Commit bc509367 authored by Shondoit's avatar Shondoit

Call weighted_forward during training

parent 21642000
......@@ -640,13 +640,14 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
with devices.autocast():
x = batch.latent_sample.to(devices.device, non_blocking=pin_memory)
w = batch.weight.to(devices.device, non_blocking=pin_memory)
if tag_drop_out != 0 or shuffle_tags:
shared.sd_model.cond_stage_model.to(devices.device)
c = shared.sd_model.cond_stage_model(batch.cond_text).to(devices.device, non_blocking=pin_memory)
shared.sd_model.cond_stage_model.to(devices.cpu)
else:
c = stack_conds(batch.cond).to(devices.device, non_blocking=pin_memory)
loss = shared.sd_model(x, c)[0] / gradient_step
loss = shared.sd_model.weighted_forward(x, c, w)[0] / gradient_step
del x
del c
......
......@@ -480,6 +480,7 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
with devices.autocast():
x = batch.latent_sample.to(devices.device, non_blocking=pin_memory)
w = batch.weight.to(devices.device, non_blocking=pin_memory)
c = shared.sd_model.cond_stage_model(batch.cond_text)
if is_training_inpainting_model:
......@@ -490,7 +491,7 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
else:
cond = c
loss = shared.sd_model(x, cond)[0] / gradient_step
loss = shared.sd_model.weighted_forward(x, cond, w)[0] / gradient_step
del x
_loss_step += loss.item()
......
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment