example API working with gradio (f80e914a) · Commits · github_fork / Stable Diffusion Webui

modules/api/api.py

+7 −2

Original line number	Diff line number	Diff line
		@@ -23,8 +23,13 @@ class Api:
		app.add_api_route("/v1/txt2img", self.text2imgapi, methods=["POST"])

		def text2imgapi(self, txt2imgreq: StableDiffusionProcessingAPI ):
		p = StableDiffusionProcessingTxt2Img(**vars(txt2imgreq))
		p.sd_model = shared.sd_model
		populate = txt2imgreq.copy(update={ # Override __init__ params
		"sd_model": shared.sd_model,
		"sampler_index": 0,
		}
		)
		p = StableDiffusionProcessingTxt2Img(**vars(populate))
		# Override object param
		processed = process_images(p)

		b64images = []

modules/api/processing.py

+38 −18

Original line number	Diff line number	Diff line
		@@ -5,6 +5,24 @@ from modules.processing import StableDiffusionProcessing, Processed, StableDiffu
		import inspect


		API_NOT_ALLOWED = [
		"self",
		"kwargs",
		"sd_model",
		"outpath_samples",
		"outpath_grids",
		"sampler_index",
		"do_not_save_samples",
		"do_not_save_grid",
		"extra_generation_params",
		"overlay_images",
		"do_not_reload_embeddings",
		"seed_enable_extras",
		"prompt_for_display",
		"sampler_noise_scheduler_override",
		"ddim_discretize"
		]

		class ModelDef(BaseModel):
		"""Assistance Class for Pydantic Dynamic Model Generation"""

		@@ -14,7 +32,7 @@ class ModelDef(BaseModel):
		field_value: Any


		class pydanticModelGenerator:
		class PydanticModelGenerator:
		"""
		Takes in created classes and stubs them out in a way FastAPI/Pydantic is happy about:
		source_data is a snapshot of the default values produced by the class
		@@ -24,30 +42,33 @@ class pydanticModelGenerator:
		def __init__(
		self,
		model_name: str = None,
		source_data: {} = {},
		params: Dict = {},
		overrides: Dict = {},
		optionals: Dict = {},
		class_instance = None
		):
		def field_type_generator(k, v, overrides, optionals):
		field_type = str if not overrides.get(k) else overrides[k]["type"]
		if v is None:
		field_type = Any
		else:
		field_type = type(v)
		def field_type_generator(k, v):
		# field_type = str if not overrides.get(k) else overrides[k]["type"]
		# print(k, v.annotation, v.default)
		field_type = v.annotation

		return Optional[field_type]

		def merge_class_params(class_):
		all_classes = list(filter(lambda x: x is not object, inspect.getmro(class_)))
		parameters = {}
		for classes in all_classes:
		parameters = {parameters, inspect.signature(classes.__init__).parameters}
		return parameters


		self._model_name = model_name
		self._json_data = source_data
		self._class_data = merge_class_params(class_instance)
		self._model_def = [
		ModelDef(
		field=underscore(k),
		field_alias=k,
		field_type=field_type_generator(k, v, overrides, optionals),
		field_value=v
		field_type=field_type_generator(k, v),
		field_value=v.default
		)
		for (k,v) in source_data.items() if k in params
		for (k,v) in self._class_data.items() if k not in API_NOT_ALLOWED
		]

		def generate_model(self):
		@@ -60,8 +81,7 @@ class pydanticModelGenerator:
		}
		DynamicModel = create_model(self._model_name, **fields)
		DynamicModel.__config__.allow_population_by_field_name = True
		DynamicModel.__config__.allow_mutation = True
		return DynamicModel

		StableDiffusionProcessingAPI = pydanticModelGenerator("StableDiffusionProcessing",
		StableDiffusionProcessing().__dict__,
		inspect.signature(StableDiffusionProcessing.__init__).parameters).generate_model()
		StableDiffusionProcessingAPI = PydanticModelGenerator("StableDiffusionProcessingTxt2Img", StableDiffusionProcessingTxt2Img).generate_model()

modules/processing.py

+15 −7

Original line number	Diff line number	Diff line
		@@ -9,6 +9,7 @@ from PIL import Image, ImageFilter, ImageOps
		import random
		import cv2
		from skimage import exposure
		from typing import Any, Dict, List, Optional

		import modules.sd_hijack
		from modules import devices, prompt_parser, masking, sd_samplers, lowvram
		@@ -51,9 +52,15 @@ def get_correct_sampler(p):
		return sd_samplers.samplers
		elif isinstance(p, modules.processing.StableDiffusionProcessingImg2Img):
		return sd_samplers.samplers_for_img2img
		elif isinstance(p, modules.api.processing.StableDiffusionProcessingAPI):
		return sd_samplers.samplers

		class StableDiffusionProcessing():
		"""
		The first set of paramaters: sd_models -> do_not_reload_embeddings represent the minimum required to create a StableDiffusionProcessing

		class StableDiffusionProcessing:
		def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt="", styles=None, seed=-1, subseed=-1, subseed_strength=0, seed_resize_from_h=-1, seed_resize_from_w=-1, seed_enable_extras=True, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, restore_faces=False, tiling=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None, negative_prompt=None, eta=None, do_not_reload_embeddings=False):
		"""
		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_index: int=0, 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 = "uniform", s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0):
		self.sd_model = sd_model
		self.outpath_samples: str = outpath_samples
		self.outpath_grids: str = outpath_grids
		@@ -86,10 +93,10 @@ class StableDiffusionProcessing:
		self.denoising_strength: float = 0
		self.sampler_noise_scheduler_override = None
		self.ddim_discretize = opts.ddim_discretize
		self.s_churn = opts.s_churn
		self.s_tmin = opts.s_tmin
		self.s_tmax = float('inf') # not representable as a standard ui option
		self.s_noise = opts.s_noise
		self.s_churn = s_churn or opts.s_churn
		self.s_tmin = s_tmin or opts.s_tmin
		self.s_tmax = s_tmax or float('inf') # not representable as a standard ui option
		self.s_noise = s_noise or opts.s_noise

		if not seed_enable_extras:
		self.subseed = -1
		@@ -97,6 +104,7 @@ class StableDiffusionProcessing:
		self.seed_resize_from_h = 0
		self.seed_resize_from_w = 0


		def init(self, all_prompts, all_seeds, all_subseeds):
		pass

		@@ -497,7 +505,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
		class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
		sampler = None

		def __init__(self, enable_hr=False, denoising_strength=0.75, firstphase_width=0, firstphase_height=0, **kwargs):
		def __init__(self, enable_hr: bool=False, denoising_strength: float=0.75, firstphase_width: int=0, firstphase_height: int=0, **kwargs):
		super().__init__(**kwargs)
		self.enable_hr = enable_hr
		self.denoising_strength = denoising_strength

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