memory optimization for CLIP interrogator

changed default cfg_scale to a higher value

1 files changed, 23 insertions, 5 deletions

diff --git a/modules/interrogate.py b/modules/interrogate.py
index 7ebb79fc..1579c042 100644
--- a/modules/interrogate.py
+++ b/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)

 

-            devices.torch_gc()

+    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