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-rw-r--r--modules/api/api.py36
-rw-r--r--modules/images.py2
-rw-r--r--modules/launch_utils.py8
-rw-r--r--modules/processing.py45
-rw-r--r--modules/scripts.py2
-rw-r--r--modules/sd_hijack.py2
-rw-r--r--modules/sd_models_xl.py4
-rw-r--r--modules/shared.py1
8 files changed, 68 insertions, 32 deletions
diff --git a/modules/api/api.py b/modules/api/api.py
index 2a4cd8a2..9d73083f 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -333,14 +333,16 @@ class Api:
p.outpath_grids = opts.outdir_txt2img_grids
p.outpath_samples = opts.outdir_txt2img_samples
- shared.state.begin(job="scripts_txt2img")
- if selectable_scripts is not None:
- p.script_args = script_args
- processed = scripts.scripts_txt2img.run(p, *p.script_args) # Need to pass args as list here
- else:
- p.script_args = tuple(script_args) # Need to pass args as tuple here
- processed = process_images(p)
- shared.state.end()
+ try:
+ shared.state.begin(job="scripts_txt2img")
+ if selectable_scripts is not None:
+ p.script_args = script_args
+ processed = scripts.scripts_txt2img.run(p, *p.script_args) # Need to pass args as list here
+ else:
+ p.script_args = tuple(script_args) # Need to pass args as tuple here
+ processed = process_images(p)
+ finally:
+ shared.state.end()
b64images = list(map(encode_pil_to_base64, processed.images)) if send_images else []
@@ -390,14 +392,16 @@ class Api:
p.outpath_grids = opts.outdir_img2img_grids
p.outpath_samples = opts.outdir_img2img_samples
- shared.state.begin(job="scripts_img2img")
- if selectable_scripts is not None:
- p.script_args = script_args
- processed = scripts.scripts_img2img.run(p, *p.script_args) # Need to pass args as list here
- else:
- p.script_args = tuple(script_args) # Need to pass args as tuple here
- processed = process_images(p)
- shared.state.end()
+ try:
+ shared.state.begin(job="scripts_img2img")
+ if selectable_scripts is not None:
+ p.script_args = script_args
+ processed = scripts.scripts_img2img.run(p, *p.script_args) # Need to pass args as list here
+ else:
+ p.script_args = tuple(script_args) # Need to pass args as tuple here
+ processed = process_images(p)
+ finally:
+ shared.state.end()
b64images = list(map(encode_pil_to_base64, processed.images)) if send_images else []
diff --git a/modules/images.py b/modules/images.py
index fb5d2e75..38aa933d 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -363,7 +363,7 @@ class FilenameGenerator:
'styles': lambda self: self.p and sanitize_filename_part(", ".join([style for style in self.p.styles if not style == "None"]) or "None", replace_spaces=False),
'sampler': lambda self: self.p and sanitize_filename_part(self.p.sampler_name, replace_spaces=False),
'model_hash': lambda self: getattr(self.p, "sd_model_hash", shared.sd_model.sd_model_hash),
- 'model_name': lambda self: sanitize_filename_part(shared.sd_model.sd_checkpoint_info.model_name, replace_spaces=False),
+ 'model_name': lambda self: sanitize_filename_part(shared.sd_model.sd_checkpoint_info.name_for_extra, replace_spaces=False),
'date': lambda self: datetime.datetime.now().strftime('%Y-%m-%d'),
'datetime': lambda self, *args: self.datetime(*args), # accepts formats: [datetime], [datetime<Format>], [datetime<Format><Time Zone>]
'job_timestamp': lambda self: getattr(self.p, "job_timestamp", shared.state.job_timestamp),
diff --git a/modules/launch_utils.py b/modules/launch_utils.py
index ea995eda..aaa671ab 100644
--- a/modules/launch_utils.py
+++ b/modules/launch_utils.py
@@ -193,7 +193,7 @@ def run_extension_installer(extension_dir):
try:
env = os.environ.copy()
- env['PYTHONPATH'] = os.path.abspath(".")
