Add CSV Save + BF16 upscale

__init__.py

@@ -50,6 +50,7 @@ from .nodes.FL_KsamplerPlus import FL_KsamplerPlus
 from .nodes.FL_KsamplerBasic import FL_KsamplerBasic
 from .nodes.FL_KsamplerFractals import FL_FractalKSampler
 from .nodes.FL_UpscaleModel import FL_UpscaleModel
+from .nodes.FL_SaveCSV import FL_SaveCSV
 
 
 
@@ -107,6 +108,7 @@ NODE_CLASS_MAPPINGS = {
     "FL_KsamplerBasic": FL_KsamplerBasic,
     "FL_FractalKSampler": FL_FractalKSampler,
     "FL_UpscaleModel": FL_UpscaleModel,
+    "FL_SaveCSV": FL_SaveCSV,
 
 }
 
@@ -163,6 +165,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "FL_KsamplerBasic": "FL KSampler Basic",
     "FL_FractalKSampler": "FL Fractal KSampler",
     "FL_UpscaleModel": "FL Upscale Model",
+    "FL_SaveCSV": "FL Save CSV",
 
 }
 

nodes/FL_SaveCSV.py

@@ -0,0 +1,43 @@
+import os
+import comfy.utils
+
+class FL_SaveCSV:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "csv_data": ("CSV",),
+                "output_directory": ("STRING", {"default": ""}),
+                "filename": ("STRING", {"default": "captions.csv"}),
+            },
+        }
+
+    RETURN_TYPES = ()
+    FUNCTION = "save_csv"
+    CATEGORY = "🏵️Fill Nodes/Captioning"
+    OUTPUT_NODE = True
+
+    def save_csv(self, csv_data, output_directory, filename):
+        # Ensure the output directory exists
+        os.makedirs(output_directory, exist_ok=True)
+
+        # Construct the full file path
+        file_path = os.path.join(output_directory, filename)
+
+        # Ensure the filename ends with .csv
+        if not file_path.lower().endswith('.csv'):
+            file_path += '.csv'
+
+        # Write the CSV data to the file
+        try:
+            with open(file_path, 'wb') as f:
+                f.write(csv_data)
+            print(f"CSV file saved successfully: {file_path}")
+        except Exception as e:
+            print(f"Error saving CSV file: {str(e)}")
+
+        return ()
+
+    @classmethod
+    def IS_CHANGED(cls, csv_data, output_directory, filename):
+        return float("NaN")

nodes/FL_UpscaleModel.py

@@ -3,10 +3,9 @@ import comfy
 from comfy_extras.nodes_upscale_model import ImageUpscaleWithModel
 from tqdm import tqdm
 
-
 class FL_UpscaleModel:
     rescale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"]
-    precision_options = ["16", "32"]
+    precision_options = ["auto", "32", "16", "bfloat16"]
 
     RETURN_TYPES = ("IMAGE",)
     FUNCTION = "upscale"
@@ -42,11 +41,21 @@ class FL_UpscaleModel:
         original_device = image.device
         original_dtype = image.dtype
 
-        if precision == "16":
-            dtype = torch.float16
+        # Determine the appropriate dtype based on precision and device
+        if precision == "auto":
+            dtype = torch.float16 if original_device.type == "cuda" else torch.float32
+        elif precision == "16":
+            dtype = torch.float16 if original_device.type == "cuda" else torch.bfloat16
+        elif precision == "bfloat16":
+            dtype = torch.bfloat16
         else:
             dtype = torch.float32
 
+        # Ensure the chosen dtype is supported on the current device
+        if dtype == torch.float16 and original_device.type != "cuda":
+            print("Warning: float16 is not supported on CPU. Falling back to bfloat16.")
+            dtype = torch.bfloat16
+
         upscale_model = upscale_model.to(dtype).to(original_device)
 
         # Split the input batch into a list of individual images
@@ -62,7 +71,7 @@ class FL_UpscaleModel:
             batch = torch.cat(image_list[i:i + batch_size]).to(dtype)
 
             with torch.no_grad():
-                if dtype == torch.float16:
+                if dtype in [torch.float16, torch.bfloat16]:
                     with torch.autocast(device_type=original_device.type, dtype=dtype):
                         upscaled_batch = self.__imageScaler.upscale(upscale_model, batch)[0]
                 else: