diff options
39 files changed, 1184 insertions, 675 deletions
diff --git a/configs/instruct-pix2pix.yaml b/configs/instruct-pix2pix.yaml index 437ddcef..4e896879 100644 --- a/configs/instruct-pix2pix.yaml +++ b/configs/instruct-pix2pix.yaml @@ -20,8 +20,7 @@ model: conditioning_key: hybrid monitor: val/loss_simple_ema scale_factor: 0.18215 - use_ema: true - load_ema: true + use_ema: false scheduler_config: # 10000 warmup steps target: ldm.lr_scheduler.LambdaLinearScheduler diff --git a/extensions-builtin/Lora/extra_networks_lora.py b/extensions-builtin/Lora/extra_networks_lora.py index 8f2e753e..6be6ef73 100644 --- a/extensions-builtin/Lora/extra_networks_lora.py +++ b/extensions-builtin/Lora/extra_networks_lora.py @@ -1,4 +1,4 @@ -from modules import extra_networks
+from modules import extra_networks, shared
import lora
class ExtraNetworkLora(extra_networks.ExtraNetwork):
@@ -6,6 +6,12 @@ class ExtraNetworkLora(extra_networks.ExtraNetwork): super().__init__('lora')
def activate(self, p, params_list):
+ additional = shared.opts.sd_lora
+
+ if additional != "" and additional in lora.available_loras and len([x for x in params_list if x.items[0] == additional]) == 0:
+ p.all_prompts = [x + f"<lora:{additional}:{shared.opts.extra_networks_default_multiplier}>" for x in p.all_prompts]
+ params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier]))
+
names = []
multipliers = []
for params in params_list:
diff --git a/extensions-builtin/Lora/scripts/lora_script.py b/extensions-builtin/Lora/scripts/lora_script.py index 544b228d..2e860160 100644 --- a/extensions-builtin/Lora/scripts/lora_script.py +++ b/extensions-builtin/Lora/scripts/lora_script.py @@ -1,4 +1,5 @@ import torch
+import gradio as gr
import lora
import extra_networks_lora
@@ -31,5 +32,7 @@ script_callbacks.on_before_ui(before_ui) shared.options_templates.update(shared.options_section(('extra_networks', "Extra Networks"), {
+ "sd_lora": shared.OptionInfo("None", "Add Lora to prompt", gr.Dropdown, lambda: {"choices": [""] + [x for x in lora.available_loras]}, refresh=lora.list_available_loras),
"lora_apply_to_outputs": shared.OptionInfo(False, "Apply Lora to outputs rather than inputs when possible (experimental)"),
+
}))
diff --git a/extensions-builtin/Lora/ui_extra_networks_lora.py b/extensions-builtin/Lora/ui_extra_networks_lora.py index 54a80d36..22cabcb0 100644 --- a/extensions-builtin/Lora/ui_extra_networks_lora.py +++ b/extensions-builtin/Lora/ui_extra_networks_lora.py @@ -20,13 +20,14 @@ class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage): preview = None
for file in previews:
if os.path.isfile(file):
- preview = "./file=" + file.replace('\\', '/') + "?mtime=" + str(os.path.getmtime(file))
+ preview = self.link_preview(file)
break
yield {
"name": name,
"filename": path,
"preview": preview,
+ "search_term": self.search_terms_from_path(lora_on_disk.filename),
"prompt": json.dumps(f"<lora:{name}:") + " + opts.extra_networks_default_multiplier + " + json.dumps(">"),
"local_preview": path + ".png",
}
diff --git a/html/extra-networks-card.html b/html/extra-networks-card.html index aa9fca87..8a5e2fbd 100644 --- a/html/extra-networks-card.html +++ b/html/extra-networks-card.html @@ -4,6 +4,7 @@ <ul> <a href="#" title="replace preview image with currently selected in gallery" onclick={save_card_preview}>replace preview</a> </ul> + <span style="display:none" class='search_term'>{search_term}</span> </div> <span class='name'>{name}</span> </div> diff --git a/javascript/extensions.js b/javascript/extensions.js index ac6e35b9..c593cd2e 100644 --- a/javascript/extensions.js +++ b/javascript/extensions.js @@ -1,7 +1,8 @@ function extensions_apply(_, _){
- disable = []
- update = []
+ var disable = []
+ var update = []
+
gradioApp().querySelectorAll('#extensions input[type="checkbox"]').forEach(function(x){
if(x.name.startsWith("enable_") && ! x.checked)
disable.push(x.name.substr(7))
@@ -16,11 +17,24 @@ function extensions_apply(_, _){ }
function extensions_check(){
+ var disable = []
+
+ gradioApp().querySelectorAll('#extensions input[type="checkbox"]').forEach(function(x){
+ if(x.name.startsWith("enable_") && ! x.checked)
+ disable.push(x.name.substr(7))
+ })
+
gradioApp().querySelectorAll('#extensions .extension_status').forEach(function(x){
x.innerHTML = "Loading..."
})
- return []
+
+ var id = randomId()
+ requestProgress(id, gradioApp().getElementById('extensions_installed_top'), null, function(){
+
+ })
+
+ return [id, JSON.stringify(disable)]
}
function install_extension_from_index(button, url){
diff --git a/javascript/extraNetworks.js b/javascript/extraNetworks.js index c5a9adb3..17bf2000 100644 --- a/javascript/extraNetworks.js +++ b/javascript/extraNetworks.js @@ -16,7 +16,7 @@ function setupExtraNetworksForTab(tabname){ searchTerm = search.value.toLowerCase()
gradioApp().querySelectorAll('#'+tabname+'_extra_tabs div.card').forEach(function(elem){
- text = elem.querySelector('.name').textContent.toLowerCase()
+ text = elem.querySelector('.name').textContent.toLowerCase() + " " + elem.querySelector('.search_term').textContent.toLowerCase()
elem.style.display = text.indexOf(searchTerm) == -1 ? "none" : ""
})
});
@@ -48,10 +48,39 @@ function setupExtraNetworks(){ onUiLoaded(setupExtraNetworks)
+var re_extranet = /<([^:]+:[^:]+):[\d\.]+>/;
+var re_extranet_g = /\s+<([^:]+:[^:]+):[\d\.]+>/g;
+
+function tryToRemoveExtraNetworkFromPrompt(textarea, text){
+ var m = text.match(re_extranet)
+ if(! m) return false
+
+ var partToSearch = m[1]
+ var replaced = false
+ var newTextareaText = textarea.value.replaceAll(re_extranet_g, function(found, index){
+ m = found.match(re_extranet);
+ if(m[1] == partToSearch){
+ replaced = true;
+ return ""
+ }
+ return found;
+ })
+
+ if(replaced){
+ textarea.value = newTextareaText
+ return true;
+ }
+
+ return false
+}
+
function cardClicked(tabname, textToAdd, allowNegativePrompt){
var textarea = allowNegativePrompt ? activePromptTextarea[tabname] : gradioApp().querySelector("#" + tabname + "_prompt > label > textarea")
- textarea.value = textarea.value + " " + textToAdd
+ if(! tryToRemoveExtraNetworkFromPrompt(textarea, textToAdd)){
+ textarea.value = textarea.value + " " + textToAdd
+ }
+
updateInput(textarea)
}
@@ -67,3 +96,12 @@ function saveCardPreview(event, tabname, filename){ event.stopPropagation()
event.preventDefault()
}
+
+function extraNetworksSearchButton(tabs_id, event){
+ searchTextarea = gradioApp().querySelector("#" + tabs_id + ' > div > textarea')
+ button = event.target
+ text = button.classList.contains("search-all") ? "" : button.textContent.trim()
+
+ searchTextarea.value = text
+ updateInput(searchTextarea)
+}
\ No newline at end of file diff --git a/javascript/ui.js b/javascript/ui.js index ba72623c..b7a8268a 100644 --- a/javascript/ui.js +++ b/javascript/ui.js @@ -191,6 +191,28 @@ function confirm_clear_prompt(prompt, negative_prompt) { return [prompt, negative_prompt] } + +promptTokecountUpdateFuncs = {} + +function recalculatePromptTokens(name){ + if(promptTokecountUpdateFuncs[name]){ + promptTokecountUpdateFuncs[name]() + } +} + +function recalculate_prompts_txt2img(){ + recalculatePromptTokens('txt2img_prompt') + recalculatePromptTokens('txt2img_neg_prompt') + return args_to_array(arguments); +} + +function recalculate_prompts_img2img(){ + recalculatePromptTokens('img2img_prompt') + recalculatePromptTokens('img2img_neg_prompt') + return args_to_array(arguments); +} + + opts = {} onUiUpdate(function(){ if(Object.keys(opts).length != 0) return; @@ -232,14 +254,12 @@ onUiUpdate(function(){ return } - prompt.parentElement.insertBefore(counter, prompt) counter.classList.add("token-counter") prompt.parentElement.style.position = "relative" - textarea.addEventListener("input", function(){ - update_token_counter(id_button); - }); + promptTokecountUpdateFuncs[id] = function(){ update_token_counter(id_button); } + textarea.addEventListener("input", promptTokecountUpdateFuncs[id]); } registerTextarea('txt2img_prompt', 'txt2img_token_counter', 'txt2img_token_button') @@ -273,7 +293,7 @@ onOptionsChanged(function(){ let txt2img_textarea, img2img_textarea = undefined; let wait_time = 800 -let token_timeout; +let token_timeouts = {}; function update_txt2img_tokens(...args) { update_token_counter("txt2img_token_button") @@ -290,9 +310,9 @@ function update_img2img_tokens(...args) { } function update_token_counter(button_id) { - if (token_timeout) - clearTimeout(token_timeout); - token_timeout = setTimeout(() => gradioApp().getElementById(button_id)?.click(), wait_time); + if (token_timeouts[button_id]) + clearTimeout(token_timeouts[button_id]); + token_timeouts[button_id] = setTimeout(() => gradioApp().getElementById(button_id)?.click(), wait_time); } function restart_reload(){ @@ -309,3 +329,10 @@ function updateInput(target){ Object.defineProperty(e, "target", {value: target}) target.dispatchEvent(e); } + + +var desiredCheckpointName = null; +function selectCheckpoint(name){ + desiredCheckpointName = name; + gradioApp().getElementById('change_checkpoint').click() +} @@ -223,6 +223,7 @@ def prepare_environment(): requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt")
commandline_args = os.environ.get('COMMANDLINE_ARGS', "")
+ xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.16rc425')
gfpgan_package = os.environ.get('GFPGAN_PACKAGE', "git+https://github.com/TencentARC/GFPGAN.git@8d2447a2d918f8eba5a4a01463fd48e45126a379")
clip_package = os.environ.get('CLIP_PACKAGE', "git+https://github.com/openai/CLIP.git@d50d76daa670286dd6cacf3bcd80b5e4823fc8e1")
openclip_package = os.environ.get('OPENCLIP_PACKAGE', "git+https://github.com/mlfoundations/open_clip.git@bb6e834e9c70d9c27d0dc3ecedeebeaeb1ffad6b")
@@ -282,7 +283,7 @@ def prepare_environment(): if (not is_installed("xformers") or reinstall_xformers) and xformers:
if platform.system() == "Windows":
if platform.python_version().startswith("3.10"):
- run_pip(f"install -U -I --no-deps xformers==0.0.16rc425", "xformers")
+ run_pip(f"install -U -I --no-deps {xformers_package}", "xformers")
else:
print("Installation of xformers is not supported in this version of Python.")
