ComfyUI/comfy_extras/nodes_hunyuan.py

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"]

        output = clip.encode_from_tokens(tokens, return_pooled=True, return_dict=True)
        cond = output.pop("cond")
        return ([[cond, output]], )

    
class ControlNetApplyAdvancedHunYuan:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {"positive": ("CONDITIONING", ),
                             "negative": ("CONDITIONING", ),
                             "control_net": ("CONTROL_NET", ),
                             "image": ("IMAGE", ),
                             "vae": ("VAE", ),
                             "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
                             "control_weight": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 2.0, "step": 0.001}),
                             "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
                             "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001})
                             }}

    RETURN_TYPES = ("CONDITIONING","CONDITIONING")
    RETURN_NAMES = ("positive", "negative")
    FUNCTION = "apply_controlnet"

    CATEGORY = "conditioning/controlnet"

    def apply_controlnet(self, positive, negative, control_net, image, strength, control_weight, start_percent, end_percent, vae=None):
        if strength == 0:
            return (positive, negative)

        control_hint = image.movedim(-1,1)
        cnets = {}

        out = []
        for conditioning in [positive, negative]:
            c = []
            for t in conditioning:
                d = t[1].copy()

                prev_cnet = d.get('control', None)
                if prev_cnet in cnets:
                    c_net = cnets[prev_cnet]
                else:
                    c_net = control_net.copy().set_cond_hint(control_hint, strength, (start_percent, end_percent), vae)
                    c_net.set_extra_arg('control_weight', control_weight)
                    
                    c_net.set_previous_controlnet(prev_cnet)
                    cnets[prev_cnet] = c_net

                d['control'] = c_net
                d['control_apply_to_uncond'] = False
                n = [t[0], d]
                c.append(n)
            out.append(c)
        return (out[0], out[1])
    
NODE_CLASS_MAPPINGS = {
    "CLIPTextEncodeHunyuanDiT": CLIPTextEncodeHunyuanDiT,
    "ControlNetApplyAdvancedHunYuan": ControlNetApplyAdvancedHunYuan,
}