diff options
| -rw-r--r-- | modules/sd_models.py | 6 | ||||
| -rw-r--r-- | modules/sd_vae.py | 3 | ||||
| -rw-r--r-- | modules/xpu_specific.py | 22 |
3 files changed, 23 insertions, 8 deletions
diff --git a/modules/sd_models.py b/modules/sd_models.py index 50bc209e..2c045771 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -842,13 +842,13 @@ def reload_model_weights(sd_model=None, info=None, forced_reload=False): sd_hijack.model_hijack.hijack(sd_model)
timer.record("hijack")
- script_callbacks.model_loaded_callback(sd_model)
- timer.record("script callbacks")
-
if not sd_model.lowvram:
sd_model.to(devices.device)
timer.record("move model to device")
+ script_callbacks.model_loaded_callback(sd_model)
+ timer.record("script callbacks")
+
print(f"Weights loaded in {timer.summary()}.")
model_data.set_sd_model(sd_model)
diff --git a/modules/sd_vae.py b/modules/sd_vae.py index 31306d8b..43687e48 100644 --- a/modules/sd_vae.py +++ b/modules/sd_vae.py @@ -273,10 +273,11 @@ def reload_vae_weights(sd_model=None, vae_file=unspecified): load_vae(sd_model, vae_file, vae_source) sd_hijack.model_hijack.hijack(sd_model) - script_callbacks.model_loaded_callback(sd_model) if not sd_model.lowvram: sd_model.to(devices.device) + script_callbacks.model_loaded_callback(sd_model) + print("VAE weights loaded.") return sd_model diff --git a/modules/xpu_specific.py b/modules/xpu_specific.py index f7687a66..2971dbc3 100644 --- a/modules/xpu_specific.py +++ b/modules/xpu_specific.py @@ -41,6 +41,8 @@ def torch_xpu_scaled_dot_product_attention( # cast to same dtype first key = key.to(query.dtype) value = value.to(query.dtype) + if attn_mask is not None and attn_mask.dtype != torch.bool: + attn_mask = attn_mask.to(query.dtype) N = query.shape[:-2] # Batch size L = query.size(-2) # Target sequence length @@ -92,11 +94,23 @@ def torch_xpu_scaled_dot_product_attention( return torch.reshape(result, (*N, L, Ev)) +def is_xpu_device(device: str | torch.device = None): + if device is None: + return False + if isinstance(device, str): + return device.startswith("xpu") + return device.type == "xpu" + + if has_xpu: - # W/A for https://github.com/intel/intel-extension-for-pytorch/issues/452: torch.Generator API doesn't support XPU device - CondFunc('torch.Generator', - lambda orig_func, device=None: torch.xpu.Generator(device), - lambda orig_func, device=None: device is not None and device.type == "xpu") + try: + # torch.Generator supports "xpu" device since 2.1 + torch.Generator("xpu") + except RuntimeError: + # W/A for https://github.com/intel/intel-extension-for-pytorch/issues/452: torch.Generator API doesn't support XPU device (for torch < 2.1) + CondFunc('torch.Generator', + lambda orig_func, device=None: torch.xpu.Generator(device), + lambda orig_func, device=None: is_xpu_device(device)) # W/A for some OPs that could not handle different input dtypes CondFunc('torch.nn.functional.layer_norm', |