Move processing's models into models.py

It didn't make sense to have two differente files for the same and
"models" is a more descriptive name.

3 files changed, 119 insertions, 156 deletions

diff --git a/modules/api/api.py b/modules/api/api.py
index 3acb1f36..20e85e82 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -1,16 +1,11 @@
-from modules.api.processing import StableDiffusionTxt2ImgProcessingAPI, StableDiffusionImg2ImgProcessingAPI
-from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
-from modules.sd_samplers import all_samplers
-import modules.shared as shared
 import uvicorn
+from gradio import processing_utils
 from fastapi import APIRouter, HTTPException
-import json
-import io
-import base64
+import modules.shared as shared
 from modules.api.models import *
-from PIL import Image
+from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
+from modules.sd_samplers import all_samplers
 from modules.extras import run_extras
-from gradio import processing_utils
 
 def upscaler_to_index(name: str):
     try:
@@ -20,29 +15,6 @@ def upscaler_to_index(name: str):
 
 sampler_to_index = lambda name: next(filter(lambda row: name.lower() == row[1].name.lower(), enumerate(all_samplers)), None)
 
-# def img_to_base64(img: str):
-#     buffer = io.BytesIO()
-#     img.save(buffer, format="png")
-#     return base64.b64encode(buffer.getvalue())
-
-# def base64_to_bytes(base64Img: str):
-#     if "," in base64Img:
-#         base64Img = base64Img.split(",")[1]
-#     return io.BytesIO(base64.b64decode(base64Img))
-
-# def base64_to_images(base64Imgs: list[str]):
-#     imgs = []
-#     for img in base64Imgs:
-#         img = Image.open(base64_to_bytes(img))
-#         imgs.append(img)
-#     return imgs
-
-class ImageToImageResponse(BaseModel):
-    images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
-    parameters: dict
-    info: str
-
-
 class Api:
     def __init__(self, app, queue_lock):
         self.router = APIRouter()
@@ -51,15 +23,7 @@ class Api:
         self.app.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"], response_model=TextToImageResponse)
         self.app.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"], response_model=ImageToImageResponse)
         self.app.add_api_route("/sdapi/v1/extra-single-image", self.extras_single_image_api, methods=["POST"], response_model=ExtrasSingleImageResponse)
-        self.app.add_api_route("/sdapi/v1/extra-batch-image", self.extras_batch_images_api, methods=["POST"], response_model=ExtrasBatchImagesResponse)
-
-    # def __base64_to_image(self, base64_string):
-    #     # if has a comma, deal with prefix
-    #     if "," in base64_string:
-    #         base64_string = base64_string.split(",")[1]
-    #     imgdata = base64.b64decode(base64_string)
-    #     # convert base64 to PIL image
-    #     return Image.open(io.BytesIO(imgdata))
+        self.app.add_api_route("/sdapi/v1/extra-batch-images", self.extras_batch_images_api, methods=["POST"], response_model=ExtrasBatchImagesResponse)
 
     def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
         sampler_index = sampler_to_index(txt2imgreq.sampler_index)
@@ -81,7 +45,7 @@ class Api:
         
         b64images = list(map(processing_utils.encode_pil_to_base64, processed.images))
         
-        return TextToImageResponse(images=b64images, parameters=json.dumps(vars(txt2imgreq)), info=processed.info)
+        return TextToImageResponse(images=b64images, parameters=vars(txt2imgreq), info=processed.info)
 
     def img2imgapi(self, img2imgreq: StableDiffusionImg2ImgProcessingAPI):
         sampler_index = sampler_to_index(img2imgreq.sampler_index)
@@ -120,10 +84,7 @@ class Api:
             processed = process_images(p)
         
         b64images = list(map(processing_utils.encode_pil_to_base64, processed.images))
-        # for i in processed.images:
-        #     buffer = io.BytesIO()
-        #     i.save(buffer, format="png")
-        #     b64images.append(base64.b64encode(buffer.getvalue()))
+       
         return ImageToImageResponse(images=b64images, parameters=vars(img2imgreq), info=processed.info)
 
     def extras_single_image_api(self, req: ExtrasSingleImageRequest):
@@ -134,12 +95,12 @@ class Api:
         reqDict.pop('upscaler_1')
         reqDict.pop('upscaler_2')
 
-        reqDict['image'] = processing_utils.decode_base64_to_file(reqDict['image'])
+        reqDict['image'] = processing_utils.decode_base64_to_image(reqDict['image'])
 
         with self.queue_lock:
             result = run_extras(**reqDict, extras_upscaler_1=upscaler1Index, extras_upscaler_2=upscaler2Index, extras_mode=0, image_folder="", input_dir="", output_dir="")
 
