diff options
Diffstat (limited to 'modules')
| -rw-r--r-- | modules/lowvram.py | 21 | ||||
| -rw-r--r-- | modules/processing.py | 3 | ||||
| -rw-r--r-- | modules/safe.py | 2 | ||||
| -rw-r--r-- | modules/script_callbacks.py | 19 | ||||
| -rw-r--r-- | modules/sd_hijack.py | 4 | ||||
| -rw-r--r-- | modules/sd_models.py | 14 | ||||
| -rw-r--r-- | modules/sd_samplers.py | 28 | ||||
| -rw-r--r-- | modules/ui_extensions.py | 112 |
8 files changed, 171 insertions, 32 deletions
diff --git a/modules/lowvram.py b/modules/lowvram.py index f327c3df..a4652cb1 100644 --- a/modules/lowvram.py +++ b/modules/lowvram.py @@ -38,13 +38,18 @@ def setup_for_low_vram(sd_model, use_medvram): # see below for register_forward_pre_hook;
# first_stage_model does not use forward(), it uses encode/decode, so register_forward_pre_hook is
# useless here, and we just replace those methods
- def first_stage_model_encode_wrap(self, encoder, x):
- send_me_to_gpu(self, None)
- return encoder(x)
- def first_stage_model_decode_wrap(self, decoder, z):
- send_me_to_gpu(self, None)
- return decoder(z)
+ first_stage_model = sd_model.first_stage_model
+ first_stage_model_encode = sd_model.first_stage_model.encode
+ first_stage_model_decode = sd_model.first_stage_model.decode
+
+ def first_stage_model_encode_wrap(x):
+ send_me_to_gpu(first_stage_model, None)
+ return first_stage_model_encode(x)
+
+ def first_stage_model_decode_wrap(z):
+ send_me_to_gpu(first_stage_model, None)
+ return first_stage_model_decode(z)
# remove three big modules, cond, first_stage, and unet from the model and then
# send the model to GPU. Then put modules back. the modules will be in CPU.
@@ -56,8 +61,8 @@ def setup_for_low_vram(sd_model, use_medvram): # register hooks for those the first two models
sd_model.cond_stage_model.transformer.register_forward_pre_hook(send_me_to_gpu)
sd_model.first_stage_model.register_forward_pre_hook(send_me_to_gpu)
- sd_model.first_stage_model.encode = lambda x, en=sd_model.first_stage_model.encode: first_stage_model_encode_wrap(sd_model.first_stage_model, en, x)
- sd_model.first_stage_model.decode = lambda z, de=sd_model.first_stage_model.decode: first_stage_model_decode_wrap(sd_model.first_stage_model, de, z)
+ sd_model.first_stage_model.encode = first_stage_model_encode_wrap
+ sd_model.first_stage_model.decode = first_stage_model_decode_wrap
parents[sd_model.cond_stage_model.transformer] = sd_model.cond_stage_model
if use_medvram:
diff --git a/modules/processing.py b/modules/processing.py index b1df4918..57d3a523 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -597,6 +597,9 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: if p.scripts is not None:
p.scripts.postprocess(p, res)
+ p.sd_model = None
+ p.sampler = None
+
return res
diff --git a/modules/safe.py b/modules/safe.py index 399165a1..348a24fc 100644 --- a/modules/safe.py +++ b/modules/safe.py @@ -32,7 +32,7 @@ class RestrictedUnpickler(pickle.Unpickler): return getattr(collections, name)
if module == 'torch._utils' and name in ['_rebuild_tensor_v2', '_rebuild_parameter']:
return getattr(torch._utils, name)
- if module == 'torch' and name in ['FloatStorage', 'HalfStorage', 'IntStorage', 'LongStorage', 'DoubleStorage']:
+ if module == 'torch' and name in ['FloatStorage', 'HalfStorage', 'IntStorage', 'LongStorage', 'DoubleStorage', 'ByteStorage']:
return getattr(torch, name)
if module == 'torch.nn.modules.container' and name in ['ParameterDict']:
return getattr(torch.nn.modules.container, name)
diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py index 6ea58d61..ce264690 100644 --- a/modules/script_callbacks.py +++ b/modules/script_callbacks.py @@ -3,6 +3,8 @@ import traceback from collections import namedtuple
import inspect
+from fastapi import FastAPI
+from gradio import Blocks
def report_exception(c, job):
