encoding', lambda orig_func, *args, **kwargs: orig_func(*args, **kwargs).float(), first_stage_cond)
-CondFunc('sgm.modules.diffusionmodules.wrappers.OpenAIWrapper.forward', apply_model, unet_needs_upcast)
-CondFunc('sgm.modules.diffusionmodules.openaimodel.timestep_embedding', lambda orig_func, timesteps, *args, **kwargs: orig_func(timesteps, *args, **kwargs).to(torch.float32 if timesteps.dtype == torch.int64 else devices.dtype_unet), unet_needs_upcast)
+CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.apply_model', apply_model)
+CondFunc('sgm.modules.diffusionmodules.wrappers.OpenAIWrapper.forward', apply_model)
+
+
+def timestep_embedding_cast_result(orig_func, timesteps, *args, **kwargs):
+ if devices.unet_needs_upcast and timesteps.dtype == torch.int64:
+ dtype = torch.float32
+ else:
+ dtype = devices.dtype_unet
+ return orig_func(timesteps, *args, **kwargs).to(dtype=dtype)
+
+
+CondFunc('ldm.modules.diffusionmodules.openaimodel.timestep_embedding', timestep_embedding_cast_result)
+CondFunc('sgm.modules.diffusionmodules.openaimodel.timestep_embedding', timestep_embedding_cast_result)
diff --git a/modules/sd_hijack_utils.py b/modules/sd_hijack_utils.py
index 79bf6e468..546f2eda4 100644
--- a/modules/sd_hijack_utils.py
+++ b/modules/sd_hijack_utils.py
@@ -1,7 +1,11 @@
import importlib
+
+always_true_func = lambda *args, **kwargs: True
+
+
class CondFunc:
- def __new__(cls, orig_func, sub_func, cond_func):
+ def __new__(cls, orig_func, sub_func, cond_func=always_true_func):
self = super(CondFunc, cls).__new__(cls)
if isinstance(orig_func, str):
func_path = orig_func.split('.')
@@ -20,13 +24,13 @@ class CondFunc:
print(f"Warning: Failed to resolve {orig_func} for CondFunc hijack")
pass
self.__init__(orig_func, sub_func, cond_func)
- return lambda *args, **kwargs: self(*args, **kwargs)
- def __init__(self, orig_func, sub_func, cond_func):
- self.__orig_func = orig_func
- self.__sub_func = sub_func
- self.__cond_func = cond_func
- def __call__(self, *args, **kwargs):
- if not self.__cond_func or self.__cond_func(self.__orig_func, *args, **kwargs):
- return self.__sub_func(self.__orig_func, *args, **kwargs)
- else:
- return self.__orig_func(*args, **kwargs)
+ return lambda *args, **kwargs: self(*args, **kwargs)
+ def __init__(self, orig_func, sub_func, cond_func):
+ self.__orig_func = orig_func
+ self.__sub_func = sub_func
+ self.__cond_func = cond_func
+ def __call__(self, *args, **kwargs):
+ if not self.__cond_func or self.__cond_func(self.__orig_func, *args, **kwargs):
+ return self.__sub_func(self.__orig_func, *args, **kwargs)
+ else:
+ return self.__orig_func(*args, **kwargs)
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 06a7cf3f0..61bd15d8f 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -149,10 +149,12 @@ def list_models():
cmd_ckpt = shared.cmd_opts.ckpt
if shared.cmd_opts.no_download_sd_model or cmd_ckpt != shared.sd_model_file or os.path.exists(cmd_ckpt):
model_url = None
+ expected_sha256 = None
else:
model_url = f"{shared.hf_endpoint}/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.safetensors"
+ expected_sha256 = '6ce0161689b3853acaa03779ec93eafe75a02f4ced659bee03f50797806fa2fa'
- model_list = modelloader.load_models(model_path=model_path, model_url=model_url, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt", ".safetensors"], download_name="v1-5-pruned-emaonly.safetensors", ext_blacklist=[".vae.ckpt", ".vae.safetensors"])
+ model_list = modelloader.load_models(model_path=model_path, model_url=model_url, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt", ".safetensors"], download_name="v1-5-pruned-emaonly.safetensors", ext_blacklist=[".vae.ckpt", ".vae.safetensors"], hash_prefix=expected_sha256)
if os.path.exists(cmd_ckpt):
checkpoint_info = CheckpointInfo(cmd_ckpt)
@@ -384,6 +386,13 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer
model.is_sd2 = not model.is_sdxl and hasattr(model.cond_stage_model, 'model')
model.is_sd1 = not model.is_sdxl and not model.is_sd2
model.is_ssd = model.is_sdxl and 'model.diffusion_model.middle_block.1.transformer_blocks.0.attn1.to_q.weight' not in state_dict.keys()
+ # Set is_sdxl_inpaint flag.
+ diffusion_model_input = state_dict.get('diffusion_model.input_blocks.0.0.weight', None)
+ model.is_sdxl_inpaint = (
+ model.is_sdxl and
+ diffusion_model_input is not None and
+ diffusion_model_input.shape[1] == 9
+ )
if model.is_sdxl:
sd_models_xl.extend_sdxl(model)
@@ -407,6 +416,7 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer
model.float()
model.alphas_cumprod_original = model.alphas_cumprod
devices.dtype_unet = torch.float32
+ assert shared.cmd_opts.precision != "half", "Cannot use --precision half with --no-half"
timer.record("apply float()")
else:
vae = model.first_stage_model
@@ -544,7 +554,7 @@ def repair_config(sd_config):
if hasattr(sd_config.model.params, 'unet_config'):
if shared.cmd_opts.no_half:
sd_config.model.params.unet_config.params.use_fp16 = False
- elif shared.cmd_opts.upcast_sampling:
+ elif shared.cmd_opts.upcast_sampling or shared.cmd_opts.precision == "half":
sd_config.model.params.unet_config.params.use_fp16 = True
if getattr(sd_config.model.params.first_stage_config.params.ddconfig, "attn_type", None) == "vanilla-xformers" and not shared.xformers_available:
@@ -555,6 +565,14 @@ def repair_config(sd_config):
karlo_path = os.path.join(paths.models_path, 'karlo')
sd_config.model.params.noise_aug_config.params.clip_stats_path = sd_config.model.params.noise_aug_config.params.clip_stats_path.replace("checkpoints/karlo_models", karlo_path)
+ # Do not use checkpoint for inference.
+ # This helps prevent extra performance overhead on checking parameters.
+ # The perf overhead is about 100ms/it on 4090 for SDXL.
+ if hasattr(sd_config.model.params, "network_config"):
+ sd_config.model.params.network_config.params.use_checkpoint = False
+ if hasattr(sd_config.model.params, "unet_config"):
+ sd_config.model.params.unet_config.params.use_checkpoint = False
+
def rescale_zero_terminal_snr_abar(alphas_cumprod):
alphas_bar_sqrt = alphas_cumprod.sqrt()
@@ -663,10 +681,11 @@ def get_empty_cond(sd_model):
def send_model_to_cpu(m):
- if m.lowvram:
- lowvram.send_everything_to_cpu()
- else:
- m.to(devices.cpu)
+ if m is not None:
+ if m.lowvram:
+ lowvram.send_everything_to_cpu()
+ else:
+ m.to(devices.cpu)
devices.torch_gc()
diff --git a/modules/sd_models_config.py b/modules/sd_models_config.py
index b38137eb5..9cec4f13d 100644
--- a/modules/sd_models_config.py
+++ b/modules/sd_models_config.py
@@ -35,7 +35,7 @@ def is_using_v_parameterization_for_sd2(state_dict):
with sd_disable_initialization.DisableInitialization():
unet = ldm.modules.diffusionmodules.openaimodel.UNetModel(
- use_checkpoint=True,
+ use_checkpoint=False,
use_fp16=False,
image_size=32,
in_channels=4,
diff --git a/modules/sd_models_xl.py b/modules/sd_models_xl.py
index 94ff973fb..1242a5936 100644
--- a/modules/sd_models_xl.py
+++ b/modules/sd_models_xl.py
@@ -35,11 +35,10 @@ def get_learned_conditioning(self: sgm.models.diffusion.DiffusionEngine, batch:
def apply_model(self: sgm.models.diffusion.DiffusionEngine, x, t, cond):
- sd = self.model.state_dict()
- diffusion_model_input = sd.get('diffusion_model.input_blocks.0.0.weight', None)
- if diffusion_model_input is not None:
- if diffusion_model_input.shape[1] == 9:
- x = torch.cat([x] + cond['c_concat'], dim=1)
+ """WARNING: This function is called once per denoising iteration. DO NOT add
+ expensive functionc calls such as `model.state_dict`. """
+ if self.is_sdxl_inpaint:
+ x = torch.cat([x] + cond['c_concat'], dim=1)
return self.model(x, t, cond)
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 6b7b84b6d..b8abac4a9 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -1,7 +1,7 @@
from __future__ import annotations
import functools
-
+import logging
from modules import sd_samplers_kdiffusion, sd_samplers_timesteps, sd_samplers_lcm, shared, sd_samplers_common, sd_schedulers
# imports for functions that previously were here and are used by other modules
@@ -122,4 +122,11 @@ def get_sampler_and_scheduler(sampler_name, scheduler_name):
