645 lines
26 KiB
Python
645 lines
26 KiB
Python
import os
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from typing import Any, Optional, Union, Tuple
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from contextlib import nullcontext
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import safetensors.torch
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import torch
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try:
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from diffusers import (
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DPMSolverMultistepScheduler,
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StableDiffusionPipeline,
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EulerDiscreteScheduler,
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EulerAncestralDiscreteScheduler,
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AutoencoderKL,
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UNet2DConditionModel,
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LCMScheduler,
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DDPMScheduler,
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DEISMultistepScheduler,
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PNDMScheduler,
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UniPCMultistepScheduler
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)
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from diffusers.loaders.single_file_utils import (
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convert_ldm_vae_checkpoint,
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convert_ldm_unet_checkpoint,
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create_vae_diffusers_config,
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create_unet_diffusers_config,
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create_text_encoder_from_ldm_clip_checkpoint
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)
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except:
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raise ImportError("Diffusers version too old. Please update to 0.26.0 minimum.")
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from .scheduling_tcd import TCDScheduler
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from contextlib import nullcontext
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from diffusers.utils import is_accelerate_available
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if is_accelerate_available():
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from accelerate import init_empty_weights
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from accelerate.utils import set_module_tensor_to_device
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from .hidiffusion import apply_hidiffusion, remove_hidiffusion
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from omegaconf import OmegaConf
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from .model import ELLA, T5TextEmbedder
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from transformers import CLIPTokenizer
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import comfy.model_management as mm
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import comfy.utils
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import folder_paths
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script_directory = os.path.dirname(os.path.abspath(__file__))
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class ELLAProxyUNet(torch.nn.Module):
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def __init__(self, ella, unet):
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super().__init__()
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# In order to still use the diffusers pipeline, including various workaround
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self.ella = ella
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self.unet = unet
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self.config = unet.config
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self.dtype = unet.dtype
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self.device = unet.device
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self.flexible_max_length_workaround = None
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def forward(
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self,
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sample: torch.FloatTensor,
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timestep: Union[torch.Tensor, float, int],
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encoder_hidden_states: torch.Tensor,
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class_labels: Optional[torch.Tensor] = None,
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timestep_cond: Optional[torch.Tensor] = None,
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attention_mask: Optional[torch.Tensor] = None,
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cross_attention_kwargs: Optional[dict[str, Any]] = None,
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added_cond_kwargs: Optional[dict[str, torch.Tensor]] = None,
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down_block_additional_residuals: Optional[tuple[torch.Tensor]] = None,
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mid_block_additional_residual: Optional[torch.Tensor] = None,
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down_intrablock_additional_residuals: Optional[tuple[torch.Tensor]] = None,
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encoder_attention_mask: Optional[torch.Tensor] = None,
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return_dict: bool = True,
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):
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if self.flexible_max_length_workaround is not None:
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time_aware_encoder_hidden_state_list = []
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for i, max_length in enumerate(self.flexible_max_length_workaround):
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time_aware_encoder_hidden_state_list.append(
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self.ella(encoder_hidden_states[i : i + 1, :max_length], timestep)
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)
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# No matter how many tokens are text features, the ella output must be 64 tokens.
