Merge branch 'main' into remove-numpy

Author: kashif

setup.py

@@ -92,7 +92,6 @@
     "jaxlib>=0.1.65,<=0.3.6",
     "modelcards>=0.1.4",
     "numpy",
-    "onnxruntime",
     "onnxruntime-gpu",
     "pytest",
     "pytest-timeout",
@@ -179,7 +178,6 @@ def run(self):
 extras["training"] = deps_list("accelerate", "datasets", "tensorboard", "modelcards")
 extras["test"] = deps_list(
     "datasets",
-    "onnxruntime",
     "onnxruntime-gpu",
     "pytest",
     "pytest-timeout",

src/diffusers/modeling_flax_utils.py

@@ -436,7 +436,7 @@ def from_pretrained(
             )
             cls._missing_keys = missing_keys
 
-        # Mistmatched keys contains tuples key/shape1/shape2 of weights in the checkpoint that have a shape not
+        # Mismatched keys contains tuples key/shape1/shape2 of weights in the checkpoint that have a shape not
         # matching the weights in the model.
         mismatched_keys = []
         for key in state.keys():

src/diffusers/pipeline_flax_utils.py

@@ -244,8 +244,8 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
 
             kwargs (remaining dictionary of keyword arguments, *optional*):
                 Can be used to overwrite load - and saveable variables - *i.e.* the pipeline components - of the
-                specific pipeline class. The overritten components are then directly passed to the pipelines `__init__`
-                method. See example below for more information.
+                specific pipeline class. The overwritten components are then directly passed to the pipelines
+                `__init__` method. See example below for more information.
 
         <Tip>
 

src/diffusers/pipeline_utils.py

@@ -235,8 +235,8 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
 
             kwargs (remaining dictionary of keyword arguments, *optional*):
                 Can be used to overwrite load - and saveable variables - *i.e.* the pipeline components - of the
-                specific pipeline class. The overritten components are then directly passed to the pipelines `__init__`
-                method. See example below for more information.
+                specific pipeline class. The overwritten components are then directly passed to the pipelines
+                `__init__` method. See example below for more information.
 
         <Tip>
 

src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py

@@ -27,7 +27,7 @@ class LDMTextToImagePipeline(DiffusionPipeline):
         vqvae ([`VQModel`]):
             Vector-quantized (VQ) Model to encode and decode images to and from latent representations.
         bert ([`LDMBertModel`]):
-            Text-encoder model based on [BERT](ttps://huggingface.co/docs/transformers/model_doc/bert) architecture.
+            Text-encoder model based on [BERT](https://huggingface.co/docs/transformers/model_doc/bert) architecture.
         tokenizer (`transformers.BertTokenizer`):
             Tokenizer of class
             [BertTokenizer](https://huggingface.co/docs/transformers/model_doc/bert#transformers.BertTokenizer).
@@ -396,7 +396,7 @@ def forward(
         attn_output = attn_output.transpose(1, 2)
 
         # Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be
-        # partitioned aross GPUs when using tensor-parallelism.
+        # partitioned across GPUs when using tensor-parallelism.
         attn_output = attn_output.reshape(bsz, tgt_len, self.inner_dim)
 
         attn_output = self.out_proj(attn_output)

src/diffusers/schedulers/scheduling_karras_ve.py

@@ -105,7 +105,7 @@ def set_timesteps(self, num_inference_steps: int):
         self.timesteps = np.arange(0, self.num_inference_steps)[::-1].copy()
         schedule = [
             (
-                self.config.sigma_max
+                self.config.sigma_max**2
                 * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
             )
             for i in self.timesteps

src/diffusers/schedulers/scheduling_karras_ve_flax.py

@@ -113,7 +113,7 @@ def set_timesteps(self, state: KarrasVeSchedulerState, num_inference_steps: int)
         timesteps = jnp.arange(0, num_inference_steps)[::-1].copy()
         schedule = [
             (
-                self.config.sigma_max
+                self.config.sigma_max**2
                 * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
             )
             for i in timesteps

src/diffusers/utils/logging.py

@@ -266,7 +266,7 @@ def reset_format() -> None:
 
 def warning_advice(self, *args, **kwargs):
     """
-    This method is identical to `logger.warninging()`, but if env var DIFFUSERS_NO_ADVISORY_WARNINGS=1 is set, this
+    This method is identical to `logger.warning()`, but if env var DIFFUSERS_NO_ADVISORY_WARNINGS=1 is set, this
     warning will not be printed
     """
     no_advisory_warnings = os.getenv("DIFFUSERS_NO_ADVISORY_WARNINGS", False)

tests/test_pipelines.py

@@ -1103,7 +1103,7 @@ def test_karras_ve_pipeline(self):
 
         image_slice = image[0, -3:, -3:, -1]
         assert image.shape == (1, 256, 256, 3)
-        expected_slice = np.array([0.26815, 0.1581, 0.2658, 0.23248, 0.1550, 0.2539, 0.1131, 0.1024, 0.0837])
+        expected_slice = np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586])
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
 
     @slow