return sampler.name, found_scheduler.label
+def fix_p_invalid_sampler_and_scheduler(p):
+ i_sampler_name, i_scheduler = p.sampler_name, p.scheduler
+ p.sampler_name, p.scheduler = get_sampler_and_scheduler(p.sampler_name, p.scheduler)
+ if p.sampler_name != i_sampler_name or i_scheduler != p.scheduler:
+ logging.warning(f'Sampler Scheduler autocorrection: "{i_sampler_name}" -> "{p.sampler_name}", "{i_scheduler}" -> "{p.scheduler}"')
+
+
set_samplers()
diff --git a/modules/sd_samplers_cfg_denoiser.py b/modules/sd_samplers_cfg_denoiser.py
index 93581c9ac..f48f58a50 100644
--- a/modules/sd_samplers_cfg_denoiser.py
+++ b/modules/sd_samplers_cfg_denoiser.py
@@ -212,9 +212,16 @@ class CFGDenoiser(torch.nn.Module):
uncond = denoiser_params.text_uncond
skip_uncond = False
- # alternating uncond allows for higher thresholds without the quality loss normally expected from raising it
- if self.step % 2 and s_min_uncond > 0 and sigma[0] < s_min_uncond and not is_edit_model:
+ if shared.opts.skip_early_cond != 0. and self.step / self.total_steps <= shared.opts.skip_early_cond:
skip_uncond = True
+ self.p.extra_generation_params["Skip Early CFG"] = shared.opts.skip_early_cond
+ elif (self.step % 2 or shared.opts.s_min_uncond_all) and s_min_uncond > 0 and sigma[0] < s_min_uncond and not is_edit_model:
+ skip_uncond = True
+ self.p.extra_generation_params["NGMS"] = s_min_uncond
+ if shared.opts.s_min_uncond_all:
+ self.p.extra_generation_params["NGMS all steps"] = shared.opts.s_min_uncond_all
+
+ if skip_uncond:
x_in = x_in[:-batch_size]
sigma_in = sigma_in[:-batch_size]
diff --git a/modules/sd_samplers_timesteps_impl.py b/modules/sd_samplers_timesteps_impl.py
index 930a64af5..84867d6ee 100644
--- a/modules/sd_samplers_timesteps_impl.py
+++ b/modules/sd_samplers_timesteps_impl.py
@@ -5,13 +5,14 @@ import numpy as np
from modules import shared
from modules.models.diffusion.uni_pc import uni_pc
+from modules.torch_utils import float64
@torch.no_grad()
def ddim(model, x, timesteps, extra_args=None, callback=None, disable=None, eta=0.0):
alphas_cumprod = model.inner_model.inner_model.alphas_cumprod
alphas = alphas_cumprod[timesteps]
- alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(torch.float64 if x.device.type != 'mps' and x.device.type != 'xpu' else torch.float32)
+ alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(float64(x))
sqrt_one_minus_alphas = torch.sqrt(1 - alphas)
sigmas = eta * np.sqrt((1 - alphas_prev.cpu().numpy()) / (1 - alphas.cpu()) * (1 - alphas.cpu() / alphas_prev.cpu().numpy()))
@@ -43,7 +44,7 @@ def ddim(model, x, timesteps, extra_args=None, callback=None, disable=None, eta=
def plms(model, x, timesteps, extra_args=None, callback=None, disable=None):
alphas_cumprod = model.inner_model.inner_model.alphas_cumprod
alphas = alphas_cumprod[timesteps]
- alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(torch.float64 if x.device.type != 'mps' and x.device.type != 'xpu' else torch.float32)
+ alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(float64(x))
sqrt_one_minus_alphas = torch.sqrt(1 - alphas)
extra_args = {} if extra_args is None else extra_args
diff --git a/modules/sd_schedulers.py b/modules/sd_schedulers.py
index 75eb3ac03..0c09af8d0 100644
--- a/modules/sd_schedulers.py
+++ b/modules/sd_schedulers.py
@@ -4,6 +4,9 @@ import torch
import k_diffusion
+import numpy as np
+
+from modules import shared
@dataclasses.dataclass
class Scheduler:
@@ -30,6 +33,41 @@ def sgm_uniform(n, sigma_min, sigma_max, inner_model, device):
sigs += [0.0]
return torch.FloatTensor(sigs).to(device)
+def get_align_your_steps_sigmas(n, sigma_min, sigma_max, device='cpu'):
+ # https://research.nvidia.com/labs/toronto-ai/AlignYourSteps/howto.html
+ def loglinear_interp(t_steps, num_steps):
+ """
+ Performs log-linear interpolation of a given array of decreasing numbers.
