diff options
Diffstat (limited to 'modules')
| -rw-r--r-- | modules/processing.py | 17 | ||||
| -rw-r--r-- | modules/safety.py | 42 | ||||
| -rw-r--r-- | modules/scripts.py | 20 | ||||
| -rw-r--r-- | modules/shared.py | 1 | ||||
| -rw-r--r-- | modules/ui_extensions.py | 13 |
5 files changed, 40 insertions, 53 deletions
diff --git a/modules/processing.py b/modules/processing.py index 81400d14..24c537d1 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -13,13 +13,15 @@ from skimage import exposure from typing import Any, Dict, List, Optional
import modules.sd_hijack
-from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste
+from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, script_callbacks
from modules.sd_hijack import model_hijack
from modules.shared import opts, cmd_opts, state
import modules.shared as shared
import modules.face_restoration
import modules.images as images
import modules.styles
+import modules.sd_models as sd_models
+import modules.sd_vae as sd_vae
import logging
from ldm.data.util import AddMiDaS
from ldm.models.diffusion.ddpm import LatentDepth2ImageDiffusion
@@ -454,8 +456,10 @@ def process_images(p: StableDiffusionProcessing) -> Processed: try:
for k, v in p.override_settings.items():
- setattr(opts, k, v) # we don't call onchange for simplicity which makes changing model impossible
- if k == 'sd_hypernetwork': shared.reload_hypernetworks() # make onchange call for changing hypernet since it is relatively fast to load on-change, while SD models are not
+ setattr(opts, k, v)
+ if k == 'sd_hypernetwork': shared.reload_hypernetworks() # make onchange call for changing hypernet
+ if k == 'sd_model_checkpoint': sd_models.reload_model_weights() # make onchange call for changing SD model
+ if k == 'sd_vae': sd_vae.reload_vae_weights() # make onchange call for changing VAE
res = process_images_inner(p)
@@ -463,6 +467,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed: for k, v in stored_opts.items():
setattr(opts, k, v)
if k == 'sd_hypernetwork': shared.reload_hypernetworks()
+ if k == 'sd_model_checkpoint': sd_models.reload_model_weights()
+ if k == 'sd_vae': sd_vae.reload_vae_weights()
return res
@@ -571,9 +577,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: devices.torch_gc()
- if opts.filter_nsfw:
- import modules.safety as safety
- x_samples_ddim = modules.safety.censor_batch(x_samples_ddim)
+ if p.scripts is not None:
+ p.scripts.postprocess_batch(p, x_samples_ddim, batch_number=n)
for i, x_sample in enumerate(x_samples_ddim):
x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
diff --git a/modules/safety.py b/modules/safety.py deleted file mode 100644 index cff4b278..00000000 --- a/modules/safety.py +++ /dev/null @@ -1,42 +0,0 @@ -import torch
-from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
-from transformers import AutoFeatureExtractor
-from PIL import Image
-
-import modules.shared as shared
-
-safety_model_id = "CompVis/stable-diffusion-safety-checker"
-safety_feature_extractor = None
-safety_checker = None
-
-def numpy_to_pil(images):
- """
- Convert a numpy image or a batch of images to a PIL image.
- """
- if images.ndim == 3:
- images = images[None, ...]
- images = (images * 255).round().astype("uint8")
- pil_images = [Image.fromarray(image) for image in images]
-
- return pil_images
-
-# check and replace nsfw content
-def check_safety(x_image):
- global safety_feature_extractor, safety_checker
-
- if safety_feature_extractor is None:
- safety_feature_extractor = AutoFeatureExtractor.from_pretrained(safety_model_id)
- safety_checker = StableDiffusionSafetyChecker.from_pretrained(safety_model_id)
-
- safety_checker_input = safety_feature_extractor(numpy_to_pil(x_image), return_tensors="pt")
- x_checked_image, has_nsfw_concept = safety_checker(images=x_image, clip_input=safety_checker_input.pixel_values)
-
- return x_checked_image, has_nsfw_concept
-
-
-def censor_batch(x):
- x_samples_ddim_numpy = x.cpu().permute(0, 2, 3, 1).numpy()
- x_checked_image, has_nsfw_concept = check_safety(x_samples_ddim_numpy)
- x = torch.from_numpy(x_checked_image).permute(0, 3, 1, 2)
-
- return x
diff --git a/modules/scripts.py b/modules/scripts.py index b934d881..23ca195d 100644 --- a/modules/scripts.py +++ b/modules/scripts.py @@ -88,6 +88,17 @@ class Script: pass
+ def postprocess_batch(self, p, *args, **kwargs):
+ """
+ Same as process_batch(), but called for every batch after it has been generated.
+
+ **kwargs will have same items as process_batch, and also:
+ - batch_number - index of current batch, from 0 to number of batches-1
+ - images - torch tensor with all generated images, with values ranging from 0 to 1;
+ """
+
+ pass
+
def postprocess(self, p, processed, *args):
"""
This function is called after processing ends for AlwaysVisible scripts.
@@ -347,6 +358,15 @@ class ScriptRunner: print(f"Error running postprocess: {script.filename}", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
+ def postprocess_batch(self, p, images, **kwargs):
+ for script in self.alwayson_scripts:
+ try:
+ script_args = p.script_args[script.args_from:script.args_to]
+ script.postprocess_batch(p, *script_args, images=images, **kwargs)
+ except Exception:
+ print(f"Error running postprocess_batch: {script.filename}", file=sys.stderr)
+ print(traceback.format_exc(), file=sys.stderr)
+
def before_component(self, component, **kwargs):
for script in self.scripts:
try:
diff --git a/modules/shared.py b/modules/shared.py index 44922c91..272267c1 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -367,7 +367,6 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), { "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"),
"comma_padding_backtrack": OptionInfo(20, "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }),
- "filter_nsfw": OptionInfo(False, "Filter NSFW content"),
'CLIP_stop_at_last_layers': OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
"random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}),
}))
diff --git a/modules/ui_extensions.py b/modules/ui_extensions.py index b487ac25..1434f25f 100644 --- a/modules/ui_extensions.py +++ b/modules/ui_extensions.py @@ -206,12 +206,13 @@ def refresh_available_extensions_from_data(hide_tags): if url is None:
continue
+ existing = installed_extension_urls.get(normalize_git_url(url), None)
+ extension_tags = extension_tags + ["installed"] if existing else extension_tags
+
if len([x for x in extension_tags if x in tags_to_hide]) > 0:
hidden += 1
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">"""
tags_text = ", ".join([f"<span class='extension-tag' title='{tags.get(x, '')}'>{x}</span>" for x in extension_tags])
@@ -222,7 +223,11 @@ def refresh_available_extensions_from_data(hide_tags): <td>{html.escape(description)}</td>
<td>{install_code}</td>
</tr>
- """
+
+ """
+
+ for tag in [x for x in extension_tags if x not in tags]:
+ tags[tag] = tag
code += """
</tbody>
@@ -272,7 +277,7 @@ def create_ui(): install_extension_button = gr.Button(elem_id="install_extension_button", visible=False)
with gr.Row():
- hide_tags = gr.CheckboxGroup(value=["ads", "localization"], label="Hide extensions with tags", choices=["script", "ads", "localization"])
+ hide_tags = gr.CheckboxGroup(value=["ads", "localization", "installed"], label="Hide extensions with tags", choices=["script", "ads", "localization", "installed"])
install_result = gr.HTML()
available_extensions_table = gr.HTML()
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