From fddb4883f4a408b3464076465e1b0949ebe0fc30 Mon Sep 17 00:00:00 2001 From: evshiron Date: Wed, 26 Oct 2022 22:33:45 +0800 Subject: prototype progress api --- modules/api/api.py | 89 +++++++++++++++++++++++++++++++++++++++++++++--------- modules/shared.py | 13 ++++++++ 2 files changed, 88 insertions(+), 14 deletions(-) (limited to 'modules') diff --git a/modules/api/api.py b/modules/api/api.py index 6e9d6097..c038f674 100644 --- a/modules/api/api.py +++ b/modules/api/api.py @@ -1,8 +1,11 @@ +import time + from modules.api.models import StableDiffusionTxt2ImgProcessingAPI, StableDiffusionImg2ImgProcessingAPI from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images from modules.sd_samplers import all_samplers from modules.extras import run_pnginfo import modules.shared as shared +from modules import devices import uvicorn from fastapi import Body, APIRouter, HTTPException from fastapi.responses import JSONResponse @@ -25,6 +28,37 @@ class ImageToImageResponse(BaseModel): parameters: Json info: Json +class ProgressResponse(BaseModel): + progress: float + eta_relative: float + state: Json + +# copy from wrap_gradio_gpu_call of webui.py +# because queue lock will be acquired in api handlers +# and time start needs to be set +# the function has been modified into two parts + +def before_gpu_call(): + devices.torch_gc() + + shared.state.sampling_step = 0 + shared.state.job_count = -1 + shared.state.job_no = 0 + shared.state.job_timestamp = shared.state.get_job_timestamp() + shared.state.current_latent = None + shared.state.current_image = None + shared.state.current_image_sampling_step = 0 + shared.state.skipped = False + shared.state.interrupted = False + shared.state.textinfo = None + shared.state.time_start = time.time() + + +def after_gpu_call(): + shared.state.job = "" + shared.state.job_count = 0 + + devices.torch_gc() class Api: def __init__(self, app, queue_lock): @@ -33,6 +67,7 @@ class Api: self.queue_lock = queue_lock self.app.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"]) self.app.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"]) + self.app.add_api_route("/sdapi/v1/progress", self.progressapi, methods=["GET"]) def __base64_to_image(self, base64_string): # if has a comma, deal with prefix @@ -44,12 +79,12 @@ class Api: def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI): sampler_index = sampler_to_index(txt2imgreq.sampler_index) - + if sampler_index is None: - raise HTTPException(status_code=404, detail="Sampler not found") - + raise HTTPException(status_code=404, detail="Sampler not found") + populate = txt2imgreq.copy(update={ # Override __init__ params - "sd_model": shared.sd_model, + "sd_model": shared.sd_model, "sampler_index": sampler_index[0], "do_not_save_samples": True, "do_not_save_grid": True @@ -57,9 +92,11 @@ class Api: ) p = StableDiffusionProcessingTxt2Img(**vars(populate)) # Override object param + before_gpu_call() with self.queue_lock: processed = process_images(p) - + after_gpu_call() + b64images = [] for i in processed.images: buffer = io.BytesIO() @@ -67,30 +104,30 @@ class Api: b64images.append(base64.b64encode(buffer.getvalue())) return TextToImageResponse(images=b64images, parameters=json.dumps(vars(txt2imgreq)), info=processed.js()) - - + + def img2imgapi(self, img2imgreq: StableDiffusionImg2ImgProcessingAPI): sampler_index = sampler_to_index(img2imgreq.sampler_index) - + if sampler_index is None: - raise HTTPException(status_code=404, detail="Sampler not found") + raise HTTPException(status_code=404, detail="Sampler not found") init_images = img2imgreq.init_images if init_images is None: - raise HTTPException(status_code=404, detail="Init image not found") + raise HTTPException(status_code=404, detail="Init image not found") mask = img2imgreq.mask if mask: mask = self.__base64_to_image(mask) - + populate = img2imgreq.copy(update={ # Override __init__ params - "sd_model": shared.sd_model, + "sd_model": shared.sd_model, "sampler_index": sampler_index[0], "do_not_save_samples": True, - "do_not_save_grid": True, + "do_not_save_grid": True, "mask": mask } ) @@ -103,9 +140,11 @@ class Api: p.init_images = imgs # Override object param + before_gpu_call() with self.queue_lock: processed = process_images(p) - + after_gpu_call() + b64images = [] for i in processed.images: buffer = io.BytesIO() @@ -118,6 +157,28 @@ class Api: return ImageToImageResponse(images=b64images, parameters=json.dumps(vars(img2imgreq)), info=processed.js()) + def progressapi(self): + # copy from check_progress_call of ui.py + + if shared.state.job_count == 0: + return ProgressResponse(progress=0, eta_relative=0, state=shared.state.js()) + + # avoid dividing zero + progress = 0.01 + + if shared.state.job_count > 0: + progress += shared.state.job_no / shared.state.job_count + if shared.state.sampling_steps > 0: + progress += 1 / shared.state.job_count * shared.state.sampling_step / shared.state.sampling_steps + + time_since_start = time.time() - shared.state.time_start + eta = (time_since_start/progress) + eta_relative = eta-time_since_start + + progress = min(progress, 1) + + return ProgressResponse(progress=progress, eta_relative=eta_relative, state=shared.state.js()) + def extrasapi(self): raise NotImplementedError diff --git a/modules/shared.py b/modules/shared.py index 1a9b8289..00f61898 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -146,6 +146,19 @@ class State: def get_job_timestamp(self): return datetime.datetime.now().strftime("%Y%m%d%H%M%S") # shouldn't this return job_timestamp? + def js(self): + obj = { + "skipped": self.skipped, + "interrupted": self.skipped, + "job": self.job, + "job_count": self.job_count, + "job_no": self.job_no, + "sampling_step": self.sampling_step, + "sampling_steps": self.sampling_steps, + } + + return json.dumps(obj) + state = State() -- cgit v1.2.3 From 9e465c8aa5616df4c6723bee007ffd3910404f12 Mon Sep 17 00:00:00 2001 From: timntorres Date: Thu, 27 Oct 2022 23:03:34 -0700 Subject: Add strength to textinfo. --- modules/processing.py | 1 + 1 file changed, 1 insertion(+) (limited to 'modules') diff --git a/modules/processing.py b/modules/processing.py index 4efba946..93066522 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -329,6 +329,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash), "Model": (None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')), "Hypernet": (None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.name), + "Hypernetwork strength": (None if shared.loaded_hypernetwork is None else shared.opts.sd_hypernetwork_strength), "Batch size": (None if p.batch_size < 2 else p.batch_size), "Batch pos": (None if p.batch_size < 2 else position_in_batch), "Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]), -- cgit v1.2.3 From d4a069a23cb19104b4e58a33d0d1670fadaefb7a Mon Sep 17 00:00:00 2001 From: timntorres Date: Thu, 27 Oct 2022 23:16:27 -0700 Subject: Read hypernet strength from PNG info. --- modules/ui.py | 1 + 1 file changed, 1 insertion(+) (limited to 'modules') diff --git a/modules/ui.py b/modules/ui.py index 0a63e357..62a2f4f3 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1812,6 +1812,7 @@ Requested path was: {f} settings_map = { 'sd_hypernetwork': 'Hypernet', + 'sd_hypernetwork_strength': 'Hypernetwork strength', 'CLIP_stop_at_last_layers': 'Clip skip', 'sd_model_checkpoint': 'Model hash', } -- cgit v1.2.3 From c0677b33161f04c3ed1a7a78f4c7288fb95787b7 Mon Sep 17 00:00:00 2001 From: timntorres Date: Thu, 27 Oct 2022 23:31:45 -0700 Subject: Explicitly state when Hypernet is none. --- modules/processing.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/processing.py b/modules/processing.py index 93066522..74a0cd64 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -328,7 +328,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration "Size": f"{p.width}x{p.height}", "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash), "Model": (None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')), - "Hypernet": (None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.name), + "Hypernet": ("None" if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.name), "Hypernetwork strength": (None if shared.loaded_hypernetwork is None else shared.opts.sd_hypernetwork_strength), "Batch size": (None if p.batch_size < 2 else p.batch_size), "Batch pos": (None if p.batch_size < 2 else position_in_batch), -- cgit v1.2.3 From db5a354c489bfd1c95e0bbf9af12ab8b5d6fe170 Mon Sep 17 00:00:00 2001 From: timntorres Date: Fri, 28 Oct 2022 01:41:57 -0700 Subject: Always ignore "None.pt" in the hypernet directory. --- modules/hypernetworks/hypernetwork.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 8113b35b..cd920df5 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -208,13 +208,16 @@ def list_hypernetworks(path): res = {} for filename in glob.iglob(os.path.join(path, '**/*.pt'), recursive=True): name = os.path.splitext(os.path.basename(filename))[0] - res[name] = filename + # Prevent a hypothetical "None.pt" from being listed. + if name != "None": + res[name] = filename return res def load_hypernetwork(filename): path = shared.hypernetworks.get(filename, None) - if path is not None: + # Prevent any file named "None.pt" from being loaded. + if path is not None and filename != "None": print(f"Loading hypernetwork {filename}") try: shared.loaded_hypernetwork = Hypernetwork() -- cgit v1.2.3 From 2c4d20388425a5e40b93eef3722e42e8d375fbb4 Mon Sep 17 00:00:00 2001 From: timntorres Date: Sat, 29 Oct 2022 00:36:51 -0700 Subject: Revert "Explicitly state when Hypernet is none." --- modules/processing.