memory optimization for CLIP interrogator

changed default cfg_scale to a higher value

modules/interrogate.py

@@ -11,7 +11,7 @@ from torchvision import transforms
 from torchvision.transforms.functional import InterpolationMode
 
 import modules.shared as shared
-from modules import devices, paths
+from modules import devices, paths, lowvram
 
 blip_image_eval_size = 384
 blip_model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_caption_capfilt_large.pth'
@@ -75,19 +75,28 @@ class InterrogateModels:
 
         self.dtype = next(self.clip_model.parameters()).dtype
 
-    def unload(self):
+    def send_clip_to_ram(self):
         if not shared.opts.interrogate_keep_models_in_memory:
             if self.clip_model is not None:
                 self.clip_model = self.clip_model.to(devices.cpu)
 
+    def send_blip_to_ram(self):
+        if not shared.opts.interrogate_keep_models_in_memory:
             if self.blip_model is not None:
                 self.blip_model = self.blip_model.to(devices.cpu)
 
+    def unload(self):
+        self.send_clip_to_ram()
+        self.send_blip_to_ram()
+
         devices.torch_gc()
 
     def rank(self, image_features, text_array, top_count=1):
         import clip
 
+        if shared.opts.interrogate_clip_dict_limit != 0:
+            text_array = text_array[0:int(shared.opts.interrogate_clip_dict_limit)]
+
         top_count = min(top_count, len(text_array))
         text_tokens = clip.tokenize([text for text in text_array]).to(shared.device)
         text_features = self.clip_model.encode_text(text_tokens).type(self.dtype)
@@ -117,16 +126,24 @@ class InterrogateModels:
         res = None
 
         try:
+
+            if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
+                lowvram.send_everything_to_cpu()
+                devices.torch_gc()
+
             self.load()
 
             caption = self.generate_caption(pil_image)
+            self.send_blip_to_ram()
+            devices.torch_gc()
+
             res = caption
 
-            images = self.clip_preprocess(pil_image).unsqueeze(0).type(self.dtype).to(shared.device)
+            cilp_image = self.clip_preprocess(pil_image).unsqueeze(0).type(self.dtype).to(shared.device)
 
             precision_scope = torch.autocast if shared.cmd_opts.precision == "autocast" else contextlib.nullcontext
             with torch.no_grad(), precision_scope("cuda"):
-                image_features = self.clip_model.encode_image(images).type(self.dtype)
+                image_features = self.clip_model.encode_image(cilp_image).type(self.dtype)
 
                 image_features /= image_features.norm(dim=-1, keepdim=True)
 
@@ -146,4 +163,5 @@ class InterrogateModels:
 
         self.unload()
 
+        res += "<error>"
         return res

modules/lowvram.py

@@ -5,6 +5,16 @@ module_in_gpu = None
 cpu = torch.device("cpu")
 device = gpu = get_optimal_device()
 
+
+def send_everything_to_cpu():
+    global module_in_gpu
+
+    if module_in_gpu is not None:
+        module_in_gpu.to(cpu)
+
+    module_in_gpu = None
+
+
 def setup_for_low_vram(sd_model, use_medvram):
     parents = {}
 

modules/shared.py

@@ -132,6 +132,7 @@ class Options:
         "interrogate_clip_num_beams": OptionInfo(1, "Interrogate: num_beams for BLIP", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1}),
         "interrogate_clip_min_length": OptionInfo(24, "Interrogate: minimum descripton length (excluding artists, etc..)", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),
         "interrogate_clip_max_length": OptionInfo(48, "Interrogate: maximum descripton length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}),
+        "interrogate_clip_dict_limit": OptionInfo(1500, "Interrogate: maximum number of lines in text file (0 = No limit)"),
     }
 
     def __init__(self):

modules/ui.py

@@ -270,7 +270,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
                     batch_count = gr.Slider(minimum=1, maximum=cmd_opts.max_batch_count, step=1, label='Batch count', value=1)
                     batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)
 
-                cfg_scale = gr.Slider(minimum=1.0, maximum=15.0, step=0.5, label='CFG Scale', value=7.0)
+                cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0)
 
                 with gr.Group():
                     height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
@@ -413,7 +413,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
                     batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)
 
                 with gr.Group():
-                    cfg_scale = gr.Slider(minimum=1.0, maximum=15.0, step=0.5, label='CFG Scale', value=7.0)
+                    cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0)
                     denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75)
                     denoising_strength_change_factor = gr.Slider(minimum=0.9, maximum=1.1, step=0.01, label='Denoising strength change factor', value=1, visible=False)