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-rw-r--r--modules/api/api.py2
-rw-r--r--modules/devices.py18
-rw-r--r--modules/errors.py8
-rw-r--r--modules/generation_parameters_copypaste.py18
-rw-r--r--modules/images.py31
-rw-r--r--modules/img2img.py3
-rw-r--r--modules/processing.py4
-rw-r--r--modules/script_callbacks.py20
-rw-r--r--modules/sd_hijack.py20
-rw-r--r--modules/sd_models.py24
-rw-r--r--modules/sd_samplers_kdiffusion.py50
-rw-r--r--modules/sd_unet.py92
-rw-r--r--modules/shared.py10
-rw-r--r--modules/shared_items.py11
-rw-r--r--modules/ui.py10
-rw-r--r--modules/ui_common.py9
16 files changed, 297 insertions, 33 deletions
diff --git a/modules/api/api.py b/modules/api/api.py
index eee99bbb..6a456861 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -700,4 +700,4 @@ class Api:
def launch(self, server_name, port):
self.app.include_router(self.router)
- uvicorn.run(self.app, host=server_name, port=port)
+ uvicorn.run(self.app, host=server_name, port=port, timeout_keep_alive=0)
diff --git a/modules/devices.py b/modules/devices.py
index d8a34a0f..1ed6ffdc 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -1,5 +1,7 @@
import sys
import contextlib
+from functools import lru_cache
+
import torch
from modules import errors
@@ -154,3 +156,19 @@ def test_for_nans(x, where):
message += " Use --disable-nan-check commandline argument to disable this check."
raise NansException(message)
+
+
+@lru_cache
+def first_time_calculation():
+ """
+ just do any calculation with pytorch layers - the first time this is done it allocaltes about 700MB of memory and
+ spends about 2.7 seconds doing that, at least wih NVidia.
+ """
+
+ x = torch.zeros((1, 1)).to(device, dtype)
+ linear = torch.nn.Linear(1, 1).to(device, dtype)
+ linear(x)
+
+ x = torch.zeros((1, 1, 3, 3)).to(device, dtype)
+ conv2d = torch.nn.Conv2d(1, 1, (3, 3)).to(device, dtype)
+ conv2d(x)
diff --git a/modules/errors.py b/modules/errors.py
index f6b80dbb..da4694f8 100644
--- a/modules/errors.py
+++ b/modules/errors.py
@@ -12,9 +12,13 @@ def print_error_explanation(message):
print('=' * max_len, file=sys.stderr)
-def display(e: Exception, task):
+def display(e: Exception, task, *, full_traceback=False):
print(f"{task or 'error'}: {type(e).__name__}", file=sys.stderr)
- print(traceback.format_exc(), file=sys.stderr)
+ te = traceback.TracebackException.from_exception(e)
+ if full_traceback:
+ # include frames leading up to the try-catch block
+ te.stack = traceback.StackSummary(traceback.extract_stack()[:-2] + te.stack)
+ print(*te.format(), sep="", file=sys.stderr)
message = str(e)
if "copying a param with shape torch.Size([640, 1024]) from checkpoint, the shape in current model is torch.Size([640, 768])" in message:
diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index d5f0a49b..071bd9ea 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -35,7 +35,7 @@ def reset():
def quote(text):
- if ',' not in str(text) and '\n' not in str(text):
+ if ',' not in str(text) and '\n' not in str(text) and ':' not in str(text):
return text
return json.dumps(text, ensure_ascii=False)
@@ -306,6 +306,18 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
if "RNG" not in res:
res["RNG"] = "GPU"
+ if "Schedule type" not in res:
+ res["Schedule type"] = "Automatic"
+
+ if "Schedule max sigma" not in res:
+ res["Schedule max sigma"] = 0
+
+ if "Schedule min sigma" not in res:
+ res["Schedule min sigma"] = 0
+
+ if "Schedule rho" not in res:
+ res["Schedule rho"] = 0
+
return res
@@ -318,6 +330,10 @@ infotext_to_setting_name_mapping = [
('Conditional mask weight', 'inpainting_mask_weight'),
('Model hash', 'sd_model_checkpoint'),
('ENSD', 'eta_noise_seed_delta'),
+ ('Schedule type', 'k_sched_type'),
+ ('Schedule max sigma', 'sigma_max'),
+ ('Schedule min sigma', 'sigma_min'),
+ ('Schedule rho', 'rho'),
('Noise multiplier', 'initial_noise_multiplier'),
('Eta', 'eta_ancestral'),
('Eta DDIM', 'eta_ddim'),
diff --git a/modules/images.py b/modules/images.py
index 4e8cd993..e21e554c 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -21,6 +21,8 @@ from modules import sd_samplers, shared, script_callbacks, errors
from modules.paths_internal import roboto_ttf_file
from modules.shared import opts
+import modules.sd_vae as sd_vae
+
LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)
@@ -336,8 +338,20 @@ def sanitize_filename_part(text, replace_spaces=True):
class FilenameGenerator:
+ def get_vae_filename(self): #get the name of the VAE file.
