126 lines
4.2 KiB
Python
126 lines
4.2 KiB
Python
import os
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import torch
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import folder_paths
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from transformers import AutoTokenizer, AutoModelForCausalLM
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from ..utils.dtype import string_to_dtype
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from huggingface_hub import snapshot_download
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tenc_root = (
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folder_paths.folder_names_and_paths.get(
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"text_encoders",
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folder_paths.folder_names_and_paths.get("clip", [[], set()])
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)
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)
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dtypes = [
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"default",
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"auto (comfy)",
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"BF16",
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"FP32",
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"FP16",
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]
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try: torch.float8_e5m2
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except AttributeError: print("Torch版本过旧,不支持FP8")
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else: dtypes += ["FP8 E4M3", "FP8 E5M2"]
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class GemmaLoader:
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@classmethod
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def INPUT_TYPES(s):
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devices = ["auto", "cpu", "cuda"]
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# 支持多GPU
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for k in range(1, torch.cuda.device_count()):
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devices.append(f"cuda:{k}")
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return {
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"required": {
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"model_name": (["Efficient-Large-Model/gemma-2-2b-it", "google/gemma-2-2b-it", "unsloth/gemma-2-2b-it-bnb-4bit"],),
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"device": (devices, {"default":"cpu"}),
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"dtype": (dtypes,),
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}
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}
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RETURN_TYPES = ("GEMMA",)
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FUNCTION = "load_model"
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CATEGORY = "ExtraModels/Gemma"
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TITLE = "Gemma Loader"
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def load_model(self, model_name, device, dtype):
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dtype = string_to_dtype(dtype, "text_encoder")
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if device == "cpu":
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assert dtype in [None, torch.float32], f"Can't use dtype '{dtype}' with CPU! Set dtype to 'default'."
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if model_name == 'google/gemma-2-2b-it':
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text_encoder_dir = os.path.join(folder_paths.models_dir, 'text_encoders', 'models--google--gemma-2-2b-it')
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if not os.path.exists(os.path.join(text_encoder_dir, 'model.safetensors')):
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snapshot_download('google/gemma-2-2b-it', local_dir=text_encoder_dir)
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elif model_name == 'unsloth/gemma-2-2b-it-bnb-4bit':
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text_encoder_dir = os.path.join(folder_paths.models_dir, 'text_encoders', 'models--unsloth--gemma-2-2b-it-bnb-4bit')
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if not os.path.exists(os.path.join(text_encoder_dir, 'model.safetensors')):
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snapshot_download('unsloth/gemma-2-2b-it-bnb-4bit', local_dir=text_encoder_dir)
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elif model_name == 'Efficient-Large-Model/gemma-2-2b-it':
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text_encoder_dir = os.path.join(folder_paths.models_dir, 'text_encoders', 'models--Efficient-Large-Model--gemma-2-2b-it')
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if not os.path.exists(os.path.join(text_encoder_dir, 'model.safetensors')):
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snapshot_download('Efficient-Large-Model/gemma-2-2b-it', local_dir=text_encoder_dir)
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else:
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raise ValueError('Not implemented!')
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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text_encoder_model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=dtype)
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tokenizer.padding_side = "right"
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text_encoder = text_encoder_model.get_decoder()
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if device != "cpu":
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text_encoder = text_encoder.to(device)
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return ({
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"tokenizer": tokenizer,
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"text_encoder": text_encoder,
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"text_encoder_model": text_encoder_model
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},)
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class GemmaTextEncode:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"text": ("STRING", {"multiline": True}),
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"GEMMA": ("GEMMA",),
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}
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}
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RETURN_TYPES = ("CONDITIONING",)
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FUNCTION = "encode"
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CATEGORY = "ExtraModels/Gemma"
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TITLE = "Gemma Text Encode"
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def encode(self, text, GEMMA=None):
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print(text)
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tokenizer = GEMMA["tokenizer"]
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text_encoder = GEMMA["text_encoder"]
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with torch.no_grad():
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tokens = tokenizer(
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text,
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max_length=300,
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padding="max_length",
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truncation=True,
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return_tensors="pt"
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).to(text_encoder.device)
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cond = text_encoder(tokens.input_ids, tokens.attention_mask)[0]
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emb_masks = tokens.attention_mask
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cond = cond * emb_masks.unsqueeze(-1)
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return ([[cond, {}]], )
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NODE_CLASS_MAPPINGS = {
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"GemmaLoader": GemmaLoader,
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"GemmaTextEncode": GemmaTextEncode,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"GemmaLoader": "Gemma Loader",
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"GemmaTextEncode": "Gemma Text Encode",
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}
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