example API working with gradio

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 = []

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()

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