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/processing.py b/modules/processing.py index 377c0978..04fdda7c 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -395,7 +395,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration "Size": f"{p.width}x{p.height}", "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash), "Model": (None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')), - "Hypernet": ("None" if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.name), + "Hypernet": (None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.name), "Hypernetwork strength": (None if shared.loaded_hypernetwork is None else shared.opts.sd_hypernetwork_strength), "Batch size": (None if p.batch_size < 2 else p.batch_size), "Batch pos": (None if p.batch_size < 2 else position_in_batch), -- cgit v1.2.3 From a5f3adbdd7d9b8245f7782216ac48913660e6bb5 Mon Sep 17 00:00:00 2001 From: Muhammad Rizqi Nur Date: Sat, 29 Oct 2022 15:37:24 +0700 Subject: Allow trailing comma in learning rate --- modules/textual_inversion/learn_schedule.py | 33 +++++++++++++++++------------ 1 file changed, 20 insertions(+), 13 deletions(-) (limited to 'modules') diff --git a/modules/textual_inversion/learn_schedule.py b/modules/textual_inversion/learn_schedule.py index 3a736065..76e611b6 100644 --- a/modules/textual_inversion/learn_schedule.py +++ b/modules/textual_inversion/learn_schedule.py @@ -11,23 +11,30 @@ class LearnScheduleIterator: self.rates = [] self.it = 0 self.maxit = 0 - for i, pair in enumerate(pairs): - tmp = pair.split(':') - if len(tmp) == 2: - step = int(tmp[1]) - if step > cur_step: - self.rates.append((float(tmp[0]), min(step, max_steps))) - self.maxit += 1 - if step > max_steps: + try: + for i, pair in enumerate(pairs): + if not pair.strip(): + continue + tmp = pair.split(':') + if len(tmp) == 2: + step = int(tmp[1]) + if step > cur_step: + self.rates.append((float(tmp[0]), min(step, max_steps))) + self.maxit += 1 + if step > max_steps: + return + elif step == -1: + self.rates.append((float(tmp[0]), max_steps)) + self.maxit += 1 return - elif step == -1: + else: self.rates.append((float(tmp[0]), max_steps)) self.maxit += 1 return - else: - self.rates.append((float(tmp[0]), max_steps)) - self.maxit += 1 - return + assert self.rates + except (ValueError, AssertionError): + raise Exception("Invalid learning rate schedule") + def __iter__(self): return self -- cgit v1.2.3 From ef4c94e1cfe66299227aa95a28c2380d21cb1600 Mon Sep 17 00:00:00 2001 From: Muhammad Rizqi Nur Date: Sat, 29 Oct 2022 15:42:51 +0700 Subject: Improve lr schedule error message --- modules/textual_inversion/learn_schedule.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/textual_inversion/learn_schedule.py b/modules/textual_inversion/learn_schedule.py index 76e611b6..dd0c0ad1 100644 --- a/modules/textual_inversion/learn_schedule.py +++ b/modules/textual_inversion/learn_schedule.py @@ -4,7 +4,7 @@ import tqdm class LearnScheduleIterator: def __init__(self, learn_rate, max_steps, cur_step=0): """ - specify learn_rate as "0.001:100, 0.00001:1000, 1e-5:10000" to have lr of 0.001 until step 100, 0.00001 until 1000, 1e-5:10000 until 10000 + specify learn_rate as "0.001:100, 0.00001:1000, 1e-5:10000" to have lr of 0.001 until step 100, 0.00001 until 1000, and 1e-5 until 10000 """ pairs = learn_rate.split(',') @@ -33,7 +33,7 @@ class LearnScheduleIterator: return assert self.rates except (ValueError, AssertionError): - raise Exception("Invalid learning rate schedule") + raise Exception('Invalid learning rate schedule. It should be a number or, for example, like "0.001:100, 0.00001:1000, 1e-5:10000" to have lr of 0.001 until step 100, 0.00001 until 1000, and 1e-5 until 10000.') def __iter__(self): -- cgit v1.2.3 From ab27c111d06ec920791c73eea25ad9a61671852e Mon Sep 17 00:00:00 2001 From: Muhammad Rizqi Nur Date: Sat, 29 Oct 2022 18:09:17 +0700 Subject: Add input validations before loading dataset for training --- modules/hypernetworks/hypernetwork.py | 38 +++++++++++--------- modules/textual_inversion/textual_inversion.py | 48 +++++++++++++++++++------- 2 files changed, 58 insertions(+), 28 deletions(-) (limited to 'modules') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 2e84583b..38f35c58 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -332,7 +332,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log # images allows training previews to have infotext. Importing it at the top causes a circular import problem. from modules import images - assert hypernetwork_name, 'hypernetwork not selected' + save_hypernetwork_every = save_hypernetwork_every or 0 + create_image_every = create_image_every or 0 + textual_inversion.validate_train_inputs(hypernetwork_name, learn_rate, batch_size, data_root, template_file, steps, save_hypernetwork_every, create_image_every, log_directory, name="hypernetwork") path = shared.hypernetworks.get(hypernetwork_name, None) shared.loaded_hypernetwork = Hypernetwork() @@ -358,39 +360,43 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log else: images_dir = None + hypernetwork = shared.loaded_hypernetwork + + ititial_step = hypernetwork.step or 0 + if ititial_step > steps: + shared.state.textinfo = f"Model has already been trained beyond specified max steps" + return hypernetwork, filename + + scheduler = LearnRateScheduler(learn_rate, steps, ititial_step) + + # dataset loading may take a while, so input validations and early returns should be done before this shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..." with torch.autocast("cuda"): ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size) + if unload: shared.sd_model.cond_stage_model.to(devices.cpu) shared.sd_model.first_stage_model.to(devices.cpu) - hypernetwork = shared.loaded_hypernetwork - weights = hypernetwork.weights() - for weight in weights: - weight.requires_grad = True - size = len(ds.indexes) loss_dict = defaultdict(lambda : deque(maxlen = 1024)) losses = torch.zeros((size,)) previous_mean_losses = [0] previous_mean_loss = 0 print("Mean loss of {} elements".format(size)) - - last_saved_file = "" - last_saved_image = "" - forced_filename = "" - - ititial_step = hypernetwork.step or 0 - if ititial_step > steps: - return hypernetwork, filename - - scheduler = LearnRateScheduler(learn_rate, steps, ititial_step) + + weights = hypernetwork.weights() + for weight in weights: + weight.requires_grad = True # if optimizer == "AdamW": or else Adam / AdamW / SGD, etc... optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate) steps_without_grad = 0 + last_saved_file = "" + last_saved_image = "" + forced_filename = "" + pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step) for i, entries in pbar: hypernetwork.step = i + ititial_step diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 17dfb223..44f06443 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -204,9 +204,30 @@ def write_loss(log_directory, filename, step, epoch_len, values): **values, }) +def validate_train_inputs(model_name, learn_rate, batch_size, data_root, template_file, steps, save_model_every, create_image_every, log_directory, name="embedding"): + assert model_name, f"{name} not selected" + assert learn_rate, "Learning rate is empty or 0" + assert isinstance(batch_size, int), "Batch size must be integer" + assert batch_size > 0, "Batch size must be positive" + assert data_root, "Dataset directory is empty" + assert os.path.isdir(data_root), "Dataset directory doesn't exist" + assert os.listdir(data_root), "Dataset directory is empty" + assert template_file, "Prompt template file is empty" + assert os.path.isfile(template_file), "Prompt template file doesn't exist" + assert steps, "Max steps is empty or 0" + assert isinstance(steps, int), "Max steps must be integer" + assert steps > 0 , "Max steps must be positive" + assert isinstance(save_model_every, int), "Save {name} must be integer" + assert save_model_every >= 0 , "Save {name} must be positive or 0" + assert isinstance(create_image_every, int), "Create image must be integer" + assert create_image_every >= 0 , "Create image must be positive or 0" + if save_model_every or create_image_every: + assert log_directory, "Log directory is empty" def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): - assert embedding_name, 'embedding not selected' + save_embedding_every = save_embedding_every or 0 + create_image_every = create_image_every or 0 + validate_train_inputs(embedding_name, learn_rate, batch_size, data_root, template_file, steps, save_embedding_every, create_image_every, log_directory, name="embedding") shared.state.textinfo = "Initializing textual inversion training..." shared.state.job_count = steps @@ -232,17 +253,27 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc os.makedirs(images_embeds_dir, exist_ok=True) else: images_embeds_dir = None - + cond_model = shared.sd_model.cond_stage_model + hijack = sd_hijack.model_hijack + + embedding = hijack.embedding_db.word_embeddings[embedding_name] + + ititial_step = embedding.step or 0 + if ititial_step > steps: + shared.state.textinfo = f"Model has already been trained beyond specified max steps" + return embedding, filename + + scheduler = LearnRateScheduler(learn_rate, steps, ititial_step) + + # dataset loading may take a while, so input validations and early returns should be done before this shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..." with torch.autocast("cuda"): ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file, batch_size=batch_size) - hijack = sd_hijack.model_hijack - - embedding = hijack.embedding_db.word_embeddings[embedding_name] embedding.vec.requires_grad = True + optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate) losses = torch.zeros((32,)) @@ -251,13 +282,6 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc forced_filename = "" embedding_yet_to_be_embedded = False - ititial_step = embedding.step or 0 - if ititial_step > steps: - return embedding, filename - - scheduler = LearnRateScheduler(learn_rate, steps, ititial_step) - optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate) - pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step) for i, entries in pbar: embedding.step = i + ititial_step -- cgit v1.2.3 From 3ce2bfdf95bd5f26d0f6e250e67338ada91980d1 Mon Sep 17 00:00:00 2001 From: Muhammad Rizqi Nur Date: Sat, 29 Oct 2022 19:43:21 +0700 Subject: Add cleanup after training --- modules/hypernetworks/hypernetwork.py | 201 +++++++++++++------------ modules/textual_inversion/textual_inversion.py | 185 ++++++++++++----------- 2 files changed, 200 insertions(+), 186 deletions(-) (limited to 'modules') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 38f35c58..170d5ea4 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -398,110 +398,112 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log forced_filename = "" pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step) - for i, entries in pbar: - hypernetwork.step = i + ititial_step - if len(loss_dict) > 0: - previous_mean_losses = [i[-1] for i in loss_dict.values()] - previous_mean_loss = mean(previous_mean_losses) - - scheduler.apply(optimizer, hypernetwork.step) - if scheduler.finished: - break - - if shared.state.interrupted: - break - - with torch.autocast("cuda"): - c = stack_conds([entry.cond for entry in entries]).to(devices.device) - # c = torch.vstack([entry.cond for entry in entries]).to(devices.device) - x = torch.stack([entry.latent for entry in entries]).to(devices.device) - loss = shared.sd_model(x, c)[0] - del x - del c - - losses[hypernetwork.step % losses.shape[0]] = loss.item() - for entry in entries: - loss_dict[entry.filename].append(loss.item()) - - optimizer.zero_grad() - weights[0].grad = None - loss.backward() - if weights[0].grad is None: - steps_without_grad += 1 + try: + for i, entries in pbar: + hypernetwork.step = i + ititial_step + if len(loss_dict) > 0: + previous_mean_losses = [i[-1] for i in loss_dict.values()] + previous_mean_loss = mean(previous_mean_losses) + + scheduler.apply(optimizer, hypernetwork.step) + if scheduler.finished: + break + + if shared.state.interrupted: + break + + with torch.autocast("cuda"): + c = stack_conds([entry.cond for entry in entries]).to(devices.device) + # c = torch.vstack([entry.cond for entry in entries]).to(devices.device) + x = torch.stack([entry.latent for entry in entries]).to(devices.device) + loss = shared.sd_model(x, c)[0] + del x + del c + + losses[hypernetwork.step % losses.shape[0]] = loss.item() + for entry in entries: + loss_dict[entry.filename].append(loss.item()) + + optimizer.zero_grad() + weights[0].grad = None + loss.backward() + + if weights[0].grad is None: + steps_without_grad += 1 + else: + steps_without_grad = 0 + 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' + + optimizer.step() + + steps_done = hypernetwork.step + 1 + + if torch.isnan(losses[hypernetwork.step % losses.shape[0]]): + raise RuntimeError("Loss diverged.") + + if len(previous_mean_losses) > 1: + std = stdev(previous_mean_losses) else: - steps_without_grad = 0 - 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' - - optimizer.step() - - steps_done = hypernetwork.step + 1 - - if torch.isnan(losses[hypernetwork.step % losses.shape[0]]): - raise RuntimeError("Loss diverged.") - - if len(previous_mean_losses) > 1: - std = stdev(previous_mean_losses) - else: - std = 0 - dataset_loss_info = f"dataset loss:{mean(previous_mean_losses):.3f}" + u"\u00B1" + f"({std / (len(previous_mean_losses) ** 0.5):.3f})" - pbar.set_description(dataset_loss_info) - - if hypernetwork_dir is not None and steps_done % save_hypernetwork_every == 0: - # Before saving, change name to match current checkpoint. - hypernetwork.name = f'{hypernetwork_name}-{steps_done}' - last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork.name}.pt') - hypernetwork.save(last_saved_file) - - textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), { - "loss": f"{previous_mean_loss:.7f}", - "learn_rate": scheduler.learn_rate - }) - - if images_dir is not None and steps_done % create_image_every == 0: - forced_filename = f'{hypernetwork_name}-{steps_done}' - last_saved_image = os.path.join(images_dir, forced_filename) - - optimizer.zero_grad() - 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, - ) + std = 0 + dataset_loss_info = f"dataset loss:{mean(previous_mean_losses):.3f}" + u"\u00B1" + f"({std / (len(previous_mean_losses) ** 0.5):.3f})" + pbar.set_description(dataset_loss_info) + + if hypernetwork_dir is not None and steps_done % save_hypernetwork_every == 0: + # Before saving, change name to match current checkpoint. + hypernetwork.name = f'{hypernetwork_name}-{steps_done}' + last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork.name}.pt') + hypernetwork.save(last_saved_file) + + textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), { + "loss": f"{previous_mean_loss:.7f}", + "learn_rate": scheduler.learn_rate + }) + + if images_dir is not None and steps_done % create_image_every == 0: + forced_filename = f'{hypernetwork_name}-{steps_done}' + last_saved_image = os.path.join(images_dir, forced_filename) + + optimizer.zero_grad() + 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, + ) - 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 + 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 - preview_text = p.prompt + preview_text = p.prompt - processed = processing.process_images(p) - image = processed.images[0] if len(processed.images)>0 else None + processed = processing.process_images(p) + image = processed.images[0] if len(processed.images)>0 else None - if unload: - shared.sd_model.cond_stage_model.to(devices.cpu) - shared.sd_model.first_stage_model.to(devices.cpu) + if unload: + shared.sd_model.cond_stage_model.to(devices.cpu) + shared.sd_model.first_stage_model.to(devices.cpu) - if image is not None: - shared.state.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}" + if image is not None: + shared.state.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}" - shared.state.job_no = hypernetwork.step + shared.state.job_no = hypernetwork.step - shared.state.textinfo = f""" + shared.state.textinfo = f"""

Loss: {previous_mean_loss:.7f}
Step: {hypernetwork.step}
@@ -510,7 +512,14 @@ Last saved hypernetwork: {html.escape(last_saved_file)}
Last saved image: {html.escape(last_saved_image)}

""" - + finally: + if weights: + for weight in weights: + weight.requires_grad = False + if unload: + shared.sd_model.cond_stage_model.to(devices.device) + shared.sd_model.first_stage_model.to(devices.device) + report_statistics(loss_dict) checkpoint = sd_models.select_checkpoint() diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 44f06443..fd7f0897 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -283,111 +283,113 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc embedding_yet_to_be_embedded = False pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step) - for i, entries in pbar: - embedding.step = i + ititial_step - scheduler.apply(optimizer, embedding.step) - if scheduler.finished: - break - - if shared.state.interrupted: - break - - with torch.autocast("cuda"): - c = cond_model([entry.cond_text for entry in entries]) - x = torch.stack([entry.latent for entry in entries]).to(devices.device) - loss = shared.sd_model(x, c)[0] - del x - - losses[embedding.step % losses.shape[0]] = loss.item() - - optimizer.zero_grad() - loss.backward() - optimizer.step() - - steps_done = embedding.step + 1 - - epoch_num = embedding.step // len(ds) - epoch_step = embedding.step % len(ds) - - pbar.set_description(f"[Epoch {epoch_num}: {epoch_step+1}/{len(ds)}]loss: {losses.mean():.7f}") - - if embedding_dir is not None and steps_done % save_embedding_every == 0: - # Before saving, change name to match current checkpoint. - embedding.name = f'{embedding_name}-{steps_done}' - last_saved_file = os.path.join(embedding_dir, f'{embedding.name}.pt') - embedding.save(last_saved_file) - embedding_yet_to_be_embedded = True - - write_loss(log_directory, "textual_inversion_loss.csv", embedding.step, len(ds), { - "loss": f"{losses.mean():.7f}", - "learn_rate": scheduler.learn_rate - }) - - if images_dir is not None and steps_done % create_image_every == 0: - forced_filename = f'{embedding_name}-{steps_done}' - last_saved_image = os.path.join(images_dir, forced_filename) - p = processing.StableDiffusionProcessingTxt2Img( - sd_model=shared.sd_model, - do_not_save_grid=True, - do_not_save_samples=True, - do_not_reload_embeddings=True, - ) - - 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.width = training_width - p.height = training_height + try: + for i, entries in pbar: + embedding.step = i + ititial_step + + scheduler.apply(optimizer, embedding.step) + if scheduler.finished: + break + + if shared.state.interrupted: + break + + with torch.autocast("cuda"): + c = cond_model([entry.cond_text for entry in entries]) + x = torch.stack([entry.latent for entry in entries]).to(devices.device) + loss = shared.sd_model(x, c)[0] + del x + + losses[embedding.step % losses.shape[0]] = loss.item() + + optimizer.zero_grad() + loss.backward() + optimizer.step() + + steps_done = embedding.step + 1 + + epoch_num = embedding.step // len(ds) + epoch_step = embedding.step % len(ds) + + pbar.set_description(f"[Epoch {epoch_num}: {epoch_step+1}/{len(ds)}]loss: {losses.mean():.7f}") + + if embedding_dir is not None and steps_done % save_embedding_every == 0: + # Before saving, change name to match current checkpoint. + embedding.name = f'{embedding_name}-{steps_done}' + last_saved_file = os.path.join(embedding_dir, f'{embedding.name}.pt') + embedding.save(last_saved_file) + embedding_yet_to_be_embedded = True + + write_loss(log_directory, "textual_inversion_loss.csv", embedding.step, len(ds), { + "loss": f"{losses.mean():.7f}", + "learn_rate": scheduler.learn_rate + }) + + if images_dir is not None and steps_done % create_image_every == 0: + forced_filename = f'{embedding_name}-{steps_done}' + last_saved_image = os.path.join(images_dir, forced_filename) + p = processing.StableDiffusionProcessingTxt2Img( + sd_model=shared.sd_model, + do_not_save_grid=True, + do_not_save_samples=True, + do_not_reload_embeddings=True, + ) + + 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.width = training_width + p.height = training_height - preview_text = p.prompt + preview_text = p.prompt - processed = processing.process_images(p) - image = processed.images[0] + processed = processing.process_images(p) + image = processed.images[0] - shared.state.current_image = image + shared.state.current_image = image - if save_image_with_stored_embedding and os.path.exists(last_saved_file) and embedding_yet_to_be_embedded: + if save_image_with_stored_embedding and os.path.exists(last_saved_file) and embedding_yet_to_be_embedded: - last_saved_image_chunks = os.path.join(images_embeds_dir, f'{embedding_name}-{steps_done}.png') + last_saved_image_chunks = os.path.join(images_embeds_dir, f'{embedding_name}-{steps_done}.png') - info = PngImagePlugin.PngInfo() - data = torch.load(last_saved_file) - info.add_text("sd-ti-embedding", embedding_to_b64(data)) + info = PngImagePlugin.PngInfo() + data = torch.load(last_saved_file) + info.add_text("sd-ti-embedding", embedding_to_b64(data)) - title = "<{}>".format(data.get('name', '???')) + title = "<{}>".format(data.get('name', '???')) - try: - vectorSize = list(data['string_to_param'].values())[0].shape[0] - except Exception as e: - vectorSize = '?' + try: + vectorSize = list(data['string_to_param'].values())[0].shape[0] + except Exception as e: + vectorSize = '?' - checkpoint = sd_models.select_checkpoint() - footer_left = checkpoint.model_name - footer_mid = '[{}]'.format(checkpoint.hash) - footer_right = '{}v {}s'.format(vectorSize, steps_done) + checkpoint = sd_models.select_checkpoint() + footer_left = checkpoint.model_name + footer_mid = '[{}]'.format(checkpoint.hash) + footer_right = '{}v {}s'.format(vectorSize, steps_done) - captioned_image = caption_image_overlay(image, title, footer_left, footer_mid, footer_right) - captioned_image = insert_image_data_embed(captioned_image, data) + captioned_image = caption_image_overlay(image, title, footer_left, footer_mid, footer_right) + captioned_image = insert_image_data_embed(captioned_image, data) - captioned_image.save(last_saved_image_chunks, "PNG", pnginfo=info) - embedding_yet_to_be_embedded = False + captioned_image.save(last_saved_image_chunks, "PNG", pnginfo=info) + embedding_yet_to_be_embedded = False - 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}" + 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}" - shared.state.job_no = embedding.step + shared.state.job_no = embedding.step - shared.state.textinfo = f""" + shared.state.textinfo = f"""

Loss: {losses.mean():.7f}
Step: {embedding.step}
@@ -396,6 +398,9 @@ Last saved embedding: {html.escape(last_saved_file)}
Last saved image: {html.escape(last_saved_image)}

""" + finally: + if embedding and embedding.vec is not None: + embedding.vec.requires_grad = False checkpoint = sd_models.select_checkpoint() -- cgit v1.2.3 From a27d19de2eff633b6a39f9f4a5c0f2d6abb81bb5 Mon Sep 17 00:00:00 2001 From: Muhammad Rizqi Nur Date: Sat, 29 Oct 2022 19:44:05 +0700 Subject: Additional assert on dataset --- modules/textual_inversion/dataset.py | 2 ++ 1 file changed, 2 insertions(+) (limited to 'modules') diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py index 8bb00d27..ad726577 100644 --- a/modules/textual_inversion/dataset.py +++ b/modules/textual_inversion/dataset.py @@ -42,6 +42,8 @@ class PersonalizedBase(Dataset): self.lines = lines assert data_root, 'dataset directory not specified' + assert os.path.isdir(data_root), "Dataset directory doesn't exist" + assert os.listdir(data_root), "Dataset directory is empty" cond_model = shared.sd_model.cond_stage_model -- cgit v1.2.3 From ab05a74ead9fabb45dd099990e34061c7eb02ca3 Mon Sep 17 00:00:00 2001 From: Muhammad Rizqi Nur Date: Sun, 30 Oct 2022 00:32:02 +0700 Subject: Revert "Add cleanup after training" This reverts commit 3ce2bfdf95bd5f26d0f6e250e67338ada91980d1. --- modules/hypernetworks/hypernetwork.py | 201 ++++++++++++------------- modules/textual_inversion/textual_inversion.py | 185 +++++++++++------------ 2 files changed, 186 insertions(+), 200 deletions(-) (limited to 'modules') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 170d5ea4..38f35c58 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -398,112 +398,110 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log forced_filename = "" pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step) - - try: - for i, entries in pbar: - hypernetwork.step = i + ititial_step - if len(loss_dict) > 0: - previous_mean_losses = [i[-1] for i in loss_dict.values()] - previous_mean_loss = mean(previous_mean_losses) - - scheduler.apply(optimizer, hypernetwork.step) - if scheduler.finished: - break - - if shared.state.interrupted: - break - - with torch.autocast("cuda"): - c = stack_conds([entry.cond for entry in entries]).to(devices.device) - # c = torch.vstack([entry.cond for entry in entries]).to(devices.device) - x = torch.stack([entry.latent for entry in entries]).to(devices.device) - loss = shared.sd_model(x, c)[0] - del x - del c - - losses[hypernetwork.step % losses.shape[0]] = loss.item() - for entry in entries: - loss_dict[entry.filename].append(loss.item()) - - optimizer.zero_grad() - weights[0].grad = None - loss.backward() - - if weights[0].grad is None: - steps_without_grad += 1 - else: - steps_without_grad = 0 - 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' - - optimizer.step() - - steps_done = hypernetwork.step + 1 - - if torch.isnan(losses[hypernetwork.step % losses.shape[0]]): - raise RuntimeError("Loss diverged.") + for i, entries in pbar: + hypernetwork.step = i + ititial_step + if len(loss_dict) > 0: + previous_mean_losses = [i[-1] for i in loss_dict.values()] + previous_mean_loss = mean(previous_mean_losses) - if len(previous_mean_losses) > 1: - std = stdev(previous_mean_losses) + scheduler.apply(optimizer, hypernetwork.step) + if scheduler.finished: + break + + if shared.state.interrupted: + break + + with torch.autocast("cuda"): + c = stack_conds([entry.cond for entry in entries]).to(devices.device) + # c = torch.vstack([entry.cond for entry in entries]).to(devices.device) + x = torch.stack([entry.latent for entry in entries]).to(devices.device) + loss = shared.sd_model(x, c)[0] + del x + del c + + losses[hypernetwork.step % losses.shape[0]] = loss.item() + for entry in entries: + loss_dict[entry.filename].append(loss.item()) + + optimizer.zero_grad() + weights[0].grad = None + loss.backward() + + if weights[0].grad is None: + steps_without_grad += 1 else: - std = 0 - dataset_loss_info = f"dataset loss:{mean(previous_mean_losses):.3f}" + u"\u00B1" + f"({std / (len(previous_mean_losses) ** 0.5):.3f})" - pbar.set_description(dataset_loss_info) - - if hypernetwork_dir is not None and steps_done % save_hypernetwork_every == 0: - # Before saving, change name to match current checkpoint. - hypernetwork.name = f'{hypernetwork_name}-{steps_done}' - last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork.name}.pt') - hypernetwork.save(last_saved_file) - - textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), { - "loss": f"{previous_mean_loss:.7f}", - "learn_rate": scheduler.learn_rate - }) - - if images_dir is not None and steps_done % create_image_every == 0: - forced_filename = f'{hypernetwork_name}-{steps_done}' - last_saved_image = os.path.join(images_dir, forced_filename) - - optimizer.zero_grad() - 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, - ) + steps_without_grad = 0 + 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' - 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 + optimizer.step() - preview_text = p.prompt + steps_done = hypernetwork.step + 1 - processed = processing.process_images(p) - image = processed.images[0] if len(processed.images)>0 else None + if torch.isnan(losses[hypernetwork.step % losses.shape[0]]): + raise RuntimeError("Loss diverged.") + + if len(previous_mean_losses) > 1: + std = stdev(previous_mean_losses) + else: + std = 0 + dataset_loss_info = f"dataset loss:{mean(previous_mean_losses):.3f}" + u"\u00B1" + f"({std / (len(previous_mean_losses) ** 0.5):.3f})" + pbar.set_description(dataset_loss_info) + + if hypernetwork_dir is not None and steps_done % save_hypernetwork_every == 0: + # Before saving, change name to match current checkpoint. + hypernetwork.name = f'{hypernetwork_name}-{steps_done}' + last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork.name}.pt') + hypernetwork.save(last_saved_file) + + textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), { + "loss": f"{previous_mean_loss:.7f}", + "learn_rate": scheduler.learn_rate + }) + + if images_dir is not None and steps_done % create_image_every == 0: + forced_filename = f'{hypernetwork_name}-{steps_done}' + last_saved_image = os.path.join(images_dir, forced_filename) + + optimizer.zero_grad() + shared.sd_model.cond_stage_model.to(devices.device) + shared.sd_model.first_stage_model.to(devices.device) - if unload: - shared.sd_model.cond_stage_model.to(devices.cpu) - shared.sd_model.first_stage_model.to(devices.cpu) + p = processing.StableDiffusionProcessingTxt2Img( + sd_model=shared.sd_model, + do_not_save_grid=True, + do_not_save_samples=True, + ) - if image is not None: - shared.state.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}" + 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 + + preview_text = p.prompt + + processed = processing.process_images(p) + image = processed.images[0] if len(processed.images)>0 else None + + if unload: + shared.sd_model.cond_stage_model.to(devices.cpu) + shared.sd_model.first_stage_model.to(devices.cpu) - shared.state.job_no = hypernetwork.step + if image is not None: + shared.state.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}" - shared.state.textinfo = f""" + shared.state.job_no = hypernetwork.step + + shared.state.textinfo = f"""