print("You can also check this and build manually: https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers#building-xformers-on-windows-by-duckness")
diff --git a/modules/devices.py b/modules/devices.py index 4687944e..655ca1d3 100644 --- a/modules/devices.py +++ b/modules/devices.py @@ -87,6 +87,14 @@ dtype_unet = torch.float16 unet_needs_upcast = False +def cond_cast_unet(input): + return input.to(dtype_unet) if unet_needs_upcast else input + + +def cond_cast_float(input): + return input.float() if unet_needs_upcast else input + + def randn(seed, shape): torch.manual_seed(seed) if device.type == 'mps': @@ -199,6 +207,3 @@ if has_mps(): cumsum_needs_bool_fix = not torch.BoolTensor([True,True]).to(device=torch.device("mps"), dtype=torch.int64).equal(torch.BoolTensor([True,False]).to(torch.device("mps")).cumsum(0)) torch.cumsum = lambda input, *args, **kwargs: ( cumsum_fix(input, orig_cumsum, *args, **kwargs) ) torch.Tensor.cumsum = lambda self, *args, **kwargs: ( cumsum_fix(self, orig_Tensor_cumsum, *args, **kwargs) ) - orig_narrow = torch.narrow - torch.narrow = lambda *args, **kwargs: ( orig_narrow(*args, **kwargs).clone() ) - diff --git a/modules/extra_networks_hypernet.py b/modules/extra_networks_hypernet.py index ff279a1f..d3a4d7ad 100644 --- a/modules/extra_networks_hypernet.py +++ b/modules/extra_networks_hypernet.py @@ -1,4 +1,4 @@ -from modules import extra_networks
+from modules import extra_networks, shared, extra_networks
from modules.hypernetworks import hypernetwork
@@ -7,6 +7,12 @@ class ExtraNetworkHypernet(extra_networks.ExtraNetwork): super().__init__('hypernet')
def activate(self, p, params_list):
+ additional = shared.opts.sd_hypernetwork
+
+ if additional != "" and additional in shared.hypernetworks and len([x for x in params_list if x.items[0] == additional]) == 0:
+ p.all_prompts = [x + f"<hypernet:{additional}:{shared.opts.extra_networks_default_multiplier}>" for x in p.all_prompts]
+ params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier]))
+
names = []
multipliers = []
for params in params_list:
diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py index 773c5c0e..fc9e17aa 100644 --- a/modules/generation_parameters_copypaste.py +++ b/modules/generation_parameters_copypaste.py @@ -1,4 +1,5 @@ import base64
+import html
import io
import math
import os
@@ -11,19 +12,28 @@ from modules import shared, ui_tempdir, script_callbacks import tempfile
from PIL import Image
-re_param_code = r'\s*([\w ]+):\s*("(?:\\|\"|[^\"])+"|[^,]*)(?:,|$)'
+re_param_code = r'\s*([\w ]+):\s*("(?:\\"[^,]|\\"|\\|[^\"])+"|[^,]*)(?:,|$)'
re_param = re.compile(re_param_code)
-re_params = re.compile(r"^(?:" + re_param_code + "){3,}$")
re_imagesize = re.compile(r"^(\d+)x(\d+)$")
re_hypernet_hash = re.compile("\(([0-9a-f]+)\)$")
type_of_gr_update = type(gr.update())
+
paste_fields = {}
-bind_list = []
+registered_param_bindings = []
+
+
+class ParamBinding:
+ def __init__(self, paste_button, tabname, source_text_component=None, source_image_component=None, source_tabname=None, override_settings_component=None):
+ self.paste_button = paste_button
+ self.tabname = tabname
+ self.source_text_component = source_text_component
+ self.source_image_component = source_image_component
+ self.source_tabname = source_tabname
+ self.override_settings_component = override_settings_component
def reset():
paste_fields.clear()
- bind_list.clear()
def quote(text):
@@ -75,26 +85,6 @@ def add_paste_fields(tabname, init_img, fields): modules.ui.img2img_paste_fields = fields
-def integrate_settings_paste_fields(component_dict):
- from modules import ui
-
- settings_map = {
- 'CLIP_stop_at_last_layers': 'Clip skip',
- '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)))
- for k, v in settings_map.items()
- ]
-
- for tabname, info in paste_fields.items():
- if info["fields"] is not None:
- info["fields"] += settings_paste_fields
-
-
def create_buttons(tabs_list):
buttons = {}
for tab in tabs_list:
@@ -102,9 +92,60 @@ def create_buttons(tabs_list): return buttons
-#if send_generate_info is a tab name, mean generate_info comes from the params fields of the tab
def bind_buttons(buttons, send_image, send_generate_info):
- bind_list.append([buttons, send_image, send_generate_info])
+ """old function for backwards compatibility; do not use this, use register_paste_params_button"""
+ for tabname, button in buttons.items():
+ source_text_component = send_generate_info if isinstance(send_generate_info, gr.components.Component) else None
+ source_tabname = send_generate_info if isinstance(send_generate_info, str) else None
+
+ register_paste_params_button(ParamBinding(paste_button=button, tabname=tabname, source_text_component=source_text_component, source_image_component=send_image, source_tabname=source_tabname))
+
+
+def register_paste_params_button(binding: ParamBinding):
+ registered_param_bindings.append(binding)
+
+
+def connect_paste_params_buttons():
+ binding: ParamBinding
+ for binding in registered_param_bindings:
+ destination_image_component = paste_fields[binding.tabname]["init_img"]
+ fields = paste_fields[binding.tabname]["fields"]
+
+ destination_width_component = next(iter([field for field, name in fields if name == "Size-1"] if fields else []), None)
+ destination_height_component = next(iter([field for field, name in fields if name == "Size-2"] if fields else []), None)
+
+ if binding.source_image_component and destination_image_component:
+ if isinstance(binding.source_image_component, gr.Gallery):
+ func = send_image_and_dimensions if destination_width_component else image_from_url_text
+ jsfunc = "extract_image_from_gallery"
+ else:
+ func = send_image_and_dimensions if destination_width_component else lambda x: x
+ jsfunc = None
+
+ binding.paste_button.click(
+ fn=func,
+ _js=jsfunc,
+ inputs=[binding.source_image_component],
+ outputs=[destination_image_component, destination_width_component, destination_height_component] if destination_width_component else [destination_image_component],
+ )
+
+ if binding.source_text_component is not None and fields is not None:
+ connect_paste(binding.paste_button, fields, binding.source_text_component, binding.override_settings_component, binding.tabname)
+
+ if binding.source_tabname is not None and fields is not None:
+ paste_field_names = ['Prompt', 'Negative prompt', 'Steps', 'Face restoration'] + (["Seed"] if shared.opts.send_seed else [])
+ binding.paste_button.click(
+ fn=lambda *x: x,
+ inputs=[field for field, name in paste_fields[binding.source_tabname]["fields"] if name in paste_field_names],
+ outputs=[field for field, name in fields if name in paste_field_names],
+ )
+
+ binding.paste_button.click(
+ fn=None,
+ _js=f"switch_to_{binding.tabname}",
+ inputs=None,
+ outputs=None,
+ )
def send_image_and_dimensions(x):
@@ -123,49 +164,6 @@ def send_image_and_dimensions(x): return img, w, h
-def run_bind():
- for buttons, source_image_component, send_generate_info in bind_list:
- for tab in buttons:
- button = buttons[tab]
- destination_image_component = paste_fields[tab]["init_img"]
- fields = paste_fields[tab]["fields"]
-
- destination_width_component = next(iter([field for field, name in fields if name == "Size-1"] if fields else []), None)
- destination_height_component = next(iter([field for field, name in fields if name == "Size-2"] if fields else []), None)
-
- if source_image_component and destination_image_component:
- if isinstance(source_image_component, gr.Gallery):
- func = send_image_and_dimensions if destination_width_component else image_from_url_text
- jsfunc = "extract_image_from_gallery"
- else:
- func = send_image_and_dimensions if destination_width_component else lambda x: x
- jsfunc = None
-
- button.click(
- fn=func,
- _js=jsfunc,
- inputs=[source_image_component],
- outputs=[destination_image_component, destination_width_component, destination_height_component] if destination_width_component else [destination_image_component],
- )
-
- if send_generate_info and fields is not None:
- if send_generate_info in paste_fields:
- paste_field_names = ['Prompt', 'Negative prompt', 'Steps', 'Face restoration'] + (["Seed"] if shared.opts.send_seed else [])
- button.click(
- fn=lambda *x: x,
- inputs=[field for field, name in paste_fields[send_generate_info]["fields"] if name in paste_field_names],
- outputs=[field for field, name in fields if name in paste_field_names],
- )
- else:
- connect_paste(button, fields, send_generate_info)
-
- button.click(
- fn=None,
- _js=f"switch_to_{tab}",
- inputs=None,
- outputs=None,
- )
-
def find_hypernetwork_key(hypernet_name, hypernet_hash=None):
"""Determines the config parameter name to use for the hypernet based on the parameters in the infotext.
@@ -243,7 +241,7 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model done_with_prompt = False
*lines, lastline = x.strip().split("\n")
- if not re_params.match(lastline):
+ if len(re_param.findall(lastline)) < 3:
lines.append(lastline)
lastline = ''
@@ -262,6 +260,7 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model res["Negative prompt"] = negative_prompt
for k, v in re_param.findall(lastline):
+ v = v[1:-1] if v[0] == '"' and v[-1] == '"' else v
m = re_imagesize.match(v)
if m is not None:
res[k+"-1"] = m.group(1)
@@ -286,7 +285,50 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model return res
-def connect_paste(button, paste_fields, input_comp, jsfunc=None):
+settings_map = {}
+
+infotext_to_setting_name_mapping = [
+ ('Clip skip', 'CLIP_stop_at_last_layers', ),
+ ('Conditional mask weight', 'inpainting_mask_weight'),
+ ('Model hash', 'sd_model_checkpoint'),
+ ('ENSD', 'eta_noise_seed_delta'),
+ ('Noise multiplier', 'initial_noise_multiplier'),
+ ('Eta', 'eta_ancestral'),
+ ('Eta DDIM', 'eta_ddim'),
+ ('Discard penultimate sigma', 'always_discard_next_to_last_sigma')
+]
+
+
+def create_override_settings_dict(text_pairs):
+ """creates processing's override_settings parameters from gradio's multiselect
+
+ Example input:
+ ['Clip skip: 2', 'Model hash: e6e99610c4', 'ENSD: 31337']
+
+ Example output:
+ {'CLIP_stop_at_last_layers': 2, 'sd_model_checkpoint': 'e6e99610c4', 'eta_noise_seed_delta': 31337}
+ """
+
+ res = {}
+
+ params = {}
+ for pair in text_pairs:
+ k, v = pair.split(":", maxsplit=1)
+
+ params[k] = v.strip()
+
+ for param_name, setting_name in infotext_to_setting_name_mapping:
+ value = params.get(param_name, None)
+
+ if value is None:
+ continue
+
+ res[setting_name] = shared.opts.cast_value(setting_name, value)
+
+ return res
+
+
+def connect_paste(button, paste_fields, input_comp, override_settings_component, tabname):
def paste_func(prompt):
if not prompt and not shared.cmd_opts.hide_ui_dir_config:
filename = os.path.join(data_path, "params.txt")
@@ -323,9 +365,35 @@ def connect_paste(button, paste_fields, input_comp, jsfunc=None): return res
+ if override_settings_component is not None:
+ def paste_settings(params):
+ vals = {}
+
+ for param_name, setting_name in infotext_to_setting_name_mapping:
+ v = params.get(param_name, None)
+ if v is None:
+ continue
+
+ if setting_name == "sd_model_checkpoint" and shared.opts.disable_weights_auto_swap:
+ continue
+
+ v = shared.opts.cast_value(setting_name, v)
+ current_value = getattr(shared.opts, setting_name, None)
+
+ if v == current_value:
+ continue
+
+ vals[param_name] = v
+
+ vals_pairs = [f"{k}: {v}" for k, v in vals.items()]
+
+ return gr.Dropdown.update(value=vals_pairs, choices=vals_pairs, visible=len(vals_pairs) > 0)
+
+ paste_fields = paste_fields + [(override_settings_component, paste_settings)]
+
button.click(
fn=paste_func,
- _js=jsfunc,
+ _js=f"recalculate_prompts_{tabname}",
inputs=[input_comp],
outputs=[x[0] for x in paste_fields],
)
diff --git a/modules/images.py b/modules/images.py index 0bc3d524..ae3cdaf4 100644 --- a/modules/images.py +++ b/modules/images.py @@ -36,6 +36,8 @@ def image_grid(imgs, batch_size=1, rows=None): else:
rows = math.sqrt(len(imgs))
rows = round(rows)
+ if rows > len(imgs):
+ rows = len(imgs)
cols = math.ceil(len(imgs) / rows)
diff --git a/modules/img2img.py b/modules/img2img.py index fe9447c7..f813299c 100644 --- a/modules/img2img.py +++ b/modules/img2img.py @@ -7,6 +7,7 @@ import numpy as np from PIL import Image, ImageOps, ImageFilter, ImageEnhance, ImageChops
from modules import devices, sd_samplers
+from modules.generation_parameters_copypaste import create_override_settings_dict
from modules.processing import Processed, StableDiffusionProcessingImg2Img, process_images
from modules.shared import opts, state
import modules.shared as shared
@@ -21,8 +22,10 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args): images = shared.listfiles(input_dir)
- inpaint_masks = shared.listfiles(inpaint_mask_dir)
- is_inpaint_batch = inpaint_mask_dir and len(inpaint_masks) > 0
+ is_inpaint_batch = False
+ if inpaint_mask_dir:
+ inpaint_masks = shared.listfiles(inpaint_mask_dir)
+ is_inpaint_batch = len(inpaint_masks) > 0
if is_inpaint_batch:
print(f"\nInpaint batch is enabled. {len(inpaint_masks)} masks found.")
@@ -73,7 +76,9 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args): processed_image.save(os.path.join(output_dir, filename))
-def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, 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, *args):
+def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, 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, *args):
+ override_settings = create_override_settings_dict(override_settings_texts)
+
is_batch = mode == 5
if mode == 0: # img2img
@@ -140,6 +145,7 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s inpaint_full_res=inpaint_full_res,
inpaint_full_res_padding=inpaint_full_res_padding,
inpainting_mask_invert=inpainting_mask_invert,
+ override_settings=override_settings,
)
p.scripts = modules.scripts.scripts_txt2img
diff --git a/modules/processing.py b/modules/processing.py index 5072fc40..e544c2e1 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -173,8 +173,7 @@ class StableDiffusionProcessing: 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.to(devices.dtype_vae) if devices.unet_needs_upcast else source_image))
- conditioning_image = conditioning_image.float() if devices.unet_needs_upcast else conditioning_image
+ 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:],
@@ -218,7 +217,7 @@ class StableDiffusionProcessing: )
# Encode the new masked image using first stage of network.
- conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image.to(devices.dtype_vae) if devices.unet_needs_upcast else conditioning_image))
+ conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image))
# Create the concatenated conditioning tensor to be fed to `c_concat`
conditioning_mask = torch.nn.functional.interpolate(conditioning_mask, size=latent_image.shape[-2:])
@@ -229,16 +228,18 @@ class StableDiffusionProcessing: return image_conditioning
def img2img_image_conditioning(self, source_image, latent_image, image_mask=None):
+ source_image = devices.cond_cast_float(source_image)
+
# 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.float() if devices.unet_needs_upcast else source_image)
+ return self.depth2img_image_conditioning(source_image)
if self.sd_model.cond_stage_key == "edit":
return self.edit_image_conditioning(source_image)
if self.sampler.conditioning_key in {'hybrid', 'concat'}:
- return self.inpainting_image_conditioning(source_image.float() if devices.unet_needs_upcast else source_image, latent_image, image_mask=image_mask)
+ 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)
@@ -418,7 +419,7 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see def decode_first_stage(model, x):
with devices.autocast(disable=x.dtype == devices.dtype_vae):
- x = model.decode_first_stage(x.to(devices.dtype_vae) if devices.unet_needs_upcast else x)
+ x = model.decode_first_stage(x)
return x
@@ -449,14 +450,11 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter "Size": f"{p.width}x{p.height}",
"Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),
"Model": (None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')),
- "Batch size": (None if p.batch_size < 2 else p.batch_size),
- "Batch pos": (None if p.batch_size < 2 else position_in_batch),
"Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]),
"Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),
"Seed resize from": (None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
"Denoising strength": getattr(p, 'denoising_strength', None),
"Conditional mask weight": getattr(p, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) if p.is_using_inpainting_conditioning else None,
- "Eta": (None if p.sampler is None or p.sampler.eta == p.sampler.default_eta else p.sampler.eta),
"Clip skip": None if clip_skip <= 1 else clip_skip,
"ENSD": None if opts.eta_noise_seed_delta == 0 else opts.eta_noise_seed_delta,
}
@@ -1007,7 +1005,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): image = torch.from_numpy(batch_images)
image = 2. * image - 1.
