fix interrogate api

CHANGELOG.md

@@ -34,6 +34,7 @@
   - xyz grid handle ip adapter name and scale  
   - lazy loading of image may prevent metadata from being loaded on time  
   - allow startup without valid models folder  
+  - fix interrogate api endpoint  
   - handle extensions that install conflicting versions of packages  
     `onnxruntime`, `opencv2-python`  
 

modules/api/endpoints.py

@@ -77,22 +77,28 @@ def post_interrogate(req: models.ReqInterrogate):
     image = helpers.decode_base64_to_image(req.image)
     image = image.convert('RGB')
     if req.model == "clip":
-        caption = shared.interrogator.interrogate(image)
-        return models.ResInterrogate(caption)
-    elif req.model == "deepdanbooru":
-        from mobules import deepbooru
+        try:
+            caption = shared.interrogator.interrogate(image)
+        except Exception as e:
+            caption = str(e)
+        return models.ResInterrogate(caption=caption)
+    elif req.model == "deepdanbooru" or req.model == 'deepbooru':
+        from modules import deepbooru
         caption = deepbooru.model.tag(image)
-        return models.ResInterrogate(caption)
+        return models.ResInterrogate(caption=caption)
     else:
         from modules.ui_interrogate import interrogate_image, analyze_image, get_models
         if req.model not in get_models():
             raise HTTPException(status_code=404, detail="Model not found")
-        caption = interrogate_image(image, model=req.model, mode=req.mode)
+        try:
+            caption = interrogate_image(image, model=req.model, mode=req.mode)
+        except Exception as e:
+            caption = str(e)
         if not req.analyze:
-            return models.ResInterrogate(caption)
+            return models.ResInterrogate(caption=caption)
         else:
             medium, artist, movement, trending, flavor = analyze_image(image, model=req.model)
-            return models.ResInterrogate(caption, medium, artist, movement, trending, flavor)
+            return models.ResInterrogate(caption=caption, medium=medium, artist=artist, movement=movement, trending=trending, flavor=flavor)
 
 def post_unload_checkpoint():
     from modules import sd_models

modules/interrogate.py

@@ -79,7 +79,8 @@ def checkpoint_wrapper(self):
 
     def load_blip_model(self):
         self.create_fake_fairscale()
-        import models.blip # pylint: disable=no-name-in-module
+        from repositories.blip import models
+        from repositories.blip.models import blip
         import modules.modelloader as modelloader
         model_path = os.path.join(paths.models_path, "BLIP")
         download_name='model_base_caption_capfilt_large.pth',
@@ -90,7 +91,7 @@ def load_blip_model(self):
             ext_filter=[".pth"],
             download_name=download_name,
         )
-        blip_model = models.blip.blip_decoder(pretrained=files[0], image_size=blip_image_eval_size, vit='base', med_config=os.path.join(paths.paths["BLIP"], "configs", "med_config.json")) # pylint: disable=c-extension-no-member
+        blip_model = blip.blip_decoder(pretrained=files[0], image_size=blip_image_eval_size, vit='base', med_config=os.path.join(paths.paths["BLIP"], "configs", "med_config.json")) # pylint: disable=c-extension-no-member
         blip_model.eval()
 
         return blip_model

modules/sd_hijack_clip.py

@@ -241,7 +241,6 @@ def tokenize(self, texts):
 
     def encode_with_transformers(self, tokens):
         clip_skip = int(opts.data['clip_skip']) or 1
-        print('HERE', type(clip_skip), clip_skip)
         outputs = self.wrapped.transformer(input_ids=tokens, output_hidden_states=-clip_skip)
         if clip_skip > 1:
             z = outputs.hidden_states[-clip_skip]

repositories/blip/models/blip.py

@@ -8,8 +8,8 @@
 import warnings
 warnings.filterwarnings("ignore")
 
-from models.vit import VisionTransformer, interpolate_pos_embed
-from models.med import BertConfig, BertModel, BertLMHeadModel
+from repositories.blip.models.vit import VisionTransformer, interpolate_pos_embed
+from repositories.blip.models.med import BertConfig, BertModel, BertLMHeadModel
 from transformers import BertTokenizer
 
 import torch