diff --git a/.gitignore b/.gitignore
index 21fa26a7..0b1d17ca 100644
--- a/.gitignore
+++ b/.gitignore
@@ -32,3 +32,4 @@ notification.mp3
/extensions
/test/stdout.txt
/test/stderr.txt
+/cache.json
diff --git a/javascript/hints.js b/javascript/hints.js
index 856e1389..244bfde2 100644
--- a/javascript/hints.js
+++ b/javascript/hints.js
@@ -92,6 +92,7 @@ titles = {
"Weighted sum": "Result = A * (1 - M) + B * M",
"Add difference": "Result = A + (B - C) * M",
+ "Initialization text": "If the number of tokens is more than the number of vectors, some may be skipped.\nLeave the textbox empty to start with zeroed out vectors",
"Learning rate": "How fast should training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG: 0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.",
"Clip skip": "Early stopping parameter for CLIP model; 1 is stop at last layer as usual, 2 is stop at penultimate layer, etc.",
diff --git a/javascript/imageviewer.js b/javascript/imageviewer.js
index b7bc2fe1..1f29ad7b 100644
--- a/javascript/imageviewer.js
+++ b/javascript/imageviewer.js
@@ -151,6 +151,7 @@ function showGalleryImage() {
e.addEventListener('mousedown', function (evt) {
if(!opts.js_modal_lightbox || evt.button != 0) return;
modalZoomSet(gradioApp().getElementById('modalImage'), opts.js_modal_lightbox_initially_zoomed)
+ evt.preventDefault()
showModal(evt)
}, true);
}
diff --git a/javascript/ui.js b/javascript/ui.js
index ee226927..1e04a8f4 100644
--- a/javascript/ui.js
+++ b/javascript/ui.js
@@ -54,7 +54,7 @@ function switch_to_img2img(){
function switch_to_inpaint(){
gradioApp().querySelector('#tabs').querySelectorAll('button')[1].click();
- gradioApp().getElementById('mode_img2img').querySelectorAll('button')[1].click();
+ gradioApp().getElementById('mode_img2img').querySelectorAll('button')[2].click();
return args_to_array(arguments);
}
@@ -143,14 +143,6 @@ function confirm_clear_prompt(prompt, negative_prompt) {
opts = {}
-function apply_settings(jsdata){
- console.log(jsdata)
-
- opts = JSON.parse(jsdata)
-
- return jsdata
-}
-
onUiUpdate(function(){
if(Object.keys(opts).length != 0) return;
@@ -160,7 +152,7 @@ onUiUpdate(function(){
textarea = json_elem.querySelector('textarea')
jsdata = textarea.value
opts = JSON.parse(jsdata)
-
+ executeCallbacks(optionsChangedCallbacks);
Object.defineProperty(textarea, 'value', {
set: function(newValue) {
@@ -171,6 +163,8 @@ onUiUpdate(function(){
if (oldValue != newValue) {
opts = JSON.parse(textarea.value)
}
+
+ executeCallbacks(optionsChangedCallbacks);
},
get: function() {
var valueProp = Object.getOwnPropertyDescriptor(HTMLTextAreaElement.prototype, 'value');
@@ -201,6 +195,19 @@ onUiUpdate(function(){
}
})
+
+onOptionsChanged(function(){
+ elem = gradioApp().getElementById('sd_checkpoint_hash')
+ sd_checkpoint_hash = opts.sd_checkpoint_hash || ""
+ shorthash = sd_checkpoint_hash.substr(0,10)
+
+ if(elem && elem.textContent != shorthash){
+ elem.textContent = shorthash
+ elem.title = sd_checkpoint_hash
+ elem.href = "https://google.com/search?q=" + sd_checkpoint_hash
+ }
+})
+
let txt2img_textarea, img2img_textarea = undefined;
let wait_time = 800
let token_timeout;
diff --git a/modules/api/api.py b/modules/api/api.py
index 6c564ad8..9814bbc2 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -286,7 +286,7 @@ 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.dict())
+ return ProgressResponse(progress=0, eta_relative=0, state=shared.state.dict(), textinfo=shared.state.textinfo)
# avoid dividing zero
progress = 0.01
@@ -308,7 +308,7 @@ class Api:
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)
+ return ProgressResponse(progress=progress, eta_relative=eta_relative, state=shared.state.dict(), current_image=current_image, textinfo=shared.state.textinfo)
def interrogateapi(self, interrogatereq: InterrogateRequest):
image_b64 = interrogatereq.image
@@ -371,7 +371,7 @@ class Api:
return upscalers
def get_sd_models(self):
- return [{"title":x.title, "model_name":x.model_name, "hash":x.hash, "filename": x.filename, "config": find_checkpoint_config(x)} for x in checkpoints_list.values()]
+ return [{"title": x.title, "model_name": x.model_name, "hash": x.shorthash, "sha256": x.sha256, "filename": x.filename, "config": find_checkpoint_config(x)} for x in checkpoints_list.values()]
def get_hypernetworks(self):
return [{"name": name, "path": shared.hypernetworks[name]} for name in shared.hypernetworks]
diff --git a/modules/api/models.py b/modules/api/models.py
index 034b4aa0..1eb1fcf1 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -168,6 +168,7 @@ class ProgressResponse(BaseModel):
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.")
+ textinfo: str = Field(default=None, title="Info text", description="Info text used by WebUI.")
class InterrogateRequest(BaseModel):
image: str = Field(default="", title="Image", description="Image to work on, must be a Base64 string containing the image's data.")
@@ -223,7 +224,8 @@ class UpscalerItem(BaseModel):
class SDModelItem(BaseModel):
title: str = Field(title="Title")
model_name: str = Field(title="Model Name")
- hash: str = Field(title="Hash")
+ hash: Optional[str] = Field(title="Short hash")
+ sha256: Optional[str] = Field(title="sha256 hash")
filename: str = Field(title="Filename")
config: str = Field(title="Config file")
diff --git a/modules/extras.py b/modules/extras.py
index 7407bfe3..a03d558e 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -3,6 +3,7 @@ import math
import os
import sys
import traceback
+import shutil
import numpy as np
from PIL import Image
@@ -248,7 +249,32 @@ def run_pnginfo(image):
return '', geninfo, info
-def run_modelmerger(primary_model_name, secondary_model_name, tertiary_model_name, interp_method, multiplier, save_as_half, custom_name, checkpoint_format):
+def create_config(ckpt_result, config_source, a, b, c):
+ def config(x):
+ return sd_models.find_checkpoint_config(x) if x else None
+
+ if config_source == 0:
+ cfg = config(a) or config(b) or config(c)
+ elif config_source == 1:
+ cfg = config(b)
+ elif config_source == 2:
+ cfg = config(c)
+ else:
+ cfg = None
+
+ if cfg is None:
+ return
+
+ filename, _ = os.path.splitext(ckpt_result)
+ checkpoint_filename = filename + ".yaml"
+
+ print("Copying config:")
+ print(" from:", cfg)
+ print(" to:", checkpoint_filename)
+ shutil.copyfile(cfg, checkpoint_filename)
+
+
+def run_modelmerger(primary_model_name, secondary_model_name, tertiary_model_name, interp_method, multiplier, save_as_half, custom_name, checkpoint_format, config_source):
shared.state.begin()
shared.state.job = 'model-merge'
@@ -356,6 +382,8 @@ def run_modelmerger(primary_model_name, secondary_model_name, tertiary_model_nam
sd_models.list_models()
+ create_config(output_modelname, config_source, primary_model_info, secondary_model_info, tertiary_model_info)
+
print("Checkpoint saved.")
shared.state.textinfo = "Checkpoint saved to " + output_modelname
shared.state.end()
diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index 620aa606..593d99ef 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -7,7 +7,7 @@ from pathlib import Path
import gradio as gr
from modules.shared import script_path
-from modules import shared, ui_tempdir
+from modules import shared, ui_tempdir, script_callbacks
import tempfile
from PIL import Image
@@ -298,6 +298,7 @@ def connect_paste(button, paste_fields, input_comp, jsfunc=None):
prompt = file.read()
params = parse_generation_parameters(prompt)
+ script_callbacks.infotext_pasted_callback(prompt, params)
res = []
for output, key in paste_fields:
diff --git a/modules/hashes.py b/modules/hashes.py
new file mode 100644
index 00000000..14231771
--- /dev/null
+++ b/modules/hashes.py
@@ -0,0 +1,84 @@
+import hashlib
+import json
+import os.path
+
+import filelock
+
+
+cache_filename = "cache.json"
+cache_data = None
+
+
+def dump_cache():
+ with filelock.FileLock(cache_filename+".lock"):
+ with open(cache_filename, "w", encoding="utf8") as file:
+ json.dump(cache_data, file, indent=4)
+
+
+def cache(subsection):
+ global cache_data
+
+ if cache_data is None:
+ with filelock.FileLock(cache_filename+".lock"):
+ if not os.path.isfile(cache_filename):
+ cache_data = {}
+ else:
+ with open(cache_filename, "r", encoding="utf8") as file:
+ cache_data = json.load(file)
+
+ s = cache_data.get(subsection, {})
+ cache_data[subsection] = s
+
+ return s
+
+
+def calculate_sha256(filename):
+ hash_sha256 = hashlib.sha256()
+
+ with open(filename, "rb") as f:
+ for chunk in iter(lambda: f.read(4096), b""):
+ hash_sha256.update(chunk)
+
+ return hash_sha256.hexdigest()
+
+
+def sha256_from_cache(filename, title):
+ hashes = cache("hashes")
+ ondisk_mtime = os.path.getmtime(filename)
+
+ if title not in hashes:
+ return None
+
+ cached_sha256 = hashes[title].get("sha256", None)
+ cached_mtime = hashes[title].get("mtime", 0)
+
+ if ondisk_mtime > cached_mtime or cached_sha256 is None:
+ return None
+
+ return cached_sha256
+
+
+def sha256(filename, title):
+ hashes = cache("hashes")
+
+ sha256_value = sha256_from_cache(filename, title)
+ if sha256_value is not None:
+ return sha256_value
+
+ print(f"Calculating sha256 for {filename}: ", end='')
+ sha256_value = calculate_sha256(filename)
+ print(f"{sha256_value}")
+
+ hashes[title] = {
+ "mtime": os.path.getmtime(filename),
+ "sha256": sha256_value,
+ }
+
+ dump_cache()
+
+ return sha256_value
+
+
+
+
+
diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 300d3975..3aebefa8 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -12,7 +12,7 @@ import torch
import tqdm
from einops import rearrange, repeat
from ldm.util import default
-from modules import devices, processing, sd_models, shared, sd_samplers
+from modules import devices, processing, sd_models, shared, sd_samplers, hashes
from modules.textual_inversion import textual_inversion, logging
from modules.textual_inversion.learn_schedule import LearnRateScheduler
from torch import einsum
@@ -24,7 +24,6 @@ from statistics import stdev, mean
optimizer_dict = {optim_name : cls_obj for optim_name, cls_obj in inspect.getmembers(torch.optim, inspect.isclass) if optim_name != "Optimizer"}
-
class HypernetworkModule(torch.nn.Module):
multiplier = 1.0
activation_dict = {
@@ -226,7 +225,7 @@ class Hypernetwork:
torch.save(state_dict, filename)
if shared.opts.save_optimizer_state and self.optimizer_state_dict:
- optimizer_saved_dict['hash'] = sd_models.model_hash(filename)
+ optimizer_saved_dict['hash'] = self.shorthash()
optimizer_saved_dict['optimizer_state_dict'] = self.optimizer_state_dict
torch.save(optimizer_saved_dict, filename + '.optim')
@@ -238,32 +237,33 @@ class Hypernetwork:
state_dict = torch.load(filename, map_location='cpu')
self.layer_structure = state_dict.get('layer_structure', [1, 2, 1])
- print(self.layer_structure)
- optional_info = state_dict.get('optional_info', None)
- if optional_info is not None:
- print(f"INFO:\n {optional_info}\n")
- self.optional_info = optional_info
+ self.optional_info = state_dict.get('optional_info', None)
self.activation_func = state_dict.get('activation_func', None)
- print(f"Activation function is {self.activation_func}")
self.weight_init = state_dict.get('weight_initialization', 'Normal')
- print(f"Weight initialization is {self.weight_init}")
self.add_layer_norm = state_dict.get('is_layer_norm', False)
- print(f"Layer norm is set to {self.add_layer_norm}")
self.dropout_structure = state_dict.get('dropout_structure', None)
self.use_dropout = True if self.dropout_structure is not None and any(self.dropout_structure) else state_dict.get('use_dropout', False)
- print(f"Dropout usage is set to {self.use_dropout}" )
self.activate_output = state_dict.get('activate_output', True)
- print(f"Activate last layer is set to {self.activate_output}")
self.last_layer_dropout = state_dict.get('last_layer_dropout', False)
# Dropout structure should have same length as layer structure, Every digits should be in [0,1), and last digit must be 0.
if self.dropout_structure is None:
- print("Using previous dropout structure")
self.dropout_structure = parse_dropout_structure(self.layer_structure, self.use_dropout, self.last_layer_dropout)
- print(f"Dropout structure is set to {self.dropout_structure}")
- optimizer_saved_dict = torch.load(self.filename + '.optim', map_location = 'cpu') if os.path.exists(self.filename + '.optim') else {}
+ if shared.opts.print_hypernet_extra:
+ if self.optional_info is not None:
+ print(f" INFO:\n {self.optional_info}\n")
- if sd_models.model_hash(filename) == optimizer_saved_dict.get('hash', None):
+ print(f" Layer structure: {self.layer_structure}")
+ print(f" Activation function: {self.activation_func}")
+ print(f" Weight initialization: {self.weight_init}")
+ print(f" Layer norm: {self.add_layer_norm}")
+ print(f" Dropout usage: {self.use_dropout}" )
+ print(f" Activate last layer: {self.activate_output}")
+ print(f" Dropout structure: {self.dropout_structure}")
+
+ optimizer_saved_dict = torch.load(self.filename + '.optim', map_location='cpu') if os.path.exists(self.filename + '.optim') else {}
+
+ if self.shorthash() == optimizer_saved_dict.get('hash', None):
self.optimizer_state_dict = optimizer_saved_dict.get('optimizer_state_dict', None)
else:
self.optimizer_state_dict = None
@@ -290,6 +290,11 @@ class Hypernetwork:
self.sd_checkpoint_name = state_dict.get('sd_checkpoint_name', None)
self.eval()
+ def shorthash(self):
+ sha256 = hashes.sha256(self.filename, f'hypernet/{self.name}')
+
+ return sha256[0:10]
+
def list_hypernetworks(path):
res = {}
@@ -297,7 +302,7 @@ def list_hypernetworks(path):
name = os.path.splitext(os.path.basename(filename))[0]
# Prevent a hypothetical "None.pt" from being listed.
if name != "None":
- res[name + f"({sd_models.model_hash(filename)})"] = filename
+ res[name] = filename
return res
@@ -498,6 +503,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step,
if clip_grad:
clip_grad_sched = LearnRateScheduler(clip_grad_value, steps, initial_step, verbose=False)
+ if shared.opts.training_enable_tensorboard:
+ tensorboard_writer = textual_inversion.tensorboard_setup(log_directory)
+
# 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)}..."
@@ -507,7 +515,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step,
if shared.opts.save_training_settings_to_txt:
saved_params = dict(
- model_name=checkpoint.model_name, model_hash=checkpoint.hash, num_of_dataset_images=len(ds),
+ model_name=checkpoint.model_name, model_hash=checkpoint.shorthash, num_of_dataset_images=len(ds),
**{field: getattr(hypernetwork, field) for field in ['layer_structure', 'activation_func', 'weight_init', 'add_layer_norm', 'use_dropout', ]}
)
logging.save_settings_to_file(log_directory, {**saved_params, **locals()})
@@ -619,7 +627,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step,
epoch_num = hypernetwork.step // steps_per_epoch
epoch_step = hypernetwork.step % steps_per_epoch
- pbar.set_description(f"[Epoch {epoch_num}: {epoch_step+1}/{steps_per_epoch}]loss: {loss_step:.7f}")
+ description = f"Training hypernetwork [Epoch {epoch_num}: {epoch_step+1}/{steps_per_epoch}]loss: {loss_step:.7f}"
+ pbar.set_description(description)
+ shared.state.textinfo = description
if hypernetwork_dir is not None and steps_done % save_hypernetwork_every == 0:
# Before saving, change name to match current checkpoint.
hypernetwork_name_every = f'{hypernetwork_name}-{steps_done}'
@@ -630,6 +640,14 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step,
save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, last_saved_file)
hypernetwork.optimizer_state_dict = None # dereference it after saving, to save memory.
+
+
+ if shared.opts.training_enable_tensorboard:
+ epoch_num = hypernetwork.step // len(ds)
+ epoch_step = hypernetwork.step - (epoch_num * len(ds)) + 1
+
+ textual_inversion.tensorboard_add(tensorboard_writer, loss=mean_loss, global_step=hypernetwork.step, step=epoch_step, learn_rate=scheduler.learn_rate, epoch_num=epoch_num)
+
textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, steps_per_epoch, {
"loss": f"{loss_step:.7f}",
"learn_rate": scheduler.learn_rate
@@ -671,6 +689,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step,
processed = processing.process_images(p)
image = processed.images[0] if len(processed.images) > 0 else None
+
+ if shared.opts.training_enable_tensorboard and shared.opts.training_tensorboard_save_images:
+ textual_inversion.tensorboard_add_image(tensorboard_writer, f"Validation at epoch {epoch_num}", image, hypernetwork.step)
if unload:
shared.sd_model.cond_stage_model.to(devices.cpu)
@@ -722,7 +743,7 @@ def save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename):
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 = checkpoint.shorthash
hypernetwork.sd_checkpoint_name = checkpoint.model_name
hypernetwork.name = hypernetwork_name
hypernetwork.save(filename)
diff --git a/modules/img2img.py b/modules/img2img.py
index ca58b5d8..f62783c6 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -59,38 +59,34 @@ def process_batch(p, input_dir, output_dir, args):
processed_image.save(os.path.join(output_dir, filename))
-def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, init_img, init_img_with_mask, init_img_with_mask_orig, init_img_inpaint, init_mask_inpaint, mask_mode, steps: int, sampler_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, *args):
- is_inpaint = mode == 1
- is_batch = mode == 2
+def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, *args):
+ is_batch = mode == 5
- if is_inpaint:
- # Drawn mask
- if mask_mode == 0:
- is_mask_sketch = isinstance(init_img_with_mask, dict)
- is_mask_paint = not is_mask_sketch
- if is_mask_sketch:
- # Sketch: mask iff. not transparent
- image, mask = init_img_with_mask["image"], init_img_with_mask["mask"]
- alpha_mask = ImageOps.invert(image.split()[-1]).convert('L').point(lambda x: 255 if x > 0 else 0, mode='1')
- mask = ImageChops.lighter(alpha_mask, mask.convert('L')).convert('L')
- else:
- # Color-sketch: mask iff. painted over
- image = init_img_with_mask
- orig = init_img_with_mask_orig or init_img_with_mask
- pred = np.any(np.array(image) != np.array(orig), axis=-1)
- mask = Image.fromarray(pred.astype(np.uint8) * 255, "L")
- mask = ImageEnhance.Brightness(mask).enhance(1 - mask_alpha / 100)
- blur = ImageFilter.GaussianBlur(mask_blur)
- image = Image.composite(image.filter(blur), orig, mask.filter(blur))
-
- image = image.convert("RGB")
- # Uploaded mask
- else:
- image = init_img_inpaint
- mask = init_mask_inpaint
- # No mask
+ if mode == 0: # img2img
+ image = init_img.convert("RGB")
+ mask = None
+ elif mode == 1: # img2img sketch
+ image = sketch.convert("RGB")
+ mask = None
+ elif mode == 2: # inpaint
+ image, mask = init_img_with_mask["image"], init_img_with_mask["mask"]
+ alpha_mask = ImageOps.invert(image.split()[-1]).convert('L').point(lambda x: 255 if x > 0 else 0, mode='1')
+ mask = ImageChops.lighter(alpha_mask, mask.convert('L')).convert('L')
+ image = image.convert("RGB")
+ elif mode == 3: # inpaint sketch
+ image = inpaint_color_sketch
+ orig = inpaint_color_sketch_orig or inpaint_color_sketch
+ pred = np.any(np.array(image) != np.array(orig), axis=-1)
+ mask = Image.fromarray(pred.astype(np.uint8) * 255, "L")
+ mask = ImageEnhance.Brightness(mask).enhance(1 - mask_alpha / 100)
+ blur = ImageFilter.GaussianBlur(mask_blur)
+ image = Image.composite(image.filter(blur), orig, mask.filter(blur))
+ image = image.convert("RGB")
+ elif mode == 4: # inpaint upload mask
+ image = init_img_inpaint
+ mask = init_mask_inpaint
else:
- image = init_img
+ image = None
mask = None
# Use the EXIF orientation of photos taken by smartphones.
diff --git a/modules/processing.py b/modules/processing.py
index f04a0e1e..849f6b19 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -437,7 +437,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
"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 hash": (None if shared.loaded_hypernetwork is None else sd_models.model_hash(shared.loaded_hypernetwork.filename)),
+ "Hypernet hash": (None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.shorthash()),
"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),
@@ -531,16 +531,16 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
def infotext(iteration=0, position_in_batch=0):
return create_infotext(p, p.all_prompts, p.all_seeds, p.all_subseeds, comments, iteration, position_in_batch)
- with open(os.path.join(shared.script_path, "params.txt"), "w", encoding="utf8") as file:
- processed = Processed(p, [], p.seed, "")
- file.write(processed.infotext(p, 0))
-
if os.path.exists(cmd_opts.embeddings_dir) and not p.do_not_reload_embeddings:
model_hijack.embedding_db.load_textual_inversion_embeddings()
if p.scripts is not None:
p.scripts.process(p)
+ with open(os.path.join(shared.script_path, "params.txt"), "w", encoding="utf8") as file:
+ processed = Processed(p, [], p.seed, "")
+ file.write(processed.infotext(p, 0))
+
infotexts = []
output_images = []
diff --git a/modules/prompt_parser.py b/modules/prompt_parser.py
index f70872c4..870218db 100644
--- a/modules/prompt_parser.py
+++ b/modules/prompt_parser.py
@@ -49,6 +49,8 @@ def get_learned_conditioning_prompt_schedules(prompts, steps):
[[5, 'a c'], [10, 'a {b|d{ c']]
>>> g("((a][:b:c [d:3]")
[[3, '((a][:b:c '], [10, '((a][:b:c d']]
+ >>> g("[a|(b:1.1)]")
+ [[1, 'a'], [2, '(b:1.1)'], [3, 'a'], [4, '(b:1.1)'], [5, 'a'], [6, '(b:1.1)'], [7, 'a'], [8, '(b:1.1)'], [9, 'a'], [10, '(b:1.1)']]
"""
def collect_steps(steps, tree):
@@ -84,7 +86,7 @@ def get_learned_conditioning_prompt_schedules(prompts, steps):
yield args[0].value
def __default__(self, data, children, meta):
for child in children:
- yield from child
+ yield child
return AtStep().transform(tree)
def get_schedule(prompt):
diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py
index 608c5300..a9e19236 100644
--- a/modules/script_callbacks.py
+++ b/modules/script_callbacks.py
@@ -2,7 +2,7 @@ import sys
import traceback
from collections import namedtuple
import inspect
-from typing import Optional
+from typing import Optional, Dict, Any
from fastapi import FastAPI
from gradio import Blocks
@@ -71,6 +71,7 @@ callback_map = dict(
callbacks_before_component=[],
callbacks_after_component=[],
callbacks_image_grid=[],
+ callbacks_infotext_pasted=[],
callbacks_script_unloaded=[],
)
@@ -172,6 +173,14 @@ def image_grid_callback(params: ImageGridLoopParams):
report_exception(c, 'image_grid')
+def infotext_pasted_callback(infotext: str, params: Dict[str, Any]):
+ for c in callback_map['callbacks_infotext_pasted']:
+ try:
+ c.callback(infotext, params)
+ except Exception:
+ report_exception(c, 'infotext_pasted')
+
+
def script_unloaded_callback():
for c in reversed(callback_map['callbacks_script_unloaded']):
try:
@@ -290,6 +299,15 @@ def on_image_grid(callback):
add_callback(callback_map['callbacks_image_grid'], callback)
+def on_infotext_pasted(callback):
+ """register a function to be called before applying an infotext.
+ The callback is called with two arguments:
+ - infotext: str - raw infotext.
+ - result: Dict[str, any] - parsed infotext parameters.
+ """
+ add_callback(callback_map['callbacks_infotext_pasted'], callback)
+
+
def on_script_unloaded(callback):
"""register a function to be called before the script is unloaded. Any hooks/hijacks/monkeying about that
the script did should be reverted here"""
diff --git a/modules/scripts.py b/modules/scripts.py
index 35164093..4ffc369b 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -152,7 +152,7 @@ def basedir():
scripts_data = []
ScriptFile = namedtuple("ScriptFile", ["basedir", "filename", "path"])
-ScriptClassData = namedtuple("ScriptClassData", ["script_class", "path", "basedir"])
+ScriptClassData = namedtuple("ScriptClassData", ["script_class", "path", "basedir", "module"])
def list_scripts(scriptdirname, extension):
@@ -206,7 +206,7 @@ def load_scripts():
for key, script_class in module.__dict__.items():
if type(script_class) == type and issubclass(script_class, Script):
- scripts_data.append(ScriptClassData(script_class, scriptfile.path, scriptfile.basedir))
+ scripts_data.append(ScriptClassData(script_class, scriptfile.path, scriptfile.basedir, module))
except Exception:
print(f"Error loading script: {scriptfile.filename}", file=sys.stderr)
@@ -241,7 +241,7 @@ class ScriptRunner:
self.alwayson_scripts.clear()
self.selectable_scripts.clear()
- for script_class, path, basedir in scripts_data:
+ for script_class, path, basedir, script_module in scripts_data:
script = script_class()
script.filename = path
script.is_txt2img = not is_img2img
diff --git a/modules/sd_disable_initialization.py b/modules/sd_disable_initialization.py
index 088ac24b..c72d8efc 100644
--- a/modules/sd_disable_initialization.py
+++ b/modules/sd_disable_initialization.py
@@ -20,6 +20,19 @@ class DisableInitialization:
```
"""
+ def __init__(self):
+ self.replaced = []
+
+ def replace(self, obj, field, func):
+ original = getattr(obj, field, None)
+ if original is None:
+ return None
+
+ self.replaced.append((obj, field, original))
+ setattr(obj, field, func)
+
+ return original
+
def __enter__(self):
def do_nothing(*args, **kwargs):
pass
@@ -37,11 +50,14 @@ class DisableInitialization:
def transformers_utils_hub_get_file_from_cache(original, url, *args, **kwargs):
# this file is always 404, prevent making request
- if url == 'https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/added_tokens.json':
- raise transformers.utils.hub.EntryNotFoundError
+ if url == 'https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/added_tokens.json' or url == 'openai/clip-vit-large-patch14' and args[0] == 'added_tokens.json':
+ return None
try:
- return original(url, *args, local_files_only=True, **kwargs)
+ res = original(url, *args, local_files_only=True, **kwargs)
+ if res is None:
+ res = original(url, *args, local_files_only=False, **kwargs)
+ return res
except Exception as e:
return original(url, *args, local_files_only=False, **kwargs)
@@ -54,42 +70,19 @@ class DisableInitialization:
def transformers_configuration_utils_cached_file(url, *args, local_files_only=False, **kwargs):
return transformers_utils_hub_get_file_from_cache(self.transformers_configuration_utils_cached_file, url, *args, **kwargs)
- self.init_kaiming_uniform = torch.nn.init.kaiming_uniform_
- self.init_no_grad_normal = torch.nn.init._no_grad_normal_
- self.init_no_grad_uniform_ = torch.nn.init._no_grad_uniform_
- self.create_model_and_transforms = open_clip.create_model_and_transforms
- self.CLIPTextModel_from_pretrained = ldm.modules.encoders.modules.CLIPTextModel.from_pretrained
- self.transformers_modeling_utils_load_pretrained_model = getattr(transformers.modeling_utils.PreTrainedModel, '_load_pretrained_model', None)
- self.transformers_tokenization_utils_base_cached_file = getattr(transformers.tokenization_utils_base, 'cached_file', None)
- self.transformers_configuration_utils_cached_file = getattr(transformers.configuration_utils, 'cached_file', None)
- self.transformers_utils_hub_get_from_cache = getattr(transformers.utils.hub, 'get_from_cache', None)
-
- torch.nn.init.kaiming_uniform_ = do_nothing
- torch.nn.init._no_grad_normal_ = do_nothing
- torch.nn.init._no_grad_uniform_ = do_nothing
- open_clip.create_model_and_transforms = create_model_and_transforms_without_pretrained
- ldm.modules.encoders.modules.CLIPTextModel.from_pretrained = CLIPTextModel_from_pretrained
- if self.transformers_modeling_utils_load_pretrained_model is not None:
- transformers.modeling_utils.PreTrainedModel._load_pretrained_model = transformers_modeling_utils_load_pretrained_model
- if self.transformers_tokenization_utils_base_cached_file is not None:
- transformers.tokenization_utils_base.cached_file = transformers_tokenization_utils_base_cached_file
- if self.transformers_configuration_utils_cached_file is not None:
- transformers.configuration_utils.cached_file = transformers_configuration_utils_cached_file
- if self.transformers_utils_hub_get_from_cache is not None:
- transformers.utils.hub.get_from_cache = transformers_utils_hub_get_from_cache
+ self.replace(torch.nn.init, 'kaiming_uniform_', do_nothing)
+ self.replace(torch.nn.init, '_no_grad_normal_', do_nothing)
+ self.replace(torch.nn.init, '_no_grad_uniform_', do_nothing)
+ self.create_model_and_transforms = self.replace(open_clip, 'create_model_and_transforms', create_model_and_transforms_without_pretrained)
+ self.CLIPTextModel_from_pretrained = self.replace(ldm.modules.encoders.modules.CLIPTextModel, 'from_pretrained', CLIPTextModel_from_pretrained)
+ self.transformers_modeling_utils_load_pretrained_model = self.replace(transformers.modeling_utils.PreTrainedModel, '_load_pretrained_model', transformers_modeling_utils_load_pretrained_model)
+ self.transformers_tokenization_utils_base_cached_file = self.replace(transformers.tokenization_utils_base, 'cached_file', transformers_tokenization_utils_base_cached_file)
+ self.transformers_configuration_utils_cached_file = self.replace(transformers.configuration_utils, 'cached_file', transformers_configuration_utils_cached_file)
+ self.transformers_utils_hub_get_from_cache = self.replace(transformers.utils.hub, 'get_from_cache', transformers_utils_hub_get_from_cache)
def __exit__(self, exc_type, exc_val, exc_tb):
- torch.nn.init.kaiming_uniform_ = self.init_kaiming_uniform
- torch.nn.init._no_grad_normal_ = self.init_no_grad_normal
- torch.nn.init._no_grad_uniform_ = self.init_no_grad_uniform_
- open_clip.create_model_and_transforms = self.create_model_and_transforms
- ldm.modules.encoders.modules.CLIPTextModel.from_pretrained = self.CLIPTextModel_from_pretrained
- if self.transformers_modeling_utils_load_pretrained_model is not None:
- transformers.modeling_utils.PreTrainedModel._load_pretrained_model = self.transformers_modeling_utils_load_pretrained_model
- if self.transformers_tokenization_utils_base_cached_file is not None:
- transformers.utils.hub.cached_file = self.transformers_tokenization_utils_base_cached_file
- if self.transformers_configuration_utils_cached_file is not None:
- transformers.utils.hub.cached_file = self.transformers_configuration_utils_cached_file
- if self.transformers_utils_hub_get_from_cache is not None:
- transformers.utils.hub.get_from_cache = self.transformers_utils_hub_get_from_cache
+ for obj, field, original in self.replaced:
+ setattr(obj, field, original)
+
+ self.replaced.clear()
diff --git a/modules/sd_models.py b/modules/sd_models.py
index b5bc12f0..6a681cef 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -14,17 +14,58 @@ import ldm.modules.midas as midas
from ldm.util import instantiate_from_config
-from modules import shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors
+from modules import shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes
from modules.paths import models_path
from modules.sd_hijack_inpainting import do_inpainting_hijack, should_hijack_inpainting
model_dir = "Stable-diffusion"
model_path = os.path.abspath(os.path.join(models_path, model_dir))
-CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name'])
checkpoints_list = {}
+checkpoint_alisases = {}
checkpoints_loaded = collections.OrderedDict()
+
+class CheckpointInfo:
+ def __init__(self, filename):
+ self.filename = filename
+ abspath = os.path.abspath(filename)
+
+ if shared.cmd_opts.ckpt_dir is not None and abspath.startswith(shared.cmd_opts.ckpt_dir):
+ name = abspath.replace(shared.cmd_opts.ckpt_dir, '')
+ elif abspath.startswith(model_path):
+ name = abspath.replace(model_path, '')
+ else:
+ name = os.path.basename(filename)
+
+ if name.startswith("\\") or name.startswith("/"):
+ name = name[1:]
+
+ self.title = name
+ self.model_name = os.path.splitext(name.replace("/", "_").replace("\\", "_"))[0]
+ self.hash = model_hash(filename)
+
+ self.sha256 = hashes.sha256_from_cache(self.filename, "checkpoint/" + self.title)
+ self.shorthash = self.sha256[0:10] if self.sha256 else None
+
+ self.ids = [self.hash, self.model_name, self.title, f'{name} [{self.hash}]'] + ([self.shorthash, self.sha256] if self.shorthash else [])
+
+ def register(self):
+ checkpoints_list[self.title] = self
+ for id in self.ids:
+ checkpoint_alisases[id] = self
+
+ def calculate_shorthash(self):
+ self.sha256 = hashes.sha256(self.filename, "checkpoint/" + self.title)
+ self.shorthash = self.sha256[0:10]
+
+ if self.shorthash not in self.ids:
+ self.ids += [self.shorthash, self.sha256]
+ self.register()
+
+ return self.shorthash
+
+
try:
# this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
@@ -43,10 +84,14 @@ def setup_model():
enable_midas_autodownload()
-def checkpoint_tiles():
- convert = lambda name: int(name) if name.isdigit() else name.lower()
- alphanumeric_key = lambda key: [convert(c) for c in re.split('([0-9]+)', key)]
- return sorted([x.title for x in checkpoints_list.values()], key = alphanumeric_key)
+def checkpoint_tiles():
+ def convert(name):
+ return int(name) if name.isdigit() else name.lower()
+
+ def alphanumeric_key(key):
+ return [convert(c) for c in re.split('([0-9]+)', key)]
+
+ return sorted([x.title for x in checkpoints_list.values()], key=alphanumeric_key)
def find_checkpoint_config(info):
@@ -62,48 +107,38 @@ def find_checkpoint_config(info):
def list_models():
checkpoints_list.clear()
+ checkpoint_alisases.clear()
model_list = modelloader.load_models(model_path=model_path, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt", ".safetensors"], ext_blacklist=[".vae.safetensors"])
- def modeltitle(path, shorthash):
- abspath = os.path.abspath(path)
-
- if shared.cmd_opts.ckpt_dir is not None and abspath.startswith(shared.cmd_opts.ckpt_dir):
- name = abspath.replace(shared.cmd_opts.ckpt_dir, '')
- elif abspath.startswith(model_path):
- name = abspath.replace(model_path, '')
- else:
- name = os.path.basename(path)
-
- if name.startswith("\\") or name.startswith("/"):
- name = name[1:]
-
- shortname = os.path.splitext(name.replace("/", "_").replace("\\", "_"))[0]
-
- return f'{name} [{shorthash}]', shortname
-
cmd_ckpt = shared.cmd_opts.ckpt
if os.path.exists(cmd_ckpt):
- h = model_hash(cmd_ckpt)
- title, short_model_name = modeltitle(cmd_ckpt, h)
- checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h, short_model_name)
- shared.opts.data['sd_model_checkpoint'] = title
+ checkpoint_info = CheckpointInfo(cmd_ckpt)
+ checkpoint_info.register()
+
+ shared.opts.data['sd_model_checkpoint'] = checkpoint_info.title
elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file:
print(f"Checkpoint in --ckpt argument not found (Possible it was moved to {model_path}: {cmd_ckpt}", file=sys.stderr)
+
for filename in model_list:
- h = model_hash(filename)
- title, short_model_name = modeltitle(filename, h)
-
- checkpoints_list[title] = CheckpointInfo(filename, title, h, short_model_name)
+ checkpoint_info = CheckpointInfo(filename)
+ checkpoint_info.register()
-def get_closet_checkpoint_match(searchString):
- applicable = sorted([info for info in checkpoints_list.values() if searchString in info.title], key = lambda x:len(x.title))
- if len(applicable) > 0:
- return applicable[0]
+def get_closet_checkpoint_match(search_string):
+ checkpoint_info = checkpoint_alisases.get(search_string, None)
+ if checkpoint_info is not None:
+ return checkpoint_info
+
+ found = sorted([info for info in checkpoints_list.values() if search_string in info.title], key=lambda x: len(x.title))
+ if found:
+ return found[0]
+
return None
def model_hash(filename):
+ """old hash that only looks at a small part of the file and is prone to collisions"""
+
try:
with open(filename, "rb") as file:
import hashlib
@@ -119,7 +154,7 @@ def model_hash(filename):
def select_checkpoint():
model_checkpoint = shared.opts.sd_model_checkpoint
- checkpoint_info = checkpoints_list.get(model_checkpoint, None)
+ checkpoint_info = checkpoint_alisases.get(model_checkpoint, None)
if checkpoint_info is not None:
return checkpoint_info
@@ -189,9 +224,8 @@ def read_state_dict(checkpoint_file, print_global_state=False, map_location=None
return sd
-def load_model_weights(model, checkpoint_info, vae_file="auto"):
- checkpoint_file = checkpoint_info.filename
- sd_model_hash = checkpoint_info.hash
+def load_model_weights(model, checkpoint_info: CheckpointInfo):
+ sd_model_hash = checkpoint_info.calculate_shorthash()
cache_enabled = shared.opts.sd_checkpoint_cache > 0
@@ -201,9 +235,9 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
model.load_state_dict(checkpoints_loaded[checkpoint_info])
else:
# load from file
- print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
+ print(f"Loading weights [{sd_model_hash}] from {checkpoint_info.filename}")
- sd = read_state_dict(checkpoint_file)
+ sd = read_state_dict(checkpoint_info.filename)
model.load_state_dict(sd, strict=False)
del sd
@@ -235,15 +269,16 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
checkpoints_loaded.popitem(last=False) # LRU
model.sd_model_hash = sd_model_hash
- model.sd_model_checkpoint = checkpoint_file
+ model.sd_model_checkpoint = checkpoint_info.filename
model.sd_checkpoint_info = checkpoint_info
+ shared.opts.data["sd_checkpoint_hash"] = checkpoint_info.sha256
model.logvar = model.logvar.to(devices.device) # fix for training
sd_vae.delete_base_vae()
sd_vae.clear_loaded_vae()
- vae_file = sd_vae.resolve_vae(checkpoint_file, vae_file=vae_file)
- sd_vae.load_vae(model, vae_file)
+ vae_file, vae_source = sd_vae.resolve_vae(checkpoint_info.filename)
+ sd_vae.load_vae(model, vae_file, vae_source)
def enable_midas_autodownload():
@@ -333,10 +368,15 @@ def load_model(checkpoint_info=None):
timer = Timer()
+ sd_model = None
+
try:
with sd_disable_initialization.DisableInitialization():
sd_model = instantiate_from_config(sd_config.model)
except Exception as e:
+ pass
+
+ if sd_model is None:
print('Failed to create model quickly; will retry using slow method.', file=sys.stderr)
sd_model = instantiate_from_config(sd_config.model)
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 01221b89..7616fded 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -138,7 +138,7 @@ def samples_to_image_grid(samples, approximation=None):
def store_latent(decoded):
state.current_latent = decoded
- if opts.show_progress_every_n_steps > 0 and shared.state.sampling_step % opts.show_progress_every_n_steps == 0:
+ if opts.live_previews_enable and opts.show_progress_every_n_steps > 0 and shared.state.sampling_step % opts.show_progress_every_n_steps == 0:
if not shared.parallel_processing_allowed:
shared.state.current_image = sample_to_image(decoded)
@@ -243,7 +243,7 @@ class VanillaStableDiffusionSampler:
self.nmask = p.nmask if hasattr(p, 'nmask') else None
def adjust_steps_if_invalid(self, p, num_steps):
- if (self.config.name == 'DDIM' and p.ddim_discretize == 'uniform') or (self.config.name == 'PLMS'):
+ if (self.config.name == 'DDIM' and p.ddim_discretize == 'uniform') or (self.config.name == 'PLMS'):
valid_step = 999 / (1000 // num_steps)
if valid_step == floor(valid_step):
return int(valid_step) + 1
@@ -266,8 +266,7 @@ class VanillaStableDiffusionSampler:
if image_conditioning is not None:
conditioning = {"c_concat": [image_conditioning], "c_crossattn": [conditioning]}
unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
-
-
+
samples = self.launch_sampling(t_enc + 1, lambda: self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning))
return samples
@@ -352,6 +351,11 @@ class CFGDenoiser(torch.nn.Module):
x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond={"c_crossattn": [uncond], "c_concat": [image_cond_in[-uncond.shape[0]:]]})
+ if opts.live_preview_content == "Prompt":
+ store_latent(x_out[0:uncond.shape[0]])
+ elif opts.live_preview_content == "Negative prompt":
+ store_latent(x_out[-uncond.shape[0]:])
+
denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)
if self.mask is not None:
@@ -423,7 +427,8 @@ class KDiffusionSampler:
def callback_state(self, d):
step = d['i']
latent = d["denoised"]
- store_latent(latent)
+ if opts.live_preview_content == "Combined":
+ store_latent(latent)
self.last_latent = latent
if self.stop_at is not None and step > self.stop_at:
diff --git a/modules/sd_vae.py b/modules/sd_vae.py
index 0a49daa1..add5cecf 100644
--- a/modules/sd_vae.py
+++ b/modules/sd_vae.py
@@ -9,23 +9,9 @@ import glob
from copy import deepcopy
-model_dir = "Stable-diffusion"
-model_path = os.path.abspath(os.path.join(models_path, model_dir))
-vae_dir = "VAE"
-vae_path = os.path.abspath(os.path.join(models_path, vae_dir))
-
-
+vae_path = os.path.abspath(os.path.join(models_path, "VAE"))
vae_ignore_keys = {"model_ema.decay", "model_ema.num_updates"}
-
-
-default_vae_dict = {"auto": "auto", "None": None, None: None}
-default_vae_list = ["auto", "None"]
-
-
-default_vae_values = [default_vae_dict[x] for x in default_vae_list]
-vae_dict = dict(default_vae_dict)
-vae_list = list(default_vae_list)
-first_load = True
+vae_dict = {}
base_vae = None
@@ -64,100 +50,69 @@ def restore_base_vae(model):
def get_filename(filepath):
- return os.path.splitext(os.path.basename(filepath))[0]
+ return os.path.basename(filepath)
-def refresh_vae_list(vae_path=vae_path, model_path=model_path):
- global vae_dict, vae_list
- res = {}
- candidates = [
- *glob.iglob(os.path.join(model_path, '**/*.vae.ckpt'), recursive=True),
- *glob.iglob(os.path.join(model_path, '**/*.vae.pt'), recursive=True),
- *glob.iglob(os.path.join(model_path, '**/*.vae.safetensors'), recursive=True),
- *glob.iglob(os.path.join(vae_path, '**/*.ckpt'), recursive=True),
- *glob.iglob(os.path.join(vae_path, '**/*.pt'), recursive=True),
- *glob.iglob(os.path.join(vae_path, '**/*.safetensors'), recursive=True),
+def refresh_vae_list():
+ vae_dict.clear()
+
+ paths = [
+ os.path.join(sd_models.model_path, '**/*.vae.ckpt'),
+ os.path.join(sd_models.model_path, '**/*.vae.pt'),
+ os.path.join(sd_models.model_path, '**/*.vae.safetensors'),
+ os.path.join(vae_path, '**/*.ckpt'),
+ os.path.join(vae_path, '**/*.pt'),
+ os.path.join(vae_path, '**/*.safetensors'),
]
- if shared.cmd_opts.vae_path is not None and os.path.isfile(shared.cmd_opts.vae_path):
- candidates.append(shared.cmd_opts.vae_path)
+
+ if shared.cmd_opts.ckpt_dir is not None and os.path.isdir(shared.cmd_opts.ckpt_dir):
+ paths += [
+ os.path.join(shared.cmd_opts.ckpt_dir, '**/*.vae.ckpt'),
+ os.path.join(shared.cmd_opts.ckpt_dir, '**/*.vae.pt'),
+ os.path.join(shared.cmd_opts.ckpt_dir, '**/*.vae.safetensors'),
+ ]
+
+ candidates = []
+ for path in paths:
+ candidates += glob.iglob(path, recursive=True)
+
for filepath in candidates:
name = get_filename(filepath)
- res[name] = filepath
- vae_list.clear()
- vae_list.extend(default_vae_list)
- vae_list.extend(list(res.keys()))
- vae_dict.clear()
- vae_dict.update(res)
- vae_dict.update(default_vae_dict)
- return vae_list
+ vae_dict[name] = filepath
-def get_vae_from_settings(vae_file="auto"):
- # else, we load from settings, if not set to be default
- if vae_file == "auto" and shared.opts.sd_vae is not None:
- # if saved VAE settings isn't recognized, fallback to auto
- vae_file = vae_dict.get(shared.opts.sd_vae, "auto")
- # if VAE selected but not found, fallback to auto
- if vae_file not in default_vae_values and not os.path.isfile(vae_file):
- vae_file = "auto"
- print(f"Selected VAE doesn't exist: {vae_file}")
- return vae_file
+def find_vae_near_checkpoint(checkpoint_file):
+ checkpoint_path = os.path.splitext(checkpoint_file)[0]
+ for vae_location in [checkpoint_path + ".vae.pt", checkpoint_path + ".vae.ckpt", checkpoint_path + ".vae.safetensors"]:
+ if os.path.isfile(vae_location):
+ return vae_location
+
+ return None
-def resolve_vae(checkpoint_file=None, vae_file="auto"):
- global first_load, vae_dict, vae_list
+def resolve_vae(checkpoint_file):
+ if shared.cmd_opts.vae_path is not None:
+ return shared.cmd_opts.vae_path, 'from commandline argument'
- # if vae_file argument is provided, it takes priority, but not saved
- if vae_file and vae_file not in default_vae_list:
- if not os.path.isfile(vae_file):
- print(f"VAE provided as function argument doesn't exist: {vae_file}")
- vae_file = "auto"
- # for the first load, if vae-path is provided, it takes priority, saved, and failure is reported
- if first_load and shared.cmd_opts.vae_path is not None:
- if os.path.isfile(shared.cmd_opts.vae_path):
- vae_file = shared.cmd_opts.vae_path
- shared.opts.data['sd_vae'] = get_filename(vae_file)
- else:
- print(f"VAE provided as command line argument doesn't exist: {vae_file}")
- # fallback to selector in settings, if vae selector not set to act as default fallback
- if not shared.opts.sd_vae_as_default:
- vae_file = get_vae_from_settings(vae_file)
- # vae-path cmd arg takes priority for auto
- if vae_file == "auto" and shared.cmd_opts.vae_path is not None:
- if os.path.isfile(shared.cmd_opts.vae_path):
- vae_file = shared.cmd_opts.vae_path
- print(f"Using VAE provided as command line argument: {vae_file}")
- # if still not found, try look for ".vae.pt" beside model
- model_path = os.path.splitext(checkpoint_file)[0]
- if vae_file == "auto":
- vae_file_try = model_path + ".vae.pt"
- if os.path.isfile(vae_file_try):
- vae_file = vae_file_try
- print(f"Using VAE found similar to selected model: {vae_file}")
- # if still not found, try look for ".vae.ckpt" beside model
- if vae_file == "auto":
- vae_file_try = model_path + ".vae.ckpt"
- if os.path.isfile(vae_file_try):
- vae_file = vae_file_try
- print(f"Using VAE found similar to selected model: {vae_file}")
- # if still not found, try look for ".vae.safetensors" beside model
- if vae_file == "auto":
- vae_file_try = model_path + ".vae.safetensors"
- if os.path.isfile(vae_file_try):
- vae_file = vae_file_try
- print(f"Using VAE found similar to selected model: {vae_file}")
- # No more fallbacks for auto
- if vae_file == "auto":
- vae_file = None
- # Last check, just because
- if vae_file and not os.path.exists(vae_file):
- vae_file = None
+ vae_near_checkpoint = find_vae_near_checkpoint(checkpoint_file)
+ if vae_near_checkpoint is not None and (shared.opts.sd_vae_as_default or shared.opts.sd_vae == "Automatic"):
+ return vae_near_checkpoint, 'found near the checkpoint'
- return vae_file
+ if shared.opts.sd_vae == "None":
+ return None, None
+
+ vae_from_options = vae_dict.get(shared.opts.sd_vae, None)
+ if vae_from_options is not None:
+ return vae_from_options, 'specified in settings'
+
+ if shared.opts.sd_vae != "Automatic":
+ print(f"Couldn't find VAE named {shared.opts.sd_vae}; using None instead")
+
+ return None, None
-def load_vae(model, vae_file=None):
- global first_load, vae_dict, vae_list, loaded_vae_file
+def load_vae(model, vae_file=None, vae_source="from unknown source"):
+ global vae_dict, loaded_vae_file
# save_settings = False
cache_enabled = shared.opts.sd_vae_checkpoint_cache > 0
@@ -165,12 +120,12 @@ def load_vae(model, vae_file=None):
if vae_file:
if cache_enabled and vae_file in checkpoints_loaded:
# use vae checkpoint cache
- print(f"Loading VAE weights [{get_filename(vae_file)}] from cache")
+ print(f"Loading VAE weights {vae_source}: cached {get_filename(vae_file)}")
store_base_vae(model)
_load_vae_dict(model, checkpoints_loaded[vae_file])
else:
- assert os.path.isfile(vae_file), f"VAE file doesn't exist: {vae_file}"
- print(f"Loading VAE weights from: {vae_file}")
+ assert os.path.isfile(vae_file), f"VAE {vae_source} doesn't exist: {vae_file}"
+ print(f"Loading VAE weights {vae_source}: {vae_file}")
store_base_vae(model)
vae_ckpt = sd_models.read_state_dict(vae_file, map_location=shared.weight_load_location)
@@ -191,14 +146,12 @@ def load_vae(model, vae_file=None):
vae_opt = get_filename(vae_file)
if vae_opt not in vae_dict:
vae_dict[vae_opt] = vae_file
- vae_list.append(vae_opt)
+
elif loaded_vae_file:
restore_base_vae(model)
loaded_vae_file = vae_file
- first_load = False
-
# don't call this from outside
def _load_vae_dict(model, vae_dict_1):
@@ -211,7 +164,10 @@ def clear_loaded_vae():
loaded_vae_file = None
-def reload_vae_weights(sd_model=None, vae_file="auto"):
+unspecified = object()
+
+
+def reload_vae_weights(sd_model=None, vae_file=unspecified):
from modules import lowvram, devices, sd_hijack
if not sd_model:
@@ -219,7 +175,11 @@ def reload_vae_weights(sd_model=None, vae_file="auto"):
checkpoint_info = sd_model.sd_checkpoint_info
checkpoint_file = checkpoint_info.filename
- vae_file = resolve_vae(checkpoint_file, vae_file=vae_file)
+
+ if vae_file == unspecified:
+ vae_file, vae_source = resolve_vae(checkpoint_file)
+ else:
+ vae_source = "from function argument"
if loaded_vae_file == vae_file:
return
@@ -231,7 +191,7 @@ def reload_vae_weights(sd_model=None, vae_file="auto"):
sd_hijack.model_hijack.undo_hijack(sd_model)
- load_vae(sd_model, vae_file)
+ load_vae(sd_model, vae_file, vae_source)
sd_hijack.model_hijack.hijack(sd_model)
script_callbacks.model_loaded_callback(sd_model)
@@ -239,5 +199,5 @@ def reload_vae_weights(sd_model=None, vae_file="auto"):
if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram:
sd_model.to(devices.device)
- print("VAE Weights loaded.")
+ print("VAE weights loaded.")
