fix(api): embed LPW pipeline (fixes #96)

api/onnx_web/diffusion/load.py

@@ -72,10 +72,10 @@ def load_pipeline(
         )
         pipe = pipeline.from_pretrained(
             model,
+            custom_pipeline="./onnx_web/diffusion/lpw_stable_diffusion_onnx.py",
             provider=device.provider,
             provider_options=device.options,
-            custom_pipeline='lpw_stable_diffusion_onnx',
-            revision='onnx',
+            revision="onnx",
             safety_checker=None,
             scheduler=scheduler,
         )

api/onnx_web/diffusion/pipeline_onnx_stable_diffusion_upscale.py

@@ -1,3 +1,10 @@
+###
+# This is based on a combination of the ONNX img2img pipeline and the PyTorch upscale pipeline:
+# https://github.com/huggingface/diffusers/blob/v0.11.1/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py
+# https://github.com/huggingface/diffusers/blob/v0.11.1/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_upscale.py
+# See also: https://github.com/huggingface/diffusers/pull/2158
+###
+
 from logging import getLogger
 from typing import Any, Callable, List, Optional, Union
 
@@ -18,12 +25,6 @@ num_channels_latents = 4
 # TODO: make this dynamic, from self.unet.config.in_channels
 unet_in_channels = 7
 
-###
-# This is based on a combination of the ONNX img2img pipeline and the PyTorch upscale pipeline:
-# https://github.com/huggingface/diffusers/blob/v0.11.1/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py
-# https://github.com/huggingface/diffusers/blob/v0.11.1/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_upscale.py
-###
-
 
 def preprocess(image):
     if isinstance(image, torch.Tensor):

api/pyproject.toml

@@ -1,2 +1,3 @@
 [tool.isort]
 profile = "black"
+force_to_top = ".logging"