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class CLIPTextEncodeControlnet:
@classmethod
def INPUT_TYPES(s):
return {"required": {"clip": ("CLIP", ), "conditioning": ("CONDITIONING", ), "text": ("STRING", {"multiline": True, "dynamicPrompts": True})}}
RETURN_TYPES = ("CONDITIONING",)
FUNCTION = "encode"
CATEGORY = "_for_testing/conditioning"
def encode(self, clip, conditioning, text):
tokens = clip.tokenize(text)
cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True)
c = []
for t in conditioning:
n = [t[0], t[1].copy()]
n[1]['cross_attn_controlnet'] = cond
n[1]['pooled_output_controlnet'] = pooled
c.append(n)
return (c, )
class T5TokenizerOptions:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"clip": ("CLIP", ),
"min_padding": ("INT", {"default": 0, "min": 0, "max": 10000, "step": 1}),
"min_length": ("INT", {"default": 0, "min": 0, "max": 10000, "step": 1}),
}
}
CATEGORY = "_for_testing/conditioning"
RETURN_TYPES = ("CLIP",)
FUNCTION = "set_options"
def set_options(self, clip, min_padding, min_length):
clip = clip.clone()
for t5_type in ["t5xxl", "pile_t5xl", "t5base", "mt5xl", "umt5xxl"]:
clip.set_tokenizer_option("{}_min_padding".format(t5_type), min_padding)
clip.set_tokenizer_option("{}_min_length".format(t5_type), min_length)
return (clip, )
NODE_CLASS_MAPPINGS = {
"CLIPTextEncodeControlnet": CLIPTextEncodeControlnet,
"T5TokenizerOptions": T5TokenizerOptions,
}
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