diff options
Diffstat (limited to 'modules/interrogate.py')
| -rw-r--r-- | modules/interrogate.py | 24 |
1 files changed, 14 insertions, 10 deletions
diff --git a/modules/interrogate.py b/modules/interrogate.py index f62a4745..9263d65a 100644 --- a/modules/interrogate.py +++ b/modules/interrogate.py @@ -21,6 +21,7 @@ Category = namedtuple("Category", ["name", "topn", "items"]) re_topn = re.compile(r"\.top(\d+)\.")
+
class InterrogateModels:
blip_model = None
clip_model = None
@@ -54,7 +55,7 @@ class InterrogateModels: model, preprocess = clip.load(clip_model_name)
model.eval()
- model = model.to(shared.device)
+ model = model.to(devices.device_interrogate)
return model, preprocess
@@ -64,14 +65,14 @@ class InterrogateModels: if not shared.cmd_opts.no_half:
self.blip_model = self.blip_model.half()
- self.blip_model = self.blip_model.to(shared.device)
+ self.blip_model = self.blip_model.to(devices.device_interrogate)
if self.clip_model is None:
self.clip_model, self.clip_preprocess = self.load_clip_model()
if not shared.cmd_opts.no_half:
self.clip_model = self.clip_model.half()
- self.clip_model = self.clip_model.to(shared.device)
+ self.clip_model = self.clip_model.to(devices.device_interrogate)
self.dtype = next(self.clip_model.parameters()).dtype
@@ -98,11 +99,11 @@ class InterrogateModels: 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], truncate=True).to(shared.device)
+ text_tokens = clip.tokenize([text for text in text_array], truncate=True).to(devices.device_interrogate)
text_features = self.clip_model.encode_text(text_tokens).type(self.dtype)
text_features /= text_features.norm(dim=-1, keepdim=True)
- similarity = torch.zeros((1, len(text_array))).to(shared.device)
+ similarity = torch.zeros((1, len(text_array))).to(devices.device_interrogate)
for i in range(image_features.shape[0]):
similarity += (100.0 * image_features[i].unsqueeze(0) @ text_features.T).softmax(dim=-1)
similarity /= image_features.shape[0]
@@ -115,14 +116,14 @@ class InterrogateModels: transforms.Resize((blip_image_eval_size, blip_image_eval_size), interpolation=InterpolationMode.BICUBIC),
transforms.ToTensor(),
transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))
- ])(pil_image).unsqueeze(0).type(self.dtype).to(shared.device)
+ ])(pil_image).unsqueeze(0).type(self.dtype).to(devices.device_interrogate)
with torch.no_grad():
caption = self.blip_model.generate(gpu_image, sample=False, num_beams=shared.opts.interrogate_clip_num_beams, min_length=shared.opts.interrogate_clip_min_length, max_length=shared.opts.interrogate_clip_max_length)
return caption[0]
- def interrogate(self, pil_image):
+ def interrogate(self, pil_image, include_ranks=False):
res = None
try:
@@ -139,11 +140,11 @@ class InterrogateModels: res = caption
- cilp_image = self.clip_preprocess(pil_image).unsqueeze(0).type(self.dtype).to(shared.device)
+ clip_image = self.clip_preprocess(pil_image).unsqueeze(0).type(self.dtype).to(devices.device_interrogate)
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(cilp_image).type(self.dtype)
+ image_features = self.clip_model.encode_image(clip_image).type(self.dtype)
image_features /= image_features.norm(dim=-1, keepdim=True)
@@ -155,7 +156,10 @@ class InterrogateModels: for name, topn, items in self.categories:
matches = self.rank(image_features, items, top_count=topn)
for match, score in matches:
- res += ", " + match
+ if include_ranks:
+ res += ", " + match
+ else:
+ res += f", ({match}:{score})"
except Exception:
print(f"Error interrogating", file=sys.stderr)
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