Merge ac18affdfd90d9af268b6da4f1ce23159e6821e8 into 7d627f764c2137d816a39adbc358cb28c1718a47

comfy/clip_vision.py

@@ -29,7 +29,12 @@ def clip_preprocess(image, size=224, mean=[0.48145466, 0.4578275, 0.40821073], s
         else:
             scale_size = (size, size)
 
-        image = torch.nn.functional.interpolate(image, size=scale_size, mode="bicubic", antialias=True)
+        if image.device.type == 'musa':
+            image = image.cpu()
+            image = torch.nn.functional.interpolate(image, size=scale_size, mode="bicubic", antialias=True)
+            image = image.to('musa')
+        else:
+            image = torch.nn.functional.interpolate(image, size=scale_size, mode="bicubic", antialias=True)
         h = (image.shape[2] - size)//2
         w = (image.shape[3] - size)//2
         image = image[:,:,h:h+size,w:w+size]

comfy/ldm/flux/math.py

@@ -27,9 +27,14 @@ def rope(pos: Tensor, dim: int, theta: int) -> Tensor:
         device = torch.device("cpu")
     else:
         device = pos.device
-
-    scale = torch.linspace(0, (dim - 2) / dim, steps=dim//2, dtype=torch.float64, device=device)
-    omega = 1.0 / (theta**scale)
+    if device.type == "musa":
+        scale = torch.linspace(0, (dim - 2) / dim, steps=dim // 2, dtype=torch.float32, device=device)
+        if not isinstance(theta, torch.Tensor):
+            theta = torch.tensor(theta, dtype=torch.float32, device=device)
+        omega = torch.exp(-scale * torch.log(theta + 1e-6))
+    else:
+        scale = torch.linspace(0, (dim - 2) / dim, steps=dim//2, dtype=torch.float64, device=device)
+        omega = 1.0 / (theta**scale)
     out = torch.einsum("...n,d->...nd", pos.to(dtype=torch.float32, device=device), omega)
     out = torch.stack([torch.cos(out), -torch.sin(out), torch.sin(out), torch.cos(out)], dim=-1)
     out = rearrange(out, "b n d (i j) -> b n d i j", i=2, j=2)

comfy/model_management.py

@@ -128,6 +128,15 @@ try:
 except:
     mlu_available = False
 
+try:
+    import torch_musa
+    _ = torch_musa.device_count()
+    musa_available = torch_musa.is_available()
+    if musa_available:
+        logging.info("MUSA device detected: {}".format(torch_musa.get_device_name(0)))
+except:
+    musa_available = False
+
 if args.cpu:
     cpu_state = CPUState.CPU
 
@@ -151,6 +160,12 @@ def is_mlu():
         return True
     return False
 
+def is_musa():
+    global musa_available
+    if musa_available:
+        return True
+    return False
+
 def get_torch_device():
     global directml_enabled
     global cpu_state
@@ -168,6 +183,8 @@ def get_torch_device():
             return torch.device("npu", torch.npu.current_device())
         elif is_mlu():
             return torch.device("mlu", torch.mlu.current_device())
+        elif is_musa():
+            return torch.device('musa', torch.musa.current_device())
         else:
             return torch.device(torch.cuda.current_device())
 
@@ -200,6 +217,12 @@ def get_total_memory(dev=None, torch_total_too=False):
             _, mem_total_mlu = torch.mlu.mem_get_info(dev)
             mem_total_torch = mem_reserved
             mem_total = mem_total_mlu
+        elif is_musa():
+            stats = torch.musa.memory_stats(dev)
+            mem_reserved = stats['reserved_bytes.all.current']
+            _, mem_total = torch.musa.mem_get_info(dev)
+            mem_total_torch = mem_reserved
+            
         else:
             stats = torch.cuda.memory_stats(dev)
             mem_reserved = stats['reserved_bytes.all.current']
@@ -1109,6 +1132,14 @@ def get_free_memory(dev=None, torch_free_too=False):
             mem_free_mlu, _ = torch.mlu.mem_get_info(dev)
             mem_free_torch = mem_reserved - mem_active
             mem_free_total = mem_free_mlu + mem_free_torch
+        elif is_musa():
+            stats = torch.musa.memory_stats(dev)
+            mem_active = stats['active_bytes.all.current']
+            mem_reserved = stats['reserved_bytes.all.current']
+            mem_free_musa, _ = torch.musa.mem_get_info(dev)
+            mem_free_torch = mem_reserved - mem_active
+            mem_free_total = mem_free_musa + mem_free_torch
+            
         else:
             stats = torch.cuda.memory_stats(dev)
             mem_active = stats['active_bytes.all.current']
@@ -1181,6 +1212,9 @@ def should_use_fp16(device=None, model_params=0, prioritize_performance=True, ma
     if is_mlu():
         return True
 
+    if is_musa():
+        return True
+
     if torch.version.hip:
         return True
 
@@ -1241,6 +1275,9 @@ def should_use_bf16(device=None, model_params=0, prioritize_performance=True, ma
     if is_ascend_npu():
         return True
 
+    if is_musa():
+        return True
+
     if is_amd():
         arch = torch.cuda.get_device_properties(device).gcnArchName
         if any((a in arch) for a in ["gfx1030", "gfx1031", "gfx1010", "gfx1011", "gfx1012", "gfx906", "gfx900", "gfx803"]):  # RDNA2 and older don't support bf16