diff --git a/modules/api/api.py b/modules/api/api.py
index fd09d3524..5e86c3bf4 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -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 = []
diff --git a/modules/api/processing.py b/modules/api/processing.py
index e4df93c54..b6798241a 100644
--- a/modules/api/processing.py
+++ b/modules/api/processing.py
@@ -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()
diff --git a/modules/processing.py b/modules/processing.py
index 4a7c6ccc4..024a4fc30 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -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:
- 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):
+class StableDiffusionProcessing():
+ """
+ The first set of paramaters: sd_models -> do_not_reload_embeddings represent the minimum required to create a StableDiffusionProcessing
+
+ """
+ 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