linter

modules/models/sd3/other_impls.py

@@ -1,6 +1,7 @@
 ### This file contains impls for underlying related models (CLIP, T5, etc)
 
-import torch, math
+import torch
+import math
 from torch import nn
 from transformers import CLIPTokenizer, T5TokenizerFast
 
@@ -14,7 +15,7 @@ def attention(q, k, v, heads, mask=None):
     """Convenience wrapper around a basic attention operation"""
     b, _, dim_head = q.shape
     dim_head //= heads
-    q, k, v = map(lambda t: t.view(b, -1, heads, dim_head).transpose(1, 2), (q, k, v))
+    q, k, v = [t.view(b, -1, heads, dim_head).transpose(1, 2) for t in (q, k, v)]
     out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False)
     return out.transpose(1, 2).reshape(b, -1, heads * dim_head)
 
@@ -89,8 +90,8 @@ def forward(self, x, mask=None, intermediate_output=None):
             if intermediate_output < 0:
                 intermediate_output = len(self.layers) + intermediate_output
         intermediate = None
-        for i, l in enumerate(self.layers):
-            x = l(x, mask)
+        for i, layer in enumerate(self.layers):
+            x = layer(x, mask)
             if i == intermediate_output:
                 intermediate = x.clone()
         return x, intermediate
@@ -215,7 +216,7 @@ def tokenize_with_weights(self, text:str):
 
 class ClipTokenWeightEncoder:
     def encode_token_weights(self, token_weight_pairs):
-        tokens = list(map(lambda a: a[0], token_weight_pairs[0]))
+        tokens = [a[0] for a in token_weight_pairs[0]]
         out, pooled = self([tokens])
         if pooled is not None:
             first_pooled = pooled[0:1].cpu()
@@ -229,7 +230,7 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
     """Uses the CLIP transformer encoder for text (from huggingface)"""
     LAYERS = ["last", "pooled", "hidden"]
     def __init__(self, device="cpu", max_length=77, layer="last", layer_idx=None, textmodel_json_config=None, dtype=None, model_class=CLIPTextModel,
-                 special_tokens={"start": 49406, "end": 49407, "pad": 49407}, layer_norm_hidden_state=True, return_projected_pooled=True):
+                 special_tokens=None, layer_norm_hidden_state=True, return_projected_pooled=True):
         super().__init__()
         assert layer in self.LAYERS
         self.transformer = model_class(textmodel_json_config, dtype, device)
@@ -240,7 +241,7 @@ def __init__(self, device="cpu", max_length=77, layer="last", layer_idx=None, te
             param.requires_grad = False
         self.layer = layer
         self.layer_idx = None
-        self.special_tokens = special_tokens
+        self.special_tokens = special_tokens if special_tokens is not None else {"start": 49406, "end": 49407, "pad": 49407}
         self.logit_scale = torch.nn.Parameter(torch.tensor(4.6055))
         self.layer_norm_hidden_state = layer_norm_hidden_state
         self.return_projected_pooled = return_projected_pooled
@@ -465,8 +466,8 @@ def forward(self, input_ids, intermediate_output=None, final_layer_norm_intermed
         intermediate = None
         x = self.embed_tokens(input_ids)
         past_bias = None
-        for i, l in enumerate(self.block):
-            x, past_bias = l(x, past_bias)
+        for i, layer in enumerate(self.block):
+            x, past_bias = layer(x, past_bias)
             if i == intermediate_output:
                 intermediate = x.clone()
         x = self.final_layer_norm(x)

modules/models/sd3/sd3_impls.py

@@ -1,6 +1,8 @@
 ### Impls of the SD3 core diffusion model and VAE
 
-import torch, math, einops
+import torch
+import math
+import einops
 from modules.models.sd3.mmdit import MMDiT
 from PIL import Image
 
@@ -214,7 +216,7 @@ def forward(self, x):
         k = self.k(hidden)
         v = self.v(hidden)
         b, c, h, w = q.shape
-        q, k, v = map(lambda x: einops.rearrange(x, "b c h w -> b 1 (h w) c").contiguous(), (q, k, v))
+        q, k, v = [einops.rearrange(x, "b c h w -> b 1 (h w) c").contiguous() for x in (q, k, v)]
         hidden = torch.nn.functional.scaled_dot_product_attention(q, k, v)  # scale is dim ** -0.5 per default
         hidden = einops.rearrange(hidden, "b 1 (h w) c -> b c h w", h=h, w=w, c=c, b=b)
         hidden = self.proj_out(hidden)
@@ -259,7 +261,7 @@ def __init__(self, ch=128, ch_mult=(1,2,4,4), num_res_blocks=2, in_channels=3, z
             attn = torch.nn.ModuleList()
             block_in = ch*in_ch_mult[i_level]
             block_out = ch*ch_mult[i_level]
-            for i_block in range(num_res_blocks):
+            for _ in range(num_res_blocks):
                 block.append(ResnetBlock(in_channels=block_in, out_channels=block_out, dtype=dtype, device=device))
                 block_in = block_out
             down = torch.nn.Module()
@@ -318,7 +320,7 @@ def __init__(self, ch=128, out_ch=3, ch_mult=(1, 2, 4, 4), num_res_blocks=2, res
         for i_level in reversed(range(self.num_resolutions)):
             block = torch.nn.ModuleList()
             block_out = ch * ch_mult[i_level]
-            for i_block in range(self.num_res_blocks + 1):
+            for _ in range(self.num_res_blocks + 1):
                 block.append(ResnetBlock(in_channels=block_in, out_channels=block_out, dtype=dtype, device=device))
                 block_in = block_out
             up = torch.nn.Module()