+ """
+ xs = np.linspace(0, 1, len(t_steps))
+ ys = np.log(t_steps[::-1])
+
+ new_xs = np.linspace(0, 1, num_steps)
+ new_ys = np.interp(new_xs, xs, ys)
+
+ interped_ys = np.exp(new_ys)[::-1].copy()
+ return interped_ys
+
+ if shared.sd_model.is_sdxl:
+ sigmas = [14.615, 6.315, 3.771, 2.181, 1.342, 0.862, 0.555, 0.380, 0.234, 0.113, 0.029]
+ else:
+ # Default to SD 1.5 sigmas.
+ sigmas = [14.615, 6.475, 3.861, 2.697, 1.886, 1.396, 0.963, 0.652, 0.399, 0.152, 0.029]
+
+ if n != len(sigmas):
+ sigmas = np.append(loglinear_interp(sigmas, n), [0.0])
+ else:
+ sigmas.append(0.0)
+
+ return torch.FloatTensor(sigmas).to(device)
+
+def kl_optimal(n, sigma_min, sigma_max, device):
+ alpha_min = torch.arctan(torch.tensor(sigma_min, device=device))
+ alpha_max = torch.arctan(torch.tensor(sigma_max, device=device))
+ step_indices = torch.arange(n + 1, device=device)
+ sigmas = torch.tan(step_indices / n * alpha_min + (1.0 - step_indices / n) * alpha_max)
+ return sigmas
+
schedulers = [
Scheduler('automatic', 'Automatic', None),
@@ -38,6 +76,8 @@ schedulers = [
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"]),
+ Scheduler('kl_optimal', 'KL Optimal', kl_optimal),
+ Scheduler('align_your_steps', 'Align Your Steps', get_align_your_steps_sigmas),
]
schedulers_map = {**{x.name: x for x in schedulers}, **{x.label: x for x in schedulers}}
diff --git a/modules/shared_init.py b/modules/shared_init.py
index 935e3a21c..a6ad0433d 100644
--- a/modules/shared_init.py
+++ b/modules/shared_init.py
@@ -31,6 +31,14 @@ def initialize():
devices.dtype_vae = torch.float32 if cmd_opts.no_half or cmd_opts.no_half_vae else torch.float16
devices.dtype_inference = torch.float32 if cmd_opts.precision == 'full' else devices.dtype
+ if cmd_opts.precision == "half":
+ msg = "--no-half and --no-half-vae conflict with --precision half"
+ assert devices.dtype == torch.float16, msg
+ assert devices.dtype_vae == torch.float16, msg
+ assert devices.dtype_inference == torch.float16, msg
+ devices.force_fp16 = True
+ devices.force_model_fp16()
+
shared.device = devices.device
shared.weight_load_location = None if cmd_opts.lowram else "cpu"
diff --git a/modules/shared_options.py b/modules/shared_options.py
index 326a317e0..05c3d9391 100644
--- a/modules/shared_options.py
+++ b/modules/shared_options.py
@@ -209,7 +209,8 @@ options_templates.update(options_section(('img2img', "img2img", "sd"), {
options_templates.update(options_section(('optimizations', "Optimizations", "sd"), {
"cross_attention_optimization": OptionInfo("Automatic", "Cross attention optimization", gr.Dropdown, lambda: {"choices": shared_items.cross_attention_optimizations()}),
- "s_min_uncond": OptionInfo(0.0, "Negative Guidance minimum sigma", gr.Slider, {"minimum": 0.0, "maximum": 15.0, "step": 0.01}).link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9177").info("skip negative prompt for some steps when the image is almost ready; 0=disable, higher=faster"),
+ "s_min_uncond": OptionInfo(0.0, "Negative Guidance minimum sigma", gr.Slider, {"minimum": 0.0, "maximum": 15.0, "step": 0.01}, infotext='NGMS').link("PR", "https://github.com/AUTOMATIC1111/stablediffusion-webui/pull/9177").info("skip negative prompt for some steps when the image is almost ready; 0=disable, higher=faster"),
+ "s_min_uncond_all": OptionInfo(False, "Negative Guidance minimum sigma all steps", infotext='NGMS all steps').info("By default, NGMS above skips every other step; this makes it skip all steps"),
"token_merging_ratio": OptionInfo(0.0, "Token merging ratio", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}, infotext='Token merging ratio').link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9256").info("0=disable, higher=faster"),
"token_merging_ratio_img2img": OptionInfo(0.0, "Token merging ratio for img2img", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}).info("only applies if non-zero and overrides above"),
"token_merging_ratio_hr": OptionInfo(0.0, "Token merging ratio for high-res pass", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}, infotext='Token merging ratio hr').info("only applies if non-zero and overrides above"),
@@ -380,7 +381,8 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
'uni_pc_skip_type': OptionInfo("time_uniform", "UniPC skip type", gr.Radio, {"choices": ["time_uniform", "time_quadratic", "logSNR"]}, infotext='UniPC skip type'),
'uni_pc_order': OptionInfo(3, "UniPC order", gr.Slider, {"minimum": 1, "maximum": 50, "step": 1}, infotext='UniPC order').info("must be < sampling steps"),
'uni_pc_lower_order_final': OptionInfo(True, "UniPC lower order final", infotext='UniPC lower order final'),
- 'sd_noise_schedule': OptionInfo("Default", "Noise schedule for sampling", gr.Radio, {"choices": ["Default", "Zero Terminal SNR"]}, infotext="Noise Schedule").info("for use with zero terminal SNR trained models")
+ 'sd_noise_schedule': OptionInfo("Default", "Noise schedule for sampling", gr.Radio, {"choices": ["Default", "Zero Terminal SNR"]}, infotext="Noise Schedule").info("for use with zero terminal SNR trained models"),
+ 'skip_early_cond': OptionInfo(0.0, "Ignore negative prompt during early sampling", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}, infotext="Skip Early CFG").info("disables CFG on a proportion of steps at the beginning of generation; 0=skip none; 1=skip all; can both improve sample diversity/quality and speed up sampling"),
}))
options_templates.update(options_section(('postprocessing', "Postprocessing", "postprocessing"), {
diff --git a/modules/torch_utils.py b/modules/torch_utils.py
index e5b52393e..a07e02853 100644
--- a/modules/torch_utils.py
+++ b/modules/torch_utils.py
@@ -1,6 +1,7 @@
from __future__ import annotations
import torch.nn
+import torch
def get_param(model) -> torch.nn.Parameter:
@@ -15,3 +16,11 @@ def get_param(model) -> torch.nn.Parameter:
return param
raise ValueError(f"No parameters found in model {model!r}")
+
+
+def float64(t: torch.Tensor):
+ """return torch.float64 if device is not mps or xpu, else return torch.float32"""
+ match t.device.type:
+ case 'mps', 'xpu':
+ return torch.float32
+ return torch.float64
diff --git a/modules/ui.py b/modules/ui.py
index 403425f29..5af34ecb0 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -38,9 +38,11 @@ warnings.filterwarnings("default" if opts.show_gradio_deprecation_warnings else
# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
mimetypes.init()
mimetypes.add_type('application/javascript', '.js')
+mimetypes.add_type('application/javascript', '.mjs')
# Likewise, add explicit content-type header for certain missing image types
mimetypes.add_type('image/webp', '.webp')
+mimetypes.add_type('image/avif', '.avif')
if not cmd_opts.share and not cmd_opts.listen:
# fix gradio phoning home
@@ -566,18 +568,25 @@ def create_ui():
init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", image_mode="RGBA", elem_id="img_inpaint_mask")
with gr.TabItem('Batch', id='batch', elem_id="img2img_batch_tab") as tab_batch:
- hidden = ' Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else ''
- gr.HTML(
- "
Process images in a directory on the same machine where the server is running." +
- " Use an empty output directory to save pictures normally instead of writing to the output directory." +
- f" Add inpaint batch mask directory to enable inpaint batch processing."
- f"{hidden}
"
- )
- img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, elem_id="img2img_batch_input_dir")
- img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, elem_id="img2img_batch_output_dir")
- img2img_batch_inpaint_mask_dir = gr.Textbox(label="Inpaint batch mask directory (required for inpaint batch processing only)", **shared.hide_dirs, elem_id="img2img_batch_inpaint_mask_dir")
+ with gr.Tabs(elem_id="img2img_batch_source"):
+ img2img_batch_source_type = gr.Textbox(visible=False, value="upload")
+ with gr.TabItem('Upload', id='batch_upload', elem_id="img2img_batch_upload_tab") as tab_batch_upload:
+ img2img_batch_upload = gr.Files(label="Files", interactive=True, elem_id="img2img_batch_upload")
+ with gr.TabItem('From directory', id='batch_from_dir', elem_id="img2img_batch_from_dir_tab") as tab_batch_from_dir:
+ hidden = ' Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else ''
+ gr.HTML(
+ "
Process images in a directory on the same machine where the server is running." +
+ " Use an empty output directory to save pictures normally instead of writing to the output directory." +
+ f" Add inpaint batch mask directory to enable inpaint batch processing."