- image = image.to(device=shared.device, dtype=devices.dtype_vae if devices.unet_needs_upcast else None)
+ image = image.to(shared.device)
self.init_latent = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image))
diff --git a/modules/realesrgan_model.py b/modules/realesrgan_model.py index 47f70251..aad4a629 100644 --- a/modules/realesrgan_model.py +++ b/modules/realesrgan_model.py @@ -46,7 +46,7 @@ class UpscalerRealESRGAN(Upscaler): scale=info.scale,
model_path=info.local_data_path,
model=info.model(),
- half=not cmd_opts.no_half,
+ half=not cmd_opts.no_half and not cmd_opts.upcast_sampling,
tile=opts.ESRGAN_tile,
tile_pad=opts.ESRGAN_tile_overlap,
)
diff --git a/modules/scripts.py b/modules/scripts.py index 6e9dc0c0..24056a12 100644 --- a/modules/scripts.py +++ b/modules/scripts.py @@ -345,6 +345,20 @@ class ScriptRunner: outputs=[script.group for script in self.selectable_scripts]
)
+ self.script_load_ctr = 0
+ def onload_script_visibility(params):
+ title = params.get('Script', None)
+ if title:
+ title_index = self.titles.index(title)
+ visibility = title_index == self.script_load_ctr
+ self.script_load_ctr = (self.script_load_ctr + 1) % len(self.titles)
+ return gr.update(visible=visibility)
+ else:
+ return gr.update(visible=False)
+
+ self.infotext_fields.append( (dropdown, lambda x: gr.update(value=x.get('Script', 'None'))) )
+ self.infotext_fields.extend( [(script.group, onload_script_visibility) for script in self.selectable_scripts] )
+
return inputs
def run(self, p, *args):
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index f9652d21..8fdc5990 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -131,6 +131,8 @@ class StableDiffusionModelHijack: m.cond_stage_model.wrapped.model.token_embedding = m.cond_stage_model.wrapped.model.token_embedding.wrapped
m.cond_stage_model = m.cond_stage_model.wrapped
+ undo_optimizations()
+
self.apply_circular(False)
self.layers = None
self.clip = None
@@ -171,7 +173,7 @@ class EmbeddingsWithFixes(torch.nn.Module): vecs = []
for fixes, tensor in zip(batch_fixes, inputs_embeds):
for offset, embedding in fixes:
- emb = embedding.vec
+ emb = devices.cond_cast_unet(embedding.vec)
emb_len = min(tensor.shape[0] - offset - 1, emb.shape[0])
tensor = torch.cat([tensor[0:offset + 1], emb[0:emb_len], tensor[offset + 1 + emb_len:]])
diff --git a/modules/sd_hijack_unet.py b/modules/sd_hijack_unet.py index a6ee577c..45cf2b18 100644 --- a/modules/sd_hijack_unet.py +++ b/modules/sd_hijack_unet.py @@ -55,8 +55,14 @@ class GELUHijack(torch.nn.GELU, torch.nn.Module): 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', lambda orig_func, *args, **kwargs: orig_func(*args, **kwargs).to(devices.dtype_unet), unet_needs_upcast)
+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("1.13.1"):
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)
CondFunc('open_clip.transformer.ResidualAttentionBlock.__init__', lambda orig_func, *args, **kwargs: kwargs.update({'act_layer': GELUHijack}) and False or orig_func(*args, **kwargs), lambda _, *args, **kwargs: kwargs.get('act_layer') is None or kwargs['act_layer'] == torch.nn.GELU)
+
+first_stage_cond = lambda _, self, *args, **kwargs: devices.unet_needs_upcast and self.model.diffusion_model.dtype == torch.float16
+first_stage_sub = lambda orig_func, self, x, **kwargs: orig_func(self, x.to(devices.dtype_vae), **kwargs)
+CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.decode_first_stage', first_stage_sub, first_stage_cond)
+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)
diff --git a/modules/sd_models.py b/modules/sd_models.py index b2d48a51..300387a9 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -41,6 +41,7 @@ class CheckpointInfo: name = name[1:]
self.name = name
+ self.name_for_extra = os.path.splitext(os.path.basename(filename))[0]
self.model_name = os.path.splitext(name.replace("/", "_").replace("\\", "_"))[0]
self.hash = model_hash(filename)
@@ -231,12 +232,10 @@ def get_checkpoint_state_dict(checkpoint_info: CheckpointInfo, timer): def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer):
- title = checkpoint_info.title
sd_model_hash = checkpoint_info.calculate_shorthash()
timer.record("calculate hash")
- if checkpoint_info.title != title:
- shared.opts.data["sd_model_checkpoint"] = checkpoint_info.title
+ shared.opts.data["sd_model_checkpoint"] = checkpoint_info.title
if state_dict is None:
state_dict = get_checkpoint_state_dict(checkpoint_info, timer)
diff --git a/modules/sd_models_config.py b/modules/sd_models_config.py index 00217990..91c21700 100644 --- a/modules/sd_models_config.py +++ b/modules/sd_models_config.py @@ -1,7 +1,9 @@ import re
import os
-from modules import shared, paths
+import torch
+
+from modules import shared, paths, sd_disable_initialization
sd_configs_path = shared.sd_configs_path
sd_repo_configs_path = os.path.join(paths.paths['Stable Diffusion'], "configs", "stable-diffusion")
@@ -16,12 +18,51 @@ config_inpainting = os.path.join(sd_configs_path, "v1-inpainting-inference.yaml" config_instruct_pix2pix = os.path.join(sd_configs_path, "instruct-pix2pix.yaml")
config_alt_diffusion = os.path.join(sd_configs_path, "alt-diffusion-inference.yaml")
-re_parametrization_v = re.compile(r'-v\b')
+def is_using_v_parameterization_for_sd2(state_dict):
+ """
+ Detects whether unet in state_dict is using v-parameterization. Returns True if it is. You're welcome.
+ """
-def guess_model_config_from_state_dict(sd, filename):
- fn = os.path.basename(filename)
+ import ldm.modules.diffusionmodules.openaimodel
+ from modules import devices
+
+ device = devices.cpu
+
+ with sd_disable_initialization.DisableInitialization():
+ unet = ldm.modules.diffusionmodules.openaimodel.UNetModel(
+ use_checkpoint=True,
+ use_fp16=False,
+ image_size=32,
+ in_channels=4,
+ out_channels=4,
+ model_channels=320,
+ attention_resolutions=[4, 2, 1],
+ num_res_blocks=2,
+ channel_mult=[1, 2, 4, 4],
+ num_head_channels=64,
+ use_spatial_transformer=True,
+ use_linear_in_transformer=True,
+ transformer_depth=1,
+ context_dim=1024,
+ legacy=False
+ )
+ unet.eval()
+
+ with torch.no_grad():
+ unet_sd = {k.replace("model.diffusion_model.", ""): v for k, v in state_dict.items() if "model.diffusion_model." in k}
+ unet.load_state_dict(unet_sd, strict=True)
+ unet.to(device=device, dtype=torch.float)
+ test_cond = torch.ones((1, 2, 1024), device=device) * 0.5
+ x_test = torch.ones((1, 4, 8, 8), device=device) * 0.5
+
+ out = (unet(x_test, torch.asarray([999], device=device), context=test_cond) - x_test).mean().item()
+
+ return out < -1
+
+
+def guess_model_config_from_state_dict(sd, filename):
sd2_cond_proj_weight = sd.get('cond_stage_model.model.transformer.resblocks.0.attn.in_proj_weight', None)
diffusion_model_input = sd.get('model.diffusion_model.input_blocks.0.0.weight', None)
@@ -31,7 +72,7 @@ def guess_model_config_from_state_dict(sd, filename): if sd2_cond_proj_weight is not None and sd2_cond_proj_weight.shape[1] == 1024:
if diffusion_model_input.shape[1] == 9:
return config_sd2_inpainting
- elif re.search(re_parametrization_v, fn):
+ elif is_using_v_parameterization_for_sd2(sd):
return config_sd2v
else:
return config_sd2
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index a7910b56..28c2136f 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -1,53 +1,11 @@ -from collections import namedtuple, deque
-import numpy as np
-from math import floor
-import torch
-import tqdm
-from PIL import Image
-import inspect
-import k_diffusion.sampling
-import torchsde._brownian.brownian_interval
-import ldm.models.diffusion.ddim
-import ldm.models.diffusion.plms
-from modules import prompt_parser, devices, processing, images, sd_vae_approx
+from modules import sd_samplers_compvis, sd_samplers_kdiffusion, shared
-from modules.shared import opts, cmd_opts, state
-import modules.shared as shared
-from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback
-
-
-SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options'])
-
-samplers_k_diffusion = [
- ('Euler a', 'sample_euler_ancestral', ['k_euler_a', 'k_euler_ancestral'], {}),
- ('Euler', 'sample_euler', ['k_euler'], {}),
- ('LMS', 'sample_lms', ['k_lms'], {}),
- ('Heun', 'sample_heun', ['k_heun'], {}),
- ('DPM2', 'sample_dpm_2', ['k_dpm_2'], {'discard_next_to_last_sigma': True}),
- ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {'discard_next_to_last_sigma': True}),
- ('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {}),
- ('DPM++ 2M', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),
- ('DPM++ SDE', 'sample_dpmpp_sde', ['k_dpmpp_sde'], {}),
- ('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {}),
- ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {}),
- ('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}),
- ('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True}),
- ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True}),
- ('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras'}),
- ('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
- ('DPM++ SDE Karras', 'sample_dpmpp_sde', ['k_dpmpp_sde_ka'], {'scheduler': 'karras'}),
-]
-
-samplers_data_k_diffusion = [
- SamplerData(label, lambda model, funcname=funcname: KDiffusionSampler(funcname, model), aliases, options)
- for label, funcname, aliases, options in samplers_k_diffusion
- if hasattr(k_diffusion.sampling, funcname)
-]
+# imports for functions that previously were here and are used by other modules
+from modules.sd_samplers_common import samples_to_image_grid, sample_to_image
all_samplers = [
- *samplers_data_k_diffusion,
- SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), [], {}),
- SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), [], {}),
+ *sd_samplers_kdiffusion.samplers_data_k_diffusion,
+ *sd_samplers_compvis.samplers_data_compvis,
]
all_samplers_map = {x.name: x for x in all_samplers}
@@ -73,8 +31,8 @@ def create_sampler(name, model): def set_samplers():
global samplers, samplers_for_img2img
- hidden = set(opts.hide_samplers)
- hidden_img2img = set(opts.hide_samplers + ['PLMS'])
+ hidden = set(shared.opts.hide_samplers)
+ hidden_img2img = set(shared.opts.hide_samplers + ['PLMS'])
samplers = [x for x in all_samplers if x.name not in hidden]
samplers_for_img2img = [x for x in all_samplers if x.name not in hidden_img2img]
@@ -87,466 +45,3 @@ def set_samplers(): set_samplers()
-
-sampler_extra_params = {
- 'sample_euler': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
- 'sample_heun': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
- 'sample_dpm_2': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
-}
-
-
-def setup_img2img_steps(p, steps=None):
- if opts.img2img_fix_steps or steps is not None:
- requested_steps = (steps or p.steps)
- steps = int(requested_steps / min(p.denoising_strength, 0.999)) if p.denoising_strength > 0 else 0
- t_enc = requested_steps - 1
- else:
- steps = p.steps
- t_enc = int(min(p.denoising_strength, 0.999) * steps)
-
- return steps, t_enc
-
-
-approximation_indexes = {"Full": 0, "Approx NN": 1, "Approx cheap": 2}
-
-
-def single_sample_to_image(sample, approximation=None):
- if approximation is None:
- approximation = approximation_indexes.get(opts.show_progress_type, 0)
-
- if approximation == 2:
- x_sample = sd_vae_approx.cheap_approximation(sample)
- elif approximation == 1:
- x_sample = sd_vae_approx.model()(sample.to(devices.device, devices.dtype).unsqueeze(0))[0].detach()
- else:
- x_sample = processing.decode_first_stage(shared.sd_model, sample.unsqueeze(0))[0]
-
- x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0)
- x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
- x_sample = x_sample.astype(np.uint8)
- return Image.fromarray(x_sample)
-
-
-def sample_to_image(samples, index=0, approximation=None):
- return single_sample_to_image(samples[index], approximation)
-
-
-def samples_to_image_grid(samples, approximation=None):
- return images.image_grid([single_sample_to_image(sample, approximation) for sample in samples])
-
-
-def store_latent(decoded):
- state.current_latent = decoded
-
- if opts.live_previews_enable and opts.show_progress_every_n_steps > 0 and shared.state.sampling_step % opts.show_progress_every_n_steps == 0:
- if not shared.parallel_processing_allowed:
- shared.state.assign_current_image(sample_to_image(decoded))
-
-
-class InterruptedException(BaseException):
- pass
-
-
-class VanillaStableDiffusionSampler:
- def __init__(self, constructor, sd_model):
- self.sampler = constructor(sd_model)
- self.is_plms = hasattr(self.sampler, 'p_sample_plms')
- self.orig_p_sample_ddim = self.sampler.p_sample_plms if self.is_plms else self.sampler.p_sample_ddim
- self.mask = None
- self.nmask = None
- self.init_latent = None
- self.sampler_noises = None
- self.step = 0
- self.stop_at = None
- self.eta = None
- self.default_eta = 0.0
- self.config = None
- self.last_latent = None
-
- self.conditioning_key = sd_model.model.conditioning_key
-
- def number_of_needed_noises(self, p):
- return 0
-
- def launch_sampling(self, steps, func):
- state.sampling_steps = steps
- state.sampling_step = 0
-
- try:
- return func()
- except InterruptedException:
- return self.last_latent
-
- def p_sample_ddim_hook(self, x_dec, cond, ts, unconditional_conditioning, *args, **kwargs):
- if state.interrupted or state.skipped:
- raise InterruptedException
-
- if self.stop_at is not None and self.step > self.stop_at:
- raise InterruptedException
-
- # Have to unwrap the inpainting conditioning here to perform pre-processing
- image_conditioning = None
- if isinstance(cond, dict):
- image_conditioning = cond["c_concat"][0]
- cond = cond["c_crossattn"][0]
- unconditional_conditioning = unconditional_conditioning["c_crossattn"][0]
-
- conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
- unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step)
-
- assert all([len(conds) == 1 for conds in conds_list]), 'composition via AND is not supported for DDIM/PLMS samplers'
- cond = tensor
-
- # for DDIM, shapes must match, we can't just process cond and uncond independently;
- # filling unconditional_conditioning with repeats of the last vector to match length is
- # not 100% correct but should work well enough
- if unconditional_conditioning.shape[1] < cond.shape[1]:
- last_vector = unconditional_conditioning[:, -1:]
- last_vector_repeated = last_vector.repeat([1, cond.shape[1] - unconditional_conditioning.shape[1], 1])
- unconditional_conditioning = torch.hstack([unconditional_conditioning, last_vector_repeated])
- elif unconditional_conditioning.shape[1] > cond.shape[1]:
- unconditional_conditioning = unconditional_conditioning[:, :cond.shape[1]]
-
- if self.mask is not None:
- img_orig = self.sampler.model.q_sample(self.init_latent, ts)
- x_dec = img_orig * self.mask + self.nmask * x_dec
-
- # Wrap the image conditioning back up since the DDIM code can accept the dict directly.
