Merge branch 'dev' of https://github.com/AUTOMATIC1111/stable-diffusion-webui into tomesd

1 files changed, 14 insertions, 15 deletions

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 1fc49537..570b5603 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -1,4 +1,3 @@
-import csv

 import datetime

 import glob

 import html

@@ -18,7 +17,7 @@ from modules.textual_inversion.learn_schedule import LearnRateScheduler
 from torch import einsum

 from torch.nn.init import normal_, xavier_normal_, xavier_uniform_, kaiming_normal_, kaiming_uniform_, zeros_

 

-from collections import defaultdict, deque

+from collections import deque

 from statistics import stdev, mean

 

 

@@ -178,34 +177,34 @@ class Hypernetwork:
 

     def weights(self):

         res = []

-        for k, layers in self.layers.items():

+        for layers in self.layers.values():

             for layer in layers:

                 res += layer.parameters()

         return res

 

     def train(self, mode=True):

-        for k, layers in self.layers.items():

+        for layers in self.layers.values():

             for layer in layers:

                 layer.train(mode=mode)

                 for param in layer.parameters():

                     param.requires_grad = mode

 

     def to(self, device):

-        for k, layers in self.layers.items():

+        for layers in self.layers.values():

             for layer in layers:

                 layer.to(device)

 

         return self

 

     def set_multiplier(self, multiplier):

-        for k, layers in self.layers.items():

+        for layers in self.layers.values():

             for layer in layers:

                 layer.multiplier = multiplier

 

         return self

 

     def eval(self):

-        for k, layers in self.layers.items():

+        for layers in self.layers.values():

             for layer in layers:

                 layer.eval()

                 for param in layer.parameters():

@@ -404,7 +403,7 @@ def attention_CrossAttention_forward(self, x, context=None, mask=None):
     k = self.to_k(context_k)

     v = self.to_v(context_v)

 

-    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))

+    q, k, v = (rearrange(t, 'b n (h d) -> (b h) n d', h=h) for t in (q, k, v))

 

     sim = einsum('b i d, b j d -> b i j', q, k) * self.scale

 

@@ -541,7 +540,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
         return hypernetwork, filename

 

     scheduler = LearnRateScheduler(learn_rate, steps, initial_step)

-    

+

     clip_grad = torch.nn.utils.clip_grad_value_ if clip_grad_mode == "value" else torch.nn.utils.clip_grad_norm_ if clip_grad_mode == "norm" else None

     if clip_grad:

         clip_grad_sched = LearnRateScheduler(clip_grad_value, steps, initial_step, verbose=False)

@@ -594,7 +593,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
             print(e)

 

     scaler = torch.cuda.amp.GradScaler()

-    

+

     batch_size = ds.batch_size

     gradient_step = ds.gradient_step

     # n steps = batch_size * gradient_step * n image processed

@@ -620,7 +619,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
     try:

         sd_hijack_checkpoint.add()

 

-        for i in range((steps-initial_step) * gradient_step):

+        for _ in range((steps-initial_step) * gradient_step):

             if scheduler.finished:

                 break

             if shared.state.interrupted:

@@ -637,7 +636,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
 

                 if clip_grad:

                     clip_grad_sched.step(hypernetwork.step)

-                

+

                 with devices.autocast():

                     x = batch.latent_sample.to(devices.device, non_blocking=pin_memory)

                     if use_weight:

@@ -658,14 +657,14 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
 

                     _loss_step += loss.item()

                 scaler.scale(loss).backward()

-                

+

                 # go back until we reach gradient accumulation steps

                 if (j + 1) % gradient_step != 0:

                     continue

                 loss_logging.append(_loss_step)

                 if clip_grad:

                     clip_grad(weights, clip_grad_sched.learn_rate)

-                

+

                 scaler.step(optimizer)

                 scaler.update()

                 hypernetwork.step += 1

@@ -675,7 +674,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
                 _loss_step = 0

 

                 steps_done = hypernetwork.step + 1

-                

+

                 epoch_num = hypernetwork.step // steps_per_epoch

                 epoch_step = hypernetwork.step % steps_per_epoch