Loss: {previous_mean_loss:.7f}
Step: {hypernetwork.step}
@@ -512,14 +510,7 @@ Last saved hypernetwork: {html.escape(last_saved_file)}
Last saved image: {html.escape(last_saved_image)}

""" - finally: - if weights: - for weight in weights: - weight.requires_grad = False - if unload: - shared.sd_model.cond_stage_model.to(devices.device) - shared.sd_model.first_stage_model.to(devices.device) - + report_statistics(loss_dict) checkpoint = sd_models.select_checkpoint() diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index fd7f0897..44f06443 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -283,113 +283,111 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc embedding_yet_to_be_embedded = False pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step) + for i, entries in pbar: + embedding.step = i + ititial_step - try: - for i, entries in pbar: - embedding.step = i + ititial_step - - scheduler.apply(optimizer, embedding.step) - if scheduler.finished: - break - - if shared.state.interrupted: - break - - with torch.autocast("cuda"): - c = cond_model([entry.cond_text for entry in entries]) - x = torch.stack([entry.latent for entry in entries]).to(devices.device) - loss = shared.sd_model(x, c)[0] - del x - - losses[embedding.step % losses.shape[0]] = loss.item() - - optimizer.zero_grad() - loss.backward() - optimizer.step() - - steps_done = embedding.step + 1 - - epoch_num = embedding.step // len(ds) - epoch_step = embedding.step % len(ds) - - pbar.set_description(f"[Epoch {epoch_num}: {epoch_step+1}/{len(ds)}]loss: {losses.mean():.7f}") - - if embedding_dir is not None and steps_done % save_embedding_every == 0: - # Before saving, change name to match current checkpoint. - embedding.name = f'{embedding_name}-{steps_done}' - last_saved_file = os.path.join(embedding_dir, f'{embedding.name}.pt') - embedding.save(last_saved_file) - embedding_yet_to_be_embedded = True - - write_loss(log_directory, "textual_inversion_loss.csv", embedding.step, len(ds), { - "loss": f"{losses.mean():.7f}", - "learn_rate": scheduler.learn_rate - }) - - if images_dir is not None and steps_done % create_image_every == 0: - forced_filename = f'{embedding_name}-{steps_done}' - last_saved_image = os.path.join(images_dir, forced_filename) - p = processing.StableDiffusionProcessingTxt2Img( - sd_model=shared.sd_model, - do_not_save_grid=True, - do_not_save_samples=True, - do_not_reload_embeddings=True, - ) - - 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.width = training_width - p.height = training_height + scheduler.apply(optimizer, embedding.step) + if scheduler.finished: + break + + if shared.state.interrupted: + break + + with torch.autocast("cuda"): + c = cond_model([entry.cond_text for entry in entries]) + x = torch.stack([entry.latent for entry in entries]).to(devices.device) + loss = shared.sd_model(x, c)[0] + del x + + losses[embedding.step % losses.shape[0]] = loss.item() + + optimizer.zero_grad() + loss.backward() + optimizer.step() + + steps_done = embedding.step + 1 + + epoch_num = embedding.step // len(ds) + epoch_step = embedding.step % len(ds) + + pbar.set_description(f"[Epoch {epoch_num}: {epoch_step+1}/{len(ds)}]loss: {losses.mean():.7f}") + + if embedding_dir is not None and steps_done % save_embedding_every == 0: + # Before saving, change name to match current checkpoint. + embedding.name = f'{embedding_name}-{steps_done}' + last_saved_file = os.path.join(embedding_dir, f'{embedding.name}.pt') + embedding.save(last_saved_file) + embedding_yet_to_be_embedded = True + + write_loss(log_directory, "textual_inversion_loss.csv", embedding.step, len(ds), { + "loss": f"{losses.mean():.7f}", + "learn_rate": scheduler.learn_rate + }) + + if images_dir is not None and steps_done % create_image_every == 0: + forced_filename = f'{embedding_name}-{steps_done}' + last_saved_image = os.path.join(images_dir, forced_filename) + p = processing.StableDiffusionProcessingTxt2Img( + sd_model=shared.sd_model, + do_not_save_grid=True, + do_not_save_samples=True, + do_not_reload_embeddings=True, + ) + + 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.width = training_width + p.height = training_height - preview_text = p.prompt + preview_text = p.prompt - processed = processing.process_images(p) - image = processed.images[0] + processed = processing.process_images(p) + image = processed.images[0] - shared.state.current_image = image + shared.state.current_image = image - if save_image_with_stored_embedding and os.path.exists(last_saved_file) and embedding_yet_to_be_embedded: + if save_image_with_stored_embedding and os.path.exists(last_saved_file) and embedding_yet_to_be_embedded: - last_saved_image_chunks = os.path.join(images_embeds_dir, f'{embedding_name}-{steps_done}.png') + last_saved_image_chunks = os.path.join(images_embeds_dir, f'{embedding_name}-{steps_done}.png') - info = PngImagePlugin.PngInfo() - data = torch.load(last_saved_file) - info.add_text("sd-ti-embedding", embedding_to_b64(data)) + info = PngImagePlugin.PngInfo() + data = torch.load(last_saved_file) + info.add_text("sd-ti-embedding", embedding_to_b64(data)) - title = "<{}>".format(data.get('name', '???')) + title = "<{}>".format(data.get('name', '???')) - try: - vectorSize = list(data['string_to_param'].values())[0].shape[0] - except Exception as e: - vectorSize = '?' + try: + vectorSize = list(data['string_to_param'].values())[0].shape[0] + except Exception as e: + vectorSize = '?' - checkpoint = sd_models.select_checkpoint() - footer_left = checkpoint.model_name - footer_mid = '[{}]'.format(checkpoint.hash) - footer_right = '{}v {}s'.format(vectorSize, steps_done) + checkpoint = sd_models.select_checkpoint() + footer_left = checkpoint.model_name + footer_mid = '[{}]'.format(checkpoint.hash) + footer_right = '{}v {}s'.format(vectorSize, steps_done) - captioned_image = caption_image_overlay(image, title, footer_left, footer_mid, footer_right) - captioned_image = insert_image_data_embed(captioned_image, data) + captioned_image = caption_image_overlay(image, title, footer_left, footer_mid, footer_right) + captioned_image = insert_image_data_embed(captioned_image, data) - captioned_image.save(last_saved_image_chunks, "PNG", pnginfo=info) - embedding_yet_to_be_embedded = False + captioned_image.save(last_saved_image_chunks, "PNG", pnginfo=info) + embedding_yet_to_be_embedded = False - 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}" + 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}" - shared.state.job_no = embedding.step + shared.state.job_no = embedding.step - shared.state.textinfo = f""" + shared.state.textinfo = f"""

Loss: {losses.mean():.7f}
Step: {embedding.step}
@@ -398,9 +396,6 @@ Last saved embedding: {html.escape(last_saved_file)}
Last saved image: {html.escape(last_saved_image)}

""" - finally: - if embedding and embedding.vec is not None: - embedding.vec.requires_grad = False checkpoint = sd_models.select_checkpoint() -- cgit v1.2.3 From a07f054c86f33360ff620d6a3fffdee366ab2d99 Mon Sep 17 00:00:00 2001 From: Muhammad Rizqi Nur Date: Sun, 30 Oct 2022 00:49:29 +0700 Subject: Add missing info on hypernetwork/embedding model log Mentioned here: https://github.com/AUTOMATIC1111/stable-diffusion-webui/discussions/1528#discussioncomment-3991513 Also group the saving into one --- modules/hypernetworks/hypernetwork.py | 31 +++++++++++++------- modules/textual_inversion/textual_inversion.py | 39 +++++++++++++++++--------- 2 files changed, 47 insertions(+), 23 deletions(-) (limited to 'modules') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 38f35c58..86daf825 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -361,6 +361,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log images_dir = None hypernetwork = shared.loaded_hypernetwork + checkpoint = sd_models.select_checkpoint() ititial_step = hypernetwork.step or 0 if ititial_step > steps: @@ -449,9 +450,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log if hypernetwork_dir is not None and steps_done % save_hypernetwork_every == 0: # Before saving, change name to match current checkpoint. - hypernetwork.name = f'{hypernetwork_name}-{steps_done}' - last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork.name}.pt') - hypernetwork.save(last_saved_file) + hypernetwork_name_every = f'{hypernetwork_name}-{steps_done}' + last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name_every}.pt') + save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, last_saved_file) textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), { "loss": f"{previous_mean_loss:.7f}", @@ -512,13 +513,23 @@ Last saved image: {html.escape(last_saved_image)}
