144 lines
4.6 KiB
Python
144 lines
4.6 KiB
Python
import torch
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import comfy.clip_model
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import comfy.model_base
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import comfy.model_management
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import comfy.sd1_clip
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import comfy.supported_models_base
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import comfy.utils
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import comfy.model_patcher
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import comfy.sd
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import comfy.latent_formats
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import folder_paths
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from pathlib import Path
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from .nodes_registry import comfy_node
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from transformers import T5EncoderModel, T5Tokenizer
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class LTXVTokenizer(comfy.sd1_clip.SDTokenizer):
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def __init__(self, tokenizer_path: str):
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self.tokenizer = T5Tokenizer.from_pretrained(
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tokenizer_path, local_files_only=True
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)
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def tokenize_with_weights(self, text: str, return_word_ids: bool = False):
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"""
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Takes a prompt and converts it to a list of (token, weight, word id) elements.
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Tokens can both be integer tokens and pre computed CLIP tensors.
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Word id values are unique per word and embedding, where the id 0 is reserved for non word tokens.
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Returned list has the dimensions NxM where M is the input size of CLIP
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"""
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text = text.lower().strip()
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text_inputs = self.tokenizer(
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text,
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padding="max_length", # do_not_pad, longest, max_length
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max_length=128,
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truncation=True,
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add_special_tokens=True,
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return_tensors="pt",
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)
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text_input_ids = text_inputs.input_ids
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prompt_attention_mask = text_inputs.attention_mask
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out = {
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"t5xxl": [
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(token, weight, i)
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for i, (token, weight) in enumerate(
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zip(text_input_ids[0], prompt_attention_mask[0])
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)
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]
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}
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if not return_word_ids:
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out = {k: [(t, w) for t, w, _ in v] for k, v in out.items()}
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return out
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class LTXVTextEncoderModel(torch.nn.Module):
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def __init__(
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self, encoder_path, dtype_t5=None, device="cpu", dtype=None, model_options={}
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):
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super().__init__()
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dtype_t5 = comfy.model_management.pick_weight_dtype(dtype_t5, dtype, device)
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self.t5xxl = (
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T5EncoderModel.from_pretrained(encoder_path, local_files_only=True)
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.to(dtype_t5)
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.to(device)
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)
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self.dtypes = set([dtype, dtype_t5])
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def set_clip_options(self, options):
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pass
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def reset_clip_options(self):
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pass
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def encode_token_weights(self, token_weight_pairs):
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token_weight_pairs_t5 = token_weight_pairs["t5xxl"]
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text_input_ids = torch.tensor(
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[[t[0] for t in token_weight_pairs_t5]],
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device=self.t5xxl.device,
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)
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prompt_attention_mask = torch.tensor(
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[[w[1] for w in token_weight_pairs_t5]],
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device=self.t5xxl.device,
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)
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out = self.t5xxl(text_input_ids, attention_mask=prompt_attention_mask)[0]
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out = out * prompt_attention_mask.unsqueeze(2)
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return out, None, {"attention_mask": prompt_attention_mask}
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def load_sd(self, sd):
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return self.t5xxl.load_state_dict(sd, strict=False)
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def ltxv_clip(encoder_path, dtype_t5=None):
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class LTXVTextEncoderModel_(LTXVTextEncoderModel):
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def __init__(self, device="cpu", dtype=None, model_options={}):
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super().__init__(
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encoder_path=encoder_path,
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dtype_t5=dtype_t5,
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device=device,
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dtype=dtype,
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model_options=model_options,
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)
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return LTXVTextEncoderModel_
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def ltxv_tokenizer(tokenizer_path):
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class LTXVTokenizer_(LTXVTokenizer):
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def __init__(self, embedding_directory=None, tokenizer_data={}):
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super().__init__(tokenizer_path)
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return LTXVTokenizer_
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@comfy_node(name="LTXVCLIPModelLoader", description="LTXV CLIP Model Loader")
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class LTXVCLIPModelLoader:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {"clip_path": (
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folder_paths.get_filename_list("text_encoders"),
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{"tooltip": "The name of the text encoder model to load."},
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)}}
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RETURN_TYPES = ("CLIP",)
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RETURN_NAMES = ("clip",)
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FUNCTION = "load_model"
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CATEGORY = "lightricks/LTXV"
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TITLE = "LTXV Model Loader"
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OUTPUT_NODE = False
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def load_model(self, clip_path):
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path = Path(folder_paths.get_full_path("text_encoders", clip_path))
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tokenizer_path = path.parents[1] / "tokenizer"
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encoder_path = path.parents[1] / "text_encoder"
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clip_target = comfy.supported_models_base.ClipTarget(
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tokenizer=ltxv_tokenizer(tokenizer_path),
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clip=ltxv_clip(encoder_path, dtype_t5=torch.bfloat16),
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)
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return (comfy.sd.CLIP(clip_target),)
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