print(f"Error executing callback {job} for {c.script}", file=sys.stderr)
@@ -25,6 +27,7 @@ class ImageSaveParams: ScriptCallback = namedtuple("ScriptCallback", ["script", "callback"])
+callbacks_app_started = []
callbacks_model_loaded = []
callbacks_ui_tabs = []
callbacks_ui_settings = []
@@ -40,6 +43,14 @@ def clear_callbacks(): callbacks_image_saved.clear()
+def app_started_callback(demo: Blocks, app: FastAPI):
+ for c in callbacks_app_started:
+ try:
+ c.callback(demo, app)
+ except Exception:
+ report_exception(c, 'app_started_callback')
+
+
def model_loaded_callback(sd_model):
for c in callbacks_model_loaded:
try:
@@ -69,7 +80,7 @@ def ui_settings_callback(): def before_image_saved_callback(params: ImageSaveParams):
- for c in callbacks_image_saved:
+ for c in callbacks_before_image_saved:
try:
c.callback(params)
except Exception:
@@ -91,6 +102,12 @@ def add_callback(callbacks, fun): callbacks.append(ScriptCallback(filename, fun))
+def on_app_started(callback):
+ """register a function to be called when the webui started, the gradio `Block` component and
+ fastapi `FastAPI` object are passed as the arguments"""
+ add_callback(callbacks_app_started, callback)
+
+
def on_model_loaded(callback):
"""register a function to be called when the stable diffusion model is created; the model is
passed as an argument"""
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index 0f10828e..bc49d235 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -94,6 +94,10 @@ class StableDiffusionModelHijack: if type(model_embeddings.token_embedding) == EmbeddingsWithFixes:
model_embeddings.token_embedding = model_embeddings.token_embedding.wrapped
+ self.layers = None
+ self.circular_enabled = False
+ self.clip = None
+
def apply_circular(self, enable):
if self.circular_enabled == enable:
return
diff --git a/modules/sd_models.py b/modules/sd_models.py index f86dc3ed..90007da3 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -1,6 +1,7 @@ import collections
import os.path
import sys
+import gc
from collections import namedtuple
import torch
import re
@@ -220,6 +221,12 @@ def load_model(checkpoint_info=None): if checkpoint_info.config != shared.cmd_opts.config:
print(f"Loading config from: {checkpoint_info.config}")
+ if shared.sd_model:
+ sd_hijack.model_hijack.undo_hijack(shared.sd_model)
+ shared.sd_model = None
+ gc.collect()
+ devices.torch_gc()
+
sd_config = OmegaConf.load(checkpoint_info.config)
if should_hijack_inpainting(checkpoint_info):
@@ -233,6 +240,7 @@ def load_model(checkpoint_info=None): checkpoint_info = checkpoint_info._replace(config=checkpoint_info.config.replace(".yaml", "-inpainting.yaml"))
do_inpainting_hijack()
+
sd_model = instantiate_from_config(sd_config.model)
load_model_weights(sd_model, checkpoint_info)
@@ -252,14 +260,18 @@ def load_model(checkpoint_info=None): return sd_model
-def reload_model_weights(sd_model, info=None):
+def reload_model_weights(sd_model=None, info=None):
from modules import lowvram, devices, sd_hijack
checkpoint_info = info or select_checkpoint()
+ if not sd_model:
+ sd_model = shared.sd_model
+
if sd_model.sd_model_checkpoint == checkpoint_info.filename:
return
if sd_model.sd_checkpoint_info.config != checkpoint_info.config or should_hijack_inpainting(checkpoint_info) != should_hijack_inpainting(sd_model.sd_checkpoint_info):
+ del sd_model
checkpoints_loaded.clear()
load_model(checkpoint_info)
return shared.sd_model
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index 3670b57d..8772db56 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -1,5 +1,6 @@ from collections import namedtuple
import numpy as np
+from math import floor
import torch
import tqdm
from PIL import Image
@@ -205,17 +206,22 @@ class VanillaStableDiffusionSampler: self.mask = p.mask if hasattr(p, 'mask') else None
