Fix typos: Successfully facilitate getting pipeline overridden (#30)

README.md

@@ -240,7 +240,7 @@ Differences may be less or more pronounced for different inputs. Please see the
 </details>
 
 <details>
-<summary> <b> <a name="low-mem-conversion"></a> Q3: </b> My Mac has 8GB RAM and I am converting models to Core ML using the example command. The process is geting killed because of memory issues. How do I fix this issue? </summary>
+<summary> <b> <a name="low-mem-conversion"></a> Q3: </b> My Mac has 8GB RAM and I am converting models to Core ML using the example command. The process is getting killed because of memory issues. How do I fix this issue? </summary>
 
 <b> A3: </b>  In order to minimize the memory impact of the model conversion process, please execute the following command instead:
 
@@ -313,7 +313,7 @@ On iOS, depending on the iPhone model, Stable Diffusion model versions, selected
 
   <b> 4. Weights and Activations Data Type </b>
 
-  When quantizing models from float32 to lower-precision data types such as float16, the generated images are [known to vary slightly](https://lambdalabs.com/blog/inference-benchmark-stable-diffusion) in semantics even when using the same PyTorch model. Core ML models generated by coremltools have float16 weights and activations by default [unless explicitly overriden](https://github.com/apple/coremltools/blob/main/coremltools/converters/_converters_entry.py#L256). This is not expected to be a major source of difference.
+  When quantizing models from float32 to lower-precision data types such as float16, the generated images are [known to vary slightly](https://lambdalabs.com/blog/inference-benchmark-stable-diffusion) in semantics even when using the same PyTorch model. Core ML models generated by coremltools have float16 weights and activations by default [unless explicitly overridden](https://github.com/apple/coremltools/blob/main/coremltools/converters/_converters_entry.py#L256). This is not expected to be a major source of difference.
 
 </details>
 

python_coreml_stable_diffusion/torch2coreml.py

@@ -576,7 +576,7 @@ def convert_unet(pipe, args):
         # Set the output descriptions
         coreml_unet.output_description["noise_pred"] = \
             "Same shape and dtype as the `sample` input. " \
-            "The predicted noise to faciliate the reverse diffusion (denoising) process"
+            "The predicted noise to facilitate the reverse diffusion (denoising) process"
 
         _save_mlpackage(coreml_unet, out_path)
         logger.info(f"Saved unet into {out_path}")

swift/StableDiffusion/pipeline/StableDiffusionPipeline+Resources.swift

@@ -75,7 +75,7 @@ public extension StableDiffusionPipeline {
             safetyChecker = SafetyChecker(modelAt: urls.safetyCheckerURL, configuration: config)
         }
 
-        // Construct pipelien
+        // Construct pipeline
         self.init(textEncoder: textEncoder,
                   unet: unet,
                   decoder: decoder,

tests/test_stable_diffusion.py

@@ -74,23 +74,23 @@ class TestStableDiffusionForTextToImage(unittest.TestCase):
         with self.subTest(model="vae_decoder"):
             logger.info("Converting vae_decoder")
             torch2coreml.convert_vae_decoder(self.pytorch_pipe, self.cli_args)
-            logger.info("Successfuly converted vae_decoder")
+            logger.info("Successfully converted vae_decoder")
 
         with self.subTest(model="unet"):
             logger.info("Converting unet")
             torch2coreml.convert_unet(self.pytorch_pipe, self.cli_args)
-            logger.info("Successfuly converted unet")
+            logger.info("Successfully converted unet")
 
         with self.subTest(model="text_encoder"):
             logger.info("Converting text_encoder")
             torch2coreml.convert_text_encoder(self.pytorch_pipe, self.cli_args)
-            logger.info("Successfuly converted text_encoder")
+            logger.info("Successfully converted text_encoder")
 
         with self.subTest(model="safety_checker"):
             logger.info("Converting safety_checker")
             torch2coreml.convert_safety_checker(self.pytorch_pipe,
                                                 self.cli_args)
-            logger.info("Successfuly converted safety_checker")
+            logger.info("Successfully converted safety_checker")
 
     def test_end_to_end_image_generation_speed(self):
         """ Tests: