diff --git a/artists.csv b/artists.csv
index 14ba2022..99cdbdc6 100644
--- a/artists.csv
+++ b/artists.csv
@@ -1045,7 +1045,6 @@ Bakemono Zukushi,0.67051035,anime
Lucy Madox Brown,0.67032814,fineart
Paul Wonner,0.6700563,scribbles
Guido Borelli Da Caluso,0.66966087,digipa-high-impact
-Guido Borelli da Caluso,0.66966087,digipa-high-impact
Emil Alzamora,0.5844039,nudity
Heinrich Brocksieper,0.64469147,fineart
Dan Smith,0.669563,digipa-high-impact
diff --git a/environment-wsl2.yaml b/environment-wsl2.yaml
index c9ce11df..f8872750 100644
--- a/environment-wsl2.yaml
+++ b/environment-wsl2.yaml
@@ -3,9 +3,9 @@ channels:
- pytorch
- defaults
dependencies:
- - python=3.8.5
- - pip=20.3
+ - python=3.10
+ - pip=22.2.2
- cudatoolkit=11.3
- - pytorch=1.11.0
- - torchvision=0.12.0
- - numpy=1.19.2
+ - pytorch=1.12.1
+ - torchvision=0.13.1
+ - numpy=1.23.1
\ No newline at end of file
diff --git a/javascript/edit-attention.js b/javascript/edit-attention.js
index 79566a2e..3f1d2fbb 100644
--- a/javascript/edit-attention.js
+++ b/javascript/edit-attention.js
@@ -25,6 +25,7 @@ addEventListener('keydown', (event) => {
} else {
end = target.value.slice(selectionEnd + 1).indexOf(")") + 1;
weight = parseFloat(target.value.slice(selectionEnd + 1, selectionEnd + 1 + end));
+ if (isNaN(weight)) return;
if (event.key == minus) weight -= 0.1;
if (event.key == plus) weight += 0.1;
diff --git a/javascript/hints.js b/javascript/hints.js
index 045f2d3c..d51ee14c 100644
--- a/javascript/hints.js
+++ b/javascript/hints.js
@@ -80,7 +80,10 @@ titles = {
"Scale latent": "Uscale the image in latent space. Alternative is to produce the full image from latent representation, upscale that, and then move it back to latent space.",
"Eta noise seed delta": "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.",
- "Do not add watermark to images": "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be bevaing in an unethical manner.",
+ "Do not add watermark to images": "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.",
+
+ "Filename word regex": "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.",
+ "Filename join string": "This string will be used to hoin split words into a single line if the option above is enabled.",
}
diff --git a/javascript/ui.js b/javascript/ui.js
index b1053201..4100944e 100644
--- a/javascript/ui.js
+++ b/javascript/ui.js
@@ -101,7 +101,8 @@ function create_tab_index_args(tabId, args){
}
function get_extras_tab_index(){
- return create_tab_index_args('mode_extras', arguments)
+ const [,,...args] = [...arguments]
+ return [get_tab_index('mode_extras'), get_tab_index('extras_resize_mode'), ...args]
}
function create_submit_args(args){
diff --git a/modules/deepbooru.py b/modules/deepbooru.py
index 32d741e2..419e6a9c 100644
--- a/modules/deepbooru.py
+++ b/modules/deepbooru.py
@@ -1,21 +1,99 @@
import os.path
from concurrent.futures import ProcessPoolExecutor
-from multiprocessing import get_context
+import multiprocessing
+import time
+import re
+
+re_special = re.compile(r'([\\()])')
+
+def get_deepbooru_tags(pil_image):
+ """
+ This method is for running only one image at a time for simple use. Used to the img2img interrogate.
+ """
+ from modules import shared # prevents circular reference
+
+ try:
+ create_deepbooru_process(shared.opts.interrogate_deepbooru_score_threshold, create_deepbooru_opts())
+ return get_tags_from_process(pil_image)
+ finally:
+ release_process()
-def _load_tf_and_return_tags(pil_image, threshold, include_ranks):
+def create_deepbooru_opts():
+ from modules import shared
+
+ return {
+ "use_spaces": shared.opts.deepbooru_use_spaces,
+ "use_escape": shared.opts.deepbooru_escape,
+ "alpha_sort": shared.opts.deepbooru_sort_alpha,
+ }
+
+
+def deepbooru_process(queue, deepbooru_process_return, threshold, deepbooru_opts):
+ model, tags = get_deepbooru_tags_model()
+ while True: # while process is running, keep monitoring queue for new image
+ pil_image = queue.get()
+ if pil_image == "QUIT":
+ break
+ else:
+ deepbooru_process_return["value"] = get_deepbooru_tags_from_model(model, tags, pil_image, threshold, deepbooru_opts)
+
+
+def create_deepbooru_process(threshold, deepbooru_opts):
+ """
+ Creates deepbooru process. A queue is created to send images into the process. This enables multiple images
+ to be processed in a row without reloading the model or creating a new process. To return the data, a shared
+ dictionary is created to hold the tags created. To wait for tags to be returned, a value of -1 is assigned
+ to the dictionary and the method adding the image to the queue should wait for this value to be updated with
+ the tags.
+ """
+ from modules import shared # prevents circular reference
+ shared.deepbooru_process_manager = multiprocessing.Manager()
+ shared.deepbooru_process_queue = shared.deepbooru_process_manager.Queue()
+ shared.deepbooru_process_return = shared.deepbooru_process_manager.dict()
+ shared.deepbooru_process_return["value"] = -1
+ shared.deepbooru_process = multiprocessing.Process(target=deepbooru_process, args=(shared.deepbooru_process_queue, shared.deepbooru_process_return, threshold, deepbooru_opts))
+ shared.deepbooru_process.start()
+
+
+def get_tags_from_process(image):
+ from modules import shared
+
+ shared.deepbooru_process_return["value"] = -1
+ shared.deepbooru_process_queue.put(image)
+ while shared.deepbooru_process_return["value"] == -1:
+ time.sleep(0.2)
+ caption = shared.deepbooru_process_return["value"]
+ shared.deepbooru_process_return["value"] = -1
+
+ return caption
+
+
+def release_process():
+ """
+ Stops the deepbooru process to return used memory
+ """
+ from modules import shared # prevents circular reference
+ shared.deepbooru_process_queue.put("QUIT")
+ shared.deepbooru_process.join()
+ shared.deepbooru_process_queue = None
+ shared.deepbooru_process = None
+ shared.deepbooru_process_return = None
+ shared.deepbooru_process_manager = None
+
+def get_deepbooru_tags_model():
import deepdanbooru as dd
import tensorflow as tf
import numpy as np
-
this_folder = os.path.dirname(__file__)
model_path = os.path.abspath(os.path.join(this_folder, '..', 'models', 'deepbooru'))
if not os.path.exists(os.path.join(model_path, 'project.json')):
# there is no point importing these every time
import zipfile
from basicsr.utils.download_util import load_file_from_url
- load_file_from_url(r"https://github.com/KichangKim/DeepDanbooru/releases/download/v3-20211112-sgd-e28/deepdanbooru-v3-20211112-sgd-e28.zip",
- model_path)
+ load_file_from_url(
+ r"https://github.com/KichangKim/DeepDanbooru/releases/download/v3-20211112-sgd-e28/deepdanbooru-v3-20211112-sgd-e28.zip",
+ model_path)
with zipfile.ZipFile(os.path.join(model_path, "deepdanbooru-v3-20211112-sgd-e28.zip"), "r") as zip_ref:
zip_ref.extractall(model_path)
os.remove(os.path.join(model_path, "deepdanbooru-v3-20211112-sgd-e28.zip"))
@@ -24,6 +102,17 @@ def _load_tf_and_return_tags(pil_image, threshold, include_ranks):
model = dd.project.load_model_from_project(
model_path, compile_model=True
)
+ return model, tags
+
+
+def get_deepbooru_tags_from_model(model, tags, pil_image, threshold, deepbooru_opts):
+ import deepdanbooru as dd
+ import tensorflow as tf
+ import numpy as np
+
+ alpha_sort = deepbooru_opts['alpha_sort']
+ use_spaces = deepbooru_opts['use_spaces']
+ use_escape = deepbooru_opts['use_escape']
width = model.input_shape[2]
height = model.input_shape[1]
@@ -46,32 +135,35 @@ def _load_tf_and_return_tags(pil_image, threshold, include_ranks):
for i, tag in enumerate(tags):
result_dict[tag] = y[i]
- result_tags_out = []
+
+ unsorted_tags_in_theshold = []
result_tags_print = []
for tag in tags:
if result_dict[tag] >= threshold:
if tag.startswith("rating:"):
continue
- tag_formatted = tag.replace('_', ' ').replace(':', ' ')
- if include_ranks:
- result_tags_out.append(f'({tag_formatted}:{result_dict[tag]})')
- else:
- result_tags_out.append(tag_formatted)
+ unsorted_tags_in_theshold.append((result_dict[tag], tag))
result_tags_print.append(f'{result_dict[tag]} {tag}')
+ # sort tags
+ result_tags_out = []
+ sort_ndx = 0
+ if alpha_sort:
+ sort_ndx = 1
+
+ # sort by reverse by likelihood and normal for alpha
+ unsorted_tags_in_theshold.sort(key=lambda y: y[sort_ndx], reverse=(not alpha_sort))
+ for weight, tag in unsorted_tags_in_theshold:
+ result_tags_out.append(tag)
+
print('\n'.join(sorted(result_tags_print, reverse=True)))
- return ', '.join(result_tags_out)
+ tags_text = ', '.join(result_tags_out)
+ if use_spaces:
+ tags_text = tags_text.replace('_', ' ')
-def subprocess_init_no_cuda():
- import os
- os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
+ if use_escape:
+ tags_text = re.sub(re_special, r'\\\1', tags_text)
-
-def get_deepbooru_tags(pil_image, threshold=0.5, include_ranks=False):
- context = get_context('spawn')
- with ProcessPoolExecutor(initializer=subprocess_init_no_cuda, mp_context=context) as executor:
- f = executor.submit(_load_tf_and_return_tags, pil_image, threshold, include_ranks)
- ret = f.result() # will rethrow any exceptions
- return ret
\ No newline at end of file
+ return tags_text.replace(':', ' ')
diff --git a/modules/extras.py b/modules/extras.py
index 41e8612c..b24d7de3 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -1,3 +1,4 @@
+import math
import os
import numpy as np
@@ -19,7 +20,7 @@ import gradio as gr
cached_images = {}
-def run_extras(extras_mode, image, image_folder, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility):
+def run_extras(extras_mode, resize_mode, image, image_folder, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility):
devices.torch_gc()
imageArr = []
@@ -67,8 +68,13 @@ def run_extras(extras_mode, image, image_folder, gfpgan_visibility, codeformer_v
info += f"CodeFormer w: {round(codeformer_weight, 2)}, CodeFormer visibility:{round(codeformer_visibility, 2)}\n"
image = res
+ if resize_mode == 1:
+ upscaling_resize = max(upscaling_resize_w/image.width, upscaling_resize_h/image.height)
+ crop_info = " (crop)" if upscaling_crop else ""
+ info += f"Resize to: {upscaling_resize_w:g}x{upscaling_resize_h:g}{crop_info}\n"
+
if upscaling_resize != 1.0:
- def upscale(image, scaler_index, resize):
+ def upscale(image, scaler_index, resize, mode, resize_w, resize_h, crop):
small = image.crop((image.width // 2, image.height // 2, image.width // 2 + 10, image.height // 2 + 10))
pixels = tuple(np.array(small).flatten().tolist())
key = (resize, scaler_index, image.width, image.height, gfpgan_visibility, codeformer_visibility, codeformer_weight) + pixels
@@ -77,15 +83,19 @@ def run_extras(extras_mode, image, image_folder, gfpgan_visibility, codeformer_v
if c is None:
upscaler = shared.sd_upscalers[scaler_index]
c = upscaler.scaler.upscale(image, resize, upscaler.data_path)
+ if mode == 1 and crop:
+ cropped = Image.new("RGB", (resize_w, resize_h))
+ cropped.paste(c, box=(resize_w // 2 - c.width // 2, resize_h // 2 - c.height // 2))
+ c = cropped
cached_images[key] = c
return c
info += f"Upscale: {round(upscaling_resize, 3)}, model:{shared.sd_upscalers[extras_upscaler_1].name}\n"
- res = upscale(image, extras_upscaler_1, upscaling_resize)
+ res = upscale(image, extras_upscaler_1, upscaling_resize, resize_mode, upscaling_resize_w, upscaling_resize_h, upscaling_crop)
if extras_upscaler_2 != 0 and extras_upscaler_2_visibility > 0:
- res2 = upscale(image, extras_upscaler_2, upscaling_resize)
+ res2 = upscale(image, extras_upscaler_2, upscaling_resize, resize_mode, upscaling_resize_w, upscaling_resize_h, upscaling_crop)
info += f"Upscale: {round(upscaling_resize, 3)}, visibility: {round(extras_upscaler_2_visibility, 3)}, model:{shared.sd_upscalers[extras_upscaler_2].name}\n"
res = Image.blend(res, res2, extras_upscaler_2_visibility)
@@ -190,7 +200,7 @@ def run_modelmerger(primary_model_name, secondary_model_name, interp_method, int
theta_0[key] = theta_func(theta_0[key], theta_1[key], (float(1.0) - interp_amount)) # Need to reverse the interp_amount to match the desired mix ration in the merged checkpoint
if save_as_half:
theta_0[key] = theta_0[key].half()
-
+
for key in theta_1.keys():
if 'model' in key and key not in theta_0:
theta_0[key] = theta_1[key]
diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 470659df..b6c06d49 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -14,7 +14,7 @@ import torch
from torch import einsum
from einops import rearrange, repeat
import modules.textual_inversion.dataset
-from modules.textual_inversion.learn_schedule import LearnSchedule
+from modules.textual_inversion.learn_schedule import LearnRateScheduler
class HypernetworkModule(torch.nn.Module):
@@ -120,6 +120,17 @@ def load_hypernetwork(filename):
shared.loaded_hypernetwork = None
+def find_closest_hypernetwork_name(search: str):
+ if not search:
+ return None
+ search = search.lower()
+ applicable = [name for name in shared.hypernetworks if search in name.lower()]
+ if not applicable:
+ return None
+ applicable = sorted(applicable, key=lambda name: len(name))
+ return applicable[0]
+
+
def apply_hypernetwork(hypernetwork, context, layer=None):
hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
@@ -164,7 +175,7 @@ def attention_CrossAttention_forward(self, x, context=None, mask=None):
def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, template_file, preview_image_prompt):
- assert hypernetwork_name, 'embedding not selected'
+ assert hypernetwork_name, 'hypernetwork not selected'
path = shared.hypernetworks.get(hypernetwork_name, None)
shared.loaded_hypernetwork = Hypernetwork()
@@ -212,31 +223,23 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
if ititial_step > steps:
return hypernetwork, filename
- schedules = iter(LearnSchedule(learn_rate, steps, ititial_step))
- (learn_rate, end_step) = next(schedules)
- print(f'Training at rate of {learn_rate} until step {end_step}')
-
- optimizer = torch.optim.AdamW(weights, lr=learn_rate)
+ scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
+ optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate)
pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)
- for i, (x, text, cond) in pbar:
+ for i, entry in pbar:
hypernetwork.step = i + ititial_step
- if hypernetwork.step > end_step:
- try:
- (learn_rate, end_step) = next(schedules)
- except Exception:
- break
- tqdm.tqdm.write(f'Training at rate of {learn_rate} until step {end_step}')
- for pg in optimizer.param_groups:
- pg['lr'] = learn_rate
+ scheduler.apply(optimizer, hypernetwork.step)
+ if scheduler.finished:
+ break
if shared.state.interrupted:
break
with torch.autocast("cuda"):
- cond = cond.to(devices.device)
- x = x.to(devices.device)
+ cond = entry.cond.to(devices.device)
+ x = entry.latent.to(devices.device)
