Changed alwayson_script_name and alwayson_script_args api params to 1 alwayson_scripts param dict
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@ -195,22 +195,17 @@ class Api:
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script_args[0] = 0
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# Now check for always on scripts
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if request.alwayson_script_name and (len(request.alwayson_script_name) > 0):
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# always on script with no arg should always run, but if you include their name in the api request, send an empty list for there args
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if not request.alwayson_script_args:
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raise HTTPException(status_code=422, detail=f"Script {request.alwayson_script_name} has no arg list")
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if len(request.alwayson_script_name) != len(request.alwayson_script_args):
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raise HTTPException(status_code=422, detail=f"Number of script names and number of script arg lists doesn't match")
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for alwayson_script_name, alwayson_script_args in zip(request.alwayson_script_name, request.alwayson_script_args):
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if request.alwayson_scripts and (len(request.alwayson_scripts) > 0):
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for alwayson_script_name in request.alwayson_scripts.keys():
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alwayson_script = self.get_script(alwayson_script_name, script_runner)
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if alwayson_script == None:
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raise HTTPException(status_code=422, detail=f"always on script {alwayson_script_name} not found")
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# Selectable script in always on script param check
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if alwayson_script.alwayson == False:
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raise HTTPException(status_code=422, detail=f"Cannot have a selectable script in the always on scripts params")
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if alwayson_script_args != []:
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script_args[alwayson_script.args_from:alwayson_script.args_to] = alwayson_script_args
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# always on script with no arg should always run so you don't really need to add them to the requests
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if "args" in request.alwayson_scripts[alwayson_script_name]:
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script_args[alwayson_script.args_from:alwayson_script.args_to] = request.alwayson_scripts[alwayson_script_name]["args"]
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return script_args
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def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
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@ -226,15 +221,13 @@ class Api:
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"do_not_save_grid": True
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}
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)
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if populate.sampler_name:
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populate.sampler_index = None # prevent a warning later on
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args = vars(populate)
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args.pop('script_name', None)
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args.pop('script_args', None) # will refeed them to the pipeline directly after initializing them
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args.pop('alwayson_script_name', None)
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args.pop('alwayson_script_args', None)
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args.pop('alwayson_scripts', None)
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script_args = self.init_script_args(txt2imgreq, selectable_scripts, selectable_script_idx, script_runner)
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@ -279,7 +272,6 @@ class Api:
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"mask": mask
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}
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)
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if populate.sampler_name:
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populate.sampler_index = None # prevent a warning later on
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@ -287,8 +279,7 @@ class Api:
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args.pop('include_init_images', None) # this is meant to be done by "exclude": True in model, but it's for a reason that I cannot determine.
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args.pop('script_name', None)
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args.pop('script_args', None) # will refeed them to the pipeline directly after initializing them
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args.pop('alwayson_script_name', None)
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args.pop('alwayson_script_args', None)
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args.pop('alwayson_scripts', None)
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script_args = self.init_script_args(img2imgreq, selectable_scripts, selectable_script_idx, script_runner)
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@ -100,13 +100,13 @@ class PydanticModelGenerator:
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StableDiffusionTxt2ImgProcessingAPI = PydanticModelGenerator(
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"StableDiffusionProcessingTxt2Img",
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StableDiffusionProcessingTxt2Img,
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[{"key": "sampler_index", "type": str, "default": "Euler"}, {"key": "script_name", "type": str, "default": None}, {"key": "script_args", "type": list, "default": []}, {"key": "alwayson_script_name", "type": list, "default": []}, {"key": "alwayson_script_args", "type": list, "default": []}]
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[{"key": "sampler_index", "type": str, "default": "Euler"}, {"key": "script_name", "type": str, "default": None}, {"key": "script_args", "type": list, "default": []}, {"key": "alwayson_scripts", "type": dict, "default": {}}]
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).generate_model()
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StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator(
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"StableDiffusionProcessingImg2Img",
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StableDiffusionProcessingImg2Img,
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[{"key": "sampler_index", "type": str, "default": "Euler"}, {"key": "init_images", "type": list, "default": None}, {"key": "denoising_strength", "type": float, "default": 0.75}, {"key": "mask", "type": str, "default": None}, {"key": "include_init_images", "type": bool, "default": False, "exclude" : True}, {"key": "script_name", "type": str, "default": None}, {"key": "script_args", "type": list, "default": []}, {"key": "alwayson_script_name", "type": list, "default": []}, {"key": "alwayson_script_args", "type": list, "default": []}]
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[{"key": "sampler_index", "type": str, "default": "Euler"}, {"key": "init_images", "type": list, "default": None}, {"key": "denoising_strength", "type": float, "default": 0.75}, {"key": "mask", "type": str, "default": None}, {"key": "include_init_images", "type": bool, "default": False, "exclude" : True}, {"key": "script_name", "type": str, "default": None}, {"key": "script_args", "type": list, "default": []}, {"key": "alwayson_scripts", "type": dict, "default": {}}]
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).generate_model()
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class TextToImageResponse(BaseModel):
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