- # Note that they need to be lists because it just concatenates them later.
- if image_conditioning is not None:
- cond = {"c_concat": [image_conditioning], "c_crossattn": [cond]}
- unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
-
- res = self.orig_p_sample_ddim(x_dec, cond, ts, unconditional_conditioning=unconditional_conditioning, *args, **kwargs)
-
- if self.mask is not None:
- self.last_latent = self.init_latent * self.mask + self.nmask * res[1]
- else:
- self.last_latent = res[1]
-
- store_latent(self.last_latent)
-
- self.step += 1
- state.sampling_step = self.step
- shared.total_tqdm.update()
-
- return res
-
- def initialize(self, p):
- self.eta = p.eta if p.eta is not None else opts.eta_ddim
-
- for fieldname in ['p_sample_ddim', 'p_sample_plms']:
- if hasattr(self.sampler, fieldname):
- setattr(self.sampler, fieldname, self.p_sample_ddim_hook)
-
- self.mask = p.mask if hasattr(p, 'mask') else None
- self.nmask = p.nmask if hasattr(p, 'nmask') else None
-
- def adjust_steps_if_invalid(self, p, num_steps):
- if (self.config.name == 'DDIM' and p.ddim_discretize == 'uniform') or (self.config.name == 'PLMS'):
- valid_step = 999 / (1000 // num_steps)
- if valid_step == floor(valid_step):
- return int(valid_step) + 1
-
- return num_steps
-
- def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
- steps, t_enc = setup_img2img_steps(p, steps)
- steps = self.adjust_steps_if_invalid(p, steps)
- self.initialize(p)
-
- self.sampler.make_schedule(ddim_num_steps=steps, ddim_eta=self.eta, ddim_discretize=p.ddim_discretize, verbose=False)
- x1 = self.sampler.stochastic_encode(x, torch.tensor([t_enc] * int(x.shape[0])).to(shared.device), noise=noise)
-
- self.init_latent = x
- self.last_latent = x
- self.step = 0
-
- # Wrap the conditioning models with additional image conditioning for inpainting model
- if image_conditioning is not None:
- conditioning = {"c_concat": [image_conditioning], "c_crossattn": [conditioning]}
- unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
-
- samples = self.launch_sampling(t_enc + 1, lambda: self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning))
-
- return samples
-
- def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
- self.initialize(p)
-
- self.init_latent = None
- self.last_latent = x
- self.step = 0
-
- steps = self.adjust_steps_if_invalid(p, steps or p.steps)
-
- # Wrap the conditioning models with additional image conditioning for inpainting model
- # dummy_for_plms is needed because PLMS code checks the first item in the dict to have the right shape
- if image_conditioning is not None:
- conditioning = {"dummy_for_plms": np.zeros((conditioning.shape[0],)), "c_crossattn": [conditioning], "c_concat": [image_conditioning]}
- unconditional_conditioning = {"c_crossattn": [unconditional_conditioning], "c_concat": [image_conditioning]}
-
- samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)[0])
-
- return samples_ddim
-
-
-class CFGDenoiser(torch.nn.Module):
- def __init__(self, model):
- super().__init__()
- self.inner_model = model
- self.mask = None
- self.nmask = None
- self.init_latent = None
- self.step = 0
-
- def combine_denoised(self, x_out, conds_list, uncond, cond_scale):
- denoised_uncond = x_out[-uncond.shape[0]:]
- denoised = torch.clone(denoised_uncond)
-
- for i, conds in enumerate(conds_list):
- for cond_index, weight in conds:
- denoised[i] += (x_out[cond_index] - denoised_uncond[i]) * (weight * cond_scale)
-
- return denoised
-
- def forward(self, x, sigma, uncond, cond, cond_scale, image_cond):
- if state.interrupted or state.skipped:
- raise InterruptedException
-
- conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
- uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step)
-
- batch_size = len(conds_list)
- repeats = [len(conds_list[i]) for i in range(batch_size)]
-
- x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
- image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond])
- sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
-
- denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps)
- cfg_denoiser_callback(denoiser_params)
- x_in = denoiser_params.x
- image_cond_in = denoiser_params.image_cond
- sigma_in = denoiser_params.sigma
-
- if tensor.shape[1] == uncond.shape[1]:
- cond_in = torch.cat([tensor, uncond])
-
- if shared.batch_cond_uncond:
- x_out = self.inner_model(x_in, sigma_in, cond={"c_crossattn": [cond_in], "c_concat": [image_cond_in]})
- else:
- x_out = torch.zeros_like(x_in)
- for batch_offset in range(0, x_out.shape[0], batch_size):
- a = batch_offset
- b = a + batch_size
- x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond={"c_crossattn": [cond_in[a:b]], "c_concat": [image_cond_in[a:b]]})
- else:
- x_out = torch.zeros_like(x_in)
- batch_size = batch_size*2 if shared.batch_cond_uncond else batch_size
- for batch_offset in range(0, tensor.shape[0], batch_size):
- a = batch_offset
- b = min(a + batch_size, tensor.shape[0])
- x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond={"c_crossattn": [tensor[a:b]], "c_concat": [image_cond_in[a:b]]})
-
- x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond={"c_crossattn": [uncond], "c_concat": [image_cond_in[-uncond.shape[0]:]]})
-
- devices.test_for_nans(x_out, "unet")
-
- if opts.live_preview_content == "Prompt":
- store_latent(x_out[0:uncond.shape[0]])
- elif opts.live_preview_content == "Negative prompt":
- store_latent(x_out[-uncond.shape[0]:])
-
- denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)
-
- if self.mask is not None:
- denoised = self.init_latent * self.mask + self.nmask * denoised
-
- self.step += 1
-
- return denoised
-
-
-class TorchHijack:
- def __init__(self, sampler_noises):
- # Using a deque to efficiently receive the sampler_noises in the same order as the previous index-based
- # implementation.
- self.sampler_noises = deque(sampler_noises)
-
- def __getattr__(self, item):
- if item == 'randn_like':
- return self.randn_like
-
- if hasattr(torch, item):
- return getattr(torch, item)
-
- raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, item))
-
- def randn_like(self, x):
- if self.sampler_noises:
- noise = self.sampler_noises.popleft()
- if noise.shape == x.shape:
- return noise
-
- if x.device.type == 'mps':
- return torch.randn_like(x, device=devices.cpu).to(x.device)
- else:
- return torch.randn_like(x)
-
-
-# MPS fix for randn in torchsde
-def torchsde_randn(size, dtype, device, seed):
- if device.type == 'mps':
- generator = torch.Generator(devices.cpu).manual_seed(int(seed))
- return torch.randn(size, dtype=dtype, device=devices.cpu, generator=generator).to(device)
- else:
- generator = torch.Generator(device).manual_seed(int(seed))
- return torch.randn(size, dtype=dtype, device=device, generator=generator)
-
-
-torchsde._brownian.brownian_interval._randn = torchsde_randn
-
-
-class KDiffusionSampler:
- def __init__(self, funcname, sd_model):
- denoiser = k_diffusion.external.CompVisVDenoiser if sd_model.parameterization == "v" else k_diffusion.external.CompVisDenoiser
-
- self.model_wrap = denoiser(sd_model, quantize=shared.opts.enable_quantization)
- self.funcname = funcname
- self.func = getattr(k_diffusion.sampling, self.funcname)
- self.extra_params = sampler_extra_params.get(funcname, [])
- self.model_wrap_cfg = CFGDenoiser(self.model_wrap)
- self.sampler_noises = None
- self.stop_at = None
- self.eta = None
- self.default_eta = 1.0
- self.config = None
- self.last_latent = None
-
- self.conditioning_key = sd_model.model.conditioning_key
-
- def callback_state(self, d):
- step = d['i']
- latent = d["denoised"]
- if opts.live_preview_content == "Combined":
- store_latent(latent)
- self.last_latent = latent
-
- if self.stop_at is not None and step > self.stop_at:
- raise InterruptedException
-
- state.sampling_step = step
- shared.total_tqdm.update()
-
- def launch_sampling(self, steps, func):
- state.sampling_steps = steps
- state.sampling_step = 0
-
- try:
- return func()
- except InterruptedException:
- return self.last_latent
-
- def number_of_needed_noises(self, p):
- return p.steps
-
- def initialize(self, p):
- self.model_wrap_cfg.mask = p.mask if hasattr(p, 'mask') else None
- self.model_wrap_cfg.nmask = p.nmask if hasattr(p, 'nmask') else None
- self.model_wrap_cfg.step = 0
- self.eta = p.eta or opts.eta_ancestral
-
- k_diffusion.sampling.torch = TorchHijack(self.sampler_noises if self.sampler_noises is not None else [])
-
- extra_params_kwargs = {}
- for param_name in self.extra_params:
- if hasattr(p, param_name) and param_name in inspect.signature(self.func).parameters:
- extra_params_kwargs[param_name] = getattr(p, param_name)
-
- if 'eta' in inspect.signature(self.func).parameters:
- extra_params_kwargs['eta'] = self.eta
-
- return extra_params_kwargs
-
- def get_sigmas(self, p, steps):
- discard_next_to_last_sigma = self.config is not None and self.config.options.get('discard_next_to_last_sigma', False)
- if opts.always_discard_next_to_last_sigma and not discard_next_to_last_sigma:
- discard_next_to_last_sigma = True
- p.extra_generation_params["Discard penultimate sigma"] = True
-
- steps += 1 if discard_next_to_last_sigma else 0
-
- if p.sampler_noise_scheduler_override:
- sigmas = p.sampler_noise_scheduler_override(steps)
- elif self.config is not None and self.config.options.get('scheduler', None) == 'karras':
- sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
-
- sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, device=shared.device)
- else:
- sigmas = self.model_wrap.get_sigmas(steps)
-
- if discard_next_to_last_sigma:
- sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])
-
- return sigmas
-
- def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
- steps, t_enc = setup_img2img_steps(p, steps)
-
- sigmas = self.get_sigmas(p, steps)
-
- sigma_sched = sigmas[steps - t_enc - 1:]
- xi = x + noise * sigma_sched[0]
-
- extra_params_kwargs = self.initialize(p)
- if 'sigma_min' in inspect.signature(self.func).parameters:
- ## last sigma is zero which isn't allowed by DPM Fast & Adaptive so taking value before last
- extra_params_kwargs['sigma_min'] = sigma_sched[-2]
- if 'sigma_max' in inspect.signature(self.func).parameters:
- extra_params_kwargs['sigma_max'] = sigma_sched[0]
- if 'n' in inspect.signature(self.func).parameters:
- extra_params_kwargs['n'] = len(sigma_sched) - 1
- if 'sigma_sched' in inspect.signature(self.func).parameters:
- extra_params_kwargs['sigma_sched'] = sigma_sched
- if 'sigmas' in inspect.signature(self.func).parameters:
- extra_params_kwargs['sigmas'] = sigma_sched
-
- self.model_wrap_cfg.init_latent = x
- self.last_latent = x
-
- samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args={
- 'cond': conditioning,
- 'image_cond': image_conditioning,
- 'uncond': unconditional_conditioning,
- 'cond_scale': p.cfg_scale
- }, disable=False, callback=self.callback_state, **extra_params_kwargs))
-
- return samples
-
- def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning = None):
- steps = steps or p.steps
-
- sigmas = self.get_sigmas(p, steps)
-
- x = x * sigmas[0]
-
- extra_params_kwargs = self.initialize(p)
- if 'sigma_min' in inspect.signature(self.func).parameters:
- extra_params_kwargs['sigma_min'] = self.model_wrap.sigmas[0].item()
- extra_params_kwargs['sigma_max'] = self.model_wrap.sigmas[-1].item()
- if 'n' in inspect.signature(self.func).parameters:
- extra_params_kwargs['n'] = steps
- else:
- extra_params_kwargs['sigmas'] = sigmas
-
- self.last_latent = x
- samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={
- 'cond': conditioning,
- 'image_cond': image_conditioning,
- 'uncond': unconditional_conditioning,