+ f"{hidden}
"
+ )
+ img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, elem_id="img2img_batch_input_dir")
+ img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, elem_id="img2img_batch_output_dir")
+ img2img_batch_inpaint_mask_dir = gr.Textbox(label="Inpaint batch mask directory (required for inpaint batch processing only)", **shared.hide_dirs, elem_id="img2img_batch_inpaint_mask_dir")
+ tab_batch_upload.select(fn=lambda: "upload", inputs=[], outputs=[img2img_batch_source_type])
+ tab_batch_from_dir.select(fn=lambda: "from dir", inputs=[], outputs=[img2img_batch_source_type])
with gr.Accordion("PNG info", open=False):
- img2img_batch_use_png_info = gr.Checkbox(label="Append png info to prompts", **shared.hide_dirs, elem_id="img2img_batch_use_png_info")
+ img2img_batch_use_png_info = gr.Checkbox(label="Append png info to prompts", elem_id="img2img_batch_use_png_info")
img2img_batch_png_info_dir = gr.Textbox(label="PNG info directory", **shared.hide_dirs, placeholder="Leave empty to use input directory", elem_id="img2img_batch_png_info_dir")
img2img_batch_png_info_props = gr.CheckboxGroup(["Prompt", "Negative prompt", "Seed", "CFG scale", "Sampler", "Steps", "Model hash"], label="Parameters to take from png info", info="Prompts from png info will be appended to prompts set in ui.")
@@ -759,6 +768,8 @@ def create_ui():
img2img_batch_use_png_info,
img2img_batch_png_info_props,
img2img_batch_png_info_dir,
+ img2img_batch_source_type,
+ img2img_batch_upload,
] + custom_inputs,
outputs=[
output_panel.gallery,
diff --git a/modules/ui_gradio_extensions.py b/modules/ui_gradio_extensions.py
index f5278d22f..18fbd6777 100644
--- a/modules/ui_gradio_extensions.py
+++ b/modules/ui_gradio_extensions.py
@@ -50,7 +50,7 @@ def reload_javascript():
def template_response(*args, **kwargs):
res = shared.GradioTemplateResponseOriginal(*args, **kwargs)
- res.body = res.body.replace(b'', f'{js}'.encode("utf8"))
+ res.body = res.body.replace(b'', f'{js}'.encode("utf8"))
res.body = res.body.replace(b'
', f'{css}'.encode("utf8"))
res.init_headers()
return res
diff --git a/requirements_versions.txt b/requirements_versions.txt
index 3df74f3d6..3037a395b 100644
--- a/requirements_versions.txt
+++ b/requirements_versions.txt
@@ -1,3 +1,4 @@
+setuptools==69.5.1 # temp fix for compatibility with some old packages
GitPython==3.1.32
Pillow==9.5.0
accelerate==0.21.0
diff --git a/scripts/xyz_grid.py b/scripts/xyz_grid.py
index b9fd66fe5..52e343c4f 100644
--- a/scripts/xyz_grid.py
+++ b/scripts/xyz_grid.py
@@ -95,15 +95,15 @@ def confirm_checkpoints_or_none(p, xs):
raise RuntimeError(f"Unknown checkpoint: {x}")
-def apply_clip_skip(p, x, xs):
- opts.data["CLIP_stop_at_last_layers"] = x
+def confirm_range(min_val, max_val, axis_label):
+ """Generates a AxisOption.confirm() function that checks all values are within the specified range."""
+ def confirm_range_fun(p, xs):
+ for x in xs:
+ if not (max_val >= x >= min_val):
+ raise ValueError(f'{axis_label} value "{x}" out of range [{min_val}, {max_val}]')
-def apply_upscale_latent_space(p, x, xs):
- if x.lower().strip() != '0':
- opts.data["use_scale_latent_for_hires_fix"] = True
- else:
- opts.data["use_scale_latent_for_hires_fix"] = False
+ return confirm_range_fun
def apply_size(p, x: str, xs) -> None:
@@ -118,21 +118,16 @@ def apply_size(p, x: str, xs) -> None:
def find_vae(name: str):
- if name.lower() in ['auto', 'automatic']:
- return modules.sd_vae.unspecified
- if name.lower() == 'none':
- return None
- else:
- choices = [x for x in sorted(modules.sd_vae.vae_dict, key=lambda x: len(x)) if name.lower().strip() in x.lower()]
- if len(choices) == 0:
- print(f"No VAE found for {name}; using automatic")
- return modules.sd_vae.unspecified
- else:
- return modules.sd_vae.vae_dict[choices[0]]
+ match name := name.lower().strip():
+ case 'auto', 'automatic':
+ return 'Automatic'
+ case 'none':
+ return 'None'
+ return next((k for k in modules.sd_vae.vae_dict if k.lower() == name), print(f'No VAE found for {name}; using Automatic') or 'Automatic')
def apply_vae(p, x, xs):
