[Stable Diffusion] Allow users to disable Safety checker if loading model from checkpoint (#2768)

* Allow user to disable SafetyChecker and enable dtypes if loading models from .ckpt or .safetensors

* Fix Import sorting (Ruff error)

* Get rid of the dtype convert method as it was implemented all along

* Fix the docstring

* Fix ruff formatting

---------

Co-authored-by: Patrick von Platen <[email protected]>

Author: Stax124

src/diffusers/pipelines/stable_diffusion/convert_from_ckpt.py

@@ -989,6 +989,7 @@ def download_from_original_stable_diffusion_ckpt(
     stable_unclip_prior: Optional[str] = None,
     clip_stats_path: Optional[str] = None,
     controlnet: Optional[bool] = None,
+    load_safety_checker: bool = True,
 ) -> StableDiffusionPipeline:
     """
     Load a Stable Diffusion pipeline object from a CompVis-style `.ckpt`/`.safetensors` file and (ideally) a `.yaml`
@@ -1028,6 +1029,8 @@ def download_from_original_stable_diffusion_ckpt(
             The device to use. Pass `None` to determine automatically. :param from_safetensors: If `checkpoint_path` is
             in `safetensors` format, load checkpoint with safetensors instead of PyTorch. :return: A
             StableDiffusionPipeline object representing the passed-in `.ckpt`/`.safetensors` file.
+        load_safety_checker (`bool`, *optional*, defaults to `True`):
+            Whether to load the safety checker or not. Defaults to `True`.
     """
     if prediction_type == "v-prediction":
         prediction_type = "v_prediction"
@@ -1270,8 +1273,13 @@ def download_from_original_stable_diffusion_ckpt(
     elif model_type == "FrozenCLIPEmbedder":
         text_model = convert_ldm_clip_checkpoint(checkpoint)
         tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
-        safety_checker = StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker")
-        feature_extractor = AutoFeatureExtractor.from_pretrained("CompVis/stable-diffusion-safety-checker")
+
+        if load_safety_checker:
+            safety_checker = StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker")
+            feature_extractor = AutoFeatureExtractor.from_pretrained("CompVis/stable-diffusion-safety-checker")
+        else:
+            safety_checker = None
+            feature_extractor = None
 
         if controlnet:
             pipe = StableDiffusionControlNetPipeline(