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time_aware_encoder_hidden_states = torch.cat(
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time_aware_encoder_hidden_state_list, dim=0
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)
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else:
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time_aware_encoder_hidden_states = self.ella(
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encoder_hidden_states, timestep
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)
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return self.unet(
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sample=sample,
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timestep=timestep,
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encoder_hidden_states=time_aware_encoder_hidden_states,
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class_labels=class_labels,
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timestep_cond=timestep_cond,
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attention_mask=attention_mask,
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cross_attention_kwargs=cross_attention_kwargs,
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added_cond_kwargs=added_cond_kwargs,
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down_block_additional_residuals=down_block_additional_residuals,
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mid_block_additional_residual=mid_block_additional_residual,
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down_intrablock_additional_residuals=down_intrablock_additional_residuals,
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encoder_attention_mask=encoder_attention_mask,
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return_dict=return_dict,
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)
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class ella_model_loader:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"model": ("MODEL",),
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"clip": ("CLIP",),
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"vae": ("VAE",),
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},
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}
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RETURN_TYPES = ("ELLAMODEL",)
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RETURN_NAMES = ("ella_model",)
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FUNCTION = "loadmodel"
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CATEGORY = "ELLA-Wrapper"
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def loadmodel(self, model, clip, vae):
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mm.soft_empty_cache()
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dtype = mm.unet_dtype()
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vae_dtype = mm.vae_dtype()
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device = mm.get_torch_device()
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custom_config = {
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'model': model,
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'vae': vae,
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}
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if not hasattr(self, 'model') or self.model == None or custom_config != self.current_config:
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pbar = comfy.utils.ProgressBar(5)
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self.current_config = custom_config
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# setup pretrained models
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original_config = OmegaConf.load(os.path.join(script_directory, f"configs/v1-inference.yaml"))
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print("loading ELLA")
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checkpoint_path = os.path.join(folder_paths.models_dir,'ella')
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ella_path = os.path.join(checkpoint_path, 'ella-sd1.5-tsc-t5xl.safetensors')
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if not os.path.exists(ella_path):
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from huggingface_hub import snapshot_download
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snapshot_download(repo_id="QQGYLab/ELLA", local_dir=checkpoint_path, local_dir_use_symlinks=False)
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with (init_empty_weights() if is_accelerate_available() else nullcontext()):
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converted_vae_config = create_vae_diffusers_config(original_config, image_size=512)
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new_vae = AutoencoderKL(**converted_vae_config)
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converted_unet_config = create_unet_diffusers_config(original_config, image_size=512)
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unet = UNet2DConditionModel(**converted_unet_config)
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clip_sd = None
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load_models = [model]
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load_models.append(clip.load_model())
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clip_sd = clip.get_sd()
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comfy.model_management.load_models_gpu(load_models)
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sd = model.model.state_dict_for_saving(clip_sd, vae.get_sd(), None)
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converted_vae = convert_ldm_vae_checkpoint(sd, converted_vae_config)
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if is_accelerate_available():
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for key in converted_vae:
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set_module_tensor_to_device(new_vae, key, device=device, dtype=dtype, value=converted_vae[key])
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else:
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new_vae.load_state_dict(converted_vae, strict=False)
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del converted_vae
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pbar.update(1)
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converted_unet = convert_ldm_unet_checkpoint(sd, converted_unet_config)
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if is_accelerate_available():
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for key in converted_unet:
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set_module_tensor_to_device(unet, key, device=device, dtype=dtype, value=converted_unet[key])
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else:
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unet.load_state_dict(converted_unet, strict=False)
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del converted_unet
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ella = ELLA()
