Merge branch 'dev' of github.com:AUTOMATIC1111/stable-diffusion-webui into dev

modules/devices.py

@@ -3,8 +3,7 @@ import contextlib
 from functools import lru_cache
 
 import torch
-from modules import errors, shared
-from modules import torch_utils
+from modules import errors, shared, npu_specific
 
 if sys.platform == "darwin":
     from modules import mac_specific
@@ -58,6 +57,9 @@ def get_optimal_device_name():
     if has_xpu():
         return xpu_specific.get_xpu_device_string()
 
+    if npu_specific.has_npu:
+        return npu_specific.get_npu_device_string()
+
     return "cpu"
 
 
@@ -85,6 +87,16 @@ def torch_gc():
     if has_xpu():
         xpu_specific.torch_xpu_gc()
 
+    if npu_specific.has_npu:
+        torch_npu_set_device()
+        npu_specific.torch_npu_gc()
+
+
+def torch_npu_set_device():
+    # Work around due to bug in torch_npu, revert me after fixed, @see https://gitee.com/ascend/pytorch/issues/I8KECW?from=project-issue
+    if npu_specific.has_npu:
+        torch.npu.set_device(0)
+
 
 def enable_tf32():
     if torch.cuda.is_available():
@@ -141,7 +153,12 @@ def manual_cast_forward(target_dtype):
             args = [arg.to(target_dtype) if isinstance(arg, torch.Tensor) else arg for arg in args]
             kwargs = {k: v.to(target_dtype) if isinstance(v, torch.Tensor) else v for k, v in kwargs.items()}
 
-        org_dtype = torch_utils.get_param(self).dtype
+        org_dtype = target_dtype
+        for param in self.parameters():
+            if param.dtype != target_dtype:
+                org_dtype = param.dtype
+                break
+
         if org_dtype != target_dtype:
             self.to(target_dtype)
         result = self.org_forward(*args, **kwargs)
@@ -170,7 +187,7 @@ def manual_cast(target_dtype):
             continue
         applied = True
         org_forward = module_type.forward
-        if module_type == torch.nn.MultiheadAttention and has_xpu():
+        if module_type == torch.nn.MultiheadAttention:
             module_type.forward = manual_cast_forward(torch.float32)
         else:
             module_type.forward = manual_cast_forward(target_dtype)
@@ -252,4 +269,3 @@ def first_time_calculation():
     x = torch.zeros((1, 1, 3, 3)).to(device, dtype)
     conv2d = torch.nn.Conv2d(1, 1, (3, 3)).to(device, dtype)
     conv2d(x)
-

modules/initialize.py

@@ -142,13 +142,14 @@ def initialize_rest(*, reload_script_modules=False):
         its optimization may be None because the list of optimizaers has neet been filled
         by that time, so we apply optimization again.
         """
+        from modules import devices
+        devices.torch_npu_set_device()
 
         shared.sd_model  # noqa: B018
 
         if sd_hijack.current_optimizer is None:
             sd_hijack.apply_optimizations()
 
-        from modules import devices
         devices.first_time_calculation()
     if not shared.cmd_opts.skip_load_model_at_start:
         Thread(target=load_model).start()

modules/launch_utils.py

@@ -338,6 +338,7 @@ def prepare_environment():
             torch_index_url = os.environ.get('TORCH_INDEX_URL', "https://pytorch-extension.intel.com/release-whl/stable/xpu/us/")
             torch_command = os.environ.get('TORCH_COMMAND', f"pip install torch==2.0.0a0 intel-extension-for-pytorch==2.0.110+gitba7f6c1 --extra-index-url {torch_index_url}")
     requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt")
+    requirements_file_for_npu = os.environ.get('REQS_FILE_FOR_NPU', "requirements_npu.txt")
 
     xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.23.post1')
     clip_package = os.environ.get('CLIP_PACKAGE', "https://github.com/openai/CLIP/archive/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1.zip")
@@ -421,6 +422,13 @@ def prepare_environment():
         run_pip(f"install -r \"{requirements_file}\"", "requirements")
         startup_timer.record("install requirements")
 
+    if not os.path.isfile(requirements_file_for_npu):
+        requirements_file_for_npu = os.path.join(script_path, requirements_file_for_npu)
+
+    if "torch_npu" in torch_command and not requirements_met(requirements_file_for_npu):
+        run_pip(f"install -r \"{requirements_file_for_npu}\"", "requirements_for_npu")
+        startup_timer.record("install requirements_for_npu")
+
     if not args.skip_install:
         run_extensions_installers(settings_file=args.ui_settings_file)
 

modules/npu_specific.py

@@ -0,0 +1,31 @@
+import importlib
+import torch
+
+from modules import shared
+
+
+def check_for_npu():
+    if importlib.util.find_spec("torch_npu") is None:
+        return False
+    import torch_npu
+
+    try:
+        # Will raise a RuntimeError if no NPU is found
+        _ = torch_npu.npu.device_count()
+        return torch.npu.is_available()
+    except RuntimeError:
+        return False
+
+
+def get_npu_device_string():
+    if shared.cmd_opts.device_id is not None:
+        return f"npu:{shared.cmd_opts.device_id}"
+    return "npu:0"
+
+
+def torch_npu_gc():
+    with torch.npu.device(get_npu_device_string()):
+        torch.npu.empty_cache()
+
+
+has_npu = check_for_npu()

modules/sd_samplers_cfg_denoiser.py

@@ -94,7 +94,7 @@ class CFGDenoiser(torch.nn.Module):
 
     def pad_cond_uncond(self, cond, uncond):
         empty = shared.sd_model.cond_stage_model_empty_prompt
-        num_repeats = (cond.shape[1] - cond.shape[1]) // empty.shape[1]
+        num_repeats = (cond.shape[1] - uncond.shape[1]) // empty.shape[1]
 
         if num_repeats < 0:
             cond = pad_cond(cond, -num_repeats, empty)

modules/textual_inversion/textual_inversion.py

@@ -150,6 +150,7 @@ class EmbeddingDatabase:
         return embedding
 
     def get_expected_shape(self):
+        devices.torch_npu_set_device()
         vec = shared.sd_model.cond_stage_model.encode_embedding_init_text(",", 1)
         return vec.shape[1]
 

modules/ui_toprow.py

@@ -96,9 +96,9 @@ class Toprow:
         with gr.Row(elem_id=f"{self.id_part}_generate_box", elem_classes=["generate-box"] + (["generate-box-compact"] if self.is_compact else []), render=not self.is_compact) as submit_box:
             self.submit_box = submit_box
 
-            self.interrupt = gr.Button('Interrupt', elem_id=f"{self.id_part}_interrupt", elem_classes="generate-box-interrupt")
-            self.skip = gr.Button('Skip', elem_id=f"{self.id_part}_skip", elem_classes="generate-box-skip")
-            self.submit = gr.Button('Generate', elem_id=f"{self.id_part}_generate", variant='primary')
+            self.interrupt = gr.Button('Interrupt', elem_id=f"{self.id_part}_interrupt", elem_classes="generate-box-interrupt", tooltip="End generation immediately or after completing current batch")
+            self.skip = gr.Button('Skip', elem_id=f"{self.id_part}_skip", elem_classes="generate-box-skip", tooltip="Stop generation of current batch and continues onto next batch")
+            self.submit = gr.Button('Generate', elem_id=f"{self.id_part}_generate", variant='primary', tooltip="Right click generate forever menu")
 
             self.skip.click(
                 fn=lambda: shared.state.skip(),

modules/upscaler_utils.py

@@ -6,7 +6,7 @@ import torch
 import tqdm
 from PIL import Image
 
-from modules import images, shared, torch_utils
+from modules import devices, images, shared, torch_utils
 
 logger = logging.getLogger(__name__)
 
@@ -44,7 +44,8 @@ def upscale_pil_patch(model, img: Image.Image) -> Image.Image:
     with torch.no_grad():
         tensor = pil_image_to_torch_bgr(img).unsqueeze(0)  # add batch dimension
         tensor = tensor.to(device=param.device, dtype=param.dtype)
-        return torch_bgr_to_pil_image(model(tensor))
+        with devices.without_autocast():
+            return torch_bgr_to_pil_image(model(tensor))
 
 
 def upscale_with_model(

requirements_npu.txt

@@ -0,0 +1,4 @@
+cloudpickle
+decorator
+synr==0.5.0
+tornado

requirements_versions.txt

@@ -19,7 +19,7 @@ piexif==1.1.3
 psutil==5.9.5
 pytorch_lightning==1.9.4
 resize-right==0.0.2
-safetensors==0.3.1
+safetensors==0.4.2
 scikit-image==0.21.0
 spandrel==0.1.6
 tomesd==0.1.3

webui.sh

@@ -158,6 +158,10 @@ then
     if echo "$gpu_info" | grep -q "AMD" && [[ -z "${TORCH_COMMAND}" ]]
     then
         export TORCH_COMMAND="pip install torch==2.0.1+rocm5.4.2 torchvision==0.15.2+rocm5.4.2 --index-url https://download.pytorch.org/whl/rocm5.4.2"
+    elif echo "$gpu_info" | grep -q "Huawei" && [[ -z "${TORCH_COMMAND}" ]]
+    then
+        export TORCH_COMMAND="pip install torch==2.1.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu; pip install torch_npu"
+    
     fi
 fi