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| MIT License | ||
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| Copyright (c) 2023 Limitex | ||
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| Permission is hereby granted, free of charge, to any person obtaining a copy | ||
| of this software and associated documentation files (the "Software"), to deal | ||
| in the Software without restriction, including without limitation the rights | ||
| to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | ||
| copies of the Software, and to permit persons to whom the Software is | ||
| furnished to do so, subject to the following conditions: | ||
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| The above copyright notice and this permission notice shall be included in all | ||
| copies or substantial portions of the Software. | ||
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| THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | ||
| IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | ||
| FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | ||
| AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | ||
| LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | ||
| OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | ||
| SOFTWARE. |
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| # comfyui-hydit | ||
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| This repository contains a customized node and workflow designed specifically for HunYuan DIT. The official tests conducted on DDPM, DDIM, and DPMMS have consistently yielded results that align with those obtained through the Diffusers library. However, it's important to note that we cannot assure the consistency of results from other ComfyUI native samplers with the Diffusers inference. We cordially invite users to explore our workflow and are open to receiving any inquiries or suggestions you may have. | ||
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| ## Overview | ||
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| ### Workflow text2image | ||
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|  | ||
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| [workflow_diffusers](workflow/hunyuan_diffusers_api.json) file for HunyuanDiT txt2image with diffusers backend. | ||
| [workflow_ksampler](workflow/hunyuan_ksampler_api.json) file for HunyuanDiT txt2image with ksampler backend. | ||
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| ## Usage | ||
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| We provide several commands to quick start: | ||
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| ```shell | ||
| # Download comfyui code | ||
| git clone https://github.com/comfyanonymous/ComfyUI.git | ||
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| # Install torch, torchvision, torchaudio | ||
| pip install torch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 --index-url https://download.pytorch.org/whl/cu117 | ||
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| # Install Comfyui essential python package | ||
| cd ComfyUI | ||
| pip install -r requirements.txt | ||
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| # ComfyUI has been successfully installed! | ||
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| # Download model weight as before or link the existing model folder to ComfyUI. | ||
| python -m pip install "huggingface_hub[cli]" | ||
| mkdir models/hunyuan | ||
| huggingface-cli download Tencent-Hunyuan/HunyuanDiT --local-dir ./models/hunyuan/ckpts | ||
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| # Move to the ComfyUI custom_nodes folder and copy comfyui-hydit folder from HunyuanDiT Repo. | ||
| cd custom_nodes | ||
| cp -r ${HunyuanDiT}/comfyui-hydit ./ | ||
| cd comfyui-hydit | ||
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| # Install some essential python Package. | ||
| pip install -r requirements.txt | ||
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| # Our tool has been successfully installed! | ||
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| # Go to ComfyUI main folder | ||
| cd ../.. | ||
| # Run the ComfyUI Lauch command | ||
| python main.py --listen --port 80 | ||
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| # Running ComfyUI successfully! | ||
| ``` | ||
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| ## Custom Node | ||
| Below I'm trying to document all the nodes, thanks for some good work[[1]](#1)[[2]](#2). | ||
| #### HunYuan Pipeline Loader | ||
| - Loads the full stack of models needed for HunYuanDiT. | ||
| - **pipeline_folder_name** is the official weight folder path for hunyuan dit including clip_text_encoder, model, mt5, sdxl-vae-fp16-fix and tokenizer. | ||
| - **model_name** is the weight list of comfyui checkpoint folder. | ||
| - **vae_name** is the weight list of comfyui vae folder. | ||
| - **backend** "diffusers" means using diffusers as the backend, while "ksampler" means using comfyui ksampler for the backend. | ||
| - **PIPELINE** is the instance of StableDiffusionPipeline. | ||
| - **MODEL** is the instance of comfyui MODEL. | ||
| - **CLIP** is the instance of comfyui CLIP. | ||
| - **VAE** is the instance of comfyui VAE. | ||
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| #### HunYuan Scheduler Loader | ||
| - Loads the scheduler algorithm for HunYuanDiT. | ||
| - **Input** is the algorithm name including ddpm, ddim and dpmms. | ||
| - **Output** is the instance of diffusers.schedulers. | ||
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| #### HunYuan Model Makeup | ||
| - Assemble the models and scheduler module. | ||
| - **Input** is the instance of StableDiffusionPipeline and diffusers.schedulers. | ||
| - **Output** is the updated instance of StableDiffusionPipeline. | ||
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| #### HunYuan Clip Text Encode | ||
| - Assemble the models and scheduler module. | ||
