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Merge branch 'master' of https://github.com/vladmandic/automatic

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RedCore
2023-04-24 18:48:45 +03:00
parent 4ed4f99d63 2825ad11a0
commit 8fbf2ab551
15 changed files with 128 additions and 90 deletions
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TODO.md
+8 -12
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@@ -5,10 +5,9 @@
Stuff to be fixed...
- ClipSkip not updated on read gen info
- Usage of `sd_vae` in quick settings
- Run VAE with hires at 1280
- Make TensorFlow optional
- Transformers version
- Move Restart Server from WebUI to Launch and reload modules
## Features
@@ -16,7 +15,6 @@ Stuff to be added...
- Add Gradio theme maker
- Create new GitHub hooks/actions for CI/CD
- Move Restart Server from WebUI to Launch and reload modules
- Redo Extensions tab: see <https://vladmandic.github.io/sd-extension-manager/pages/extensions.html>
- Stream-load models as option for slow storage
- Autodetect nVidia and AMD: `nvidia-smi` vs `rocm-smi`
@@ -58,11 +56,9 @@ Tech that can be integrated as part of the core workflow...
### Pending Code Updates
- fix VAE dtype
should fix most issues with NaN or black images
- add built-in Gradio themes
- fix setup race conditions
- reduce requirements
- more AMD specific work
- initial work on Apple platform support
- additional PR merges
- use samples format for live preview
- identify race condition where generate locks up while fetching preview
- use **Approx NN** for live preview
- create default `styles.csv`
- fix setup not installing `tensorflow` dependencies
- update default git flags to reduce number of warnings
extensions-builtin/SwinIR/scripts/swinir_model.py
+1 -1
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@@ -89,7 +89,7 @@ class UpscalerSwinIR(Upscaler):
with progress.open(filename, 'rb', description=f'Loading weights: [cyan]{filename}', auto_refresh=True) as f:
pretrained_model = torch.load(filename)
if params is not None:
if params is not None and params in pretrained_model:
model.load_state_dict(pretrained_model[params], strict=True)
else:
model.load_state_dict(pretrained_model, strict=True)
modules/cmd_args.py
+2
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@@ -17,6 +17,7 @@ parser.add_argument("--lowram", action='store_true', help="Load checkpoint weigh
parser.add_argument("--ckpt", type=str, default=sd_model_file, help="Path to checkpoint of stable diffusion model to load immediately",)
parser.add_argument('--vae', type=str, help='Path to checkpoint of stable diffusion VAE model to load immediately', default=None)
parser.add_argument("--data-dir", type=str, default=os.path.dirname(os.path.dirname(os.path.realpath(__file__))), help="Base path where all user data is stored")
parser.add_argument("--models-dir", type=str, default="models", help="Nase path where all models are stored",)
parser.add_argument("--allow-code", action='store_true', help="Allow custom script execution")
parser.add_argument("--share", action='store_true', help="Enable to make the UI accessible through Gradio site")
@@ -43,6 +44,7 @@ parser.add_argument("--no-hashing", action='store_true', help="Disable sha256 ha
parser.add_argument("--no-download-sd-model", action='store_true', help="Disable download of default model even if no model is found", default=False)
parser.add_argument("--profile", action='store_true', help="Run profiler, default: %(default)s")
parser.add_argument("--disable-queue", action='store_true', help="Disable Gradio queues and force use of HTTP instead of WebSockets, default: %(default)s")
parser.add_argument("--rollback-vae", action='store_true', help="trying to roll back vae when produced nan image, need to enable nan check", default=False)
parser.add_argument("--token-merging", action='store_true', help="Provides speed and memory improvements by merging redundant tokens. This has a more pronounced effect on higher resolutions.", default=False)
modules/generation_parameters_copypaste.py
+4 -2
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@@ -3,10 +3,10 @@ import io
import os
import re
from PIL import Image
import gradio as gr
from modules.paths import data_path
from modules import shared, ui_tempdir, script_callbacks
from PIL import Image
re_param_code = r'\s*([\w ]+):\s*("(?:\\"[^,]|\\"|\\|[^\"])+"|[^,]*)(?:,|$)'
re_param = re.compile(re_param_code)
@@ -251,7 +251,7 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
lines.append(lastline)
lastline = ''
for i, line in enumerate(lines):
for _i, line in enumerate(lines):
line = line.strip()
if line.startswith("Negative prompt:"):
done_with_prompt = True
@@ -382,6 +382,8 @@ def connect_paste(button, paste_fields, input_comp, override_settings_component,
if os.path.exists(filename):
with open(filename, "r", encoding="utf8") as file:
prompt = file.read()
else:
prompt = ''
params = parse_generation_parameters(prompt)
script_callbacks.infotext_pasted_callback(prompt, params)
modules/paths_internal.py
-1
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@@ -15,7 +15,6 @@ parser_pre.add_argument("--data-dir", type=str, default=os.path.dirname(os.path.
