diff options
Diffstat (limited to 'modules')
| -rw-r--r-- | modules/api/api.py | 111 | ||||
| -rw-r--r-- | modules/api/models.py | 4 |
2 files changed, 100 insertions, 15 deletions
diff --git a/modules/api/api.py b/modules/api/api.py index 5a9ac5f1..a1cdebb8 100644 --- a/modules/api/api.py +++ b/modules/api/api.py @@ -163,20 +163,26 @@ class Api: raise HTTPException(status_code=401, detail="Incorrect username or password", headers={"WWW-Authenticate": "Basic"}) - def get_script(self, script_name, script_runner): - if script_name is None: + def get_selectable_script(self, script_name, script_runner): + if script_name is None or script_name == "": return None, None - if not script_runner.scripts: - script_runner.initialize_scripts(False) - ui.create_ui() - script_idx = script_name_to_index(script_name, script_runner.selectable_scripts) script = script_runner.selectable_scripts[script_idx] return script, script_idx + def get_script(self, script_name, script_runner): + for script in script_runner.scripts: + if script_name.lower() == script.title().lower(): + return script + return None + def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI): - script, script_idx = self.get_script(txt2imgreq.script_name, scripts.scripts_txt2img) + script_runner = scripts.scripts_txt2img + if not script_runner.scripts: + script_runner.initialize_scripts(False) + ui.create_ui() + api_selectable_scripts, api_selectable_script_idx = self.get_selectable_script(txt2imgreq.script_name, script_runner) populate = txt2imgreq.copy(update={ # Override __init__ params "sampler_name": validate_sampler_name(txt2imgreq.sampler_name or txt2imgreq.sampler_index), @@ -184,22 +190,59 @@ class Api: "do_not_save_grid": True } ) + if populate.sampler_name: populate.sampler_index = None # prevent a warning later on args = vars(populate) args.pop('script_name', None) + args.pop('script_args', None) # will refeed them later with script_args + args.pop('alwayson_script_name', None) + args.pop('alwayson_script_args', None) + + #find max idx from the scripts in runner and generate a none array to init script_args + last_arg_index = 1 + for script in script_runner.scripts: + if last_arg_index < script.args_to: + last_arg_index = script.args_to + # None everywhere exepct position 0 to initialize script args + script_args = [None]*last_arg_index + # position 0 in script_arg is the idx+1 of the selectable script that is going to be run + if api_selectable_scripts: + script_args[api_selectable_scripts.args_from:api_selectable_scripts.args_to] = txt2imgreq.script_args + script_args[0] = api_selectable_script_idx + 1 + else: + # if 0 then none + script_args[0] = 0 + + # Now check for always on scripts + if len(txt2imgreq.alwayson_script_name) > 0: + # 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 + if len(txt2imgreq.alwayson_script_name) != len(txt2imgreq.alwayson_script_args): + raise HTTPException(status_code=422, detail=f"Number of script names and number of script arg lists doesn't match") + + for alwayson_script_name, alwayson_script_args in zip(txt2imgreq.alwayson_script_name, txt2imgreq.alwayson_script_args): + alwayson_script = self.get_script(alwayson_script_name, script_runner) + if alwayson_script == None: + raise HTTPException(status_code=422, detail=f"always on script {alwayson_script_name} not found") + # Selectable script in always on script param check + if alwayson_script.alwayson == False: + raise HTTPException(status_code=422, detail=f"Cannot have a selectable script in the always on scripts params") + if alwayson_script_args != []: + script_args[alwayson_script.args_from:alwayson_script.args_to] = alwayson_script_args with self.queue_lock: p = StableDiffusionProcessingTxt2Img(sd_model=shared.sd_model, **args) + p.scripts = script_runner shared.state.begin() - if script is not None: + if api_selectable_scripts != None: + p.script_args = script_args p.outpath_grids = opts.outdir_txt2img_grids p.outpath_samples = opts.outdir_txt2img_samples - p.script_args = [script_idx + 1] + [None] * (script.args_from - 1) + p.script_args processed = scripts.scripts_txt2img.run(p, *p.script_args) else: + p.script_args = tuple(script_args) processed = process_images(p) shared.state.end() @@ -212,12 +255,16 @@ class Api: if init_images is None: raise HTTPException(status_code=404, detail="Init image not found") - script, script_idx = self.get_script(img2imgreq.script_name, scripts.scripts_img2img) - mask = img2imgreq.mask if mask: mask = decode_base64_to_image(mask) + script_runner = scripts.scripts_img2img + if not script_runner.scripts: + script_runner.initialize_scripts(True) + ui.create_ui() + api_selectable_scripts, api_selectable_script_idx = self.get_selectable_script(img2imgreq.script_name, script_runner) + populate = img2imgreq.copy(update={ # Override __init__ params "sampler_name": validate_sampler_name(img2imgreq.sampler_name or img2imgreq.sampler_index), "do_not_save_samples": True, @@ -225,24 +272,62 @@ class Api: "mask": mask } ) + if populate.sampler_name: populate.sampler_index = None # prevent a warning later on args = vars(populate) 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. args.pop('script_name', None) + args.pop('script_args', None) # will refeed them later with script_args + args.pop('alwayson_script_name', None) + args.pop('alwayson_script_args', None) + + #find max idx from the scripts in runner and generate a none array to init script_args + last_arg_index = 1 + for script in script_runner.scripts: + if last_arg_index < script.args_to: + last_arg_index = script.args_to + # None everywhere exepct position 0 to initialize script args + script_args = [None]*last_arg_index + # position 0 in script_arg is the idx+1 of the selectable script that is going to be run + if api_selectable_scripts: + script_args[api_selectable_scripts.args_from:api_selectable_scripts.args_to] = img2imgreq.script_args + script_args[0] = api_selectable_script_idx + 1 + else: + # if 0 then none + script_args[0] = 0 + + # Now check for always on scripts + if len(img2imgreq.alwayson_script_name) > 0: + # 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 + if len(img2imgreq.alwayson_script_name) != len(img2imgreq.alwayson_script_args): + raise HTTPException(status_code=422, detail=f"Number of script names and number of script arg lists doesn't match") + + for alwayson_script_name, alwayson_script_args in zip(img2imgreq.alwayson_script_name, img2imgreq.alwayson_script_args): + alwayson_script = self.get_script(alwayson_script_name, script_runner) + if alwayson_script == None: + raise HTTPException(status_code=422, detail=f"always on script {alwayson_script_name} not found") + # Selectable script in always on script param check + if alwayson_script.alwayson == False: + raise HTTPException(status_code=422, detail=f"Cannot have a selectable script in the always on scripts params") + if alwayson_script_args != []: + script_args[alwayson_script.args_from:alwayson_script.args_to] = alwayson_script_args + with self.queue_lock: p = StableDiffusionProcessingImg2Img(sd_model=shared.sd_model, **args) p.init_images = [decode_base64_to_image(x) for x in init_images] + p.scripts = script_runner shared.state.begin() - if script is not None: + if api_selectable_scripts != None: + p.script_args = script_args p.outpath_grids = opts.outdir_img2img_grids p.outpath_samples = opts.outdir_img2img_samples - p.script_args = [script_idx + 1] + [None] * (script.args_from - 1) + p.script_args processed = scripts.scripts_img2img.run(p, *p.script_args) else: + p.script_args = tuple(script_args) processed = process_images(p) shared.state.end() diff --git a/modules/api/models.py b/modules/api/models.py index cba43d3b..86c70178 100644 --- a/modules/api/models.py +++ b/modules/api/models.py @@ -100,13 +100,13 @@ class PydanticModelGenerator: StableDiffusionTxt2ImgProcessingAPI = PydanticModelGenerator( "StableDiffusionProcessingTxt2Img", StableDiffusionProcessingTxt2Img, - [{"key": "sampler_index", "type": str, "default": "Euler"}, {"key": "script_name", "type": str, "default": None}, {"key": "script_args", "type": list, "default": []}] + [{"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": []}] ).generate_model() StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator( "StableDiffusionProcessingImg2Img", StableDiffusionProcessingImg2Img, - [{"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": "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": []}] ).generate_model() class TextToImageResponse(BaseModel): |