+ if sd_vae.loaded_vae_file is None:
+ return "NoneType"
+ file_name = os.path.basename(sd_vae.loaded_vae_file)
+ split_file_name = file_name.split('.')
+ if len(split_file_name) > 1 and split_file_name[0] == '':
+ return split_file_name[1] # if the first character of the filename is "." then [1] is obtained.
+ else:
+ return split_file_name[0]
+
replacements = {
'seed': lambda self: self.seed if self.seed is not None else '',
+ 'seed_first': lambda self: self.seed if self.p.batch_size == 1 else self.p.all_seeds[0],
+ 'seed_last': lambda self: NOTHING_AND_SKIP_PREVIOUS_TEXT if self.p.batch_size == 1 else self.p.all_seeds[-1],
'steps': lambda self: self.p and self.p.steps,
'cfg': lambda self: self.p and self.p.cfg_scale,
'width': lambda self: self.image.width,
@@ -354,19 +368,23 @@ class FilenameGenerator:
'prompt_no_styles': lambda self: self.prompt_no_style(),
'prompt_spaces': lambda self: sanitize_filename_part(self.prompt, replace_spaces=False),
'prompt_words': lambda self: self.prompt_words(),
- 'batch_number': lambda self: NOTHING_AND_SKIP_PREVIOUS_TEXT if self.p.batch_size == 1 else self.p.batch_index + 1,
- 'generation_number': lambda self: NOTHING_AND_SKIP_PREVIOUS_TEXT if self.p.n_iter == 1 and self.p.batch_size == 1 else self.p.iteration * self.p.batch_size + self.p.batch_index + 1,
+ 'batch_number': lambda self: NOTHING_AND_SKIP_PREVIOUS_TEXT if self.p.batch_size == 1 or self.zip else self.p.batch_index + 1,
+ 'batch_size': lambda self: self.p.batch_size,
+ 'generation_number': lambda self: NOTHING_AND_SKIP_PREVIOUS_TEXT if (self.p.n_iter == 1 and self.p.batch_size == 1) or self.zip else self.p.iteration * self.p.batch_size + self.p.batch_index + 1,
'hasprompt': lambda self, *args: self.hasprompt(*args), # accepts formats:[hasprompt<prompt1|default><prompt2>..]
