Fix ONNX/Olive generation.

modules/onnx_impl/pipelines/onnx_stable_diffusion_pipeline.py

@@ -62,6 +62,9 @@ def __call__(
         if generator is None:
             generator = torch.Generator("cpu")
 
+        # set timesteps
+        self.scheduler.set_timesteps(num_inference_steps)
+
         # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
         # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
         # corresponds to doing no classifier free guidance.
@@ -84,12 +87,9 @@ def __call__(
             width,
             prompt_embeds.dtype,
             generator,
-            latents
+            latents,
         )
 
-        # set timesteps
-        self.scheduler.set_timesteps(num_inference_steps)
-
         # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
         # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
         # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502

modules/onnx_impl/pipelines/onnx_stable_diffusion_upscale_pipeline.py

@@ -70,6 +70,9 @@ def __call__(
         if generator is None:
             generator = torch.Generator("cpu")
 
+        self.scheduler.set_timesteps(num_inference_steps)
+        timesteps = self.scheduler.timesteps
+
         # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
         # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
         # corresponds to doing no classifier free guidance.
@@ -97,9 +100,6 @@ def __call__(
             generator,
         )
 
-        self.scheduler.set_timesteps(num_inference_steps)
-        timesteps = self.scheduler.timesteps
-
         # 5. Add noise to image
         noise_level = np.array([noise_level]).astype(np.int64)
         noise = randn_tensor(

modules/onnx_impl/pipelines/utils.py

@@ -26,21 +26,14 @@ def prepare_latents(
     width: int,
     dtype: np.dtype,
     generator: Union[torch.Generator, List[torch.Generator]],
-    latents: Union[np.ndarray, None]=None,
-    num_channels_latents=4,
-    vae_scale_factor=8,
+    latents: Union[np.ndarray, None] = None,
+    num_channels_latents = 4,
+    vae_scale_factor = 8,
 ):
     shape = (batch_size, num_channels_latents, height // vae_scale_factor, width // vae_scale_factor)
-    if isinstance(generator, list) and len(generator) != batch_size:
-        raise ValueError(
-            f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
-            f" size of {batch_size}. Make sure the batch size matches the length of the generators."
-        )
 
     if latents is None:
         latents = randn_tensor(shape, dtype, generator)
-    elif latents.shape != shape:
-        raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
 
     # scale the initial noise by the standard deviation required by the scheduler
     latents = latents * np.float64(init_noise_sigma)