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https://git.datalinker.icu/comfyanonymous/ComfyUI
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convert nodes_stable3d.py to V3 schema (#10204)
This commit is contained in:
Alexander Piskun
GitHub
parent
2ed74f7ac7
commit
b1fa1922df
@ -1,6 +1,8 @@
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import torch
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import nodes
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import comfy.utils
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from typing_extensions import override
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from comfy_api.latest import ComfyExtension, io
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def camera_embeddings(elevation, azimuth):
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elevation = torch.as_tensor([elevation])
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@ -20,26 +22,31 @@ def camera_embeddings(elevation, azimuth):
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return embeddings
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class StableZero123_Conditioning:
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class StableZero123_Conditioning(io.ComfyNode):
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "clip_vision": ("CLIP_VISION",),
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"init_image": ("IMAGE",),
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"vae": ("VAE",),
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"width": ("INT", {"default": 256, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
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"height": ("INT", {"default": 256, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
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"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
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"elevation": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}),
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"azimuth": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}),
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}}
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RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
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RETURN_NAMES = ("positive", "negative", "latent")
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def define_schema(cls):
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return io.Schema(
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node_id="StableZero123_Conditioning",
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category="conditioning/3d_models",
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inputs=[
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io.ClipVision.Input("clip_vision"),
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io.Image.Input("init_image"),
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io.Vae.Input("vae"),
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io.Int.Input("width", default=256, min=16, max=nodes.MAX_RESOLUTION, step=8),
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io.Int.Input("height", default=256, min=16, max=nodes.MAX_RESOLUTION, step=8),
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io.Int.Input("batch_size", default=1, min=1, max=4096),
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io.Float.Input("elevation", default=0.0, min=-180.0, max=180.0, step=0.1, round=False),
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io.Float.Input("azimuth", default=0.0, min=-180.0, max=180.0, step=0.1, round=False)
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],
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outputs=[
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io.Conditioning.Output(display_name="positive"),
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io.Conditioning.Output(display_name="negative"),
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io.Latent.Output(display_name="latent")
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]
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)
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FUNCTION = "encode"
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CATEGORY = "conditioning/3d_models"
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def encode(self, clip_vision, init_image, vae, width, height, batch_size, elevation, azimuth):
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@classmethod
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def execute(cls, clip_vision, init_image, vae, width, height, batch_size, elevation, azimuth) -> io.NodeOutput:
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output = clip_vision.encode_image(init_image)
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pooled = output.image_embeds.unsqueeze(0)
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pixels = comfy.utils.common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1)
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@ -51,30 +58,35 @@ class StableZero123_Conditioning:
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positive = [[cond, {"concat_latent_image": t}]]
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negative = [[torch.zeros_like(pooled), {"concat_latent_image": torch.zeros_like(t)}]]
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latent = torch.zeros([batch_size, 4, height // 8, width // 8])
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return (positive, negative, {"samples":latent})
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return io.NodeOutput(positive, negative, {"samples":latent})
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class StableZero123_Conditioning_Batched:
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class StableZero123_Conditioning_Batched(io.ComfyNode):
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "clip_vision": ("CLIP_VISION",),
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"init_image": ("IMAGE",),
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"vae": ("VAE",),
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"width": ("INT", {"default": 256, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
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"height": ("INT", {"default": 256, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
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"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
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"elevation": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}),
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"azimuth": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}),
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"elevation_batch_increment": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}),
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"azimuth_batch_increment": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}),
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}}
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RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
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RETURN_NAMES = ("positive", "negative", "latent")
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def define_schema(cls):
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return io.Schema(
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node_id="StableZero123_Conditioning_Batched",
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category="conditioning/3d_models",
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inputs=[
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io.ClipVision.Input("clip_vision"),
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io.Image.Input("init_image"),
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io.Vae.Input("vae"),
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io.Int.Input("width", default=256, min=16, max=nodes.MAX_RESOLUTION, step=8),
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io.Int.Input("height", default=256, min=16, max=nodes.MAX_RESOLUTION, step=8),
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io.Int.Input("batch_size", default=1, min=1, max=4096),
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io.Float.Input("elevation", default=0.0, min=-180.0, max=180.0, step=0.1, round=False),
