Basic support for Intel XPU (Arc Graphics)

comfy/model_management.py

@@ -7,6 +7,7 @@
 MPS = 5
 
 accelerate_enabled = False
+xpu_available = False
 vram_state = NORMAL_VRAM
 
 total_vram = 0
@@ -21,7 +22,12 @@
 
 try:
     import torch
-    total_vram = torch.cuda.mem_get_info(torch.cuda.current_device())[1] / (1024 * 1024)
+    import intel_extension_for_pytorch as ipex
+    if torch.xpu.is_available():
+        xpu_available = True
+        total_vram = torch.xpu.get_device_properties(torch.xpu.current_device()).total_memory / (1024 * 1024)
+    else:
+        total_vram = torch.cuda.mem_get_info(torch.cuda.current_device())[1] / (1024 * 1024)
     total_ram = psutil.virtual_memory().total / (1024 * 1024)
     forced_normal_vram = "--normalvram" in sys.argv
     if not forced_normal_vram and not forced_cpu:
@@ -125,6 +131,7 @@ def load_model_gpu(model):
     global current_loaded_model
     global vram_state
     global model_accelerated
+    global xpu_available
 
     if model is current_loaded_model:
         return
@@ -143,14 +150,17 @@ def load_model_gpu(model):
         pass
     elif vram_state == NORMAL_VRAM or vram_state == HIGH_VRAM:
         model_accelerated = False
-        real_model.cuda()
+        if xpu_available:
+            real_model.to("xpu")
+        else:
+            real_model.cuda()
     else:
         if vram_state == NO_VRAM:
             device_map = accelerate.infer_auto_device_map(real_model, max_memory={0: "256MiB", "cpu": "16GiB"})
         elif vram_state == LOW_VRAM:
             device_map = accelerate.infer_auto_device_map(real_model, max_memory={0: "{}MiB".format(total_vram_available_mb), "cpu": "16GiB"})
 
-        accelerate.dispatch_model(real_model, device_map=device_map, main_device="cuda")
+        accelerate.dispatch_model(real_model, device_map=device_map, main_device="xpu" if xpu_available else "cuda")
         model_accelerated = True
     return current_loaded_model
 
@@ -176,8 +186,12 @@ def load_controlnet_gpu(models):
 
 def load_if_low_vram(model):
     global vram_state
+    global xpu_available
     if vram_state == LOW_VRAM or vram_state == NO_VRAM:
-        return model.cuda()
+        if xpu_available:
+            return model.to("xpu")
+        else:
+            return model.cuda()
     return model
 
 def unload_if_low_vram(model):
@@ -187,12 +201,16 @@ def unload_if_low_vram(model):
     return model
 
 def get_torch_device():
+    global xpu_available
     if vram_state == MPS:
         return torch.device("mps")
     if vram_state == CPU:
         return torch.device("cpu")
     else:
-        return torch.cuda.current_device()
+        if xpu_available:
+            return torch.device("xpu")
+        else:
+            return torch.cuda.current_device()
 
 def get_autocast_device(dev):
     if hasattr(dev, 'type'):
@@ -222,19 +240,24 @@ def pytorch_attention_enabled():
     return ENABLE_PYTORCH_ATTENTION
 
 def get_free_memory(dev=None, torch_free_too=False):
+    global xpu_available
     if dev is None:
         dev = get_torch_device()
 
     if hasattr(dev, 'type') and (dev.type == 'cpu' or dev.type == 'mps'):
         mem_free_total = psutil.virtual_memory().available
         mem_free_torch = mem_free_total
     else:
-        stats = torch.cuda.memory_stats(dev)
-        mem_active = stats['active_bytes.all.current']
-        mem_reserved = stats['reserved_bytes.all.current']
-        mem_free_cuda, _ = torch.cuda.mem_get_info(dev)
-        mem_free_torch = mem_reserved - mem_active
-        mem_free_total = mem_free_cuda + mem_free_torch
+        if xpu_available:
+            mem_free_total = torch.xpu.get_device_properties(dev).total_memory - torch.xpu.memory_allocated(dev)
+            mem_free_torch = mem_free_total
+        else:
+            stats = torch.cuda.memory_stats(dev)
+            mem_active = stats['active_bytes.all.current']
+            mem_reserved = stats['reserved_bytes.all.current']
+            mem_free_cuda, _ = torch.cuda.mem_get_info(dev)
+            mem_free_torch = mem_reserved - mem_active
+            mem_free_total = mem_free_cuda + mem_free_torch
 
     if torch_free_too:
         return (mem_free_total, mem_free_torch)
@@ -259,7 +282,8 @@ def mps_mode():
     return vram_state == MPS
 
 def should_use_fp16():
-    if cpu_mode() or mps_mode():
+    global xpu_available
+    if cpu_mode() or mps_mode() or xpu_available:
         return False #TODO ?
 
     if torch.cuda.is_bf16_supported():

requirements.txt

@@ -4,7 +4,7 @@ torchsde
 einops
 open-clip-torch
 transformers>=4.25.1
-safetensors
+safetensors>=0.3.0
 pytorch_lightning
 aiohttp
 accelerate