fix: 添加MPS设备对float8数据类型的兼容性处理

在MPS设备上，PyTorch不支持直接创建或转换float8数据类型。这会导致在尝试将float8张量移至MPS设备时出现运行时错误。

- 在 `stochastic_rounding` 函数中添加递归回退到CPU的逻辑，当检测到MPS设备且目标类型为float8时。
- 在 `cast_to` 函数中添加针对MPS设备的特殊处理，确保float8张量在移动到MPS前先转换为float16。
- 测试了两个workflow：templates-6-key-frames和image_z_image_turbo

相比https://github.com/Comfy-Org/ComfyUI/pull/12378 更具有维护性

comfy/float.py

@@ -55,6 +55,12 @@ def stochastic_rounding(value, dtype, seed=0):
     if dtype == torch.bfloat16:
         return value.to(dtype=torch.bfloat16)
     if dtype == torch.float8_e4m3fn or dtype == torch.float8_e5m2:
+        # MPS workaround: if we're on MPS and trying to create float8, force CPU
+        if value.device.type == "mps":
+             cpu_value = value.to("cpu")
+             # Recursive call on CPU
+             return stochastic_rounding(cpu_value, dtype, seed)
+
         generator = torch.Generator(device=value.device)
         generator.manual_seed(seed)
         output = torch.empty_like(value, dtype=dtype)

comfy/model_management.py

@@ -55,11 +55,6 @@ cpu_state = CPUState.GPU
 
 total_vram = 0
 
-
-# Training Related State
-in_training = False
-
-
 def get_supported_float8_types():
     float8_types = []
     try:
@@ -1233,6 +1228,23 @@ def cast_to(weight, dtype=None, device=None, non_blocking=False, copy=False, str
 
         return r
 
+    target_device = device
+    if target_device is not None and is_device_mps(target_device):
+        is_quantized = hasattr(weight, "storage_dtype")
+        real_dtype = weight.storage_dtype if is_quantized else weight.dtype
+        if real_dtype in FLOAT8_TYPES:
+            if is_quantized:
+                # MPS workaround: ensure everything is on CPU before dequantizing
+                # This prevents 'Trying to convert Float8_e4m3fn to the MPS backend' errors
+                # if the tensor or its scale are partially on MPS.
+                if weight.device.type != "cpu":
+                    weight = weight.to("cpu")
+                weight = weight.dequantize()
+            if weight.dtype in FLOAT8_TYPES:
+                weight = weight.float()
+            weight = weight.to(target_device, dtype=torch.float16)
+            dtype = torch.float16 if dtype is None else dtype
+
     if device is None or weight.device == device:
         if not copy:
             if dtype is None or weight.dtype == dtype: