example API working with gradio
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arcticfaded
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commit
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3 changed files with 60 additions and 27 deletions
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@ -23,8 +23,13 @@ class Api:
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app.add_api_route("/v1/txt2img", self.text2imgapi, methods=["POST"])
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def text2imgapi(self, txt2imgreq: StableDiffusionProcessingAPI ):
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p = StableDiffusionProcessingTxt2Img(**vars(txt2imgreq))
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p.sd_model = shared.sd_model
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populate = txt2imgreq.copy(update={ # Override __init__ params
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"sd_model": shared.sd_model,
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"sampler_index": 0,
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}
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)
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p = StableDiffusionProcessingTxt2Img(**vars(populate))
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# Override object param
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processed = process_images(p)
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b64images = []
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@ -5,6 +5,24 @@ from modules.processing import StableDiffusionProcessing, Processed, StableDiffu
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import inspect
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API_NOT_ALLOWED = [
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"self",
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"kwargs",
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"sd_model",
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"outpath_samples",
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"outpath_grids",
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"sampler_index",
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"do_not_save_samples",
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"do_not_save_grid",
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"extra_generation_params",
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"overlay_images",
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"do_not_reload_embeddings",
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"seed_enable_extras",
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"prompt_for_display",
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"sampler_noise_scheduler_override",
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"ddim_discretize"
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]
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class ModelDef(BaseModel):
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"""Assistance Class for Pydantic Dynamic Model Generation"""
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@ -14,7 +32,7 @@ class ModelDef(BaseModel):
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field_value: Any
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class pydanticModelGenerator:
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class PydanticModelGenerator:
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"""
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Takes in created classes and stubs them out in a way FastAPI/Pydantic is happy about:
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source_data is a snapshot of the default values produced by the class
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@ -24,30 +42,33 @@ class pydanticModelGenerator:
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def __init__(
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self,
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model_name: str = None,
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source_data: {} = {},
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params: Dict = {},
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overrides: Dict = {},
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optionals: Dict = {},
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class_instance = None
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):
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def field_type_generator(k, v, overrides, optionals):
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field_type = str if not overrides.get(k) else overrides[k]["type"]
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if v is None:
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field_type = Any
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else:
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field_type = type(v)
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def field_type_generator(k, v):
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# field_type = str if not overrides.get(k) else overrides[k]["type"]
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# print(k, v.annotation, v.default)
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field_type = v.annotation
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return Optional[field_type]
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def merge_class_params(class_):
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all_classes = list(filter(lambda x: x is not object, inspect.getmro(class_)))
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parameters = {}
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for classes in all_classes:
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parameters = {**parameters, **inspect.signature(classes.__init__).parameters}
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return parameters
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self._model_name = model_name
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self._json_data = source_data
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self._class_data = merge_class_params(class_instance)
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self._model_def = [
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ModelDef(
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field=underscore(k),
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field_alias=k,
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field_type=field_type_generator(k, v, overrides, optionals),
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field_value=v
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field_type=field_type_generator(k, v),
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field_value=v.default
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)
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for (k,v) in source_data.items() if k in params
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for (k,v) in self._class_data.items() if k not in API_NOT_ALLOWED
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]
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def generate_model(self):
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@ -60,8 +81,7 @@ class pydanticModelGenerator:
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}
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DynamicModel = create_model(self._model_name, **fields)
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DynamicModel.__config__.allow_population_by_field_name = True
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DynamicModel.__config__.allow_mutation = True
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return DynamicModel
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StableDiffusionProcessingAPI = pydanticModelGenerator("StableDiffusionProcessing",
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StableDiffusionProcessing().__dict__,
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inspect.signature(StableDiffusionProcessing.__init__).parameters).generate_model()
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StableDiffusionProcessingAPI = PydanticModelGenerator("StableDiffusionProcessingTxt2Img", StableDiffusionProcessingTxt2Img).generate_model()
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@ -9,6 +9,7 @@ from PIL import Image, ImageFilter, ImageOps
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import random
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import cv2
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from skimage import exposure
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from typing import Any, Dict, List, Optional
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import modules.sd_hijack
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from modules import devices, prompt_parser, masking, sd_samplers, lowvram
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@ -51,9 +52,15 @@ def get_correct_sampler(p):
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return sd_samplers.samplers
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elif isinstance(p, modules.processing.StableDiffusionProcessingImg2Img):
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return sd_samplers.samplers_for_img2img
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elif isinstance(p, modules.api.processing.StableDiffusionProcessingAPI):
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return sd_samplers.samplers
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class StableDiffusionProcessing:
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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):
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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_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):
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self.sd_model = sd_model
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self.outpath_samples: str = outpath_samples
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self.outpath_grids: str = outpath_grids
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@ -86,10 +93,10 @@ class StableDiffusionProcessing:
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self.denoising_strength: float = 0
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self.sampler_noise_scheduler_override = None
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self.ddim_discretize = opts.ddim_discretize
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self.s_churn = opts.s_churn
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self.s_tmin = opts.s_tmin
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self.s_tmax = float('inf') # not representable as a standard ui option
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self.s_noise = opts.s_noise
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self.s_churn = s_churn or opts.s_churn
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self.s_tmin = s_tmin or opts.s_tmin
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self.s_tmax = s_tmax or float('inf') # not representable as a standard ui option
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self.s_noise = s_noise or opts.s_noise
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if not seed_enable_extras:
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self.subseed = -1
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@ -97,6 +104,7 @@ class StableDiffusionProcessing:
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self.seed_resize_from_h = 0
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self.seed_resize_from_w = 0
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def init(self, all_prompts, all_seeds, all_subseeds):
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pass
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@ -497,7 +505,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
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class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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sampler = None
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def __init__(self, enable_hr=False, denoising_strength=0.75, firstphase_width=0, firstphase_height=0, **kwargs):
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def __init__(self, enable_hr: bool=False, denoising_strength: float=0.75, firstphase_width: int=0, firstphase_height: int=0, **kwargs):
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super().__init__(**kwargs)
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self.enable_hr = enable_hr
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self.denoising_strength = denoising_strength
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