self.nmask = p.nmask if hasattr(p, 'nmask') else None
+
+ def adjust_steps_if_invalid(self, p, num_steps):
+ if (self.config.name == 'DDIM' and p.ddim_discretize == 'uniform') or (self.config.name == 'PLMS'):
+ valid_step = 999 / (1000 // num_steps)
+ if valid_step == floor(valid_step):
+ return int(valid_step) + 1
+
+ return num_steps
+
+
def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
steps, t_enc = setup_img2img_steps(p, steps)
-
+ steps = self.adjust_steps_if_invalid(p, steps)
self.initialize(p)
- # existing code fails with certain step counts, like 9
- try:
- self.sampler.make_schedule(ddim_num_steps=steps, ddim_eta=self.eta, ddim_discretize=p.ddim_discretize, verbose=False)
- except Exception:
- self.sampler.make_schedule(ddim_num_steps=steps+1, ddim_eta=self.eta, ddim_discretize=p.ddim_discretize, verbose=False)
-
+ self.sampler.make_schedule(ddim_num_steps=steps, ddim_eta=self.eta, ddim_discretize=p.ddim_discretize, verbose=False)
x1 = self.sampler.stochastic_encode(x, torch.tensor([t_enc] * int(x.shape[0])).to(shared.device), noise=noise)
self.init_latent = x
@@ -239,18 +245,14 @@ class VanillaStableDiffusionSampler: self.last_latent = x
self.step = 0
- steps = steps or p.steps
+ steps = self.adjust_steps_if_invalid(p, steps or p.steps)
# Wrap the conditioning models with additional image conditioning for inpainting model
if image_conditioning is not None:
conditioning = {"c_concat": [image_conditioning], "c_crossattn": [conditioning]}
unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
- # existing code fails with certain step counts, like 9
- try:
- samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)[0])
- except Exception:
- samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps+1, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)[0])
+ samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)[0])
return samples_ddim
diff --git a/modules/ui_extensions.py b/modules/ui_extensions.py index e74b7d68..ab807722 100644 --- a/modules/ui_extensions.py +++ b/modules/ui_extensions.py @@ -13,6 +13,9 @@ import html from modules import extensions, shared, paths
+available_extensions = {"extensions": []}
+
+
def check_access():
assert not shared.cmd_opts.disable_extension_access, "extension access disabed because of commandline flags"
@@ -96,6 +99,14 @@ def extension_table(): return code
+def normalize_git_url(url):
+ if url is None:
+ return ""
+
+ url = url.replace(".git", "")
+ return url
+
+
def install_extension_from_url(dirname, url):
check_access()
@@ -103,14 +114,15 @@ def install_extension_from_url(dirname, url): if dirname is None or dirname == "":
*parts, last_part = url.split('/')
- last_part = last_part.replace(".git", "")
+ last_part = normalize_git_url(last_part)
dirname = last_part
target_dir = os.path.join(extensions.extensions_dir, dirname)
assert not os.path.exists(target_dir), f'Extension directory already exists: {target_dir}'
- assert len([x for x in extensions.extensions if x.remote == url]) == 0, 'Extension with this URL is already installed'
+ normalized_url = normalize_git_url(url)
+ assert len([x for x in extensions.extensions if normalize_git_url(x.remote) == normalized_url]) == 0, 'Extension with this URL is already installed'
tmpdir = os.path.join(paths.script_path, "tmp", dirname)
@@ -128,18 +140,80 @@ def install_extension_from_url(dirname, url): shutil.rmtree(tmpdir, True)
+def install_extension_from_index(url):
+ ext_table, message = install_extension_from_url(None, url)
+
+ return refresh_available_extensions_from_data(), ext_table, message
+
+
+def refresh_available_extensions(url):
+ global available_extensions
+
+ import urllib.request
+ with urllib.request.urlopen(url) as response:
+ text = response.read()
+
+ available_extensions = json.loads(text)
+