'clip_skip': lambda self: opts.data["CLIP_stop_at_last_layers"],
'denoising': lambda self: self.p.denoising_strength if self.p and self.p.denoising_strength else NOTHING_AND_SKIP_PREVIOUS_TEXT,
+ 'vae_filename': lambda self: self.get_vae_filename(),
+
}
default_time_format = '%Y%m%d%H%M%S'
- def __init__(self, p, seed, prompt, image):
+ def __init__(self, p, seed, prompt, image, zip=False):
self.p = p
self.seed = seed
self.prompt = prompt
self.image = image
+ self.zip = zip
def hasprompt(self, *args):
lower = self.prompt.lower()
@@ -665,9 +683,10 @@ def read_info_from_image(image):
items['exif comment'] = exif_comment
geninfo = exif_comment
- for field in ['jfif', 'jfif_version', 'jfif_unit', 'jfif_density', 'dpi', 'exif',
- 'loop', 'background', 'timestamp', 'duration']:
- items.pop(field, None)
+ for field in ['jfif', 'jfif_version', 'jfif_unit', 'jfif_density', 'dpi', 'exif',
+ 'loop', 'background', 'timestamp', 'duration', 'progressive', 'progression',
+ 'icc_profile', 'chromaticity']:
+ items.pop(field, None)
if items.get("Software", None) == "NovelAI":
try:
diff --git a/modules/img2img.py b/modules/img2img.py
index d704bf90..4c12c2c5 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -92,7 +92,8 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
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')
+ mask = mask.convert('L').point(lambda x: 255 if x > 128 else 0, mode='1')
+ mask = ImageChops.lighter(alpha_mask, mask).convert('L')
image = image.convert("RGB")
elif mode == 3: # inpaint sketch
image = inpaint_color_sketch
diff --git a/modules/processing.py b/modules/processing.py
index 29a3743f..b75f2515 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -13,7 +13,7 @@ from skimage import exposure
from typing import Any, Dict, List
import modules.sd_hijack
-from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, extra_networks, sd_vae_approx, scripts, sd_samplers_common
+from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, extra_networks, sd_vae_approx, scripts, sd_samplers_common, sd_unet
from modules.sd_hijack import model_hijack
from modules.shared import opts, cmd_opts, state
import modules.shared as shared
@@ -674,6 +674,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
if shared.opts.live_previews_enable and opts.show_progress_type == "Approx NN":
sd_vae_approx.model()
+ sd_unet.apply_unet()
+
if state.job_count == -1:
state.job_count = p.n_iter
diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py
index 40f388a5..d2728e12 100644
--- a/modules/script_callbacks.py
+++ b/modules/script_callbacks.py
@@ -111,6 +111,7 @@ callback_map = dict(
callbacks_before_ui=[],
callbacks_on_reload=[],
callbacks_list_optimizers=[],
+ callbacks_list_unets=[],
)
@@ -271,6 +272,18 @@ def list_optimizers_callback():
return res
+def list_unets_callback():
+ res = []
+
+ for c in callback_map['callbacks_list_unets']:
+ try:
+ c.callback(res)
+ except Exception:
+ report_exception(c, 'list_unets')
+
+ return res
+
+
def add_callback(callbacks, fun):
stack = [x for x in inspect.stack() if x.filename != __file__]
filename = stack[0].filename if len(stack) > 0 else 'unknown file'
@@ -430,3 +443,10 @@ def on_list_optimizers(callback):
to it."""
add_callback(callback_map['callbacks_list_optimizers'], callback)
+
+
+def on_list_unets(callback):
+ """register a function to be called when UI is making a list of alternative options for unet.
+ The function will be called with one argument, a list, and shall add objects of type modules.sd_unet.SdUnetOption to it."""
+
+ add_callback(callback_map['callbacks_list_unets'], callback)
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index f93df0a6..487dfd60 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -3,7 +3,7 @@ from torch.nn.functional import silu
from types import MethodType
import modules.textual_inversion.textual_inversion
-from modules import devices, sd_hijack_optimizations, shared, script_callbacks, errors
+from modules import devices, sd_hijack_optimizations, shared, script_callbacks, errors, sd_unet
from modules.hypernetworks import hypernetwork
from modules.shared import cmd_opts
from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet, sd_hijack_xlmr, xlmr
@@ -43,7 +43,7 @@ def list_optimizers():
optimizers.extend(new_optimizers)
-def apply_optimizations():
+def apply_optimizations(option=None):
global current_optimizer
undo_optimizations()
@@ -60,7 +60,7 @@ def apply_optimizations():
current_optimizer.undo()
current_optimizer = None
- selection = shared.opts.cross_attention_optimization
+ selection = option or shared.opts.cross_attention_optimization
if selection == "Automatic" and len(optimizers) > 0:
matching_optimizer = next(iter([x for x in optimizers if x.cmd_opt and getattr(shared.cmd_opts, x.cmd_opt, False)]), optimizers[0])
else:
@@ -72,12 +72,13 @@ def apply_optimizations():
matching_optimizer = optimizers[0]
if matching_optimizer is not None:
- print(f"Applying optimization: {matching_optimizer.name}... ", end='')
+ print(f"Applying attention optimization: {matching_optimizer.name}... ", end='')
matching_optimizer.apply()
print("done.")