| - **Input** is the string of positive and negative prompts. | ||
| - **Output** is the converted string for model. | ||
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| #### HunYuan Sampler | ||
| - Similar with KSampler in ComfyUI. | ||
| - **Input** is the instance of StableDiffusionPipeline and some hyper-parameters for sampling. | ||
| - **Output** is the generated image. | ||
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| ## Reference | ||
| <a id="1">[1]</a> | ||
| https://github.com/Limitex/ComfyUI-Diffusers | ||
| <a id="2">[2]</a> | ||
| https://github.com/Tencent/HunyuanDiT/pull/59 |
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| from .nodes import * | ||
| #aa = DiffusersSampler() | ||
| #print(aa) | ||
| __all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"] |
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| import comfy.supported_models_base | ||
| import comfy.latent_formats | ||
| import comfy.model_patcher | ||
| import comfy.model_base | ||
| import comfy.utils | ||
| from .hydit.modules.text_encoder import MT5Embedder | ||
| from transformers import BertModel, BertTokenizer | ||
| import torch | ||
| import os | ||
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| class CLIP: | ||
| def __init__(self, root): | ||
| self.device = "cuda" if torch.cuda.is_available() else "cpu" | ||
| text_encoder_path = os.path.join(root,"clip_text_encoder") | ||
| clip_text_encoder = BertModel.from_pretrained(str(text_encoder_path), False, revision=None).to(self.device) | ||
| tokenizer_path = os.path.join(root,"tokenizer") | ||
| self.tokenizer = HyBertTokenizer(tokenizer_path) | ||
| t5_text_encoder_path = os.path.join(root,'mt5') | ||
| embedder_t5 = MT5Embedder(t5_text_encoder_path, torch_dtype=torch.float16, max_length=256) | ||
| self.tokenizer_t5 = HyT5Tokenizer(embedder_t5.tokenizer, max_length=embedder_t5.max_length) | ||
| self.embedder_t5 = embedder_t5.model | ||
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| self.cond_stage_model = clip_text_encoder | ||
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| def tokenize(self, text): | ||
| tokens = self.tokenizer.tokenize(text) | ||
| t5_tokens = self.tokenizer_t5.tokenize(text) | ||
| tokens.update(t5_tokens) | ||
| return tokens | ||
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| def tokenize_t5(self, text): | ||
| return self.tokenizer_t5.tokenize(text) | ||
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| def encode_from_tokens(self, tokens, return_pooled=False): | ||
| attention_mask = tokens['attention_mask'].to(self.device) | ||
| with torch.no_grad(): | ||
| prompt_embeds = self.cond_stage_model( | ||
| tokens['text_input_ids'].to(self.device), | ||
| attention_mask=attention_mask | ||
| ) | ||
| prompt_embeds = prompt_embeds[0] | ||
| t5_attention_mask = tokens['t5_attention_mask'].to(self.device) | ||
| with torch.no_grad(): | ||
| t5_prompt_cond = self.embedder_t5( | ||
| tokens['t5_text_input_ids'].to(self.device), | ||
| attention_mask=t5_attention_mask | ||
| ) | ||
| t5_embeds = t5_prompt_cond[0] | ||
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| addit_embeds = { | ||
| "t5_embeds": t5_embeds, | ||
| "attention_mask": attention_mask.float(), | ||
| "t5_attention_mask": t5_attention_mask.float() | ||
| } | ||
| prompt_embeds.addit_embeds = addit_embeds | ||
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| if return_pooled: | ||
| return prompt_embeds, None | ||
| else: | ||
| return prompt_embeds | ||
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| class HyBertTokenizer: | ||
| def __init__(self, tokenizer_path=None, max_length=77, truncation=True, return_attention_mask=True, device='cpu'): | ||
| self.tokenizer = BertTokenizer.from_pretrained(str(tokenizer_path)) | ||
| self.max_length = self.tokenizer.model_max_length or max_length | ||
| self.truncation = truncation | ||
| self.return_attention_mask = return_attention_mask | ||
| self.device = device | ||
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| def tokenize(self, text:str): | ||
| text_inputs = self.tokenizer( | ||
| text, | ||
| padding="max_length", | ||
| max_length=self.max_length, | ||
| truncation=self.truncation, | ||
| return_attention_mask=self.return_attention_mask, | ||
| add_special_tokens = True, | ||
| return_tensors="pt", | ||
| ) | ||
| text_input_ids = text_inputs.input_ids | ||
| attention_mask = text_inputs.attention_mask | ||
| tokens = { | ||
| 'text_input_ids': text_input_ids, | ||
| 'attention_mask': attention_mask | ||
| } | ||
| return tokens | ||
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| class HyT5Tokenizer: | ||
| def __init__(self, tokenizer, max_length=77, truncation=True, return_attention_mask=True, device='cpu'): | ||
| self.tokenizer = tokenizer | ||
| self.max_length = max_length | ||
| self.truncation = truncation | ||
| self.return_attention_mask = return_attention_mask | ||
| self.device = device | ||
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| def tokenize(self, text:str): | ||
| text_inputs = self.tokenizer( | ||
| text, | ||
| padding="max_length", | ||
| max_length=self.max_length, | ||
| truncation=self.truncation, | ||
| return_attention_mask=self.return_attention_mask, | ||
| add_special_tokens = True, | ||
| return_tensors="pt", | ||
| ) | ||
| text_input_ids = text_inputs.input_ids | ||
| attention_mask = text_inputs.attention_mask | ||
| tokens = { | ||
| 't5_text_input_ids': text_input_ids, | ||
| 't5_attention_mask': attention_mask | ||
| } | ||
| return tokens | ||
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| import os | ||
| from .hydit.constants import SAMPLER_FACTORY | ||
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| base_path = os.path.dirname(os.path.realpath(__file__)) | ||
| HUNYUAN_PATH = os.path.join(base_path, "..", "..", "models", "hunyuan") | ||
| SCHEDULERS_hunyuan = list(SAMPLER_FACTORY.keys()) |
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