loss = shared.sd_model(x.unsqueeze(0), cond)[0]
del x
del cond
@@ -256,7 +259,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0:
last_saved_image = os.path.join(images_dir, f'{hypernetwork_name}-{hypernetwork.step}.png')
- preview_text = text if preview_image_prompt == "" else preview_image_prompt
+ preview_text = entry.cond_text if preview_image_prompt == "" else preview_image_prompt
optimizer.zero_grad()
shared.sd_model.cond_stage_model.to(devices.device)
@@ -271,16 +274,16 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
)
processed = processing.process_images(p)
- image = processed.images[0]
+ 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.current_image = image
- image.save(last_saved_image)
-
- last_saved_image += f", prompt: {preview_text}"
+ if image is not None:
+ shared.state.current_image = image
+ image.save(last_saved_image)
+ last_saved_image += f", prompt: {preview_text}"
shared.state.job_no = hypernetwork.step
@@ -288,7 +291,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
Loss: {losses.mean():.7f}
Step: {hypernetwork.step}
-Last prompt: {html.escape(text)}
+Last prompt: {html.escape(entry.cond_text)}
Last saved embedding: {html.escape(last_saved_file)}
Last saved image: {html.escape(last_saved_image)}
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index ac70f876..c81722a0 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -321,7 +321,17 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
fixes.append(fix[1])
self.hijack.fixes.append(fixes)
- z1 = self.process_tokens([x[:75] for x in remade_batch_tokens], [x[:75] for x in batch_multipliers])
+ tokens = []
+ multipliers = []
+ for j in range(len(remade_batch_tokens)):
+ if len(remade_batch_tokens[j]) > 0:
+ tokens.append(remade_batch_tokens[j][:75])
+ multipliers.append(batch_multipliers[j][:75])
+ else:
+ tokens.append([self.wrapped.tokenizer.eos_token_id] * 75)
+ multipliers.append([1.0] * 75)
+
+ z1 = self.process_tokens(tokens, multipliers)
z = z1 if z is None else torch.cat((z, z1), axis=-2)
remade_batch_tokens = rem_tokens
diff --git a/modules/shared.py b/modules/shared.py
index 3e0bfd72..8b62e259 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -86,6 +86,7 @@ parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram
xformers_available = False
config_filename = cmd_opts.ui_settings_file
+os.makedirs(cmd_opts.hypernetwork_dir, exist_ok=True)
hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir)
loaded_hypernetwork = None
@@ -229,7 +230,10 @@ options_templates.update(options_section(('system', "System"), {
}))
options_templates.update(options_section(('training', "Training"), {
- "unload_models_when_training": OptionInfo(False, "Unload VAE and CLIP form VRAM when training"),
+ "unload_models_when_training": OptionInfo(False, "Unload VAE and CLIP from VRAM when training"),
+ "dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
+ "dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
+ "training_image_repeats_per_epoch": OptionInfo(100, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}),
}))
options_templates.update(options_section(('sd', "Stable Diffusion"), {
@@ -255,8 +259,10 @@ options_templates.update(options_section(('interrogate', "Interrogate Options"),
"interrogate_clip_num_beams": OptionInfo(1, "Interrogate: num_beams for BLIP", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1}),
"interrogate_clip_min_length": OptionInfo(24, "Interrogate: minimum description length (excluding artists, etc..)", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),
"interrogate_clip_max_length": OptionInfo(48, "Interrogate: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}),
- "interrogate_clip_dict_limit": OptionInfo(1500, "Interrogate: maximum number of lines in text file (0 = No limit)"),
"interrogate_deepbooru_score_threshold": OptionInfo(0.5, "Interrogate: deepbooru score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
+ "deepbooru_sort_alpha": OptionInfo(True, "Interrogate: deepbooru sort alphabetically"),
+ "deepbooru_use_spaces": OptionInfo(False, "use spaces for tags in deepbooru"),
+ "deepbooru_escape": OptionInfo(True, "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)"),
}))
options_templates.update(options_section(('ui', "User interface"), {
diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index f61f40d3..67e90afe 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -11,11 +11,21 @@ import tqdm
from modules import devices, shared
import re
-re_tag = re.compile(r"[a-zA-Z][_\w\d()]+")
+re_numbers_at_start = re.compile(r"^[-\d]+\s*")
+
+
+class DatasetEntry:
+ def __init__(self, filename=None, latent=None, filename_text=None):
+ self.filename = filename
+ self.latent = latent
+ self.filename_text = filename_text
+ self.cond = None
+ self.cond_text = None
class PersonalizedBase(Dataset):
def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None, include_cond=False):
+ re_word = re.compile(shared.opts.dataset_filename_word_regex) if len(shared.opts.dataset_filename_word_regex)>0 else None
self.placeholder_token = placeholder_token
@@ -42,9 +52,18 @@ class PersonalizedBase(Dataset):
except Exception:
continue
+ text_filename = os.path.splitext(path)[0] + ".txt"
filename = os.path.basename(path)
- filename_tokens = os.path.splitext(filename)[0]
- filename_tokens = re_tag.findall(filename_tokens)
+
+ if os.path.exists(text_filename):
+ with open(text_filename, "r", encoding="utf8") as file:
+ filename_text = file.read()
+ else:
+ filename_text = os.path.splitext(filename)[0]
+ filename_text = re.sub(re_numbers_at_start, '', filename_text)
+ if re_word:
+ tokens = re_word.findall(filename_text)
+ filename_text = (shared.opts.dataset_filename_join_string or "").join(tokens)
npimage = np.array(image).astype(np.uint8)
npimage = (npimage / 127.5 - 1.0).astype(np.float32)
@@ -55,13 +74,13 @@ class PersonalizedBase(Dataset):
init_latent = model.get_first_stage_encoding(model.encode_first_stage(torchdata.unsqueeze(dim=0))).squeeze()
init_latent = init_latent.to(devices.cpu)
- if include_cond:
- text = self.create_text(filename_tokens)
- cond = cond_model([text]).to(devices.cpu)
- else:
- cond = None
+ entry = DatasetEntry(filename=path, filename_text=filename_text, latent=init_latent)
- self.dataset.append((init_latent, filename_tokens, cond))
+ if include_cond:
+ entry.cond_text = self.create_text(filename_text)
+ entry.cond = cond_model([entry.cond_text]).to(devices.cpu)
+
+ self.dataset.append(entry)
self.length = len(self.dataset) * repeats
@@ -72,10 +91,10 @@ class PersonalizedBase(Dataset):
def shuffle(self):
self.indexes = self.initial_indexes[torch.randperm(self.initial_indexes.shape[0])]
- def create_text(self, filename_tokens):
+ def create_text(self, filename_text):
text = random.choice(self.lines)
text = text.replace("[name]", self.placeholder_token)
- text = text.replace("[filewords]", ' '.join(filename_tokens))
+ text = text.replace("[filewords]", filename_text)
return text
def __len__(self):
@@ -86,7 +105,9 @@ class PersonalizedBase(Dataset):
self.shuffle()
index = self.indexes[i % len(self.indexes)]
- x, filename_tokens, cond = self.dataset[index]
+ entry = self.dataset[index]
- text = self.create_text(filename_tokens)
- return x, text, cond
+ if entry.cond is None:
+ entry.cond_text = self.create_text(entry.filename_text)
+
+ return entry
