Rollback of UI Always Visible Overhaul
While the line `return scripts.AlwaysVisible if is_img2img else False` correctly makes the script always visible in the img2img tab, for some reason A1111 is not using the script. This requires further investigation and troubleshooting, however, the other features of this script patch is ready for use. So the AlwaysVisible feature is being rolled back while the other features are kept.pull/10/head
Smirking Kitsune
GitHub
parent
6b1b47f392
commit
b7f1440367
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@ -51,7 +51,8 @@ class Script(scripts.Script):
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return NAME
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def show(self, is_img2img):
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return scripts.AlwaysVisible if is_img2img else False
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# return scripts.AlwaysVisible if is_img2img else False
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return is_img2img
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def b_clicked(o):
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return gr.Button.update(interactive=True)
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@ -100,82 +101,81 @@ class Script(scripts.Script):
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def ui(self, is_img2img):
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with gr.Group():
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with gr.Accordion(NAME, open=False):
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model_options = ["CLIP (API)", "CLIP (Native)", "Deepbooru (Native)", "WD (API)"]
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model_selection = gr.Dropdown(choices=model_options, label="Select Interrogation Model(s)", multiselect=True, value=None)
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model_options = ["CLIP (API)", "CLIP (Native)", "Deepbooru (Native)", "WD (API)"]
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model_selection = gr.Dropdown(choices=model_options, label="Select Interrogation Model(s)", multiselect=True, value=None)
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in_front = gr.Radio(
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choices=["Prepend to prompt", "Append to prompt"],
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value="Prepend to prompt",
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label="Interrogator result position"
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)
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def update_prompt_weight_visibility(use_weight):
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return gr.Slider.update(visible=use_weight)
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in_front = gr.Radio(
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choices=["Prepend to prompt", "Append to prompt"],
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value="Prepend to prompt",
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label="Interrogator result position"
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)
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def update_prompt_weight_visibility(use_weight):
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return gr.Slider.update(visible=use_weight)
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use_weight = gr.Checkbox(label="Use Interrogator Prompt Weight", value=True)
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prompt_weight = gr.Slider(0.0, 1.0, value=0.5, step=0.01, label="Interrogator Prompt Weight", visible=True)
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# CLIP API Options
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def update_clip_api_visibility(model_selection):
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is_visible = "CLIP (API)" in model_selection
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if is_visible:
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clip_models = self.load_clip_models()
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return gr.Accordion.update(visible=True), clip_models
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else:
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return gr.Accordion.update(visible=False), gr.Dropdown.update()
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use_weight = gr.Checkbox(label="Use Interrogator Prompt Weight", value=True)
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prompt_weight = gr.Slider(0.0, 1.0, value=0.5, step=0.01, label="Interrogator Prompt Weight", visible=True)
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clip_api_accordion = gr.Accordion("CLIP API Options:", open=False, visible=False)
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with clip_api_accordion:
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clip_api_model = gr.Dropdown(choices=[], value='ViT-L-14/openai', label="CLIP API Model")
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clip_api_mode = gr.Radio(choices=["best", "fast", "classic", "negative"], value='best', label="CLIP API Mode")
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# CLIP API Options
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def update_clip_api_visibility(model_selection):
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is_visible = "CLIP (API)" in model_selection
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if is_visible:
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clip_models = self.load_clip_models()
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return gr.Accordion.update(visible=True), clip_models
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else:
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return gr.Accordion.update(visible=False), gr.Dropdown.update()
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# WD API Options
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def update_wd_api_visibility(model_selection):
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is_visible = "WD (API)" in model_selection
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if is_visible:
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wd_models = self.load_wd_models()
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return gr.Accordion.update(visible=True), wd_models
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else:
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return gr.Accordion.update(visible=False), gr.Dropdown.update()
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wd_api_accordion = gr.Accordion("WD API Options:", open=False, visible=False)
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with wd_api_accordion:
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wd_api_model = gr.Dropdown(choices=[], value='wd-v1-4-moat-tagger.v2', label="WD API Model")
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wd_threshold = gr.Slider(0.0, 1.0, value=0.35, step=0.01, label="Threshold")
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wd_underscore_fix = gr.Checkbox(label="Remove Underscores from Tags", value=True)
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unload_wd_models_afterwords = gr.Checkbox(label="Unload WD Model After Use", value=True)
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unload_wd_models_button = gr.Button(value="Unload WD Models")
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clip_api_accordion = gr.Accordion("CLIP API Options:", open=False, visible=False)
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with clip_api_accordion:
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clip_api_model = gr.Dropdown(choices=[], value='ViT-L-14/openai', label="CLIP API Model")
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clip_api_mode = gr.Radio(choices=["best", "fast", "classic", "negative"], label="CLIP API Mode", value="best")
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# WD API Options
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def update_wd_api_visibility(model_selection):
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is_visible = "WD (API)" in model_selection
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if is_visible:
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wd_models = self.load_wd_models()
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return gr.Accordion.update(visible=True), wd_models
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else:
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return gr.Accordion.update(visible=False), gr.Dropdown.update()
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wd_api_accordion = gr.Accordion("WD API Options:", open=False, visible=False)
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with wd_api_accordion:
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wd_api_model = gr.Dropdown(choices=[], value='wd-v1-4-moat-tagger.v2', label="WD API Model")
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wd_threshold = gr.Slider(0.0, 1.0, value=0.35, step=0.01, label="Threshold")
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wd_underscore_fix = gr.Checkbox(label="Remove Underscores from Tags", value=True)
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unload_wd_models_afterwords = gr.Checkbox(label="Unload WD Model After Use", value=True)
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unload_wd_models_button = gr.Button(value="Unload WD Models")
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# Function to load custom filter from file
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def load_custom_filter(custom_filter):
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with open("extensions/sd-Img2img-batch-interrogator/custom_filter.txt", "r") as file:
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custom_filter = file.read()
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return custom_filter
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with gr.Accordion("Filtering tools:"):
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no_duplicates = gr.Checkbox(label="Filter Duplicate Prompt Content from Interrogation", value=False)
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use_negatives = gr.Checkbox(label="Filter Negative Prompt Content from Interrogation", value=False)
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use_custom_filter = gr.Checkbox(label="Filter Custom Prompt Content from Interrogation", value=False)
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custom_filter = gr.Textbox(
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label="Custom Filter Prompt",
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placeholder="Prompt content separated by commas. Warning ignores attention syntax, parentheses '()' and colon suffix ':XX.XX' are discarded.",
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show_copy_button=True
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)
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# Button to load custom filter from file
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load_custom_filter_button = gr.Button(value="Load Last Custom Filter")
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# Listeners
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model_selection.select(fn=self.update_model_choices, inputs=[model_selection], outputs=[model_selection])
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model_selection.change(fn=update_clip_api_visibility, inputs=[model_selection], outputs=[clip_api_accordion, clip_api_model])
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model_selection.change(fn=update_wd_api_visibility, inputs=[model_selection], outputs=[wd_api_accordion, wd_api_model])
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load_custom_filter_button.click(load_custom_filter, inputs=custom_filter, outputs=custom_filter)
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unload_wd_models_button.click(self.post_wd_api_unload, inputs=None, outputs=None)
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use_weight.change(fn=update_prompt_weight_visibility, inputs=[use_weight], outputs=[prompt_weight])
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# Function to load custom filter from file
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def load_custom_filter(custom_filter):
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with open("extensions/sd-Img2img-batch-interrogator/custom_filter.txt", "r") as file:
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custom_filter = file.read()
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return custom_filter
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with gr.Accordion("Filtering tools:"):
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no_duplicates = gr.Checkbox(label="Filter Duplicate Prompt Content from Interrogation", value=False)
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use_negatives = gr.Checkbox(label="Filter Negative Prompt Content from Interrogation", value=False)
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use_custom_filter = gr.Checkbox(label="Filter Custom Prompt Content from Interrogation", value=False)
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custom_filter = gr.Textbox(
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label="Custom Filter Prompt",
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placeholder="Prompt content separated by commas. Warning ignores attention syntax, parentheses '()' and colon suffix ':XX.XX' are discarded.",
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show_copy_button=True
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)
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# Button to load custom filter from file
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load_custom_filter_button = gr.Button(value="Load Last Custom Filter")
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# Listeners
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model_selection.select(fn=self.update_model_choices, inputs=[model_selection], outputs=[model_selection])
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model_selection.change(fn=update_clip_api_visibility, inputs=[model_selection], outputs=[clip_api_accordion, clip_api_model])
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model_selection.change(fn=update_wd_api_visibility, inputs=[model_selection], outputs=[wd_api_accordion, wd_api_model])
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load_custom_filter_button.click(load_custom_filter, inputs=custom_filter, outputs=custom_filter)
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unload_wd_models_button.click(self.post_wd_api_unload, inputs=None, outputs=None)
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use_weight.change(fn=update_prompt_weight_visibility, inputs=[use_weight], outputs=[prompt_weight])
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return [in_front, prompt_weight, model_selection, use_weight, no_duplicates, use_negatives, use_custom_filter, custom_filter, clip_api_model, clip_api_mode, wd_api_model, wd_threshold, wd_underscore_fix, unload_wd_models_afterwords]
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# Required to parse information from a string that is between () or has :##.## suffix
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def remove_attention(self, words):
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# Define a regular expression pattern to match attention-related suffixes
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@ -219,68 +219,65 @@ class Script(scripts.Script):
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raw_prompt = p.prompt
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interrogator = ""
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# If no model selected or no image, interrogation should not run
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if model_selection and not p.init_images[0]:
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# fix alpha channel
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p.init_images[0] = p.init_images[0].convert("RGB")
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first = True # Two interrogator concatenation correction boolean
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for model in model_selection:
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# This prevents two interrogators from being incorrectly concatenated
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if first == False:
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interrogator += ", "
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first = False
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# Should add the interrogators in the order determined by the model_selection list
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if model == "Deepbooru (Native)":
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interrogator += deepbooru.model.tag(p.init_images[0])
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elif model == "CLIP (Native)":
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interrogator += shared.interrogator.interrogate(p.init_images[0])
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elif model == "CLIP (API)":
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interrogator += self.post_clip_api_prompt(p.init_images[0], clip_api_model, clip_api_mode)
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elif model == "WD (API)":
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interrogator += self.post_wd_api_tagger(p.init_images[0], wd_api_model, wd_threshold, wd_underscore_fix)
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# Remove duplicate prompt content from interrogator prompt
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if no_duplicates:
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interrogator = self.filter_words(interrogator, raw_prompt)
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# Remove negative prompt content from interrogator prompt
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if use_negatives:
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interrogator = self.filter_words(interrogator, p.negative_prompt)
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# Remove custom prompt content from interrogator prompt
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if use_custom_filter:
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interrogator = self.filter_words(interrogator, custom_filter)
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# Save custom filter to text file
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with open("extensions/sd-Img2img-batch-interrogator/custom_filter.txt", "w") as file:
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file.write(custom_filter)
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if use_weight:
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if p.prompt == "":
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p.prompt = interrogator
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elif in_front == "Append to prompt":
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p.prompt = f"{p.prompt}, ({interrogator}:{prompt_weight})"
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else:
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p.prompt = f"({interrogator}:{prompt_weight}), {p.prompt}"
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# fix alpha channel
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p.init_images[0] = p.init_images[0].convert("RGB")
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first = True # Two interrogator concatenation correction boolean
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for model in model_selection:
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# This prevents two interrogators from being incorrectly concatenated
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if first == False:
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interrogator += ", "
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first = False
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# Should add the interrogators in the order determined by the model_selection list
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if model == "Deepbooru (Native)":
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interrogator += deepbooru.model.tag(p.init_images[0])
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elif model == "CLIP (Native)":
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interrogator += shared.interrogator.interrogate(p.init_images[0])
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elif model == "CLIP (API)":
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interrogator += self.post_clip_api_prompt(p.init_images[0], clip_api_model, clip_api_mode)
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elif model == "WD (API)":
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interrogator += self.post_wd_api_tagger(p.init_images[0], wd_api_model, wd_threshold, wd_underscore_fix)
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# Remove duplicate prompt content from interrogator prompt
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if no_duplicates:
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interrogator = self.filter_words(interrogator, raw_prompt)
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# Remove negative prompt content from interrogator prompt
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if use_negatives:
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interrogator = self.filter_words(interrogator, p.negative_prompt)
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# Remove custom prompt content from interrogator prompt
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if use_custom_filter:
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interrogator = self.filter_words(interrogator, custom_filter)
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# Save custom filter to text file
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with open("extensions/sd-Img2img-batch-interrogator/custom_filter.txt", "w") as file:
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file.write(custom_filter)
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if use_weight:
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if p.prompt == "":
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p.prompt = interrogator
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elif in_front == "Append to prompt":
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p.prompt = f"{p.prompt}, ({interrogator}:{prompt_weight})"
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else:
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if p.prompt == "":
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p.prompt = interrogator
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elif in_front == "Append to prompt":
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p.prompt = f"{p.prompt}, {interrogator}"
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else:
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p.prompt = f"{interrogator}, {p.prompt}"
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if unload_wd_models_afterwords and "WD (API)" in model_selection:
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self.post_wd_api_unload()
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print(f"Prompt: {p.prompt}")
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p.prompt = f"({interrogator}:{prompt_weight}), {p.prompt}"
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else:
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if p.prompt == "":
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p.prompt = interrogator
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elif in_front == "Append to prompt":
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p.prompt = f"{p.prompt}, {interrogator}"
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else:
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p.prompt = f"{interrogator}, {p.prompt}"
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if unload_wd_models_afterwords and "WD (API)" in model_selection:
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self.post_wd_api_unload()
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print(f"Prompt: {p.prompt}")
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processed = process_images(p)
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# Restore the UI elements we modified
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p.prompt = raw_prompt
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return processed
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# CLIP API Model Identification
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def get_clip_API_models(self):
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# Ensure CLIP Interrogator is present and accessible
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@ -366,7 +363,7 @@ class Script(scripts.Script):
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# WARNING: Removing `timeout` could result in a frozen client if the queue_lock is locked. If you need more time add more time, do not remove timeout or risk DEADLOCK.
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# Note: If WD Tagger did not load a model, it is likely that WD Tagger specifically queue_lock (FIFOLock) is concerned with your system's threading and thinks running could cause processes starvation...
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# Note: It would be advisable to download models in the WD tab due to the timeout
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response = requests.post(api_address, json=payload, timeout=120)
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response = requests.post(api_address, json=payload, timeout=120)
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response.raise_for_status()
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result = response.json()
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@ -381,16 +378,16 @@ class Script(scripts.Script):
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return tags_string
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except Exception as error:
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print(f"Error generating prompt with WD API: {error}")
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return ""
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return ""
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# WD API Model Unloader
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def post_wd_api_unload(self):
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try:
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api_address = f"{self.get_server_address()}tagger/v1/unload-interrogators"
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response = requests.post(api_address, json='')
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response = requests.post(api_address, json='')
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response.raise_for_status()
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except Exception as error:
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print(f"Error Unloading models with WD API: {error}")
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print(f"Error Unloading models with WD API: {error}")
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script_callbacks.on_app_started(Script.set_server_address)
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Loading…
Reference in New Issue