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-rw-r--r--modules/bsrgan_model.py6
-rw-r--r--modules/codeformer_model.py12
-rw-r--r--modules/devices.py15
-rw-r--r--modules/esrgan_model.py9
-rw-r--r--modules/gfpgan_model.py22
-rw-r--r--modules/images.py39
-rw-r--r--modules/img2img.py7
-rw-r--r--modules/processing.py18
-rw-r--r--modules/prompt_parser.py119
-rw-r--r--modules/scunet_model.py8
-rw-r--r--modules/shared.py13
-rw-r--r--modules/textual_inversion/dataset.py7
-rw-r--r--modules/textual_inversion/preprocess.py4
-rw-r--r--modules/textual_inversion/textual_inversion.py2
-rw-r--r--modules/ui.py14
15 files changed, 165 insertions, 130 deletions
diff --git a/modules/bsrgan_model.py b/modules/bsrgan_model.py
index e62c6657..3bd80791 100644
--- a/modules/bsrgan_model.py
+++ b/modules/bsrgan_model.py
@@ -8,7 +8,7 @@ import torch
from basicsr.utils.download_util import load_file_from_url
import modules.upscaler
-from modules import shared, modelloader
+from modules import devices, modelloader
from modules.bsrgan_model_arch import RRDBNet
from modules.paths import models_path
@@ -44,13 +44,13 @@ class UpscalerBSRGAN(modules.upscaler.Upscaler):
model = self.load_model(selected_file)
if model is None:
return img
- model.to(shared.device)
+ model.to(devices.device_bsrgan)
torch.cuda.empty_cache()
img = np.array(img)
img = img[:, :, ::-1]
img = np.moveaxis(img, 2, 0) / 255
img = torch.from_numpy(img).float()
- img = img.unsqueeze(0).to(shared.device)
+ img = img.unsqueeze(0).to(devices.device_bsrgan)
with torch.no_grad():
output = model(img)
output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
diff --git a/modules/codeformer_model.py b/modules/codeformer_model.py
index a29f3855..e6d9fa4f 100644
--- a/modules/codeformer_model.py
+++ b/modules/codeformer_model.py
@@ -69,10 +69,14 @@ def setup_model(dirname):
self.net = net
self.face_helper = face_helper
- self.net.to(devices.device_codeformer)
return net, face_helper
+ def send_model_to(self, device):
+ self.net.to(device)
+ self.face_helper.face_det.to(device)
+ self.face_helper.face_parse.to(device)
+
def restore(self, np_image, w=None):
np_image = np_image[:, :, ::-1]
@@ -82,6 +86,8 @@ def setup_model(dirname):
if self.net is None or self.face_helper is None:
return np_image
+ self.send_model_to(devices.device_codeformer)
+
self.face_helper.clean_all()
self.face_helper.read_image(np_image)
self.face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5)
@@ -113,8 +119,10 @@ def setup_model(dirname):
if original_resolution != restored_img.shape[0:2]:
restored_img = cv2.resize(restored_img, (0, 0), fx=original_resolution[1]/restored_img.shape[1], fy=original_resolution[0]/restored_img.shape[0], interpolation=cv2.INTER_LINEAR)
+ self.face_helper.clean_all()
+
if shared.opts.face_restoration_unload:
- self.net.to(devices.cpu)
+ self.send_model_to(devices.cpu)
return restored_img
diff --git a/modules/devices.py b/modules/devices.py
index ff82f2f6..0158b11f 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -1,8 +1,10 @@
+import contextlib
+
import torch
-# has_mps is only available in nightly pytorch (for now), `getattr` for compatibility
from modules import errors
+# has_mps is only available in nightly pytorch (for now), `getattr` for compatibility
has_mps = getattr(torch, 'has_mps', False)
cpu = torch.device("cpu")
@@ -32,8 +34,7 @@ def enable_tf32():
errors.run(enable_tf32, "Enabling TF32")
-device = get_optimal_device()
-device_codeformer = cpu if has_mps else device
+device = device_gfpgan = device_bsrgan = device_esrgan = device_scunet = device_codeformer = get_optimal_device()
dtype = torch.float16
def randn(seed, shape):
@@ -57,3 +58,11 @@ def randn_without_seed(shape):
return torch.randn(shape, device=device)
+
+def autocast():
+ from modules import shared
+
+ if dtype == torch.float32 or shared.cmd_opts.precision == "full":
+ return contextlib.nullcontext()
+
+ return torch.autocast("cuda")
diff --git a/modules/esrgan_model.py b/modules/esrgan_model.py
index 4aed9283..d17e730f 100644
--- a/modules/esrgan_model.py
+++ b/modules/esrgan_model.py
@@ -6,8 +6,7 @@ from PIL import Image
from basicsr.utils.download_util import load_file_from_url
import modules.esrgam_model_arch as arch
-from modules import shared, modelloader, images
-from modules.devices import has_mps
+from modules import shared, modelloader, images, devices
from modules.paths import models_path
from modules.upscaler import Upscaler, UpscalerData
from modules.shared import opts
@@ -97,7 +96,7 @@ class UpscalerESRGAN(Upscaler):
model = self.load_model(selected_model)
if model is None:
return img
- model.to(shared.device)
+ model.to(devices.device_esrgan)
img = esrgan_upscale(model, img)
return img
@@ -112,7 +111,7 @@ class UpscalerESRGAN(Upscaler):
print("Unable to load %s from %s" % (self.model_path, filename))
return None
- pretrained_net = torch.load(filename, map_location='cpu' if has_mps else None)
+ pretrained_net = torch.load(filename, map_location='cpu' if shared.device.type == 'mps' else None)
crt_model = arch.RRDBNet(3, 3, 64, 23, gc=32)
pretrained_net = fix_model_layers(crt_model, pretrained_net)
@@ -127,7 +126,7 @@ def upscale_without_tiling(model, img):
img = img[:, :, ::-1]
img = np.moveaxis(img, 2, 0) / 255
img = torch.from_numpy(img).float()
- img = img.unsqueeze(0).to(shared.device)
+ img = img.unsqueeze(0).to(devices.device_esrgan)
with torch.no_grad():
output = model(img)
output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
diff --git a/modules/gfpgan_model.py b/modules/gfpgan_model.py
index bb30d733..a9452dce 100644
--- a/modules/gfpgan_model.py
+++ b/modules/gfpgan_model.py
@@ -21,7 +21,7 @@ def gfpgann():
global loaded_gfpgan_model
global model_path
if loaded_gfpgan_model is not None:
- loaded_gfpgan_model.gfpgan.to(shared.device)
+ loaded_gfpgan_model.gfpgan.to(devices.device_gfpgan)
return loaded_gfpgan_model
if gfpgan_constructor is None:
@@ -37,22 +37,32 @@ def gfpgann():
print("Unable to load gfpgan model!")
return None
model = gfpgan_constructor(model_path=model_file, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None)
- model.gfpgan.to(shared.device)
loaded_gfpgan_model = model
return model
+def send_model_to(model, device):
+ model.gfpgan.to(device)
+ model.face_helper.face_det.to(device)
+ model.face_helper.face_parse.to(device)
+
+
def gfpgan_fix_faces(np_image):
model = gfpgann()
if model is None:
return np_image
+
+ send_model_to(model, devices.device_gfpgan)
+
np_image_bgr = np_image[:, :, ::-1]
cropped_faces, restored_faces, gfpgan_output_bgr = model.enhance(np_image_bgr, has_aligned=False, only_center_face=False, paste_back=True)
np_image = gfpgan_output_bgr[:, :, ::-1]
+ model.face_helper.clean_all()
+
if shared.opts.face_restoration_unload:
- model.gfpgan.to(devices.cpu)
+ send_model_to(model, devices.cpu)
return np_image
@@ -97,11 +107,7 @@ def setup_model(dirname):
return "GFPGAN"
def restore(self, np_image):
- np_image_bgr = np_image[:, :, ::-1]
- cropped_faces, restored_faces, gfpgan_output_bgr = gfpgann().enhance(np_image_bgr, has_aligned=False, only_center_face=False, paste_back=True)
- np_image = gfpgan_output_bgr[:, :, ::-1]
-
- return np_image
+ return gfpgan_fix_faces(np_image)
shared.face_restorers.append(FaceRestorerGFPGAN())
except Exception:
diff --git a/modules/images.py b/modules/images.py
index 1a046aca..c2fadab9 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -287,6 +287,25 @@ def apply_filename_pattern(x, p, seed, prompt):
if seed is not None:
x = x.replace("[seed]", str(seed))
+ if p is not None:
+ x = x.replace("[steps]", str(p.steps))
+ x = x.replace("[cfg]", str(p.cfg_scale))
+ x = x.replace("[width]", str(p.width))
+ x = x.replace("[height]", str(p.height))
+
+ #currently disabled if using the save button, will work otherwise
+ # if enabled it will cause a bug because styles is not included in the save_files data dictionary
+ if hasattr(p, "styles"):
+ x = x.replace("[styles]", sanitize_filename_part(", ".join([x for x in p.styles if not x == "None"]) or "None", replace_spaces=False))
+
+ x = x.replace("[sampler]", sanitize_filename_part(sd_samplers.samplers[p.sampler_index].name, replace_spaces=False))
+
+ x = x.replace("[model_hash]", shared.sd_model.sd_model_hash)
+ x = x.replace("[date]", datetime.date.today().isoformat())
+ x = x.replace("[datetime]", datetime.datetime.now().strftime("%Y%m%d%H%M%S"))
+ x = x.replace("[job_timestamp]", shared.state.job_timestamp)
+
+ # Apply [prompt] at last. Because it may contain any replacement word.^M
if prompt is not None:
x = x.replace("[prompt]", sanitize_filename_part(prompt))
if "[prompt_no_styles]" in x:
@@ -295,7 +314,7 @@ def apply_filename_pattern(x, p, seed, prompt):
if len(style) > 0:
style_parts = [y for y in style.split("{prompt}")]
for part in style_parts:
- prompt_no_style = prompt_no_style.replace(part, "").replace(", ,", ",").strip().strip(',')
+ prompt_no_style = prompt_no_style.replace(part, "").replace(", ,", ",").strip().strip(',')
prompt_no_style = prompt_no_style.replace(style, "").strip().strip(',').strip()
x = x.replace("[prompt_no_styles]", sanitize_filename_part(prompt_no_style, replace_spaces=False))
@@ -306,24 +325,6 @@ def apply_filename_pattern(x, p, seed, prompt):
words = ["empty"]
x = x.replace("[prompt_words]", sanitize_filename_part(" ".join(words[0:max_prompt_words]), replace_spaces=False))
- if p is not None:
- x = x.replace("[steps]", str(p.steps))
- x = x.replace("[cfg]", str(p.cfg_scale))
- x = x.replace("[width]", str(p.width))
- x = x.replace("[height]", str(p.height))
-
- #currently disabled if using the save button, will work otherwise
- # if enabled it will cause a bug because styles is not included in the save_files data dictionary
- if hasattr(p, "styles"):
- x = x.replace("[styles]", sanitize_filename_part(", ".join([x for x in p.styles if not x == "None"]) or "None", replace_spaces=False))
-
- x = x.replace("[sampler]", sanitize_filename_part(sd_samplers.samplers[p.sampler_index].name, replace_spaces=False))
-
- x = x.replace("[model_hash]", shared.sd_model.sd_model_hash)
- x = x.replace("[date]", datetime.date.today().isoformat())
- x = x.replace("[datetime]", datetime.datetime.now().strftime("%Y%m%d%H%M%S"))
- x = x.replace("[job_timestamp]", shared.state.job_timestamp)
-
if cmd_opts.hide_ui_dir_config:
x = re.sub(r'^[\\/]+|\.{2,}[\\/]+|[\\/]+\.{2,}', '', x)
diff --git a/modules/img2img.py b/modules/img2img.py
index f4455c90..2ff8e261 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -23,8 +23,10 @@ def process_batch(p, input_dir, output_dir, args):
print(f"Will process {len(images)} images, creating {p.n_iter * p.batch_size} new images for each.")
+ save_normally = output_dir == ''
+
p.do_not_save_grid = True
- p.do_not_save_samples = True
+ p.do_not_save_samples = not save_normally
state.job_count = len(images) * p.n_iter
@@ -48,7 +50,8 @@ def process_batch(p, input_dir, output_dir, args):
left, right = os.path.splitext(filename)
filename = f"{left}-{n}{right}"
- processed_image.save(os.path.join(output_dir, filename))
+ if not save_normally:
+ 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_inpaint, init_mask_inpaint, mask_mode, steps: int, sampler_index: int, mask_blur: int, 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):
diff --git a/modules/processing.py b/modules/processing.py
index 0a4b6198..6f5599c7 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -1,4 +1,3 @@
-import contextlib
import json
import math
import os
@@ -330,9 +329,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
infotexts = []
output_images = []
- precision_scope = torch.autocast if cmd_opts.precision == "autocast" else contextlib.nullcontext
- ema_scope = (contextlib.nullcontext if cmd_opts.lowvram else p.sd_model.ema_scope)
- with torch.no_grad(), precision_scope("cuda"), ema_scope():
+
+ with torch.no_grad():
p.init(all_prompts, all_seeds, all_subseeds)
if state.job_count == -1:
@@ -351,8 +349,9 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
#uc = p.sd_model.get_learned_conditioning(len(prompts) * [p.negative_prompt])
#c = p.sd_model.get_learned_conditioning(prompts)
- uc = prompt_parser.get_learned_conditioning(len(prompts) * [p.negative_prompt], p.steps)
- c = prompt_parser.get_learned_conditioning(prompts, p.steps)
+ with devices.autocast():
+ uc = prompt_parser.get_learned_conditioning(len(prompts) * [p.negative_prompt], p.steps)
+ c = prompt_parser.get_learned_conditioning(prompts, p.steps)
if len(model_hijack.comments) > 0:
for comment in model_hijack.comments:
@@ -361,13 +360,17 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
if p.n_iter > 1:
shared.state.job = f"Batch {n+1} out of {p.n_iter}"
- samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength)
+ with devices.autocast():
+ samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength)
+
if state.interrupted:
# if we are interruped, sample returns just noise
# use the image collected previously in sampler loop
samples_ddim = shared.state.current_latent
+ samples_ddim = samples_ddim.to(devices.dtype)
+
x_samples_ddim = p.sd_model.decode_first_stage(samples_ddim)
x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
@@ -386,6 +389,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
devices.torch_gc()
x_sample = modules.face_restoration.restore_faces(x_sample)
+ devices.torch_gc()
image = Image.fromarray(x_sample)
diff --git a/modules/prompt_parser.py b/modules/prompt_parser.py
index e811eb9e..99c8ed99 100644
--- a/modules/prompt_parser.py
+++ b/modules/prompt_parser.py
@@ -1,20 +1,11 @@
import re
from collections import namedtuple
import torch
+from lark import Lark, Transformer, Visitor
+import functools
import modules.shared as shared
-re_prompt = re.compile(r'''
-(.*?)
-\[
- ([^]:]+):
- (?:([^]:]*):)?
- ([0-9]*\.?[0-9]+)
-]
-|
-(.+)
-''', re.X)
-
# a prompt like this: "fantasy landscape with a [mountain:lake:0.25] and [an oak:a christmas tree:0.75][ in foreground::0.6][ in background:0.25] [shoddy:masterful:0.5]"
# will be represented with prompt_schedule like this (assuming steps=100):
# [25, 'fantasy landscape with a mountain and an oak in foreground shoddy']
@@ -25,61 +16,57 @@ re_prompt = re.compile(r'''
def get_learned_conditioning_prompt_schedules(prompts, steps):
- res = []
- cache = {}
-
- for prompt in prompts:
- prompt_schedule: list[list[str | int]] = [[steps, ""]]
-
- cached = cache.get(prompt, None)
- if cached is not None:
- res.append(cached)
- continue
-
- for m in re_prompt.finditer(prompt):
- plaintext = m.group(1) if m.group(5) is None else m.group(5)
- concept_from = m.group(2)
- concept_to = m.group(3)
- if concept_to is None:
- concept_to = concept_from
- concept_from = ""
- swap_position = float(m.group(4)) if m.group(4) is not None else None
-
- if swap_position is not None:
- if swap_position < 1:
- swap_position = swap_position * steps
- swap_position = int(min(swap_position, steps))
-
- swap_index = None
- found_exact_index = False
- for i in range(len(prompt_schedule)):
- end_step = prompt_schedule[i][0]
- prompt_schedule[i][1] += plaintext
-
- if swap_position is not None and swap_index is None:
- if swap_position == end_step:
- swap_index = i
- found_exact_index = True
-
- if swap_position < end_step:
- swap_index = i
-
- if swap_index is not None:
- if not found_exact_index:
- prompt_schedule.insert(swap_index, [swap_position, prompt_schedule[swap_index][1]])
-
- for i in range(len(prompt_schedule)):
- end_step = prompt_schedule[i][0]
- must_replace = swap_position < end_step
-
- prompt_schedule[i][1] += concept_to if must_replace else concept_from
-
- res.append(prompt_schedule)
- cache[prompt] = prompt_schedule
- #for t in prompt_schedule:
- # print(t)
-
- return res
+ grammar = r"""
+ start: prompt
+ prompt: (emphasized | scheduled | weighted | plain)*
+ !emphasized: "(" prompt ")"
+ | "(" prompt ":" prompt ")"
+ | "[" prompt "]"
+ scheduled: "[" (prompt ":")? prompt ":" NUMBER "]"
+ !weighted: "{" weighted_item ("|" weighted_item)* "}"
+ !weighted_item: prompt (":" prompt)?
+ plain: /([^\\\[\](){}:|]|\\.)+/
+ %import common.SIGNED_NUMBER -> NUMBER
+ """
+ parser = Lark(grammar, parser='lalr')
+ def collect_steps(steps, tree):
+ l = [steps]
+ class CollectSteps(Visitor):
+ def scheduled(self, tree):
+ tree.children[-1] = float(tree.children[-1])
+ if tree.children[-1] < 1:
+ tree.children[-1] *= steps
+ tree.children[-1] = min(steps, int(tree.children[-1]))
+ l.append(tree.children[-1])
+ CollectSteps().visit(tree)
+ return sorted(set(l))
+ def at_step(step, tree):
+ class AtStep(Transformer):
+ def scheduled(self, args):
+ if len(args) == 2:
+ before, after, when = (), *args
+ else:
+ before, after, when = args
+ yield before if step <= when else after
+ def start(self, args):
+ def flatten(x):
+ if type(x) == str:
+ yield x
+ else:
+ for gen in x:
+ yield from flatten(gen)
+ return ''.join(flatten(args[0]))
+ def plain(self, args):
+ yield args[0].value
+ def __default__(self, data, children, meta):
+ for child in children:
+ yield from child
+ return AtStep().transform(tree)
+ @functools.cache
+ def get_schedule(prompt):
+ tree = parser.parse(prompt)
+ return [[t, at_step(t, tree)] for t in collect_steps(steps, tree)]
+ return [get_schedule(prompt) for prompt in prompts]
ScheduledPromptConditioning = namedtuple("ScheduledPromptConditioning", ["end_at_step", "cond"])
diff --git a/modules/scunet_model.py b/modules/scunet_model.py
index 7987ac14..fb64b740 100644
--- a/modules/scunet_model.py
+++ b/modules/scunet_model.py
@@ -8,7 +8,7 @@ import torch
from basicsr.utils.download_util import load_file_from_url
import modules.upscaler
-from modules import shared, modelloader
+from modules import devices, modelloader
from modules.paths import models_path
from modules.scunet_model_arch import SCUNet as net
@@ -51,12 +51,12 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
if model is None:
return img
- device = shared.device
+ device = devices.device_scunet
img = np.array(img)
img = img[:, :, ::-1]
img = np.moveaxis(img, 2, 0) / 255
img = torch.from_numpy(img).float()
- img = img.unsqueeze(0).to(shared.device)
+ img = img.unsqueeze(0).to(device)
img = img.to(device)
with torch.no_grad():
@@ -69,7 +69,7 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
return PIL.Image.fromarray(output, 'RGB')
def load_model(self, path: str):
- device = shared.device
+ device = devices.device_scunet
if "http" in path:
filename = load_file_from_url(url=self.model_url, model_dir=self.model_path, file_name="%s.pth" % self.name,
progress=True)
diff --git a/modules/shared.py b/modules/shared.py
index 2a599e9c..a7d13b2d 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -12,7 +12,7 @@ import modules.interrogate
import modules.memmon
import modules.sd_models
import modules.styles
-from modules.devices import get_optimal_device
+import modules.devices as devices
from modules.paths import script_path, sd_path
sd_model_file = os.path.join(script_path, 'model.ckpt')
@@ -46,6 +46,7 @@ parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with
parser.add_argument("--opt-split-attention", action='store_true', help="force-enables cross-attention layer optimization. By default, it's on for torch.cuda and off for other torch devices.")
parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")
+parser.add_argument("--use-cpu", nargs='+',choices=['SD', 'GFPGAN', 'BSRGAN', 'ESRGAN', 'SCUNet', 'CodeFormer'], help="use CPU as torch device for specified modules", default=[])
parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests")
parser.add_argument("--port", type=int, help="launch gradio with given server port, you need root/admin rights for ports < 1024, defaults to 7860 if available", default=None)
parser.add_argument("--show-negative-prompt", action='store_true', help="does not do anything", default=False)
@@ -63,7 +64,11 @@ parser.add_argument("--enable-console-prompts", action='store_true', help="print
cmd_opts = parser.parse_args()
-device = get_optimal_device()
+
+devices.device, devices.device_gfpgan, devices.device_bsrgan, devices.device_esrgan, devices.device_scunet, devices.device_codeformer = \
+(devices.cpu if x in cmd_opts.use_cpu else devices.get_optimal_device() for x in ['SD', 'GFPGAN', 'BSRGAN', 'ESRGAN', 'SCUNet', 'CodeFormer'])
+
+device = devices.device
batch_cond_uncond = cmd_opts.always_batch_cond_uncond or not (cmd_opts.lowvram or cmd_opts.medvram)
parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram
@@ -195,7 +200,7 @@ options_templates.update(options_section(('face-restoration', "Face restoration"
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. Broken in PyCharm console."),
+ "multiple_tqdm": OptionInfo(True, "Add a second progress bar to the console that shows progress for an entire job."),
}))
options_templates.update(options_section(('sd', "Stable Diffusion"), {
@@ -204,7 +209,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
"save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
"img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."),
"enable_quantization": OptionInfo(False, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply."),
- "enable_emphasis": OptionInfo(True, "Eemphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention"),
+ "enable_emphasis": OptionInfo(True, "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention"),
"use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),
"enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
"filter_nsfw": OptionInfo(False, "Filter NSFW content"),
diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index e8394ff6..7c44ea5b 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -9,6 +9,9 @@ from torchvision import transforms
import random
import tqdm
from modules import devices
+import re
+
+re_tag = re.compile(r"[a-zA-Z][_\w\d()]+")
class PersonalizedBase(Dataset):
@@ -38,8 +41,8 @@ class PersonalizedBase(Dataset):
image = image.resize((self.width, self.height), PIL.Image.BICUBIC)
filename = os.path.basename(path)
- filename_tokens = os.path.splitext(filename)[0].replace('_', '-').replace(' ', '-').split('-')
- filename_tokens = [token for token in filename_tokens if token.isalpha()]
+ filename_tokens = os.path.splitext(filename)[0]
+ filename_tokens = re_tag.findall(filename_tokens)
npimage = np.array(image).astype(np.uint8)
npimage = (npimage / 127.5 - 1.0).astype(np.float32)
diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index 209e928f..f545a993 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -26,7 +26,9 @@ def preprocess(process_src, process_dst, process_flip, process_split, process_ca
if process_caption:
caption = "-" + shared.interrogator.generate_caption(image)
else:
- caption = ""
+ caption = filename
+ caption = os.path.splitext(caption)[0]
+ caption = os.path.basename(caption)
image.save(os.path.join(dst, f"{index:05}-{subindex[0]}{caption}.png"))
subindex[0] += 1
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 8686f534..cd9f3498 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -164,7 +164,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')
- log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%d-%m"), embedding_name)
+ log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%m-%d"), embedding_name)
if save_embedding_every > 0:
embedding_dir = os.path.join(log_directory, "embeddings")
diff --git a/modules/ui.py b/modules/ui.py
index 16432151..20dc8c37 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -386,14 +386,22 @@ def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info:
outputs=[seed, dummy_component]
)
+
def update_token_counter(text, steps):
- prompt_schedules = get_learned_conditioning_prompt_schedules([text], steps)
+ try:
+ prompt_schedules = get_learned_conditioning_prompt_schedules([text], steps)
+ except Exception:
+ # a parsing error can happen here during typing, and we don't want to bother the user with
+ # messages related to it in console
+ prompt_schedules = [[[steps, text]]]
+
flat_prompts = reduce(lambda list1, list2: list1+list2, prompt_schedules)
- prompts = [prompt_text for step,prompt_text in flat_prompts]
+ prompts = [prompt_text for step, prompt_text in flat_prompts]
tokens, token_count, max_length = max([model_hijack.tokenize(prompt) for prompt in prompts], key=lambda args: args[1])
style_class = ' class="red"' if (token_count > max_length) else ""
return f"<span {style_class}>{token_count}/{max_length}</span>"
+
def create_toprow(is_img2img):
id_part = "img2img" if is_img2img else "txt2img"
@@ -658,7 +666,7 @@ def create_ui(wrap_gradio_gpu_call):
with gr.TabItem('Batch img2img', id='batch'):
hidden = '<br>Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else ''
- gr.HTML(f"<p class=\"text-gray-500\">Process images in a directory on the same machine where the server is running.{hidden}</p>")
+ gr.HTML(f"<p class=\"text-gray-500\">Process images in a directory on the same machine where the server is running.<br>Use an empty output directory to save pictures normally instead of writing to the output directory.{hidden}</p>")
img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs)
img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs)
generated by cgit v1.2.3 (git 2.25.1) at 2025-05-02 16:03:06 +0000