diff --git a/modules/textual_inversion/image_embedding.py b/modules/textual_inversion/image_embedding.py
new file mode 100644
index 00000000..898ce3b3
--- /dev/null
+++ b/modules/textual_inversion/image_embedding.py
@@ -0,0 +1,219 @@
+import base64
+import json
+import numpy as np
+import zlib
+from PIL import Image, PngImagePlugin, ImageDraw, ImageFont
+from fonts.ttf import Roboto
+import torch
+
+
+class EmbeddingEncoder(json.JSONEncoder):
+ def default(self, obj):
+ if isinstance(obj, torch.Tensor):
+ return {'TORCHTENSOR': obj.cpu().detach().numpy().tolist()}
+ return json.JSONEncoder.default(self, obj)
+
+
+class EmbeddingDecoder(json.JSONDecoder):
+ def __init__(self, *args, **kwargs):
+ json.JSONDecoder.__init__(self, object_hook=self.object_hook, *args, **kwargs)
+
+ def object_hook(self, d):
+ if 'TORCHTENSOR' in d:
+ return torch.from_numpy(np.array(d['TORCHTENSOR']))
+ return d
+
+
+def embedding_to_b64(data):
+ d = json.dumps(data, cls=EmbeddingEncoder)
+ return base64.b64encode(d.encode())
+
+
+def embedding_from_b64(data):
+ d = base64.b64decode(data)
+ return json.loads(d, cls=EmbeddingDecoder)
+
+
+def lcg(m=2**32, a=1664525, c=1013904223, seed=0):
+ while True:
+ seed = (a * seed + c) % m
+ yield seed % 255
+
+
+def xor_block(block):
+ g = lcg()
+ randblock = np.array([next(g) for _ in range(np.product(block.shape))]).astype(np.uint8).reshape(block.shape)
+ return np.bitwise_xor(block.astype(np.uint8), randblock & 0x0F)
+
+
+def style_block(block, sequence):
+ im = Image.new('RGB', (block.shape[1], block.shape[0]))
+ draw = ImageDraw.Draw(im)
+ i = 0
+ for x in range(-6, im.size[0], 8):
+ for yi, y in enumerate(range(-6, im.size[1], 8)):
+ offset = 0
+ if yi % 2 == 0:
+ offset = 4
+ shade = sequence[i % len(sequence)]
+ i += 1
+ draw.ellipse((x+offset, y, x+6+offset, y+6), fill=(shade, shade, shade))
+
+ fg = np.array(im).astype(np.uint8) & 0xF0
+
+ return block ^ fg
+
+
+def insert_image_data_embed(image, data):
+ d = 3
+ data_compressed = zlib.compress(json.dumps(data, cls=EmbeddingEncoder).encode(), level=9)
+ data_np_ = np.frombuffer(data_compressed, np.uint8).copy()
+ data_np_high = data_np_ >> 4
+ data_np_low = data_np_ & 0x0F
+
+ h = image.size[1]
+ next_size = data_np_low.shape[0] + (h-(data_np_low.shape[0] % h))
+ next_size = next_size + ((h*d)-(next_size % (h*d)))
+
+ data_np_low.resize(next_size)
+ data_np_low = data_np_low.reshape((h, -1, d))
+
+ data_np_high.resize(next_size)
+ data_np_high = data_np_high.reshape((h, -1, d))
+
+ edge_style = list(data['string_to_param'].values())[0].cpu().detach().numpy().tolist()[0][:1024]
+ edge_style = (np.abs(edge_style)/np.max(np.abs(edge_style))*255).astype(np.uint8)
+
+ data_np_low = style_block(data_np_low, sequence=edge_style)
+ data_np_low = xor_block(data_np_low)
+ data_np_high = style_block(data_np_high, sequence=edge_style[::-1])
+ data_np_high = xor_block(data_np_high)
+
+ im_low = Image.fromarray(data_np_low, mode='RGB')
+ im_high = Image.fromarray(data_np_high, mode='RGB')
+
+ background = Image.new('RGB', (image.size[0]+im_low.size[0]+im_high.size[0]+2, image.size[1]), (0, 0, 0))
+ background.paste(im_low, (0, 0))
+ background.paste(image, (im_low.size[0]+1, 0))
+ background.paste(im_high, (im_low.size[0]+1+image.size[0]+1, 0))
+
+ return background
+
+
+def crop_black(img, tol=0):
+ mask = (img > tol).all(2)
+ mask0, mask1 = mask.any(0), mask.any(1)
+ col_start, col_end = mask0.argmax(), mask.shape[1]-mask0[::-1].argmax()
+ row_start, row_end = mask1.argmax(), mask.shape[0]-mask1[::-1].argmax()
+ return img[row_start:row_end, col_start:col_end]
+
+
+def extract_image_data_embed(image):
+ d = 3
+ outarr = crop_black(np.array(image.convert('RGB').getdata()).reshape(image.size[1], image.size[0], d).astype(np.uint8)) & 0x0F
+ black_cols = np.where(np.sum(outarr, axis=(0, 2)) == 0)
+ if black_cols[0].shape[0] < 2:
+ print('No Image data blocks found.')
+ return None
+
+ data_block_lower = outarr[:, :black_cols[0].min(), :].astype(np.uint8)
+ data_block_upper = outarr[:, black_cols[0].max()+1:, :].astype(np.uint8)
+
+ data_block_lower = xor_block(data_block_lower)
+ data_block_upper = xor_block(data_block_upper)
+
+ data_block = (data_block_upper << 4) | (data_block_lower)
+ data_block = data_block.flatten().tobytes()
+
+ data = zlib.decompress(data_block)
+ return json.loads(data, cls=EmbeddingDecoder)
+
+
+def caption_image_overlay(srcimage, title, footerLeft, footerMid, footerRight, textfont=None):
+ from math import cos
+
+ image = srcimage.copy()
+
+ if textfont is None:
+ try:
+ textfont = ImageFont.truetype(opts.font or Roboto, fontsize)
+ textfont = opts.font or Roboto
+ except Exception:
+ textfont = Roboto
+
+ factor = 1.5
+ gradient = Image.new('RGBA', (1, image.size[1]), color=(0, 0, 0, 0))
+ for y in range(image.size[1]):
+ mag = 1-cos(y/image.size[1]*factor)
+ mag = max(mag, 1-cos((image.size[1]-y)/image.size[1]*factor*1.1))
+ gradient.putpixel((0, y), (0, 0, 0, int(mag*255)))
+ image = Image.alpha_composite(image.convert('RGBA'), gradient.resize(image.size))
+
+ draw = ImageDraw.Draw(image)
+ fontsize = 32
+ font = ImageFont.truetype(textfont, fontsize)
+ padding = 10
+
+ _, _, w, h = draw.textbbox((0, 0), title, font=font)
+ fontsize = min(int(fontsize * (((image.size[0]*0.75)-(padding*4))/w)), 72)
+ font = ImageFont.truetype(textfont, fontsize)
+ _, _, w, h = draw.textbbox((0, 0), title, font=font)
+ draw.text((padding, padding), title, anchor='lt', font=font, fill=(255, 255, 255, 230))
+
+ _, _, w, h = draw.textbbox((0, 0), footerLeft, font=font)
+ fontsize_left = min(int(fontsize * (((image.size[0]/3)-(padding))/w)), 72)
+ _, _, w, h = draw.textbbox((0, 0), footerMid, font=font)
+ fontsize_mid = min(int(fontsize * (((image.size[0]/3)-(padding))/w)), 72)
+ _, _, w, h = draw.textbbox((0, 0), footerRight, font=font)
+ fontsize_right = min(int(fontsize * (((image.size[0]/3)-(padding))/w)), 72)
+
+ font = ImageFont.truetype(textfont, min(fontsize_left, fontsize_mid, fontsize_right))
+
+ draw.text((padding, image.size[1]-padding), footerLeft, anchor='ls', font=font, fill=(255, 255, 255, 230))
+ draw.text((image.size[0]/2, image.size[1]-padding), footerMid, anchor='ms', font=font, fill=(255, 255, 255, 230))
+ draw.text((image.size[0]-padding, image.size[1]-padding), footerRight, anchor='rs', font=font, fill=(255, 255, 255, 230))
+
+ return image
+
+
+if __name__ == '__main__':
+
+ testEmbed = Image.open('test_embedding.png')
+ data = extract_image_data_embed(testEmbed)
+ assert data is not None
+
+ data = embedding_from_b64(testEmbed.text['sd-ti-embedding'])
+ assert data is not None
+
+ image = Image.new('RGBA', (512, 512), (255, 255, 200, 255))
+ cap_image = caption_image_overlay(image, 'title', 'footerLeft', 'footerMid', 'footerRight')
+
+ test_embed = {'string_to_param': {'*': torch.from_numpy(np.random.random((2, 4096)))}}
+
+ embedded_image = insert_image_data_embed(cap_image, test_embed)
+
+ retrived_embed = extract_image_data_embed(embedded_image)
+
+ assert str(retrived_embed) == str(test_embed)
+
+ embedded_image2 = insert_image_data_embed(cap_image, retrived_embed)
+
+ assert embedded_image == embedded_image2
+
+ g = lcg()
+ shared_random = np.array([next(g) for _ in range(100)]).astype(np.uint8).tolist()
+
+ reference_random = [253, 242, 127, 44, 157, 27, 239, 133, 38, 79, 167, 4, 177,
