Merge branch 'master' into improved-hr-conflict-test
This commit is contained in:
InvincibleDude
GitHub
commit
3ec2eb8bf1
@ -20,8 +20,7 @@ model:
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conditioning_key: hybrid
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monitor: val/loss_simple_ema
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scale_factor: 0.18215
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use_ema: true
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load_ema: true
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use_ema: false
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scheduler_config: # 10000 warmup steps
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target: ldm.lr_scheduler.LambdaLinearScheduler
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@ -191,6 +191,28 @@ function confirm_clear_prompt(prompt, negative_prompt) {
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return [prompt, negative_prompt]
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}
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promptTokecountUpdateFuncs = {}
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function recalculatePromptTokens(name){
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if(promptTokecountUpdateFuncs[name]){
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promptTokecountUpdateFuncs[name]()
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}
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}
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function recalculate_prompts_txt2img(){
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recalculatePromptTokens('txt2img_prompt')
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recalculatePromptTokens('txt2img_neg_prompt')
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return args_to_array(arguments);
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}
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function recalculate_prompts_img2img(){
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recalculatePromptTokens('img2img_prompt')
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recalculatePromptTokens('img2img_neg_prompt')
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return args_to_array(arguments);
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}
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opts = {}
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onUiUpdate(function(){
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if(Object.keys(opts).length != 0) return;
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@ -232,14 +254,12 @@ onUiUpdate(function(){
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return
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}
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prompt.parentElement.insertBefore(counter, prompt)
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counter.classList.add("token-counter")
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prompt.parentElement.style.position = "relative"
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textarea.addEventListener("input", function(){
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update_token_counter(id_button);
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});
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promptTokecountUpdateFuncs[id] = function(){ update_token_counter(id_button); }
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textarea.addEventListener("input", promptTokecountUpdateFuncs[id]);
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}
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registerTextarea('txt2img_prompt', 'txt2img_token_counter', 'txt2img_token_button')
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@ -273,7 +293,7 @@ onOptionsChanged(function(){
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let txt2img_textarea, img2img_textarea = undefined;
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let wait_time = 800
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let token_timeout;
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let token_timeouts = {};
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function update_txt2img_tokens(...args) {
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update_token_counter("txt2img_token_button")
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@ -290,9 +310,9 @@ function update_img2img_tokens(...args) {
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}
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function update_token_counter(button_id) {
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if (token_timeout)
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clearTimeout(token_timeout);
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token_timeout = setTimeout(() => gradioApp().getElementById(button_id)?.click(), wait_time);
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if (token_timeouts[button_id])
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clearTimeout(token_timeouts[button_id]);
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token_timeouts[button_id] = setTimeout(() => gradioApp().getElementById(button_id)?.click(), wait_time);
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}
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function restart_reload(){
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@ -223,6 +223,7 @@ def prepare_environment():
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requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt")
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commandline_args = os.environ.get('COMMANDLINE_ARGS', "")
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xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.16rc425')
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gfpgan_package = os.environ.get('GFPGAN_PACKAGE', "git+https://github.com/TencentARC/GFPGAN.git@8d2447a2d918f8eba5a4a01463fd48e45126a379")
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clip_package = os.environ.get('CLIP_PACKAGE', "git+https://github.com/openai/CLIP.git@d50d76daa670286dd6cacf3bcd80b5e4823fc8e1")
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openclip_package = os.environ.get('OPENCLIP_PACKAGE', "git+https://github.com/mlfoundations/open_clip.git@bb6e834e9c70d9c27d0dc3ecedeebeaeb1ffad6b")
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@ -282,7 +283,7 @@ def prepare_environment():
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if (not is_installed("xformers") or reinstall_xformers) and xformers:
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if platform.system() == "Windows":
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if platform.python_version().startswith("3.10"):
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run_pip(f"install -U -I --no-deps xformers==0.0.16rc425", "xformers")
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run_pip(f"install -U -I --no-deps {xformers_package}", "xformers")
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else:
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print("Installation of xformers is not supported in this version of Python.")
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print("You can also check this and build manually: https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers#building-xformers-on-windows-by-duckness")
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@ -1,4 +1,5 @@
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import base64
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import html
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import io
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import math
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import os
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@ -16,13 +17,23 @@ re_param = re.compile(re_param_code)
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re_imagesize = re.compile(r"^(\d+)x(\d+)$")
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re_hypernet_hash = re.compile("\(([0-9a-f]+)\)$")
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type_of_gr_update = type(gr.update())
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paste_fields = {}
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bind_list = []
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registered_param_bindings = []
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class ParamBinding:
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def __init__(self, paste_button, tabname, source_text_component=None, source_image_component=None, source_tabname=None, override_settings_component=None):
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self.paste_button = paste_button
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self.tabname = tabname
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self.source_text_component = source_text_component
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self.source_image_component = source_image_component
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self.source_tabname = source_tabname
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self.override_settings_component = override_settings_component
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def reset():
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paste_fields.clear()
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bind_list.clear()
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def quote(text):
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@ -74,26 +85,6 @@ def add_paste_fields(tabname, init_img, fields):
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modules.ui.img2img_paste_fields = fields
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def integrate_settings_paste_fields(component_dict):
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from modules import ui
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settings_map = {
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'CLIP_stop_at_last_layers': 'Clip skip',
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'inpainting_mask_weight': 'Conditional mask weight',
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'sd_model_checkpoint': 'Model hash',
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'eta_noise_seed_delta': 'ENSD',
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'initial_noise_multiplier': 'Noise multiplier',
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}
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settings_paste_fields = [
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(component_dict[k], lambda d, k=k, v=v: ui.apply_setting(k, d.get(v, None)))
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for k, v in settings_map.items()
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]
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for tabname, info in paste_fields.items():
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if info["fields"] is not None:
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info["fields"] += settings_paste_fields
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def create_buttons(tabs_list):
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buttons = {}
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for tab in tabs_list:
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@ -101,9 +92,60 @@ def create_buttons(tabs_list):
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return buttons
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#if send_generate_info is a tab name, mean generate_info comes from the params fields of the tab
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def bind_buttons(buttons, send_image, send_generate_info):
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bind_list.append([buttons, send_image, send_generate_info])
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"""old function for backwards compatibility; do not use this, use register_paste_params_button"""
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for tabname, button in buttons.items():
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source_text_component = send_generate_info if isinstance(send_generate_info, gr.components.Component) else None
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source_tabname = send_generate_info if isinstance(send_generate_info, str) else None
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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))
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def register_paste_params_button(binding: ParamBinding):
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registered_param_bindings.append(binding)
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def connect_paste_params_buttons():
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binding: ParamBinding
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for binding in registered_param_bindings:
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destination_image_component = paste_fields[binding.tabname]["init_img"]
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fields = paste_fields[binding.tabname]["fields"]
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destination_width_component = next(iter([field for field, name in fields if name == "Size-1"] if fields else []), None)
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destination_height_component = next(iter([field for field, name in fields if name == "Size-2"] if fields else []), None)
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if binding.source_image_component and destination_image_component:
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if isinstance(binding.source_image_component, gr.Gallery):
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func = send_image_and_dimensions if destination_width_component else image_from_url_text
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jsfunc = "extract_image_from_gallery"
