Add variant argument to dreambooth lora sdxl advanced (#6021)

Co-authored-by: Sayak Paul <[email protected]>

examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py

@@ -225,6 +225,12 @@ def parse_args(input_args=None):
         required=False,
         help="Revision of pretrained model identifier from huggingface.co/models.",
     )
+    parser.add_argument(
+        "--variant",
+        type=str,
+        default=None,
+        help="Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16",
+    )
     parser.add_argument(
         "--dataset_name",
         type=str,
@@ -1064,6 +1070,7 @@ def main(args):
                 args.pretrained_model_name_or_path,
                 torch_dtype=torch_dtype,
                 revision=args.revision,
+                variant=args.variant,
             )
             pipeline.set_progress_bar_config(disable=True)
 
@@ -1102,10 +1109,18 @@ def main(args):
 
     # Load the tokenizers
     tokenizer_one = AutoTokenizer.from_pretrained(
-        args.pretrained_model_name_or_path, subfolder="tokenizer", revision=args.revision, use_fast=False
+        args.pretrained_model_name_or_path,
+        subfolder="tokenizer",
+        revision=args.revision,
+        variant=args.variant,
+        use_fast=False,
     )
     tokenizer_two = AutoTokenizer.from_pretrained(
-        args.pretrained_model_name_or_path, subfolder="tokenizer_2", revision=args.revision, use_fast=False
+        args.pretrained_model_name_or_path,
+        subfolder="tokenizer_2",
+        revision=args.revision,
+        variant=args.variant,
+        use_fast=False,
     )
 
     # import correct text encoder classes
@@ -1119,21 +1134,24 @@ def main(args):
     # Load scheduler and models
     noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
     text_encoder_one = text_encoder_cls_one.from_pretrained(
-        args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision
+        args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision, variant=args.variant
     )
     text_encoder_two = text_encoder_cls_two.from_pretrained(
-        args.pretrained_model_name_or_path, subfolder="text_encoder_2", revision=args.revision
+        args.pretrained_model_name_or_path, subfolder="text_encoder_2", revision=args.revision, variant=args.variant
     )
     vae_path = (
         args.pretrained_model_name_or_path
         if args.pretrained_vae_model_name_or_path is None
         else args.pretrained_vae_model_name_or_path
     )
     vae = AutoencoderKL.from_pretrained(
-        vae_path, subfolder="vae" if args.pretrained_vae_model_name_or_path is None else None, revision=args.revision
+        vae_path,
+        subfolder="vae" if args.pretrained_vae_model_name_or_path is None else None,
+        revision=args.revision,
+        variant=args.variant,
     )
     unet = UNet2DConditionModel.from_pretrained(
-        args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision
+        args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision, variant=args.variant
     )
 
     if args.train_text_encoder_ti:
@@ -1843,10 +1861,16 @@ def compute_text_embeddings(prompt, text_encoders, tokenizers):
                 # create pipeline
                 if freeze_text_encoder:
                     text_encoder_one = text_encoder_cls_one.from_pretrained(
-                        args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision
+                        args.pretrained_model_name_or_path,
+                        subfolder="text_encoder",
+                        revision=args.revision,
+                        variant=args.variant,
                     )
                     text_encoder_two = text_encoder_cls_two.from_pretrained(
-                        args.pretrained_model_name_or_path, subfolder="text_encoder_2", revision=args.revision
+                        args.pretrained_model_name_or_path,
+                        subfolder="text_encoder_2",
+                        revision=args.revision,
+                        variant=args.variant,
                     )
                 pipeline = StableDiffusionXLPipeline.from_pretrained(
                     args.pretrained_model_name_or_path,
@@ -1855,6 +1879,7 @@ def compute_text_embeddings(prompt, text_encoders, tokenizers):
                     text_encoder_2=accelerator.unwrap_model(text_encoder_two),
                     unet=accelerator.unwrap_model(unet),
                     revision=args.revision,
+                    variant=args.variant,
                     torch_dtype=weight_dtype,
                 )
 
@@ -1932,10 +1957,15 @@ def compute_text_embeddings(prompt, text_encoders, tokenizers):
             vae_path,
             subfolder="vae" if args.pretrained_vae_model_name_or_path is None else None,
             revision=args.revision,
+            variant=args.variant,
             torch_dtype=weight_dtype,
         )
         pipeline = StableDiffusionXLPipeline.from_pretrained(
-            args.pretrained_model_name_or_path, vae=vae, revision=args.revision, torch_dtype=weight_dtype
+            args.pretrained_model_name_or_path,
+            vae=vae,
+            revision=args.revision,
+            variant=args.variant,
+            torch_dtype=weight_dtype,
         )
 
         # We train on the simplified learning objective. If we were previously predicting a variance, we need the scheduler to ignore it