[Bugfix][API] - Fix API response for colab users

modules/api/api.py

@@ -7,6 +7,7 @@ import uvicorn
 from fastapi import Body, APIRouter, HTTPException
 from fastapi.responses import JSONResponse
 from pydantic import BaseModel, Field, Json
+from typing import List
 import json
 import io
 import base64
@@ -15,12 +16,12 @@ from PIL import Image
 sampler_to_index = lambda name: next(filter(lambda row: name.lower() == row[1].name.lower(), enumerate(all_samplers)), None)
 
 class TextToImageResponse(BaseModel):
-    images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
+    images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
     parameters: Json
     info: Json
 
 class ImageToImageResponse(BaseModel):
-    images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
+    images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
     parameters: Json
     info: Json
 
@@ -41,6 +42,9 @@ class Api:
         # convert base64 to PIL image
         return Image.open(io.BytesIO(imgdata))
 
+    def __processed_info_to_json(self, processed):
+        return json.dumps(processed.info)
+
     def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
         sampler_index = sampler_to_index(txt2imgreq.sampler_index)
         
@@ -65,7 +69,7 @@ class Api:
             i.save(buffer, format="png")
             b64images.append(base64.b64encode(buffer.getvalue()))
 
-        return TextToImageResponse(images=b64images, parameters=json.dumps(vars(txt2imgreq)), info=json.dumps(processed.info))
+        return TextToImageResponse(images=b64images, parameters=json.dumps(vars(txt2imgreq)), info=processed.js())
         
         
 
@@ -111,7 +115,12 @@ class Api:
             i.save(buffer, format="png")
             b64images.append(base64.b64encode(buffer.getvalue()))
 
-        return ImageToImageResponse(images=b64images, parameters=json.dumps(vars(img2imgreq)), info=json.dumps(processed.info))
+        if (not img2imgreq.include_init_images):
+            # remove img2imgreq.init_images and img2imgreq.mask
+            img2imgreq.init_images = None
+            img2imgreq.mask = None
+
+        return ImageToImageResponse(images=b64images, parameters=json.dumps(vars(img2imgreq)), info=processed.js())
 
     def extrasapi(self):
         raise NotImplementedError

modules/api/models.py

@@ -31,6 +31,7 @@ class ModelDef(BaseModel):
     field_alias: str
     field_type: Any
     field_value: Any
+    field_exclude: bool = False
 
 
 class PydanticModelGenerator:
@@ -68,7 +69,7 @@ class PydanticModelGenerator:
                 field=underscore(k),
                 field_alias=k,
                 field_type=field_type_generator(k, v),
-                field_value=v.default
+                field_value=v.default,
             )
             for (k,v) in self._class_data.items() if k not in API_NOT_ALLOWED
         ]
@@ -78,7 +79,8 @@ class PydanticModelGenerator:
                 field=underscore(fields["key"]), 
                 field_alias=fields["key"], 
                 field_type=fields["type"],
-                field_value=fields["default"]))
+                field_value=fields["default"],
+                field_exclude=fields["exclude"] if "exclude" in fields else False))
 
     def generate_model(self):
         """
@@ -86,7 +88,7 @@ class PydanticModelGenerator:
         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
+            d.field: (d.field_type, Field(default=d.field_value, alias=d.field_alias, exclude=d.field_exclude)) for d in self._model_def
         }
         DynamicModel = create_model(self._model_name, **fields)
         DynamicModel.__config__.allow_population_by_field_name = True
@@ -102,5 +104,5 @@ StableDiffusionTxt2ImgProcessingAPI = PydanticModelGenerator(
 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}]
+    [{"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}, {"key": "include_init_images", "type": bool, "default": False, "exclude" : True}]
 ).generate_model()
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