diff --git a/essentials.py b/essentials.py
index b3ddbd0..e6981dd 100644
--- a/essentials.py
+++ b/essentials.py
@@ -727,7 +727,47 @@ class ConsoleDebug:
         return (None,)
 
 
+from comfy_extras.nodes_stable3d import camera_embeddings
+class StableZero123_Increments:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "clip_vision": ("CLIP_VISION",),
+                              "init_image": ("IMAGE",),
+                              "vae": ("VAE",),
+                              "width": ("INT", {"default": 256, "min": 16, "max": MAX_RESOLUTION, "step": 8}),
+                              "height": ("INT", {"default": 256, "min": 16, "max": MAX_RESOLUTION, "step": 8}),
+                              "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
+                              "elevation": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0}),
+                              "azimuth": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0}),
+                              "elevation_inc": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0}),
+                              "azimuth_inc": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0}),
+                             }}
+    RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
+    RETURN_NAMES = ("positive", "negative", "latent")
+
+    FUNCTION = "encode"
+
+    CATEGORY = "essentials"
+
+    def encode(self, clip_vision, init_image, vae, width, height, batch_size, elevation, azimuth, elevation_inc, azimuth_inc):
+        output = clip_vision.encode_image(init_image)
+        pooled = output.image_embeds.unsqueeze(0)
+        pixels = comfy.utils.common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1)
+        encode_pixels = pixels[:,:,:,:3]
+        t = vae.encode(encode_pixels)
+
+        cam_embeds = [camera_embeddings(elevation + i * elevation_inc, azimuth + i * azimuth_inc) for i in range(batch_size)]
+        cam_embeds = torch.cat(cam_embeds, dim=0)
+        cond = torch.cat([pooled.repeat((batch_size, 1, 1)), cam_embeds], dim=-1)
+       
+        positive = [[cond, {"concat_latent_image": t}]]
+        negative = [[torch.zeros_like(pooled), {"concat_latent_image": torch.zeros_like(t)}]]
+        latent = torch.zeros([batch_size, 4, height // 8, width // 8])
+        return (positive, negative, {"samples":latent})
+
 NODE_CLASS_MAPPINGS = {
+    "StableZero123_Increments": StableZero123_Increments,
+
     "GetImageSize+": GetImageSize,
 
     "ImageResize+": ImageResize,
@@ -755,6 +795,8 @@ NODE_CLASS_MAPPINGS = {
 }
 
 NODE_DISPLAY_NAME_MAPPINGS = {
+    "StableZero123_Increments": "🔧 StableZero123 with Increments (temporary)",
+
     "GetImageSize+": "🔧 Get Image Size",
 
     "ImageResize+": "🔧 Image Resize",