replace deprecated pipeline names in ONNX docs

Author: ssube

docs/source/en/optimization/onnx.mdx

@@ -21,13 +21,13 @@ specific language governing permissions and limitations under the License.
 
 ## Stable Diffusion Inference
 
-The snippet below demonstrates how to use the ONNX runtime. You need to use `StableDiffusionOnnxPipeline` instead of `StableDiffusionPipeline`. You also need to download the weights from the `onnx` branch of the repository, and indicate the runtime provider you want to use.
+The snippet below demonstrates how to use the ONNX runtime. You need to use `OnnxStableDiffusionPipeline` instead of `StableDiffusionPipeline`. You also need to download the weights from the `onnx` branch of the repository, and indicate the runtime provider you want to use.
 
 ```python
 # make sure you're logged in with `huggingface-cli login`
-from diffusers import StableDiffusionOnnxPipeline
+from diffusers import OnnxStableDiffusionPipeline
 
-pipe = StableDiffusionOnnxPipeline.from_pretrained(
+pipe = OnnxStableDiffusionPipeline.from_pretrained(
     "runwayml/stable-diffusion-v1-5",
     revision="onnx",
     provider="CUDAExecutionProvider",
@@ -40,12 +40,12 @@ image = pipe(prompt).images[0]
 The snippet below demonstrates how to use the ONNX runtime with the Stable Diffusion upscaling pipeline.
 
 ```python
-from diffusers import StableDiffusionOnnxPipeline
+from diffusers import OnnxStableDiffusionPipeline, OnnxStableDiffusionUpscalePipeline
 
 prompt = "a photo of an astronaut riding a horse on mars"
 steps = 50
 
-txt2img = StableDiffusionOnnxPipeline.from_pretrained(
+txt2img = OnnxStableDiffusionPipeline.from_pretrained(
     "runwayml/stable-diffusion-v1-5",
     revision="onnx",
     provider="CUDAExecutionProvider",

tests/pipelines/stable_diffusion/test_onnx_stable_diffusion_upscale.py

@@ -138,9 +138,9 @@ def test_pipeline_euler_ancestral(self):
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1
 
 
-@nightly
-@require_onnxruntime
-@require_torch_gpu
+# @nightly
+# @require_onnxruntime
+# @require_torch_gpu
 class OnnxStableDiffusionUpscalePipelineIntegrationTests(unittest.TestCase):
     @property
     def gpu_provider(self):