better caption output

nodes.py

@@ -6,7 +6,7 @@ import matplotlib
 matplotlib.use('Agg')
 import matplotlib.pyplot as plt
 import matplotlib.patches as patches
-from PIL import Image, ImageDraw, ImageFont 
+from PIL import Image, ImageDraw 
 import random
 import numpy as np
 
@@ -23,7 +23,7 @@ def fixed_get_imports(filename: str | os.PathLike) -> list[str]:
 
 
 import comfy.model_management as mm
-from comfy.utils import ProgressBar, load_torch_file
+from comfy.utils import ProgressBar
 import folder_paths
 
 script_directory = os.path.dirname(os.path.abspath(__file__))
@@ -100,7 +100,7 @@ class Florence2Run:
                 "text_input": ("STRING", {"default": "", "multiline": True}),
                 "task": (
                     [ 
-                    'annotate',
+                    'region_caption',
                     'dense_region_caption',
                     'caption',
                     'detailed_caption',
@@ -134,7 +134,7 @@ class Florence2Run:
         colormap = ['blue','orange','green','purple','brown','pink','gray','olive','cyan','red',
                     'lime','indigo','violet','aqua','magenta','coral','gold','tan','skyblue']
 
-        if task == 'annotate':
+        if task == 'region_caption':
             prompt = "<OD>"
         elif task == 'dense_region_caption':
             prompt = '<DENSE_REGION_CAPTION>'
@@ -148,7 +148,10 @@ class Florence2Run:
             prompt = '<CAPTION_TO_PHRASE_GROUNDING>'
         elif task == 'referring_expression_segmentation': 
             prompt = '<REFERRING_EXPRESSION_SEGMENTATION>'
-        
+
+        if (task!= 'referring_expression_segmentation' and task!= 'caption_to_phrase_grounding') and text_input:
+            raise ValueError("text_input is only supported for 'referring_expression_segmentation' and 'caption_to_phrase_grounding'")
+
         if text_input is not None:
             prompt = prompt + text_input
 
@@ -171,8 +174,19 @@ class Florence2Run:
             )
 
             results = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
-            out_results.append(results)
-            if task == 'annotate' or task == 'dense_region_caption' or task == 'caption_to_phrase_grounding':
+
+            # cleanup the special tokens from the final list
+            clean_results = str(results)       
+            clean_results = clean_results.replace('</s>', '')
+            clean_results = clean_results.replace('<s>', '')
+
+            #return single string if only one image for compatibility with nodes that can't handle string lists
+            if len(image) == 1:
+                out_results = clean_results
+            else:
+                out_results.append(clean_results)
+
+            if task == 'region_caption' or task == 'dense_region_caption' or task == 'caption_to_phrase_grounding':
                 parsed_answer = processor.post_process_generation(results, task="<OD>", image_size=(image_pil.width, image_pil.height))
                 
                 fig, ax = plt.subplots(figsize=(image_pil.width / 100, image_pil.height / 100), dpi=100)
@@ -284,7 +298,6 @@ class Florence2Run:
             out_tensor = torch.cat(out, dim=0)
         else:
             out_tensor = torch.zeros((1, 64,64, 3), dtype=torch.float32, device="cpu")
-            print(out_tensor.shape)
         if len(out_masks) > 0:
             out_mask_tensor = torch.cat(out_masks, dim=0)
         else:
@@ -293,6 +306,7 @@ class Florence2Run:
         if not keep_model_loaded:
             print("Offloading model...")
             model.to(offload_device)
+            mm.soft_empty_cache()
         
         return (out_tensor, out_mask_tensor, out_results,)