Iterate seed after apply

ai_diffusion/backend/workflow.py

@@ -61,6 +61,8 @@ def generate_seed():
     # Currently only using 32 bit because Qt widgets don't support int64
     return random.randint(0, 2**32 - 1)
 
+def iterate_seed(old_seed):
+    return (old_seed + 1) % (2**32 - 1)
 
 def sampling_from_style(style: Style, strength: float, is_live: bool):
     sampler_name = style.live_sampler if is_live else style.sampler

ai_diffusion/model/model.py

@@ -928,6 +928,9 @@
     def generate_seed(self):
         self.seed = workflow.generate_seed()
 
+    def iterate_seed(self):
+        self.seed = workflow.iterate_seed(self.seed)
+
     def save_result(self, job_id: str, index: int):
         _save_job_result(self, self.jobs.find(job_id), index)
 
@@ -1322,9 +1325,12 @@
             region_behavior = ApplyRegionBehavior.layer_group
         self.model.apply_result(self.result, params, behavior, region_behavior)
 
-        if settings.new_seed_after_apply:
+        if settings.new_seed_after_apply == random:
             self.model.generate_seed()
 
+        if settings.new_seed_after_apply == iterate:
+            self.model.iterate_seed()
+
     @property
     def result(self):
         return self._result

ai_diffusion/settings.py

@@ -122,6 +122,11 @@
             return ImageFileFormat.jpeg
         return self
 
+class NewSeedMode(Enum):
+    disabled = (_("Disabled"), 0)
+    random = (_("Random"), 1)
+    iterate = (_("Iterate"), 2)
+
 
 class PerformancePresetSettings(NamedTuple):
     batch_size: int = 4
@@ -256,10 +261,10 @@
         _("NSFW Filter"), 0.0, _("Attempt to filter out images with explicit content")
     )
 
-    new_seed_after_apply: bool
+    new_seed_after_apply: NewSeedMode
     _new_seed_after_apply = Setting(
         _("Live: New Seed after Apply"),
-        False,
+        undefined,
         _("Pick a new seed after copying the result to the canvas in Live mode"),
     )