parser_pre.add_argument("--models-dir", type=str, default="models", help="base path where all models are stored",)
cmd_opts_pre = parser_pre.parse_known_args()[0]
data_path = cmd_opts_pre.data_dir
models_path = os.path.join(data_path, cmd_opts_pre.models_dir)
extensions_dir = os.path.join(data_path, "extensions")
extensions_builtin_dir = os.path.join(script_path, "extensions-builtin")
modules/processing.py
+15 -4
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@@ -442,9 +442,8 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
def decode_first_stage(model, x):
with devices.autocast(disable=x.dtype == devices.dtype_vae):
with devices.autocast(disable = x.dtype==devices.dtype_vae):
x = model.decode_first_stage(x)
return x
@@ -690,8 +689,20 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, prompts=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")
try:
for x in x_samples_ddim:
devices.test_for_nans(x, "vae")
except devices.NansException as e:
if not shared.cmd_opts.no_half and not shared.cmd_opts.no_half_vae and shared.cmd_opts.rollback_vae:
print('\nA tensor with all NaNs was produced in VAE, try converting to bf16.')
devices.dtype_vae = torch.bfloat16
vae_file, vae_source = sd_vae.resolve_vae(p.sd_model.sd_model_checkpoint)
sd_vae.load_vae(p.sd_model, vae_file, vae_source)
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")
else:
raise e
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)
modules/progress.py
+7 -22
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@@ -1,11 +1,7 @@
import base64
import io
import time
from pydantic import BaseModel, Field
from modules.shared import opts
from pydantic import BaseModel, Field # pylint: disable=no-name-in-module
import modules.shared as shared
@@ -15,18 +11,15 @@ finished_tasks = []
def start_task(id_task):
global current_task
global current_task # pylint: disable=global-statement
current_task = id_task
pending_tasks.pop(id_task, None)
def finish_task(id_task):
global current_task
global current_task # pylint: disable=global-statement
if current_task == id_task:
current_task = None
finished_tasks.append(id_task)
if len(finished_tasks) > 16:
finished_tasks.pop(0)
@@ -60,39 +53,31 @@ def progressapi(req: ProgressRequest):
active = req.id_task == current_task
queued = req.id_task in pending_tasks
completed = req.id_task in finished_tasks
if not active:
return ProgressResponse(active=active, queued=queued, completed=completed, id_live_preview=-1, textinfo="In queue..." if queued else "Waiting...")
progress = 0
job_count, job_no = shared.state.job_count, shared.state.job_no
sampling_steps, sampling_step = shared.state.sampling_steps, shared.state.sampling_step
if job_count > 0:
progress += job_no / job_count
if sampling_steps > 0 and job_count > 0:
progress += 1 / job_count * sampling_step / sampling_steps
progress = min(progress, 1)
elapsed_since_start = time.time() - shared.state.time_start
predicted_duration = elapsed_since_start / progress if progress > 0 else None
eta = predicted_duration - elapsed_since_start if predicted_duration is not None else None
id_live_preview = req.id_live_preview
shared.state.set_current_image()
if opts.live_previews_enable and shared.state.id_live_preview != req.id_live_preview:
if shared.opts.live_previews_enable and shared.state.id_live_preview != req.id_live_preview:
image = shared.state.current_image
if image is not None:
buffered = io.BytesIO()
image.save(buffered, format="png")
live_preview = 'data:image/png;base64,' + base64.b64encode(buffered.getvalue()).decode("ascii")
fmt = 'jpeg' if shared.opts.samples_format == 'jpg' else shared.opts.samples_format
image.save(buffered, format=fmt)
live_preview = f'data:image/{fmt};base64,{base64.b64encode(buffered.getvalue()).decode("ascii")}'
id_live_preview = shared.state.id_live_preview
else:
live_preview = None
else:
live_preview = None
return ProgressResponse(active=active, queued=queued, completed=completed, progress=progress, eta=eta, live_preview=live_preview, id_live_preview=id_live_preview, textinfo=shared.state.textinfo)
modules/sd_samplers_common.py
-2
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@@ -28,14 +28,12 @@ approximation_indexes = {"Full": 0, "Approx NN": 1, "Approx cheap": 2}
def single_sample_to_image(sample, approximation=None):
if approximation is None:
approximation = approximation_indexes.get(opts.show_progress_type, 0)
if approximation == 2:
x_sample = sd_vae_approx.cheap_approximation(sample)
elif approximation == 1:
x_sample = sd_vae_approx.model()(sample.to(devices.device, devices.dtype).unsqueeze(0))[0].detach()
else:
x_sample = processing.decode_first_stage(shared.sd_model, sample.unsqueeze(0))[0]
x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0)
x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
x_sample = x_sample.astype(np.uint8)
modules/sd_vae.py
+2
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@@ -197,6 +197,8 @@ def reload_vae_weights(sd_model=None, vae_file=unspecified):
sd_model.to(devices.cpu)
sd_hijack.model_hijack.undo_hijack(sd_model)
if shared.cmd_opts.rollback_vae and devices.dtype_vae == torch.bfloat16:
devices.dtype_vae = torch.float16
load_vae(sd_model, vae_file, vae_source)
modules/shared.py
+2 -6
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@@ -112,7 +112,6 @@ class State:
"sampling_step": self.sampling_step,
"sampling_steps": self.sampling_steps,
}
return obj
def begin(self):
@@ -142,20 +141,17 @@ class State:
"""sets self.current_image from self.current_latent if enough sampling steps have been made after the last call to this"""
if not parallel_processing_allowed:
return
if self.sampling_step - self.current_image_sampling_step >= opts.show_progress_every_n_steps and opts.live_previews_enable and opts.show_progress_every_n_steps != -1:
self.do_set_current_image()
def do_set_current_image(self):
if self.current_latent is None:
return
import modules.sd_samplers # pylint: disable=W0621
if opts.show_progress_grid:
self.assign_current_image(modules.sd_samplers.samples_to_image_grid(self.current_latent))
else:
self.assign_current_image(modules.sd_samplers.sample_to_image(self.current_latent))
self.current_image_sampling_step = self.sampling_step
def assign_current_image(self, image):
@@ -425,8 +421,8 @@ options_templates.update(options_section(('ui', "Live previews"), {
"show_progressbar": OptionInfo(True, "Show progressbar"),
"live_previews_enable": OptionInfo(True, "Show live previews of the created image"),
"show_progress_grid": OptionInfo(True, "Show previews of all images generated in a batch as a grid"),
