diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml
index 2c519ede5..09fea712e 100644
--- a/.github/ISSUE_TEMPLATE/config.yml
+++ b/.github/ISSUE_TEMPLATE/config.yml
@@ -1,5 +1,8 @@
 blank_issues_enabled: true
 contact_links:
+  - name: ComfyUI Frontend Issues
+    url: https://github.com/Comfy-Org/ComfyUI_frontend/issues
+    about: Issues related to the ComfyUI frontend (display issues, user interaction bugs), please go to the frontend repo to file the issue
   - name: ComfyUI Matrix Space
     url: https://app.element.io/#/room/%23comfyui_space%3Amatrix.org
     about: The ComfyUI Matrix Space is available for support and general discussion related to ComfyUI (Matrix is like Discord but open source).
diff --git a/comfy/float.py b/comfy/float.py
index 51ae9872a..1dbdafd59 100644
--- a/comfy/float.py
+++ b/comfy/float.py
@@ -19,25 +19,29 @@ def manual_stochastic_round_to_float8(x, dtype):
     )
 
     # Combine mantissa calculation and rounding
-    mantissa = abs_x / (2.0 ** (exponent - EXPONENT_BIAS)) - 1.0
-    mantissa_scaled = mantissa * (2**MANTISSA_BITS)
+    # min_normal = 2.0 ** (-EXPONENT_BIAS + 1)
+    # zero_mask = (abs_x == 0)
+    # subnormal_mask = (exponent == 0) & (abs_x != 0)
+    normal_mask = ~(exponent == 0)
+
+    mantissa_scaled = torch.where(
+        normal_mask,
+        (abs_x / (2.0 ** (exponent - EXPONENT_BIAS)) - 1.0) * (2**MANTISSA_BITS),
+        (abs_x / (2.0 ** (-EXPONENT_BIAS + 1 - MANTISSA_BITS)))
+    )
     mantissa_floor = mantissa_scaled.floor()
     mantissa = torch.where(
         torch.rand_like(mantissa_scaled) < (mantissa_scaled - mantissa_floor),
         (mantissa_floor + 1) / (2**MANTISSA_BITS),
         mantissa_floor / (2**MANTISSA_BITS)
     )
+    result = torch.where(
+        normal_mask,
+        sign * (2.0 ** (exponent - EXPONENT_BIAS)) * (1.0 + mantissa),
+        sign * (2.0 ** (-EXPONENT_BIAS + 1)) * mantissa
+    )
 
-    # Combine final result calculation
-    result = sign * (2.0 ** (exponent - EXPONENT_BIAS)) * (1.0 + mantissa)
-
-    # Handle zero case
-    zero_mask = (abs_x == 0)
-    result = torch.where(zero_mask, torch.zeros_like(result), result)
-
-    # Handle subnormal numbers
-    min_normal = 2.0 ** (-EXPONENT_BIAS + 1)
-    result = torch.where((abs_x < min_normal) & (~zero_mask), torch.round(x / (2.0 ** (-EXPONENT_BIAS + 1 - MANTISSA_BITS))) * (2.0 ** (-EXPONENT_BIAS + 1 - MANTISSA_BITS)), result)
+    result = torch.where(abs_x == 0, 0, result)
     return result.to(dtype=dtype)
 
 
diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py
index c6fb0eff1..6be8f7730 100644
--- a/comfy/model_patcher.py
+++ b/comfy/model_patcher.py
@@ -644,40 +644,46 @@ class ModelPatcher:
     def partially_unload(self, device_to, memory_to_free=0):
         memory_freed = 0
         patch_counter = 0
+        unload_list = []
 
-        for n, m in list(self.model.named_modules())[::-1]:
-            if memory_to_free < memory_freed:
-                break
-
+        for n, m in self.model.named_modules():
             shift_lowvram = False
             if hasattr(m, "comfy_cast_weights"):
                 module_mem = comfy.model_management.module_size(m)
-                weight_key = "{}.weight".format(n)
-                bias_key = "{}.bias".format(n)
+                unload_list.append((module_mem, n, m))
 
+        unload_list.sort()
+        for unload in unload_list:
+            if memory_to_free < memory_freed:
+                break
+            module_mem = unload[0]
+            n = unload[1]
+            m = unload[2]
+            weight_key = "{}.weight".format(n)
+            bias_key = "{}.bias".format(n)
 
-                if m.weight is not None and m.weight.device != device_to:
-                    for key in [weight_key, bias_key]:
-                        bk = self.backup.get(key, None)
-                        if bk is not None:
-                            if bk.inplace_update:
-                                comfy.utils.copy_to_param(self.model, key, bk.weight)
-                            else:
-                                comfy.utils.set_attr_param(self.model, key, bk.weight)
-                            self.backup.pop(key)
+            if m.weight is not None and m.weight.device != device_to:
+                for key in [weight_key, bias_key]:
+                    bk = self.backup.get(key, None)
+                    if bk is not None:
+                        if bk.inplace_update:
+                            comfy.utils.copy_to_param(self.model, key, bk.weight)
+                        else:
+                            comfy.utils.set_attr_param(self.model, key, bk.weight)
+                        self.backup.pop(key)
 
-                    m.to(device_to)
-                    if weight_key in self.patches:
-                        m.weight_function = LowVramPatch(weight_key, self)
-                        patch_counter += 1
-                    if bias_key in self.patches:
-                        m.bias_function = LowVramPatch(bias_key, self)
-                        patch_counter += 1
+                m.to(device_to)
+                if weight_key in self.patches:
+                    m.weight_function = LowVramPatch(weight_key, self)
+                    patch_counter += 1
+                if bias_key in self.patches:
+                    m.bias_function = LowVramPatch(bias_key, self)
+                    patch_counter += 1
 
-                    m.prev_comfy_cast_weights = m.comfy_cast_weights
-                    m.comfy_cast_weights = True
-                    memory_freed += module_mem
-                    logging.debug("freed {}".format(n))
+                m.prev_comfy_cast_weights = m.comfy_cast_weights
+                m.comfy_cast_weights = True
+                memory_freed += module_mem
+                logging.debug("freed {}".format(n))
 
         self.model.model_lowvram = True
         self.model.lowvram_patch_counter += patch_counter