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Revert "fix bugs and optimizations" 

This reverts commit 108be15500.
This commit is contained in:
aria1th 2022-10-21 01:37:11 +09:00
parent 108be15500
commit f89829ec3a
1 changed files with 40 additions and 53 deletions
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93
modules/hypernetworks/hypernetwork.py
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@ -36,14 +36,14 @@ class HypernetworkModule(torch.nn.Module):
linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1])))
# if skip_first_layer because first parameters potentially contain negative values
# if i < 1: continue
if add_layer_norm:
linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1])))
if activation_func in HypernetworkModule.activation_dict:
linears.append(HypernetworkModule.activation_dict[activation_func]())
else:
print("Invalid key {} encountered as activation function!".format(activation_func))
# if use_dropout:
# linears.append(torch.nn.Dropout(p=0.3))
if add_layer_norm:
linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1])))
self.linear = torch.nn.Sequential(*linears)
@ -115,24 +115,11 @@ class Hypernetwork:
for k, layers in self.layers.items():
for layer in layers:
layer.train()
res += layer.trainables()
return res
def eval(self):
for k, layers in self.layers.items():
for layer in layers:
layer.eval()
for items in self.weights():
items.requires_grad = False
def train(self):
for k, layers in self.layers.items():
for layer in layers:
layer.train()
for items in self.weights():
items.requires_grad = True
def save(self, filename):
state_dict = {}
@ -303,6 +290,10 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
shared.sd_model.first_stage_model.to(devices.cpu)
hypernetwork = shared.loaded_hypernetwork
weights = hypernetwork.weights()
for weight in weights:
weight.requires_grad = True
losses = torch.zeros((32,))
last_saved_file = "<none>"
@ -313,10 +304,10 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
return hypernetwork, filename
scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
optimizer = torch.optim.AdamW(hypernetwork.weights(), lr=scheduler.learn_rate)
# if optimizer == "AdamW": or else Adam / AdamW / SGD, etc...
optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate)
pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)
hypernetwork.train()
for i, entries in pbar:
hypernetwork.step = i + ititial_step
@ -337,9 +328,8 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
losses[hypernetwork.step % losses.shape[0]] = loss.item()
optimizer.zero_grad(set_to_none=True)
optimizer.zero_grad()
loss.backward()
del loss
optimizer.step()
mean_loss = losses.mean()
if torch.isnan(mean_loss):
@ -356,47 +346,44 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
})
if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0:
torch.cuda.empty_cache()
last_saved_image = os.path.join(images_dir, f'{hypernetwork_name}-{hypernetwork.step}.png')
with torch.no_grad():
hypernetwork.eval()
shared.sd_model.cond_stage_model.to(devices.device)
shared.sd_model.first_stage_model.to(devices.device)
p = processing.StableDiffusionProcessingTxt2Img(
sd_model=shared.sd_model,
do_not_save_grid=True,
do_not_save_samples=True,
)
optimizer.zero_grad()
shared.sd_model.cond_stage_model.to(devices.device)
shared.sd_model.first_stage_model.to(devices.device)
if preview_from_txt2img:
p.prompt = preview_prompt
p.negative_prompt = preview_negative_prompt
p.steps = preview_steps
p.sampler_index = preview_sampler_index
p.cfg_scale = preview_cfg_scale
p.seed = preview_seed
p.width = preview_width
p.height = preview_height
else:
p.prompt = entries[0].cond_text
p.steps = 20
p = processing.StableDiffusionProcessingTxt2Img(
sd_model=shared.sd_model,
do_not_save_grid=True,
do_not_save_samples=True,
)
preview_text = p.prompt
if preview_from_txt2img:
p.prompt = preview_prompt
p.negative_prompt = preview_negative_prompt
p.steps = preview_steps
p.sampler_index = preview_sampler_index
p.cfg_scale = preview_cfg_scale
p.seed = preview_seed
p.width = preview_width
p.height = preview_height
else:
p.prompt = entries[0].cond_text
p.steps = 20
processed = processing.process_images(p)
image = processed.images[0] if len(processed.images)>0 else None
preview_text = p.prompt
if unload:
shared.sd_model.cond_stage_model.to(devices.cpu)
shared.sd_model.first_stage_model.to(devices.cpu)
processed = processing.process_images(p)
image = processed.images[0] if len(processed.images)>0 else None
if image is not None:
shared.state.current_image = image
image.save(last_saved_image)
last_saved_image += f", prompt: {preview_text}"
if unload:
shared.sd_model.cond_stage_model.to(devices.cpu)
shared.sd_model.first_stage_model.to(devices.cpu)
hypernetwork.train()
if image is not None:
shared.state.current_image = image
image.save(last_saved_image)
last_saved_image += f", prompt: {preview_text}"
shared.state.job_no = hypernetwork.step