Merge remote-tracking branch 'upstream/master' into sub-quad_attn_opt

modules/shared.py

@@ -14,7 +14,7 @@ import modules.interrogate
 import modules.memmon
 import modules.styles
 import modules.devices as devices
-from modules import localization, sd_vae, extensions, script_loading
+from modules import localization, sd_vae, extensions, script_loading, errors
 from modules.paths import models_path, script_path, sd_path
 
 
@@ -86,6 +86,7 @@ parser.add_argument('--vae-path', type=str, help='Path to Variational Autoencode
 parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False)
 parser.add_argument("--api", action='store_true', help="use api=True to launch the API together with the webui (use --nowebui instead for only the API)")
 parser.add_argument("--api-auth", type=str, help='Set authentication for API like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None)
+parser.add_argument("--api-log", action='store_true', help="use api-log=True to enable logging of all API requests")
 parser.add_argument("--nowebui", action='store_true', help="use api=True to launch the API instead of the webui")
 parser.add_argument("--ui-debug-mode", action='store_true', help="Don't load model to quickly launch UI")
 parser.add_argument("--device-id", type=str, help="Select the default CUDA device to use (export CUDA_VISIBLE_DEVICES=0,1,etc might be needed before)", default=None)
@@ -156,6 +157,7 @@ class State:
     job = ""
     job_no = 0
     job_count = 0
+    processing_has_refined_job_count = False
     job_timestamp = '0'
     sampling_step = 0
     sampling_steps = 0
@@ -186,6 +188,7 @@ class State:
             "interrupted": self.interrupted,
             "job": self.job,
             "job_count": self.job_count,
+            "job_timestamp": self.job_timestamp,
             "job_no": self.job_no,
             "sampling_step": self.sampling_step,
             "sampling_steps": self.sampling_steps,
@@ -196,6 +199,7 @@ class State:
     def begin(self):
         self.sampling_step = 0
         self.job_count = -1
+        self.processing_has_refined_job_count = False
         self.job_no = 0
         self.job_timestamp = datetime.datetime.now().strftime("%Y%m%d%H%M%S")
         self.current_latent = None
@@ -216,12 +220,13 @@ class State:
 
     """sets self.current_image from self.current_latent if enough sampling steps have been made after the last call to this"""
     def set_current_image(self):
+        if not parallel_processing_allowed:
+            return
+
         if self.sampling_step - self.current_image_sampling_step >= opts.show_progress_every_n_steps and opts.show_progress_every_n_steps > 0:
             self.do_set_current_image()
 
     def do_set_current_image(self):
-        if not parallel_processing_allowed:
-            return
         if self.current_latent is None:
             return
 
@@ -233,6 +238,7 @@ class State:
 
         self.current_image_sampling_step = self.sampling_step
 
+
 state = State()
 
 artist_db = modules.artists.ArtistsDatabase(os.path.join(script_path, 'artists.csv'))
@@ -359,7 +365,7 @@ options_templates.update(options_section(('system', "System"), {
 options_templates.update(options_section(('training', "Training"), {
     "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."),
     "pin_memory": OptionInfo(False, "Turn on pin_memory for DataLoader. Makes training slightly faster but can increase memory usage."),
-    "save_optimizer_state": OptionInfo(False, "Saves Optimizer state as separate *.optim file. Training can be resumed with HN itself and matching optim file."),
+    "save_optimizer_state": OptionInfo(False, "Saves Optimizer state as separate *.optim file. Training of embedding or HN can be resumed with the matching optim file."),
     "dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
     "dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
     "training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}),
@@ -498,7 +504,12 @@ class Options:
             return False
 
         if self.data_labels[key].onchange is not None:
-            self.data_labels[key].onchange()
+            try:
+                self.data_labels[key].onchange()
+            except Exception as e:
+                errors.display(e, f"changing setting {key} to {value}")
+                setattr(self, key, oldval)
+                return False
 
         return True
 
@@ -563,8 +574,11 @@ if os.path.exists(config_filename):
 
 latent_upscale_default_mode = "Latent"
 latent_upscale_modes = {
-    "Latent": "bilinear",
-    "Latent (nearest)": "nearest",
+    "Latent": {"mode": "bilinear", "antialias": False},
+    "Latent (antialiased)": {"mode": "bilinear", "antialias": True},
+    "Latent (bicubic)": {"mode": "bicubic", "antialias": False},
+    "Latent (bicubic antialiased)": {"mode": "bicubic", "antialias": True},
+    "Latent (nearest)": {"mode": "nearest", "antialias": False},
 }
 
 sd_upscalers = []
@@ -600,7 +614,7 @@ class TotalTQDM:
             return
         if self._tqdm is None:
             self.reset()
-        self._tqdm.total=new_total
+        self._tqdm.total = new_total
 
     def clear(self):
         if self._tqdm is not None: