18f9f7dc31
* Initial commit
* Update README.md
* sync with original main Fooocus repo
* update with my gitignore setup
* add max lora config feature
* Revert "add max lora config feature"
This reverts commit cfe7463fe2.
* add max loras config feature
* Update README.md
* Update .gitignore
* update
* merge
* revert
* refactor: rename default_loras_max_number to default_max_lora_number, validate config for int
* fix: add missing patch_all call and imports again
---------
Co-authored-by: Manuel Schmid <manuel.schmid@odt.net>
151 lines
3.9 KiB
Python
151 lines
3.9 KiB
Python
import json
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import gradio as gr
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import modules.config
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def load_parameter_button_click(raw_prompt_txt, is_generating):
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loaded_parameter_dict = json.loads(raw_prompt_txt)
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assert isinstance(loaded_parameter_dict, dict)
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results = [True, 1]
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try:
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h = loaded_parameter_dict.get('Prompt', None)
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assert isinstance(h, str)
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results.append(h)
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except:
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results.append(gr.update())
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try:
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h = loaded_parameter_dict.get('Negative Prompt', None)
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assert isinstance(h, str)
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results.append(h)
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except:
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results.append(gr.update())
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try:
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h = loaded_parameter_dict.get('Styles', None)
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h = eval(h)
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assert isinstance(h, list)
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results.append(h)
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except:
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results.append(gr.update())
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try:
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h = loaded_parameter_dict.get('Performance', None)
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assert isinstance(h, str)
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results.append(h)
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except:
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results.append(gr.update())
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try:
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h = loaded_parameter_dict.get('Resolution', None)
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width, height = eval(h)
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formatted = modules.config.add_ratio(f'{width}*{height}')
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if formatted in modules.config.available_aspect_ratios:
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results.append(formatted)
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results.append(-1)
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results.append(-1)
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else:
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results.append(gr.update())
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results.append(width)
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results.append(height)
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except:
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results.append(gr.update())
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results.append(gr.update())
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results.append(gr.update())
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try:
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h = loaded_parameter_dict.get('Sharpness', None)
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assert h is not None
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h = float(h)
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results.append(h)
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except:
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results.append(gr.update())
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try:
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h = loaded_parameter_dict.get('Guidance Scale', None)
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assert h is not None
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h = float(h)
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results.append(h)
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except:
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results.append(gr.update())
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try:
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h = loaded_parameter_dict.get('ADM Guidance', None)
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p, n, e = eval(h)
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results.append(float(p))
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results.append(float(n))
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results.append(float(e))
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except:
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results.append(gr.update())
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results.append(gr.update())
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results.append(gr.update())
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try:
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h = loaded_parameter_dict.get('Base Model', None)
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assert isinstance(h, str)
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results.append(h)
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except:
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results.append(gr.update())
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try:
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h = loaded_parameter_dict.get('Refiner Model', None)
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assert isinstance(h, str)
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results.append(h)
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except:
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results.append(gr.update())
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try:
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h = loaded_parameter_dict.get('Refiner Switch', None)
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assert h is not None
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h = float(h)
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results.append(h)
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except:
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results.append(gr.update())
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try:
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h = loaded_parameter_dict.get('Sampler', None)
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assert isinstance(h, str)
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results.append(h)
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except:
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results.append(gr.update())
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try:
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h = loaded_parameter_dict.get('Scheduler', None)
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assert isinstance(h, str)
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results.append(h)
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except:
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results.append(gr.update())
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try:
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h = loaded_parameter_dict.get('Seed', None)
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assert h is not None
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h = int(h)
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results.append(False)
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results.append(h)
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except:
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results.append(gr.update())
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results.append(gr.update())
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if is_generating:
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results.append(gr.update())
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else:
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results.append(gr.update(visible=True))
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results.append(gr.update(visible=False))
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for i in range(1, modules.config.default_max_lora_number + 1):
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try:
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n, w = loaded_parameter_dict.get(f'LoRA {i}', ' : ').split(' : ')
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w = float(w)
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results.append(True)
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results.append(n)
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results.append(w)
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except:
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results.append(True)
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results.append('None')
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results.append(1.0)
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return results
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