+ 95, 130, 79, 78, 14, 52, 215, 220, 194, 126, 28, 240, 179,
+ 160, 153, 149, 50, 105, 14, 21, 218, 199, 18, 54, 198, 193,
+ 38, 128, 19, 53, 195, 124, 75, 205, 12, 6, 145, 0, 28,
+ 30, 148, 8, 45, 218, 171, 55, 249, 97, 166, 12, 35, 0,
+ 41, 221, 122, 215, 170, 31, 113, 186, 97, 119, 31, 23, 185,
+ 66, 140, 30, 41, 37, 63, 137, 109, 216, 55, 159, 145, 82,
+ 204, 86, 73, 222, 44, 198, 118, 240, 97]
+
+ assert shared_random == reference_random
+
+ hunna_kay_random_sum = sum(np.array([next(g) for _ in range(100000)]).astype(np.uint8).tolist())
+
+ assert 12731374 == hunna_kay_random_sum
diff --git a/modules/textual_inversion/learn_schedule.py b/modules/textual_inversion/learn_schedule.py
index db720271..2062726a 100644
--- a/modules/textual_inversion/learn_schedule.py
+++ b/modules/textual_inversion/learn_schedule.py
@@ -1,6 +1,12 @@
+import tqdm
-class LearnSchedule:
+
+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
+ """
+
pairs = learn_rate.split(',')
self.rates = []
self.it = 0
@@ -32,3 +38,32 @@ class LearnSchedule:
return self.rates[self.it - 1]
else:
raise StopIteration
+
+
+class LearnRateScheduler:
+ def __init__(self, learn_rate, max_steps, cur_step=0, verbose=True):
+ self.schedules = LearnScheduleIterator(learn_rate, max_steps, cur_step)
+ (self.learn_rate, self.end_step) = next(self.schedules)
+ self.verbose = verbose
+
+ if self.verbose:
+ print(f'Training at rate of {self.learn_rate} until step {self.end_step}')
+
+ self.finished = False
+
+ def apply(self, optimizer, step_number):
+ if step_number <= self.end_step:
+ return
+
+ try:
+ (self.learn_rate, self.end_step) = next(self.schedules)
+ except Exception:
+ self.finished = True
+ return
+
+ if self.verbose:
+ tqdm.tqdm.write(f'Training at rate of {self.learn_rate} until step {self.end_step}')
+
+ for pg in optimizer.param_groups:
+ pg['lr'] = self.learn_rate
+
diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index 1a672725..3047bede 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -3,11 +3,35 @@ from PIL import Image, ImageOps
import platform
import sys
import tqdm
+import time
from modules import shared, images
+from modules.shared import opts, cmd_opts
+if cmd_opts.deepdanbooru:
+ import modules.deepbooru as deepbooru
-def preprocess(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption):
+def preprocess(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru=False):
+ try:
+ if process_caption:
+ shared.interrogator.load()
+
+ if process_caption_deepbooru:
+ deepbooru.create_deepbooru_process(opts.interrogate_deepbooru_score_threshold, deepbooru.create_deepbooru_opts())
+
+ preprocess_work(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru)
+
+ finally:
+
+ if process_caption:
+ shared.interrogator.send_blip_to_ram()
+
+ if process_caption_deepbooru:
+ deepbooru.release_process()
+
+
+
+def preprocess_work(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru=False):
width = process_width
height = process_height
src = os.path.abspath(process_src)
@@ -22,19 +46,28 @@ def preprocess(process_src, process_dst, process_width, process_height, process_
shared.state.textinfo = "Preprocessing..."
shared.state.job_count = len(files)
- if process_caption:
- shared.interrogator.load()
-
def save_pic_with_caption(image, index):
- if process_caption:
- caption = "-" + shared.interrogator.generate_caption(image)
- caption = sanitize_caption(os.path.join(dst, f"{index:05}-{subindex[0]}"), caption, ".png")
- else:
- caption = filename
- caption = os.path.splitext(caption)[0]
- caption = os.path.basename(caption)
+ caption = ""
+
+ if process_caption:
+ caption += shared.interrogator.generate_caption(image)
+
+ if process_caption_deepbooru:
+ if len(caption) > 0:
+ caption += ", "
+ caption += deepbooru.get_tags_from_process(image)
+
+ filename_part = filename
+ filename_part = os.path.splitext(filename_part)[0]
+ filename_part = os.path.basename(filename_part)
+
+ basename = f"{index:05}-{subindex[0]}-{filename_part}"
+ image.save(os.path.join(dst, f"{basename}.png"))
+
+ if len(caption) > 0:
+ with open(os.path.join(dst, f"{basename}.txt"), "w", encoding="utf8") as file:
+ file.write(caption)
- image.save(os.path.join(dst, f"{index:05}-{subindex[0]}{caption}.png"))
subindex[0] += 1
def save_pic(image, index):
@@ -79,30 +112,3 @@ def preprocess(process_src, process_dst, process_width, process_height, process_
save_pic(img, index)
shared.state.nextjob()
-
- if process_caption:
- shared.interrogator.send_blip_to_ram()
-
-def sanitize_caption(base_path, original_caption, suffix):
- operating_system = platform.system().lower()
- if (operating_system == "windows"):
- invalid_path_characters = "\\/:*?\"<>|"
- max_path_length = 259
- else:
- invalid_path_characters = "/" #linux/macos
- max_path_length = 1023
- caption = original_caption
- for invalid_character in invalid_path_characters:
- caption = caption.replace(invalid_character, "")
- fixed_path_length = len(base_path) + len(suffix)
- if fixed_path_length + len(caption) <= max_path_length:
- return caption
- caption_tokens = caption.split()
- new_caption = ""
- for token in caption_tokens:
- last_caption = new_caption
- new_caption = new_caption + token + " "
- if (len(new_caption) + fixed_path_length - 1 > max_path_length):
- break
- print(f"\nPath will be too long. Truncated caption: {original_caption}\nto: {last_caption}", file=sys.stderr)
- return last_caption.strip()
diff --git a/modules/textual_inversion/test_embedding.png b/modules/textual_inversion/test_embedding.png
new file mode 100644
index 00000000..07e2d9af
Binary files /dev/null and b/modules/textual_inversion/test_embedding.png differ
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 7717837d..fa0e33a2 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -7,11 +7,15 @@ import tqdm
import html
import datetime
+from PIL import Image, PngImagePlugin
from modules import shared, devices, sd_hijack, processing, sd_models
import modules.textual_inversion.dataset
-from modules.textual_inversion.learn_schedule import LearnSchedule
+from modules.textual_inversion.learn_schedule import LearnRateScheduler
+from modules.textual_inversion.image_embedding import (embedding_to_b64, embedding_from_b64,
+ insert_image_data_embed, extract_image_data_embed,
+ caption_image_overlay)
class Embedding:
def __init__(self, vec, name, step=None):
@@ -81,7 +85,18 @@ class EmbeddingDatabase:
def process_file(path, filename):
name = os.path.splitext(filename)[0]
- data = torch.load(path, map_location="cpu")
+ data = []
+
+ if filename.upper().endswith('.PNG'):
+ embed_image = Image.open(path)
+ if 'sd-ti-embedding' in embed_image.text:
+ data = embedding_from_b64(embed_image.text['sd-ti-embedding'])
+ name = data.get('name', name)
+ else:
+ data = extract_image_data_embed(embed_image)
+ name = data.get('name', name)
+ else:
+ data = torch.load(path, map_location="cpu")
# textual inversion embeddings
if 'string_to_param' in data:
@@ -157,7 +172,7 @@ def create_embedding(name, num_vectors_per_token, init_text='*'):
return fn
-def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, num_repeats, create_image_every, save_embedding_every, template_file, preview_image_prompt):
+def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_image_prompt):
assert embedding_name, 'embedding not selected'
shared.state.textinfo = "Initializing textual inversion training..."