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else:
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func = send_image_and_dimensions if destination_width_component else lambda x: x
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jsfunc = None
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binding.paste_button.click(
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fn=func,
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_js=jsfunc,
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inputs=[binding.source_image_component],
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outputs=[destination_image_component, destination_width_component, destination_height_component] if destination_width_component else [destination_image_component],
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)
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if binding.source_text_component is not None and fields is not None:
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connect_paste(binding.paste_button, fields, binding.source_text_component, binding.override_settings_component, binding.tabname)
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if binding.source_tabname is not None and fields is not None:
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paste_field_names = ['Prompt', 'Negative prompt', 'Steps', 'Face restoration'] + (["Seed"] if shared.opts.send_seed else [])
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binding.paste_button.click(
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fn=lambda *x: x,
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inputs=[field for field, name in paste_fields[binding.source_tabname]["fields"] if name in paste_field_names],
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outputs=[field for field, name in fields if name in paste_field_names],
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)
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binding.paste_button.click(
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fn=None,
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_js=f"switch_to_{binding.tabname}",
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inputs=None,
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outputs=None,
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)
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def send_image_and_dimensions(x):
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@ -122,49 +164,6 @@ def send_image_and_dimensions(x):
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return img, w, h
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def run_bind():
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for buttons, source_image_component, send_generate_info in bind_list:
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for tab in buttons:
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button = buttons[tab]
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destination_image_component = paste_fields[tab]["init_img"]
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fields = paste_fields[tab]["fields"]
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destination_width_component = next(iter([field for field, name in fields if name == "Size-1"] if fields else []), None)
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destination_height_component = next(iter([field for field, name in fields if name == "Size-2"] if fields else []), None)
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if source_image_component and destination_image_component:
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if isinstance(source_image_component, gr.Gallery):
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func = send_image_and_dimensions if destination_width_component else image_from_url_text
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jsfunc = "extract_image_from_gallery"
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else:
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func = send_image_and_dimensions if destination_width_component else lambda x: x
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jsfunc = None
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button.click(
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fn=func,
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_js=jsfunc,
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inputs=[source_image_component],
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outputs=[destination_image_component, destination_width_component, destination_height_component] if destination_width_component else [destination_image_component],
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)
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if send_generate_info and fields is not None:
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if send_generate_info in paste_fields:
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paste_field_names = ['Prompt', 'Negative prompt', 'Steps', 'Face restoration'] + (["Seed"] if shared.opts.send_seed else [])
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button.click(
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fn=lambda *x: x,
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inputs=[field for field, name in paste_fields[send_generate_info]["fields"] if name in paste_field_names],
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outputs=[field for field, name in fields if name in paste_field_names],
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)
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else:
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connect_paste(button, fields, send_generate_info)
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button.click(
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fn=None,
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_js=f"switch_to_{tab}",
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inputs=None,
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outputs=None,
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)
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def find_hypernetwork_key(hypernet_name, hypernet_hash=None):
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"""Determines the config parameter name to use for the hypernet based on the parameters in the infotext.
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@ -290,7 +289,50 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
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return res
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def connect_paste(button, paste_fields, input_comp, jsfunc=None):
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settings_map = {}
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infotext_to_setting_name_mapping = [
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('Clip skip', 'CLIP_stop_at_last_layers', ),
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('Conditional mask weight', 'inpainting_mask_weight'),
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('Model hash', 'sd_model_checkpoint'),
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('ENSD', 'eta_noise_seed_delta'),
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('Noise multiplier', 'initial_noise_multiplier'),
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('Eta', 'eta_ancestral'),
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('Eta DDIM', 'eta_ddim'),
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('Discard penultimate sigma', 'always_discard_next_to_last_sigma')
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]
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def create_override_settings_dict(text_pairs):
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"""creates processing's override_settings parameters from gradio's multiselect
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Example input:
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['Clip skip: 2', 'Model hash: e6e99610c4', 'ENSD: 31337']
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Example output:
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{'CLIP_stop_at_last_layers': 2, 'sd_model_checkpoint': 'e6e99610c4', 'eta_noise_seed_delta': 31337}
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"""
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res = {}
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params = {}
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for pair in text_pairs:
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k, v = pair.split(":", maxsplit=1)
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params[k] = v.strip()
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for param_name, setting_name in infotext_to_setting_name_mapping:
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value = params.get(param_name, None)
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if value is None:
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continue
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res[setting_name] = shared.opts.cast_value(setting_name, value)
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return res
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def connect_paste(button, paste_fields, input_comp, override_settings_component, tabname):
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def paste_func(prompt):
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if not prompt and not shared.cmd_opts.hide_ui_dir_config:
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filename = os.path.join(data_path, "params.txt")
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@ -327,9 +369,35 @@ def connect_paste(button, paste_fields, input_comp, jsfunc=None):
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return res
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if override_settings_component is not None:
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def paste_settings(params):
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vals = {}
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for param_name, setting_name in infotext_to_setting_name_mapping:
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v = params.get(param_name, None)
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if v is None:
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continue
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if setting_name == "sd_model_checkpoint" and shared.opts.disable_weights_auto_swap:
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continue
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v = shared.opts.cast_value(setting_name, v)
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current_value = getattr(shared.opts, setting_name, None)
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if v == current_value:
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continue
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vals[param_name] = v
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vals_pairs = [f"{k}: {v}" for k, v in vals.items()]
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return gr.Dropdown.update(value=vals_pairs, choices=vals_pairs, visible=len(vals_pairs) > 0)
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paste_fields = paste_fields + [(override_settings_component, paste_settings)]
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button.click(
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fn=paste_func,
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_js=jsfunc,
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_js=f"recalculate_prompts_{tabname}",
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inputs=[input_comp],
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outputs=[x[0] for x in paste_fields],
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)
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@ -7,6 +7,7 @@ import numpy as np
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from PIL import Image, ImageOps, ImageFilter, ImageEnhance, ImageChops
|
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from modules import devices, sd_samplers
|
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from modules.generation_parameters_copypaste import create_override_settings_dict
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from modules.processing import Processed, StableDiffusionProcessingImg2Img, process_images
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from modules.shared import opts, state
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import modules.shared as shared
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@ -75,7 +76,9 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args):
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processed_image.save(os.path.join(output_dir, filename))
|
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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):
|
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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):
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override_settings = create_override_settings_dict(override_settings_texts)
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is_batch = mode == 5
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if mode == 0: # img2img
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@ -142,6 +145,7 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
|
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inpaint_full_res=inpaint_full_res,
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inpaint_full_res_padding=inpaint_full_res_padding,
|
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inpainting_mask_invert=inpainting_mask_invert,
|
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override_settings=override_settings,
|
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)
|
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|
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p.scripts = modules.scripts.scripts_txt2img
|
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|
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@ -455,7 +455,6 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
|
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"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}"),
|
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"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),
|
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"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,
|
||||
}
|
||||
|
||||
@ -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
|
||||
|
||||
|
||||
78
modules/sd_samplers_common.py
Normal file
78
modules/sd_samplers_common.py
Normal file
@ -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
|
||||
|
||||
160
modules/sd_samplers_compvis.py
Normal file
160
modules/sd_samplers_compvis.py
Normal file
@ -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
|
||||
298
modules/sd_samplers_kdiffusion.py
Normal file
298
modules/sd_samplers_kdiffusion.py
Normal file
@ -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
|
||||
|
||||
@ -105,6 +105,8 @@ parser.add_argument("--tls-keyfile", type=str, help="Partially enables TLS, requ
|
||||
parser.add_argument("--tls-certfile", type=str, help="Partially enables TLS, requires --tls-keyfile to fully function", default=None)
|
||||
parser.add_argument("--server-name", type=str, help="Sets hostname of server", default=None)
|
||||
parser.add_argument("--gradio-queue", action='store_true', help="Uses gradio queue; experimental option; breaks restart UI button")
|
||||
parser.add_argument("--skip-version-check", action='store_true', help="Do not check versions of torch and xformers")
|
||||
|
||||
|
||||
|
||||
script_loading.preload_extensions(extensions.extensions_dir, parser)
|
||||
@ -127,12 +129,13 @@ restricted_opts = {
|
||||
ui_reorder_categories = [
|
||||
"inpaint",
|
||||
"sampler",
|
||||
"checkboxes",
|
||||
"hires_fix",
|
||||
"dimensions",
|
||||
"cfg",
|
||||
"seed",
|
||||
"checkboxes",
|
||||
"hires_fix",
|
||||
"batch",
|
||||
"override_settings",
|
||||
"scripts",
|
||||
]
|
||||
|
||||
@ -346,10 +349,10 @@ 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}),
|
||||
}))
|
||||
|
||||
@ -440,7 +443,7 @@ 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"),
|
||||
@ -605,11 +608,37 @@ 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},
|
||||
|
||||
@ -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, hr_sampler_index: int, hr_prompt: str, hr_negative_prompt, *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, hr_sampler_index: int, hr_prompt: str, hr_negative_prompt, 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,
|
||||
@ -40,7 +43,8 @@ def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, step
|
||||
hr_resize_y=hr_resize_y,
|
||||
hr_sampler=sd_samplers.samplers_for_img2img[hr_sampler_index - 1].name if hr_sampler_index != 0 else '---',
|
||||
hr_prompt=hr_prompt,
|
||||
hr_negative_prompt=hr_negative_prompt
|
||||
hr_negative_prompt=hr_negative_prompt,
|
||||
override_settings=override_settings,
|
||||
)
|
||||
|
||||
p.scripts = modules.scripts.scripts_txt2img
|
||||
|
||||
@ -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
|
||||
@ -514,6 +527,10 @@ 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()
|
||||
@ -535,7 +552,6 @@ 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)
|
||||
@ -569,6 +585,8 @@ def create_ui():
|
||||
hr_sampler_index,
|
||||
hr_prompt,
|
||||
hr_negative_prompt,
|
||||
override_settings,
|
||||
|
||||
] + custom_inputs,
|
||||
|
||||
outputs=[
|
||||
@ -632,6 +650,9 @@ 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,
|
||||
@ -779,6 +800,10 @@ 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()
|
||||
@ -813,7 +838,6 @@ 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)
|
||||
@ -866,7 +890,8 @@ 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,
|
||||
@ -954,6 +979,9 @@ 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
|
||||
|
||||
@ -971,7 +999,11 @@ 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),
|
||||
@ -1380,6 +1412,7 @@ def create_ui():
|
||||
|
||||
components = []
|
||||
component_dict = {}
|
||||
shared.settings_components = component_dict
|
||||
|
||||
script_callbacks.ui_settings_callback()
|
||||
opts.reorder()
|
||||
@ -1546,8 +1579,7 @@ 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:
|
||||
|
||||
@ -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
|
||||
|
||||
@ -14,6 +14,7 @@ class ExtraNetworksPageCheckpoints(ui_extra_networks.ExtraNetworksPage):
|
||||
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"]
|
||||
@ -28,7 +29,7 @@ class ExtraNetworksPageCheckpoints(ui_extra_networks.ExtraNetworksPage):
|
||||
"name": checkpoint.name_for_extra,
|
||||
"filename": path,
|
||||
"preview": preview,
|
||||
"search_term": self.search_terms_from_path(checkpoint.filename),
|
||||
"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",
|
||||
}
|
||||
|
||||
10
webui.py
10
webui.py
@ -52,6 +52,9 @@ else:
|
||||
|
||||
|
||||
def check_versions():
|
||||
if shared.cmd_opts.skip_version_check:
|
||||
return
|
||||
|
||||
expected_torch_version = "1.13.1"
|
||||
|
||||
if version.parse(torch.__version__) < version.parse(expected_torch_version):
|
||||
@ -59,7 +62,10 @@ def check_versions():
|
||||
You are running torch {torch.__version__}.
|
||||
The program is tested to work with torch {expected_torch_version}.
|
||||
To reinstall the desired version, run with commandline flag --reinstall-torch.
|
||||
Beware that this will cause a lot of large files to be downloaded.
|
||||
Beware that this will cause a lot of large files to be downloaded, as well as
|
||||
there are reports of issues with training tab on the latest version.
|
||||
|
||||
Use --skip-version-check commandline argument to disable this check.
|
||||
""".strip())
|
||||
|
||||
expected_xformers_version = "0.0.16rc425"
|
||||
@ -71,6 +77,8 @@ Beware that this will cause a lot of large files to be downloaded.
|
||||
You are running xformers {xformers.__version__}.
|
||||
The program is tested to work with xformers {expected_xformers_version}.
|
||||
To reinstall the desired version, run with commandline flag --reinstall-xformers.
|
||||
|
||||
Use --skip-version-check commandline argument to disable this check.
|
||||
""".strip())
|
||||
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user