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import nodes
import node_helpers
import torch
import comfy.model_management
from typing_extensions import override
from comfy_api.latest import ComfyExtension, io
class CLIPTextEncodeHunyuanDiT(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="CLIPTextEncodeHunyuanDiT",
category="advanced/conditioning",
inputs=[
io.Clip.Input("clip"),
io.String.Input("bert", multiline=True, dynamic_prompts=True),
io.String.Input("mt5xl", multiline=True, dynamic_prompts=True),
],
outputs=[
io.Conditioning.Output(),
],
)
@classmethod
def execute(cls, clip, bert, mt5xl) -> io.NodeOutput:
tokens = clip.tokenize(bert)
tokens["mt5xl"] = clip.tokenize(mt5xl)["mt5xl"]
return io.NodeOutput(clip.encode_from_tokens_scheduled(tokens))
encode = execute # TODO: remove
class EmptyHunyuanLatentVideo(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="EmptyHunyuanLatentVideo",
category="latent/video",
inputs=[
io.Int.Input("width", default=848, min=16, max=nodes.MAX_RESOLUTION, step=16),
io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16),
io.Int.Input("length", default=25, min=1, max=nodes.MAX_RESOLUTION, step=4),
io.Int.Input("batch_size", default=1, min=1, max=4096),
],
outputs=[
io.Latent.Output(),
],
)
@classmethod
def execute(cls, width, height, length, batch_size=1) -> io.NodeOutput:
latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
return io.NodeOutput({"samples":latent})
generate = execute # TODO: remove
PROMPT_TEMPLATE_ENCODE_VIDEO_I2V = (
"<|start_header_id|>system<|end_header_id|>\n\n<image>\nDescribe the video by detailing the following aspects according to the reference image: "
"1. The main content and theme of the video."
"2. The color, shape, size, texture, quantity, text, and spatial relationships of the objects."
"3. Actions, events, behaviors temporal relationships, physical movement changes of the objects."
"4. background environment, light, style and atmosphere."
"5. camera angles, movements, and transitions used in the video:<|eot_id|>\n\n"
"<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>"
"<|start_header_id|>assistant<|end_header_id|>\n\n"
)
class TextEncodeHunyuanVideo_ImageToVideo(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="TextEncodeHunyuanVideo_ImageToVideo",
category="advanced/conditioning",
inputs=[
io.Clip.Input("clip"),
io.ClipVisionOutput.Input("clip_vision_output"),
io.String.Input("prompt", multiline=True, dynamic_prompts=True),
io.Int.Input(
"image_interleave",
default=2,
min=1,
max=512,
tooltip="How much the image influences things vs the text prompt. Higher number means more influence from the text prompt.",
),
],
outputs=[
io.Conditioning.Output(),
],
)
@classmethod
def execute(cls, clip, clip_vision_output, prompt, image_interleave) -> io.NodeOutput:
tokens = clip.tokenize(prompt, llama_template=PROMPT_TEMPLATE_ENCODE_VIDEO_I2V, image_embeds=clip_vision_output.mm_projected, image_interleave=image_interleave)
return io.NodeOutput(clip.encode_from_tokens_scheduled(tokens))
encode = execute # TODO: remove
class HunyuanImageToVideo(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="HunyuanImageToVideo",
category="conditioning/video_models",
inputs=[
io.Conditioning.Input("positive"),
io.Vae.Input("vae"),
io.Int.Input("width", default=848, min=16, max=nodes.MAX_RESOLUTION, step=16),
io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16),
io.Int.Input("length", default=53, min=1, max=nodes.MAX_RESOLUTION, step=4),
io.Int.Input("batch_size", default=1, min=1, max=4096),
io.Combo.Input("guidance_type", options=["v1 (concat)", "v2 (replace)", "custom"]),
io.Image.Input("start_image", optional=True),
],
outputs=[
io.Conditioning.Output(display_name="positive"),
io.Latent.Output(display_name="latent"),
],
)
@classmethod
def execute(cls, positive, vae, width, height, length, batch_size, guidance_type, start_image=None) -> io.NodeOutput:
latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
out_latent = {}
if start_image is not None:
start_image = comfy.utils.common_upscale(start_image[:length, :, :, :3].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
concat_latent_image = vae.encode(start_image)
mask = torch.ones((1, 1, latent.shape[2], concat_latent_image.shape[-2], concat_latent_image.shape[-1]), device=start_image.device, dtype=start_image.dtype)
mask[:, :, :((start_image.shape[0] - 1) // 4) + 1] = 0.0
if guidance_type == "v1 (concat)":
cond = {"concat_latent_image": concat_latent_image, "concat_mask": mask}
elif guidance_type == "v2 (replace)":
cond = {'guiding_frame_index': 0}
latent[:, :, :concat_latent_image.shape[2]] = concat_latent_image
out_latent["noise_mask"] = mask
elif guidance_type == "custom":
cond = {"ref_latent": concat_latent_image}
positive = node_helpers.conditioning_set_values(positive, cond)
out_latent["samples"] = latent
return io.NodeOutput(positive, out_latent)
encode = execute # TODO: remove
class EmptyHunyuanImageLatent(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="EmptyHunyuanImageLatent",
category="latent",
inputs=[
io.Int.Input("width", default=2048, min=64, max=nodes.MAX_RESOLUTION, step=32),
io.Int.Input("height", default=2048, min=64, max=nodes.MAX_RESOLUTION, step=32),
io.Int.Input("batch_size", default=1, min=1, max=4096),
],
outputs=[
io.Latent.Output(),
],
)
@classmethod
def execute(cls, width, height, batch_size=1) -> io.NodeOutput:
latent = torch.zeros([batch_size, 64, height // 32, width // 32], device=comfy.model_management.intermediate_device())
return io.NodeOutput({"samples":latent})
generate = execute # TODO: remove
class HunyuanRefinerLatent(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="HunyuanRefinerLatent",
inputs=[
io.Conditioning.Input("positive"),
io.Conditioning.Input("negative"),
io.Latent.Input("latent"),
io.Float.Input("noise_augmentation", default=0.10, min=0.0, max=1.0, step=0.01),
],
outputs=[
io.Conditioning.Output(display_name="positive"),
io.Conditioning.Output(display_name="negative"),
io.Latent.Output(display_name="latent"),
],
)
@classmethod
def execute(cls, positive, negative, latent, noise_augmentation) -> io.NodeOutput:
latent = latent["samples"]
positive = node_helpers.conditioning_set_values(positive, {"concat_latent_image": latent, "noise_augmentation": noise_augmentation})
negative = node_helpers.conditioning_set_values(negative, {"concat_latent_image": latent, "noise_augmentation": noise_augmentation})
out_latent = {}
out_latent["samples"] = torch.zeros([latent.shape[0], 32, latent.shape[-3], latent.shape[-2], latent.shape[-1]], device=comfy.model_management.intermediate_device())
return io.NodeOutput(positive, negative, out_latent)
class HunyuanExtension(ComfyExtension):
@override
async def get_node_list(self) -> list[type[io.ComfyNode]]:
return [
CLIPTextEncodeHunyuanDiT,
TextEncodeHunyuanVideo_ImageToVideo,
EmptyHunyuanLatentVideo,
HunyuanImageToVideo,
EmptyHunyuanImageLatent,
HunyuanRefinerLatent,
]
async def comfy_entrypoint() -> HunyuanExtension:
return HunyuanExtension()
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