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safetensors.torch.load_model(ella, ella_path, strict=True)
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ella.to(device, dtype=dtype)
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ella_unet = ELLAProxyUNet(ella, unet)
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pbar.update(1)
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# 3. text_model
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print("loading text model")
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text_encoder = create_text_encoder_from_ldm_clip_checkpoint("openai/clip-vit-large-patch14",sd)
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scheduler_config = {
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'num_train_timesteps': 1000,
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'beta_start': 0.00085,
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'beta_end': 0.012,
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'beta_schedule': "scaled_linear",
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'steps_offset': 1
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}
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# 4. tokenizer
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tokenizer_path = os.path.join(script_directory, "configs/tokenizer")
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tokenizer = CLIPTokenizer.from_pretrained(tokenizer_path)
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scheduler=DPMSolverMultistepScheduler(**scheduler_config)
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pbar.update(1)
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del sd
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pbar.update(1)
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print("creating pipeline")
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self.pipe = StableDiffusionPipeline(
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unet=ella_unet,
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vae=new_vae,
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text_encoder=text_encoder,
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tokenizer=tokenizer,
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scheduler=scheduler,
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safety_checker=None,
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feature_extractor=None,
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requires_safety_checker=False,
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image_encoder=None
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)
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print("pipeline created")
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pbar.update(1)
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self.pipe.enable_model_cpu_offload()
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ella_model = {
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'pipe': self.pipe,
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}
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return (ella_model,)
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class diffusers_model_loader:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"model": ("MODEL",),
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"clip": ("CLIP",),
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"vae": ("VAE",),
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},
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}
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RETURN_TYPES = ("DIFFUSERSMODEL",)
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RETURN_NAMES = ("diffusers_model",)
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FUNCTION = "loadmodel"
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CATEGORY = "ELLA-Wrapper"
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def loadmodel(self, model, clip, vae):
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mm.soft_empty_cache()
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dtype = mm.unet_dtype()
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vae_dtype = mm.vae_dtype()
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device = mm.get_torch_device()
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custom_config = {
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'model': model,
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'vae': vae,
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}
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if not hasattr(self, 'model') or self.model == None or custom_config != self.current_config:
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pbar = comfy.utils.ProgressBar(5)
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self.current_config = custom_config
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# setup pretrained models
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original_config = OmegaConf.load(os.path.join(script_directory, f"configs/v1-inference.yaml"))
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print("loading ELLA")
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checkpoint_path = os.path.join(folder_paths.models_dir,'ella')
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ella_path = os.path.join(checkpoint_path, 'ella-sd1.5-tsc-t5xl.safetensors')
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if not os.path.exists(ella_path):
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from huggingface_hub import snapshot_download
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snapshot_download(repo_id="QQGYLab/ELLA", local_dir=checkpoint_path, local_dir_use_symlinks=False)
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with (init_empty_weights() if is_accelerate_available() else nullcontext()):
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converted_vae_config = create_vae_diffusers_config(original_config, image_size=512)
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new_vae = AutoencoderKL(**converted_vae_config)
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converted_unet_config = create_unet_diffusers_config(original_config, image_size=512)
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unet = UNet2DConditionModel(**converted_unet_config)
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clip_sd = None
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load_models = [model]
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load_models.append(clip.load_model())
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clip_sd = clip.get_sd()
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comfy.model_management.load_models_gpu(load_models)
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sd = model.model.state_dict_for_saving(clip_sd, vae.get_sd(), None)
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converted_vae = convert_ldm_vae_checkpoint(sd, converted_vae_config)
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if is_accelerate_available():
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for key in converted_vae:
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set_module_tensor_to_device(new_vae, key, device=device, dtype=dtype, value=converted_vae[key])
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else:
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new_vae.load_state_dict(converted_vae, strict=False)
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del converted_vae
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pbar.update(1)
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converted_unet = convert_ldm_unet_checkpoint(sd, converted_unet_config)
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if is_accelerate_available():
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for key in converted_unet:
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set_module_tensor_to_device(unet, key, device=device, dtype=dtype, value=converted_unet[key])
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else:
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unet.load_state_dict(converted_unet, strict=False)
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del converted_unet
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pbar.update(1)
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# 3. text_model
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print("loading text model")
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text_encoder = create_text_encoder_from_ldm_clip_checkpoint("openai/clip-vit-large-patch14",sd)
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scheduler_config = {
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'num_train_timesteps': 1000,
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'beta_start': 0.00085,
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'beta_end': 0.012,
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'beta_schedule': "scaled_linear",
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'steps_offset': 1
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}
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# 4. tokenizer
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tokenizer_path = os.path.join(script_directory, "configs/tokenizer")
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tokenizer = CLIPTokenizer.from_pretrained(tokenizer_path)
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scheduler=DPMSolverMultistepScheduler(**scheduler_config)
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pbar.update(1)
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del sd
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pbar.update(1)
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print("creating pipeline")
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self.pipe = StableDiffusionPipeline(
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unet=unet,
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vae=new_vae,
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text_encoder=text_encoder,
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tokenizer=tokenizer,
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scheduler=scheduler,
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safety_checker=None,
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feature_extractor=None,
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requires_safety_checker=False,
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image_encoder=None
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)
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print("pipeline created")
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pbar.update(1)
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#self.pipe.enable_model_cpu_offload()
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diffusers_model = {
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'pipe': self.pipe,
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}
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return (diffusers_model,)
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class diffusers_sampler:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"diffusers_model": ("DIFFUSERSMODEL",),
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"width": ("INT", {"default": 512, "min": 64, "max": 2048, "step": 64}),
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"height": ("INT", {"default": 512, "min": 64, "max": 2048, "step": 64}),
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"steps": ("INT", {"default": 25, "min": 1, "max": 200, "step": 1}),
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"guidance_scale": ("FLOAT", {"default": 10.0, "min": 1.01, "max": 20.0, "step": 0.01}),
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"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
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"scheduler": (
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[
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'DPMSolverMultistepScheduler',
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'DPMSolverMultistepScheduler_SDE_karras',
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'DDPMScheduler',
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'LCMScheduler',
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'PNDMScheduler',
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'DEISMultistepScheduler',
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'EulerDiscreteScheduler',
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'EulerAncestralDiscreteScheduler',
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'UniPCMultistepScheduler',
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'TCDScheduler'
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], {
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"default": 'DPMSolverMultistepScheduler'
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}),
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"prompt": ("STRING", {"default": "positive", "multiline": True}),
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"n_prompt": ("STRING", {"default": "negative", "multiline": True}),
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"hidiffusion": ("BOOLEAN", {"default": False}),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("images",)
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FUNCTION = "process"
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CATEGORY = "ELLA-Wrapper"
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def process(self, diffusers_model, width, height, steps, guidance_scale, seed, scheduler, prompt, n_prompt, hidiffusion):
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device = mm.get_torch_device()
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mm.unload_all_models()
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mm.soft_empty_cache()
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dtype = mm.unet_dtype()
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pipe=diffusers_model['pipe']
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pipe.to(device, dtype=dtype)
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scheduler_config = {
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'num_train_timesteps': 1000,
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'beta_start': 0.00085,
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'beta_end': 0.012,
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'beta_schedule': "scaled_linear",
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'steps_offset': 1,
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}
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if scheduler == 'DPMSolverMultistepScheduler':
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noise_scheduler = DPMSolverMultistepScheduler(**scheduler_config)
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elif scheduler == 'DPMSolverMultistepScheduler_SDE_karras':
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scheduler_config.update({"algorithm_type": "sde-dpmsolver++"})
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scheduler_config.update({"use_karras_sigmas": True})
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noise_scheduler = DPMSolverMultistepScheduler(**scheduler_config)
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elif scheduler == 'DDPMScheduler':
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noise_scheduler = DDPMScheduler(**scheduler_config)
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elif scheduler == 'LCMScheduler':
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noise_scheduler = LCMScheduler(**scheduler_config)
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elif scheduler == 'PNDMScheduler':
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scheduler_config.update({"set_alpha_to_one": False})
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scheduler_config.update({"trained_betas": None})
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noise_scheduler = PNDMScheduler(**scheduler_config)
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elif scheduler == 'DEISMultistepScheduler':
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noise_scheduler = DEISMultistepScheduler(**scheduler_config)
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elif scheduler == 'EulerDiscreteScheduler':
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noise_scheduler = EulerDiscreteScheduler(**scheduler_config)
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elif scheduler == 'EulerAncestralDiscreteScheduler':
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noise_scheduler = EulerAncestralDiscreteScheduler(**scheduler_config)
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elif scheduler == 'UniPCMultistepScheduler':
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noise_scheduler = UniPCMultistepScheduler(**scheduler_config)
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elif scheduler == 'TCDScheduler':
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noise_scheduler = TCDScheduler(**scheduler_config)
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pipe.scheduler = noise_scheduler
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if hidiffusion:
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apply_hidiffusion(pipe)
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else:
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remove_hidiffusion(pipe)
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autocast_condition = (dtype != torch.float32) and not mm.is_device_mps(device)
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with torch.autocast(mm.get_autocast_device(device), dtype=dtype) if autocast_condition else nullcontext():
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images = pipe(
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prompt = prompt,
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negative_prompt = n_prompt,
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prompt_embeds=None,
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negative_prompt_embeds=None,
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guidance_scale=guidance_scale,
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num_inference_steps=steps,
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height=height,
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width=width,
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generator=[
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torch.Generator(device=device).manual_seed(seed + i)
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for i in range(1)
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],
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output_type="pt",
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).images
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image_out = images.permute(0, 2, 3, 1).cpu().float()
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return (image_out,)
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class ella_sampler:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"ella_model": ("ELLAMODEL",),
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"ella_embeds": ("ELLAEMBEDS",),
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"width": ("INT", {"default": 512, "min": 64, "max": 8192, "step": 64}),
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"height": ("INT", {"default": 512, "min": 64, "max": 8192, "step": 64}),
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"steps": ("INT", {"default": 25, "min": 1, "max": 200, "step": 1}),
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"guidance_scale": ("FLOAT", {"default": 10.0, "min": 1.01, "max": 20.0, "step": 0.01}),
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"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
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"scheduler": (
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[
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'DPMSolverMultistepScheduler',
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'DPMSolverMultistepScheduler_SDE_karras',
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'DDPMScheduler',
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'LCMScheduler',
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'PNDMScheduler',
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'DEISMultistepScheduler',
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'EulerDiscreteScheduler',
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'EulerAncestralDiscreteScheduler',
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|
'UniPCMultistepScheduler',
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|
'TCDScheduler'
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|
], {
|
|
"default": 'DPMSolverMultistepScheduler'
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|
}),
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|
},
|
|
"optional": {
|
|
"hidiffusion": ("BOOLEAN", {"default": False}),
