reorg imports for faster startup

modules/loader.py

@@ -37,13 +37,17 @@ if ".dev" in torch.__version__ or "+git" in torch.__version__:
     torch.__version__ = re.search(r'[\d.]+[\d]', torch.__version__).group(0)
 timer.startup.record("torch")
 
+import transformers # pylint: disable=W0611,C0411
+timer.startup.record("transformers")
+
 import onnxruntime
 onnxruntime.set_default_logger_severity(3)
 timer.startup.record("onnx")
 
-from modules.onnx_impl import initialize_olive # pylint: disable=ungrouped-imports
-initialize_olive()
-timer.startup.record("olive")
+# moved to webui.py:initialize()
+# from modules.onnx_impl import initialize_olive # pylint: disable=ungrouped-imports
+# initialize_olive()
+# timer.startup.record("olive")
 
 from fastapi import FastAPI # pylint: disable=W0611,C0411
 import gradio # pylint: disable=W0611,C0411

repositories/ldm/modules/encoders/modules.py

@@ -1,14 +1,10 @@
 import torch
 import torch.nn as nn
-import kornia
 from torch.utils.checkpoint import checkpoint
 
-from transformers import T5Tokenizer, T5EncoderModel, CLIPTokenizer, CLIPTextModel
-
 import open_clip
 from ldm.util import default, count_params, autocast
 
-
 class AbstractEncoder(nn.Module):
     def __init__(self):
         super().__init__()
@@ -62,6 +58,7 @@ class FrozenT5Embedder(AbstractEncoder):
     def __init__(self, version="google/t5-v1_1-large", device="cuda", max_length=77,
                  freeze=True):  # others are google/t5-v1_1-xl and google/t5-v1_1-xxl
         super().__init__()
+        from transformers import T5Tokenizer, T5EncoderModel
         self.tokenizer = T5Tokenizer.from_pretrained(version)
         self.transformer = T5EncoderModel.from_pretrained(version)
         self.device = device
@@ -99,6 +96,7 @@ class FrozenCLIPEmbedder(AbstractEncoder):
     def __init__(self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77,
                  freeze=True, layer="last", layer_idx=None):  # clip-vit-base-patch32
         super().__init__()
+        from transformers import CLIPTokenizer, CLIPTextModel
         assert layer in self.LAYERS
         self.tokenizer = CLIPTokenizer.from_pretrained(version)
         self.transformer = CLIPTextModel.from_pretrained(version)
@@ -155,6 +153,7 @@ class ClipImageEmbedder(nn.Module):
         self.ucg_rate = ucg_rate
 
     def preprocess(self, x):
+        import kornia
         # normalize to [0,1]
         x = kornia.geometry.resize(x, (224, 224),
                                    interpolation='bicubic', align_corners=True,
@@ -265,6 +264,7 @@ class FrozenOpenCLIPImageEmbedder(AbstractEncoder):
         self.ucg_rate = ucg_rate
 
     def preprocess(self, x):
+        import kornia
         # normalize to [0,1]
         x = kornia.geometry.resize(x, (224, 224),
                                    interpolation='bicubic', align_corners=True,

webui.py

@@ -81,6 +81,12 @@ def initialize():
     log.debug('Initializing')
     check_rollback_vae()
 
+    if shared.opts.cuda_compile_backend == "olive-ai":
+        from modules.onnx_impl import initialize_olive, install_olive
+        install_olive()
+        initialize_olive()
+        timer.startup.record("olive")
+
     modules.sd_samplers.list_samplers()
     timer.startup.record("samplers")