"show_progress_every_n_steps": OptionInfo(-1, "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("Full", "Image creation progress preview mode", gr.Radio, {"choices": ["Full", "Approx NN", "Approx cheap"]}),
"show_progress_every_n_steps": OptionInfo(1, "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("Combined", "Live preview subject", gr.Radio, {"choices": ["Combined", "Prompt", "Negative prompt"]}),
"live_preview_refresh_period": OptionInfo(250, "Progressbar/preview update period, in milliseconds")
}))
modules/styles.py
+3 -6
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@@ -49,7 +49,8 @@ class StyleDatabase:
self.styles.clear()
if not os.path.exists(self.path):
return
print(f'Creating styles database: {self.path}')
self.save_styles(self.path)
with open(self.path, "r", encoding="utf-8-sig", newline='') as file:
reader = csv.DictReader(file)
@@ -79,9 +80,5 @@ class StyleDatabase:
# and collections.NamedTuple has explicit documentation for accessing _fields. Same goes for _asdict()
writer = csv.DictWriter(file, fieldnames=PromptStyle._fields)
writer.writeheader()
writer.writerows(style._asdict() for k, style in self.styles.items())
# Always keep a backup file around
if os.path.exists(path):
shutil.move(path, path + ".bak")
writer.writerows(style._asdict() for k, style in self.styles.items())
shutil.move(temp_path, path)
requirements.txt
-1
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@@ -49,7 +49,6 @@ tqdm
voluptuous
yapf
scikit-image
accelerate==0.18.0
opencv-python==4.7.0.72
diffusers==0.15.0
scripts/xyz_grid.py
+50 -21
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@@ -374,16 +374,19 @@ class Script(scripts.Script):
with gr.Row():
x_type = gr.Dropdown(label="X type", choices=[x.label for x in self.current_axis_options], value=self.current_axis_options[1].label, type="index", elem_id=self.elem_id("x_type"))
x_values = gr.Textbox(label="X values", lines=1, elem_id=self.elem_id("x_values"))
x_values_dropdown = gr.Dropdown(label="X values",visible=False,multiselect=True,interactive=True)
fill_x_button = ToolButton(value=fill_values_symbol, elem_id="xyz_grid_fill_x_tool_button", visible=False)
with gr.Row():
y_type = gr.Dropdown(label="Y type", choices=[x.label for x in self.current_axis_options], value=self.current_axis_options[0].label, type="index", elem_id=self.elem_id("y_type"))
y_values = gr.Textbox(label="Y values", lines=1, elem_id=self.elem_id("y_values"))
y_values_dropdown = gr.Dropdown(label="Y values",visible=False,multiselect=True,interactive=True)
fill_y_button = ToolButton(value=fill_values_symbol, elem_id="xyz_grid_fill_y_tool_button", visible=False)
with gr.Row():
z_type = gr.Dropdown(label="Z type", choices=[x.label for x in self.current_axis_options], value=self.current_axis_options[0].label, type="index", elem_id=self.elem_id("z_type"))
z_values = gr.Textbox(label="Z values", lines=1, elem_id=self.elem_id("z_values"))
z_values_dropdown = gr.Dropdown(label="Z values",visible=False,multiselect=True,interactive=True)
fill_z_button = ToolButton(value=fill_values_symbol, elem_id="xyz_grid_fill_z_tool_button", visible=False)
with gr.Row(variant="compact", elem_id="axis_options"):
@@ -401,54 +404,74 @@ class Script(scripts.Script):
swap_yz_axes_button = gr.Button(value="Swap Y/Z axes", elem_id="yz_grid_swap_axes_button")
swap_xz_axes_button = gr.Button(value="Swap X/Z axes", elem_id="xz_grid_swap_axes_button")
def swap_axes(axis1_type, axis1_values, axis2_type, axis2_values):
return self.current_axis_options[axis2_type].label, axis2_values, self.current_axis_options[axis1_type].label, axis1_values
def swap_axes(axis1_type, axis1_values, axis1_values_dropdown, axis2_type, axis2_values, axis2_values_dropdown):
return self.current_axis_options[axis2_type].label, axis2_values, axis2_values_dropdown, self.current_axis_options[axis1_type].label, axis1_values, axis1_values_dropdown
xy_swap_args = [x_type, x_values, y_type, y_values]
xy_swap_args = [x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown]
swap_xy_axes_button.click(swap_axes, inputs=xy_swap_args, outputs=xy_swap_args)
yz_swap_args = [y_type, y_values, z_type, z_values]
yz_swap_args = [y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown]