- 'cond_scale': p.cfg_scale
- }, disable=False, callback=self.callback_state, **extra_params_kwargs))
-
- return samples
-
diff --git a/modules/sd_samplers_common.py b/modules/sd_samplers_common.py new file mode 100644 index 00000000..3c03d442 --- /dev/null +++ b/modules/sd_samplers_common.py @@ -0,0 +1,78 @@ +from collections import namedtuple
+import numpy as np
+import torch
+from PIL import Image
+import torchsde._brownian.brownian_interval
+from modules import devices, processing, images, sd_vae_approx
+
+from modules.shared import opts, state
+import modules.shared as shared
+
+SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options'])
+
+
+def setup_img2img_steps(p, steps=None):
+ if opts.img2img_fix_steps or steps is not None:
+ requested_steps = (steps or p.steps)
+ steps = int(requested_steps / min(p.denoising_strength, 0.999)) if p.denoising_strength > 0 else 0
+ t_enc = requested_steps - 1
+ else:
+ steps = p.steps
+ t_enc = int(min(p.denoising_strength, 0.999) * steps)
+
+ return steps, t_enc
+
+
+approximation_indexes = {"Full": 0, "Approx NN": 1, "Approx cheap": 2}
+
+
+def single_sample_to_image(sample, approximation=None):
+ if approximation is None:
+ approximation = approximation_indexes.get(opts.show_progress_type, 0)
+
+ if approximation == 2:
+ x_sample = sd_vae_approx.cheap_approximation(sample)
+ elif approximation == 1:
+ x_sample = sd_vae_approx.model()(sample.to(devices.device, devices.dtype).unsqueeze(0))[0].detach()
+ else:
+ x_sample = processing.decode_first_stage(shared.sd_model, sample.unsqueeze(0))[0]
+
+ x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0)
+ x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
+ x_sample = x_sample.astype(np.uint8)
+ return Image.fromarray(x_sample)
+
+
+def sample_to_image(samples, index=0, approximation=None):
+ return single_sample_to_image(samples[index], approximation)
+
+
+def samples_to_image_grid(samples, approximation=None):
+ return images.image_grid([single_sample_to_image(sample, approximation) for sample in samples])
+
+
+def store_latent(decoded):
+ state.current_latent = decoded
+
+ if opts.live_previews_enable and opts.show_progress_every_n_steps > 0 and shared.state.sampling_step % opts.show_progress_every_n_steps == 0:
+ if not shared.parallel_processing_allowed:
+ shared.state.assign_current_image(sample_to_image(decoded))
+
+
+class InterruptedException(BaseException):
+ pass
+
+
+# MPS fix for randn in torchsde
+# XXX move this to separate file for MPS
+def torchsde_randn(size, dtype, device, seed):
+ if device.type == 'mps':
+ generator = torch.Generator(devices.cpu).manual_seed(int(seed))
+ return torch.randn(size, dtype=dtype, device=devices.cpu, generator=generator).to(device)
+ else:
+ generator = torch.Generator(device).manual_seed(int(seed))
+ return torch.randn(size, dtype=dtype, device=device, generator=generator)
+
+
+torchsde._brownian.brownian_interval._randn = torchsde_randn
+
diff --git a/modules/sd_samplers_compvis.py b/modules/sd_samplers_compvis.py new file mode 100644 index 00000000..d03131cd --- /dev/null +++ b/modules/sd_samplers_compvis.py @@ -0,0 +1,160 @@ +import math
+import ldm.models.diffusion.ddim
+import ldm.models.diffusion.plms
+
+import numpy as np
+import torch
+
+from modules.shared import state
+from modules import sd_samplers_common, prompt_parser, shared
+
+
+samplers_data_compvis = [
+ sd_samplers_common.SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), [], {}),
+ sd_samplers_common.SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), [], {}),
+]
+
+
+class VanillaStableDiffusionSampler:
+ def __init__(self, constructor, sd_model):
+ self.sampler = constructor(sd_model)
+ self.is_plms = hasattr(self.sampler, 'p_sample_plms')
+ self.orig_p_sample_ddim = self.sampler.p_sample_plms if self.is_plms else self.sampler.p_sample_ddim
+ self.mask = None
+ self.nmask = None
+ self.init_latent = None
+ self.sampler_noises = None
+ self.step = 0
+ self.stop_at = None
+ self.eta = None
+ self.config = None
+ self.last_latent = None
+
+ self.conditioning_key = sd_model.model.conditioning_key
+
+ def number_of_needed_noises(self, p):
+ return 0
+
+ def launch_sampling(self, steps, func):
+ state.sampling_steps = steps
+ state.sampling_step = 0
+
+ try:
+ return func()
+ except sd_samplers_common.InterruptedException:
+ return self.last_latent
+
+ def p_sample_ddim_hook(self, x_dec, cond, ts, unconditional_conditioning, *args, **kwargs):
+ if state.interrupted or state.skipped:
+ raise sd_samplers_common.InterruptedException
+
+ if self.stop_at is not None and self.step > self.stop_at:
+ raise sd_samplers_common.InterruptedException
+
+ # Have to unwrap the inpainting conditioning here to perform pre-processing
+ image_conditioning = None
+ if isinstance(cond, dict):
+ image_conditioning = cond["c_concat"][0]
+ cond = cond["c_crossattn"][0]
+ unconditional_conditioning = unconditional_conditioning["c_crossattn"][0]
+
+ conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
+ unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step)
+
+ assert all([len(conds) == 1 for conds in conds_list]), 'composition via AND is not supported for DDIM/PLMS samplers'
+ cond = tensor
+
+ # for DDIM, shapes must match, we can't just process cond and uncond independently;
+ # filling unconditional_conditioning with repeats of the last vector to match length is
+ # not 100% correct but should work well enough
+ if unconditional_conditioning.shape[1] < cond.shape[1]:
+ last_vector = unconditional_conditioning[:, -1:]
+ last_vector_repeated = last_vector.repeat([1, cond.shape[1] - unconditional_conditioning.shape[1], 1])
+ unconditional_conditioning = torch.hstack([unconditional_conditioning, last_vector_repeated])
+ elif unconditional_conditioning.shape[1] > cond.shape[1]:
+ unconditional_conditioning = unconditional_conditioning[:, :cond.shape[1]]
+
+ if self.mask is not None:
+ img_orig = self.sampler.model.q_sample(self.init_latent, ts)
+ x_dec = img_orig * self.mask + self.nmask * x_dec
+
+ # Wrap the image conditioning back up since the DDIM code can accept the dict directly.
+ # Note that they need to be lists because it just concatenates them later.
+ if image_conditioning is not None:
+ cond = {"c_concat": [image_conditioning], "c_crossattn": [cond]}
+ unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
+
+ res = self.orig_p_sample_ddim(x_dec, cond, ts, unconditional_conditioning=unconditional_conditioning, *args, **kwargs)
+
+ if self.mask is not None:
+ self.last_latent = self.init_latent * self.mask + self.nmask * res[1]
+ else:
+ self.last_latent = res[1]
+
+ sd_samplers_common.store_latent(self.last_latent)
+
+ self.step += 1
+ state.sampling_step = self.step
+ shared.total_tqdm.update()
+
+ return res
+
+ def initialize(self, p):
+ self.eta = p.eta if p.eta is not None else shared.opts.eta_ddim
+ if self.eta != 0.0:
+ p.extra_generation_params["Eta DDIM"] = self.eta
+
+ for fieldname in ['p_sample_ddim', 'p_sample_plms']:
+ if hasattr(self.sampler, fieldname):
+ setattr(self.sampler, fieldname, self.p_sample_ddim_hook)
+
+ self.mask = p.mask if hasattr(p, 'mask') else None
+ self.nmask = p.nmask if hasattr(p, 'nmask') else None
+
+ def adjust_steps_if_invalid(self, p, num_steps):
+ if (self.config.name == 'DDIM' and p.ddim_discretize == 'uniform') or (self.config.name == 'PLMS'):
+ valid_step = 999 / (1000 // num_steps)
+ if valid_step == math.floor(valid_step):
+ return int(valid_step) + 1
+
+ return num_steps
+
+ def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
+ steps, t_enc = sd_samplers_common.setup_img2img_steps(p, steps)
+ steps = self.adjust_steps_if_invalid(p, steps)
+ self.initialize(p)
+
+ self.sampler.make_schedule(ddim_num_steps=steps, ddim_eta=self.eta, ddim_discretize=p.ddim_discretize, verbose=False)
+ x1 = self.sampler.stochastic_encode(x, torch.tensor([t_enc] * int(x.shape[0])).to(shared.device), noise=noise)
+
+ self.init_latent = x
+ self.last_latent = x
+ self.step = 0
+
+ # Wrap the conditioning models with additional image conditioning for inpainting model
+ if image_conditioning is not None:
+ conditioning = {"c_concat": [image_conditioning], "c_crossattn": [conditioning]}
+ unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
+
+ samples = self.launch_sampling(t_enc + 1, lambda: self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning))
+
+ return samples
+
+ def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
+ self.initialize(p)
+
+ self.init_latent = None
+ self.last_latent = x
+ self.step = 0
+
+ steps = self.adjust_steps_if_invalid(p, steps or p.steps)
+
+ # Wrap the conditioning models with additional image conditioning for inpainting model
+ # dummy_for_plms is needed because PLMS code checks the first item in the dict to have the right shape
+ if image_conditioning is not None:
+ conditioning = {"dummy_for_plms": np.zeros((conditioning.shape[0],)), "c_crossattn": [conditioning], "c_concat": [image_conditioning]}
+ unconditional_conditioning = {"c_crossattn": [unconditional_conditioning], "c_concat": [image_conditioning]}
+
+ samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)[0])
+
+ return samples_ddim
diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py new file mode 100644 index 00000000..aa7f106b --- /dev/null +++ b/modules/sd_samplers_kdiffusion.py @@ -0,0 +1,298 @@ +from collections import deque
+import torch
+import inspect
+import k_diffusion.sampling
+from modules import prompt_parser, devices, sd_samplers_common
+
+from modules.shared import opts, state
+import modules.shared as shared
+from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback
+
+samplers_k_diffusion = [
+ ('Euler a', 'sample_euler_ancestral', ['k_euler_a', 'k_euler_ancestral'], {}),
+ ('Euler', 'sample_euler', ['k_euler'], {}),
+ ('LMS', 'sample_lms', ['k_lms'], {}),
+ ('Heun', 'sample_heun', ['k_heun'], {}),
+ ('DPM2', 'sample_dpm_2', ['k_dpm_2'], {'discard_next_to_last_sigma': True}),
+ ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {'discard_next_to_last_sigma': True}),
+ ('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {}),
+ ('DPM++ 2M', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),
+ ('DPM++ SDE', 'sample_dpmpp_sde', ['k_dpmpp_sde'], {}),
+ ('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {}),
+ ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {}),
+ ('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}),
+ ('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True}),
+ ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True}),
+ ('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras'}),
+ ('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
+ ('DPM++ SDE Karras', 'sample_dpmpp_sde', ['k_dpmpp_sde_ka'], {'scheduler': 'karras'}),
+]
+
+samplers_data_k_diffusion = [
+ sd_samplers_common.SamplerData(label, lambda model, funcname=funcname: KDiffusionSampler(funcname, model), aliases, options)
+ for label, funcname, aliases, options in samplers_k_diffusion
+ if hasattr(k_diffusion.sampling, funcname)
+]
+
+sampler_extra_params = {
+ 'sample_euler': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
+ 'sample_heun': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
+ 'sample_dpm_2': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
+}
+
+
+class CFGDenoiser(torch.nn.Module):
+ """
+ Classifier free guidance denoiser. A wrapper for stable diffusion model (specifically for unet)
+ that can take a noisy picture and produce a noise-free picture using two guidances (prompts)
+ instead of one. Originally, the second prompt is just an empty string, but we use non-empty
+ negative prompt.