@@ -179,11 +194,17 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
else:
images_dir = None
+ if create_image_every > 0 and save_image_with_stored_embedding:
+ images_embeds_dir = os.path.join(log_directory, "image_embeddings")
+ os.makedirs(images_embeds_dir, exist_ok=True)
+ else:
+ images_embeds_dir = None
+
cond_model = shared.sd_model.cond_stage_model
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=num_repeats, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file)
+ 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)
hijack = sd_hijack.model_hijack
@@ -199,32 +220,24 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
if ititial_step > steps:
return embedding, filename
- schedules = iter(LearnSchedule(learn_rate, steps, ititial_step))
- (learn_rate, end_step) = next(schedules)
- print(f'Training at rate of {learn_rate} until step {end_step}')
-
- optimizer = torch.optim.AdamW([embedding.vec], lr=learn_rate)
+ 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, (x, text, _) in pbar:
+ for i, entry in pbar:
embedding.step = i + ititial_step
- if embedding.step > end_step:
- try:
- (learn_rate, end_step) = next(schedules)
- except:
- break
- tqdm.tqdm.write(f'Training at rate of {learn_rate} until step {end_step}')
- for pg in optimizer.param_groups:
- pg['lr'] = learn_rate
+ scheduler.apply(optimizer, embedding.step)
+ if scheduler.finished:
+ break
if shared.state.interrupted:
break
with torch.autocast("cuda"):
- c = cond_model([text])
+ c = cond_model([entry.cond_text])
- x = x.to(devices.device)
+ x = entry.latent.to(devices.device)
loss = shared.sd_model(x.unsqueeze(0), c)[0]
del x
@@ -246,7 +259,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
if embedding.step > 0 and images_dir is not None and embedding.step % create_image_every == 0:
last_saved_image = os.path.join(images_dir, f'{embedding_name}-{embedding.step}.png')
- preview_text = text if preview_image_prompt == "" else preview_image_prompt
+ preview_text = entry.cond_text if preview_image_prompt == "" else preview_image_prompt
p = processing.StableDiffusionProcessingTxt2Img(
sd_model=shared.sd_model,
@@ -262,6 +275,26 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
image = processed.images[0]
shared.state.current_image = image
+
+ if save_image_with_stored_embedding and os.path.exists(last_saved_file):
+
+ last_saved_image_chunks = os.path.join(images_embeds_dir, f'{embedding_name}-{embedding.step}.png')
+
+ info = PngImagePlugin.PngInfo()
+ data = torch.load(last_saved_file)
+ info.add_text("sd-ti-embedding", embedding_to_b64(data))
+
+ title = "<{}>".format(data.get('name', '???'))
+ checkpoint = sd_models.select_checkpoint()
+ footer_left = checkpoint.model_name
+ footer_mid = '[{}]'.format(checkpoint.hash)
+ footer_right = '{}'.format(embedding.step)
+
+ 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)
+
image.save(last_saved_image)
last_saved_image += f", prompt: {preview_text}"
@@ -272,7 +305,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
Loss: {losses.mean():.7f}
Step: {embedding.step}
-Last prompt: {html.escape(text)}
+Last prompt: {html.escape(entry.cond_text)}
Last saved embedding: {html.escape(last_saved_file)}
Last saved image: {html.escape(last_saved_image)}
diff --git a/modules/ui.py b/modules/ui.py
index f4dbe247..e07ee0e1 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -107,7 +107,7 @@ def send_gradio_gallery_to_image(x):
def save_files(js_data, images, do_make_zip, index):
- import csv
+ import csv
filenames = []
fullfns = []
@@ -131,6 +131,8 @@ def save_files(js_data, images, do_make_zip, index):
images = [images[index]]
start_index = index
+ os.makedirs(opts.outdir_save, exist_ok=True)
+
with open(os.path.join(opts.outdir_save, "log.csv"), "a", encoding="utf8", newline='') as file:
at_start = file.tell() == 0
writer = csv.writer(file)
@@ -181,8 +183,15 @@ def wrap_gradio_call(func, extra_outputs=None):
try:
res = list(func(*args, **kwargs))
except Exception as e:
+ # When printing out our debug argument list, do not print out more than a MB of text
+ max_debug_str_len = 131072 # (1024*1024)/8
+
print("Error completing request", file=sys.stderr)
- print("Arguments:", args, kwargs, file=sys.stderr)
+ argStr = f"Arguments: {str(args)} {str(kwargs)}"
+ print(argStr[:max_debug_str_len], file=sys.stderr)
+ if len(argStr) > max_debug_str_len:
+ print(f"(Argument list truncated at {max_debug_str_len}/{len(argStr)} characters)", file=sys.stderr)
+
print(traceback.format_exc(), file=sys.stderr)
shared.state.job = ""
@@ -311,12 +320,13 @@ def apply_styles(prompt, prompt_neg, style1_name, style2_name):
def interrogate(image):
- prompt = shared.interrogator.interrogate(image, include_ranks=opts.interrogate_return_ranks)
+ prompt = shared.interrogator.interrogate(image)
+
return gr_show(True) if prompt is None else prompt
def interrogate_deepbooru(image):
- prompt = get_deepbooru_tags(image, opts.interrogate_deepbooru_score_threshold, opts.interrogate_return_ranks)
+ prompt = get_deepbooru_tags(image)
return gr_show(True) if prompt is None else prompt
@@ -557,11 +567,11 @@ def create_ui(wrap_gradio_gpu_call):
button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder'
open_txt2img_folder = gr.Button(folder_symbol, elem_id=button_id)
- with gr.Row():
- do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
-
- with gr.Row():
- download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
+ with gr.Row():
+ do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
+
+ with gr.Row():
+ download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
with gr.Group():
html_info = gr.HTML()
@@ -745,11 +755,11 @@ def create_ui(wrap_gradio_gpu_call):
button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder'
open_img2img_folder = gr.Button(folder_symbol, elem_id=button_id)
- with gr.Row():
- do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
-
- with gr.Row():
- download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
+ with gr.Row():
+ do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
+
+ with gr.Row():
+ download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
with gr.Group():
html_info = gr.HTML()
@@ -911,7 +921,15 @@ def create_ui(wrap_gradio_gpu_call):
with gr.TabItem('Batch Process'):
image_batch = gr.File(label="Batch Process", file_count="multiple", interactive=True, type="file")
- upscaling_resize = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Resize", value=2)
+ with gr.Tabs(elem_id="extras_resize_mode"):
+ with gr.TabItem('Scale by'):
+ upscaling_resize = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Resize", value=2)
+ with gr.TabItem('Scale to'):
+ with gr.Group():
+ with gr.Row():
+ upscaling_resize_w = gr.Number(label="Width", value=512, precision=0)
+ upscaling_resize_h = gr.Number(label="Height", value=512, precision=0)
+ upscaling_crop = gr.Checkbox(label='Crop to fit', value=True)
with gr.Group():
extras_upscaler_1 = gr.Radio(label='Upscaler 1', choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
@@ -942,6 +960,7 @@ def create_ui(wrap_gradio_gpu_call):
fn=wrap_gradio_gpu_call(modules.extras.run_extras),
_js="get_extras_tab_index",
inputs=[
+ dummy_component,
dummy_component,
extras_image,
image_batch,
@@ -949,6 +968,9 @@ def create_ui(wrap_gradio_gpu_call):
codeformer_visibility,
codeformer_weight,
upscaling_resize,
+ upscaling_resize_w,
+ upscaling_resize_h,
+ upscaling_crop,
extras_upscaler_1,
extras_upscaler_2,
extras_upscaler_2_visibility,
@@ -959,14 +981,14 @@ def create_ui(wrap_gradio_gpu_call):
html_info,
]
)
-
+
extras_send_to_img2img.click(
fn=lambda x: image_from_url_text(x),
_js="extract_image_from_gallery_img2img",
inputs=[result_images],
outputs=[init_img],
)
-
+
extras_send_to_inpaint.click(
fn=lambda x: image_from_url_text(x),
_js="extract_image_from_gallery_inpaint",
@@ -1013,14 +1035,14 @@ def create_ui(wrap_gradio_gpu_call):
sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings()
- with gr.Blocks() as textual_inversion_interface:
+ with gr.Blocks() as train_interface:
with gr.Row().style(equal_height=False):
- with gr.Column():
- with gr.Group():
- gr.HTML(value="See wiki for detailed explanation.
