| from comfy import sd1_clip |
| from comfy import sdxl_clip |
| from transformers import T5TokenizerFast |
| import comfy.t5 |
| import torch |
| import os |
| import comfy.model_management |
| import logging |
|
|
| class T5XXLModel(sd1_clip.SDClipModel): |
| def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None): |
| textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_config_xxl.json") |
| super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.t5.T5) |
|
|
| class T5XXLTokenizer(sd1_clip.SDTokenizer): |
| def __init__(self, embedding_directory=None): |
| tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer") |
| super().__init__(tokenizer_path, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=77) |
|
|
| class SDT5XXLTokenizer(sd1_clip.SD1Tokenizer): |
| def __init__(self, embedding_directory=None): |
| super().__init__(embedding_directory=embedding_directory, clip_name="t5xxl", tokenizer=T5XXLTokenizer) |
|
|
| class SDT5XXLModel(sd1_clip.SD1ClipModel): |
| def __init__(self, device="cpu", dtype=None, **kwargs): |
| super().__init__(device=device, dtype=dtype, clip_name="t5xxl", clip_model=T5XXLModel, **kwargs) |
|
|
|
|
|
|
| class SD3Tokenizer: |
| def __init__(self, embedding_directory=None): |
| self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory) |
| self.clip_g = sdxl_clip.SDXLClipGTokenizer(embedding_directory=embedding_directory) |
| self.t5xxl = T5XXLTokenizer(embedding_directory=embedding_directory) |
|
|
| def tokenize_with_weights(self, text:str, return_word_ids=False): |
| out = {} |
| out["g"] = self.clip_g.tokenize_with_weights(text, return_word_ids) |
| out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids) |
| out["t5xxl"] = self.t5xxl.tokenize_with_weights(text, return_word_ids) |
| return out |
|
|
| def untokenize(self, token_weight_pair): |
| return self.clip_g.untokenize(token_weight_pair) |
|
|
| class SD3ClipModel(torch.nn.Module): |
| def __init__(self, clip_l=True, clip_g=True, t5=True, dtype_t5=None, device="cpu", dtype=None): |
| super().__init__() |
| self.dtypes = set() |
| if clip_l: |
| self.clip_l = sd1_clip.SDClipModel(layer="hidden", layer_idx=-2, device=device, dtype=dtype, layer_norm_hidden_state=False, return_projected_pooled=False) |
| self.dtypes.add(dtype) |
| else: |
| self.clip_l = None |
|
|
| if clip_g: |
| self.clip_g = sdxl_clip.SDXLClipG(device=device, dtype=dtype) |
| self.dtypes.add(dtype) |
| else: |
| self.clip_g = None |
|
|
| if t5: |
| if dtype_t5 is None: |
| dtype_t5 = dtype |
| elif comfy.model_management.dtype_size(dtype_t5) > comfy.model_management.dtype_size(dtype): |
| dtype_t5 = dtype |
|
|
| if not comfy.model_management.supports_cast(device, dtype_t5): |
| dtype_t5 = dtype |
|
|
| self.t5xxl = T5XXLModel(device=device, dtype=dtype_t5) |
| self.dtypes.add(dtype_t5) |
| else: |
| self.t5xxl = None |
|
|
| logging.debug("Created SD3 text encoder with: clip_l {}, clip_g {}, t5xxl {}:{}".format(clip_l, clip_g, t5, dtype_t5)) |
|
|
| def set_clip_options(self, options): |
| if self.clip_l is not None: |
| self.clip_l.set_clip_options(options) |
| if self.clip_g is not None: |
| self.clip_g.set_clip_options(options) |
| if self.t5xxl is not None: |
| self.t5xxl.set_clip_options(options) |
|
|
| def reset_clip_options(self): |
| if self.clip_l is not None: |
| self.clip_l.reset_clip_options() |
| if self.clip_g is not None: |
| self.clip_g.reset_clip_options() |
| if self.t5xxl is not None: |
| self.t5xxl.reset_clip_options() |
|
|
| def encode_token_weights(self, token_weight_pairs): |
| token_weight_pairs_l = token_weight_pairs["l"] |
| token_weight_pairs_g = token_weight_pairs["g"] |
| token_weight_pars_t5 = token_weight_pairs["t5xxl"] |
| lg_out = None |
| pooled = None |
| out = None |
|
|
| if len(token_weight_pairs_g) > 0 or len(token_weight_pairs_l) > 0: |
| if self.clip_l is not None: |
| lg_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l) |
| else: |
| l_pooled = torch.zeros((1, 768), device=comfy.model_management.intermediate_device()) |
|
|
| if self.clip_g is not None: |
| g_out, g_pooled = self.clip_g.encode_token_weights(token_weight_pairs_g) |
| if lg_out is not None: |
| lg_out = torch.cat([lg_out, g_out], dim=-1) |
| else: |
| lg_out = torch.nn.functional.pad(g_out, (768, 0)) |
| else: |
| g_out = None |
| g_pooled = torch.zeros((1, 1280), device=comfy.model_management.intermediate_device()) |
|
|
| if lg_out is not None: |
| lg_out = torch.nn.functional.pad(lg_out, (0, 4096 - lg_out.shape[-1])) |
| out = lg_out |
| pooled = torch.cat((l_pooled, g_pooled), dim=-1) |
|
|
| if self.t5xxl is not None: |
| t5_out, t5_pooled = self.t5xxl.encode_token_weights(token_weight_pars_t5) |
| if lg_out is not None: |
| out = torch.cat([lg_out, t5_out], dim=-2) |
| else: |
| out = t5_out |
|
|
| if out is None: |
| out = torch.zeros((1, 77, 4096), device=comfy.model_management.intermediate_device()) |
|
|
| if pooled is None: |
| pooled = torch.zeros((1, 768 + 1280), device=comfy.model_management.intermediate_device()) |
|
|
| return out, pooled |
|
|
| def load_sd(self, sd): |
| if "text_model.encoder.layers.30.mlp.fc1.weight" in sd: |
| return self.clip_g.load_sd(sd) |
| elif "text_model.encoder.layers.1.mlp.fc1.weight" in sd: |
| return self.clip_l.load_sd(sd) |
| else: |
| return self.t5xxl.load_sd(sd) |
|
|
| def sd3_clip(clip_l=True, clip_g=True, t5=True, dtype_t5=None): |
| class SD3ClipModel_(SD3ClipModel): |
| def __init__(self, device="cpu", dtype=None): |
| super().__init__(clip_l=clip_l, clip_g=clip_g, t5=t5, dtype_t5=dtype_t5, device=device, dtype=dtype) |
| return SD3ClipModel_ |
|
|
