Hotfix for AttributeErrors in OnnxStableDiffusionInpaintPipelineLegacy (huggingface#1448)

Author: anton-l

src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint_legacy.py

@@ -5,7 +5,6 @@
 import torch
 
 import PIL
-from packaging import version
 from transformers import CLIPFeatureExtractor, CLIPTokenizer
 
 from ...configuration_utils import FrozenDict
@@ -68,6 +67,8 @@ class OnnxStableDiffusionInpaintPipelineLegacy(DiffusionPipeline):
         feature_extractor ([`CLIPFeatureExtractor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """
+    _optional_components = ["safety_checker", "feature_extractor"]
+
     vae_encoder: OnnxRuntimeModel
     vae_decoder: OnnxRuntimeModel
     text_encoder: OnnxRuntimeModel
@@ -134,27 +135,6 @@ def __init__(
                 " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
             )
 
-        is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
-            version.parse(unet.config._diffusers_version).base_version
-        ) < version.parse("0.9.0.dev0")
-        is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
-        if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
-            deprecation_message = (
-                "The configuration file of the unet has set the default `sample_size` to smaller than"
-                " 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the"
-                " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
-                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
-                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
-                " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
-                " in the config might lead to incorrect results in future versions. If you have downloaded this"
-                " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
-                " the `unet/config.json` file"
-            )
-            deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
-            new_config = dict(unet.config)
-            new_config["sample_size"] = 64
-            unet._internal_dict = FrozenDict(new_config)
-
         self.register_modules(
             vae_encoder=vae_encoder,
             vae_decoder=vae_decoder,
@@ -165,7 +145,6 @@ def __init__(
             safety_checker=safety_checker,
             feature_extractor=feature_extractor,
         )
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
         self.register_to_config(requires_safety_checker=requires_safety_checker)
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_onnx_stable_diffusion.OnnxStableDiffusionPipeline._encode_prompt
@@ -372,7 +351,7 @@ def __call__(
 
         # preprocess mask
         if not isinstance(mask_image, np.ndarray):
-            mask_image = preprocess_mask(mask_image, self.vae_scale_factor)
+            mask_image = preprocess_mask(mask_image, 8)
         mask_image = mask_image.astype(latents_dtype)
         mask = np.concatenate([mask_image] * num_images_per_prompt, axis=0)