""" report_statistics(loss_dict) - checkpoint = sd_models.select_checkpoint() - hypernetwork.sd_checkpoint = checkpoint.hash - hypernetwork.sd_checkpoint_name = checkpoint.model_name - # Before saving for the last time, change name back to the base name (as opposed to the save_hypernetwork_every step-suffixed naming convention). - hypernetwork.name = hypernetwork_name - filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork.name}.pt') - hypernetwork.save(filename) + filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt') + save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename) return hypernetwork, filename + +def save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename): + old_hypernetwork_name = hypernetwork.name + old_sd_checkpoint = hypernetwork.sd_checkpoint if hasattr(hypernetwork, "sd_checkpoint") else None + old_sd_checkpoint_name = hypernetwork.sd_checkpoint_name if hasattr(hypernetwork, "sd_checkpoint_name") else None + try: + hypernetwork.sd_checkpoint = checkpoint.hash + hypernetwork.sd_checkpoint_name = checkpoint.model_name + hypernetwork.name = hypernetwork_name + hypernetwork.save(filename) + except: + hypernetwork.sd_checkpoint = old_sd_checkpoint + hypernetwork.sd_checkpoint_name = old_sd_checkpoint_name + hypernetwork.name = old_hypernetwork_name + raise diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 44f06443..ee9917ce 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -119,7 +119,7 @@ class EmbeddingDatabase: vec = emb.detach().to(devices.device, dtype=torch.float32) embedding = Embedding(vec, name) embedding.step = data.get('step', None) - embedding.sd_checkpoint = data.get('hash', None) + embedding.sd_checkpoint = data.get('sd_checkpoint', None) embedding.sd_checkpoint_name = data.get('sd_checkpoint_name', None) self.register_embedding(embedding, shared.sd_model) @@ -259,6 +259,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc hijack = sd_hijack.model_hijack embedding = hijack.embedding_db.word_embeddings[embedding_name] + checkpoint = sd_models.select_checkpoint() ititial_step = embedding.step or 0 if ititial_step > steps: @@ -314,9 +315,9 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc if embedding_dir is not None and steps_done % save_embedding_every == 0: # Before saving, change name to match current checkpoint. - embedding.name = f'{embedding_name}-{steps_done}' - last_saved_file = os.path.join(embedding_dir, f'{embedding.name}.pt') - embedding.save(last_saved_file) + embedding_name_every = f'{embedding_name}-{steps_done}' + last_saved_file = os.path.join(embedding_dir, f'{embedding_name_every}.pt') + save_embedding(embedding, checkpoint, embedding_name_every, last_saved_file, remove_cached_checksum=True) embedding_yet_to_be_embedded = True write_loss(log_directory, "textual_inversion_loss.csv", embedding.step, len(ds), { @@ -397,14 +398,26 @@ Last saved image: {html.escape(last_saved_image)}

""" - checkpoint = sd_models.select_checkpoint() - - embedding.sd_checkpoint = checkpoint.hash - embedding.sd_checkpoint_name = checkpoint.model_name - embedding.cached_checksum = None - # Before saving for the last time, change name back to base name (as opposed to the save_embedding_every step-suffixed naming convention). - embedding.name = embedding_name - filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding.name}.pt') - embedding.save(filename) + filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt') + save_embedding(embedding, checkpoint, embedding_name, filename, remove_cached_checksum=True) return embedding, filename + +def save_embedding(embedding, checkpoint, embedding_name, filename, remove_cached_checksum=True): + old_embedding_name = embedding.name + old_sd_checkpoint = embedding.sd_checkpoint if hasattr(embedding, "sd_checkpoint") else None + old_sd_checkpoint_name = embedding.sd_checkpoint_name if hasattr(embedding, "sd_checkpoint_name") else None + old_cached_checksum = embedding.cached_checksum if hasattr(embedding, "cached_checksum") else None + try: + embedding.sd_checkpoint = checkpoint.hash + embedding.sd_checkpoint_name = checkpoint.model_name + if remove_cached_checksum: + embedding.cached_checksum = None + embedding.name = embedding_name + embedding.save(filename) + except: + embedding.sd_checkpoint = old_sd_checkpoint + embedding.sd_checkpoint_name = old_sd_checkpoint_name + embedding.name = old_embedding_name + embedding.cached_checksum = old_cached_checksum + raise -- cgit v1.2.3 From 3d58510f214c645ce5cdb261aa47df6573b239e9 Mon Sep 17 00:00:00 2001 From: Muhammad Rizqi Nur Date: Sun, 30 Oct 2022 00:54:59 +0700 Subject: Fix dataset still being loaded even when training will be skipped --- modules/hypernetworks/hypernetwork.py | 2 +- modules/textual_inversion/textual_inversion.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 86daf825..07acadc9 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -364,7 +364,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log checkpoint = sd_models.select_checkpoint() ititial_step = hypernetwork.step or 0 - if ititial_step > steps: + if ititial_step >= steps: shared.state.textinfo = f"Model has already been trained beyond specified max steps" return hypernetwork, filename diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index ee9917ce..e0babb46 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -262,7 +262,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc checkpoint = sd_models.select_checkpoint() ititial_step = embedding.step or 0 - if ititial_step > steps: + if ititial_step >= steps: shared.state.textinfo = f"Model has already been trained beyond specified max steps" return embedding, filename -- cgit v1.2.3 From 4609b83cd496013a05e77c42af031d89f07785a9 Mon Sep 17 00:00:00 2001 From: Bruno Seoane Date: Sat, 29 Oct 2022 16:09:19 -0300 Subject: Add PNG Info endpoint --- modules/api/api.py | 12 +++++++++--- modules/api/models.py | 9 ++++++++- 2 files changed, 17 insertions(+), 4 deletions(-) (limited to 'modules') diff --git a/modules/api/api.py b/modules/api/api.py index 49c213ea..8fcd068d 100644 --- a/modules/api/api.py +++ b/modules/api/api.py @@ -5,7 +5,7 @@ import modules.shared as shared from modules.api.models import * from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images from modules.sd_samplers import all_samplers -from modules.extras import run_extras +from modules.extras import run_extras, run_pnginfo def upscaler_to_index(name: str): try: @@ -32,6 +32,7 @@ class Api: self.app.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"], response_model=ImageToImageResponse) self.app.add_api_route("/sdapi/v1/extra-single-image", self.extras_single_image_api, methods=["POST"], response_model=ExtrasSingleImageResponse) self.app.add_api_route("/sdapi/v1/extra-batch-images", self.extras_batch_images_api, methods=["POST"], response_model=ExtrasBatchImagesResponse) + self.app.add_api_route("/sdapi/v1/png-info", self.pnginfoapi, methods=["POST"], response_model=PNGInfoResponse) def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI): sampler_index = sampler_to_index(txt2imgreq.sampler_index) @@ -125,8 +126,13 @@ class Api: return ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info=result[1]) - def pnginfoapi(self): - raise NotImplementedError + def pnginfoapi(self, req:PNGInfoRequest): + if(not req.image.strip()): + return PNGInfoResponse(info="") + + result = run_pnginfo(decode_base64_to_image(req.image.strip())) + + return PNGInfoResponse(info=result[1]) def launch(self, server_name, port): self.app.include_router(self.router) diff --git a/modules/api/models.py b/modules/api/models.py index dd122321..58e8e58b 100644 --- a/modules/api/models.py +++ b/modules/api/models.py @@ -1,4 +1,5 @@ import inspect +from click import prompt from pydantic import BaseModel, Field, create_model from typing import Any, Optional from typing_extensions import Literal @@ -148,4 +149,10 @@ class ExtrasBatchImagesRequest(ExtrasBaseRequest): imageList: list[FileData] = Field(title="Images", description="List of images to work on. Must be Base64 strings") class ExtrasBatchImagesResponse(ExtraBaseResponse): - images: list[str] = Field(title="Images", description="The generated images in base64 format.") \ No newline at end of file + images: list[str] = Field(title="Images", description="The generated images in base64 format.") + +class PNGInfoRequest(BaseModel): + image: str = Field(title="Image", description="The base64 encoded PNG image") + +class PNGInfoResponse(BaseModel): + info: str = Field(title="Image info", description="A string with all the info the image had") \ No newline at end of file -- cgit v1.2.3 From 83a1f44ae26cb89492064bb8be0321b14a75efe4 Mon Sep 17 00:00:00 2001 From: Bruno Seoane Date: Sat, 29 Oct 2022 16:10:00 -0300 Subject: Fix space --- modules/api/api.