return sd_model
diff --git a/modules/shared.py b/modules/shared.py
index 264264a6..9756adea 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -74,8 +74,8 @@ parser.add_argument("--freeze-settings", action='store_true', help="disable edit
parser.add_argument("--ui-settings-file", type=str, help="filename to use for ui settings", default=os.path.join(script_path, 'config.json'))
parser.add_argument("--gradio-debug", action='store_true', help="launch gradio with --debug option")
parser.add_argument("--gradio-auth", type=str, help='set gradio authentication like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None)
-parser.add_argument("--gradio-img2img-tool", type=str, help='gradio image uploader tool: can be either editor for ctopping, or color-sketch for drawing', choices=["color-sketch", "editor"], default="editor")
-parser.add_argument("--gradio-inpaint-tool", type=str, choices=["sketch", "color-sketch"], default="sketch", help="gradio inpainting editor: can be either sketch to only blur/noise the input, or color-sketch to paint over it")
+parser.add_argument("--gradio-img2img-tool", type=str, help='does not do anything')
+parser.add_argument("--gradio-inpaint-tool", type=str, help="does not do anything")
parser.add_argument("--opt-channelslast", action='store_true', help="change memory type for stable diffusion to channels last")
parser.add_argument("--styles-file", type=str, help="filename to use for styles", default=os.path.join(script_path, 'styles.csv'))
parser.add_argument("--autolaunch", action='store_true', help="open the webui URL in the system's default browser upon launch", default=False)
@@ -83,7 +83,7 @@ parser.add_argument("--theme", type=str, help="launches the UI with light or dar
parser.add_argument("--use-textbox-seed", action='store_true', help="use textbox for seeds in UI (no up/down, but possible to input long seeds)", default=False)
parser.add_argument("--disable-console-progressbars", action='store_true', help="do not output progressbars to console", default=False)
parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False)
-parser.add_argument('--vae-path', type=str, help='Path to Variational Autoencoders model', default=None)
+parser.add_argument('--vae-path', type=str, help='Checkpoint to use as VAE; setting this argument disables all settings related to VAE', default=None)
parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False)
parser.add_argument("--api", action='store_true', help="use api=True to launch the API together with the webui (use --nowebui instead for only the API)")
parser.add_argument("--api-auth", type=str, help='Set authentication for API like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None)
@@ -176,7 +176,7 @@ class State:
self.interrupted = True
def nextjob(self):
- if opts.show_progress_every_n_steps == -1:
+ if opts.live_previews_enable and opts.show_progress_every_n_steps == -1:
self.do_set_current_image()
self.job_no += 1
@@ -224,7 +224,7 @@ class State:
if not parallel_processing_allowed:
return
- if self.sampling_step - self.current_image_sampling_step >= opts.show_progress_every_n_steps and opts.show_progress_every_n_steps > 0:
+ if self.sampling_step - self.current_image_sampling_step >= opts.show_progress_every_n_steps and opts.live_previews_enable:
self.do_set_current_image()
def do_set_current_image(self):
@@ -361,6 +361,7 @@ options_templates.update(options_section(('system', "System"), {
"memmon_poll_rate": OptionInfo(8, "VRAM usage polls per second during generation. Set to 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 40, "step": 1}),
"samples_log_stdout": OptionInfo(False, "Always print all generation info to standard output"),
"multiple_tqdm": OptionInfo(True, "Add a second progress bar to the console that shows progress for an entire job."),
+ "print_hypernet_extra": OptionInfo(False, "Print extra hypernetwork information to console."),
}))
options_templates.update(options_section(('training', "Training"), {
@@ -373,13 +374,16 @@ options_templates.update(options_section(('training', "Training"), {
"training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}),
"training_write_csv_every": OptionInfo(500, "Save an csv containing the loss to log directory every N steps, 0 to disable"),
"training_xattention_optimizations": OptionInfo(False, "Use cross attention optimizations while training"),
+ "training_enable_tensorboard": OptionInfo(False, "Enable tensorboard logging."),
+ "training_tensorboard_save_images": OptionInfo(False, "Save generated images within tensorboard."),
+ "training_tensorboard_flush_every": OptionInfo(120, "How often, in seconds, to flush the pending tensorboard events and summaries to disk."),
}))
options_templates.update(options_section(('sd', "Stable Diffusion"), {
"sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": list_checkpoint_tiles()}, refresh=refresh_checkpoints),
"sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
"sd_vae_checkpoint_cache": OptionInfo(0, "VAE Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
- "sd_vae": OptionInfo("auto", "SD VAE", gr.Dropdown, lambda: {"choices": sd_vae.vae_list}, refresh=sd_vae.refresh_vae_list),
+ "sd_vae": OptionInfo("Automatic", "SD VAE", gr.Dropdown, lambda: {"choices": ["Automatic", "None"] + list(sd_vae.vae_dict)}, refresh=sd_vae.refresh_vae_list),
"sd_vae_as_default": OptionInfo(False, "Ignore selected VAE for stable diffusion checkpoints that have their own .vae.pt next to them"),
"sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
"sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}),
@@ -419,13 +423,11 @@ options_templates.update(options_section(('interrogate', "Interrogate Options"),
options_templates.update(options_section(('ui', "User interface"), {
"show_progressbar": OptionInfo(True, "Show progressbar"),
- "show_progress_every_n_steps": OptionInfo(0, "Show image creation progress every N sampling steps. Set to 0 to disable. Set to -1 to show after completion of batch.", gr.Slider, {"minimum": -1, "maximum": 32, "step": 1}),
- "show_progress_type": OptionInfo("Full", "Image creation progress preview mode", gr.Radio, {"choices": ["Full", "Approx NN", "Approx cheap"]}),
"show_progress_grid": OptionInfo(True, "Show previews of all images generated in a batch as a grid"),
"return_grid": OptionInfo(True, "Show grid in results for web"),
"do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
"add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
- "add_model_name_to_info": OptionInfo(False, "Add model name to generation information"),
+ "add_model_name_to_info": OptionInfo(True, "Add model name to generation information"),
"disable_weights_auto_swap": OptionInfo(False, "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint."),
"send_seed": OptionInfo(True, "Send seed when sending prompt or image to other interface"),
"send_size": OptionInfo(True, "Send size when sending prompt or image to another interface"),
@@ -440,6 +442,13 @@ options_templates.update(options_section(('ui', "User interface"), {
'localization': OptionInfo("None", "Localization (requires restart)", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)),
}))
+options_templates.update(options_section(('ui', "Live previews"), {
+ "live_previews_enable": OptionInfo(True, "Show live previews of the created image"),
+ "show_progress_every_n_steps": OptionInfo(10, "Show new live preview image every N sampling steps. Set to -1 to show after completion of batch.", gr.Slider, {"minimum": -1, "maximum": 32, "step": 1}),
+ "show_progress_type": OptionInfo("Approx NN", "Image creation progress preview mode", gr.Radio, {"choices": ["Full", "Approx NN", "Approx cheap"]}),
+ "live_preview_content": OptionInfo("Prompt", "Live preview subject", gr.Radio, {"choices": ["Combined", "Prompt", "Negative prompt"]}),
+}))
+
options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
"hide_samplers": OptionInfo([], "Hide samplers in user interface (requires restart)", gr.CheckboxGroup, lambda: {"choices": [x.name for x in list_samplers()]}),
"eta_ddim": OptionInfo(0.0, "eta (noise multiplier) for DDIM", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
@@ -454,6 +463,7 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
options_templates.update(options_section((None, "Hidden options"), {
"disabled_extensions": OptionInfo([], "Disable those extensions"),
+ "sd_checkpoint_hash": OptionInfo("", "SHA256 hash of the current checkpoint"),
}))
options_templates.update()
diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index fa48708e..d31963d4 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -3,8 +3,10 @@ import numpy as np
import PIL
import torch
from PIL import Image
-from torch.utils.data import Dataset, DataLoader
+from torch.utils.data import Dataset, DataLoader, Sampler
from torchvision import transforms
+from collections import defaultdict
+from random import shuffle, choices
import random
import tqdm
@@ -45,12 +47,12 @@ class PersonalizedBase(Dataset):
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"
- assert batch_size == 1 or not varsize, 'variable img size must have batch size 1'
self.image_paths = [os.path.join(data_root, file_path) for file_path in os.listdir(data_root)]
self.shuffle_tags = shuffle_tags
self.tag_drop_out = tag_drop_out
+ groups = defaultdict(list)
print("Preparing dataset...")
for path in tqdm.tqdm(self.image_paths):
@@ -103,18 +105,25 @@ class PersonalizedBase(Dataset):
if include_cond and not (self.tag_drop_out != 0 or self.shuffle_tags):
with devices.autocast():
entry.cond = cond_model([entry.cond_text]).to(devices.cpu).squeeze(0)
-
+ groups[image.size].append(len(self.dataset))
self.dataset.append(entry)
del torchdata
del latent_dist
del latent_sample
self.length = len(self.dataset)
+ self.groups = list(groups.values())
assert self.length > 0, "No images have been found in the dataset."
self.batch_size = min(batch_size, self.length)
self.gradient_step = min(gradient_step, self.length // self.batch_size)
self.latent_sampling_method = latent_sampling_method
+ if len(groups) > 1:
+ print("Buckets:")
+ for (w, h), ids in sorted(groups.items(), key=lambda x: x[0]):
+ print(f" {w}x{h}: {len(ids)}")
+ print()
+
def create_text(self, filename_text):
text = random.choice(self.lines)
tags = filename_text.split(',')
@@ -137,9 +146,44 @@ class PersonalizedBase(Dataset):
entry.latent_sample = shared.sd_model.get_first_stage_encoding(entry.latent_dist).to(devices.cpu)
return entry
+
+class GroupedBatchSampler(Sampler):
+ def __init__(self, data_source: PersonalizedBase, batch_size: int):
+ super().__init__(data_source)
+
+ n = len(data_source)
+ self.groups = data_source.groups
+ self.len = n_batch = n // batch_size
+ expected = [len(g) / n * n_batch * batch_size for g in data_source.groups]
+ self.base = [int(e) // batch_size for e in expected]
+ self.n_rand_batches = nrb = n_batch - sum(self.base)
+ self.probs = [e%batch_size/nrb/batch_size if nrb>0 else 0 for e in expected]
+ self.batch_size = batch_size
+
+ def __len__(self):
+ return self.len
+
+ def __iter__(self):
+ b = self.batch_size
+
+ for g in self.groups:
+ shuffle(g)
+
+ batches = []
+ for g in self.groups:
+ batches.extend(g[i*b:(i+1)*b] for i in range(len(g) // b))
+ for _ in range(self.n_rand_batches):
+ rand_group = choices(self.groups, self.probs)[0]
+ batches.append(choices(rand_group, k=b))
+
+ shuffle(batches)
+
+ yield from batches
+
+
class PersonalizedDataLoader(DataLoader):
def __init__(self, dataset, latent_sampling_method="once", batch_size=1, pin_memory=False):
- super(PersonalizedDataLoader, self).__init__(dataset, shuffle=True, drop_last=True, batch_size=batch_size, pin_memory=pin_memory)
+ super(PersonalizedDataLoader, self).__init__(dataset, batch_sampler=GroupedBatchSampler(dataset, batch_size), pin_memory=pin_memory)
if latent_sampling_method == "random":
self.collate_fn = collate_wrapper_random
else:
diff --git a/modules/textual_inversion/image_embedding.py b/modules/textual_inversion/image_embedding.py
index ea653806..5593f88c 100644
--- a/modules/textual_inversion/image_embedding.py
+++ b/modules/textual_inversion/image_embedding.py
@@ -76,10 +76,10 @@ def insert_image_data_embed(image, data):
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 = np.resize(data_np_low, next_size)
data_np_low = data_np_low.reshape((h, -1, d))
- data_np_high.resize(next_size)
+ data_np_high = np.resize(data_np_high, 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]
diff --git a/modules/textual_inversion/logging.py b/modules/textual_inversion/logging.py
index 8b1981d5..31e50b64 100644
--- a/modules/textual_inversion/logging.py
+++ b/modules/textual_inversion/logging.py
@@ -2,7 +2,7 @@ import datetime
import json
import os
-saved_params_shared = {"model_name", "model_hash", "initial_step", "num_of_dataset_images", "learn_rate", "batch_size", "data_root", "log_directory", "training_width", "training_height", "steps", "create_image_every", "template_file"}
+saved_params_shared = {"model_name", "model_hash", "initial_step", "num_of_dataset_images", "learn_rate", "batch_size", "clip_grad_mode", "clip_grad_value", "gradient_step", "data_root", "log_directory", "training_width", "training_height", "steps", "create_image_every", "template_file"}
saved_params_ti = {"embedding_name", "num_vectors_per_token", "save_embedding_every", "save_image_with_stored_embedding"}
saved_params_hypernet = {"hypernetwork_name", "layer_structure", "activation_func", "weight_init", "add_layer_norm", "use_dropout", "save_hypernetwork_every"}
saved_params_all = saved_params_shared | saved_params_ti | saved_params_hypernet
diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index feb876c6..3c1042ad 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -135,7 +135,8 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pre
params.process_caption_deepbooru = process_caption_deepbooru
params.preprocess_txt_action = preprocess_txt_action
- for index, imagefile in enumerate(tqdm.tqdm(files)):
+ pbar = tqdm.tqdm(files)
+ for index, imagefile in enumerate(pbar):
params.subindex = 0
filename = os.path.join(src, imagefile)
try:
@@ -143,6 +144,10 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pre
except Exception:
continue
+ description = f"Preprocessing [Image {index}/{len(files)}]"
+ pbar.set_description(description)
+ shared.state.textinfo = description
+
params.src = filename
existing_caption = None
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 5420903f..63935878 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -9,8 +9,11 @@ import tqdm
import html
import datetime
import csv
+import safetensors.torch
+import numpy as np
from PIL import Image, PngImagePlugin
+from torch.utils.tensorboard import SummaryWriter
from modules import shared, devices, sd_hijack, processing, sd_models, images, sd_samplers
import modules.textual_inversion.dataset
@@ -150,6 +153,8 @@ class EmbeddingDatabase:
name = data.get('name', name)
elif ext in ['.BIN', '.PT']:
data = torch.load(path, map_location="cpu")
+ elif ext in ['.SAFETENSORS']:
+ data = safetensors.torch.load_file(path, device="cpu")
else:
return
@@ -245,11 +250,14 @@ def create_embedding(name, num_vectors_per_token, overwrite_old, init_text='*'):
with devices.autocast():
cond_model([""]) # will send cond model to GPU if lowvram/medvram is active
- embedded = cond_model.encode_embedding_init_text(init_text, num_vectors_per_token)
+ #cond_model expects at least some text, so we provide '*' as backup.