")
+ gr.HTML(value="See wiki for detailed explanation.
")
- gr.HTML(value="Create a new embedding
")
+ with gr.Row().style(equal_height=False):
+ with gr.Tabs(elem_id="train_tabs"):
+ with gr.Tab(label="Create embedding"):
new_embedding_name = gr.Textbox(label="Name")
initialization_text = gr.Textbox(label="Initialization text", value="*")
nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1)
@@ -1032,9 +1054,7 @@ def create_ui(wrap_gradio_gpu_call):
with gr.Column():
create_embedding = gr.Button(value="Create embedding", variant='primary')
- with gr.Group():
- gr.HTML(value="Create a new hypernetwork
")
-
+ with gr.Tab(label="Create hypernetwork"):
new_hypernetwork_name = gr.Textbox(label="Name")
new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
@@ -1045,9 +1065,7 @@ def create_ui(wrap_gradio_gpu_call):
with gr.Column():
create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary')
- with gr.Group():
- gr.HTML(value="Preprocess images
")
-
+ with gr.Tab(label="Preprocess images"):
process_src = gr.Textbox(label='Source directory')
process_dst = gr.Textbox(label='Destination directory')
process_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
@@ -1056,7 +1074,8 @@ def create_ui(wrap_gradio_gpu_call):
with gr.Row():
process_flip = gr.Checkbox(label='Create flipped copies')
process_split = gr.Checkbox(label='Split oversized images into two')
- process_caption = gr.Checkbox(label='Use BLIP caption as filename')
+ process_caption = gr.Checkbox(label='Use BLIP for caption')
+ process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True if cmd_opts.deepdanbooru else False)
with gr.Row():
with gr.Column(scale=3):
@@ -1065,7 +1084,7 @@ def create_ui(wrap_gradio_gpu_call):
with gr.Column():
run_preprocess = gr.Button(value="Preprocess", variant='primary')
- with gr.Group():
+ with gr.Tab(label="Train"):
gr.HTML(value="Train an embedding; must specify a directory with a set of 1:1 ratio images
")
train_embedding_name = gr.Dropdown(label='Embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', choices=[x for x in shared.hypernetworks.keys()])
@@ -1076,9 +1095,9 @@ def create_ui(wrap_gradio_gpu_call):
training_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
training_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
steps = gr.Number(label='Max steps', value=100000, precision=0)
- num_repeats = gr.Number(label='Number of repeats for a single input image per epoch', value=100, precision=0)
create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
+ save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True)
preview_image_prompt = gr.Textbox(label='Preview prompt', value="")
with gr.Row():
@@ -1134,6 +1153,7 @@ def create_ui(wrap_gradio_gpu_call):
process_flip,
process_split,
process_caption,
+ process_caption_deepbooru
],
outputs=[
ti_output,
@@ -1152,10 +1172,10 @@ def create_ui(wrap_gradio_gpu_call):
training_width,
training_height,
steps,
- num_repeats,
create_image_every,
save_embedding_every,
template_file,
+ save_image_with_stored_embedding,
preview_image_prompt,
],
outputs=[
@@ -1351,7 +1371,7 @@ Requested path was: {f}
outputs=[],
_js='function(){restart_reload()}'
)
-
+
if column is not None:
column.__exit__()
@@ -1361,7 +1381,7 @@ Requested path was: {f}
(extras_interface, "Extras", "extras"),
(pnginfo_interface, "PNG Info", "pnginfo"),
(modelmerger_interface, "Checkpoint Merger", "modelmerger"),
- (textual_inversion_interface, "Textual inversion", "ti"),
+ (train_interface, "Train", "ti"),
(settings_interface, "Settings", "settings"),
]
@@ -1383,12 +1403,12 @@ Requested path was: {f}
component_dict[k] = component
settings_interface.gradio_ref = demo
-
+
with gr.Tabs() as tabs:
for interface, label, ifid in interfaces:
with gr.TabItem(label, id=ifid, elem_id='tab_' + ifid):
interface.render()
-
+
if os.path.exists(os.path.join(script_path, "notification.mp3")):
audio_notification = gr.Audio(interactive=False, value=os.path.join(script_path, "notification.mp3"), elem_id="audio_notification", visible=False)
@@ -1521,10 +1541,10 @@ Requested path was: {f}
if getattr(obj,'custom_script_source',None) is not None:
key = 'customscript/' + obj.custom_script_source + '/' + key
-
+
if getattr(obj, 'do_not_save_to_config', False):
return
-
+
saved_value = ui_settings.get(key, None)
if saved_value is None:
ui_settings[key] = getattr(obj, field)
@@ -1548,10 +1568,10 @@ Requested path was: {f}
if type(x) == gr.Textbox:
apply_field(x, 'value')
-
+
if type(x) == gr.Number:
apply_field(x, 'value')
-
+
visit(txt2img_interface, loadsave, "txt2img")
visit(img2img_interface, loadsave, "img2img")
visit(extras_interface, loadsave, "extras")
diff --git a/scripts/img2imgalt.py b/scripts/img2imgalt.py
index f9894cb0..313a55d2 100644
--- a/scripts/img2imgalt.py
+++ b/scripts/img2imgalt.py
@@ -129,8 +129,6 @@ class Script(scripts.Script):
return [original_prompt, original_negative_prompt, cfg, st, randomness, sigma_adjustment]
def run(self, p, original_prompt, original_negative_prompt, cfg, st, randomness, sigma_adjustment):
- p.batch_size = 1
- p.batch_count = 1
def sample_extra(conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
@@ -154,7 +152,7 @@ class Script(scripts.Script):
rec_noise = find_noise_for_image(p, cond, uncond, cfg, st)
self.cache = Cached(rec_noise, cfg, st, lat, original_prompt, original_negative_prompt, sigma_adjustment)
- rand_noise = processing.create_random_tensors(p.init_latent.shape[1:], [p.seed + x + 1 for x in range(p.init_latent.shape[0])])
+ rand_noise = processing.create_random_tensors(p.init_latent.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, seed_resize_from_h=p.seed_resize_from_h, seed_resize_from_w=p.seed_resize_from_w, p=p)
combined_noise = ((1 - randomness) * rec_noise + randomness * rand_noise) / ((randomness**2 + (1-randomness)**2) ** 0.5)
diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py
index ef431105..3bb080bf 100644
--- a/scripts/xy_grid.py
+++ b/scripts/xy_grid.py
@@ -77,14 +77,42 @@ def apply_sampler(p, x, xs):
p.sampler_index = sampler_index
+def confirm_samplers(p, xs):
+ samplers_dict = build_samplers_dict(p)
+ for x in xs:
+ if x.lower() not in samplers_dict.keys():
+ raise RuntimeError(f"Unknown sampler: {x}")
+
+
def apply_checkpoint(p, x, xs):
info = modules.sd_models.get_closet_checkpoint_match(x)
- assert info is not None, f'Checkpoint for {x} not found'
+ if info is None:
+ raise RuntimeError(f"Unknown checkpoint: {x}")
modules.sd_models.reload_model_weights(shared.sd_model, info)
+def confirm_checkpoints(p, xs):
+ for x in xs:
+ if modules.sd_models.get_closet_checkpoint_match(x) is None:
+ raise RuntimeError(f"Unknown checkpoint: {x}")
+
+
def apply_hypernetwork(p, x, xs):
- hypernetwork.load_hypernetwork(x)
+ if x.lower() in ["", "none"]:
+ name = None
+ else:
