fix(api): apply fp16 optimizations to LoRA and Textual Inversion blen…

…ding

api/onnx_web/convert/diffusion/diffusers.py

@@ -97,8 +97,7 @@ def convert_diffusion_diffusers(
     single_vae = model.get("single_vae")
     replace_vae = model.get("vae")
 
-    torch_half = "torch-fp16" in ctx.optimizations
-    torch_dtype = torch.float16 if torch_half else torch.float32
+    torch_dtype = ctx.torch_dtype()
     logger.debug("using Torch dtype %s for pipeline", torch_dtype)
 
     dest_path = path.join(ctx.model_path, name)

api/onnx_web/convert/diffusion/lora.py

@@ -62,6 +62,7 @@ def blend_loras(
 ):
     # always load to CPU for blending
     device = torch.device("cpu")
+    dtype = context.torch_dtype()
 
     base_model = base_name if isinstance(base_name, ModelProto) else load(base_name)
     lora_models = [load_tensor(name, map_location=device) for name, _weight in loras]
@@ -88,11 +89,11 @@ def blend_loras(
                     "blending weights for keys: %s, %s, %s", key, up_key, alpha_key
                 )
 
-                down_weight = lora_model[key].to(dtype=torch.float32)
-                up_weight = lora_model[up_key].to(dtype=torch.float32)
+                down_weight = lora_model[key].to(dtype=dtype)
+                up_weight = lora_model[up_key].to(dtype=dtype)
 
                 dim = down_weight.size()[0]
-                alpha = lora_model.get(alpha_key, dim).to(torch.float32).numpy()
+                alpha = lora_model.get(alpha_key, dim).to(dtype).numpy()
 
                 try:
                     if len(up_weight.size()) == 2:
@@ -203,7 +204,7 @@ def blend_loras(
             logger.trace("blended weight shape: %s", blended.shape)
 
             # replace the original initializer
-            updated_node = numpy_helper.from_array(blended, weight_node.name)
+            updated_node = numpy_helper.from_array(blended.astype(base_weights.dtype), weight_node.name)
             del base_model.graph.initializer[weight_idx]
             base_model.graph.initializer.insert(weight_idx, updated_node)
         elif matmul_key in fixed_node_names:
@@ -232,7 +233,7 @@ def blend_loras(
             logger.trace("blended weight shape: %s", blended.shape)
 
             # replace the original initializer
-            updated_node = numpy_helper.from_array(blended, matmul_node.name)
+            updated_node = numpy_helper.from_array(blended.astype(base_weights.dtype), matmul_node.name)
             del base_model.graph.initializer[matmul_idx]
             base_model.graph.initializer.insert(matmul_idx, updated_node)
         else:

api/onnx_web/convert/diffusion/textual_inversion.py

@@ -22,7 +22,7 @@ def blend_textual_inversions(
 ) -> Tuple[ModelProto, CLIPTokenizer]:
     # always load to CPU for blending
     device = torch.device("cpu")
-    dtype = np.float
+    dtype = context.numpy_dtype()
     embeds = {}
 
     for name, weight, base_token, inversion_format in inversions:
@@ -149,7 +149,7 @@ def blend_textual_inversions(
                 == "text_model.embeddings.token_embedding.weight"
             ):
                 new_initializer = numpy_helper.from_array(
-                    embedding_weights.astype(np.float32), embedding_node.name
+                    embedding_weights.astype(dtype), embedding_node.name
                 )
                 logger.trace("new initializer data type: %s", new_initializer.data_type)
                 del text_encoder.graph.initializer[i]

api/onnx_web/server/context.py

@@ -2,6 +2,9 @@
 from os import environ, path
 from typing import List, Optional
 
+import torch
+import numpy as np
+
 from ..utils import get_boolean
 from .model_cache import ModelCache
 
@@ -77,3 +80,15 @@ def from_environ(cls):
             job_limit=int(environ.get("ONNX_WEB_JOB_LIMIT", DEFAULT_JOB_LIMIT)),
             memory_limit=memory_limit,
         )
+
+    def torch_dtype(self):
+        if "torch-fp16" in self.optimizations:
+            return torch.float16
+        else:
+            return torch.float32
+
+    def numpy_dtype(self):
+        if "torch-fp16" in self.optimizations or "onnx-fp16" in self.optimizations:
+            return np.float16
+        else:
+            return np.float32