Muhammad Rizqi Nur
parent
a27d19de2e
commit
ab05a74ead
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@ -398,112 +398,110 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
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forced_filename = "<none>"
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pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)
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try:
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for i, entries in pbar:
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hypernetwork.step = i + ititial_step
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if len(loss_dict) > 0:
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previous_mean_losses = [i[-1] for i in loss_dict.values()]
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previous_mean_loss = mean(previous_mean_losses)
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scheduler.apply(optimizer, hypernetwork.step)
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if scheduler.finished:
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break
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if shared.state.interrupted:
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break
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with torch.autocast("cuda"):
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c = stack_conds([entry.cond for entry in entries]).to(devices.device)
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# c = torch.vstack([entry.cond for entry in entries]).to(devices.device)
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x = torch.stack([entry.latent for entry in entries]).to(devices.device)
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loss = shared.sd_model(x, c)[0]
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del x
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del c
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losses[hypernetwork.step % losses.shape[0]] = loss.item()
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for entry in entries:
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loss_dict[entry.filename].append(loss.item())
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optimizer.zero_grad()
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weights[0].grad = None
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loss.backward()
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if weights[0].grad is None:
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steps_without_grad += 1
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else:
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steps_without_grad = 0
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assert steps_without_grad < 10, 'no gradient found for the trained weight after backward() for 10 steps in a row; this is a bug; training cannot continue'
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optimizer.step()
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steps_done = hypernetwork.step + 1
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if torch.isnan(losses[hypernetwork.step % losses.shape[0]]):
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raise RuntimeError("Loss diverged.")
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for i, entries in pbar:
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hypernetwork.step = i + ititial_step
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if len(loss_dict) > 0:
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previous_mean_losses = [i[-1] for i in loss_dict.values()]
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previous_mean_loss = mean(previous_mean_losses)
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if len(previous_mean_losses) > 1:
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std = stdev(previous_mean_losses)
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scheduler.apply(optimizer, hypernetwork.step)
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if scheduler.finished:
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break
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if shared.state.interrupted:
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break
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with torch.autocast("cuda"):
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c = stack_conds([entry.cond for entry in entries]).to(devices.device)
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# c = torch.vstack([entry.cond for entry in entries]).to(devices.device)
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x = torch.stack([entry.latent for entry in entries]).to(devices.device)
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loss = shared.sd_model(x, c)[0]
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del x
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del c
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losses[hypernetwork.step % losses.shape[0]] = loss.item()
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for entry in entries:
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loss_dict[entry.filename].append(loss.item())
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optimizer.zero_grad()
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weights[0].grad = None
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loss.backward()
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if weights[0].grad is None:
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steps_without_grad += 1
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else:
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std = 0
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dataset_loss_info = f"dataset loss:{mean(previous_mean_losses):.3f}" + u"\u00B1" + f"({std / (len(previous_mean_losses) ** 0.5):.3f})"
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pbar.set_description(dataset_loss_info)
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steps_without_grad = 0
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assert steps_without_grad < 10, 'no gradient found for the trained weight after backward() for 10 steps in a row; this is a bug; training cannot continue'
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if hypernetwork_dir is not None and steps_done % save_hypernetwork_every == 0:
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# Before saving, change name to match current checkpoint.
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hypernetwork.name = f'{hypernetwork_name}-{steps_done}'
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last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork.name}.pt')
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hypernetwork.save(last_saved_file)
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optimizer.step()
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textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), {
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"loss": f"{previous_mean_loss:.7f}",
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"learn_rate": scheduler.learn_rate
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})
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steps_done = hypernetwork.step + 1
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if images_dir is not None and steps_done % create_image_every == 0:
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forced_filename = f'{hypernetwork_name}-{steps_done}'
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last_saved_image = os.path.join(images_dir, forced_filename)
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if torch.isnan(losses[hypernetwork.step % losses.shape[0]]):
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raise RuntimeError("Loss diverged.")