+ embedded = cond_model.encode_embedding_init_text(init_text or '*', num_vectors_per_token)
vec = torch.zeros((num_vectors_per_token, embedded.shape[1]), device=devices.device)
- for i in range(num_vectors_per_token):
- vec[i] = embedded[i * int(embedded.shape[0]) // num_vectors_per_token]
+ #Only copy if we provided an init_text, otherwise keep vectors as zeros
+ if init_text:
+ for i in range(num_vectors_per_token):
+ vec[i] = embedded[i * int(embedded.shape[0]) // num_vectors_per_token]
# Remove illegal characters from name.
name = "".join( x for x in name if (x.isalnum() or x in "._- "))
@@ -288,6 +296,30 @@ def write_loss(log_directory, filename, step, epoch_len, values):
**values,
})
+def tensorboard_setup(log_directory):
+ os.makedirs(os.path.join(log_directory, "tensorboard"), exist_ok=True)
+ return SummaryWriter(
+ log_dir=os.path.join(log_directory, "tensorboard"),
+ flush_secs=shared.opts.training_tensorboard_flush_every)
+
+def tensorboard_add(tensorboard_writer, loss, global_step, step, learn_rate, epoch_num):
+ tensorboard_add_scaler(tensorboard_writer, "Loss/train", loss, global_step)
+ tensorboard_add_scaler(tensorboard_writer, f"Loss/train/epoch-{epoch_num}", loss, step)
+ tensorboard_add_scaler(tensorboard_writer, "Learn rate/train", learn_rate, global_step)
+ tensorboard_add_scaler(tensorboard_writer, f"Learn rate/train/epoch-{epoch_num}", learn_rate, step)
+
+def tensorboard_add_scaler(tensorboard_writer, tag, value, step):
+ tensorboard_writer.add_scalar(tag=tag,
+ scalar_value=value, global_step=step)
+
+def tensorboard_add_image(tensorboard_writer, tag, pil_image, step):
+ # Convert a pil image to a torch tensor
+ img_tensor = torch.as_tensor(np.array(pil_image, copy=True))
+ img_tensor = img_tensor.view(pil_image.size[1], pil_image.size[0],
+ len(pil_image.getbands()))
+ img_tensor = img_tensor.permute((2, 0, 1))
+
+ tensorboard_writer.add_image(tag, img_tensor, global_step=step)
def validate_train_inputs(model_name, learn_rate, batch_size, gradient_step, data_root, template_file, template_filename, steps, save_model_every, create_image_every, log_directory, name="embedding"):
assert model_name, f"{name} not selected"
@@ -366,13 +398,16 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
# 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)}..."
old_parallel_processing_allowed = shared.parallel_processing_allowed
+
+ if shared.opts.training_enable_tensorboard:
+ tensorboard_writer = tensorboard_setup(log_directory)
pin_memory = shared.opts.pin_memory
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, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method, varsize=varsize)
if shared.opts.save_training_settings_to_txt:
- save_settings_to_file(log_directory, {**dict(model_name=checkpoint.model_name, model_hash=checkpoint.hash, num_of_dataset_images=len(ds), num_vectors_per_token=len(embedding.vec)), **locals()})
+ save_settings_to_file(log_directory, {**dict(model_name=checkpoint.model_name, model_hash=checkpoint.shorthash, num_of_dataset_images=len(ds), num_vectors_per_token=len(embedding.vec)), **locals()})
latent_sampling_method = ds.latent_sampling_method
@@ -473,7 +508,9 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
epoch_num = embedding.step // steps_per_epoch
epoch_step = embedding.step % steps_per_epoch
- pbar.set_description(f"[Epoch {epoch_num}: {epoch_step+1}/{steps_per_epoch}]loss: {loss_step:.7f}")
+ description = f"Training textual inversion [Epoch {epoch_num}: {epoch_step+1}/{steps_per_epoch}] loss: {loss_step:.7f}"
+ pbar.set_description(description)
+ shared.state.textinfo = description
if embedding_dir is not None and steps_done % save_embedding_every == 0:
# Before saving, change name to match current checkpoint.
embedding_name_every = f'{embedding_name}-{steps_done}'
@@ -527,6 +564,9 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
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 shared.opts.training_enable_tensorboard and shared.opts.training_tensorboard_save_images:
+ tensorboard_add_image(tensorboard_writer, f"Validation at epoch {epoch_num}", image, embedding.step)
+
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')
@@ -544,7 +584,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
checkpoint = sd_models.select_checkpoint()
footer_left = checkpoint.model_name
- footer_mid = '[{}]'.format(checkpoint.hash)
+ footer_mid = '[{}]'.format(checkpoint.shorthash)
footer_right = '{}v {}s'.format(vectorSize, steps_done)
captioned_image = caption_image_overlay(image, title, footer_left, footer_mid, footer_right)
@@ -586,7 +626,7 @@ def save_embedding(embedding, optimizer, checkpoint, embedding_name, filename, r
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 = checkpoint.shorthash
embedding.sd_checkpoint_name = checkpoint.model_name
if remove_cached_checksum:
embedding.cached_checksum = None
diff --git a/modules/ui.py b/modules/ui.py
index 3c458ce8..2625ae32 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -795,53 +795,67 @@ def create_ui():
with FormRow().style(equal_height=False):
with gr.Column(variant='panel', elem_id="img2img_settings"):
+ with gr.Tabs(elem_id="mode_img2img"):
+ with gr.TabItem('img2img', id='img2img', elem_id="img2img_img2img_tab") as tab_img2img:
+ init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool="editor", image_mode="RGBA").style(height=480)
- with gr.Tabs(elem_id="mode_img2img") as tabs_img2img_mode:
- with gr.TabItem('img2img', id='img2img', elem_id="img2img_img2img_tab"):
- init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool=cmd_opts.gradio_img2img_tool, image_mode="RGBA").style(height=480)
+ with gr.TabItem('Sketch', id='img2img_sketch', elem_id="img2img_img2img_sketch_tab") as tab_sketch:
+ sketch = gr.Image(label="Image for img2img", elem_id="img2img_sketch", show_label=False, source="upload", interactive=True, type="pil", tool="color-sketch", image_mode="RGBA").style(height=480)
- with gr.TabItem('Inpaint', id='inpaint', elem_id="img2img_inpaint_tab"):
- init_img_with_mask = gr.Image(label="Image for inpainting with mask", show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool=cmd_opts.gradio_inpaint_tool, image_mode="RGBA").style(height=480)
- init_img_with_mask_orig = gr.State(None)
+ with gr.TabItem('Inpaint', id='inpaint', elem_id="img2img_inpaint_tab") as tab_inpaint:
+ init_img_with_mask = gr.Image(label="Image for inpainting with mask", show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA").style(height=480)
- use_color_sketch = cmd_opts.gradio_inpaint_tool == "color-sketch"
- if use_color_sketch:
- def update_orig(image, state):
- if image is not None:
- same_size = state is not None and state.size == image.size
- has_exact_match = np.any(np.all(np.array(image) == np.array(state), axis=-1))
- edited = same_size and has_exact_match
- return image if not edited or state is None else state
+ with gr.TabItem('Inpaint sketch', id='inpaint_sketch', elem_id="img2img_inpaint_sketch_tab") as tab_inpaint_color:
+ inpaint_color_sketch = gr.Image(label="Color sketch inpainting", show_label=False, elem_id="inpaint_sketch", source="upload", interactive=True, type="pil", tool="color-sketch", image_mode="RGBA").style(height=480)
+ inpaint_color_sketch_orig = gr.State(None)
- init_img_with_mask.change(update_orig, [init_img_with_mask, init_img_with_mask_orig], init_img_with_mask_orig)
+ def update_orig(image, state):
+ if image is not None:
+ same_size = state is not None and state.size == image.size
+ has_exact_match = np.any(np.all(np.array(image) == np.array(state), axis=-1))
+ edited = same_size and has_exact_match
+ return image if not edited or state is None else state
- init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_base")
- init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_mask")
+ inpaint_color_sketch.change(update_orig, [inpaint_color_sketch, inpaint_color_sketch_orig], inpaint_color_sketch_orig)
- with FormRow():
- mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4, elem_id="img2img_mask_blur")
- mask_alpha = gr.Slider(label="Mask transparency", interactive=use_color_sketch, visible=use_color_sketch, elem_id="img2img_mask_alpha")
+ with gr.TabItem('Inpaint upload', id='inpaint_upload', elem_id="img2img_inpaint_upload_tab") as tab_inpaint_upload:
+ init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", elem_id="img_inpaint_base")
+ init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", elem_id="img_inpaint_mask")
- with FormRow():
- mask_mode = gr.Radio(label="Mask source", choices=["Draw mask", "Upload mask"], type="index", value="Draw mask", elem_id="mask_mode")
- inpainting_mask_invert = gr.Radio(label='Mask mode', choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index", elem_id="img2img_mask_mode")
-
- with FormRow():
- inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='original', type="index", elem_id="img2img_inpainting_fill")
-
- with FormRow():
- with gr.Column():
- inpaint_full_res = gr.Radio(label="Inpaint area", choices=["Whole picture", "Only masked"], type="index", value="Whole picture", elem_id="img2img_inpaint_full_res")
-
- with gr.Column(scale=4):
- inpaint_full_res_padding = gr.Slider(label='Only masked padding, pixels', minimum=0, maximum=256, step=4, value=32, elem_id="img2img_inpaint_full_res_padding")
-
- with gr.TabItem('Batch img2img', id='batch', elem_id="img2img_batch_tab"):
+ with gr.TabItem('Batch', id='batch', elem_id="img2img_batch_tab") as tab_batch:
hidden = '
Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else ''
gr.HTML(f"Process images in a directory on the same machine where the server is running.
Use an empty output directory to save pictures normally instead of writing to the output directory.{hidden}
")
img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, elem_id="img2img_batch_input_dir")
img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, elem_id="img2img_batch_output_dir")
+ with FormGroup(elem_id="inpaint_controls", visible=False) as inpaint_controls:
+ with FormRow():
+ mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4, elem_id="img2img_mask_blur")
+ mask_alpha = gr.Slider(label="Mask transparency", visible=False, elem_id="img2img_mask_alpha")
+
+ with FormRow():
+ inpainting_mask_invert = gr.Radio(label='Mask mode', choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index", elem_id="img2img_mask_mode")
+
+ with FormRow():
+ inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='original', type="index", elem_id="img2img_inpainting_fill")
+
+ with FormRow():
+ with gr.Column():
+ inpaint_full_res = gr.Radio(label="Inpaint area", choices=["Whole picture", "Only masked"], type="index", value="Whole picture", elem_id="img2img_inpaint_full_res")
+
+ with gr.Column(scale=4):
+ inpaint_full_res_padding = gr.Slider(label='Only masked padding, pixels', minimum=0, maximum=256, step=4, value=32, elem_id="img2img_inpaint_full_res_padding")
+
+ def select_img2img_tab(tab):
+ return gr.update(visible=tab in [2, 3, 4]), gr.update(visible=tab == 3),
+
+ for i, elem in enumerate([tab_img2img, tab_sketch, tab_inpaint, tab_inpaint_color, tab_inpaint_upload, tab_batch]):
+ elem.select(
+ fn=lambda tab=i: select_img2img_tab(tab),
+ inputs=[],
+ outputs=[inpaint_controls, mask_alpha],
+ )
+
with FormRow():
resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", choices=["Just resize", "Crop and resize", "Resize and fill", "Just resize (latent upscale)"], type="index", value="Just resize")
@@ -900,20 +914,6 @@ def create_ui():
]
)
- mask_mode.change(
- lambda mode, img: {
- init_img_with_mask: gr_show(mode == 0),
- init_img_inpaint: gr_show(mode == 1),
- init_mask_inpaint: gr_show(mode == 1),
- },
- inputs=[mask_mode, init_img_with_mask],
- outputs=[
- init_img_with_mask,
- init_img_inpaint,
- init_mask_inpaint,
- ],
- )
-
img2img_args = dict(
fn=wrap_gradio_gpu_call(modules.img2img.img2img, extra_outputs=[None, '', '']),
_js="submit_img2img",
@@ -924,11 +924,12 @@ def create_ui():
img2img_prompt_style,
img2img_prompt_style2,
init_img,
+ sketch,
init_img_with_mask,
- init_img_with_mask_orig,
+ inpaint_color_sketch,
+ inpaint_color_sketch_orig,
init_img_inpaint,
init_mask_inpaint,
- mask_mode,
steps,
sampler_index,
mask_blur,
@@ -1129,7 +1130,7 @@ def create_ui():
with gr.Column(variant='panel'):
gr.HTML(value="A merger of the two checkpoints will be generated in your checkpoint directory.
")
- with gr.Row():
+ with FormRow():
primary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary model (A)")
create_refresh_button(primary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_A")
@@ -1143,11 +1144,13 @@ def create_ui():
interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Multiplier (M) - set to 0 to get model A', value=0.3, elem_id="modelmerger_interp_amount")
interp_method = gr.Radio(choices=["Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method", elem_id="modelmerger_interp_method")
- with gr.Row():
+ with FormRow():
checkpoint_format = gr.Radio(choices=["ckpt", "safetensors"], value="ckpt", label="Checkpoint format", elem_id="modelmerger_checkpoint_format")
save_as_half = gr.Checkbox(value=False, label="Save as float16", elem_id="modelmerger_save_as_half")
- modelmerger_merge = gr.Button(elem_id="modelmerger_merge", label="Merge", variant='primary')
+ config_source = gr.Radio(choices=["A, B or C", "B", "C", "Don't"], value="A, B or C", label="Copy config from", type="index", elem_id="modelmerger_config_method")
+
+ modelmerger_merge = gr.Button(elem_id="modelmerger_merge", value="Merge", variant='primary')
with gr.Column(variant='panel'):
submit_result = gr.Textbox(elem_id="modelmerger_result", show_label=False)
@@ -1703,6 +1706,7 @@ def create_ui():
save_as_half,
custom_name,
checkpoint_format,
+ config_source,
],
outputs=[
submit_result,
@@ -1837,4 +1841,6 @@ xformers: {xformers_version}
gradio: {gr.__version__}
•
commit: {short_commit}
+ •
+checkpoint: N/A
"""
diff --git a/modules/ui_progress.py b/modules/ui_progress.py
index 592fda55..7cd312e4 100644
--- a/modules/ui_progress.py
+++ b/modules/ui_progress.py
@@ -52,7 +52,7 @@ def check_progress_call(id_part):
image = gr.update(visible=False)
preview_visibility = gr.update(visible=False)
- if opts.show_progress_every_n_steps != 0:
+ if opts.live_previews_enable:
shared.state.set_current_image()
image = shared.state.current_image
diff --git a/script.js b/script.js
index 0e117d06..3345e32b 100644
--- a/script.js
+++ b/script.js
@@ -1,5 +1,6 @@
function gradioApp() {
- const gradioShadowRoot = document.getElementsByTagName('gradio-app')[0].shadowRoot
+ const elems = document.getElementsByTagName('gradio-app')
+ const gradioShadowRoot = elems.length == 0 ? null : elems[0].shadowRoot
return !!gradioShadowRoot ? gradioShadowRoot : document;
}
@@ -13,6 +14,7 @@ function get_uiCurrentTabContent() {
uiUpdateCallbacks = []
uiTabChangeCallbacks = []
+optionsChangedCallbacks = []
let uiCurrentTab = null
function onUiUpdate(callback){
@@ -21,6 +23,9 @@ function onUiUpdate(callback){
function onUiTabChange(callback){
uiTabChangeCallbacks.push(callback)
}
+function onOptionsChanged(callback){
+ optionsChangedCallbacks.push(callback)
+}
function runCallback(x, m){
try {
diff --git a/scripts/prompts_from_file.py b/scripts/prompts_from_file.py
index 2751f98a..f3e711d7 100644
--- a/scripts/prompts_from_file.py
+++ b/scripts/prompts_from_file.py
@@ -146,11 +146,7 @@ class Script(scripts.Script):
else:
args = {"prompt": line}
- n_iter = args.get("n_iter", 1)
- if n_iter != 1:
- job_count += n_iter
- else:
- job_count += 1
+ job_count += args.get("n_iter", p.n_iter)
jobs.append(args)
diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py
index f04d9b7e..bd3087d4 100644
--- a/scripts/xy_grid.py
+++ b/scripts/xy_grid.py
@@ -125,24 +125,21 @@ def apply_upscale_latent_space(p, x, xs):
def find_vae(name: str):
- if name.lower() in ['auto', 'none']:
- return name
+ if name.lower() in ['auto', 'automatic']:
+ return modules.sd_vae.unspecified
+ if name.lower() == 'none':
+ return None
else:
- vae_path = os.path.abspath(os.path.join(paths.models_path, 'VAE'))
- found = glob.glob(os.path.join(vae_path, f'**/{name}.*pt'), recursive=True)
- if found:
- return found[0]
+ choices = [x for x in sorted(modules.sd_vae.vae_dict, key=lambda x: len(x)) if name.lower().strip() in x.lower()]
+ if len(choices) == 0:
+ print(f"No VAE found for {name}; using automatic")
+ return modules.sd_vae.unspecified
else:
- return 'auto'
+ return modules.sd_vae.vae_dict[choices[0]]
def apply_vae(p, x, xs):
- if x.lower().strip() == 'none':
- modules.sd_vae.reload_vae_weights(shared.sd_model, vae_file='None')
- else:
- found = find_vae(x)
- if found:
- v = modules.sd_vae.reload_vae_weights(shared.sd_model, vae_file=found)
+ modules.sd_vae.reload_vae_weights(shared.sd_model, vae_file=find_vae(x))
def apply_styles(p: StableDiffusionProcessingTxt2Img, x: str, _):
@@ -271,7 +268,9 @@ class SharedSettingsStackHelper(object):
def __exit__(self, exc_type, exc_value, tb):
modules.sd_models.reload_model_weights(self.model)
- modules.sd_vae.reload_vae_weights(self.model, vae_file=find_vae(self.vae))
+
+ opts.data["sd_vae"] = self.vae
+ modules.sd_vae.reload_vae_weights(self.model)
hypernetwork.load_hypernetwork(self.hypernetwork)
hypernetwork.apply_strength()
diff --git a/style.css b/style.css
index ec5e4182..ffd6307f 100644
--- a/style.css
+++ b/style.css
@@ -557,7 +557,9 @@ canvas[key="mask"] {
}
#img2img_image, #img2img_image > .h-60, #img2img_image > .h-60 > div, #img2img_image > .h-60 > div > img,
-img2maskimg, #img2maskimg > .h-60, #img2maskimg > .h-60 > div, #img2maskimg > .h-60 > div > img
+#img2img_sketch, #img2img_sketch > .h-60, #img2img_sketch > .h-60 > div, #img2img_sketch > .h-60 > div > img,
+#img2maskimg, #img2maskimg > .h-60, #img2maskimg > .h-60 > div, #img2maskimg > .h-60 > div > img,
+#inpaint_sketch, #inpaint_sketch > .h-60, #inpaint_sketch > .h-60 > div, #inpaint_sketch > .h-60 > div > img
{
height: 480px !important;
max-height: 480px !important;
diff --git a/webui.py b/webui.py
index 47d372c7..1fff80da 100644
--- a/webui.py
+++ b/webui.py
@@ -78,6 +78,8 @@ def initialize():
print("Stable diffusion model failed to load, exiting", file=sys.stderr)
exit(1)
+ shared.opts.data["sd_model_checkpoint"] = shared.sd_model.sd_checkpoint_info.title
+
shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights()))
shared.opts.onchange("sd_vae", wrap_queued_call(lambda: modules.sd_vae.reload_vae_weights()), call=False)
shared.opts.onchange("sd_vae_as_default", wrap_queued_call(lambda: modules.sd_vae.reload_vae_weights()), call=False)