feat(api): add flag for ORT float16 optimizations

api/onnx_web/diffusers/load.py

@@ -353,7 +353,8 @@ timestep_dtype = None
 
 
 class UNetWrapper(object):
-    def __init__(self, wrapped):
+    def __init__(self, server, wrapped):
+        self.server = server
         self.wrapped = wrapped
 
     def __call__(self, sample=None, timestep=None, encoder_hidden_states=None):
@@ -361,8 +362,12 @@ class UNetWrapper(object):
         timestep_dtype = timestep.dtype
 
         logger.trace("UNet parameter types: %s, %s", sample.dtype, timestep.dtype)
-        if sample.dtype != timestep.dtype:
-            logger.info("converting UNet sample dtype")
+        if "onnx-fp16" in self.server.optimizations:
+            logger.info("converting UNet sample to ONNX fp16")
+            sample = sample.astype(np.float16)
+            encoder_hidden_states = encoder_hidden_states.astype(np.float16)
+        elif sample.dtype != timestep.dtype:
+            logger.info("converting UNet sample to timestep dtype")
             sample = sample.astype(timestep.dtype)
 
         return self.wrapped(
@@ -376,7 +381,8 @@ class UNetWrapper(object):
 
 
 class VAEWrapper(object):
-    def __init__(self, wrapped):
+    def __init__(self, server, wrapped):
+        self.server = server
         self.wrapped = wrapped
 
     def __call__(self, latent_sample=None):
@@ -404,5 +410,5 @@ def patch_pipeline(
     original_unet = pipe.unet
     original_vae = pipe.vae_decoder
 
-    pipe.unet = UNetWrapper(original_unet)
-    pipe.vae_decoder = VAEWrapper(original_vae)
+    pipe.unet = UNetWrapper(server, original_unet)
+    pipe.vae_decoder = VAEWrapper(server, original_vae)

docs/server-admin.md

@@ -99,8 +99,12 @@ Others:
     - not available for ONNX pipelines (most of them)
     - https://huggingface.co/docs/diffusers/optimization/fp16#sliced-vae-decode-for-larger-batches
 - `onnx-*`
-  - `onnx-low-memory`
-    - disable ONNX features that allocate more memory than is strictly required or keep memory after use
+  - `onnx-deterministic-compute`
+    - enable ONNX deterministic compute
+  - `onnx-fp16`
+    - force 16-bit floating point values when running pipelines
+    - use with https://github.com/microsoft/onnxruntime/tree/main/onnxruntime/python/tools/transformers/models/stable_diffusion#optimize-onnx-pipeline
+      and the `--float16` flag
   - `onnx-graph-*`
     - `onnx-graph-disable`
       - disable all ONNX graph optimizations
@@ -108,11 +112,11 @@ Others:
       - enable basic ONNX graph optimizations
     - `onnx-graph-all`
       - enable all ONNX graph optimizations
-  - `onnx-deterministic-compute`
-    - enable ONNX deterministic compute
+  - `onnx-low-memory`
+    - disable ONNX features that allocate more memory than is strictly required or keep memory after use
 - `torch-*`
   - `torch-fp16`
-    - use 16-bit floating point values when loading and converting pipelines
+    - use 16-bit floating point values when converting and running pipelines
     - applies during conversion as well
     - only available on CUDA platform