From f00877a13bce50b02cfc3790f2d18a325e9ff95b Mon Sep 17 00:00:00 2001
From: Volpeon <git@volpeon.ink>
Date: Sat, 14 Jan 2023 22:42:44 +0100
Subject: Update

---
 training/functional.py | 34 ++++++++++++++++++++++++----------
 1 file changed, 24 insertions(+), 10 deletions(-)

(limited to 'training/functional.py')

diff --git a/training/functional.py b/training/functional.py
index c100ea2..c5b514a 100644
--- a/training/functional.py
+++ b/training/functional.py
@@ -25,17 +25,31 @@ def const(result=None):
     return fn
 
 
+def get_models(pretrained_model_name_or_path: str):
+    tokenizer = MultiCLIPTokenizer.from_pretrained(pretrained_model_name_or_path, subfolder='tokenizer')
+    text_encoder = CLIPTextModel.from_pretrained(pretrained_model_name_or_path, subfolder='text_encoder')
+    vae = AutoencoderKL.from_pretrained(pretrained_model_name_or_path, subfolder='vae')
+    unet = UNet2DConditionModel.from_pretrained(pretrained_model_name_or_path, subfolder='unet')
+    noise_scheduler = DDPMScheduler.from_pretrained(pretrained_model_name_or_path, subfolder='scheduler')
+    sample_scheduler = DPMSolverMultistepScheduler.from_pretrained(
+        pretrained_model_name_or_path, subfolder='scheduler')
+
+    embeddings = patch_managed_embeddings(text_encoder)
+
+    return tokenizer, text_encoder, vae, unet, noise_scheduler, sample_scheduler, embeddings
+
+
 def generate_class_images(
-    accelerator,
-    text_encoder,
-    vae,
-    unet,
-    tokenizer,
-    scheduler,
+    accelerator: Accelerator,
+    text_encoder: CLIPTextModel,
+    vae: AutoencoderKL,
+    unet: UNet2DConditionModel,
+    tokenizer: MultiCLIPTokenizer,
+    sample_scheduler: DPMSolverMultistepScheduler,
     data_train,
-    sample_batch_size,
-    sample_image_size,
-    sample_steps
+    sample_batch_size: int,
+    sample_image_size: int,
+    sample_steps: int
 ):
     missing_data = [item for item in data_train if not item.class_image_path.exists()]
 
@@ -52,7 +66,7 @@ def generate_class_images(
         vae=vae,
         unet=unet,
         tokenizer=tokenizer,
-        scheduler=scheduler,
+        scheduler=sample_scheduler,
     ).to(accelerator.device)
     pipeline.set_progress_bar_config(dynamic_ncols=True)
 
-- 
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