Remove redundant SD3 API Node

__init__.py

@@ -155,7 +155,6 @@ NODE_CONFIG = {
     "InterpolateCoords": {"class": InterpolateCoords, "name": "Interpolate Coords"},
     "PointsEditor": {"class": PointsEditor, "name": "Points Editor"},
     #experimental
-    "StabilityAPI_SD3": {"class": StabilityAPI_SD3, "name": "Stability API SD3"},
     "SoundReactive": {"class": SoundReactive, "name": "Sound Reactive"},
     "StableZero123_BatchSchedule": {"class": StableZero123_BatchSchedule, "name": "Stable Zero123 Batch Schedule"},
     "SV3D_BatchSchedule": {"class": SV3D_BatchSchedule, "name": "SV3D Batch Schedule"},

config.json

@@ -1,3 +0,0 @@
-{
-    "sai_api_key": "your_api_key_here"
-}

nodes/nodes.py

@@ -1693,158 +1693,7 @@ or a .txt file with RealEstate camera intrinsics and coordinates, in a 3D plot.
         ret_poses = [transform_matrix @ x for x in ret_poses]
         return np.array(ret_poses, dtype=np.float32)
     
-
+    
-
-class StabilityAPI_SD3:
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "prompt": ("STRING", {"multiline": True}),
-                "n_prompt": ("STRING", {"multiline": True}),
-                "seed": ("INT", {"default": 123,"min": 0, "max": 4294967294, "step": 1}),
-                "model": (
-                [   
-                    'sd3',
-                    'sd3-turbo',
-                ],
-                {
-                "default": 'sd3'
-                 }),
-                 "aspect_ratio": (
-                [   
-                    '1:1',
-                    '16:9',
-                    '21:9',
-                    '2:3',
-                    '3:2',
-                    '4:5',
-                    '5:4',
-                    '9:16',
-                    '9:21',
-                ],
-                {
-                "default": '1:1'
-                }),
-                "output_format": (
-                [   
-                    'png',
-                    'jpeg',
-                ],
-                {
-                "default": 'jpeg'
-                 }),                 
-            },
-            "optional": {
-                "api_key": ("STRING", {"multiline": True}),
-                "image": ("IMAGE",),
-                "img2img_strength": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
-                "disable_metadata": ("BOOLEAN", {"default": True}),
-            },
-        }
-
-    RETURN_TYPES = ("IMAGE",)
-    FUNCTION = "apicall"
-
-    CATEGORY = "KJNodes/experimental"
-    DESCRIPTION = """
-## Calls StabilityAI API
-   
-Although you may have multiple keys in your account,  
-you should use the same key for all requests to this API.  
-
-Get your API key here: https://platform.stability.ai/account/keys  
-Recommended to set the key in the config.json -file under this  
-node packs folder.  
-# WARNING:  
-Otherwise the API key may get saved in the image metadata even  
-with "disable_metadata" on if the workflow includes save nodes  
-separate from this node.  
-   
-sd3 requires 6.5 credits per generation  
-sd3-turbo requires 4 credits per generation  
-
-If no image is provided, mode is set to text-to-image  
-
-"""
-
-    def apicall(self, prompt, n_prompt, model, seed, aspect_ratio, output_format, 
-                img2img_strength=0.5, image=None, disable_metadata=True, api_key=""):
-        from comfy.cli_args import args
-        if disable_metadata:
-            args.disable_metadata = True
-        else:
-            args.disable_metadata = False
-        
-        import requests
-        from torchvision import transforms
-        
-        data = {
-                "mode": "text-to-image",
-                "prompt": prompt,
-                "model": model,
-                "seed": seed,
-                "output_format": output_format
-                }
-        
-        if image is not None:
-            image = image.permute(0, 3, 1, 2).squeeze(0)
-            to_pil = transforms.ToPILImage()
-            pil_image = to_pil(image)
-            # Save the PIL Image to a BytesIO object
-            buffer = io.BytesIO()
-            pil_image.save(buffer, format='PNG')
-            buffer.seek(0)
-            files = {"image": ("image.png", buffer, "image/png")}
-           
-            data["mode"] = "image-to-image"
-            data["image"] = pil_image
-            data["strength"] = img2img_strength
-        else:
-            data["aspect_ratio"] = aspect_ratio,
-            files = {"none": ''}
-        
-        if model != "sd3-turbo":
-            data["negative_prompt"] = n_prompt
-
-        headers={
-                "accept": "image/*"
-            }
-        
-        if api_key != "":
-            headers["authorization"] = api_key
-        else:
-            config_file_path = os.path.join(script_directory,"config.json")
-            with open(config_file_path, 'r') as file:
-                config = json.load(file)
-            api_key_from_config = config.get("sai_api_key")
-            headers["authorization"] = api_key_from_config            
-        
-        response = requests.post(
-            f"https://api.stability.ai/v2beta/stable-image/generate/sd3",
-            headers=headers,
-            files = files,
-            data = data,
-        )
-
-        if response.status_code == 200:
-            # Convert the response content to a PIL Image
-            image = Image.open(io.BytesIO(response.content))
-            # Convert the PIL Image to a PyTorch tensor
-            transform = transforms.ToTensor()
-            tensor_image = transform(image)
-            tensor_image = tensor_image.unsqueeze(0)
-            tensor_image = tensor_image.permute(0, 2, 3, 1).cpu().float()
-            return (tensor_image,)
-        else:
-            try:
-                # Attempt to parse the response as JSON
-                error_data = response.json()
-                raise Exception(f"Server error: {error_data}")
-            except json.JSONDecodeError:
-                # If the response is not valid JSON, raise a different exception
-                raise Exception(f"Server error: {response.text}")
             
 class CheckpointPerturbWeights: