use pipeline type to decide scheduler patch options

api/onnx_web/diffusers/load.py

@@ -642,6 +642,19 @@ def optimize_pipeline(
             logger.warning("error while enabling memory efficient attention: %s", e)
 
 
+IMAGE_PIPELINES = [
+    OnnxStableDiffusionControlNetPipeline,
+    OnnxStableDiffusionImg2ImgPipeline,
+    OnnxStableDiffusionInpaintPipeline,
+    OnnxStableDiffusionInstructPix2PixPipeline,
+    OnnxStableDiffusionLongPromptWeightingPipeline,
+    OnnxStableDiffusionPanoramaPipeline,
+    OnnxStableDiffusionUpscalePipeline,
+    ORTStableDiffusionXLImg2ImgPipeline,
+    ORTStableDiffusionXLPanoramaPipeline,
+]
+
+
 def patch_pipeline(
     server: ServerContext,
     pipe: StableDiffusionPipeline,
@@ -654,9 +667,15 @@ def patch_pipeline(
         logger.debug("patching prompt encoder")
         pipe._encode_prompt = expand_prompt.__get__(pipe, pipeline)
 
-    logger.debug("patching pipeline scheduler")
+    # the pipeline requested in params may not be the one currently being used, especially during the later img2img
+    # stages of a highres pipeline, so we need to check the pipeline type
+    is_text_pipeline = type(pipe) not in IMAGE_PIPELINES
+    logger.debug(
+        "patching pipeline scheduler for %s pipeline",
+        "txt2img" if is_text_pipeline else "img2img",
+    )
     original_scheduler = pipe.scheduler
-    pipe.scheduler = SchedulerPatch(server, original_scheduler, params.is_txt2img())
+    pipe.scheduler = SchedulerPatch(server, original_scheduler, is_text_pipeline)
 
     logger.debug("patching pipeline UNet")
     original_unet = pipe.unet

api/onnx_web/params.py

@@ -321,9 +321,6 @@ class ImageParams:
     def is_pix2pix(self):
         return self.pipeline == "pix2pix"
 
-    def is_txt2img(self):
-        return self.pipeline in ["txt2img", "txt2img-sdxl"]
-
     def is_xl(self):
         return self.pipeline.endswith("-sdxl")