current_optimizer = matching_optimizer
return current_optimizer.name
else:
+ print("Disabling attention optimization")
return ''
@@ -155,9 +156,9 @@ class StableDiffusionModelHijack:
def __init__(self):
self.embedding_db.add_embedding_dir(cmd_opts.embeddings_dir)
- def apply_optimizations(self):
+ def apply_optimizations(self, option=None):
try:
- self.optimization_method = apply_optimizations()
+ self.optimization_method = apply_optimizations(option)
except Exception as e:
errors.display(e, "applying cross attention optimization")
undo_optimizations()
@@ -194,6 +195,11 @@ class StableDiffusionModelHijack:
self.layers = flatten(m)
+ if not hasattr(ldm.modules.diffusionmodules.openaimodel, 'copy_of_UNetModel_forward_for_webui'):
+ ldm.modules.diffusionmodules.openaimodel.copy_of_UNetModel_forward_for_webui = ldm.modules.diffusionmodules.openaimodel.UNetModel.forward
+
+ ldm.modules.diffusionmodules.openaimodel.UNetModel.forward = sd_unet.UNetModel_forward
+
def undo_hijack(self, m):
if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation:
m.cond_stage_model = m.cond_stage_model.wrapped
@@ -215,6 +221,8 @@ class StableDiffusionModelHijack:
self.layers = None
self.clip = None
+ ldm.modules.diffusionmodules.openaimodel.UNetModel.forward = ldm.modules.diffusionmodules.openaimodel.copy_of_UNetModel_forward_for_webui
+
def apply_circular(self, enable):
if self.circular_enabled == enable:
return
diff --git a/modules/sd_models.py b/modules/sd_models.py
index b1afbaa7..232eb9c4 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -14,7 +14,7 @@ import ldm.modules.midas as midas
from ldm.util import instantiate_from_config
-from modules import paths, shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config
+from modules import paths, shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config, sd_unet
from modules.sd_hijack_inpainting import do_inpainting_hijack
from modules.timer import Timer
import tomesd
@@ -164,6 +164,7 @@ def model_hash(filename):
def select_checkpoint():
+ """Raises `FileNotFoundError` if no checkpoints are found."""
model_checkpoint = shared.opts.sd_model_checkpoint
checkpoint_info = checkpoint_alisases.get(model_checkpoint, None)
@@ -171,14 +172,14 @@ def select_checkpoint():
return checkpoint_info
if len(checkpoints_list) == 0:
- print("No checkpoints found. When searching for checkpoints, looked at:", file=sys.stderr)
+ error_message = "No checkpoints found. When searching for checkpoints, looked at:"
if shared.cmd_opts.ckpt is not None:
- print(f" - file {os.path.abspath(shared.cmd_opts.ckpt)}", file=sys.stderr)
- print(f" - directory {model_path}", file=sys.stderr)
+ error_message += f"\n - file {os.path.abspath(shared.cmd_opts.ckpt)}"
+ error_message += f"\n - directory {model_path}"
if shared.cmd_opts.ckpt_dir is not None:
- print(f" - directory {os.path.abspath(shared.cmd_opts.ckpt_dir)}", file=sys.stderr)
- print("Can't run without a checkpoint. Find and place a .ckpt or .safetensors file into any of those locations. The program will exit.", file=sys.stderr)
- exit(1)
+ error_message += f"\n - directory {os.path.abspath(shared.cmd_opts.ckpt_dir)}"
+ error_message += "Can't run without a checkpoint. Find and place a .ckpt or .safetensors file into any of those locations."
+ raise FileNotFoundError(error_message)
checkpoint_info = next(iter(checkpoints_list.values()))
if model_checkpoint is not None:
@@ -423,7 +424,7 @@ class SdModelData:
try:
load_model()
except Exception as e:
- errors.display(e, "loading stable diffusion model")
+ errors.display(e, "loading stable diffusion model", full_traceback=True)
print("", file=sys.stderr)
print("Stable diffusion model failed to load", file=sys.stderr)
self.sd_model = None
@@ -508,6 +509,11 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None):
timer.record("scripts callbacks")
+ with devices.autocast(), torch.no_grad():
+ sd_model.cond_stage_model_empty_prompt = sd_model.cond_stage_model([""])
+
+ timer.record("calculate empty prompt")
+
print(f"Model loaded in {timer.summary()}.")
return sd_model
@@ -527,6 +533,8 @@ def reload_model_weights(sd_model=None, info=None):
if sd_model.sd_model_checkpoint == checkpoint_info.filename:
return
+ sd_unet.apply_unet("None")
+
if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
lowvram.send_everything_to_cpu()
else:
diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py
index 59982fc9..e9ba2c61 100644
--- a/modules/sd_samplers_kdiffusion.py
+++ b/modules/sd_samplers_kdiffusion.py
@@ -44,6 +44,14 @@ sampler_extra_params = {
'sample_dpm_2': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
}
+k_diffusion_samplers_map = {x.name: x for x in samplers_data_k_diffusion}
+k_diffusion_scheduler = {
+ 'Automatic': None,
+ 'karras': k_diffusion.sampling.get_sigmas_karras,
+ 'exponential': k_diffusion.sampling.get_sigmas_exponential,
+ 'polyexponential': k_diffusion.sampling.get_sigmas_polyexponential
+}
+
class CFGDenoiser(torch.nn.Module):
"""
@@ -125,6 +133,16 @@ class CFGDenoiser(torch.nn.Module):
x_in = x_in[:-batch_size]
sigma_in = sigma_in[:-batch_size]
+ # TODO add infotext entry
+ if shared.opts.pad_cond_uncond and tensor.shape[1] != uncond.shape[1]:
+ empty = shared.sd_model.cond_stage_model_empty_prompt
+ num_repeats = (tensor.shape[1] - uncond.shape[1]) // empty.shape[1]
+
+ if num_repeats < 0:
+ tensor = torch.cat([tensor, empty.repeat((tensor.shape[0], -num_repeats, 1))], axis=1)
+ elif num_repeats > 0:
+ uncond = torch.cat([uncond, empty.repeat((uncond.shape[0], num_repeats, 1))], axis=1)
+
if tensor.shape[1] == uncond.shape[1] or skip_uncond:
if is_edit_model:
cond_in = torch.cat([tensor, uncond, uncond])
@@ -255,6 +273,13 @@ class KDiffusionSampler:
try:
return func()
+ except RecursionError:
+ print(
+ 'Encountered RecursionError during sampling, returning last latent. '
+ 'rho >5 with a polyexponential scheduler may cause this error. '
+ 'You should try to use a smaller rho value instead.'
+ )
+ return self.last_latent
except sd_samplers_common.InterruptedException:
return self.last_latent
@@ -294,6 +319,31 @@ class KDiffusionSampler:
if p.sampler_noise_scheduler_override:
sigmas = p.sampler_noise_scheduler_override(steps)
+ elif opts.k_sched_type != "Automatic":
+ m_sigma_min, m_sigma_max = (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
+ sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (m_sigma_min, m_sigma_max)
+ sigmas_kwargs = {
+ 'sigma_min': sigma_min,
+ 'sigma_max': sigma_max,
+ }
+
+ sigmas_func = k_diffusion_scheduler[opts.k_sched_type]
+ p.extra_generation_params["Schedule type"] = opts.k_sched_type
+
+ if opts.sigma_min != m_sigma_min and opts.sigma_min != 0:
+ sigmas_kwargs['sigma_min'] = opts.sigma_min
+ p.extra_generation_params["Schedule min sigma"] = opts.sigma_min
+ if opts.sigma_max != m_sigma_max and opts.sigma_max != 0:
+ sigmas_kwargs['sigma_max'] = opts.sigma_max
+ p.extra_generation_params["Schedule max sigma"] = opts.sigma_max
+
+ default_rho = 1. if opts.k_sched_type == "polyexponential" else 7.
