Initial commit.

t5_encoder.py

@@ -0,0 +1,143 @@
+import torch
+import comfy.clip_model
+import comfy.model_base
+import comfy.model_management
+import comfy.sd1_clip
+import comfy.supported_models_base
+import comfy.utils
+import comfy.model_patcher
+import comfy.sd
+import comfy.latent_formats
+import folder_paths
+from pathlib import Path
+from .nodes_registry import comfy_node
+
+from transformers import T5EncoderModel, T5Tokenizer
+
+
+class LTXVTokenizer(comfy.sd1_clip.SDTokenizer):
+    def __init__(self, tokenizer_path: str):
+        self.tokenizer = T5Tokenizer.from_pretrained(
+            tokenizer_path, local_files_only=True
+        )
+
+    def tokenize_with_weights(self, text: str, return_word_ids: bool = False):
+        """
+        Takes a prompt and converts it to a list of (token, weight, word id) elements.
+        Tokens can both be integer tokens and pre computed CLIP tensors.
+        Word id values are unique per word and embedding, where the id 0 is reserved for non word tokens.
+        Returned list has the dimensions NxM where M is the input size of CLIP
+        """
+        text = text.lower().strip()
+        text_inputs = self.tokenizer(
+            text,
+            padding="max_length",  # do_not_pad, longest, max_length
+            max_length=128,
+            truncation=True,
+            add_special_tokens=True,
+            return_tensors="pt",
+        )
+        text_input_ids = text_inputs.input_ids
+        prompt_attention_mask = text_inputs.attention_mask
+
+        out = {
+            "t5xxl": [
+                (token, weight, i)
+                for i, (token, weight) in enumerate(
+                    zip(text_input_ids[0], prompt_attention_mask[0])
+                )
+            ]
+        }
+
+        if not return_word_ids:
+            out = {k: [(t, w) for t, w, _ in v] for k, v in out.items()}
+
+        return out
+
+
+class LTXVTextEncoderModel(torch.nn.Module):
+    def __init__(
+        self, encoder_path, dtype_t5=None, device="cpu", dtype=None, model_options={}
+    ):
+        super().__init__()
+        dtype_t5 = comfy.model_management.pick_weight_dtype(dtype_t5, dtype, device)
+        self.t5xxl = (
+            T5EncoderModel.from_pretrained(encoder_path, local_files_only=True)
+            .to(dtype_t5)
+            .to(device)
+        )
+        self.dtypes = set([dtype, dtype_t5])
+
+    def set_clip_options(self, options):
+        pass
+
+    def reset_clip_options(self):
+        pass
+
+    def encode_token_weights(self, token_weight_pairs):
+        token_weight_pairs_t5 = token_weight_pairs["t5xxl"]
+        text_input_ids = torch.tensor(
+            [[t[0] for t in token_weight_pairs_t5]],
+            device=self.t5xxl.device,
+        )
+        prompt_attention_mask = torch.tensor(
+            [[w[1] for w in token_weight_pairs_t5]],
+            device=self.t5xxl.device,
+        )
+        out = self.t5xxl(text_input_ids, attention_mask=prompt_attention_mask)[0]
+        out = out * prompt_attention_mask.unsqueeze(2)
+        return out, None, {"attention_mask": prompt_attention_mask}
+
+    def load_sd(self, sd):
+        return self.t5xxl.load_state_dict(sd, strict=False)
+
+
+def ltxv_clip(encoder_path, dtype_t5=None):
+    class LTXVTextEncoderModel_(LTXVTextEncoderModel):
+        def __init__(self, device="cpu", dtype=None, model_options={}):
+            super().__init__(
+                encoder_path=encoder_path,
+                dtype_t5=dtype_t5,
+                device=device,
+                dtype=dtype,
+                model_options=model_options,
+            )
+
+    return LTXVTextEncoderModel_
+
+
+def ltxv_tokenizer(tokenizer_path):
+    class LTXVTokenizer_(LTXVTokenizer):
+        def __init__(self, embedding_directory=None, tokenizer_data={}):
+            super().__init__(tokenizer_path)
+
+    return LTXVTokenizer_
+
+
+@comfy_node(name="LTXVCLIPModelLoader", description="LTXV CLIP Model Loader")
+class LTXVCLIPModelLoader:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"clip_path": (
+            folder_paths.get_filename_list("text_encoders"),
+            {"tooltip": "The name of the text encoder model to load."},
+        )}}
+
+    RETURN_TYPES = ("CLIP",)
+    RETURN_NAMES = ("clip",)
+    FUNCTION = "load_model"
+    CATEGORY = "lightricks/LTXV"
+    TITLE = "LTXV Model Loader"
+    OUTPUT_NODE = False
+
+    def load_model(self, clip_path):
+        path = Path(folder_paths.get_full_path("text_encoders", clip_path))
+        tokenizer_path = path.parents[1] / "tokenizer"
+        encoder_path = path.parents[1] / "text_encoder"
+
+        clip_target = comfy.supported_models_base.ClipTarget(
+            tokenizer=ltxv_tokenizer(tokenizer_path),
+            clip=ltxv_clip(encoder_path, dtype_t5=torch.bfloat16),
+        )
+
+        return (comfy.sd.CLIP(clip_target),)