Minor bug fixes

Author: flowstatelabs

FlowStateLatentChooser.py

@@ -137,28 +137,41 @@ def create_latent(self, model_type, resolution, orientation, latent_type, image,
         print(f'\n\n\nFlowState Latent Chooser - {model_type}')
 
         selected_model = model_type if isinstance(model_type, str) else model_type[0]
+        loaded_latent, loaded_img = self.load_and_encode(image, vae)
+
+        using_custom = resolution == 'custom'
+        horizontal_img = orientation == 'horizontal'
+        using_empty = latent_type == 'empty_latent'
+        using_input = latent_type == 'input_img'
+        have_pixels = pixels != None
+
+        width_to_use = loaded_img.shape[2]
+        height_to_use = loaded_img.shape[1]
+        res = None
+        res_split = None
+
+        if using_custom and using_empty:
+            width_to_use = custom_width
+            height_to_use = custom_height
+
+        if not using_custom and using_empty:
+            res_split = res.split(' - ')[0].split('x')
+            width_to_use = int(res_split[0]) if horizontal_img else int(res_split[1])
+            height_to_use = int(res_split[1]) if horizontal_img else int(res_split[0])
+
+        if using_input and have_pixels:
+            width_to_use = pixels.shape[2]
+            height_to_use = pixels.shape[1]
 
         if latent_type == 'empty_latent':
             print(f'  - Preparing empty latent.\n')
-            using_custom = resolution == 'custom'
-            horizontal_img = orientation == 'horizontal'
-            aspect_split = resolution.split(' - ')[0].split('x')
-            aspect_w = int(aspect_split[0])
-            aspect_h = int(aspect_split[1])
-            width_to_use = custom_width if using_custom else (aspect_w if horizontal_img else aspect_h)
-            height_to_use = custom_height if using_custom else (aspect_h if horizontal_img else aspect_w)
-            latent = self.generate(width_to_use, height_to_use, selected_model)
-            return ({'samples':latent}, vae, image, width_to_use, height_to_use, )
+            generated_latent = self.generate(width_to_use, height_to_use, selected_model)
+            return ({'samples': generated_latent}, vae, loaded_img, width_to_use, height_to_use, )
         elif latent_type == 'input_img':
             print(f'  - Preparing latent from input image.')
-            latent = vae.encode(pixels[:,:,:,:3])
-            img_width = pixels.shape[2]
-            img_height = pixels.shape[1]
-            return ({'samples':latent}, vae, pixels, img_width, img_height, )
+            input_latent = vae.encode(pixels[:,:,:,:3])
+            return ({'samples': input_latent}, vae, pixels, width_to_use, height_to_use, )
         else:
             print(f'  - Preparing latent from imported image.')
-            latent, loaded_img = self.load_and_encode(image, vae)
-            img_width = loaded_img.shape[2]
-            img_height = loaded_img.shape[1]
-            return ({'samples':latent}, vae, loaded_img, img_width, img_height, )
+            return ({'samples': loaded_latent}, vae, loaded_img, width_to_use, height_to_use, )
 

FlowStateUnifiedStyler.py

@@ -267,15 +267,15 @@ def apply_style(self,
 
         styler_duration, styler_mins, styler_secs = get_mins_and_secs(start_time)
 
-        # model_mem_sizes = [ m.size for m in styler_out[0] ]
-        # kb = [ round(mem / 1024, 2) for mem in model_mem_sizes ]
-        # mb = [ round(mem / 1024, 2) for mem in kb ]
-        # gb = [ round(mem / 1024, 2) for mem in mb ]
+        model_mem_sizes = [ m.size for m in styler_out[0] ]
+        kb = [ round(mem / 1024, 2) for mem in model_mem_sizes ]
+        mb = [ round(mem / 1024, 2) for mem in kb ]
+        gb = [ round(mem / 1024, 2) for mem in mb ]
 
         print(
             f'\n\n\nFlowState Unified Styler - Styling generation complete.'
             f'\n  - Styling Time: {styler_mins}m {styler_secs}s'
-            # f'\n  - Model Sizes (bytes): {model_mem_sizes}'
+            # f'\n  - Model Sizes (bytes): {image}'
             # f'\n  - Model Sizes (kb): {kb}'
             # f'\n  - Model Sizes (mb): {mb}'
             # f'\n  - Model Sizes (gb): {gb}'

README.md

@@ -57,15 +57,16 @@ COMING SOON: Install through the ComfyUI Custom Node Manager!
 <br/><br/>
 
 
-# LATEST PUSH 10/29/24 - 11:15 am (GMT-5) #
+# 1.1.2 RELEASE (LATEST) 10/29/24 - 1:30 pm (GMT-5) #
 
 ## Updates
 * Introduction of orientation selector
 * Fixed "unload_loras" bug
 * Converted static model directories to dynamic, so no restart required when downloading new models or changing filenames
+* Fixed issue with custom resolution inputs
 
 
-# PUSH 10/27/24 - 3:00 pm (GMT-5) #
+# 1.1.0 RELEASE 10/27/24 - 3:00 pm (GMT-5) #
 
 ## Updates
 * Video Sampler node is available but still experimental

imgs/FlowState-Custom-Nodes-Banner.png

@@ -1,7 +1,7 @@
 [project]
 name = "flowstate-nodes"
 description = "A collection of custom nodes to consolidate functionality of many other nodes, enable unified workflows, and add custom functionality you won't find with other nodes."
-version = "1.1.1"
+version = "1.1.2"
 license = {file = "LICENSE"}
 dependencies = [
     "transformers>=4.0.0",