+
+ if opts.k_sched_type != 'exponential' and opts.rho != 0 and opts.rho != default_rho:
+ sigmas_kwargs['rho'] = opts.rho
+ p.extra_generation_params["Schedule rho"] = opts.rho
+
+ sigmas = sigmas_func(n=steps, **sigmas_kwargs, device=shared.device)
elif self.config is not None and self.config.options.get('scheduler', None) == 'karras':
sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
diff --git a/modules/sd_unet.py b/modules/sd_unet.py
new file mode 100644
index 00000000..6d708ad2
--- /dev/null
+++ b/modules/sd_unet.py
@@ -0,0 +1,92 @@
+import torch.nn
+import ldm.modules.diffusionmodules.openaimodel
+
+from modules import script_callbacks, shared, devices
+
+unet_options = []
+current_unet_option = None
+current_unet = None
+
+
+def list_unets():
+ new_unets = script_callbacks.list_unets_callback()
+
+ unet_options.clear()
+ unet_options.extend(new_unets)
+
+
+def get_unet_option(option=None):
+ option = option or shared.opts.sd_unet
+
+ if option == "None":
+ return None
+
+ if option == "Automatic":
+ name = shared.sd_model.sd_checkpoint_info.model_name
+
+ options = [x for x in unet_options if x.model_name == name]
+
+ option = options[0].label if options else "None"
+
+ return next(iter([x for x in unet_options if x.label == option]), None)
+
+
+def apply_unet(option=None):
+ global current_unet_option
+ global current_unet
+
+ new_option = get_unet_option(option)
+ if new_option == current_unet_option:
+ return
+
+ if current_unet is not None:
+ print(f"Dectivating unet: {current_unet.option.label}")
+ current_unet.deactivate()
+
+ current_unet_option = new_option
+ if current_unet_option is None:
+ current_unet = None
+
+ if not (shared.cmd_opts.lowvram or shared.cmd_opts.medvram):
+ shared.sd_model.model.diffusion_model.to(devices.device)
+
+ return
+
+ shared.sd_model.model.diffusion_model.to(devices.cpu)
+ devices.torch_gc()
+
+ current_unet = current_unet_option.create_unet()
+ current_unet.option = current_unet_option
+ print(f"Activating unet: {current_unet.option.label}")
+ current_unet.activate()
+
+
+class SdUnetOption:
+ model_name = None
+ """name of related checkpoint - this option will be selected automatically for unet if the name of checkpoint matches this"""
+
+ label = None
+ """name of the unet in UI"""
+
+ def create_unet(self):
+ """returns SdUnet object to be used as a Unet instead of built-in unet when making pictures"""
+ raise NotImplementedError()
+
+
+class SdUnet(torch.nn.Module):
+ def forward(self, x, timesteps, context, *args, **kwargs):
+ raise NotImplementedError()
+
+ def activate(self):
+ pass
+
+ def deactivate(self):
+ pass
+
+
+def UNetModel_forward(self, x, timesteps=None, context=None, *args, **kwargs):
+ if current_unet is not None:
+ return current_unet.forward(x, timesteps, context, *args, **kwargs)
+
+ return ldm.modules.diffusionmodules.openaimodel.copy_of_UNetModel_forward_for_webui(self, x, timesteps, context, *args, **kwargs)
+
diff --git a/modules/shared.py b/modules/shared.py
index 3099d1d2..f1d8c24b 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -314,6 +314,7 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
"grid_extended_filename": OptionInfo(False, "Add extended info (seed, prompt) to filename when saving grid"),
"grid_only_if_multiple": OptionInfo(True, "Do not save grids consisting of one picture"),
"grid_prevent_empty_spots": OptionInfo(False, "Prevent empty spots in grid (when set to autodetect)"),
+ "grid_zip_filename_pattern": OptionInfo("", "Archive filename pattern", component_args=hide_dirs).link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Images-Filename-Name-and-Subdirectory"),
"n_rows": OptionInfo(-1, "Grid row count; use -1 for autodetect and 0 for it to be same as batch size", gr.Slider, {"minimum": -1, "maximum": 16, "step": 1}),
"enable_pnginfo": OptionInfo(True, "Save text information about generation parameters as chunks to png files"),
@@ -403,6 +404,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
"sd_vae_checkpoint_cache": OptionInfo(0, "VAE Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
"sd_vae": OptionInfo("Automatic", "SD VAE", gr.Dropdown, lambda: {"choices": shared_items.sd_vae_items()}, refresh=shared_items.refresh_vae_list).info("choose VAE model: Automatic = use one with same filename as checkpoint; None = use VAE from checkpoint"),
"sd_vae_as_default": OptionInfo(True, "Ignore selected VAE for stable diffusion checkpoints that have their own .vae.pt next to them"),
+ "sd_unet": OptionInfo("Automatic", "SD Unet", gr.Dropdown, lambda: {"choices": shared_items.sd_unet_items()}, refresh=shared_items.refresh_unet_list).info("choose Unet model: Automatic = use one with same filename as checkpoint; None = use Unet from checkpoint"),
"inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
"initial_noise_multiplier": OptionInfo(1.0, "Noise multiplier for img2img", gr.Slider, {"minimum": 0.5, "maximum": 1.5, "step": 0.01}),
"img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
@@ -419,10 +421,11 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
options_templates.update(options_section(('optimizations', "Optimizations"), {
"cross_attention_optimization": OptionInfo("Automatic", "Cross attention optimization", gr.Dropdown, lambda: {"choices": shared_items.cross_attention_optimizations()}),
- "s_min_uncond": OptionInfo(0, "Negative Guidance minimum sigma", gr.Slider, {"minimum": 0.0, "maximum": 4.0, "step": 0.01}).link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9177").info("skip negative prompt for some steps when the image is almost ready; 0=disable, higher=faster"),
+ "s_min_uncond": OptionInfo(0.0, "Negative Guidance minimum sigma", gr.Slider, {"minimum": 0.0, "maximum": 4.0, "step": 0.01}).link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9177").info("skip negative prompt for some steps when the image is almost ready; 0=disable, higher=faster"),
"token_merging_ratio": OptionInfo(0.0, "Token merging ratio", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}).link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9256").info("0=disable, higher=faster"),
"token_merging_ratio_img2img": OptionInfo(0.0, "Token merging ratio for img2img", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}).info("only applies if non-zero and overrides above"),
"token_merging_ratio_hr": OptionInfo(0.0, "Token merging ratio for high-res pass", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}).info("only applies if non-zero and overrides above"),
+ "pad_cond_uncond": OptionInfo(False, "Pad prompt/negative prompt to be same length").info("improves performance when prompt and negative prompt have different lengths; changes seeds"),
}))
options_templates.update(options_section(('compatibility', "Compatibility"), {
@@ -487,6 +490,7 @@ options_templates.update(options_section(('ui', "User interface"), {
"ui_reorder": OptionInfo(", ".join(ui_reorder_categories), "txt2img/img2img UI item order").needs_restart(),
"hires_fix_show_sampler": OptionInfo(False, "Hires fix: show hires sampler selection").needs_restart(),
"hires_fix_show_prompts": OptionInfo(False, "Hires fix: show hires prompt and negative prompt").needs_restart(),
+ "disable_token_counters": OptionInfo(False, "Disable prompt token counters").needs_restart(),
}))
options_templates.update(options_section(('infotext', "Infotext"), {
@@ -515,6 +519,10 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
's_tmin': OptionInfo(0.0, "sigma tmin", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+ 'k_sched_type': OptionInfo("Automatic", "scheduler type", gr.Dropdown, {"choices": ["Automatic", "karras", "exponential", "polyexponential"]}).info("lets you override the noise schedule for k-diffusion samplers; choosing Automatic disables the three parameters below"),
+ 'sigma_min': OptionInfo(0.0, "sigma min", gr.Number).info("0 = default (~0.03); minimum noise strength for k-diffusion noise scheduler"),
+ 'sigma_max': OptionInfo(0.0, "sigma max", gr.Number).info("0 = default (~14.6); maximum noise strength for k-diffusion noise schedule"),
+ 'rho': OptionInfo(0.0, "rho", gr.Number).info("0 = default (7 for karras, 1 for polyexponential); higher values result in a more steep noise schedule (decreases faster)"),
'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}).info("ENSD; does not improve anything, just produces different results for ancestral samplers - only useful for reproducing images"),
'always_discard_next_to_last_sigma': OptionInfo(False, "Always discard next-to-last sigma").link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/6044"),
'uni_pc_variant': OptionInfo("bh1", "UniPC variant", gr.Radio, {"choices": ["bh1", "bh2", "vary_coeff"]}),
diff --git a/modules/shared_items.py b/modules/shared_items.py
index 2a8713c8..7f306a06 100644
--- a/modules/shared_items.py
+++ b/modules/shared_items.py
@@ -29,3 +29,14 @@ def cross_attention_optimizations():
return ["Automatic"] + [x.title() for x in modules.sd_hijack.optimizers] + ["None"]
+def sd_unet_items():
+ import modules.sd_unet
+
+ return ["Automatic"] + [x.label for x in modules.sd_unet.unet_options] + ["None"]
+
+
+def refresh_unet_list():
+ import modules.sd_unet
+
+ modules.sd_unet.list_unets()
+
diff --git a/modules/ui.py b/modules/ui.py
index e62182da..6189ceeb 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -505,10 +505,10 @@ def create_ui():
with FormRow(elem_id="txt2img_hires_fix_row4", variant="compact", visible=opts.hires_fix_show_prompts) as hr_prompts_container:
with gr.Column(scale=80):
with gr.Row():
- hr_prompt = gr.Textbox(label="Prompt", elem_id="hires_prompt", show_label=False, lines=3, placeholder="Prompt for hires fix pass.\nLeave empty to use the same prompt as in first pass.", elem_classes=["prompt"])
+ hr_prompt = gr.Textbox(label="Hires prompt", elem_id="hires_prompt", show_label=False, lines=3, placeholder="Prompt for hires fix pass.\nLeave empty to use the same prompt as in first pass.", elem_classes=["prompt"])
with gr.Column(scale=80):
with gr.Row():
- hr_negative_prompt = gr.Textbox(label="Negative prompt", elem_id="hires_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt for hires fix pass.\nLeave empty to use the same negative prompt as in first pass.", elem_classes=["prompt"])
+ hr_negative_prompt = gr.Textbox(label="Hires negative prompt", elem_id="hires_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt for hires fix pass.\nLeave empty to use the same negative prompt as in first pass.", elem_classes=["prompt"])
elif category == "batch":
if not opts.dimensions_and_batch_together:
@@ -616,7 +616,8 @@ def create_ui():
outputs=[
txt2img_prompt,
txt_prompt_img
- ]
+ ],
+ show_progress=False,
)
enable_hr.change(
@@ -902,7 +903,8 @@ def create_ui():
outputs=[
img2img_prompt,
img2img_prompt_img
- ]
+ ],
+ show_progress=False,
)
img2img_args = dict(
diff --git a/modules/ui_common.py b/modules/ui_common.py
index 27ab3ebb..5a9204a4 100644
--- a/modules/ui_common.py
+++ b/modules/ui_common.py
@@ -50,9 +50,10 @@ def save_files(js_data, images, do_make_zip, index):
save_to_dirs = shared.opts.use_save_to_dirs_for_ui
extension: str = shared.opts.samples_format
start_index = 0
+ only_one = False
if index > -1 and shared.opts.save_selected_only and (index >= data["index_of_first_image"]): # ensures we are looking at a specific non-grid picture, and we have save_selected_only
-
+ only_one = True
images = [images[index]]
start_index = index
@@ -70,6 +71,7 @@ def save_files(js_data, images, do_make_zip, index):
is_grid = image_index < p.index_of_first_image
i = 0 if is_grid else (image_index - p.index_of_first_image)
+ p.batch_index = image_index-1
fullfn, txt_fullfn = modules.images.save_image(image, path, "", seed=p.all_seeds[i], prompt=p.all_prompts[i], extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs)
filename = os.path.relpath(fullfn, path)
@@ -83,7 +85,10 @@ def save_files(js_data, images, do_make_zip, index):
# Make Zip
if do_make_zip:
- zip_filepath = os.path.join(path, "images.zip")
+ zip_fileseed = p.all_seeds[index-1] if only_one else p.all_seeds[0]
+ namegen = modules.images.FilenameGenerator(p, zip_fileseed, p.all_prompts[0], image, True)
+ zip_filename = namegen.apply(shared.opts.grid_zip_filename_pattern or "[datetime]_[[model_name]]_[seed]-[seed_last]")
+ zip_filepath = os.path.join(path, f"{zip_filename}.zip")
from zipfile import ZipFile
with ZipFile(zip_filepath, "w") as zip_file:
generated by cgit v1.2.1 (git 2.18.0) at 2024-09-21 00:17:31 +0000