- modules.sd_vae.reload_vae_weights(shared.sd_model, vae_file=find_vae(x))
+ p.override_settings['sd_vae'] = find_vae(x)
def apply_styles(p: StableDiffusionProcessingTxt2Img, x: str, _):
@@ -140,7 +135,7 @@ def apply_styles(p: StableDiffusionProcessingTxt2Img, x: str, _):
def apply_uni_pc_order(p, x, xs):
- opts.data["uni_pc_order"] = min(x, p.steps - 1)
+ p.override_settings['uni_pc_order'] = min(x, p.steps - 1)
def apply_face_restore(p, opt, x):
@@ -162,12 +157,14 @@ def apply_override(field, boolean: bool = False):
if boolean:
x = True if x.lower() == "true" else False
p.override_settings[field] = x
+
return fun
def boolean_choice(reverse: bool = False):
def choice():
return ["False", "True"] if reverse else ["True", "False"]
+
return choice
@@ -212,7 +209,7 @@ def list_to_csv_string(data_list):
def csv_string_to_list_strip(data_str):
- return list(map(str.strip, chain.from_iterable(csv.reader(StringIO(data_str)))))
+ return list(map(str.strip, chain.from_iterable(csv.reader(StringIO(data_str), skipinitialspace=True))))
class AxisOption:
@@ -264,13 +261,13 @@ axis_options = [
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("Clip skip", int, apply_override('CLIP_stop_at_last_layers')),
AxisOption("Denoising", float, apply_field("denoising_strength")),
AxisOption("Initial noise multiplier", float, apply_field("initial_noise_multiplier")),
AxisOption("Extra noise", float, apply_override("img2img_extra_noise")),
AxisOptionTxt2Img("Hires upscaler", str, apply_field("hr_upscaler"), choices=lambda: [*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]]),
AxisOptionImg2Img("Cond. Image Mask Weight", float, apply_field("inpainting_mask_weight")),
- AxisOption("VAE", str, apply_vae, cost=0.7, choices=lambda: ['None'] + list(sd_vae.vae_dict)),
+ AxisOption("VAE", str, apply_vae, cost=0.7, choices=lambda: ['Automatic', 'None'] + list(sd_vae.vae_dict)),
AxisOption("Styles", str, apply_styles, choices=lambda: list(shared.prompt_styles.styles)),
AxisOption("UniPC Order", int, apply_uni_pc_order, cost=0.5),
AxisOption("Face restore", str, apply_face_restore, format_value=format_value),
@@ -399,18 +396,12 @@ def draw_xyz_grid(p, xs, ys, zs, x_labels, y_labels, z_labels, cell, draw_legend
class SharedSettingsStackHelper(object):
def __enter__(self):
- self.CLIP_stop_at_last_layers = opts.CLIP_stop_at_last_layers
- self.vae = opts.sd_vae
- self.uni_pc_order = opts.uni_pc_order
+ pass
def __exit__(self, exc_type, exc_value, tb):
- opts.data["sd_vae"] = self.vae
- opts.data["uni_pc_order"] = self.uni_pc_order
modules.sd_models.reload_model_weights()
modules.sd_vae.reload_vae_weights()
- opts.data["CLIP_stop_at_last_layers"] = self.CLIP_stop_at_last_layers
-
re_range = re.compile(r"\s*([+-]?\s*\d+)\s*-\s*([+-]?\s*\d+)(?:\s*\(([+-]\d+)\s*\))?\s*")
re_range_float = re.compile(r"\s*([+-]?\s*\d+(?:.\d*)?)\s*-\s*([+-]?\s*\d+(?:.\d*)?)(?:\s*\(([+-]\d+(?:.\d*)?)\s*\))?\s*")
@@ -572,7 +563,7 @@ class Script(scripts.Script):
mc = re_range_count.fullmatch(val)
if m is not None:
start = int(m.group(1))
- end = int(m.group(2))+1
+ end = int(m.group(2)) + 1
step = int(m.group(3)) if m.group(3) is not None else 1
valslist_ext += list(range(start, end, step))
@@ -725,11 +716,11 @@ class Script(scripts.Script):
ydim = len(ys) if vary_seeds_y else 1
if vary_seeds_x:
- pc.seed += ix
+ pc.seed += ix
if vary_seeds_y:
- pc.seed += iy * xdim
+ pc.seed += iy * xdim
if vary_seeds_z:
- pc.seed += iz * xdim * ydim
+ pc.seed += iz * xdim * ydim
try:
res = process_images(pc)
@@ -797,18 +788,18 @@ class Script(scripts.Script):
z_count = len(zs)
# Set the grid infotexts to the real ones with extra_generation_params (1 main grid + z_count sub-grids)
- processed.infotexts[:1+z_count] = grid_infotext[:1+z_count]
+ processed.infotexts[:1 + z_count] = grid_infotext[:1 + z_count]
if not include_lone_images:
# Don't need sub-images anymore, drop from list:
- processed.images = processed.images[:z_count+1]
+ processed.images = processed.images[:z_count + 1]
if opts.grid_save:
# Auto-save main and sub-grids:
grid_count = z_count + 1 if z_count > 1 else 1
for g in range(grid_count):
# TODO: See previous comment about intentional data misalignment.
- adj_g = g-1 if g > 0 else g
+ adj_g = g - 1 if g > 0 else g
images.save_image(processed.images[g], p.outpath_grids, "xyz_grid", info=processed.infotexts[g], extension=opts.grid_format, prompt=processed.all_prompts[adj_g], seed=processed.all_seeds[adj_g], grid=True, p=processed)
if not include_sub_grids: # if not include_sub_grids then skip saving after the first grid
break
diff --git a/style.css b/style.css
index f6a89b8f9..bfc460c51 100644
--- a/style.css
+++ b/style.css
@@ -1,6 +1,6 @@
/* temporary fix to load default gradio font in frontend instead of backend */
-@import url('/webui-assets/css/sourcesanspro.css');
+@import url('webui-assets/css/sourcesanspro.css');
/* temporary fix to hide gradio crop tool until it's fixed https://github.com/gradio-app/gradio/issues/3810 */
@@ -780,9 +780,9 @@ table.popup-table .link{
position:absolute;
display:block;
padding:0px 0;
- border:2px solid #a55000;
+ border:2px solid var(--primary-800);
border-radius:8px;
- box-shadow:1px 1px 2px #CE6400;
+ box-shadow:1px 1px 2px var(--primary-500);
width: 200px;
}
@@ -799,7 +799,7 @@ table.popup-table .link{
}
.context-menu-items a:hover{
- background: #a55000;
+ background: var(--primary-700);
}
@@ -807,6 +807,8 @@ table.popup-table .link{
#tab_extensions table{
border-collapse: collapse;
+ overflow-x: auto;
+ display: block;
}
#tab_extensions table td, #tab_extensions table th{
diff --git a/webui-macos-env.sh b/webui-macos-env.sh
index db7e8b1a0..ad0736378 100644
--- a/webui-macos-env.sh
+++ b/webui-macos-env.sh
@@ -11,7 +11,12 @@ fi
export install_dir="$HOME"
export COMMANDLINE_ARGS="--skip-torch-cuda-test --upcast-sampling --no-half-vae --use-cpu interrogate"
-export TORCH_COMMAND="pip install torch==2.1.0 torchvision==0.16.0"
export PYTORCH_ENABLE_MPS_FALLBACK=1
+if [[ "$(sysctl -n machdep.cpu.brand_string)" =~ ^.*"Intel".*$ ]]; then
+ export TORCH_COMMAND="pip install torch==2.1.2 torchvision==0.16.2"
+else
+ export TORCH_COMMAND="pip install torch==2.3.0 torchvision==0.18.0"
+fi
+
####################################################################
diff --git a/webui.bat b/webui.bat
index e2c9079d2..a8d479b05 100644
--- a/webui.bat
+++ b/webui.bat
@@ -37,10 +37,15 @@ if %ERRORLEVEL% == 0 goto :activate_venv
for /f "delims=" %%i in ('CALL %PYTHON% -c "import sys; print(sys.executable)"') do set PYTHON_FULLNAME="%%i"
echo Creating venv in directory %VENV_DIR% using python %PYTHON_FULLNAME%
%PYTHON_FULLNAME% -m venv "%VENV_DIR%" >tmp/stdout.txt 2>tmp/stderr.txt
-if %ERRORLEVEL% == 0 goto :activate_venv
+if %ERRORLEVEL% == 0 goto :upgrade_pip
echo Unable to create venv in directory "%VENV_DIR%"
goto :show_stdout_stderr
+:upgrade_pip
+"%VENV_DIR%\Scripts\Python.exe" -m pip install --upgrade pip
+if %ERRORLEVEL% == 0 goto :activate_venv
+echo Warning: Failed to upgrade PIP version
+
:activate_venv
set PYTHON="%VENV_DIR%\Scripts\Python.exe"
echo venv %PYTHON%
diff --git a/webui.sh b/webui.sh
index c7c4bee98..7acea902c 100755
--- a/webui.sh
+++ b/webui.sh
@@ -210,6 +210,7 @@ then
if [[ ! -d "${venv_dir}" ]]
then
"${python_cmd}" -m venv "${venv_dir}"
+ "${venv_dir}"/bin/python -m pip install --upgrade pip
first_launch=1
fi
# shellcheck source=/dev/null