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big rework of progressbar/preview system to allow multiple users to prompts at the same time and do not get previews of each other

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AUTOMATIC
2023-01-15 18:50:56 +03:00
parent ebfdd7baeb
commit d8b90ac121
16 changed files with 395 additions and 280 deletions
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6
modules/hypernetworks/hypernetwork.py
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@@ -453,7 +453,7 @@ def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None,
shared.reload_hypernetworks()
def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, create_image_every, save_hypernetwork_every, template_filename, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, create_image_every, save_hypernetwork_every, template_filename, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
# images allows training previews to have infotext. Importing it at the top causes a circular import problem.
from modules import images
@@ -629,7 +629,6 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step,
description = f"Training hypernetwork [Epoch {epoch_num}: {epoch_step+1}/{steps_per_epoch}]loss: {loss_step:.7f}"
pbar.set_description(description)
shared.state.textinfo = description
if hypernetwork_dir is not None and steps_done % save_hypernetwork_every == 0:
# Before saving, change name to match current checkpoint.
hypernetwork_name_every = f'{hypernetwork_name}-{steps_done}'
@@ -701,7 +700,8 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step,
torch.cuda.set_rng_state_all(cuda_rng_state)
hypernetwork.train()
if image is not None:
shared.state.current_image = image
shared.state.assign_current_image(image)
last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False)
last_saved_image += f", prompt: {preview_text}"