Merge pull request #6469 from noodleanon/scripts-from-api
Run scripts from API
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AUTOMATIC1111
GitHub
commit
e7f2f1e1b6
4 changed files with 48 additions and 9 deletions
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@ -11,7 +11,7 @@ from fastapi.security import HTTPBasic, HTTPBasicCredentials
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from secrets import compare_digest
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import modules.shared as shared
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from modules import sd_samplers, deepbooru, sd_hijack, images
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from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui
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from modules.api.models import *
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from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
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from modules.extras import run_extras
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@ -28,8 +28,13 @@ def upscaler_to_index(name: str):
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try:
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return [x.name.lower() for x in shared.sd_upscalers].index(name.lower())
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except:
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raise HTTPException(status_code=400, detail=f"Invalid upscaler, needs to be on of these: {' , '.join([x.name for x in sd_upscalers])}")
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raise HTTPException(status_code=400, detail=f"Invalid upscaler, needs to be one of these: {' , '.join([x.name for x in sd_upscalers])}")
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def script_name_to_index(name, scripts):
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try:
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return [script.title().lower() for script in scripts].index(name.lower())
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except:
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raise HTTPException(status_code=422, detail=f"Script '{name}' not found")
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def validate_sampler_name(name):
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config = sd_samplers.all_samplers_map.get(name, None)
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@ -144,6 +149,14 @@ class Api:
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raise HTTPException(status_code=401, detail="Incorrect username or password", headers={"WWW-Authenticate": "Basic"})
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def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
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if txt2imgreq.script_name is not None:
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if scripts.scripts_txt2img.scripts == []:
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scripts.scripts_txt2img.initialize_scripts(False)
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ui.create_ui()
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script_idx = script_name_to_index(txt2imgreq.script_name, scripts.scripts_txt2img.selectable_scripts)
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script = scripts.scripts_txt2img.selectable_scripts[script_idx]
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populate = txt2imgreq.copy(update={ # Override __init__ params
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"sampler_name": validate_sampler_name(txt2imgreq.sampler_name or txt2imgreq.sampler_index),
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"do_not_save_samples": True,
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@ -153,11 +166,20 @@ class Api:
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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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with self.queue_lock:
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p = StableDiffusionProcessingTxt2Img(sd_model=shared.sd_model, **vars(populate))
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p = StableDiffusionProcessingTxt2Img(sd_model=shared.sd_model, **args)
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shared.state.begin()
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processed = process_images(p)
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if 'script' in locals():
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p.outpath_grids = opts.outdir_txt2img_grids
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p.outpath_samples = opts.outdir_txt2img_samples
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p.script_args = [script_idx + 1] + [None] * (script.args_from - 1) + p.script_args
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processed = scripts.scripts_txt2img.run(p, *p.script_args)
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else:
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processed = process_images(p)
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shared.state.end()
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@ -170,6 +192,14 @@ class Api:
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if init_images is None:
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raise HTTPException(status_code=404, detail="Init image not found")
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if img2imgreq.script_name is not None:
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if scripts.scripts_img2img.scripts == []:
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scripts.scripts_img2img.initialize_scripts(True)
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ui.create_ui()
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script_idx = script_name_to_index(img2imgreq.script_name, scripts.scripts_img2img.selectable_scripts)
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script = scripts.scripts_img2img.selectable_scripts[script_idx]
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mask = img2imgreq.mask
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if mask:
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mask = decode_base64_to_image(mask)
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@ -186,13 +216,20 @@ class Api:
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args = vars(populate)
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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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with self.queue_lock:
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p = StableDiffusionProcessingImg2Img(sd_model=shared.sd_model, **args)
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p.init_images = [decode_base64_to_image(x) for x in init_images]
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shared.state.begin()
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processed = process_images(p)
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if 'script' in locals():
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p.outpath_grids = opts.outdir_img2img_grids
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p.outpath_samples = opts.outdir_img2img_samples
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p.script_args = [script_idx + 1] + [None] * (script.args_from - 1) + p.script_args
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processed = scripts.scripts_img2img.run(p, *p.script_args)
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else:
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processed = process_images(p)
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shared.state.end()
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b64images = list(map(encode_pil_to_base64, processed.images))
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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"}]
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[{"key": "sampler_index", "type": str, "default": "Euler"}, {"key": "script_name", "type": str, "default": None}, {"key": "script_args", "type": list, "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}]
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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": []}]
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).generate_model()
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class TextToImageResponse(BaseModel):
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@ -98,7 +98,7 @@ class StableDiffusionProcessing():
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"""
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The first set of paramaters: sd_models -> do_not_reload_embeddings represent the minimum required to create a StableDiffusionProcessing
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"""
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def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0, override_settings: Dict[str, Any] = None, override_settings_restore_afterwards: bool = True, sampler_index: int = None):
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def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0, override_settings: Dict[str, Any] = None, override_settings_restore_afterwards: bool = True, sampler_index: int = None, script_args: list = None):
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if sampler_index is not None:
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print("sampler_index argument for StableDiffusionProcessing does not do anything; use sampler_name", file=sys.stderr)
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@ -149,7 +149,7 @@ class StableDiffusionProcessing():
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self.seed_resize_from_w = 0
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self.scripts = None
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self.script_args = None
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self.script_args = script_args
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self.all_prompts = None
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self.all_negative_prompts = None
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self.all_seeds = None
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@ -25,6 +25,8 @@ class Script(scripts.Script):
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return [info, overlap, upscaler_index, scale_factor]
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def run(self, p, _, overlap, upscaler_index, scale_factor):
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if isinstance(upscaler_index, str):
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upscaler_index = [x.name.lower() for x in shared.sd_upscalers].index(upscaler_index.lower())
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processing.fix_seed(p)
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upscaler = shared.sd_upscalers[upscaler_index]
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