swap_yz_axes_button.click(swap_axes, inputs=yz_swap_args, outputs=yz_swap_args)
xz_swap_args = [x_type, x_values, z_type, z_values]
xz_swap_args = [x_type, x_values, x_values_dropdown, z_type, z_values, z_values_dropdown]
swap_xz_axes_button.click(swap_axes, inputs=xz_swap_args, outputs=xz_swap_args)
def fill(x_type):
axis = self.current_axis_options[x_type]
return ", ".join(axis.choices()) if axis.choices else gr.update()
return axis.choices() if axis.choices else gr.update()
fill_x_button.click(fn=fill, inputs=[x_type], outputs=[x_values])
fill_y_button.click(fn=fill, inputs=[y_type], outputs=[y_values])
fill_z_button.click(fn=fill, inputs=[z_type], outputs=[z_values])
fill_x_button.click(fn=fill, inputs=[x_type], outputs=[x_values_dropdown])
fill_y_button.click(fn=fill, inputs=[y_type], outputs=[y_values_dropdown])
fill_z_button.click(fn=fill, inputs=[z_type], outputs=[z_values_dropdown])
def select_axis(x_type):
return gr.Button.update(visible=self.current_axis_options[x_type].choices is not None)
def select_axis(axis_type,axis_values_dropdown):
choices = self.current_axis_options[axis_type].choices
has_choices = choices is not None
current_values = axis_values_dropdown
if has_choices:
choices = choices()
if isinstance(current_values,str):
current_values = current_values.split(",")
current_values = list(filter(lambda x: x in choices, current_values))
return gr.Button.update(visible=has_choices),gr.Textbox.update(visible=not has_choices),gr.update(choices=choices if has_choices else None,visible=has_choices,value=current_values)
x_type.change(fn=select_axis, inputs=[x_type], outputs=[fill_x_button])
y_type.change(fn=select_axis, inputs=[y_type], outputs=[fill_y_button])
z_type.change(fn=select_axis, inputs=[z_type], outputs=[fill_z_button])
x_type.change(fn=select_axis, inputs=[x_type,x_values_dropdown], outputs=[fill_x_button,x_values,x_values_dropdown])
y_type.change(fn=select_axis, inputs=[y_type,y_values_dropdown], outputs=[fill_y_button,y_values,y_values_dropdown])
z_type.change(fn=select_axis, inputs=[z_type,z_values_dropdown], outputs=[fill_z_button,z_values,z_values_dropdown])
def get_dropdown_update_from_params(axis,params):
val_key = axis + " Values"
vals = params.get(val_key,"")
valslist = [x.strip() for x in chain.from_iterable(csv.reader(StringIO(vals))) if x]
return gr.update(value = valslist)
self.infotext_fields = (
(x_type, "X Type"),
(x_values, "X Values"),
(x_values_dropdown, lambda params:get_dropdown_update_from_params("X",params)),
(y_type, "Y Type"),
(y_values, "Y Values"),
(y_values_dropdown, lambda params:get_dropdown_update_from_params("Y",params)),
(z_type, "Z Type"),
(z_values, "Z Values"),
(z_values_dropdown, lambda params:get_dropdown_update_from_params("Z",params)),
)
return [x_type, x_values, y_type, y_values, z_type, z_values, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, margin_size]
return [x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, margin_size]
def run(self, p, x_type, x_values, y_type, y_values, z_type, z_values, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, margin_size):
def run(self, p, x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, margin_size):
if not no_fixed_seeds:
modules.processing.fix_seed(p)
if not opts.return_grid:
p.batch_size = 1
def process_axis(opt, vals):
def process_axis(opt, vals, vals_dropdown):
if opt.label == 'Nothing':
return [0]
valslist = [x.strip() for x in chain.from_iterable(csv.reader(StringIO(vals)))]
if opt.choices is not None:
valslist = vals_dropdown
else:
valslist = [x.strip() for x in chain.from_iterable(csv.reader(StringIO(vals))) if x]
if opt.type == int:
valslist_ext = []
@@ -506,13 +529,19 @@ class Script(scripts.Script):
return valslist
x_opt = self.current_axis_options[x_type]
xs = process_axis(x_opt, x_values)
if x_opt.choices is not None:
x_values = ",".join(x_values_dropdown)
xs = process_axis(x_opt, x_values, x_values_dropdown)
y_opt = self.current_axis_options[y_type]
ys = process_axis(y_opt, y_values)
if y_opt.choices is not None:
y_values = ",".join(y_values_dropdown)
