| from comfy import sd1_clip | |
| from comfy import sdxl_clip | |
| from transformers import T5TokenizerFast | |
| import comfy.text_encoders.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, attention_mask=False, model_options={}): | |
| textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_config_xxl.json") | |
| t5xxl_scaled_fp8 = model_options.get("t5xxl_scaled_fp8", None) | |
| if t5xxl_scaled_fp8 is not None: | |
| model_options = model_options.copy() | |
| model_options["scaled_fp8"] = t5xxl_scaled_fp8 | |
| 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.text_encoders.t5.T5, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options) | |
| def t5_xxl_detect(state_dict, prefix=""): | |
| out = {} | |
| t5_key = "{}encoder.final_layer_norm.weight".format(prefix) | |
| if t5_key in state_dict: | |
| out["dtype_t5"] = state_dict[t5_key].dtype | |
| scaled_fp8_key = "{}scaled_fp8".format(prefix) | |
| if scaled_fp8_key in state_dict: | |
| out["t5xxl_scaled_fp8"] = state_dict[scaled_fp8_key].dtype | |
| return out | |
| class T5XXLTokenizer(sd1_clip.SDTokenizer): | |
| def __init__(self, embedding_directory=None, tokenizer_data={}): | |
| tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer") | |
| super().__init__(tokenizer_path, embedding_directory=embedding_directory, 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 SD3Tokenizer: | |
| def __init__(self, embedding_directory=None, tokenizer_data={}): | |
| clip_l_tokenizer_class = tokenizer_data.get("clip_l_tokenizer_class", sd1_clip.SDTokenizer) | |
| self.clip_l = clip_l_tokenizer_class(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) | |
| def state_dict(self): | |
| return {} | |
| class SD3ClipModel(torch.nn.Module): | |
| def __init__(self, clip_l=True, clip_g=True, t5=True, dtype_t5=None, t5_attention_mask=False, device="cpu", dtype=None, model_options={}): | |
| super().__init__() | |
| self.dtypes = set() | |
| if clip_l: | |
| clip_l_class = model_options.get("clip_l_class", sd1_clip.SDClipModel) | |
| self.clip_l = clip_l_class(layer="hidden", layer_idx=-2, device=device, dtype=dtype, layer_norm_hidden_state=False, return_projected_pooled=False, model_options=model_options) | |
| self.dtypes.add(dtype) | |
| else: | |
| self.clip_l = None | |
| if clip_g: | |
| self.clip_g = sdxl_clip.SDXLClipG(device=device, dtype=dtype, model_options=model_options) | |
| self.dtypes.add(dtype) | |
| else: | |
| self.clip_g = None | |
| if t5: | |
| dtype_t5 = comfy.model_management.pick_weight_dtype(dtype_t5, dtype, device) | |
| self.t5_attention_mask = t5_attention_mask | |
| self.t5xxl = T5XXLModel(device=device, dtype=dtype_t5, model_options=model_options, attention_mask=self.t5_attention_mask) | |
| 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_pairs_t5 = token_weight_pairs["t5xxl"] | |
| lg_out = None | |
| pooled = None | |
| out = None | |
| extra = {} | |
| 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: | |
| cut_to = min(lg_out.shape[1], g_out.shape[1]) | |
| lg_out = torch.cat([lg_out[:,:cut_to], g_out[:,:cut_to]], 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_output = self.t5xxl.encode_token_weights(token_weight_pairs_t5) | |
| t5_out, t5_pooled = t5_output[:2] | |
| if self.t5_attention_mask: | |
| extra["attention_mask"] = t5_output[2]["attention_mask"] | |
| 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, extra | |
| 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, t5xxl_scaled_fp8=None, t5_attention_mask=False): | |
| class SD3ClipModel_(SD3ClipModel): | |
| def __init__(self, device="cpu", dtype=None, model_options={}): | |
| if t5xxl_scaled_fp8 is not None and "t5xxl_scaled_fp8" not in model_options: | |
| model_options = model_options.copy() | |
| model_options["t5xxl_scaled_fp8"] = t5xxl_scaled_fp8 | |
| super().__init__(clip_l=clip_l, clip_g=clip_g, t5=t5, dtype_t5=dtype_t5, t5_attention_mask=t5_attention_mask, device=device, dtype=dtype, model_options=model_options) | |
| return SD3ClipModel_ | |