-        return ExtrasSingleImageResponse(image=processing_utils.encode_pil_to_base64(result[0]), html_info_x=result[1], html_info=result[2])
+        return ExtrasSingleImageResponse(image=processing_utils.encode_pil_to_base64(result[0][0]), html_info_x=result[1], html_info=result[2])
 
     def extras_batch_images_api(self, req: ExtrasBatchImagesRequest):
         upscaler1Index = upscaler_to_index(req.upscaler_1)
diff --git a/modules/api/models.py b/modules/api/models.py
index 209f8af5..362e6277 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -1,10 +1,118 @@
-from pydantic import BaseModel, Field, Json
+import inspect
+from pydantic import BaseModel, Field, Json, create_model
+from typing import Any, Optional
 from typing_extensions import Literal
+from inflection import underscore
+from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img
 from modules.shared import sd_upscalers
 
+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"""
+
+    field: str
+    field_alias: str
+    field_type: Any
+    field_value: Any
+
+
+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
+    params are the names of the actual keys required by __init__
+    """
+
+    def __init__(
+        self,
+        model_name: str = None,
+        class_instance = None,
+        additional_fields = None,
+    ):
+        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._class_data = merge_class_params(class_instance)
+        self._model_def = [
+            ModelDef(
+                field=underscore(k),
+                field_alias=k,
+                field_type=field_type_generator(k, v),
+                field_value=v.default
+            )
+            for (k,v) in self._class_data.items() if k not in API_NOT_ALLOWED
+        ]
+        
+        for fields in additional_fields:
+            self._model_def.append(ModelDef(
+                field=underscore(fields["key"]), 
+                field_alias=fields["key"], 
+                field_type=fields["type"],
+                field_value=fields["default"]))
+
+    def generate_model(self):
+        """
+        Creates a pydantic BaseModel
+        from the json and overrides provided at initialization
+        """
+        fields = {
+            d.field: (d.field_type, Field(default=d.field_value, alias=d.field_alias)) for d in self._model_def
+        }
+        DynamicModel = create_model(self._model_name, **fields)
+        DynamicModel.__config__.allow_population_by_field_name = True
+        DynamicModel.__config__.allow_mutation = True
+        return DynamicModel
+    
+StableDiffusionTxt2ImgProcessingAPI = PydanticModelGenerator(
+    "StableDiffusionProcessingTxt2Img", 
+    StableDiffusionProcessingTxt2Img,
+    [{"key": "sampler_index", "type": str, "default": "Euler"}]
+).generate_model()
+
+StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator(
+    "StableDiffusionProcessingImg2Img", 
+    StableDiffusionProcessingImg2Img,
+    [{"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}]
+).generate_model()
+
 class TextToImageResponse(BaseModel):
     images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
-    parameters: str
+    parameters: dict
+    info: str
+
+class ImageToImageResponse(BaseModel):
+    images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
+    parameters: dict
     info: str
 
 class ExtrasBaseRequest(BaseModel):
diff --git a/modules/api/processing.py b/modules/api/processing.py
deleted file mode 100644
index f551fa35..00000000
--- a/modules/api/processing.py
+++ /dev/null
@@ -1,106 +0,0 @@
-from array import array
-from inflection import underscore
-from typing import Any, Dict, Optional
-from pydantic import BaseModel, Field, create_model
-from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img
-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"""
-
-    field: str
-    field_alias: str
-    field_type: Any
-    field_value: Any
-
-
-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
-    params are the names of the actual keys required by __init__
-    """
-
-    def __init__(
-        self,
-        model_name: str = None,
-        class_instance = None,
-        additional_fields = None,
-    ):
-        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._class_data = merge_class_params(class_instance)
-        self._model_def = [
-            ModelDef(
-                field=underscore(k),
-                field_alias=k,
-                field_type=field_type_generator(k, v),
-                field_value=v.default
-            )
-            for (k,v) in self._class_data.items() if k not in API_NOT_ALLOWED
-        ]
-        
-        for fields in additional_fields:
-            self._model_def.append(ModelDef(
-                field=underscore(fields["key"]), 
-                field_alias=fields["key"], 
-                field_type=fields["type"],
-                field_value=fields["default"]))
-
-    def generate_model(self):
-        """
-        Creates a pydantic BaseModel
-        from the json and overrides provided at initialization
-        """
-        fields = {
-            d.field: (d.field_type, Field(default=d.field_value, alias=d.field_alias)) for d in self._model_def
-        }
-        DynamicModel = create_model(self._model_name, **fields)
-        DynamicModel.__config__.allow_population_by_field_name = True
-        DynamicModel.__config__.allow_mutation = True
-        return DynamicModel
-    
-StableDiffusionTxt2ImgProcessingAPI = PydanticModelGenerator(
-    "StableDiffusionProcessingTxt2Img", 
-    StableDiffusionProcessingTxt2Img,
-    [{"key": "sampler_index", "type": str, "default": "Euler"}]
-).generate_model()
-
-StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator(
-    "StableDiffusionProcessingImg2Img", 
-    StableDiffusionProcessingImg2Img,
-    [{"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}]
-).generate_model()
\ No newline at end of file