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|
}
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|
}
|
|
|
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("images",)
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|
FUNCTION = "process"
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|
CATEGORY = "ELLA-Wrapper"
|
|
|
|
def process(self, ella_embeds, width, height, steps, guidance_scale, seed, ella_model, scheduler, hidiffusion=False):
|
|
device = mm.get_torch_device()
|
|
mm.unload_all_models()
|
|
mm.soft_empty_cache()
|
|
dtype = mm.unet_dtype()
|
|
pipe=ella_model['pipe']
|
|
pipe.to(device, dtype=dtype)
|
|
|
|
scheduler_config = {
|
|
'num_train_timesteps': 1000,
|
|
'beta_start': 0.00085,
|
|
'beta_end': 0.012,
|
|
'beta_schedule': "scaled_linear",
|
|
'steps_offset': 1,
|
|
}
|
|
if scheduler == 'DPMSolverMultistepScheduler':
|
|
noise_scheduler = DPMSolverMultistepScheduler(**scheduler_config)
|
|
elif scheduler == 'DPMSolverMultistepScheduler_SDE_karras':
|
|
scheduler_config.update({"algorithm_type": "sde-dpmsolver++"})
|
|
scheduler_config.update({"use_karras_sigmas": True})
|
|
noise_scheduler = DPMSolverMultistepScheduler(**scheduler_config)
|
|
elif scheduler == 'DDPMScheduler':
|
|
noise_scheduler = DDPMScheduler(**scheduler_config)
|
|
elif scheduler == 'LCMScheduler':
|
|
noise_scheduler = LCMScheduler(**scheduler_config)
|
|
elif scheduler == 'PNDMScheduler':
|
|
scheduler_config.update({"set_alpha_to_one": False})
|
|
scheduler_config.update({"trained_betas": None})
|
|
noise_scheduler = PNDMScheduler(**scheduler_config)
|
|
elif scheduler == 'DEISMultistepScheduler':
|
|
noise_scheduler = DEISMultistepScheduler(**scheduler_config)
|
|
elif scheduler == 'EulerDiscreteScheduler':
|
|
noise_scheduler = EulerDiscreteScheduler(**scheduler_config)
|
|
elif scheduler == 'EulerAncestralDiscreteScheduler':
|
|
noise_scheduler = EulerAncestralDiscreteScheduler(**scheduler_config)
|
|
elif scheduler == 'UniPCMultistepScheduler':
|
|
noise_scheduler = UniPCMultistepScheduler(**scheduler_config)
|
|
elif scheduler == 'TCDScheduler':
|
|
noise_scheduler = TCDScheduler(**scheduler_config)
|
|
|
|
pipe.scheduler = noise_scheduler
|
|
if hidiffusion:
|
|
apply_hidiffusion(pipe)
|
|
else:
|
|
remove_hidiffusion(pipe)
|
|
|
|
autocast_condition = (dtype != torch.float32) and not mm.is_device_mps(device)
|
|
with torch.autocast(mm.get_autocast_device(device), dtype=dtype) if autocast_condition else nullcontext():
|
|
|
|
# diffusers pipeline concatenate `prompt_embeds` too early...
|
|
# https://github.com/huggingface/diffusers/blob/b6d7e31d10df675d86c6fe7838044712c6dca4e9/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py#L913
|
|
pipe.unet.flexible_max_length_workaround = [ella_embeds["negative_prompt_embeds"].size(1)] * ella_embeds["batch_size"] + [ella_embeds["prompt_embeds"].size(1)] * ella_embeds["batch_size"]
|
|
|
|
images = pipe(
|
|
prompt_embeds=ella_embeds["prompt_embeds"],
|
|
negative_prompt_embeds=ella_embeds["negative_prompt_embeds"],
|
|
guidance_scale=guidance_scale,
|
|
num_inference_steps=steps,
|
|
height=height,
|
|
width=width,
|
|
generator=[
|
|
torch.Generator(device=device).manual_seed(seed + i)
|
|
for i in range(ella_embeds["batch_size"])
|
|
],
|
|
output_type="pt",
|
|
).images
|
|
|
|
image_out = images.permute(0, 2, 3, 1).cpu().float()
|
|
return (image_out,)
|
|
|
|
|
|
class ella_t5_embeds:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {
|
|
"prompt": ("STRING", {"multiline": True, "default": "A vivid red book with a smooth, matte cover lies next to a glossy yellow vase. The vase, with a slightly curved silhouette, stands on a dark wood table with a noticeable grain pattern. The book appears slightly worn at the edges, suggesting frequent use, while the vase holds a fresh array of multicolored wildflowers.",}),
|
|
"batch_size": ("INT", {"default": 1, "min": 1, "max": 256, "step": 1}),
|
|
"max_length": ("INT", {"default": 128, "min": 1, "max": 512, "step": 1}),
|
|
"fixed_negative": ("BOOLEAN", {"default": False}),
|
|
},
|
|
"optional": {
|
|
"flexible_max_length": ("BOOLEAN", {"default": True}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("ELLAEMBEDS",)
|
|
RETURN_NAMES = ("ella_embeds",)
|
|
FUNCTION = "process"
|
|
CATEGORY = "ELLA-Wrapper"
|
|
|
|
def process(self, prompt, batch_size, max_length, fixed_negative, flexible_max_length=True):
|
|
device = mm.get_torch_device()
|
|
offload_device = mm.unet_offload_device()
|
|
mm.unload_all_models()
|
|
mm.soft_empty_cache()
|
|
dtype = mm.unet_dtype()
|
|
|
|
#old checkpoint location
|
|
repo_id_old = "ybelkada/flan-t5-xl-sharded-bf16"
|
|
checkpoint_path_old = os.path.join(script_directory, 'checkpoints')
|
|
t5_path_old = os.path.join(checkpoint_path_old, repo_id_old)
|
|
|
|
if os.path.exists(t5_path_old):
|
|
t5_encoder = T5TextEmbedder(pretrained_path=t5_path_old).to(device, dtype=dtype)
|
|
else:
|
|
t5_path = os.path.join(folder_paths.models_dir,'t5_model', 'flan-t5-xl-encoder-only-bf16')
|
|
if not os.path.exists(t5_path):
|
|
from huggingface_hub import snapshot_download
|
|
snapshot_download(repo_id="Kijai/flan-t5-xl-encoder-only-bf16", local_dir=t5_path, local_dir_use_symlinks=False)
|
|
t5_encoder = T5TextEmbedder(pretrained_path=t5_path).to(device, dtype=dtype)
|
|
|
|
t5_encoder.to(device)
|
|
autocast_condition = (dtype != torch.float32) and not mm.is_device_mps(device)
|
|
with torch.autocast(mm.get_autocast_device(device), dtype=dtype) if autocast_condition else nullcontext():
|
|
print("generating embeds")
|
|
prompt = [prompt] * batch_size
|
|
prompt = [prompt] if isinstance(prompt, str) else prompt
|
|
if flexible_max_length:
|
|
max_length = None
|
|
prompt_embeds = t5_encoder(prompt, max_length=max_length).to(device, dtype)
|
|
negative_prompt_embeds = t5_encoder([""] * batch_size, max_length=max_length if fixed_negative else None).to(device, dtype)
|
|
|
|
max_length = max([prompt_embeds.size(1), negative_prompt_embeds.size(1)])
|
|
b, _, d = prompt_embeds.shape
|
|
prompt_embeds = torch.cat(
|
|
[
|
|
prompt_embeds,
|
|
torch.zeros(
|
|
(b, max_length - prompt_embeds.size(1), d), device=device, dtype=dtype
|
|
),
|
|
],
|
|
dim=1,
|
|
)
|
|
negative_prompt_embeds = torch.cat(
|
|
[
|
|
negative_prompt_embeds,
|
|
torch.zeros(
|
|
(b, max_length - negative_prompt_embeds.size(1), d),
|
|
device=device,
|
|
dtype=dtype,
|
|
),
|
|
],
|
|
dim=1,
|
|
)
|
|
embeds = {
|
|
"prompt_embeds": prompt_embeds,
|
|
"negative_prompt_embeds": negative_prompt_embeds,
|
|
"batch_size": batch_size
|
|
}
|
|
t5_encoder.to(offload_device)
|
|
return (embeds,)
|
|
|
|
NODE_CLASS_MAPPINGS = {
|
|
"ella_model_loader": ella_model_loader,
|
|
"ella_sampler": ella_sampler,
|
|
"ella_t5_embeds": ella_t5_embeds,
|
|
"diffusers_model_loader": diffusers_model_loader,
|
|
"diffusers_sampler": diffusers_sampler
|
|
}
|
|
NODE_DISPLAY_NAME_MAPPINGS = {
|
|
"ella_model_loader": "ELLA Model Loader",
|
|
"ella_sampler": "ELLA Sampler",
|
|
"ella_t5_embeds": "ELLA T5 Embeds",
|
|
"diffusers_model_loader": "Diffusers Model Loader",
|
|
"diffusers_sampler": "Diffusers Sampler"
|
|
|
|
}
|