Remove hardcoded names from PT scripts (#1778)

* Remove hardcoded names from PT scripts

* Apply suggestions from code review
Co-authored-by: Suraj Patil <surajp815@gmail.com>

examples/textual_inversion/textual_inversion.py

@@ -16,9 +16,8 @@ import PIL
 from accelerate import Accelerator
 from accelerate.logging import get_logger
 from accelerate.utils import set_seed
-from diffusers import AutoencoderKL, DDPMScheduler, PNDMScheduler, StableDiffusionPipeline, UNet2DConditionModel
+from diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionPipeline, UNet2DConditionModel
 from diffusers.optimization import get_scheduler
-from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
 from diffusers.utils import check_min_version
 from diffusers.utils.import_utils import is_xformers_available
 from huggingface_hub import HfFolder, Repository, whoami
@@ -28,7 +27,7 @@ from packaging import version
 from PIL import Image
 from torchvision import transforms
 from tqdm.auto import tqdm
-from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
+from transformers import CLIPTextModel, CLIPTokenizer
 
 
 if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
@@ -678,14 +677,12 @@ def main():
         else:
             save_full_model = not args.only_save_embeds
         if save_full_model:
-            pipeline = StableDiffusionPipeline(
+            pipeline = StableDiffusionPipeline.from_pretrained(
+                args.pretrained_model_name_or_path,
                 text_encoder=accelerator.unwrap_model(text_encoder),
                 vae=vae,
                 unet=unet,
                 tokenizer=tokenizer,
-                scheduler=PNDMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler"),
-                safety_checker=StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker"),
-                feature_extractor=CLIPFeatureExtractor.from_pretrained("openai/clip-vit-base-patch32"),
             )
             pipeline.save_pretrained(args.output_dir)
         # Save the newly trained embeddings