diff --git a/comfy/ldm/modules/attention.py b/comfy/ldm/modules/attention.py
index 0e3821ef0..dd8c6ba72 100644
--- a/comfy/ldm/modules/attention.py
+++ b/comfy/ldm/modules/attention.py
@@ -98,7 +98,7 @@ def var_attn_arg(kwargs):
     cu_seqlens_k = kwargs.get("cu_seqlens_k", cu_seqlens_q)
     max_seqlen_q = kwargs.get("max_seqlen_q", None)
     max_seqlen_k = kwargs.get("max_seqlen_k", max_seqlen_q)
-    assert cu_seqlens_q != None, "cu_seqlens_q shouldn't be None when var_length is True"
+    assert cu_seqlens_q is not None, "cu_seqlens_q shouldn't be None when var_length is True"
     return cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k
 # feedforward
 class GEGLU(nn.Module):
@@ -449,9 +449,12 @@ def attention_xformers(q, k, v, heads, mask=None, attn_precision=None, skip_resh
             k = k.view(1, total_tokens, heads, dim_head)
             v = v.view(1, total_tokens, heads, dim_head)
         else:
-            if q.ndim == 3: q = q.unsqueeze(0)
-            if k.ndim == 3: k = k.unsqueeze(0)
-            if v.ndim == 3: v = v.unsqueeze(0)
+            if q.ndim == 3:
+                q = q.unsqueeze(0)
+            if k.ndim == 3:
+                k = k.unsqueeze(0)
+            if v.ndim == 3:
+                v = v.unsqueeze(0)
             dim_head = q.shape[-1]
 
         target_output_shape = (q.shape[1], -1)
@@ -526,7 +529,8 @@ def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_resha
             k = k.view(k.shape[0], heads, head_dim)
             v = v.view(v.shape[0], heads, head_dim)
 
-        b = q.size(0); dim_head = q.shape[-1]
+        b = q.size(0)
+        dim_head = q.shape[-1]
         q = torch.nested.nested_tensor_from_jagged(q, offsets=cu_seqlens_q.long())
         k = torch.nested.nested_tensor_from_jagged(k, offsets=cu_seqlens_k.long())
         v = torch.nested.nested_tensor_from_jagged(v, offsets=cu_seqlens_k.long())
diff --git a/comfy_extras/nodes_seedvr.py b/comfy_extras/nodes_seedvr.py
index 945cf966b..c4e8f3958 100644
--- a/comfy_extras/nodes_seedvr.py
+++ b/comfy_extras/nodes_seedvr.py
@@ -78,8 +78,10 @@ def tiled_vae(x, vae_model, tile_size=(512, 512), tile_overlap=(64, 64), tempora
             else:
                 out = vae_model.decode_(t_chunk)
 
-            if isinstance(out, (tuple, list)): out = out[0]
-            if out.ndim == 4: out = out.unsqueeze(2)
+            if isinstance(out, (tuple, list)):
+                out = out[0]
+            if out.ndim == 4:
+                out = out.unsqueeze(2)
 
             if pad_amount > 0:
                 if encode:
@@ -136,13 +138,17 @@ def tiled_vae(x, vae_model, tile_size=(512, 512), tile_overlap=(64, 64), tempora
 
             if cur_ov_h > 0:
                 r = get_ramp(cur_ov_h)
-                if y_idx > 0: w_h[:cur_ov_h] = r
-                if y_end < h: w_h[-cur_ov_h:] = 1.0 - r
+                if y_idx > 0:
+                    w_h[:cur_ov_h] = r
+                if y_end < h:
+                    w_h[-cur_ov_h:] = 1.0 - r
 
             if cur_ov_w > 0:
                 r = get_ramp(cur_ov_w)
-                if x_idx > 0: w_w[:cur_ov_w] = r
-                if x_end < w: w_w[-cur_ov_w:] = 1.0 - r
+                if x_idx > 0:
+                    w_w[:cur_ov_w] = r
+                if x_end < w:
+                    w_w[-cur_ov_w:] = 1.0 - r
 
             final_weight = w_h.view(1,1,1,-1,1) * w_w.view(1,1,1,1,-1)
 
@@ -335,7 +341,8 @@ class SeedVR2InputProcessing(io.ComfyNode):
 
         comfy.model_management.load_models_gpu([vae.patcher])
         vae_model = vae.first_stage_model
-        scale = 0.9152; shift = 0
+        scale = 0.9152
+        shift = 0
         if images.dim() != 5: # add the t dim
             images = images.unsqueeze(0)
         images = images.permute(0, 1, 4, 2, 3)