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if len(previous_mean_losses) > 1:
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std = stdev(previous_mean_losses)
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else:
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std = 0
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dataset_loss_info = f"dataset loss:{mean(previous_mean_losses):.3f}" + u"\u00B1" + f"({std / (len(previous_mean_losses) ** 0.5):.3f})"
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pbar.set_description(dataset_loss_info)
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optimizer.zero_grad()
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shared.sd_model.cond_stage_model.to(devices.device)
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shared.sd_model.first_stage_model.to(devices.device)
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if hypernetwork_dir is not None and steps_done % save_hypernetwork_every == 0:
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# Before saving, change name to match current checkpoint.
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hypernetwork.name = f'{hypernetwork_name}-{steps_done}'
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last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork.name}.pt')
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hypernetwork.save(last_saved_file)
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p = processing.StableDiffusionProcessingTxt2Img(
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sd_model=shared.sd_model,
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do_not_save_grid=True,
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do_not_save_samples=True,
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)
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textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), {
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"loss": f"{previous_mean_loss:.7f}",
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"learn_rate": scheduler.learn_rate
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})
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if preview_from_txt2img:
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p.prompt = preview_prompt
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p.negative_prompt = preview_negative_prompt
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p.steps = preview_steps
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p.sampler_index = preview_sampler_index
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p.cfg_scale = preview_cfg_scale
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p.seed = preview_seed
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p.width = preview_width
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p.height = preview_height
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else:
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p.prompt = entries[0].cond_text
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p.steps = 20
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if images_dir is not None and steps_done % create_image_every == 0:
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forced_filename = f'{hypernetwork_name}-{steps_done}'
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last_saved_image = os.path.join(images_dir, forced_filename)
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preview_text = p.prompt
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optimizer.zero_grad()
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shared.sd_model.cond_stage_model.to(devices.device)
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shared.sd_model.first_stage_model.to(devices.device)
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processed = processing.process_images(p)
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image = processed.images[0] if len(processed.images)>0 else None
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p = processing.StableDiffusionProcessingTxt2Img(
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sd_model=shared.sd_model,
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do_not_save_grid=True,
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do_not_save_samples=True,
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)
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if unload:
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shared.sd_model.cond_stage_model.to(devices.cpu)
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shared.sd_model.first_stage_model.to(devices.cpu)
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if preview_from_txt2img:
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p.prompt = preview_prompt
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p.negative_prompt = preview_negative_prompt
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p.steps = preview_steps
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p.sampler_index = preview_sampler_index
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p.cfg_scale = preview_cfg_scale
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p.seed = preview_seed
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p.width = preview_width
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p.height = preview_height
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else:
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p.prompt = entries[0].cond_text
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p.steps = 20
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if image is not None:
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shared.state.current_image = image
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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)
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last_saved_image += f", prompt: {preview_text}"
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preview_text = p.prompt
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shared.state.job_no = hypernetwork.step
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processed = processing.process_images(p)
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image = processed.images[0] if len(processed.images)>0 else None
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shared.state.textinfo = f"""
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if unload:
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shared.sd_model.cond_stage_model.to(devices.cpu)
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shared.sd_model.first_stage_model.to(devices.cpu)
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if image is not None:
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shared.state.current_image = image
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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)
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last_saved_image += f", prompt: {preview_text}"
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shared.state.job_no = hypernetwork.step
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shared.state.textinfo = f"""
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<p>
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Loss: {previous_mean_loss:.7f}<br/>
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Step: {hypernetwork.step}<br/>
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@ -512,14 +510,7 @@ Last saved hypernetwork: {html.escape(last_saved_file)}<br/>
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Last saved image: {html.escape(last_saved_image)}<br/>
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</p>
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"""
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finally:
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if weights:
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for weight in weights:
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weight.requires_grad = False
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if unload:
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shared.sd_model.cond_stage_model.to(devices.device)
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shared.sd_model.first_stage_model.to(devices.device)
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report_statistics(loss_dict)
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checkpoint = sd_models.select_checkpoint()
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@ -283,113 +283,111 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
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embedding_yet_to_be_embedded = False
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pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
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for i, entries in pbar:
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embedding.step = i + ititial_step
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try:
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for i, entries in pbar:
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embedding.step = i + ititial_step
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scheduler.apply(optimizer, embedding.step)
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if scheduler.finished:
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break
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scheduler.apply(optimizer, embedding.step)
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if scheduler.finished:
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break
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if shared.state.interrupted:
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break
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if shared.state.interrupted:
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break
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with torch.autocast("cuda"):
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c = cond_model([entry.cond_text for entry in entries])
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x = torch.stack([entry.latent for entry in entries]).to(devices.device)
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loss = shared.sd_model(x, c)[0]
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del x
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with torch.autocast("cuda"):
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c = cond_model([entry.cond_text for entry in entries])
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x = torch.stack([entry.latent for entry in entries]).to(devices.device)
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loss = shared.sd_model(x, c)[0]
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del x
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losses[embedding.step % losses.shape[0]] = loss.item()
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losses[embedding.step % losses.shape[0]] = loss.item()
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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steps_done = embedding.step + 1
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steps_done = embedding.step + 1
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epoch_num = embedding.step // len(ds)
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epoch_step = embedding.step % len(ds)
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epoch_num = embedding.step // len(ds)
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epoch_step = embedding.step % len(ds)
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pbar.set_description(f"[Epoch {epoch_num}: {epoch_step+1}/{len(ds)}]loss: {losses.mean():.7f}")
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pbar.set_description(f"[Epoch {epoch_num}: {epoch_step+1}/{len(ds)}]loss: {losses.mean():.7f}")
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if embedding_dir is not None and steps_done % save_embedding_every == 0:
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# Before saving, change name to match current checkpoint.
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embedding.name = f'{embedding_name}-{steps_done}'
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last_saved_file = os.path.join(embedding_dir, f'{embedding.name}.pt')
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embedding.save(last_saved_file)
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embedding_yet_to_be_embedded = True
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if embedding_dir is not None and steps_done % save_embedding_every == 0:
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# Before saving, change name to match current checkpoint.
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embedding.name = f'{embedding_name}-{steps_done}'
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last_saved_file = os.path.join(embedding_dir, f'{embedding.name}.pt')
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embedding.save(last_saved_file)
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embedding_yet_to_be_embedded = True
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write_loss(log_directory, "textual_inversion_loss.csv", embedding.step, len(ds), {
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"loss": f"{losses.mean():.7f}",
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"learn_rate": scheduler.learn_rate
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})
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write_loss(log_directory, "textual_inversion_loss.csv", embedding.step, len(ds), {
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"loss": f"{losses.mean():.7f}",
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"learn_rate": scheduler.learn_rate
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})
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if images_dir is not None and steps_done % create_image_every == 0:
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forced_filename = f'{embedding_name}-{steps_done}'
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last_saved_image = os.path.join(images_dir, forced_filename)
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p = processing.StableDiffusionProcessingTxt2Img(
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sd_model=shared.sd_model,
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do_not_save_grid=True,
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do_not_save_samples=True,
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do_not_reload_embeddings=True,
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)
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if images_dir is not None and steps_done % create_image_every == 0:
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forced_filename = f'{embedding_name}-{steps_done}'
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last_saved_image = os.path.join(images_dir, forced_filename)
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p = processing.StableDiffusionProcessingTxt2Img(
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sd_model=shared.sd_model,
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do_not_save_grid=True,
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do_not_save_samples=True,
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do_not_reload_embeddings=True,
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)
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if preview_from_txt2img:
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p.prompt = preview_prompt
