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[V3] convert nodes_stable3d.py to V3 schema #10204

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Oct 4, 2025
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[V3] convert nodes_stable3d.py to V3 schema #10204

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155 changes: 89 additions & 66 deletions comfy_extras/nodes_stable3d.py
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Original file line number Diff line number Diff line change
@@ -1,6 +1,8 @@
import torch
import nodes
import comfy.utils
from typing_extensions import override
from comfy_api.latest import ComfyExtension, io

def camera_embeddings(elevation, azimuth):
elevation = torch.as_tensor([elevation])
Expand All @@ -20,26 +22,31 @@ def camera_embeddings(elevation, azimuth):
return embeddings


class StableZero123_Conditioning:
class StableZero123_Conditioning(io.ComfyNode):
@classmethod
def INPUT_TYPES(s):
return {"required": { "clip_vision": ("CLIP_VISION",),
"init_image": ("IMAGE",),
"vae": ("VAE",),
"width": ("INT", {"default": 256, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
"height": ("INT", {"default": 256, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
"elevation": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}),
"azimuth": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}),
}}
RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
RETURN_NAMES = ("positive", "negative", "latent")

FUNCTION = "encode"

CATEGORY = "conditioning/3d_models"

def encode(self, clip_vision, init_image, vae, width, height, batch_size, elevation, azimuth):
def define_schema(cls):
return io.Schema(
node_id="StableZero123_Conditioning",
category="conditioning/3d_models",
inputs=[
io.ClipVision.Input("clip_vision"),
io.Image.Input("init_image"),
io.Vae.Input("vae"),
io.Int.Input("width", default=256, min=16, max=nodes.MAX_RESOLUTION, step=8),
io.Int.Input("height", default=256, min=16, max=nodes.MAX_RESOLUTION, step=8),
io.Int.Input("batch_size", default=1, min=1, max=4096),
io.Float.Input("elevation", default=0.0, min=-180.0, max=180.0, step=0.1, round=False),
io.Float.Input("azimuth", default=0.0, min=-180.0, max=180.0, step=0.1, round=False)
],
outputs=[
io.Conditioning.Output(display_name="positive"),
io.Conditioning.Output(display_name="negative"),
io.Latent.Output(display_name="latent")
]
)

@classmethod
def execute(cls, clip_vision, init_image, vae, width, height, batch_size, elevation, azimuth) -> io.NodeOutput:
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)
Expand All @@ -51,30 +58,35 @@ def encode(self, clip_vision, init_image, vae, width, height, batch_size, elevat
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})
return io.NodeOutput(positive, negative, {"samples":latent})

class StableZero123_Conditioning_Batched(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="StableZero123_Conditioning_Batched",
category="conditioning/3d_models",
inputs=[
io.ClipVision.Input("clip_vision"),
io.Image.Input("init_image"),
io.Vae.Input("vae"),
io.Int.Input("width", default=256, min=16, max=nodes.MAX_RESOLUTION, step=8),
io.Int.Input("height", default=256, min=16, max=nodes.MAX_RESOLUTION, step=8),
io.Int.Input("batch_size", default=1, min=1, max=4096),
io.Float.Input("elevation", default=0.0, min=-180.0, max=180.0, step=0.1, round=False),
io.Float.Input("azimuth", default=0.0, min=-180.0, max=180.0, step=0.1, round=False),
io.Float.Input("elevation_batch_increment", default=0.0, min=-180.0, max=180.0, step=0.1, round=False),
io.Float.Input("azimuth_batch_increment", default=0.0, min=-180.0, max=180.0, step=0.1, round=False)
],
outputs=[
io.Conditioning.Output(display_name="positive"),
io.Conditioning.Output(display_name="negative"),
io.Latent.Output(display_name="latent")
]
)

class StableZero123_Conditioning_Batched:
@classmethod
def INPUT_TYPES(s):
return {"required": { "clip_vision": ("CLIP_VISION",),
"init_image": ("IMAGE",),
"vae": ("VAE",),
"width": ("INT", {"default": 256, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
"height": ("INT", {"default": 256, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
"elevation": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}),
"azimuth": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}),
"elevation_batch_increment": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}),
"azimuth_batch_increment": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}),
}}
RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
RETURN_NAMES = ("positive", "negative", "latent")

FUNCTION = "encode"

CATEGORY = "conditioning/3d_models"

def encode(self, clip_vision, init_image, vae, width, height, batch_size, elevation, azimuth, elevation_batch_increment, azimuth_batch_increment):
def execute(cls, clip_vision, init_image, vae, width, height, batch_size, elevation, azimuth, elevation_batch_increment, azimuth_batch_increment) -> io.NodeOutput:
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)
Expand All @@ -93,27 +105,32 @@ def encode(self, clip_vision, init_image, vae, width, height, batch_size, elevat
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, "batch_index": [0] * batch_size})
return io.NodeOutput(positive, negative, {"samples":latent, "batch_index": [0] * batch_size})

class SV3D_Conditioning:
class SV3D_Conditioning(io.ComfyNode):
@classmethod
def INPUT_TYPES(s):
return {"required": { "clip_vision": ("CLIP_VISION",),
"init_image": ("IMAGE",),
"vae": ("VAE",),
"width": ("INT", {"default": 576, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
"height": ("INT", {"default": 576, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
"video_frames": ("INT", {"default": 21, "min": 1, "max": 4096}),
"elevation": ("FLOAT", {"default": 0.0, "min": -90.0, "max": 90.0, "step": 0.1, "round": False}),
}}
RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
RETURN_NAMES = ("positive", "negative", "latent")

FUNCTION = "encode"

CATEGORY = "conditioning/3d_models"

def encode(self, clip_vision, init_image, vae, width, height, video_frames, elevation):
def define_schema(cls):
return io.Schema(
node_id="SV3D_Conditioning",
category="conditioning/3d_models",
inputs=[
io.ClipVision.Input("clip_vision"),
io.Image.Input("init_image"),
io.Vae.Input("vae"),
io.Int.Input("width", default=576, min=16, max=nodes.MAX_RESOLUTION, step=8),
io.Int.Input("height", default=576, min=16, max=nodes.MAX_RESOLUTION, step=8),
io.Int.Input("video_frames", default=21, min=1, max=4096),
io.Float.Input("elevation", default=0.0, min=-90.0, max=90.0, step=0.1, round=False)
],
outputs=[
io.Conditioning.Output(display_name="positive"),
io.Conditioning.Output(display_name="negative"),
io.Latent.Output(display_name="latent")
]
)

@classmethod
def execute(cls, clip_vision, init_image, vae, width, height, video_frames, elevation) -> io.NodeOutput:
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)
Expand All @@ -133,11 +150,17 @@ def encode(self, clip_vision, init_image, vae, width, height, video_frames, elev
positive = [[pooled, {"concat_latent_image": t, "elevation": elevations, "azimuth": azimuths}]]
negative = [[torch.zeros_like(pooled), {"concat_latent_image": torch.zeros_like(t), "elevation": elevations, "azimuth": azimuths}]]
latent = torch.zeros([video_frames, 4, height // 8, width // 8])
return (positive, negative, {"samples":latent})
return io.NodeOutput(positive, negative, {"samples":latent})


class Stable3DExtension(ComfyExtension):
@override
async def get_node_list(self) -> list[type[io.ComfyNode]]:
return [
StableZero123_Conditioning,
StableZero123_Conditioning_Batched,
SV3D_Conditioning,
]

NODE_CLASS_MAPPINGS = {
"StableZero123_Conditioning": StableZero123_Conditioning,
"StableZero123_Conditioning_Batched": StableZero123_Conditioning_Batched,
"SV3D_Conditioning": SV3D_Conditioning,
}
async def comfy_entrypoint() -> Stable3DExtension:
return Stable3DExtension()