+ env['PYTHONPATH'] = f"{os.path.abspath('.')}{os.pathsep}{env['PYTHONPATH']}"
print(run(f'"{python}" "{path_installer}"', errdesc=f"Error running install.py for extension {extension_dir}", custom_env=env))
except Exception as e:
@@ -230,7 +230,7 @@ def run_extensions_installers(settings_file):
re_requirement = re.compile(r"\s*([-_a-zA-Z0-9]+)\s*(?:==\s*([-+_.a-zA-Z0-9]+))?\s*")
-def requrements_met(requirements_file):
+def requirements_met(requirements_file):
"""
Does a simple parse of a requirements.txt file to determine if all rerqirements in it
are already installed. Returns True if so, False if not installed or parsing fails.
@@ -290,7 +290,7 @@ def prepare_environment():
try:
# the existance of this file is a signal to webui.sh/bat that webui needs to be restarted when it stops execution
os.remove(os.path.join(script_path, "tmp", "restart"))
- os.environ.setdefault('SD_WEBUI_RESTARTING ', '1')
+ os.environ.setdefault('SD_WEBUI_RESTARTING', '1')
except OSError:
pass
@@ -351,7 +351,7 @@ def prepare_environment():
if not os.path.isfile(requirements_file):
requirements_file = os.path.join(script_path, requirements_file)
- if not requrements_met(requirements_file):
+ if not requirements_met(requirements_file):
run_pip(f"install -r \"{requirements_file}\"", "requirements")
run_extensions_installers(settings_file=args.ui_settings_file)
diff --git a/modules/processing.py b/modules/processing.py
index 6567b3cf..a74a5302 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -14,7 +14,7 @@ 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, sd_unet
+from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, extra_networks, sd_vae_approx, scripts, sd_samplers_common, sd_unet, errors
from modules.sd_hijack import model_hijack
from modules.shared import opts, cmd_opts, state
import modules.shared as shared
@@ -538,6 +538,40 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
return x
+def decode_latent_batch(model, batch, target_device=None, check_for_nans=False):
+ samples = []
+
+ for i in range(batch.shape[0]):
+ sample = decode_first_stage(model, batch[i:i + 1])[0]
+
+ if check_for_nans:
+ try:
+ devices.test_for_nans(sample, "vae")
+ except devices.NansException as e:
+ if devices.dtype_vae == torch.float32 or not shared.opts.auto_vae_precision:
+ raise e
+
+ errors.print_error_explanation(
+ "A tensor with all NaNs was produced in VAE.\n"
+ "Web UI will now convert VAE into 32-bit float and retry.\n"
+ "To disable this behavior, disable the 'Automaticlly revert VAE to 32-bit floats' setting.\n"
+ "To always start with 32-bit VAE, use --no-half-vae commandline flag."
+ )
+
+ devices.dtype_vae = torch.float32
+ model.first_stage_model.to(devices.dtype_vae)
+ batch = batch.to(devices.dtype_vae)
+
+ sample = decode_first_stage(model, batch[i:i + 1])[0]
+
+ if target_device is not None:
+ sample = sample.to(target_device)
+
+ samples.append(sample)
+
+ return samples
+
+
def decode_first_stage(model, x):
x = model.decode_first_stage(x.to(devices.dtype_vae))
@@ -587,7 +621,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
"Face restoration": (opts.face_restoration_model if p.restore_faces else None),
"Size": f"{p.width}x{p.height}",
"Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),
- "Model": (None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')),
+ "Model": (None if not opts.add_model_name_to_info else shared.sd_model.sd_checkpoint_info.name_for_extra),
"Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]),
"Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),
"Seed resize from": (None if p.seed_resize_from_w <= 0 or p.seed_resize_from_h <= 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
@@ -758,10 +792,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
with devices.without_autocast() if devices.unet_needs_upcast else devices.autocast():
samples_ddim = p.sample(conditioning=p.c, unconditional_conditioning=p.uc, seeds=p.seeds, subseeds=p.subseeds, subseed_strength=p.subseed_strength, prompts=p.prompts)
