ComfyUI/comfy_extras/nodes_hunyuan.py
2024-12-16 19:42:01 -05:00

45 lines
1.6 KiB
Python

import nodes
import torch
import comfy.model_management
class CLIPTextEncodeHunyuanDiT:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"clip": ("CLIP", ),
"bert": ("STRING", {"multiline": True, "dynamicPrompts": True}),
"mt5xl": ("STRING", {"multiline": True, "dynamicPrompts": True}),
}}
RETURN_TYPES = ("CONDITIONING",)
FUNCTION = "encode"
CATEGORY = "advanced/conditioning"
def encode(self, clip, bert, mt5xl):
tokens = clip.tokenize(bert)
tokens["mt5xl"] = clip.tokenize(mt5xl)["mt5xl"]
return (clip.encode_from_tokens_scheduled(tokens), )
class EmptyHunyuanLatentVideo:
@classmethod
def INPUT_TYPES(s):
return {"required": { "width": ("INT", {"default": 848, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
"height": ("INT", {"default": 480, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
"length": ("INT", {"default": 25, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 4}),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096})}}
RETURN_TYPES = ("LATENT",)
FUNCTION = "generate"
CATEGORY = "latent/video"
def generate(self, width, height, length, batch_size=1):
latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
return ({"samples":latent}, )
NODE_CLASS_MAPPINGS = {
"CLIPTextEncodeHunyuanDiT": CLIPTextEncodeHunyuanDiT,
"EmptyHunyuanLatentVideo": EmptyHunyuanLatentVideo,
}