Explicitly set dtypes based on precision argument

models/turbine_models/custom_models/sdxl_inference/sdxl_pipeline.py

@@ -203,7 +203,8 @@ def export_submodel(args, submodel):
 
 def generate_images(args, vmfbs: dict, weights: dict):
     pipe_start = time.time()
-    dtype = torch.float16 if args.precision == "fp16" else torch.float32
+    iree_dtype = "float32" if args.precision == "fp32" else "float16"
+    torch_dtype = torch.float32 if args.precision == "fp32" else torch.float16
 
     all_imgs = []
     generator = torch.manual_seed(0)
@@ -215,7 +216,7 @@ def generate_images(args, vmfbs: dict, weights: dict):
             args.width // 8,
         ),
         generator=generator,
-        dtype=dtype,
+        dtype=torch_dtype,
     )
 
     pipe_runner = vmfbRunner(
@@ -250,19 +251,23 @@ def generate_images(args, vmfbs: dict, weights: dict):
     prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
     add_text_embeds = torch.cat([pooled_negative_prompt_embeds, add_text_embeds], dim=0)
 
-    add_text_embeds = add_text_embeds.to(dtype)
-    prompt_embeds = prompt_embeds.to(dtype)
+    add_text_embeds = add_text_embeds.to(torch_dtype)
+    prompt_embeds = prompt_embeds.to(torch_dtype)
 
     unet_start = time.time()
 
     unet_inputs = [
-        ireert.asdevicearray(pipe_runner.config.device, rand_sample),
-        ireert.asdevicearray(pipe_runner.config.device, prompt_embeds),
-        ireert.asdevicearray(pipe_runner.config.device, add_text_embeds),
+        ireert.asdevicearray(pipe_runner.config.device, rand_sample, dtype=iree_dtype),
+        ireert.asdevicearray(
+            pipe_runner.config.device, prompt_embeds, dtype=iree_dtype
+        ),
+        ireert.asdevicearray(
+            pipe_runner.config.device, add_text_embeds, dtype=iree_dtype
+        ),
         ireert.asdevicearray(
             pipe_runner.config.device,
             np.asarray([args.guidance_scale]),
-            dtype="float32" if args.precision == "fp32" else "float16",
+            dtype=iree_dtype,
         ),
     ]
     latents = pipe_runner.ctx.modules.sdxl_compiled_pipeline["produce_image_latents"](
@@ -395,6 +400,5 @@ def is_prepared(args, vmfbs, weights):
                     vmfbs[submodel] = vmfb
                     if weights[submodel] is None:
                         weights[submodel] = weight
-            assert is_prepared(args, vmfbs, weights)[0]
     generate_images(args, vmfbs, weights)
     print("Image generation complete.")