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io.Float.Input("azimuth", default=0.0, min=-180.0, max=180.0, step=0.1, round=False),
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io.Float.Input("elevation_batch_increment", default=0.0, min=-180.0, max=180.0, step=0.1, round=False),
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io.Float.Input("azimuth_batch_increment", default=0.0, min=-180.0, max=180.0, step=0.1, round=False)
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],
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outputs=[
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io.Conditioning.Output(display_name="positive"),
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io.Conditioning.Output(display_name="negative"),
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io.Latent.Output(display_name="latent")
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]
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)
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FUNCTION = "encode"
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CATEGORY = "conditioning/3d_models"
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def encode(self, clip_vision, init_image, vae, width, height, batch_size, elevation, azimuth, elevation_batch_increment, azimuth_batch_increment):
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@classmethod
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def execute(cls, clip_vision, init_image, vae, width, height, batch_size, elevation, azimuth, elevation_batch_increment, azimuth_batch_increment) -> io.NodeOutput:
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output = clip_vision.encode_image(init_image)
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pooled = output.image_embeds.unsqueeze(0)
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pixels = comfy.utils.common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1)
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@ -93,27 +105,32 @@ class StableZero123_Conditioning_Batched:
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positive = [[cond, {"concat_latent_image": t}]]
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negative = [[torch.zeros_like(pooled), {"concat_latent_image": torch.zeros_like(t)}]]
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latent = torch.zeros([batch_size, 4, height // 8, width // 8])
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return (positive, negative, {"samples":latent, "batch_index": [0] * batch_size})
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return io.NodeOutput(positive, negative, {"samples":latent, "batch_index": [0] * batch_size})
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class SV3D_Conditioning:
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class SV3D_Conditioning(io.ComfyNode):
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "clip_vision": ("CLIP_VISION",),
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"init_image": ("IMAGE",),
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"vae": ("VAE",),
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"width": ("INT", {"default": 576, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
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"height": ("INT", {"default": 576, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
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"video_frames": ("INT", {"default": 21, "min": 1, "max": 4096}),
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"elevation": ("FLOAT", {"default": 0.0, "min": -90.0, "max": 90.0, "step": 0.1, "round": False}),
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}}
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RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
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RETURN_NAMES = ("positive", "negative", "latent")
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def define_schema(cls):
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return io.Schema(
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node_id="SV3D_Conditioning",
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category="conditioning/3d_models",
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inputs=[
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io.ClipVision.Input("clip_vision"),
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io.Image.Input("init_image"),
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io.Vae.Input("vae"),
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io.Int.Input("width", default=576, min=16, max=nodes.MAX_RESOLUTION, step=8),
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io.Int.Input("height", default=576, min=16, max=nodes.MAX_RESOLUTION, step=8),
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io.Int.Input("video_frames", default=21, min=1, max=4096),
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io.Float.Input("elevation", default=0.0, min=-90.0, max=90.0, step=0.1, round=False)
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],
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outputs=[
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io.Conditioning.Output(display_name="positive"),
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io.Conditioning.Output(display_name="negative"),
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io.Latent.Output(display_name="latent")
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]
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)
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FUNCTION = "encode"
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CATEGORY = "conditioning/3d_models"
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def encode(self, clip_vision, init_image, vae, width, height, video_frames, elevation):
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@classmethod
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def execute(cls, clip_vision, init_image, vae, width, height, video_frames, elevation) -> io.NodeOutput:
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output = clip_vision.encode_image(init_image)
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pooled = output.image_embeds.unsqueeze(0)
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pixels = comfy.utils.common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1)
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@ -133,11 +150,17 @@ class SV3D_Conditioning:
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positive = [[pooled, {"concat_latent_image": t, "elevation": elevations, "azimuth": azimuths}]]
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negative = [[torch.zeros_like(pooled), {"concat_latent_image": torch.zeros_like(t), "elevation": elevations, "azimuth": azimuths}]]
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latent = torch.zeros([video_frames, 4, height // 8, width // 8])
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return (positive, negative, {"samples":latent})
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return io.NodeOutput(positive, negative, {"samples":latent})
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NODE_CLASS_MAPPINGS = {
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"StableZero123_Conditioning": StableZero123_Conditioning,
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"StableZero123_Conditioning_Batched": StableZero123_Conditioning_Batched,
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"SV3D_Conditioning": SV3D_Conditioning,
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}
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class Stable3DExtension(ComfyExtension):
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@override
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async def get_node_list(self) -> list[type[io.ComfyNode]]:
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return [
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StableZero123_Conditioning,
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StableZero123_Conditioning_Batched,
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SV3D_Conditioning,
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]
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async def comfy_entrypoint() -> Stable3DExtension:
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return Stable3DExtension()
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