+ return url, refresh_available_extensions_from_data(), ''
+
+
+def refresh_available_extensions_from_data():
+ extlist = available_extensions["extensions"]
+ installed_extension_urls = {normalize_git_url(extension.remote): extension.name for extension in extensions.extensions}
+
+ code = f"""<!-- {time.time()} -->
+ <table id="available_extensions">
+ <thead>
+ <tr>
+ <th>Extension</th>
+ <th>Description</th>
+ <th>Action</th>
+ </tr>
+ </thead>
+ <tbody>
+ """
+
+ for ext in extlist:
+ name = ext.get("name", "noname")
+ url = ext.get("url", None)
+ description = ext.get("description", "")
+
+ if url is None:
+ continue
+
+ existing = installed_extension_urls.get(normalize_git_url(url), None)
+
+ install_code = f"""<input onclick="install_extension_from_index(this, '{html.escape(url)}')" type="button" value="{"Install" if not existing else "Installed"}" {"disabled=disabled" if existing else ""} class="gr-button gr-button-lg gr-button-secondary">"""
+
+ code += f"""
+ <tr>
+ <td><a href="{html.escape(url)}">{html.escape(name)}</a></td>
+ <td>{html.escape(description)}</td>
+ <td>{install_code}</td>
+ </tr>
+ """
+
+ code += """
+ </tbody>
+ </table>
+ """
+
+ return code
+
+
def create_ui():
import modules.ui
with gr.Blocks(analytics_enabled=False) as ui:
with gr.Tabs(elem_id="tabs_extensions") as tabs:
with gr.TabItem("Installed"):
- extensions_disabled_list = gr.Text(elem_id="extensions_disabled_list", visible=False)
- extensions_update_list = gr.Text(elem_id="extensions_update_list", visible=False)
with gr.Row():
apply = gr.Button(value="Apply and restart UI", variant="primary")
check = gr.Button(value="Check for updates")
+ extensions_disabled_list = gr.Text(elem_id="extensions_disabled_list", visible=False).style(container=False)
+ extensions_update_list = gr.Text(elem_id="extensions_update_list", visible=False).style(container=False)
extensions_table = gr.HTML(lambda: extension_table())
@@ -157,16 +231,38 @@ def create_ui(): outputs=[extensions_table],
)
+ with gr.TabItem("Available"):
+ with gr.Row():
+ refresh_available_extensions_button = gr.Button(value="Load from:", variant="primary")
+ available_extensions_index = gr.Text(value="https://raw.githubusercontent.com/wiki/AUTOMATIC1111/stable-diffusion-webui/Extensions-index.md", label="Extension index URL").style(container=False)
+ extension_to_install = gr.Text(elem_id="extension_to_install", visible=False)
+ install_extension_button = gr.Button(elem_id="install_extension_button", visible=False)
+
+ install_result = gr.HTML()
+ available_extensions_table = gr.HTML()
+
+ refresh_available_extensions_button.click(
+ fn=modules.ui.wrap_gradio_call(refresh_available_extensions, extra_outputs=[gr.update(), gr.update()]),
+ inputs=[available_extensions_index],
+ outputs=[available_extensions_index, available_extensions_table, install_result],
+ )
+
+ install_extension_button.click(
+ fn=modules.ui.wrap_gradio_call(install_extension_from_index, extra_outputs=[gr.update(), gr.update()]),
+ inputs=[extension_to_install],
+ outputs=[available_extensions_table, extensions_table, install_result],
+ )
+
with gr.TabItem("Install from URL"):
install_url = gr.Text(label="URL for extension's git repository")
install_dirname = gr.Text(label="Local directory name", placeholder="Leave empty for auto")
- intall_button = gr.Button(value="Install", variant="primary")
- intall_result = gr.HTML(elem_id="extension_install_result")
+ install_button = gr.Button(value="Install", variant="primary")
+ install_result = gr.HTML(elem_id="extension_install_result")
- intall_button.click(
+ install_button.click(
fn=modules.ui.wrap_gradio_call(install_extension_from_url, extra_outputs=[gr.update()]),
inputs=[install_dirname, install_url],
- outputs=[extensions_table, intall_result],
+ outputs=[extensions_table, install_result],
)
return ui
|