Merge branch 'main' into cnet-union-img2img-fixes

Author: asomoza

src/diffusers/configuration_utils.py

@@ -763,4 +763,7 @@ def from_config(cls, config: Union[FrozenDict, Dict[str, Any]] = None, return_un
         # resolve remapping
         remapped_class = _fetch_remapped_cls_from_config(config, cls)
 
-        return remapped_class.from_config(config, return_unused_kwargs, **kwargs)
+        if remapped_class is cls:
+            return super(LegacyConfigMixin, remapped_class).from_config(config, return_unused_kwargs, **kwargs)
+        else:
+            return remapped_class.from_config(config, return_unused_kwargs, **kwargs)

src/diffusers/models/modeling_utils.py

@@ -1877,4 +1877,9 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
         # resolve remapping
         remapped_class = _fetch_remapped_cls_from_config(config, cls)
 
-        return remapped_class.from_pretrained(pretrained_model_name_or_path, **kwargs_copy)
+        if remapped_class is cls:
+            return super(LegacyModelMixin, remapped_class).from_pretrained(
+                pretrained_model_name_or_path, **kwargs_copy
+            )
+        else:
+            return remapped_class.from_pretrained(pretrained_model_name_or_path, **kwargs_copy)

src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion.py

@@ -383,7 +383,8 @@ def __call__(
         # set timesteps
         self.scheduler.set_timesteps(num_inference_steps)
 
-        latents = latents * np.float64(self.scheduler.init_noise_sigma)
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = latents * self.scheduler.init_noise_sigma
 
         # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
         # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.

src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint.py

@@ -483,7 +483,7 @@ def __call__(
         self.scheduler.set_timesteps(num_inference_steps)
 
         # scale the initial noise by the standard deviation required by the scheduler
-        latents = latents * np.float64(self.scheduler.init_noise_sigma)
+        latents = latents * self.scheduler.init_noise_sigma
 
         # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
         # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.

src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_upscale.py

@@ -481,7 +481,7 @@ def __call__(
         timesteps = self.scheduler.timesteps
 
         # Scale the initial noise by the standard deviation required by the scheduler
-        latents = latents * np.float64(self.scheduler.init_noise_sigma)
+        latents = latents * self.scheduler.init_noise_sigma
 
         # 5. Add noise to image
         noise_level = np.array([noise_level]).astype(np.int64)