ys = process_axis(y_opt, y_values, y_values_dropdown)
z_opt = self.current_axis_options[z_type]
zs = process_axis(z_opt, z_values)
if z_opt.choices is not None:
z_values = ",".join(z_values_dropdown)
zs = process_axis(z_opt, z_values, z_values_dropdown)
# this could be moved to common code, but unlikely to be ever triggered anywhere else
Image.MAX_IMAGE_PIXELS = None # disable check in Pillow and rely on check below to allow large custom image sizes
setup.py
+22 -12
View File
@@ -140,7 +140,7 @@ def update(folder):
git('checkout master', folder)
else:
log.warning(f'Unknown branch for: {folder}')
git('pull --rebase --autostash', folder)
git('pull --autostash', folder)
branch = git('branch', folder)
@@ -217,7 +217,7 @@ def check_torch():
log.debug(f'Cannot install xformers package: {e}')
try:
tensorflow_package = os.environ.get('TENSORFLOW_PACKAGE', 'tensorflow==2.12.0')
install(f'--no-deps {tensorflow_package}', ignore=True)
install(tensorflow_package, ignore=True)
except Exception as e:
log.debug(f'Cannot install tensorflow package: {e}')
@@ -237,7 +237,6 @@ def install_packages():
def install_repositories():
def d(name):
return os.path.join(os.path.dirname(__file__), 'repositories', name)
log.info('Installing repositories')
os.makedirs(os.path.join(os.path.dirname(__file__), 'repositories'), exist_ok=True)
stable_diffusion_repo = os.environ.get('STABLE_DIFFUSION_REPO', "https://github.com/Stability-AI/stablediffusion.git")
@@ -263,7 +262,7 @@ def run_extension_installer(folder):
if not os.path.isfile(path_installer):
return
try:
log.debug(f"Running extension installer: {path_installer}")
log.debug(f"Running extension installer: {folder} / {path_installer}")
env = os.environ.copy()
env['PYTHONPATH'] = os.path.abspath(".")
result = subprocess.run(f'"{sys.executable}" "{path_installer}"', shell=True, env=env, check=False, stdout=subprocess.PIPE, stderr=subprocess.PIPE, cwd=folder)
@@ -334,7 +333,7 @@ def install_submodules():
def ensure_package(pkg):
try:
import pkg
import pkg # type: ignore
except ImportError:
install(pkg)
@@ -394,6 +393,13 @@ def check_extensions():
# check version of the main repo and optionally upgrade it
def check_version():
if not os.path.exists('.git'):
log.error('Not a git repository')
exit(1)
status = git('status')
if 'branch' not in status:
log.error('Cannot get git repository status')
exit(1)
ver = git('log -1 --pretty=format:"%h %ad"')
log.info(f'Version: {ver}')
commit = git('rev-parse HEAD')
@@ -420,17 +426,20 @@ def check_version():
log.error('Error upgrading repository')
else:
log.info(f'Latest published version: {commits["commit"]["sha"]} {commits["commit"]["commit"]["author"]["date"]}')
if not args.noupdate:
log.info('Updating Wiki')
try:
update(os.path.join(os.path.dirname(__file__), "wiki"))
update(os.path.join(os.path.dirname(__file__), "wiki", "origin-wiki"))
except:
log.error('Error updating wiki')
except Exception as e:
log.error(f'Failed to check version: {e} {commits}')
def update_wiki():
if not args.noupdate:
log.info('Updating Wiki')
try:
update(os.path.join(os.path.dirname(__file__), "wiki"))
update(os.path.join(os.path.dirname(__file__), "wiki", "origin-wiki"))
except:
log.error('Error updating wiki')
# check if we can run setup in quick mode
def check_timestamp():
if not quick_allowed or not os.path.isfile('setup.log'):
@@ -535,6 +544,7 @@ def run_setup():
install_repositories()
install_submodules()
install_extensions()
update_wiki()
if errors == 0:
log.debug(f'Setup complete without errors: {round(time.time())}')
else:
webui.py
+12
View File
@@ -64,7 +64,19 @@ else:
server_name = "0.0.0.0" if cmd_opts.listen else None
def check_rollback_vae():
if shared.cmd_opts.rollback_vae:
if version.parse(torch.__version__) < version.parse('2.1'):
print("If your PyTorch version is lower than PyTorch 2.1, Rollback VAE will not work.")
shared.cmd_opts.rollback_vae = False
elif 0 < torch.cuda.get_device_capability()[0] < 8:
print('Rollback VAE will not work because your device does not support it.')
shared.cmd_opts.rollback_vae = False
def initialize():
check_rollback_vae()
extensions.list_extensions()
startup_timer.record("extensions")