- x_samples_ddim = [decode_first_stage(p.sd_model, samples_ddim[i:i+1].to(dtype=devices.dtype_vae))[0].cpu() for i in range(samples_ddim.size(0))]
- for x in x_samples_ddim:
- devices.test_for_nans(x, "vae")
-
+ x_samples_ddim = decode_latent_batch(p.sd_model, samples_ddim, target_device=devices.cpu, check_for_nans=True)
x_samples_ddim = torch.stack(x_samples_ddim).float()
x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
@@ -1029,7 +1060,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
image = sd_samplers.sample_to_image(image, index, approximation=0)
info = create_infotext(self, self.all_prompts, self.all_seeds, self.all_subseeds, [], iteration=self.iteration, position_in_batch=index)
- images.save_image(image, self.outpath_samples, "", seeds[index], prompts[index], opts.samples_format, info=info, suffix="-before-highres-fix")
+ images.save_image(image, self.outpath_samples, "", seeds[index], prompts[index], opts.samples_format, info=info, p=self, suffix="-before-highres-fix")
if latent_scale_mode is not None:
for i in range(samples.shape[0]):
diff --git a/modules/scripts.py b/modules/scripts.py
index 7d9dd59f..f34240a0 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -119,7 +119,7 @@ class Script:
def after_extra_networks_activate(self, p, *args, **kwargs):
"""
- Calledafter extra networks activation, before conds calculation
+ Called after extra networks activation, before conds calculation
allow modification of the network after extra networks activation been applied
won't be call if p.disable_extra_networks
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index f5615967..c8fdd4f1 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -243,7 +243,7 @@ class StableDiffusionModelHijack:
ldm.modules.diffusionmodules.openaimodel.UNetModel.forward = sd_unet.UNetModel_forward
def undo_hijack(self, m):
- if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation:
+ if type(m.cond_stage_model) == sd_hijack_xlmr.FrozenXLMREmbedderWithCustomWords:
m.cond_stage_model = m.cond_stage_model.wrapped
elif type(m.cond_stage_model) == sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords:
diff --git a/modules/sd_models_xl.py b/modules/sd_models_xl.py
index 01320c7a..40559208 100644
--- a/modules/sd_models_xl.py
+++ b/modules/sd_models_xl.py
@@ -12,8 +12,8 @@ def get_learned_conditioning(self: sgm.models.diffusion.DiffusionEngine, batch:
for embedder in self.conditioner.embedders:
embedder.ucg_rate = 0.0
- width = getattr(self, 'target_width', 1024)
- height = getattr(self, 'target_height', 1024)
+ width = getattr(batch, 'width', 1024)
+ height = getattr(batch, 'height', 1024)
is_negative_prompt = getattr(batch, 'is_negative_prompt', False)
aesthetic_score = shared.opts.sdxl_refiner_low_aesthetic_score if is_negative_prompt else shared.opts.sdxl_refiner_high_aesthetic_score
diff --git a/modules/shared.py b/modules/shared.py
index 1ce7b49e..aa72c9c8 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -427,6 +427,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
"comma_padding_backtrack": OptionInfo(20, "Prompt word wrap length limit", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1}).info("in tokens - for texts shorter than specified, if they don't fit into 75 token limit, move them to the next 75 token chunk"),
"CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}).link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#clip-skip").info("ignore last layers of CLIP network; 1 ignores none, 2 ignores one layer"),
"upcast_attn": OptionInfo(False, "Upcast cross attention layer to float32"),
+ "auto_vae_precision": OptionInfo(True, "Automaticlly revert VAE to 32-bit floats").info("triggers when a tensor with NaNs is produced in VAE; disabling the option in this case will result in a black square image"),
"randn_source": OptionInfo("GPU", "Random number generator source.", gr.Radio, {"choices": ["GPU", "CPU"]}).info("changes seeds drastically; use CPU to produce the same picture across different videocard vendors"),
}))
generated by cgit v1.2.1 (git 2.18.0) at 2025-05-20 00:34:32 +0000