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p.negative_prompt = preview_negative_prompt
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p.steps = preview_steps
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p.sampler_index = preview_sampler_index
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p.cfg_scale = preview_cfg_scale
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p.seed = preview_seed
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p.width = preview_width
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p.height = preview_height
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else:
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p.prompt = entries[0].cond_text
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p.steps = 20
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p.width = training_width
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p.height = training_height
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if preview_from_txt2img:
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p.prompt = preview_prompt
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p.negative_prompt = preview_negative_prompt
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p.steps = preview_steps
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p.sampler_index = preview_sampler_index
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p.cfg_scale = preview_cfg_scale
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p.seed = preview_seed
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p.width = preview_width
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p.height = preview_height
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else:
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p.prompt = entries[0].cond_text
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p.steps = 20
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p.width = training_width
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p.height = training_height
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preview_text = p.prompt
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preview_text = p.prompt
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processed = processing.process_images(p)
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image = processed.images[0]
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processed = processing.process_images(p)
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image = processed.images[0]
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shared.state.current_image = image
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shared.state.current_image = image
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if save_image_with_stored_embedding and os.path.exists(last_saved_file) and embedding_yet_to_be_embedded:
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if save_image_with_stored_embedding and os.path.exists(last_saved_file) and embedding_yet_to_be_embedded:
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last_saved_image_chunks = os.path.join(images_embeds_dir, f'{embedding_name}-{steps_done}.png')
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last_saved_image_chunks = os.path.join(images_embeds_dir, f'{embedding_name}-{steps_done}.png')
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info = PngImagePlugin.PngInfo()
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data = torch.load(last_saved_file)
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info.add_text("sd-ti-embedding", embedding_to_b64(data))
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info = PngImagePlugin.PngInfo()
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data = torch.load(last_saved_file)
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info.add_text("sd-ti-embedding", embedding_to_b64(data))
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title = "<{}>".format(data.get('name', '???'))
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title = "<{}>".format(data.get('name', '???'))
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try:
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vectorSize = list(data['string_to_param'].values())[0].shape[0]
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except Exception as e:
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vectorSize = '?'
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try:
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vectorSize = list(data['string_to_param'].values())[0].shape[0]
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except Exception as e:
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vectorSize = '?'
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checkpoint = sd_models.select_checkpoint()
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footer_left = checkpoint.model_name
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footer_mid = '[{}]'.format(checkpoint.hash)
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footer_right = '{}v {}s'.format(vectorSize, steps_done)
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checkpoint = sd_models.select_checkpoint()
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footer_left = checkpoint.model_name
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footer_mid = '[{}]'.format(checkpoint.hash)
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footer_right = '{}v {}s'.format(vectorSize, steps_done)
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captioned_image = caption_image_overlay(image, title, footer_left, footer_mid, footer_right)
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captioned_image = insert_image_data_embed(captioned_image, data)
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captioned_image = caption_image_overlay(image, title, footer_left, footer_mid, footer_right)
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captioned_image = insert_image_data_embed(captioned_image, data)
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captioned_image.save(last_saved_image_chunks, "PNG", pnginfo=info)
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embedding_yet_to_be_embedded = False
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captioned_image.save(last_saved_image_chunks, "PNG", pnginfo=info)
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embedding_yet_to_be_embedded = False
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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)
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last_saved_image += f", prompt: {preview_text}"
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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)
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last_saved_image += f", prompt: {preview_text}"
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shared.state.job_no = embedding.step
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shared.state.job_no = embedding.step
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shared.state.textinfo = f"""
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shared.state.textinfo = f"""
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<p>
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Loss: {losses.mean():.7f}<br/>
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Step: {embedding.step}<br/>
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@ -398,9 +396,6 @@ Last saved embedding: {html.escape(last_saved_file)}<br/>
|
|||
Last saved image: {html.escape(last_saved_image)}<br/>
|
||||
</p>
|
||||
"""
|
||||
finally:
|
||||
if embedding and embedding.vec is not None:
|
||||
embedding.vec.requires_grad = False
|
||||
|
||||
checkpoint = sd_models.select_checkpoint()
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user