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/api/api.py b/modules/api/api.py index 8fcd068d..d0f488ca 100644 --- a/modules/api/api.py +++ b/modules/api/api.py @@ -126,7 +126,7 @@ class Api: return ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info=result[1]) - def pnginfoapi(self, req:PNGInfoRequest): + def pnginfoapi(self, req: PNGInfoRequest): if(not req.image.strip()): return PNGInfoResponse(info="") -- cgit v1.2.3 From 9bb6b6509aff8c1e6546d5a798ef9e9922758dc4 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sat, 29 Oct 2022 22:20:02 +0300 Subject: add postprocess call for scripts --- modules/processing.py | 12 +++++++++--- modules/scripts.py | 24 +++++++++++++++++++++--- 2 files changed, 30 insertions(+), 6 deletions(-) (limited to 'modules') diff --git a/modules/processing.py b/modules/processing.py index 548eec29..50343846 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -478,7 +478,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: model_hijack.embedding_db.load_textual_inversion_embeddings() if p.scripts is not None: - p.scripts.run_alwayson_scripts(p) + p.scripts.process(p) infotexts = [] output_images = [] @@ -501,7 +501,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: seeds = p.all_seeds[n * p.batch_size:(n + 1) * p.batch_size] subseeds = p.all_subseeds[n * p.batch_size:(n + 1) * p.batch_size] - if (len(prompts) == 0): + if len(prompts) == 0: break with devices.autocast(): @@ -590,7 +590,13 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: images.save_image(grid, p.outpath_grids, "grid", p.all_seeds[0], p.all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename, p=p, grid=True) devices.torch_gc() - return Processed(p, output_images, p.all_seeds[0], infotext() + "".join(["\n\n" + x for x in comments]), subseed=p.all_subseeds[0], all_prompts=p.all_prompts, all_seeds=p.all_seeds, all_subseeds=p.all_subseeds, index_of_first_image=index_of_first_image, infotexts=infotexts) + + res = Processed(p, output_images, p.all_seeds[0], infotext() + "".join(["\n\n" + x for x in comments]), subseed=p.all_subseeds[0], all_prompts=p.all_prompts, all_seeds=p.all_seeds, all_subseeds=p.all_subseeds, index_of_first_image=index_of_first_image, infotexts=infotexts) + + if p.scripts is not None: + p.scripts.postprocess(p, res) + + return res class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): diff --git a/modules/scripts.py b/modules/scripts.py index a7f36012..96e44bfd 100644 --- a/modules/scripts.py +++ b/modules/scripts.py @@ -64,7 +64,16 @@ class Script: def process(self, p, *args): """ This function is called before processing begins for AlwaysVisible scripts. - scripts. You can modify the processing object (p) here, inject hooks, etc. + You can modify the processing object (p) here, inject hooks, etc. + args contains all values returned by components from ui() + """ + + pass + + def postprocess(self, p, processed, *args): + """ + This function is called after processing ends for AlwaysVisible scripts. + args contains all values returned by components from ui() """ pass @@ -289,13 +298,22 @@ class ScriptRunner: return processed - def run_alwayson_scripts(self, p): + def process(self, p): for script in self.alwayson_scripts: try: script_args = p.script_args[script.args_from:script.args_to] script.process(p, *script_args) except Exception: - print(f"Error running alwayson script: {script.filename}", file=sys.stderr) + print(f"Error running process: {script.filename}", file=sys.stderr) + print(traceback.format_exc(), file=sys.stderr) + + def postprocess(self, p, processed): + for script in self.alwayson_scripts: + try: + script_args = p.script_args[script.args_from:script.args_to] + script.postprocess(p, processed, *script_args) + except Exception: + print(f"Error running postprocess: {script.filename}", file=sys.stderr) print(traceback.format_exc(), file=sys.stderr) def reload_sources(self, cache): -- cgit v1.2.3 From f62db4d5c753bc32d2ae166606ce41f4c5fa5c43 Mon Sep 17 00:00:00 2001 From: evshiron Date: Sun, 30 Oct 2022 03:55:43 +0800 Subject: fix progress response model --- modules/api/api.py | 30 ------------------------------ modules/api/models.py | 8 ++++---- 2 files changed, 4 insertions(+), 34 deletions(-) (limited to 'modules') diff --git a/modules/api/api.py b/modules/api/api.py index e93cddcb..7e8522a2 100644 --- a/modules/api/api.py +++ b/modules/api/api.py @@ -1,33 +1,3 @@ -# import time - -# from modules.api.models import StableDiffusionTxt2ImgProcessingAPI, StableDiffusionImg2ImgProcessingAPI -# from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images -# from modules.sd_samplers import all_samplers -# from modules.extras import run_pnginfo -# import modules.shared as shared -# from modules import devices -# import uvicorn -# from fastapi import Body, APIRouter, HTTPException -# from fastapi.responses import JSONResponse -# from pydantic import BaseModel, Field, Json -# from typing import List -# import json -# import io -# import base64 -# from PIL import Image - -# sampler_to_index = lambda name: next(filter(lambda row: name.lower() == row[1].name.lower(), enumerate(all_samplers)), None) - -# class TextToImageResponse(BaseModel): -# images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.") -# parameters: Json -# info: Json - -# class ImageToImageResponse(BaseModel): -# images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.") -# parameters: Json -# info: Json - import time import uvicorn from gradio.processing_utils import encode_pil_to_base64, decode_base64_to_file, decode_base64_to_image diff --git a/modules/api/models.py b/modules/api/models.py index 8d4abc39..e1762fb9 100644 --- a/modules/api/models.py +++ b/modules/api/models.py @@ -1,6 +1,6 @@ import inspect from click import prompt -from pydantic import BaseModel, Field, create_model +from pydantic import BaseModel, Field, Json, create_model from typing import Any, Optional from typing_extensions import Literal from inflection import underscore @@ -158,6 +158,6 @@ class PNGInfoResponse(BaseModel): info: str = Field(title="Image info", description="A string with all the info the image had") class ProgressResponse(BaseModel): - progress: float - eta_relative: float - state: dict + progress: float = Field(title="Progress", description="The progress with a range of 0 to 1") + eta_relative: float = Field(title="ETA in secs") + state: Json -- cgit v1.2.3 From e9c6c2a51f972fd7cd88ea740ade4ac3d8108b67 Mon Sep 17 00:00:00 2001 From: evshiron Date: Sun, 30 Oct 2022 04:02:56 +0800 Subject: add description for state field --- modules/api/models.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/api/models.py b/modules/api/models.py index e1762fb9..709ab5a6 100644 --- a/modules/api/models.py +++ b/modules/api/models.py @@ -160,4 +160,4 @@ class PNGInfoResponse(BaseModel): class ProgressResponse(BaseModel): progress: float = Field(title="Progress", description="The progress with a range of 0 to 1") eta_relative: float = Field(title="ETA in secs") - state: Json + state: Json = Field(title="State", description="The current state snapshot") -- cgit v1.2.3 From 88f46a5bec610cf03641f18becbe3deda541e982 Mon Sep 17 00:00:00 2001 From: evshiron Date: Sun, 30 Oct 2022 05:04:29 +0800 Subject: update progress response model --- modules/api/api.py | 6 +++--- modules/api/models.py | 4 ++-- modules/shared.py | 4 ++-- 3 files changed, 7 insertions(+), 7 deletions(-) (limited to 'modules') diff --git a/modules/api/api.py b/modules/api/api.py index 7e8522a2..5912d289 100644 --- a/modules/api/api.py +++ b/modules/api/api.py @@ -61,7 +61,7 @@ class Api: self.app.add_api_route("/sdapi/v1/extra-single-image", self.extras_single_image_api, methods=["POST"], response_model=ExtrasSingleImageResponse) self.app.add_api_route("/sdapi/v1/extra-batch-images", self.extras_batch_images_api, methods=["POST"], response_model=ExtrasBatchImagesResponse) self.app.add_api_route("/sdapi/v1/png-info", self.pnginfoapi, methods=["POST"], response_model=PNGInfoResponse) - self.app.add_api_route("/sdapi/v1/progress", self.progressapi, methods=["GET"]) + self.app.add_api_route("/sdapi/v1/progress", self.progressapi, methods=["GET"], response_model=ProgressResponse) def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI): sampler_index = sampler_to_index(txt2imgreq.sampler_index) @@ -171,7 +171,7 @@ class Api: # copy from check_progress_call of ui.py if shared.state.job_count == 0: - return ProgressResponse(progress=0, eta_relative=0, state=shared.state.js()) + return ProgressResponse(progress=0, eta_relative=0, state=shared.state.dict()) # avoid dividing zero progress = 0.01 @@ -187,7 +187,7 @@ class Api: progress = min(progress, 1) - return ProgressResponse(progress=progress, eta_relative=eta_relative, state=shared.state.js()) + return ProgressResponse(progress=progress, eta_relative=eta_relative, state=shared.state.dict()) def launch(self, server_name, port): self.app.include_router(self.router) diff --git a/modules/api/models.py b/modules/api/models.py index 709ab5a6..0ab85ec5 100644 --- a/modules/api/models.py +++ b/modules/api/models.py @@ -1,6 +1,6 @@ import inspect from click import prompt -from pydantic import BaseModel, Field, Json, create_model +from pydantic import BaseModel, Field, create_model from typing import Any, Optional from typing_extensions import Literal from inflection import underscore @@ -160,4 +160,4 @@ class PNGInfoResponse(BaseModel): class ProgressResponse(BaseModel): progress: float = Field(title="Progress", description="The progress with a range of 0 to 1") eta_relative: float = Field(title="ETA in secs") - state: Json = Field(title="State", description="The current state snapshot") + state: dict = Field(title="State", description="The current state snapshot") diff --git a/modules/shared.py b/modules/shared.py index 0f4c035d..f7b0990c 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -147,7 +147,7 @@ class State: def get_job_timestamp(self): return datetime.datetime.now().strftime("%Y%m%d%H%M%S") # shouldn't this return job_timestamp? - def js(self): + def dict(self): obj = { "skipped": self.skipped, "interrupted": self.skipped, @@ -158,7 +158,7 @@ class State: "sampling_steps": self.sampling_steps, } - return json.dumps(obj) + return obj state = State() -- cgit v1.2.3 From 9f104b53c425e248595e5b6481336d2a339e015e Mon Sep 17 00:00:00 2001 From: evshiron Date: Sun, 30 Oct 2022 05:19:17 +0800 Subject: preview current image when opts.show_progress_every_n_steps is enabled --- modules/api/api.py | 8 ++++++-- modules/api/models.py | 1 + 2 files changed, 7 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/api/api.py b/modules/api/api.py index 5912d289..e960bb7b 100644 --- a/modules/api/api.py +++ b/modules/api/api.py @@ -1,7 +1,7 @@ import time import uvicorn from gradio.processing_utils import encode_pil_to_base64, decode_base64_to_file, decode_base64_to_image -from fastapi import APIRouter, HTTPException +from fastapi import APIRouter, Depends, HTTPException import modules.shared as shared from modules import devices from modules.api.models