add quicksettings reset button

Signed-off-by: Vladimir Mandic <mandic00@live.com>

CHANGELOG.md

@@ -14,6 +14,8 @@
   - new embedded docs/wiki search!  
     **Docs** search: fully-local and works in real-time on all document pages  
     **Wiki** search: uses github api to search online wiki pages  
+  - quicksettings reset button to restore all quicksettings to default values  
+    because things do sometimes get wrong...  
   - modernui checkbox/radio styling  
 - **Offloading**
   - changed **default** values for offloading based on detected gpu memory  

TODO.md

@@ -31,13 +31,14 @@ Main ToDo list can be found at [GitHub projects](https://github.com/users/vladma
 
 ## ModernUI
 
-- Extensions tab: Full CSS
+- Extensions tab:
+  - full CSS redesign
 - Models tab: 
-  - Current
   - Validate: broken table
   - Update: broken table
   - CivitAI: redesign downloader
-- History
+- History tab:
+  - empty?
 
 ### Under Consideration
 

html/locale_en.json

@@ -13,6 +13,7 @@
   {"id":"","label":"※","localized":"","hint":"Load model as refiner model when selected, otherwise load as base model"},
   {"id":"","label":"🔎︎","localized":"","hint":"Scan CivitAI for missing metadata and previews"},
   {"id":"","label":"☲","localized":"","hint":"Change view type"},
+  {"id":"","label":"⊗","localized":"","hint":"Reset values"},
   {"id":"","label":"📐","localized":"","hint":"Measure"},
   {"id":"","label":"🔍","localized":"","hint":"Search"},
   {"id":"","label":"🖌️","localized":"","hint":"LaMa remove selected object from image"},

javascript/sdnext.css

@@ -680,8 +680,19 @@ div#extras_scale_to_tab div.form {
 
 #quicksettings>button {
   align-self: end;
-  margin-bottom: 6px;
-  padding: 0 1em 0 0;
+  height: 1.3em;
+  margin: 0em 0.7em 0.3em -0.5em;
+  line-height: 1em;
+  background-color: var(--background-fill-primary);
+}
+
+#quicksettings>button:hover {
+  background-color: var(--button-cancel-border-color-hover);
+}
+
+button#quicksettings_reset {
+  position: absolute;
+  right: 0;
 }
 
 #settings {

modules/ui_settings.py

@@ -1,6 +1,7 @@
 import os
 import gradio as gr
-from modules import timer, shared, paths, theme, sd_models, modelloader, ui_common, ui_loadsave, ui_history, generation_parameters_copypaste, call_queue, script_callbacks
+from modules import timer, shared, paths, theme, sd_models, modelloader, generation_parameters_copypaste, call_queue, script_callbacks
+from modules import ui_common, ui_loadsave, ui_history, ui_components, ui_symbols
 
 
 text_settings = None # holds json of entire shared.opts
@@ -275,6 +276,15 @@ def create_ui():
     shutdown_submit.click(fn=lambda: shared.restart_server(restart=False), _js="restartReload")
 
 
+def reset_quicksettings(components):
+    components = components.split(',')
+    updates = []
+    for key in components:
+        shared.log.warning(f'Reset: setting={key}')
+        updates.append(gr.update(value=shared.opts.get_default(key)))
+    return updates
+
+
 def create_quicksettings(interfaces):
     shared.tab_names = []
     for _interface, label, _ifid in interfaces:
@@ -282,9 +292,16 @@ def create_quicksettings(interfaces):
 
     with gr.Blocks(theme=theme.gradio_theme, analytics_enabled=False, title="SD.Next") as ui_app:
         with gr.Row(elem_id="quicksettings", variant="compact"):
+            quicksetting_components = []
+            quicksetting_keys = []
             for k, _item in sorted(quicksettings_list, key=lambda x: quicksettings_names.get(x[1], x[0])):
                 component = create_setting_component(k, is_quicksettings=True)
+                quicksetting_components.append(component)
+                quicksetting_keys.append(k)
                 shared.settings_components[k] = component
+            quicksetting_keys = gr.State(value=','.join(quicksetting_keys), elem_id="quicksettings_keys")
+            btn_reset = ui_components.ToolButton(value=ui_symbols.clear, visible=True, elem_id="quicksettings_reset")
+            btn_reset.click(fn=reset_quicksettings, inputs=[quicksetting_keys], outputs=quicksetting_components)
 
         generation_parameters_copypaste.connect_paste_params_buttons()
 

pipelines/chroma/convert_chroma.py

@@ -0,0 +1,95 @@
+from rich import print as rprint
+rprint('starting chroma conversion')
+
+import os
+import torch
+import transformers
+import diffusers
+import huggingface_hub as hf
+from rich.traceback import install as install_traceback
+
+
+convert = True
+test = False
+upload = True
+input_files = [
+    'chroma-unlocked-v48.safetensors',
+    'chroma-unlocked-v48-detail-calibrated.safetensors',
+    'chroma-unlocked-v46-flash.safetensors',
+]
+input_folder = '/mnt/models/UNET'
+output_folder = '/mnt/models/Diffusers'
+cache_dir = '/mnt/models/huggingface'
+hf_token = ''
+dtype = torch.bfloat16
+device = torch.device('cuda')
+
+
+install_traceback(show_locals=False)
+rprint(f'torch={torch.__version__} diffusers={diffusers.__version__} transformers={transformers.__version__}')
+for input_file in input_files:
+    input_basename = os.path.splitext(input_file)[0]
+    input_model = os.path.join(input_folder, input_file)
+    output_model = os.path.join(output_folder, input_basename)
+
+    if convert:
+        rprint(f'load transformer: {input_model}')
+        transformer = diffusers.ChromaTransformer2DModel.from_single_file(
+            input_model,
+            torch_dtype=dtype,
+            cache_dir=cache_dir,
+        ).to(device)
+
+        rprint(f'load text-encoder')
+        text_encoder = transformers.T5EncoderModel.from_pretrained(
+            "black-forest-labs/FLUX.1-schnell",
+            subfolder="text_encoder_2",
+            torch_dtype=dtype,
+            cache_dir=cache_dir,
+        ).to(device)
+
+        rprint(f'load tokenizer')
+        tokenizer = transformers.T5Tokenizer.from_pretrained(
+            "black-forest-labs/FLUX.1-schnell",
+            subfolder="tokenizer_2",
+            cache_dir=cache_dir,
+        )
+
+        rprint(f'load pipeline')
+        pipe = diffusers.ChromaPipeline.from_pretrained(
+            "black-forest-labs/FLUX.1-dev",
+            transformer=transformer,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            torch_dtype=dtype,
+            cache_dir=cache_dir,
+        ).to(device)
+
+
+        rprint(f'save pipeline: {output_model}')
+        pipe.save_pretrained(
+            output_model,
+        )
+
+    if test:
+        rprint('test load')
+        pipe = diffusers.ChromaPipeline.from_pretrained(
+            output_model,
+            torch_dtype=dtype,
+            cache_dir=cache_dir,
+        )
+
+    if upload:
+        rprint('hf login')
+        hf.logout()
+        hf.login(token=hf_token, add_to_git_credential=False, write_permission=True)
+        rprint('upload model')
+        pipe.push_to_hub(
+            input_basename,
+            private=False,
+            token=hf_token,
+        )
+    
+    pipe = None
+
+rprint(f'done')