+ name = hypernetwork.find_closest_hypernetwork_name(x)
+ if not name:
+ raise RuntimeError(f"Unknown hypernetwork: {x}")
+ hypernetwork.load_hypernetwork(name)
+
+
+def confirm_hypernetworks(p, xs):
+ for x in xs:
+ if x.lower() in ["", "none"]:
+ continue
+ if not hypernetwork.find_closest_hypernetwork_name(x):
+ raise RuntimeError(f"Unknown hypernetwork: {x}")
def apply_clip_skip(p, x, xs):
@@ -121,29 +149,29 @@ def str_permutations(x):
return x
-AxisOption = namedtuple("AxisOption", ["label", "type", "apply", "format_value"])
-AxisOptionImg2Img = namedtuple("AxisOptionImg2Img", ["label", "type", "apply", "format_value"])
+AxisOption = namedtuple("AxisOption", ["label", "type", "apply", "format_value", "confirm"])
+AxisOptionImg2Img = namedtuple("AxisOptionImg2Img", ["label", "type", "apply", "format_value", "confirm"])
axis_options = [
- AxisOption("Nothing", str, do_nothing, format_nothing),
- AxisOption("Seed", int, apply_field("seed"), format_value_add_label),
- AxisOption("Var. seed", int, apply_field("subseed"), format_value_add_label),
- AxisOption("Var. strength", float, apply_field("subseed_strength"), format_value_add_label),
- AxisOption("Steps", int, apply_field("steps"), format_value_add_label),
- AxisOption("CFG Scale", float, apply_field("cfg_scale"), format_value_add_label),
- AxisOption("Prompt S/R", str, apply_prompt, format_value),
- AxisOption("Prompt order", str_permutations, apply_order, format_value_join_list),
- AxisOption("Sampler", str, apply_sampler, format_value),
- AxisOption("Checkpoint name", str, apply_checkpoint, format_value),
- AxisOption("Hypernetwork", str, apply_hypernetwork, format_value),
- AxisOption("Sigma Churn", float, apply_field("s_churn"), format_value_add_label),
- AxisOption("Sigma min", float, apply_field("s_tmin"), format_value_add_label),
- AxisOption("Sigma max", float, apply_field("s_tmax"), format_value_add_label),
- AxisOption("Sigma noise", float, apply_field("s_noise"), format_value_add_label),
- AxisOption("Eta", float, apply_field("eta"), format_value_add_label),
- AxisOption("Clip skip", int, apply_clip_skip, format_value_add_label),
- AxisOptionImg2Img("Denoising", float, apply_field("denoising_strength"), format_value_add_label), # as it is now all AxisOptionImg2Img items must go after AxisOption ones
+ AxisOption("Nothing", str, do_nothing, format_nothing, None),
+ AxisOption("Seed", int, apply_field("seed"), format_value_add_label, None),
+ AxisOption("Var. seed", int, apply_field("subseed"), format_value_add_label, None),
+ AxisOption("Var. strength", float, apply_field("subseed_strength"), format_value_add_label, None),
+ AxisOption("Steps", int, apply_field("steps"), format_value_add_label, None),
+ AxisOption("CFG Scale", float, apply_field("cfg_scale"), format_value_add_label, None),
+ AxisOption("Prompt S/R", str, apply_prompt, format_value, None),
+ AxisOption("Prompt order", str_permutations, apply_order, format_value_join_list, None),
+ AxisOption("Sampler", str, apply_sampler, format_value, confirm_samplers),
+ AxisOption("Checkpoint name", str, apply_checkpoint, format_value, confirm_checkpoints),
+ AxisOption("Hypernetwork", str, apply_hypernetwork, format_value, confirm_hypernetworks),
+ AxisOption("Sigma Churn", float, apply_field("s_churn"), format_value_add_label, None),
+ AxisOption("Sigma min", float, apply_field("s_tmin"), format_value_add_label, None),
+ AxisOption("Sigma max", float, apply_field("s_tmax"), format_value_add_label, None),
+ AxisOption("Sigma noise", float, apply_field("s_noise"), format_value_add_label, None),
+ AxisOption("Eta", float, apply_field("eta"), format_value_add_label, None),
+ AxisOption("Clip skip", int, apply_clip_skip, format_value_add_label, None),
+ AxisOptionImg2Img("Denoising", float, apply_field("denoising_strength"), format_value_add_label, None), # as it is now all AxisOptionImg2Img items must go after AxisOption ones
]
@@ -269,17 +297,10 @@ class Script(scripts.Script):
valslist = list(permutations(valslist))
valslist = [opt.type(x) for x in valslist]
-
+
# Confirm options are valid before starting
- if opt.label == "Sampler":
- samplers_dict = build_samplers_dict(p)
- for sampler_val in valslist:
- if sampler_val.lower() not in samplers_dict.keys():
- raise RuntimeError(f"Unknown sampler: {sampler_val}")
- elif opt.label == "Checkpoint name":
- for ckpt_val in valslist:
- if modules.sd_models.get_closet_checkpoint_match(ckpt_val) is None:
- raise RuntimeError(f"Checkpoint for {ckpt_val} not found")
+ if opt.confirm:
+ opt.confirm(p, valslist)
return valslist
diff --git a/webui.py b/webui.py
index ca278e94..33ba7905 100644
--- a/webui.py
+++ b/webui.py
@@ -31,12 +31,7 @@ from modules.paths import script_path
from modules.shared import cmd_opts
import modules.hypernetworks.hypernetwork
-modelloader.cleanup_models()
-modules.sd_models.setup_model()
-codeformer.setup_model(cmd_opts.codeformer_models_path)
-gfpgan.setup_model(cmd_opts.gfpgan_models_path)
-shared.face_restorers.append(modules.face_restoration.FaceRestoration())
-modelloader.load_upscalers()
+
queue_lock = threading.Lock()
@@ -78,15 +73,24 @@ def wrap_gradio_gpu_call(func, extra_outputs=None):
return modules.ui.wrap_gradio_call(f, extra_outputs=extra_outputs)
-modules.scripts.load_scripts(os.path.join(script_path, "scripts"))
+def initialize():
+ modelloader.cleanup_models()
+ modules.sd_models.setup_model()
+ codeformer.setup_model(cmd_opts.codeformer_models_path)
+ gfpgan.setup_model(cmd_opts.gfpgan_models_path)
+ shared.face_restorers.append(modules.face_restoration.FaceRestoration())
+ modelloader.load_upscalers()
-shared.sd_model = modules.sd_models.load_model()
-shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights(shared.sd_model)))
+ modules.scripts.load_scripts(os.path.join(script_path, "scripts"))
-shared.opts.onchange("sd_hypernetwork", wrap_queued_call(lambda: modules.hypernetworks.hypernetwork.load_hypernetwork(shared.opts.sd_hypernetwork)))
+ shared.sd_model = modules.sd_models.load_model()
+ shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights(shared.sd_model)))
+ shared.opts.onchange("sd_hypernetwork", wrap_queued_call(lambda: modules.hypernetworks.hypernetwork.load_hypernetwork(shared.opts.sd_hypernetwork)))
def webui():
+ initialize()
+
# make the program just exit at ctrl+c without waiting for anything
def sigint_handler(sig, frame):
print(f'Interrupted with signal {sig} in {frame}')
@@ -98,7 +102,7 @@ def webui():
demo = modules.ui.create_ui(wrap_gradio_gpu_call=wrap_gradio_gpu_call)
- app,local_url,share_url = demo.launch(
+ app, local_url, share_url = demo.launch(
share=cmd_opts.share,
server_name="0.0.0.0" if cmd_opts.listen else None,
server_port=cmd_opts.port,
@@ -129,6 +133,5 @@ def webui():
print('Restarting Gradio')
-
if __name__ == "__main__":
webui()