import * @@ -187,7 +187,11 @@ class Api: progress = min(progress, 1) - return ProgressResponse(progress=progress, eta_relative=eta_relative, state=shared.state.dict()) + current_image = None + if shared.state.current_image: + current_image = encode_pil_to_base64(shared.state.current_image) + + return ProgressResponse(progress=progress, eta_relative=eta_relative, state=shared.state.dict(), current_image=current_image) def launch(self, server_name, port): self.app.include_router(self.router) diff --git a/modules/api/models.py b/modules/api/models.py index 0ab85ec5..c8bc719a 100644 --- a/modules/api/models.py +++ b/modules/api/models.py @@ -161,3 +161,4 @@ class ProgressResponse(BaseModel): progress: float = Field(title="Progress", description="The progress with a range of 0 to 1") eta_relative: float = Field(title="ETA in secs") state: dict = Field(title="State", description="The current state snapshot") + current_image: str = Field(default=None, title="Current image", description="The current image in base64 format. opts.show_progress_every_n_steps is required for this to work.") -- cgit v1.2.3 From 66d038f6a41507af2243ff1f6618a745a092c290 Mon Sep 17 00:00:00 2001 From: timntorres Date: Sat, 29 Oct 2022 15:00:08 -0700 Subject: Read hypernet strength from PNG info. --- modules/generation_parameters_copypaste.py | 1 + 1 file changed, 1 insertion(+) (limited to 'modules') diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py index bbaad42e..59c6d7da 100644 --- a/modules/generation_parameters_copypaste.py +++ b/modules/generation_parameters_copypaste.py @@ -66,6 +66,7 @@ def integrate_settings_paste_fields(component_dict): settings_map = { 'sd_hypernetwork': 'Hypernet', + 'sd_hypernetwork_strength': 'Hypernetwork strength', 'CLIP_stop_at_last_layers': 'Clip skip', 'sd_model_checkpoint': 'Model hash', } -- cgit v1.2.3 From 9f4f894d74b57c3d02ebccaa59f9c22fca2b6c90 Mon Sep 17 00:00:00 2001 From: evshiron Date: Sun, 30 Oct 2022 06:03:32 +0800 Subject: allow skip current image in progress api --- modules/api/api.py | 4 ++-- modules/api/models.py | 3 +++ 2 files changed, 5 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/api/api.py b/modules/api/api.py index e960bb7b..5c5b210f 100644 --- a/modules/api/api.py +++ b/modules/api/api.py @@ -167,7 +167,7 @@ class Api: return PNGInfoResponse(info=result[1]) - def progressapi(self): + def progressapi(self, req: ProgressRequest = Depends()): # copy from check_progress_call of ui.py if shared.state.job_count == 0: @@ -188,7 +188,7 @@ class Api: progress = min(progress, 1) current_image = None - if shared.state.current_image: + if shared.state.current_image and not req.skip_current_image: current_image = encode_pil_to_base64(shared.state.current_image) return ProgressResponse(progress=progress, eta_relative=eta_relative, state=shared.state.dict(), current_image=current_image) diff --git a/modules/api/models.py b/modules/api/models.py index c8bc719a..9ee42a17 100644 --- a/modules/api/models.py +++ b/modules/api/models.py @@ -157,6 +157,9 @@ class PNGInfoRequest(BaseModel): class PNGInfoResponse(BaseModel): info: str = Field(title="Image info", description="A string with all the info the image had") +class ProgressRequest(BaseModel): + skip_current_image: bool = Field(default=False, title="Skip current image", description="Skip current image serialization") + class ProgressResponse(BaseModel): progress: float = Field(title="Progress", description="The progress with a range of 0 to 1") eta_relative: float = Field(title="ETA in secs") -- cgit v1.2.3 From 05a657dd357eaca6940c4775daa946bd33f1167d Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sun, 30 Oct 2022 07:36:56 +0300 Subject: fix broken hires fix --- modules/processing.py | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) (limited to 'modules') diff --git a/modules/processing.py b/modules/processing.py index 50343846..947ce6fa 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -686,15 +686,12 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self) + image_conditioning = self.txt2img_image_conditioning(x) + # GC now before running the next img2img to prevent running out of memory x = None devices.torch_gc() - image_conditioning = self.img2img_image_conditioning( - decoded_samples, - samples, - decoded_samples.new_ones(decoded_samples.shape[0], 1, decoded_samples.shape[2], decoded_samples.shape[3]) - ) samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps, image_conditioning=image_conditioning) return samples -- cgit v1.2.3 From 61836bd544fc8f4ef62f311c9d5964fbdaeb3f4c Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sun, 30 Oct 2022 08:48:53 +0300 Subject: shorten Hypernetwork strength in infotext and omit it when it's the default value. --- modules/generation_parameters_copypaste.py | 2 +- modules/processing.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py index 59c6d7da..df70c728 100644 --- a/modules/generation_parameters_copypaste.py +++ b/modules/generation_parameters_copypaste.py @@ -66,7 +66,7 @@ def integrate_settings_paste_fields(component_dict): settings_map = { 'sd_hypernetwork': 'Hypernet', - 'sd_hypernetwork_strength': 'Hypernetwork strength', + 'sd_hypernetwork_strength': 'Hypernet strength', 'CLIP_stop_at_last_layers': 'Clip skip', 'sd_model_checkpoint': 'Model hash', } diff --git a/modules/processing.py b/modules/processing.py index ecaa78e2..b1df4918 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -396,7 +396,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash), "Model": (None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')), "Hypernet": (None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.name), - "Hypernetwork strength": (None if shared.loaded_hypernetwork is None else shared.opts.sd_hypernetwork_strength), + "Hypernet strength": (None if shared.loaded_hypernetwork is None or shared.opts.sd_hypernetwork_strength >= 1 else shared.opts.sd_hypernetwork_strength), "Batch size": (None if p.batch_size < 2 else p.batch_size), "Batch pos": (None if p.batch_size < 2 else position_in_batch), "Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]), -- cgit v1.2.3 From 149784202cca8612b43629c601ee27cfda64e623 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sun, 30 Oct 2022 09:10:22 +0300 Subject: rework #3722 to not introduce duplicate code --- modules/api/api.py | 43 +++++++++++++------------------------------ modules/shared.py | 22 +++++++++++++++++++--- 2 files changed, 32 insertions(+), 33 deletions(-) (limited to 'modules') diff --git a/modules/api/api.py b/modules/api/api.py index 5c5b210f..6c06d449 100644 --- a/modules/api/api.py +++ b/modules/api/api.py @@ -9,31 +9,6 @@ from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusion from modules.sd_samplers import all_samplers from modules.extras import run_extras, run_pnginfo -# copy from wrap_gradio_gpu_call of webui.py -# because queue lock will be acquired in api handlers -# and time start needs to be set -# the function has been modified into two parts - -def before_gpu_call(): - devices.torch_gc() - - shared.state.sampling_step = 0 - shared.state.job_count = -1 - shared.state.job_no = 0 - shared.state.job_timestamp = shared.state.get_job_timestamp() - shared.state.current_latent = None - shared.state.current_image = None - shared.state.current_image_sampling_step = 0 - shared.state.skipped = False - shared.state.interrupted = False - shared.state.textinfo = None - shared.state.time_start = time.time() - -def after_gpu_call(): - shared.state.job = "" - shared.state.job_count = 0 - - devices.torch_gc() def upscaler_to_index(name: str): try: @@ -41,8 +16,10 @@ def upscaler_to_index(name: str): except: raise HTTPException(status_code=400, detail=f"Invalid upscaler, needs to be on of these: {' , '.join([x.name for x in sd_upscalers])}") + sampler_to_index = lambda name: next(filter(lambda row: name.lower() == row[1].name.lower(), enumerate(all_samplers)), None) + def setUpscalers(req: dict): reqDict = vars(req) reqDict['extras_upscaler_1'] = upscaler_to_index(req.upscaler_1) @@ -51,6 +28,7 @@ def setUpscalers(req: dict): reqDict.pop('upscaler_2') return reqDict + class Api: def __init__(self, app, queue_lock): self.router = APIRouter() @@ -78,10 +56,13 @@ class Api: ) p = StableDiffusionProcessingTxt2Img(**vars(populate)) # Override object param - before_gpu_call() + + shared.state.begin() + with self.queue_lock: processed = process_images(p) - after_gpu_call() + + shared.state.end() b64images = list(map(encode_pil_to_base64, processed.images)) @@ -119,11 +100,13 @@ class Api: imgs = [img] * p.batch_size p.init_images = imgs - # Override object param - before_gpu_call() + + shared.state.begin() + with self.queue_lock: processed = process_images(p) - after_gpu_call() + + shared.state.end() b64images = list(map(encode_pil_to_base64, processed.images)) diff --git a/modules/shared.py b/modules/shared.py index f7b0990c..e4f163c1 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -144,9 +144,6 @@ class State: self.sampling_step = 0 self.current_image_sampling_step = 0 - def get_job_timestamp(self): - return datetime.datetime.now().strftime("%Y%m%d%H%M%S") # shouldn't this return job_timestamp? - def dict(self): obj = { "skipped": self.skipped, @@ -160,6 +157,25 @@ class State: return obj + def begin(self): + self.sampling_step = 0 + self.job_count = -1 + self.job_no = 0 + self.job_timestamp = datetime.datetime.now().strftime("%Y%m%d%H%M%S") + self.current_latent = None + self.current_image = None + self.current_image_sampling_step = 0 + self.skipped = False + self.interrupted = False + self.textinfo = None + + devices.torch_gc() + + def end(self): + self.job = "" + self.job_count = 0 + + devices.torch_gc() state = State() -- cgit v1.2.3