+ """
+
+ def __init__(self, model):
+ super().__init__()
+ self.inner_model = model
+ self.mask = None
+ self.nmask = None
+ self.init_latent = None
+ self.step = 0
+
+ def combine_denoised(self, x_out, conds_list, uncond, cond_scale):
+ denoised_uncond = x_out[-uncond.shape[0]:]
+ denoised = torch.clone(denoised_uncond)
+
+ for i, conds in enumerate(conds_list):
+ for cond_index, weight in conds:
+ denoised[i] += (x_out[cond_index] - denoised_uncond[i]) * (weight * cond_scale)
+
+ return denoised
+
+ def forward(self, x, sigma, uncond, cond, cond_scale, image_cond):
+ if state.interrupted or state.skipped:
+ raise sd_samplers_common.InterruptedException
+
+ conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
+ uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step)
+
+ batch_size = len(conds_list)
+ repeats = [len(conds_list[i]) for i in range(batch_size)]
+
+ x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
+ image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond])
+ sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
+
+ denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps)
+ cfg_denoiser_callback(denoiser_params)
+ x_in = denoiser_params.x
+ image_cond_in = denoiser_params.image_cond
+ sigma_in = denoiser_params.sigma
+
+ if tensor.shape[1] == uncond.shape[1]:
+ cond_in = torch.cat([tensor, uncond])
+
+ if shared.batch_cond_uncond:
+ x_out = self.inner_model(x_in, sigma_in, cond={"c_crossattn": [cond_in], "c_concat": [image_cond_in]})
+ else:
+ x_out = torch.zeros_like(x_in)
+ for batch_offset in range(0, x_out.shape[0], batch_size):
+ a = batch_offset
+ b = a + batch_size
+ x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond={"c_crossattn": [cond_in[a:b]], "c_concat": [image_cond_in[a:b]]})
+ else:
+ x_out = torch.zeros_like(x_in)
+ batch_size = batch_size*2 if shared.batch_cond_uncond else batch_size
+ for batch_offset in range(0, tensor.shape[0], batch_size):
+ a = batch_offset
+ b = min(a + batch_size, tensor.shape[0])
+ x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond={"c_crossattn": [tensor[a:b]], "c_concat": [image_cond_in[a:b]]})
+
+ x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond={"c_crossattn": [uncond], "c_concat": [image_cond_in[-uncond.shape[0]:]]})
+
+ devices.test_for_nans(x_out, "unet")
+
+ if opts.live_preview_content == "Prompt":
+ sd_samplers_common.store_latent(x_out[0:uncond.shape[0]])
+ elif opts.live_preview_content == "Negative prompt":
+ sd_samplers_common.store_latent(x_out[-uncond.shape[0]:])
+
+ denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)
+
+ if self.mask is not None:
+ denoised = self.init_latent * self.mask + self.nmask * denoised
+
+ self.step += 1
+
+ return denoised
+
+
+class TorchHijack:
+ def __init__(self, sampler_noises):
+ # Using a deque to efficiently receive the sampler_noises in the same order as the previous index-based
+ # implementation.
+ self.sampler_noises = deque(sampler_noises)
+
+ def __getattr__(self, item):
+ if item == 'randn_like':
+ return self.randn_like
+
+ if hasattr(torch, item):
+ return getattr(torch, item)
+
+ raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, item))
+
+ def randn_like(self, x):
+ if self.sampler_noises:
+ noise = self.sampler_noises.popleft()
+ if noise.shape == x.shape:
+ return noise
+
+ if x.device.type == 'mps':
+ return torch.randn_like(x, device=devices.cpu).to(x.device)
+ else:
+ return torch.randn_like(x)
+
+
+class KDiffusionSampler:
+ def __init__(self, funcname, sd_model):
+ denoiser = k_diffusion.external.CompVisVDenoiser if sd_model.parameterization == "v" else k_diffusion.external.CompVisDenoiser
+
+ self.model_wrap = denoiser(sd_model, quantize=shared.opts.enable_quantization)
+ self.funcname = funcname
+ self.func = getattr(k_diffusion.sampling, self.funcname)
+ self.extra_params = sampler_extra_params.get(funcname, [])
+ self.model_wrap_cfg = CFGDenoiser(self.model_wrap)
+ self.sampler_noises = None
+ self.stop_at = None
+ self.eta = None
+ self.config = None
+ self.last_latent = None
+
+ self.conditioning_key = sd_model.model.conditioning_key
+
+ def callback_state(self, d):
+ step = d['i']
+ latent = d["denoised"]
+ if opts.live_preview_content == "Combined":
+ sd_samplers_common.store_latent(latent)
+ self.last_latent = latent
+
+ if self.stop_at is not None and step > self.stop_at:
+ raise sd_samplers_common.InterruptedException
+
+ state.sampling_step = step
+ shared.total_tqdm.update()
+
+ def launch_sampling(self, steps, func):
+ state.sampling_steps = steps
+ state.sampling_step = 0
+
+ try:
+ return func()
+ except sd_samplers_common.InterruptedException:
+ return self.last_latent
+
+ def number_of_needed_noises(self, p):
+ return p.steps
+
+ def initialize(self, p):
+ self.model_wrap_cfg.mask = p.mask if hasattr(p, 'mask') else None
+ self.model_wrap_cfg.nmask = p.nmask if hasattr(p, 'nmask') else None
+ self.model_wrap_cfg.step = 0
+ self.eta = p.eta if p.eta is not None else opts.eta_ancestral
+
+ k_diffusion.sampling.torch = TorchHijack(self.sampler_noises if self.sampler_noises is not None else [])
+
+ extra_params_kwargs = {}
+ for param_name in self.extra_params:
+ if hasattr(p, param_name) and param_name in inspect.signature(self.func).parameters:
+ extra_params_kwargs[param_name] = getattr(p, param_name)
+
+ if 'eta' in inspect.signature(self.func).parameters:
+ if self.eta != 1.0:
+ p.extra_generation_params["Eta"] = self.eta
+
+ extra_params_kwargs['eta'] = self.eta
+
+ return extra_params_kwargs
+
+ def get_sigmas(self, p, steps):
+ discard_next_to_last_sigma = self.config is not None and self.config.options.get('discard_next_to_last_sigma', False)
+ if opts.always_discard_next_to_last_sigma and not discard_next_to_last_sigma:
+ discard_next_to_last_sigma = True
+ p.extra_generation_params["Discard penultimate sigma"] = True
+
+ steps += 1 if discard_next_to_last_sigma else 0
+
+ if p.sampler_noise_scheduler_override:
+ sigmas = p.sampler_noise_scheduler_override(steps)
+ elif self.config is not None and self.config.options.get('scheduler', None) == 'karras':
+ sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
+
+ sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, device=shared.device)
+ else:
+ sigmas = self.model_wrap.get_sigmas(steps)
+
+ if discard_next_to_last_sigma:
+ sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])
+
+ return sigmas
+
+ def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
+ steps, t_enc = sd_samplers_common.setup_img2img_steps(p, steps)
+
+ sigmas = self.get_sigmas(p, steps)
+
+ sigma_sched = sigmas[steps - t_enc - 1:]
+ xi = x + noise * sigma_sched[0]
+
+ extra_params_kwargs = self.initialize(p)
+ if 'sigma_min' in inspect.signature(self.func).parameters:
+ ## last sigma is zero which isn't allowed by DPM Fast & Adaptive so taking value before last
+ extra_params_kwargs['sigma_min'] = sigma_sched[-2]
+ if 'sigma_max' in inspect.signature(self.func).parameters:
+ extra_params_kwargs['sigma_max'] = sigma_sched[0]
+ if 'n' in inspect.signature(self.func).parameters:
+ extra_params_kwargs['n'] = len(sigma_sched) - 1
+ if 'sigma_sched' in inspect.signature(self.func).parameters:
+ extra_params_kwargs['sigma_sched'] = sigma_sched
+ if 'sigmas' in inspect.signature(self.func).parameters:
+ extra_params_kwargs['sigmas'] = sigma_sched
+
+ self.model_wrap_cfg.init_latent = x
+ self.last_latent = x
+
+ samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args={
+ 'cond': conditioning,
+ 'image_cond': image_conditioning,
+ 'uncond': unconditional_conditioning,
+ 'cond_scale': p.cfg_scale
+ }, disable=False, callback=self.callback_state, **extra_params_kwargs))
+
+ return samples
+
+ def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning = None):
+ steps = steps or p.steps
+
+ sigmas = self.get_sigmas(p, steps)
+
+ x = x * sigmas[0]
+
+ extra_params_kwargs = self.initialize(p)
+ if 'sigma_min' in inspect.signature(self.func).parameters:
+ extra_params_kwargs['sigma_min'] = self.model_wrap.sigmas[0].item()
+ extra_params_kwargs['sigma_max'] = self.model_wrap.sigmas[-1].item()
+ if 'n' in inspect.signature(self.func).parameters:
+ extra_params_kwargs['n'] = steps
+ else:
+ extra_params_kwargs['sigmas'] = sigmas
+
+ self.last_latent = x
+ samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={
+ 'cond': conditioning,
+ 'image_cond': image_conditioning,
+ 'uncond': unconditional_conditioning,
+ 'cond_scale': p.cfg_scale
+ }, disable=False, callback=self.callback_state, **extra_params_kwargs))
+
+ return samples
+
diff --git a/modules/shared.py b/modules/shared.py index 474fcc42..96a2572f 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -127,12 +127,13 @@ restricted_opts = { ui_reorder_categories = [
"inpaint",
"sampler",
+ "checkboxes",
+ "hires_fix",
"dimensions",
"cfg",
"seed",
- "checkboxes",
- "hires_fix",
"batch",
+ "override_settings",
"scripts",
]
@@ -346,10 +347,10 @@ options_templates.update(options_section(('saving-paths', "Paths for saving"), { }))
options_templates.update(options_section(('saving-to-dirs', "Saving to a directory"), {
- "save_to_dirs": OptionInfo(False, "Save images to a subdirectory"),
- "grid_save_to_dirs": OptionInfo(False, "Save grids to a subdirectory"),
+ "save_to_dirs": OptionInfo(True, "Save images to a subdirectory"),
+ "grid_save_to_dirs": OptionInfo(True, "Save grids to a subdirectory"),
"use_save_to_dirs_for_ui": OptionInfo(False, "When using \"Save\" button, save images to a subdirectory"),
- "directories_filename_pattern": OptionInfo("", "Directory name pattern", component_args=hide_dirs),
+ "directories_filename_pattern": OptionInfo("[date]", "Directory name pattern", component_args=hide_dirs),
"directories_max_prompt_words": OptionInfo(8, "Max prompt words for [prompt_words] pattern", gr.Slider, {"minimum": 1, "maximum": 20, "step": 1, **hide_dirs}),
}))
@@ -405,7 +406,6 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), { "enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
"comma_padding_backtrack": OptionInfo(20, "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }),
"CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
- "extra_networks_default_multiplier": OptionInfo(1.0, "Multiplier for extra networks", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
"upcast_attn": OptionInfo(False, "Upcast cross attention layer to float32"),
}))
@@ -431,7 +431,9 @@ options_templates.update(options_section(('interrogate', "Interrogate Options"), }))
options_templates.update(options_section(('extra_networks', "Extra Networks"), {
- "extra_networks_default_view": OptionInfo("cards", "Default view for Extra Networks", gr.Dropdown, { "choices": ["cards", "thumbs"] }),
+ "extra_networks_default_view": OptionInfo("cards", "Default view for Extra Networks", gr.Dropdown, {"choices": ["cards", "thumbs"]}),
+ "extra_networks_default_multiplier": OptionInfo(1.0, "Multiplier for extra networks", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+ "sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, lambda: {"choices": [""] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
}))
options_templates.update(options_section(('ui', "User interface"), {
@@ -439,7 +441,7 @@ options_templates.update(options_section(('ui', "User interface"), { "do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
"add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
"add_model_name_to_info": OptionInfo(True, "Add model name to generation information"),
- "disable_weights_auto_swap": OptionInfo(False, "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint."),
+ "disable_weights_auto_swap": OptionInfo(True, "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint."),
"send_seed": OptionInfo(True, "Send seed when sending prompt or image to other interface"),
"send_size": OptionInfo(True, "Send size when sending prompt or image to another interface"),
"font": OptionInfo("", "Font for image grids that have text"),
@@ -604,11 +606,37 @@ class Options: self.data_labels = {k: v for k, v in sorted(settings_items, key=lambda x: section_ids[x[1].section])}
+ def cast_value(self, key, value):
+ """casts an arbitrary to the same type as this setting's value with key
+ Example: cast_value("eta_noise_seed_delta", "12") -> returns 12 (an int rather than str)
+ """
+
+ if value is None:
+ return None
+
+ default_value = self.data_labels[key].default
+ if default_value is None:
+ default_value = getattr(self, key, None)
+ if default_value is None:
+ return None
+
+ expected_type = type(default_value)
+ if expected_type == bool and value == "False":
+ value = False
+ else:
+ value = expected_type(value)
+
+ return value
+
+
opts = Options()
if os.path.exists(config_filename):
opts.load(config_filename)
+settings_components = None
+"""assinged from ui.py, a mapping on setting anmes to gradio components repsponsible for those settings"""
+
latent_upscale_default_mode = "Latent"
latent_upscale_modes = {
"Latent": {"mode": "bilinear", "antialias": False},
