DALL·E node

__init__.py

@@ -2,12 +2,14 @@ from .cyberdolphin_gradio import CyberDolphinGradioApi
 from .cyberdolphin_openai_advanced import CyberdolphinOpenAIAdvanced
 from .cyberdolphin_openai_simple import CyberdolphinOpenAISimple
 from .cyberdolphin_openai_compatible import CyberdolphinOpenAICompatible
+from .cyberdolphin_imageneering import CyberDolphinImageneering
 
 NODE_CLASS_MAPPINGS = {
     "🐬 Gradio ChatInterface": CyberDolphinGradioApi,
     "🐬 OpenAI Simple": CyberdolphinOpenAISimple,
     "🐬 OpenAI Advanced": CyberdolphinOpenAIAdvanced,
     "🐬 OpenAI Compatible": CyberdolphinOpenAICompatible,
+    "🐬 OpenAI DALL·E": CyberDolphinImageneering,
 }
 
 NODE_DISPLAY_NAME_MAPPINGS = {
@@ -15,4 +17,5 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "CyberDolphin GPT-3.5 (Simple)": "🐬 CyberDolphin GPT-3.5 (Simple)",
     "CyberDolphin OpenAI (Advanced)": "🐬 CyberDolphin OpenAI (Advanced)",
     "CyberDolphin OpenAI Compatible": "🐬 CyberDolphin OpenAI Compatible",
+    "CyberDolphin OpenAI DALL·E": "🐬 CyberDolphin OpenAI DALL·E",
 }

cyberdolphin_imageneering.py

@@ -0,0 +1,64 @@
+import hashlib
+import os
+
+from PIL import Image, ImageOps
+import torch
+import numpy as np
+
+import folder_paths
+from .openai_client import convert_bson_to_image, OpenAiClient
+from .settings import load_settings
+import openai
+
+
+class CyberDolphinImageneering:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "prompt": ('STRING', {'default': 'a white siamese cat'}),
+            "size": (["256x256", "512x512", "1024x1024"], {'default': "1024x1024"}),
+        }}
+
+    CATEGORY = "🐬 CyberDolphin"
+    RETURN_TYPES = ("IMAGE", "MASK")
+    FUNCTION = "load_image"
+
+    def load_image(self, prompt: str, size: str):
+        """
+        Loads an image from api.openai.com.
+        https://platform.openai.com/docs/api-reference/images/create
+
+        Args:
+            prompt: A text description of the desired image. The maximum length is 1000 characters.
+            size: Must be one of: 256x256, 512x512, 1024x1024
+
+        Returns:
+
+        """
+        i = OpenAiClient.image_create(prompt=prompt, size=size)
+        i = ImageOps.exif_transpose(i)
+
+        image = i.convert("RGB")
+        image = np.array(image).astype(np.float32) / 255.0
+        image = torch.from_numpy(image)[None,]
+        if 'A' in i.getbands():
+            mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
+            mask = 1. - torch.from_numpy(mask)
+        else:
+            mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
+        return (image, mask.unsqueeze(0))
+
+    @classmethod
+    def IS_CHANGED(s, image):
+        image_path = folder_paths.get_annotated_filepath(image)
+        m = hashlib.sha256()
+        with open(image_path, 'rb') as f:
+            m.update(f.read())
+        return m.digest().hex()
+
+    # @classmethod
+    # def VALIDATE_INPUTS(s, image):
+    #     # if not folder_paths.exists_annotated_filepath(image):
+    #     #     return "Invalid image file: {}".format(image)
+    #
+    #     return True

examples/README.md

@@ -1,11 +1,10 @@
 # Examples
 
 ## Cyber Dolphin
+
+---
+
 ![img.png](img.png)
-[dolphin-openai workflow](./dolphin-openai.workflow.json)
+![img_1.png](img_1.png)
 
 ---
-
-![Comfy-PNG](./Gpt-3.5-turbo-gen_00005_.png)
-_(comfyui png depends on a stable api)
----

openai_client.py

@@ -1,3 +1,4 @@
+import PIL.Image
 import openai
 
 from custom_nodes.cyberdolphin.settings import api_settings
@@ -18,8 +19,30 @@ def validation(temperature: float, top_p: float = None) -> list[str]:
     return errors_list
 
 
+def convert_bson_to_image(image_bson: str):
+    import base64
+    import io
+    from PIL import Image
+    image_bytes = base64.b64decode(image_bson)
+    image = Image.open(io.BytesIO(image_bytes))
+    return image
+
+
 class OpenAiClient:
 
+    @staticmethod
+    def image_create(prompt: str, size: str = "1024x1024", api='openai') -> PIL.Image.Image:
+        openai.api_base, openai.api_key, openai.organization = api_settings(api)
+        response = openai.Image.create(
+            n=1,
+            size=size,
+            prompt=prompt,
+            response_format="b64_json"
+        )
+        image_bson = response['data'][0]['b64_json']
+        i = convert_bson_to_image(image_bson)
+        return i
+
     @staticmethod
     def complete(key: str, model: str, temperature: float, top_p: float, system_content: str, user_content: str):
         errors = validation(temperature, top_p)