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refactor devices

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Signed-off-by: Vladimir Mandic <mandic00@live.com>
This commit is contained in:
Vladimir Mandic
2024-09-29 20:17:03 -04:00
parent fe94edf781
commit 47755dce6b
14 changed files with 182 additions and 182 deletions
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modules/interrogate.py
+8 -8
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@@ -67,7 +67,7 @@ class InterrogateModels:
self.loaded_categories = None
self.skip_categories = []
self.content_dir = content_dir
self.running_on_cpu = devices.device_interrogate == torch.device("cpu")
self.running_on_cpu = False
def categories(self):
if not os.path.exists(self.content_dir):
@@ -123,7 +123,7 @@ class InterrogateModels:
else:
model, preprocess = clip.load(clip_model_name, download_root=shared.opts.clip_models_path)
model.eval()
model = model.to(devices.device_interrogate)
model = model.to(devices.device)
return model, preprocess
def load(self):
@@ -131,12 +131,12 @@ class InterrogateModels:
self.blip_model = self.load_blip_model()
if not shared.opts.no_half and not self.running_on_cpu:
self.blip_model = self.blip_model.half()
self.blip_model = self.blip_model.to(devices.device_interrogate)
self.blip_model = self.blip_model.to(devices.device)
if self.clip_model is None:
self.clip_model, self.clip_preprocess = self.load_clip_model()
if not shared.opts.no_half and not self.running_on_cpu:
self.clip_model = self.clip_model.half()
self.clip_model = self.clip_model.to(devices.device_interrogate)
self.clip_model = self.clip_model.to(devices.device)
self.dtype = next(self.clip_model.parameters()).dtype
def send_clip_to_ram(self):
@@ -160,10 +160,10 @@ class InterrogateModels:
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(list(text_array), truncate=True).to(devices.device_interrogate)
text_tokens = clip.tokenize(list(text_array), truncate=True).to(devices.device)
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(devices.device_interrogate)
similarity = torch.zeros((1, len(text_array))).to(devices.device)
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]
@@ -175,7 +175,7 @@ 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(devices.device_interrogate)
])(pil_image).unsqueeze(0).type(self.dtype).to(devices.device)
with devices.inference_context():
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]
@@ -199,7 +199,7 @@ class InterrogateModels:
self.send_blip_to_ram()
devices.torch_gc()
res = caption
clip_image = self.clip_preprocess(pil_image).unsqueeze(0).type(self.dtype).to(devices.device_interrogate)
clip_image = self.clip_preprocess(pil_image).unsqueeze(0).type(self.dtype).to(devices.device)
with devices.inference_context(), devices.autocast():
image_features = self.clip_model.encode_image(clip_image).type(self.dtype)
image_features /= image_features.norm(dim=-1, keepdim=True)