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 6cf00e65..a1a406c2 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -112,6 +112,7 @@ class EmbeddingDatabase: self.skipped_embeddings = {}
self.expected_shape = -1
self.embedding_dirs = {}
+ self.previously_displayed_embeddings = ()
def add_embedding_dir(self, path):
self.embedding_dirs[path] = DirWithTextualInversionEmbeddings(path)
@@ -228,9 +229,12 @@ class EmbeddingDatabase: self.load_from_dir(embdir)
embdir.update()
- print(f"Textual inversion embeddings loaded({len(self.word_embeddings)}): {', '.join(self.word_embeddings.keys())}")
- if len(self.skipped_embeddings) > 0:
- print(f"Textual inversion embeddings skipped({len(self.skipped_embeddings)}): {', '.join(self.skipped_embeddings.keys())}")
+ displayed_embeddings = (tuple(self.word_embeddings.keys()), tuple(self.skipped_embeddings.keys()))
+ if self.previously_displayed_embeddings != displayed_embeddings:
+ self.previously_displayed_embeddings = displayed_embeddings
+ print(f"Textual inversion embeddings loaded({len(self.word_embeddings)}): {', '.join(self.word_embeddings.keys())}")
+ if len(self.skipped_embeddings) > 0:
+ print(f"Textual inversion embeddings skipped({len(self.skipped_embeddings)}): {', '.join(self.skipped_embeddings.keys())}")
def find_embedding_at_position(self, tokens, offset):
token = tokens[offset]
diff --git a/modules/txt2img.py b/modules/txt2img.py index e945fd69..16841d0f 100644 --- a/modules/txt2img.py +++ b/modules/txt2img.py @@ -1,5 +1,6 @@ import modules.scripts
from modules import sd_samplers
+from modules.generation_parameters_copypaste import create_override_settings_dict
from modules.processing import StableDiffusionProcessing, Processed, StableDiffusionProcessingTxt2Img, \
StableDiffusionProcessingImg2Img, process_images
from modules.shared import opts, cmd_opts
@@ -8,7 +9,9 @@ import modules.processing as processing from modules.ui import plaintext_to_html
-def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, hr_scale: float, hr_upscaler: str, hr_second_pass_steps: int, hr_resize_x: int, hr_resize_y: int, *args):
+def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, hr_scale: float, hr_upscaler: str, hr_second_pass_steps: int, hr_resize_x: int, hr_resize_y: int, override_settings_texts, *args):
+ override_settings = create_override_settings_dict(override_settings_texts)
+
p = StableDiffusionProcessingTxt2Img(
sd_model=shared.sd_model,
outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples,
@@ -38,6 +41,7 @@ def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, step hr_second_pass_steps=hr_second_pass_steps,
hr_resize_x=hr_resize_x,
hr_resize_y=hr_resize_y,
+ override_settings=override_settings,
)
p.scripts = modules.scripts.scripts_txt2img
diff --git a/modules/ui.py b/modules/ui.py index 9f4cfda1..f910c582 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -380,6 +380,7 @@ def apply_setting(key, value): opts.save(shared.config_filename)
return getattr(opts, key)
+
def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id):
def refresh():
refresh_method()
@@ -433,6 +434,18 @@ def get_value_for_setting(key): return gr.update(value=value, **args)
+def create_override_settings_dropdown(tabname, row):
+ dropdown = gr.Dropdown([], label="Override settings", visible=False, elem_id=f"{tabname}_override_settings", multiselect=True)
+
+ dropdown.change(
+ fn=lambda x: gr.Dropdown.update(visible=len(x) > 0),
+ inputs=[dropdown],
+ outputs=[dropdown],
+ )
+
+ return dropdown
+
+
def create_ui():
import modules.img2img
import modules.txt2img
@@ -503,6 +516,10 @@ def create_ui(): batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="txt2img_batch_count")
batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="txt2img_batch_size")
+ elif category == "override_settings":
+ with FormRow(elem_id="txt2img_override_settings_row") as row:
+ override_settings = create_override_settings_dropdown('txt2img', row)
+
elif category == "scripts":
with FormGroup(elem_id="txt2img_script_container"):
custom_inputs = modules.scripts.scripts_txt2img.setup_ui()
@@ -524,7 +541,6 @@ def create_ui(): )
txt2img_gallery, generation_info, html_info, html_log = create_output_panel("txt2img", opts.outdir_txt2img_samples)
- parameters_copypaste.bind_buttons({"txt2img": txt2img_paste}, None, txt2img_prompt)
connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
@@ -555,6 +571,7 @@ def create_ui(): hr_second_pass_steps,
hr_resize_x,
hr_resize_y,
+ override_settings,
] + custom_inputs,
outputs=[
@@ -615,6 +632,9 @@ def create_ui(): *modules.scripts.scripts_txt2img.infotext_fields
]
parameters_copypaste.add_paste_fields("txt2img", None, txt2img_paste_fields)
+ parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
+ paste_button=txt2img_paste, tabname="txt2img", source_text_component=txt2img_prompt, source_image_component=None, override_settings_component=override_settings,
+ ))
txt2img_preview_params = [
txt2img_prompt,
@@ -762,6 +782,10 @@ def create_ui(): batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="img2img_batch_count")
batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="img2img_batch_size")
+ elif category == "override_settings":
+ with FormRow(elem_id="img2img_override_settings_row") as row:
+ override_settings = create_override_settings_dropdown('img2img', row)
+
elif category == "scripts":
with FormGroup(elem_id="img2img_script_container"):
custom_inputs = modules.scripts.scripts_img2img.setup_ui()
@@ -796,7 +820,6 @@ def create_ui(): )
img2img_gallery, generation_info, html_info, html_log = create_output_panel("img2img", opts.outdir_img2img_samples)
- parameters_copypaste.bind_buttons({"img2img": img2img_paste}, None, img2img_prompt)
connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
@@ -849,7 +872,8 @@ def create_ui(): inpainting_mask_invert,
img2img_batch_input_dir,
img2img_batch_output_dir,
- img2img_batch_inpaint_mask_dir
+ img2img_batch_inpaint_mask_dir,
+ override_settings,
] + custom_inputs,
outputs=[
img2img_gallery,
@@ -937,6 +961,9 @@ def create_ui(): ]
parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields)
parameters_copypaste.add_paste_fields("inpaint", init_img_with_mask, img2img_paste_fields)
+ parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
+ paste_button=img2img_paste, tabname="img2img", source_text_component=img2img_prompt, source_image_component=None, override_settings_component=override_settings,
+ ))
modules.scripts.scripts_current = None
@@ -954,7 +981,11 @@ def create_ui(): html2 = gr.HTML()
with gr.Row():
buttons = parameters_copypaste.create_buttons(["txt2img", "img2img", "inpaint", "extras"])
- parameters_copypaste.bind_buttons(buttons, image, generation_info)
+
+ for tabname, button in buttons.items():
+ parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
+ paste_button=button, tabname=tabname, source_text_component=generation_info, source_image_component=image,
+ ))
image.change(
fn=wrap_gradio_call(modules.extras.run_pnginfo),
@@ -1363,6 +1394,7 @@ def create_ui(): components = []
component_dict = {}
+ shared.settings_components = component_dict
script_callbacks.ui_settings_callback()
opts.reorder()
@@ -1529,8 +1561,7 @@ def create_ui(): component = create_setting_component(k, is_quicksettings=True)
component_dict[k] = component
- parameters_copypaste.integrate_settings_paste_fields(component_dict)
- parameters_copypaste.run_bind()
+ parameters_copypaste.connect_paste_params_buttons()
with gr.Tabs(elem_id="tabs") as tabs:
for interface, label, ifid in interfaces:
@@ -1560,6 +1591,14 @@ def create_ui(): outputs=[component, text_settings],
)
+ button_set_checkpoint = gr.Button('Change checkpoint', elem_id='change_checkpoint', visible=False)
+ button_set_checkpoint.click(
+ fn=lambda value, _: run_settings_single(value, key='sd_model_checkpoint'),
+ _js="function(v){ var res = desiredCheckpointName; desiredCheckpointName = ''; return [res || v, null]; }",
+ inputs=[component_dict['sd_model_checkpoint'], dummy_component],
+ outputs=[component_dict['sd_model_checkpoint'], text_settings],
+ )
+
component_keys = [k for k in opts.data_labels.keys() if k in component_dict]
def get_settings_values():
@@ -1692,14 +1731,14 @@ def create_ui(): def reload_javascript():
- head = f'<script type="text/javascript" src="file={os.path.abspath("script.js")}"></script>\n'
+ head = f'<script type="text/javascript" src="file={os.path.abspath("script.js")}?{os.path.getmtime("script.js")}"></script>\n'
inline = f"{localization.localization_js(shared.opts.localization)};"
if cmd_opts.theme is not None:
inline += f"set_theme('{cmd_opts.theme}');"
for script in modules.scripts.list_scripts("javascript", ".js"):
- head += f'<script type="text/javascript" src="file={script.path}"></script>\n'
+ head += f'<script type="text/javascript" src="file={script.path}?{os.path.getmtime(script.path)}"></script>\n'
head += f'<script type="text/javascript">{inline}</script>\n'
diff --git a/modules/ui_common.py b/modules/ui_common.py index 9405ac1f..fd047f31 100644 --- a/modules/ui_common.py +++ b/modules/ui_common.py @@ -198,5 +198,9 @@ Requested path was: {f} html_info = gr.HTML(elem_id=f'html_info_{tabname}')
html_log = gr.HTML(elem_id=f'html_log_{tabname}')
- parameters_copypaste.bind_buttons(buttons, result_gallery, "txt2img" if tabname == "txt2img" else None)
+ for paste_tabname, paste_button in buttons.items():
+ parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
+ paste_button=paste_button, tabname=paste_tabname, source_tabname="txt2img" if tabname == "txt2img" else None, source_image_component=result_gallery
+ ))
+
return result_gallery, generation_info if tabname != "extras" else html_info_x, html_info, html_log
diff --git a/modules/ui_extensions.py b/modules/ui_extensions.py index 66a41865..37d30e1f 100644 --- a/modules/ui_extensions.py +++ b/modules/ui_extensions.py @@ -13,7 +13,7 @@ import shutil import errno
from modules import extensions, shared, paths
-
+from modules.call_queue import wrap_gradio_gpu_call
available_extensions = {"extensions": []}
@@ -50,12 +50,17 @@ def apply_and_restart(disable_list, update_list): shared.state.need_restart = True
-def check_updates():
+def check_updates(id_task, disable_list):
check_access()
- for ext in extensions.extensions:
- if ext.remote is None:
- continue
+ disabled = json.loads(disable_list)
+ assert type(disabled) == list, f"wrong disable_list data for apply_and_restart: {disable_list}"
+
+ exts = [ext for ext in extensions.extensions if ext.remote is not None and ext.name not in disabled]
+ shared.state.job_count = len(exts)
+
+ for ext in exts:
+ shared.state.textinfo = ext.name
try:
ext.check_updates()
@@ -63,7 +68,9 @@ def check_updates(): print(f"Error checking updates for {ext.name}:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
- return extension_table()
+ shared.state.nextjob()
+
+ return extension_table(), ""
def extension_table():
@@ -273,12 +280,13 @@ def create_ui(): with gr.Tabs(elem_id="tabs_extensions") as tabs:
with gr.TabItem("Installed"):
- with gr.Row():
+ with gr.Row(elem_id="extensions_installed_top"):
apply = gr.Button(value="Apply and restart UI", variant="primary")
check = gr.Button(value="Check for updates")
extensions_disabled_list = gr.Text(elem_id="extensions_disabled_list", visible=False).style(container=False)
extensions_update_list = gr.Text(elem_id="extensions_update_list", visible=False).style(container=False)
+ info = gr.HTML()
extensions_table = gr.HTML(lambda: extension_table())
apply.click(
@@ -289,10 +297,10 @@ def create_ui(): )
check.click(
- fn=check_updates,
+ fn=wrap_gradio_gpu_call(check_updates, extra_outputs=[gr.update()]),
_js="extensions_check",
- inputs=[],
- outputs=[extensions_table],
+ inputs=[info, extensions_disabled_list],
+ outputs=[extensions_table, info],
)
with gr.TabItem("Available"):
diff --git a/modules/ui_extra_networks.py b/modules/ui_extra_networks.py index c6ff889a..83367968 100644 --- a/modules/ui_extra_networks.py +++ b/modules/ui_extra_networks.py @@ -1,4 +1,7 @@ +import glob
import os.path
+import urllib.parse
+from pathlib import Path
from modules import shared
import gradio as gr
@@ -8,12 +11,31 @@ import html from modules.generation_parameters_copypaste import image_from_url_text
extra_pages = []
+allowed_dirs = set()
def register_page(page):
"""registers extra networks page for the UI; recommend doing it in on_before_ui() callback for extensions"""
extra_pages.append(page)
+ allowed_dirs.clear()
+ allowed_dirs.update(set(sum([x.allowed_directories_for_previews() for x in extra_pages], [])))
+
+
+def add_pages_to_demo(app):
+ def fetch_file(filename: str = ""):
+ from starlette.responses import FileResponse
+
+ if not any([Path(x).resolve() in Path(filename).resolve().parents for x in allowed_dirs]):
+ raise ValueError(f"File cannot be fetched: {filename}. Must be in one of directories registered by extra pages.")
+
+ if os.path.splitext(filename)[1].lower() != ".png":
+ raise ValueError(f"File cannot be fetched: {filename}. Only png.")
+
+ # would profit from returning 304
+ return FileResponse(filename, headers={"Accept-Ranges": "bytes"})
+
+ app.add_api_route("/sd_extra_networks/thumb", fetch_file, methods=["GET"])
class ExtraNetworksPage:
@@ -26,10 +48,44 @@ class ExtraNetworksPage: def refresh(self):
pass
+ def link_preview(self, filename):
+ return "./sd_extra_networks/thumb?filename=" + urllib.parse.quote(filename.replace('\\', '/')) + "&mtime=" + str(os.path.getmtime(filename))
+
+ def search_terms_from_path(self, filename, possible_directories=None):
+ abspath = os.path.abspath(filename)
+
+ for parentdir in (possible_directories if possible_directories is not None else self.allowed_directories_for_previews()):
+ parentdir = os.path.abspath(parentdir)
+ if abspath.startswith(parentdir):
+ return abspath[len(parentdir):].replace('\\', '/')
+
+ return ""
+
def create_html(self, tabname):
view = shared.opts.extra_networks_default_view
items_html = ''
+ subdirs = {}
+ for parentdir in [os.path.abspath(x) for x in self.allowed_directories_for_previews()]:
+ for x in glob.glob(os.path.join(parentdir, '**/*'), recursive=True):
+ if not os.path.isdir(x):
+ continue
+
+ subdir = os.path.abspath(x)[len(parentdir):].replace("\\", "/")
+ while subdir.startswith("/"):
+ subdir = subdir[1:]
+
+ subdirs[subdir] = 1
+
+ if subdirs:
+ subdirs = {"": 1, **subdirs}
+
+ subdirs_html = "".join([f"""
+<button class='gr-button gr-button-lg gr-button-secondary{" search-all" if subdir=="" else ""}' onclick='extraNetworksSearchButton("{tabname}_extra_tabs", event)'>
+{html.escape(subdir if subdir!="" else "all")}
+</button>
+""" for subdir in subdirs])
+
for item in self.list_items():
items_html += self.create_html_for_item(item, tabname)
@@ -38,6 +94,9 @@ class ExtraNetworksPage: items_html = shared.html("extra-networks-no-cards.html").format(dirs=dirs)
res = f"""
+<div id='{tabname}_{self.name}_subdirs' class='extra-network-subdirs extra-network-subdirs-{view}'>
+{subdirs_html}
+</div>
<div id='{tabname}_{self.name}_cards' class='extra-network-{view}'>
{items_html}
</div>
@@ -54,14 +113,19 @@ class ExtraNetworksPage: def create_html_for_item(self, item, tabname):
preview = item.get("preview", None)
+ onclick = item.get("onclick", None)
+ if onclick is None:
+ onclick = '"' + html.escape(f"""return cardClicked({json.dumps(tabname)}, {item["prompt"]}, {"true" if self.allow_negative_prompt else "false"})""") + '"'
+
args = {
"preview_html": "style='background-image: url(\"" + html.escape(preview) + "\")'" if preview else '',
- "prompt": item["prompt"],
+ "prompt": item.get("prompt", None),
"tabname": json.dumps(tabname),
"local_preview": json.dumps(item["local_preview"]),
"name": item["name"],
- "card_clicked": '"' + html.escape(f"""return cardClicked({json.dumps(tabname)}, {item["prompt"]}, {"true" if self.allow_negative_prompt else "false"})""") + '"',
+ "card_clicked": onclick,
"save_card_preview": '"' + html.escape(f"""return saveCardPreview(event, {json.dumps(tabname)}, {json.dumps(item["local_preview"])})""") + '"',
+ "search_term": item.get("search_term", ""),
}
return self.card_page.format(**args)
@@ -143,7 +207,7 @@ def path_is_parent(parent_path, child_path): parent_path = os.path.abspath(parent_path)
child_path = os.path.abspath(child_path)
- return os.path.commonpath([parent_path]) == os.path.commonpath([parent_path, child_path])
+ return child_path.startswith(parent_path)
def setup_ui(ui, gallery):
@@ -173,7 +237,8 @@ def setup_ui(ui, gallery): ui.button_save_preview.click(
fn=save_preview,
- _js="function(x, y, z){console.log(x, y, z); return [selected_gallery_index(), y, z]}",
+ _js="function(x, y, z){return [selected_gallery_index(), y, z]}",
inputs=[ui.preview_target_filename, gallery, ui.preview_target_filename],
outputs=[*ui.pages]
)
+
diff --git a/modules/ui_extra_networks_checkpoints.py b/modules/ui_extra_networks_checkpoints.py new file mode 100644 index 00000000..04097a79 --- /dev/null +++ b/modules/ui_extra_networks_checkpoints.py @@ -0,0 +1,39 @@ +import html
+import json
+import os
+import urllib.parse
+
+from modules import shared, ui_extra_networks, sd_models
+
+
+class ExtraNetworksPageCheckpoints(ui_extra_networks.ExtraNetworksPage):
+ def __init__(self):
+ super().__init__('Checkpoints')
+
+ def refresh(self):
+ shared.refresh_checkpoints()
+
+ def list_items(self):
+ checkpoint: sd_models.CheckpointInfo
+ for name, checkpoint in sd_models.checkpoints_list.items():
+ path, ext = os.path.splitext(checkpoint.filename)
+ previews = [path + ".png", path + ".preview.png"]
+
+ preview = None
+ for file in previews:
+ if os.path.isfile(file):
+ preview = self.link_preview(file)
+ break
+
+ yield {
+ "name": checkpoint.name_for_extra,
+ "filename": path,
+ "preview": preview,
+ "search_term": self.search_terms_from_path(checkpoint.filename) + " " + (checkpoint.sha256 or ""),
+ "onclick": '"' + html.escape(f"""return selectCheckpoint({json.dumps(name)})""") + '"',
+ "local_preview": path + ".png",
+ }
+
+ def allowed_directories_for_previews(self):
+ return [v for v in [shared.cmd_opts.ckpt_dir, sd_models.model_path] if v is not None]
+
diff --git a/modules/ui_extra_networks_hypernets.py b/modules/ui_extra_networks_hypernets.py index 65d000cf..57851088 100644 --- a/modules/ui_extra_networks_hypernets.py +++ b/modules/ui_extra_networks_hypernets.py @@ -19,13 +19,14 @@ class ExtraNetworksPageHypernetworks(ui_extra_networks.ExtraNetworksPage): preview = None
for file in previews:
if os.path.isfile(file):
- preview = "./file=" + file.replace('\\', '/') + "?mtime=" + str(os.path.getmtime(file))
+ preview = self.link_preview(file)
break
yield {
"name": name,
"filename": path,
"preview": preview,
+ "search_term": self.search_terms_from_path(path),
"prompt": json.dumps(f"<hypernet:{name}:") + " + opts.extra_networks_default_multiplier + " + json.dumps(">"),
"local_preview": path + ".png",
}
diff --git a/modules/ui_extra_networks_textual_inversion.py b/modules/ui_extra_networks_textual_inversion.py index dbd23d2d..bb64eb81 100644 --- a/modules/ui_extra_networks_textual_inversion.py +++ b/modules/ui_extra_networks_textual_inversion.py @@ -19,12 +19,13 @@ class ExtraNetworksPageTextualInversion(ui_extra_networks.ExtraNetworksPage): preview = None
if os.path.isfile(preview_file):
- preview = "./file=" + preview_file.replace('\\', '/') + "?mtime=" + str(os.path.getmtime(preview_file))
+ preview = self.link_preview(preview_file)
yield {
"name": embedding.name,
"filename": embedding.filename,
"preview": preview,
+ "search_term": self.search_terms_from_path(embedding.filename),
"prompt": json.dumps(embedding.name),
"local_preview": path + ".preview.png",
}
diff --git a/scripts/xyz_grid.py b/scripts/xyz_grid.py index f0116055..3df40483 100644 --- a/scripts/xyz_grid.py +++ b/scripts/xyz_grid.py @@ -383,6 +383,15 @@ class Script(scripts.Script): y_type.change(fn=select_axis, inputs=[y_type], outputs=[fill_y_button])
z_type.change(fn=select_axis, inputs=[z_type], outputs=[fill_z_button])
+ self.infotext_fields = (
+ (x_type, "X Type"),
+ (x_values, "X Values"),
+ (y_type, "Y Type"),
+ (y_values, "Y Values"),
+ (z_type, "Z Type"),
+ (z_values, "Z Values"),
+ )
+
return [x_type, x_values, y_type, y_values, z_type, z_values, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds]
def run(self, p, x_type, x_values, y_type, y_values, z_type, z_values, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds):
@@ -542,6 +551,7 @@ class Script(scripts.Script): if grid_infotext[0] is None:
pc.extra_generation_params = copy(pc.extra_generation_params)
+ pc.extra_generation_params['Script'] = self.title()
if x_opt.label != 'Nothing':
pc.extra_generation_params["X Type"] = x_opt.label
@@ -74,7 +74,12 @@ #txt2img_gallery img, #img2img_gallery img{
object-fit: scale-down;
}
-
+#txt2img_actions_column, #img2img_actions_column {
+ margin: 0.35rem 0.75rem 0.35rem 0;
+}
+#script_list {
+ padding: .625rem .75rem 0 .625rem;
+}
.justify-center.overflow-x-scroll {
justify-content: left;
}
@@ -126,6 +131,7 @@ #txt2img_actions_column, #img2img_actions_column{
gap: 0;
+ margin-right: .75rem;
}
#txt2img_tools, #img2img_tools{
@@ -150,6 +156,7 @@ #txt2img_styles_row, #img2img_styles_row{
gap: 0.25em;
+ margin-top: 0.3em;
}
#txt2img_styles_row > button, #img2img_styles_row > button{
@@ -311,11 +318,11 @@ input[type="range"]{ .min-h-\[6rem\] { min-height: unset !important; }
.progressDiv{
- position: absolute;
+ position: relative;
height: 20px;
- top: -20px;
background: #b4c0cc;
border-radius: 3px !important;
+ margin-bottom: -3px;
}
.dark .progressDiv{
@@ -535,7 +542,7 @@ input[type="range"]{ }
#quicksettings {
- gap: 0.4em;
+ width: fit-content;
}
#quicksettings > div, #quicksettings > fieldset{
@@ -545,6 +552,7 @@ input[type="range"]{ border: none;
box-shadow: none;
background: none;
+ margin-right: 10px;
}
#quicksettings > div > div > div > label > span {
@@ -567,7 +575,7 @@ canvas[key="mask"] { right: 0.5em;
top: -0.6em;
z-index: 400;
- width: 8em;
+ width: 6em;
}
#quicksettings .gr-box > div > div > input.gr-text-input {
top: -1.12em;
@@ -665,11 +673,27 @@ canvas[key="mask"] { #quicksettings .gr-button-tool{
margin: 0;
+ border-color: unset;
+ background-color: unset;
}
-
+#modelmerger_interp_description>p {
+ margin: 0!important;
+ text-align: center;
+}
+#modelmerger_interp_description {
+ margin: 0.35rem 0.75rem 1.23rem;
+}
#img2img_settings > div.gr-form, #txt2img_settings > div.gr-form {
padding-top: 0.9em;
+ padding-bottom: 0.9em;
+}
+#txt2img_settings {
+ padding-top: 1.16em;
+ padding-bottom: 0.9em;
+}
+#img2img_settings {
+ padding-bottom: 0.9em;
}
#img2img_settings div.gr-form .gr-form, #txt2img_settings div.gr-form .gr-form, #train_tabs div.gr-form .gr-form{
@@ -741,6 +765,7 @@ footer { .dark .gr-compact{
background-color: rgb(31 41 55 / var(--tw-bg-opacity));
+ margin-left: 0;
}
.gr-compact{
@@ -782,7 +807,13 @@ footer { margin: 0.3em;
}
+.extra-network-subdirs{
+ padding: 0.2em 0.35em;
+}
+.extra-network-subdirs button{
+ margin: 0 0.15em;
+}
#txt2img_extra_networks .search, #img2img_extra_networks .search{
display: inline-block;
@@ -925,3 +956,6 @@ footer { color: red;
}
+[id*='_prompt_container'] > div {
+ margin: 0!important;
+}
diff --git a/webui-macos-env.sh b/webui-macos-env.sh index fa187dd1..37cac4fb 100644 --- a/webui-macos-env.sh +++ b/webui-macos-env.sh @@ -10,7 +10,7 @@ then fi export install_dir="$HOME" -export COMMANDLINE_ARGS="--skip-torch-cuda-test --upcast-sampling --use-cpu interrogate" +export COMMANDLINE_ARGS="--skip-torch-cuda-test --upcast-sampling --no-half-vae --use-cpu interrogate" export TORCH_COMMAND="pip install torch==1.12.1 torchvision==0.13.1" export K_DIFFUSION_REPO="https://github.com/brkirch/k-diffusion.git" export K_DIFFUSION_COMMIT_HASH="51c9778f269cedb55a4d88c79c0246d35bdadb71" @@ -12,7 +12,7 @@ from packaging import version import logging
logging.getLogger("xformers").addFilter(lambda record: 'A matching Triton is not available' not in record.getMessage())
-from modules import import_hook, errors, extra_networks
+from modules import import_hook, errors, extra_networks, ui_extra_networks_checkpoints
from modules import extra_networks_hypernet, ui_extra_networks_hypernets, ui_extra_networks_textual_inversion
from modules.call_queue import wrap_queued_call, queue_lock, wrap_gradio_gpu_call
@@ -119,6 +119,7 @@ def initialize(): ui_extra_networks.intialize()
ui_extra_networks.register_page(ui_extra_networks_textual_inversion.ExtraNetworksPageTextualInversion())
ui_extra_networks.register_page(ui_extra_networks_hypernets.ExtraNetworksPageHypernetworks())
+ ui_extra_networks.register_page(ui_extra_networks_checkpoints.ExtraNetworksPageCheckpoints())
extra_networks.initialize()
extra_networks.register_extra_network(extra_networks_hypernet.ExtraNetworkHypernet())
@@ -227,6 +228,8 @@ def webui(): if launch_api:
create_api(app)
+ ui_extra_networks.add_pages_to_demo(app)
+
modules.script_callbacks.app_started_callback(shared.demo, app)
wait_on_server(shared.demo)
@@ -254,6 +257,7 @@ def webui(): ui_extra_networks.intialize()
ui_extra_networks.register_page(ui_extra_networks_textual_inversion.ExtraNetworksPageTextualInversion())
ui_extra_networks.register_page(ui_extra_networks_hypernets.ExtraNetworksPageHypernetworks())
+ ui_extra_networks.register_page(ui_extra_networks_checkpoints.ExtraNetworksPageCheckpoints())
extra_networks.initialize()
extra_networks.register_extra_network(extra_networks_hypernet.ExtraNetworkHypernet())
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