From 32a422656b28e2b2fc8359487149287daf5bebe2 Mon Sep 17 00:00:00 2001 From: xuhuaren Date: Mon, 8 Jul 2024 10:07:36 +0800 Subject: [PATCH] add hunyuan dit txt2image --- .gitignore | 2 +- custom_nodes/comfyui-hydit_min/.DS_Store | Bin 0 -> 6148 bytes custom_nodes/comfyui-hydit_min/LICENSE | 21 + custom_nodes/comfyui-hydit_min/README.md | 91 + custom_nodes/comfyui-hydit_min/__init__.py | 4 + custom_nodes/comfyui-hydit_min/clip.py | 150 + .../comfyui-hydit_min/config_clip.json | 34 + .../comfyui-hydit_min/config_mt5.json | 28 + custom_nodes/comfyui-hydit_min/constant.py | 10 + custom_nodes/comfyui-hydit_min/dit.py | 145 + .../comfyui-hydit_min/hydit/__init__.py | 0 .../comfyui-hydit_min/hydit/config_comfyui.py | 227 + .../comfyui-hydit_min/hydit/constants.py | 82 + .../hydit/diffusion/__init__.py | 49 + .../hydit/diffusion/diffusion_utils.py | 69 + .../hydit/diffusion/gaussian_diffusion.py | 1369 + .../hydit/diffusion/respace.py | 144 + .../comfyui-hydit_min/hydit/ds_config.py | 140 + .../hydit/inference_comfyui.py | 510 + .../comfyui-hydit_min/hydit/lr_scheduler.py | 761 + .../hydit/modules/__init__.py | 0 .../hydit/modules/attn_layers.py | 401 + .../hydit/modules/attn_layers.py.bak | 377 + .../comfyui-hydit_min/hydit/modules/ema.py | 127 + .../hydit/modules/embedders.py | 111 + .../hydit/modules/fp16_layers.py | 86 + .../comfyui-hydit_min/hydit/modules/models.py | 556 + .../hydit/modules/norm_layers.py | 68 + .../hydit/modules/poolers.py | 39 + .../hydit/modules/posemb_layers.py | 224 + .../hydit/modules/text_encoder.py | 128 + .../comfyui-hydit_min/hydit/utils/tools.py | 190 + .../comfyui-hydit_min/img/work_diffusers.png | Bin 0 -> 733472 bytes .../img/workflow_ksampler.png | Bin 0 -> 994885 bytes .../img/workflow_lora_controlnet.png | Bin 0 -> 802830 bytes custom_nodes/comfyui-hydit_min/nodes.py | 94 + .../comfyui-hydit_min/requirements.txt | 15 + .../comfyui-hydit_min/supported_dit_models.py | 115 + .../tokenizer_clip/special_tokens_map.json | 7 + .../tokenizer_clip/tokenizer_config.json | 16 + .../tokenizer_clip/vocab.txt | 47020 ++++++++++++++++ .../tokenizer_clip/vocab_org.txt | 21128 +++++++ .../tokenizer_mt5/config.json | 28 + .../tokenizer_mt5/special_tokens_map.json | 1 + .../tokenizer_mt5/spiece.model | Bin 0 -> 4309802 bytes .../tokenizer_mt5/tokenizer_config.json | 1 + custom_nodes/comfyui-hydit_min/utils.py | 215 + .../workflow/workflow_ksampler.json | 424 + 48 files changed, 75206 insertions(+), 1 deletion(-) create mode 100644 custom_nodes/comfyui-hydit_min/.DS_Store create mode 100644 custom_nodes/comfyui-hydit_min/LICENSE create mode 100644 custom_nodes/comfyui-hydit_min/README.md create mode 100644 custom_nodes/comfyui-hydit_min/__init__.py create mode 100644 custom_nodes/comfyui-hydit_min/clip.py create mode 100644 custom_nodes/comfyui-hydit_min/config_clip.json create mode 100644 custom_nodes/comfyui-hydit_min/config_mt5.json create mode 100644 custom_nodes/comfyui-hydit_min/constant.py create mode 100644 custom_nodes/comfyui-hydit_min/dit.py create mode 100644 custom_nodes/comfyui-hydit_min/hydit/__init__.py create mode 100644 custom_nodes/comfyui-hydit_min/hydit/config_comfyui.py create mode 100644 custom_nodes/comfyui-hydit_min/hydit/constants.py create mode 100644 custom_nodes/comfyui-hydit_min/hydit/diffusion/__init__.py create mode 100644 custom_nodes/comfyui-hydit_min/hydit/diffusion/diffusion_utils.py create mode 100644 custom_nodes/comfyui-hydit_min/hydit/diffusion/gaussian_diffusion.py create mode 100644 custom_nodes/comfyui-hydit_min/hydit/diffusion/respace.py create mode 100644 custom_nodes/comfyui-hydit_min/hydit/ds_config.py create mode 100644 custom_nodes/comfyui-hydit_min/hydit/inference_comfyui.py create mode 100644 custom_nodes/comfyui-hydit_min/hydit/lr_scheduler.py create mode 100644 custom_nodes/comfyui-hydit_min/hydit/modules/__init__.py create mode 100644 custom_nodes/comfyui-hydit_min/hydit/modules/attn_layers.py create mode 100644 custom_nodes/comfyui-hydit_min/hydit/modules/attn_layers.py.bak create mode 100644 custom_nodes/comfyui-hydit_min/hydit/modules/ema.py create mode 100644 custom_nodes/comfyui-hydit_min/hydit/modules/embedders.py create mode 100644 custom_nodes/comfyui-hydit_min/hydit/modules/fp16_layers.py create mode 100644 custom_nodes/comfyui-hydit_min/hydit/modules/models.py create mode 100644 custom_nodes/comfyui-hydit_min/hydit/modules/norm_layers.py create mode 100644 custom_nodes/comfyui-hydit_min/hydit/modules/poolers.py create mode 100644 custom_nodes/comfyui-hydit_min/hydit/modules/posemb_layers.py create mode 100644 custom_nodes/comfyui-hydit_min/hydit/modules/text_encoder.py create mode 100644 custom_nodes/comfyui-hydit_min/hydit/utils/tools.py create mode 100644 custom_nodes/comfyui-hydit_min/img/work_diffusers.png create mode 100644 custom_nodes/comfyui-hydit_min/img/workflow_ksampler.png create mode 100644 custom_nodes/comfyui-hydit_min/img/workflow_lora_controlnet.png create mode 100644 custom_nodes/comfyui-hydit_min/nodes.py create mode 100644 custom_nodes/comfyui-hydit_min/requirements.txt create mode 100644 custom_nodes/comfyui-hydit_min/supported_dit_models.py create mode 100644 custom_nodes/comfyui-hydit_min/tokenizer_clip/special_tokens_map.json create mode 100644 custom_nodes/comfyui-hydit_min/tokenizer_clip/tokenizer_config.json create mode 100644 custom_nodes/comfyui-hydit_min/tokenizer_clip/vocab.txt create mode 100644 custom_nodes/comfyui-hydit_min/tokenizer_clip/vocab_org.txt create mode 100644 custom_nodes/comfyui-hydit_min/tokenizer_mt5/config.json create mode 100644 custom_nodes/comfyui-hydit_min/tokenizer_mt5/special_tokens_map.json create mode 100644 custom_nodes/comfyui-hydit_min/tokenizer_mt5/spiece.model create mode 100644 custom_nodes/comfyui-hydit_min/tokenizer_mt5/tokenizer_config.json create mode 100644 custom_nodes/comfyui-hydit_min/utils.py create mode 100644 custom_nodes/comfyui-hydit_min/workflow/workflow_ksampler.json diff --git a/.gitignore b/.gitignore index a9beebe73..71747858b 100644 --- a/.gitignore +++ b/.gitignore @@ -5,7 +5,7 @@ __pycache__/ !/input/example.png /models/ /temp/ -/custom_nodes/ +#/custom_nodes/ !custom_nodes/example_node.py.example extra_model_paths.yaml /.vs diff --git a/custom_nodes/comfyui-hydit_min/.DS_Store b/custom_nodes/comfyui-hydit_min/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..e4c1db0e899560e5b63f188934ffef8a19a0b801 GIT binary patch literal 6148 zcmeHK%}T>S5Z>*NO({YS3VK`cTClN)B3?qQFJMFuDm5X+24l7~sYOa5cYPsW#OHBl zcLOa3k0N#kcE8#A+0A^A{b7u8Zyq^}*^Ds@8X`xfLeO04s+nL!uI9+YMK%o+8CFd5 zH%<8MO*Ub#EMhTR|Nf6~n#5V&>wWTCz0urhSuLw$-TM={@be&_XI?P7#nF|NNm%MZ zcpZ-yBYW>$rg;#jtAkyraK>GY)Dr{5z$OEAJ+$%szkpw+ z@{zxpLL*{;82D!l@W#*|dQg-(TfdcuXRUzt01XA>3RFNq-?#+80QZpt<fmK W3ynAn+EqFrT?7;%)DZ)}z`z%UxJxkr literal 0 HcmV?d00001 diff --git a/custom_nodes/comfyui-hydit_min/LICENSE b/custom_nodes/comfyui-hydit_min/LICENSE new file mode 100644 index 000000000..150218d6c --- /dev/null +++ b/custom_nodes/comfyui-hydit_min/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023 Limitex + +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: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +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. diff --git a/custom_nodes/comfyui-hydit_min/README.md b/custom_nodes/comfyui-hydit_min/README.md new file mode 100644 index 000000000..92840bdf7 --- /dev/null +++ b/custom_nodes/comfyui-hydit_min/README.md @@ -0,0 +1,91 @@ +# comfyui-hydit + +This repository houses a tailored 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. + +## Overview + + +### Workflow text2image + + +![Workflow](img/workflow_ksampler.png) +[workflow_ksampler](workflow/workflow_ksampler.json) file for HunyuanDiT txt2image with ksampler backend. + + +## Usage + +Make sure you run the following command inside [ComfyUI](https://github.com/comfyanonymous/ComfyUI) project with our [comfyui-hydit](.) and have correct conda environment. + +```shell +# Download comfyui code +git clone https://github.com/comfyanonymous/ComfyUI.git + +# 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 --default-timeout=100 future + +# Install Comfyui essential python package +cd ComfyUI +pip install -r requirements.txt + +# ComfyUI has been successfully installed! + + +# Move to the ComfyUI custom_nodes folder and copy comfyui-hydit folder from HunyuanDiT Repo. +cd custom_nodes +git clone https://github.com/Tencent/HunyuanDiT.git +cp -r HunyuanDiT/comfyui-hydit ./ +rm -rf HunyuanDiT +cd comfyui-hydit + +# If using windows system !! +cd custom_nodes +git clone https://github.com/Tencent/HunyuanDiT.git +xcopy /E /I HunyuanDiT\comfyui-hydit comfyui-hydit +rmdir /S /Q HunyuanDiT +cd comfyui-hydit + +# Install some essential python Package. +pip install -r requirements.txt + +# Our tool has been successfully installed! + +# Go to ComfyUI main folder +cd ../.. +# Run the ComfyUI Lauch command +python main.py --listen --port 80 + +# Running ComfyUI successfully! +``` + + +## Download weights for ksampler mode +Download the [clip encoder](https://huggingface.co/Tencent-Hunyuan/HunyuanDiT-v1.2/blob/main/t2i/clip_text_encoder/pytorch_model.bin) and place it in `ComfyUI/models/clip` +Download the [mt5](https://huggingface.co/Tencent-Hunyuan/HunyuanDiT-v1.2/blob/main/t2i/mt5/pytorch_model.bin) and place it in `ComfyUI/models/t5` +Download the [base model](https://huggingface.co/Tencent-Hunyuan/HunyuanDiT-v1.2/blob/main/t2i/model/pytorch_model_ema.pt) and place it in `ComfyUI/models/checkpoints` +Download the [sdxl vae](https://huggingface.co/Tencent-Hunyuan/HunyuanDiT-v1.2/blob/main/t2i/sdxl-vae-fp16-fix/diffusion_pytorch_model.bin) and place it in `ComfyUI/models/vae` + + +## Custom Node +Below I'm trying to document all the nodes, thanks for some good work[[1]](#1)[[2]](#2). + +#### HunYuan Checkpoint Loader +- Loads the base model for HunYuanDiT in ksampler backend. +- **model_name** is the weight list of comfyui checkpoint folder. + + +#### HunYuan CLIP Loader +- Loads the clip and mt5 model for HunYuanDiT in ksampler backend. +- **text_encoder_path** is the weight list of comfyui clip model folder. +- **t5_text_encoder_path** is the weight list of comfyui t5 model folder. + +#### HunYuan VAE Loader +- Loads the vae model for HunYuanDiT in ksampler backend. +- **model_name** is the weight list of comfyui vae model folder. + + + +## Reference +[1] +https://github.com/Limitex/ComfyUI-Diffusers +[2] +https://github.com/Tencent/HunyuanDiT/pull/59 diff --git a/custom_nodes/comfyui-hydit_min/__init__.py b/custom_nodes/comfyui-hydit_min/__init__.py new file mode 100644 index 000000000..ee645d67e --- /dev/null +++ b/custom_nodes/comfyui-hydit_min/__init__.py @@ -0,0 +1,4 @@ +from .nodes import * +#aa = DiffusersSampler() +#print(aa) +__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"] diff --git a/custom_nodes/comfyui-hydit_min/clip.py b/custom_nodes/comfyui-hydit_min/clip.py new file mode 100644 index 000000000..ef2d30021 --- /dev/null +++ b/custom_nodes/comfyui-hydit_min/clip.py @@ -0,0 +1,150 @@ +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, AutoTokenizer +import torch +import os +from transformers import BertConfig, BertModel +from transformers import AutoTokenizer, modeling_utils +#import pdb + +class CLIP: + def __init__(self, root, text_encoder_path = None, t5_text_encoder_path = None): + self.device = "cuda" if torch.cuda.is_available() else "cpu" + if text_encoder_path == None: + 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) + else: + config = BertConfig.from_json_file(os.path.join(os.path.dirname(os.path.realpath(__file__)),"config_clip.json")) + with modeling_utils.no_init_weights(): + clip_text_encoder = BertModel(config) + sd = comfy.utils.load_torch_file(text_encoder_path) + prefix = "bert." + state_dict = {} + for key in sd: + nkey = key + if key.startswith(prefix): + nkey = key[len(prefix):] + state_dict[nkey] = sd[key] + + m, e = clip_text_encoder.load_state_dict(state_dict, strict=False) + if len(m) > 0 or len(e) > 0: + print(f"HYDiT: clip missing {len(m)} keys ({len(e)} extra)") + #clip_text_encoder.load_state_dict(sd, strict=False) + clip_text_encoder.eval().to(self.device) + + tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "tokenizer_clip") + self.tokenizer = HyBertTokenizer(tokenizer_path) + #assert(0) + + if t5_text_encoder_path == None: + 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 + else: + + embedder_t5 = MT5Embedder(t5_text_encoder_path, torch_dtype=torch.float16, max_length=256, ksampler=True) + self.tokenizer_t5 = HyT5Tokenizer(embedder_t5.tokenizer, max_length=embedder_t5.max_length) + self.embedder_t5 = embedder_t5.model + + + self.cond_stage_model = clip_text_encoder + + + def tokenize(self, text): + tokens = self.tokenizer.tokenize(text) + t5_tokens = self.tokenizer_t5.tokenize(text) + tokens.update(t5_tokens) + return tokens + + def tokenize_t5(self, text): + return self.tokenizer_t5.tokenize(text) + + 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] + + addit_embeds = { + "t5_embeds": t5_embeds, + "attention_mask": attention_mask.float(), + "t5_attention_mask": t5_attention_mask.float() + } + prompt_embeds.addit_embeds = addit_embeds + + if return_pooled: + return prompt_embeds, None + else: + return prompt_embeds + +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 + + 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 + +class HyT5Tokenizer: + def __init__(self, tokenizer, tokenizer_path = None, max_length=77, truncation=True, return_attention_mask=True, device='cpu'): + if tokenizer_path: + self.tokenizer = AutoTokenizer.from_pretrained(str(tokenizer_path)) + else: + self.tokenizer = tokenizer + + self.max_length = max_length + self.truncation = truncation + self.return_attention_mask = return_attention_mask + self.device = device + + 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 + diff --git a/custom_nodes/comfyui-hydit_min/config_clip.json b/custom_nodes/comfyui-hydit_min/config_clip.json new file mode 100644 index 000000000..f6298741b --- /dev/null +++ b/custom_nodes/comfyui-hydit_min/config_clip.json @@ -0,0 +1,34 @@ +{ + "_name_or_path": "hfl/chinese-roberta-wwm-ext-large", + "architectures": [ + "BertModel" + ], + "attention_probs_dropout_prob": 0.1, + "bos_token_id": 0, + "classifier_dropout": null, + "directionality": "bidi", + "eos_token_id": 2, + "hidden_act": "gelu", + "hidden_dropout_prob": 0.1, + "hidden_size": 1024, + "initializer_range": 0.02, + "intermediate_size": 4096, + "layer_norm_eps": 1e-12, + "max_position_embeddings": 512, + "model_type": "bert", + "num_attention_heads": 16, + "num_hidden_layers": 24, + "output_past": true, + "pad_token_id": 0, + "pooler_fc_size": 768, + "pooler_num_attention_heads": 12, + "pooler_num_fc_layers": 3, + "pooler_size_per_head": 128, + "pooler_type": "first_token_transform", + "position_embedding_type": "absolute", + "torch_dtype": "float32", + "transformers_version": "4.22.1", + "type_vocab_size": 2, + "use_cache": true, + "vocab_size": 47020 +} diff --git a/custom_nodes/comfyui-hydit_min/config_mt5.json b/custom_nodes/comfyui-hydit_min/config_mt5.json new file mode 100644 index 000000000..a9f8cc780 --- /dev/null +++ b/custom_nodes/comfyui-hydit_min/config_mt5.json @@ -0,0 +1,28 @@ +{ + "_name_or_path": "/home/patrick/t5/mt5-xl", + "architectures": [ + "MT5ForConditionalGeneration" + ], + "d_ff": 5120, + "d_kv": 64, + "d_model": 2048, + "decoder_start_token_id": 0, + "dropout_rate": 0.1, + "eos_token_id": 1, + "feed_forward_proj": "gated-gelu", + "initializer_factor": 1.0, + "is_encoder_decoder": true, + "layer_norm_epsilon": 1e-06, + "model_type": "mt5", + "num_decoder_layers": 24, + "num_heads": 32, + "num_layers": 24, + "output_past": true, + "pad_token_id": 0, + "relative_attention_num_buckets": 32, + "tie_word_embeddings": false, + "tokenizer_class": "T5Tokenizer", + "transformers_version": "4.10.0.dev0", + "use_cache": true, + "vocab_size": 250112 +} diff --git a/custom_nodes/comfyui-hydit_min/constant.py b/custom_nodes/comfyui-hydit_min/constant.py new file mode 100644 index 000000000..0937ffd87 --- /dev/null +++ b/custom_nodes/comfyui-hydit_min/constant.py @@ -0,0 +1,10 @@ +import os +from .hydit.constants import SAMPLER_FACTORY + +base_path = os.path.dirname(os.path.realpath(__file__)) +HUNYUAN_PATH = os.path.join(base_path, "..", "..", "models", "hunyuan") +SCHEDULERS_hunyuan = list(SAMPLER_FACTORY.keys()) + + +T5_PATH = os.path.join(base_path, "..", "..", "models", "t5") +LORA_PATH = os.path.join(base_path, "..", "..", "models", "loras") \ No newline at end of file diff --git a/custom_nodes/comfyui-hydit_min/dit.py b/custom_nodes/comfyui-hydit_min/dit.py new file mode 100644 index 000000000..961d44468 --- /dev/null +++ b/custom_nodes/comfyui-hydit_min/dit.py @@ -0,0 +1,145 @@ +import comfy.supported_models_base +import comfy.latent_formats +import comfy.model_patcher +import comfy.model_base +import comfy.utils +from comfy import model_management +from .supported_dit_models import HunYuan_DiT, HYDiT_Model, ModifiedHunYuanDiT +from .clip import CLIP +import os +from .hydit.config_comfyui import get_args + +sampling_settings = { + "beta_schedule" : "linear", + "linear_start" : 0.00085, + "linear_end" : 0.03, + "timesteps" : 1000, +} + +hydit_conf = { + "G/2": { # Seems to be the main one + "unet_config": { + "depth" : 40, + "num_heads" : 16, + "patch_size" : 2, + "hidden_size" : 1408, + "mlp_ratio" : 4.3637, + "input_size": (1024//8, 1024//8), + #"disable_unet_model_creation": True, + }, + "sampling_settings" : sampling_settings, + }, +} + +def load_dit(model_path, output_clip=True, output_model=True, output_vae=True, MODEL_PATH = None, VAE_PATH = None): + if MODEL_PATH: + state_dict = comfy.utils.load_torch_file(MODEL_PATH) + else: + state_dict = comfy.utils.load_torch_file(os.path.join(model_path, "t2i", "model", "pytorch_model_ema.pt")) + + + state_dict = state_dict.get("model", state_dict) + parameters = comfy.utils.calculate_parameters(state_dict) + unet_dtype = model_management.unet_dtype(model_params=parameters) + load_device = comfy.model_management.get_torch_device() + offload_device = comfy.model_management.unet_offload_device() + clip = None, + vae = None + model_patcher = None + + # ignore fp8/etc and use directly for now + manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device) + root = os.path.join(model_path, "t2i") + if manual_cast_dtype: + print(f"DiT: falling back to {manual_cast_dtype}") + unet_dtype = manual_cast_dtype + + #model_conf["unet_config"]["num_classes"] = state_dict["y_embedder.embedding_table.weight"].shape[0] - 1 # adj. for empty + + if output_model: + model_conf = HunYuan_DiT(hydit_conf["G/2"]) + model = HYDiT_Model( + model_conf, + model_type=comfy.model_base.ModelType.V_PREDICTION, + device=model_management.get_torch_device() + ) + + #print(model_conf.unet_config) + #assert(0) + all_args =get_args() + + model.diffusion_model = ModifiedHunYuanDiT(model_conf.dit_conf, **model_conf.unet_config).half().to(load_device) + + model.diffusion_model.load_state_dict(state_dict) + model.diffusion_model.eval() + model.diffusion_model.to(unet_dtype) + + model_patcher = comfy.model_patcher.ModelPatcher( + model, + load_device = load_device, + offload_device = offload_device, + current_device = "cpu", + ) + if output_clip: + clip = CLIP(root) + + if output_vae: + if VAE_PATH: + vae_path = VAE_PATH + else: + vae_path = os.path.join(root, "sdxl-vae-fp16-fix", "diffusion_pytorch_model.safetensors") + #print(vae_path) + sd = comfy.utils.load_torch_file(vae_path) + vae = comfy.sd.VAE(sd=sd) + + return (model_patcher, clip, vae) + + +def load_checkpoint(model_path): + + state_dict = comfy.utils.load_torch_file(model_path) + + + state_dict = state_dict.get("model", state_dict) + parameters = comfy.utils.calculate_parameters(state_dict) + unet_dtype = model_management.unet_dtype(model_params=parameters) + load_device = comfy.model_management.get_torch_device() + offload_device = comfy.model_management.unet_offload_device() + model_patcher = None + + # ignore fp8/etc and use directly for now + manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device) + if manual_cast_dtype: + print(f"DiT: falling back to {manual_cast_dtype}") + unet_dtype = manual_cast_dtype + + + model_conf = HunYuan_DiT(hydit_conf["G/2"]) + model = HYDiT_Model( + model_conf, + model_type=comfy.model_base.ModelType.V_PREDICTION, + device=model_management.get_torch_device() + ) + + + model.diffusion_model = ModifiedHunYuanDiT(model_conf.dit_conf, **model_conf.unet_config).half().to(load_device) + + model.diffusion_model.load_state_dict(state_dict) + model.diffusion_model.eval() + model.diffusion_model.to(unet_dtype) + + model_patcher = comfy.model_patcher.ModelPatcher( + model, + load_device = load_device, + offload_device = offload_device, + current_device = "cpu",) + return (model_patcher,) + + + +def load_vae(vae_path): + + sd = comfy.utils.load_torch_file(vae_path) + vae = comfy.sd.VAE(sd=sd) + + return (vae,) \ No newline at end of file diff --git a/custom_nodes/comfyui-hydit_min/hydit/__init__.py b/custom_nodes/comfyui-hydit_min/hydit/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/custom_nodes/comfyui-hydit_min/hydit/config_comfyui.py b/custom_nodes/comfyui-hydit_min/hydit/config_comfyui.py new file mode 100644 index 000000000..b062cc180 --- /dev/null +++ b/custom_nodes/comfyui-hydit_min/hydit/config_comfyui.py @@ -0,0 +1,227 @@ + +import argparse +#import deepspeed + +from .constants import * +from .diffusion.gaussian_diffusion import ModelVarType +from .modules.models import HUNYUAN_DIT_CONFIG + + +def model_var_type(value): + try: + return ModelVarType[value] + except KeyError: + raise ValueError(f"Invalid choice '{value}', valid choices are {[v.name for v in ModelVarType]}") + + +def get_args(default_args=None): + parser = argparse.ArgumentParser() + parser.add_argument("--task-flag", type=str) + + # General Setting + parser.add_argument("--batch-size", type=int, default=1, help="Per-GPU batch size") + parser.add_argument('--seed', type=int, default=42, help="A seed for all the prompts.") + parser.add_argument("--use-fp16", action="store_true", help="Use FP16 precision.") + parser.add_argument("--no-fp16", dest="use_fp16", action="store_false") + parser.set_defaults(use_fp16=True) + parser.add_argument("--extra-fp16", action="store_true", help="Use extra fp16 for vae and text_encoder.") + + # HunYuan-DiT + parser.add_argument("--model", type=str, choices=list(HUNYUAN_DIT_CONFIG.keys()), default='DiT-g/2') + parser.add_argument("--image-size", type=int, nargs='+', default=[1024, 1024], + help='Image size (h, w). If a single value is provided, the image will be treated to ' + '(value, value).') + parser.add_argument("--qk-norm", action="store_true", + help="Query Key normalization. See http://arxiv.org/abs/2302.05442 for details.") + parser.set_defaults(qk_norm=True) + parser.add_argument("--norm", type=str, choices=["rms", "laryer"], default="layer", help="Normalization layer type") + parser.add_argument("--text-states-dim", type=int, default=1024, help="Hidden size of CLIP text encoder.") + parser.add_argument("--text-len", type=int, default=77, help="Token length of CLIP text encoder output.") + parser.add_argument("--text-states-dim-t5", type=int, default=2048, help="Hidden size of CLIP text encoder.") + parser.add_argument("--text-len-t5", type=int, default=256, help="Token length of T5 text encoder output.") + + # LoRA config + parser.add_argument("--training-parts", type=str, default='all', choices=['all', 'lora'], help="Training parts") + parser.add_argument("--rank", type=int, default=64, help="Rank of LoRA") + parser.add_argument("--lora-ckpt", type=str, default=None, help="LoRA checkpoint") + parser.add_argument('--target-modules', type=str, nargs='+', default=['Wqkv', 'q_proj', 'kv_proj', 'out_proj'], + help="Target modules for LoRA fine tune") + parser.add_argument("--output-merge-path", type=str, default=None, help="Output path for merged model") + + # controlnet config + parser.add_argument("--control-type", type=str, default='canny', choices=['canny', 'depth', 'pose'], + help="Controlnet condition type") + parser.add_argument("--control-weight", type=str, default=1.0, + help="Controlnet weight, You can use a float to specify the weight for all layers, " + "or use a list to separately specify the weight for each layer, for example, " + "'[1.0 * (0.825 ** float(19 - i)) for i in range(19)]'") + parser.add_argument("--condition-image-path", type=str, default=None, help="Inference condition image path") + + # Diffusion + parser.add_argument("--learn-sigma", action="store_true", help="Learn extra channels for sigma.") + parser.add_argument("--no-learn-sigma", dest="learn_sigma", action="store_false") + parser.set_defaults(learn_sigma=True) + parser.add_argument("--predict-type", type=str, choices=list(PREDICT_TYPE), default="v_prediction", + help="Diffusion predict type") + parser.add_argument("--noise-schedule", type=str, choices=list(NOISE_SCHEDULES), default="scaled_linear", + help="Noise schedule") + parser.add_argument("--beta-start", type=float, default=0.00085, help="Beta start value") + parser.add_argument("--beta-end", type=float, default=0.02, help="Beta end value") + parser.add_argument("--sigma-small", action="store_true") + parser.add_argument("--mse-loss-weight-type", type=str, default="constant", + help="Min-SNR-gamma. Can be constant or min_snr_ where gamma is a integer. " + "5 is recommended in the paper.") + parser.add_argument("--model-var-type", type=model_var_type, default=None, help="Specify the model variable type.") + parser.add_argument("--noise-offset", type=float, default=0.0, help="Add extra noise to the input image.") + + # ======================================================================================================== + # Inference + # ======================================================================================================== + + # Basic Setting + parser.add_argument("--prompt", type=str, default="一只小猫", help="The prompt for generating images.") + parser.add_argument("--model-root", type=str, default="ckpts", + help="Root path of all the models, including t2i model and dialoggen model.") + parser.add_argument("--dit-weight", type=str, default=None, + help="Path to the HunYuan-DiT model. If None, search the model in the args.model_root." + "1. If it is a directory, search the model in the directory. Support two types of models: " + "1) named `pytorch_model_*.pt`, where * is specified by the --load-key. " + "2) named `*_model_states.pt`, where * can be `mp_rank_00`. *_model_states.pt contains " + "both 'module' and 'ema' weights. Therefore, you still use --load-key to specify the " + "weights to load. By default, load 'ema' weights. " + "2. If it is a file, load the model directly. In this case, the --load-key is ignored." + ) + + # Model setting + parser.add_argument("--load-key", type=str, choices=["ema", "module", "distill", 'merge'], default="ema", + help="Load model key for HunYuanDiT checkpoint.") + parser.add_argument('--use-style-cond', action="store_true", + help="Use style condition in hydit. Only for hydit version <= 1.1") + parser.add_argument('--size-cond', type=int, nargs='+', default=None, + help="Size condition used in sampling. 2 values are required for height and width. " + "If a single value is provided, the image will be treated to (value, value)." + "Recommended values are [1024, 1024]. Only for hydit version <= 1.1") + parser.add_argument('--target-ratios', type=str, nargs='+', default=None, + help="Target ratios for multi-resolution training.") + parser.add_argument("--cfg-scale", type=float, default=6.0, help="Guidance scale for classifier-free.") + parser.add_argument("--negative", type=str, default=None, help="Negative prompt.") + + # Acceleration + parser.add_argument("--infer-mode", type=str, choices=["fa", "torch", "trt"], default="fa", + help="Inference mode") + parser.add_argument("--onnx-workdir", type=str, default="onnx_model", help="Path to save ONNX model") + + # Sampling + parser.add_argument("--sampler", type=str, choices=SAMPLER_FACTORY, default="ddpm", help="Diffusion sampler") + parser.add_argument("--infer-steps", type=int, default=100, help="Inference steps") + + # Prompt enhancement + parser.add_argument("--enhance", action="store_true", help="Enhance prompt with mllm.") + parser.add_argument("--no-enhance", dest="enhance", action="store_false") + parser.add_argument("--load-4bit", help="load DialogGen model with 4bit quantization.", action="store_true") + parser.set_defaults(enhance=True) + + # App + parser.add_argument("--lang", type=str, default="zh", choices=["zh", "en"], help="Language") + + + # ======================================================================================================== + # Training + # ======================================================================================================== + + # Basic Setting + parser.add_argument("--lr", type=float, default=1e-4) + parser.add_argument("--epochs", type=int, default=10) + parser.add_argument("--max-training-steps", type=int, default=10_000_000) + parser.add_argument("--gc-interval", type=int, default=40, + help='To address the memory bottleneck encountered during the preprocessing of the dataset,' + ' memory fragments are reclaimed here by invoking the gc.collect() function.') + parser.add_argument("--log-every", type=int, default=100) + parser.add_argument("--ckpt-every", type=int, default=100_000, help="Create a ckpt every a few steps.") + parser.add_argument("--ckpt-latest-every", type=int, default=10_000, + help="Create a ckpt named `latest.pt` every a few steps.") + parser.add_argument("--ckpt-every-n-epoch", type=int, default=0, + help="Create a ckpt every a few epochs. If 0, do not create ckpt based on epoch. Default is 0.") + parser.add_argument("--num-workers", type=int, default=4) + parser.add_argument("--global-seed", type=int, default=1234) + parser.add_argument("--warmup-min-lr", type=float, default=1e-6) + parser.add_argument("--warmup-num-steps", type=float, default=0) + parser.add_argument("--weight-decay", type=float, default=0, help="weight-decay in optimizer") + parser.add_argument("--rope-img", type=str, default=None, choices=['extend', 'base512', 'base1024'], + help="Extend or interpolate the positional embedding of the image.") + parser.add_argument("--rope-real", action="store_true", + help="Use real part and imaginary part separately for RoPE.") + + # Classifier-free + parser.add_argument("--uncond-p", type=float, default=0.2, + help="The probability of dropping training text used for CLIP feature extraction") + parser.add_argument("--uncond-p-t5", type=float, default=0.2, + help="The probability of dropping training text used for mT5 feature extraction") + + # Directory + parser.add_argument("--results-dir", type=str, default="results") + parser.add_argument("--resume", type=str, default=None, help="Resume experiment from a checkpoint") + parser.add_argument("--no-strict", dest="strict", action="store_false", help="Strict loading of checkpoint") + parser.set_defaults(strict=True) + parser.add_argument("--resume-deepspeed", action="store_true", + help="Resume model and ema states from a checkpoint saved by Deepspeed version of DIT.") + parser.add_argument("--resume-split", action="store_true", + help="Resume model and ema states from two checkpoint separated from DeepSpeed ckpt.") + parser.add_argument("--ema-to-module", action="store_true", + help="If true, initialize the module with EMA weights.") + parser.add_argument("--module-to-ema", action="store_true", + help="if true, initialize the ema with Module weights.") + + # Dataset + parser.add_argument("--index-file", type=str, nargs='+', help="During training, provide a JSON file with data " + "indices.") + parser.add_argument("--random-flip", action="store_true", help="Random flip image") + parser.add_argument("--reset-loader", action="store_true", + help="Reset the data loader. It is useful when resuming from a checkpoint but switch to a new " + "dataset.") + parser.add_argument("--multireso", action="store_true", help="Use multi-resolution training.") + parser.add_argument("--reso-step", type=int, default=None, help="Step size for multi-resolution training.") + + # Additional condition + parser.add_argument("--random-shrink-size-cond", action="store_true", + help="Randomly shrink the original size condition.") + parser.add_argument("--merge-src-cond", action="store_true", help="Merge the source condition into a single value.") + + # EMA Model + parser.add_argument("--use-ema", action="store_true", help="Use EMA model") + parser.add_argument("--ema-dtype", type=str, choices=['fp16', 'fp32', 'none'], default="none", + help="EMA data type. If none, use the same data type as the model.") + parser.add_argument("--ema-decay", type=float, default=None, + help="EMA decay rate. If None, use the default value of the model.") + parser.add_argument("--ema-warmup", action="store_true", + help="EMA warmup. If True, perform ema_decay warmup from 0 to ema_decay.") + parser.add_argument("--ema-warmup-power", type=float, default=None, + help="EMA power. If None, use the default value of the model.") + parser.add_argument("--ema-reset-decay", action="store_true", + help="Reset EMA decay to 0 and restart increasing the EMA decay." + "Only works when --ema-warmup is enabled.") + # Acceleration + parser.add_argument("--use-flash-attn", action="store_true", help="During training, " + "flash attention is used to accelerate training.") + parser.add_argument("--no-flash-attn", dest="use_flash_attn", action="store_false", + help="During training, flash attention is not used to accelerate training.") + parser.add_argument("--use-zero-stage", type=int, default=1, help="Use AngelPTM zero stage. Support 2 and 3") + parser.add_argument("--grad-accu-steps", type=int, default=1, help="Gradient accumulation steps.") + parser.add_argument("--gradient-checkpointing", action="store_true", help="Use gradient checkpointing.") + parser.add_argument("--save-optimizer-state", action="store_true", help="Save optimizer state in the checkpoint.") + + # ======================================================================================================== + # Deepspeed config + # ======================================================================================================== + #parser = deepspeed.add_config_arguments(parser) + parser.add_argument('--local_rank', type=int, default=None, + help='local rank passed from distributed launcher.') + parser.add_argument('--deepspeed-optimizer', action='store_true', + help='Switching to the optimizers in DeepSpeed') + parser.add_argument('--remote-device', type=str, default='none', choices=['none', 'cpu', 'nvme'], + help='Remote device for ZeRO-3 initialized parameters.') + parser.add_argument('--zero-stage', type=int, default=1) + + args = parser.parse_known_args() + + return args diff --git a/custom_nodes/comfyui-hydit_min/hydit/constants.py b/custom_nodes/comfyui-hydit_min/hydit/constants.py new file mode 100644 index 000000000..3cf719ea4 --- /dev/null +++ b/custom_nodes/comfyui-hydit_min/hydit/constants.py @@ -0,0 +1,82 @@ +import torch + +# ======================================================= + +NOISE_SCHEDULES = { + "linear", + "scaled_linear", + "squaredcos_cap_v2", +} + +PREDICT_TYPE = { + "epsilon", + "sample", + "v_prediction", +} + +# ======================================================= + +NEGATIVE_PROMPT = '错误的眼睛，糟糕的人脸，毁容，糟糕的艺术，变形，多余的肢体，模糊的颜色，模糊，重复，病态，残缺，' + +# ======================================================= +TRT_MAX_BATCH_SIZE = 1 +TRT_MAX_WIDTH = 1280 +TRT_MAX_HEIGHT = 1280 + +# ======================================================= +# Constants about models +# ======================================================= + +VAE_EMA_PATH = "ckpts/t2i/sdxl-vae-fp16-fix" +TOKENIZER = "ckpts/t2i/tokenizer" +TEXT_ENCODER = 'ckpts/t2i/clip_text_encoder' +T5_ENCODER = { + 'MT5': 'ckpts/t2i/mt5', + 'attention_mask': True, + 'layer_index': -1, + 'attention_pool': True, + 'torch_dtype': torch.float16, + 'learnable_replace': True +} + +SAMPLER_FACTORY = { + 'ddpm': { + 'scheduler': 'DDPMScheduler', + 'name': 'DDPM', + 'kwargs': { + 'steps_offset': 1, + 'clip_sample': False, + 'clip_sample_range': 1.0, + 'beta_schedule': 'scaled_linear', + 'beta_start': 0.00085, + 'beta_end': 0.02, + 'prediction_type': 'v_prediction', + } + }, + 'ddim': { + 'scheduler': 'DDIMScheduler', + 'name': 'DDIM', + 'kwargs': { + 'steps_offset': 1, + 'clip_sample': False, + 'clip_sample_range': 1.0, + 'beta_schedule': 'scaled_linear', + 'beta_start': 0.00085, + 'beta_end': 0.02, + 'prediction_type': 'v_prediction', + } + }, + 'dpmms': { + 'scheduler': 'DPMSolverMultistepScheduler', + 'name': 'DPMMS', + 'kwargs': { + 'beta_schedule': 'scaled_linear', + 'beta_start': 0.00085, + 'beta_end': 0.02, + 'prediction_type': 'v_prediction', + 'trained_betas': None, + 'solver_order': 2, + 'algorithm_type': 'dpmsolver++', + } + }, +} diff --git a/custom_nodes/comfyui-hydit_min/hydit/diffusion/__init__.py b/custom_nodes/comfyui-hydit_min/hydit/diffusion/__init__.py new file mode 100644 index 000000000..f6a681113 --- /dev/null +++ b/custom_nodes/comfyui-hydit_min/hydit/diffusion/__init__.py @@ -0,0 +1,49 @@ +from . import gaussian_diffusion as gd +from .respace import SpacedDiffusion, space_timesteps + + +def create_diffusion( + *, + steps=1000, + learn_sigma=True, + sigma_small=False, + noise_schedule="linear", + use_kl=False, + predict_type='epsilon', + rescale_timesteps=False, + rescale_learned_sigmas=False, + timestep_respacing="", + mse_loss_weight_type='constant', + beta_start=0.0001, + beta_end=0.02, + noise_offset=0.0, +): + betas = gd.get_named_beta_schedule(noise_schedule, steps, beta_start, beta_end) + if use_kl: + loss_type = gd.LossType.RESCALED_KL + elif rescale_learned_sigmas: + loss_type = gd.LossType.RESCALED_MSE + else: + loss_type = gd.LossType.MSE + if timestep_respacing is None or timestep_respacing == "": + timestep_respacing = [steps] + mean_type = gd.predict_type_dict[predict_type] + + return SpacedDiffusion( + use_timesteps=space_timesteps(steps, timestep_respacing), + betas=betas, + model_mean_type=mean_type, + model_var_type=( + ( + gd.ModelVarType.FIXED_LARGE + if not sigma_small + else gd.ModelVarType.FIXED_SMALL + ) + if not learn_sigma + else gd.ModelVarType.LEARNED_RANGE + ), + loss_type=loss_type, + rescale_timesteps=rescale_timesteps, + mse_loss_weight_type=mse_loss_weight_type, + noise_offset=noise_offset, + ) diff --git a/custom_nodes/comfyui-hydit_min/hydit/diffusion/diffusion_utils.py b/custom_nodes/comfyui-hydit_min/hydit/diffusion/diffusion_utils.py new file mode 100644 index 000000000..9c62d5b0f --- /dev/null +++ b/custom_nodes/comfyui-hydit_min/hydit/diffusion/diffusion_utils.py @@ -0,0 +1,69 @@ +import torch as th +import numpy as np + + +def normal_kl(mean1, logvar1, mean2, logvar2): + """ + Compute the KL divergence between two gaussians. + Shapes are automatically broadcasted, so batches can be compared to + scalars, among other use cases. + """ + tensor = None + for obj in (mean1, logvar1, mean2, logvar2): + if isinstance(obj, th.Tensor): + tensor = obj + break + assert tensor is not None, "at least one argument must be a Tensor" + + # Force variances to be Tensors. Broadcasting helps convert scalars to + # Tensors, but it does not work for th.exp(). + logvar1, logvar2 = [ + x if isinstance(x, th.Tensor) else th.tensor(x).to(tensor) + for x in (logvar1, logvar2) + ] + + return 0.5 * ( + -1.0 + + logvar2 + - logvar1 + + th.exp(logvar1 - logvar2) + + ((mean1 - mean2) ** 2) * th.exp(-logvar2) + ) + + +def approx_standard_normal_cdf(x): + """ + A fast approximation of the cumulative distribution function of the + standard normal. + """ + return 0.5 * (1.0 + th.tanh(np.sqrt(2.0 / np.pi) * (x + 0.044715 * th.pow(x, 3)))) + + +def discretized_gaussian_log_likelihood(x, *, means, log_scales): + """ + Compute the log-likelihood of a Gaussian distribution discretizing to a + given image. + :param x: the target images. It is assumed that this was uint8 values, + rescaled to the range [-1, 1]. + :param means: the Gaussian mean Tensor. + :param log_scales: the Gaussian log stddev Tensor. + :return: a tensor like x of log probabilities (in nats). + """ + assert x.shape == means.shape == log_scales.shape + centered_x = x - means + inv_stdv = th.exp(-log_scales) + plus_in = inv_stdv * (centered_x + 1.0 / 255.0) + cdf_plus = approx_standard_normal_cdf(plus_in) + min_in = inv_stdv * (centered_x - 1.0 / 255.0) + cdf_min = approx_standard_normal_cdf(min_in) + log_cdf_plus = th.log(cdf_plus.clamp(min=1e-12)) + log_one_minus_cdf_min = th.log((1.0 - cdf_min).clamp(min=1e-12)) + cdf_delta = cdf_plus - cdf_min + log_probs = th.where( + x < -0.999, + log_cdf_plus, + th.where(x > 0.999, log_one_minus_cdf_min, th.log(cdf_delta.clamp(min=1e-12))), + ) + assert log_probs.shape == x.shape + return log_probs + diff --git a/custom_nodes/comfyui-hydit_min/hydit/diffusion/gaussian_diffusion.py b/custom_nodes/comfyui-hydit_min/hydit/diffusion/gaussian_diffusion.py new file mode 100644 index 000000000..23b0f44a3 --- /dev/null +++ b/custom_nodes/comfyui-hydit_min/hydit/diffusion/gaussian_diffusion.py @@ -0,0 +1,1369 @@ +import math + +import numpy as np +import torch as th +import enum + +from .diffusion_utils import discretized_gaussian_log_likelihood, normal_kl +from ..utils.tools import assert_shape + + +def mean_flat(tensor): + """ + Take the mean over all non-batch dimensions. + """ + return tensor.mean(dim=list(range(1, len(tensor.shape)))) + + +class ModelMeanType(enum.Enum): + """ + Which type of output the model predicts. + """ + + PREVIOUS_X = enum.auto() # the model predicts x_{t-1} + START_X = enum.auto() # the model predicts x_0 + EPSILON = enum.auto() # the model predicts epsilon + VELOCITY = enum.auto() # the model predicts v + + +predict_type_dict = { + 'epsilon': ModelMeanType.EPSILON, + 'sample': ModelMeanType.START_X, + 'v_prediction': ModelMeanType.VELOCITY, +} + + +class ModelVarType(enum.Enum): + """ + What is used as the model's output variance. + The LEARNED_RANGE option has been added to allow the model to predict + values between FIXED_SMALL and FIXED_LARGE, making its job easier. + """ + + LEARNED = enum.auto() + FIXED_SMALL = enum.auto() + FIXED_LARGE = enum.auto() + LEARNED_RANGE = enum.auto() + + +class LossType(enum.Enum): + MSE = enum.auto() # use raw MSE loss (and KL when learning variances) + RESCALED_MSE = ( + enum.auto() + ) # use raw MSE loss (with RESCALED_KL when learning variances) + KL = enum.auto() # use the variational lower-bound + RESCALED_KL = enum.auto() # like KL, but rescale to estimate the full VLB + + def is_vb(self): + return self == LossType.KL or self == LossType.RESCALED_KL + + +def _warmup_beta(beta_start, beta_end, num_diffusion_timesteps, warmup_frac): + betas = beta_end * np.ones(num_diffusion_timesteps, dtype=np.float64) + warmup_time = int(num_diffusion_timesteps * warmup_frac) + betas[:warmup_time] = np.linspace(beta_start, beta_end, warmup_time, dtype=np.float64) + return betas + + +def get_beta_schedule(beta_schedule, *, beta_start, beta_end, num_diffusion_timesteps): + """ + This is the deprecated API for creating beta schedules. + See get_named_beta_schedule() for the new library of schedules. + """ + if beta_schedule == "quad": + betas = ( + np.linspace( + beta_start ** 0.5, + beta_end ** 0.5, + num_diffusion_timesteps, + dtype=np.float64, + ) + ** 2 + ) + elif beta_schedule == "linear": + betas = np.linspace(beta_start, beta_end, num_diffusion_timesteps, dtype=np.float64) + elif beta_schedule == "warmup10": + betas = _warmup_beta(beta_start, beta_end, num_diffusion_timesteps, 0.1) + elif beta_schedule == "warmup50": + betas = _warmup_beta(beta_start, beta_end, num_diffusion_timesteps, 0.5) + elif beta_schedule == "const": + betas = beta_end * np.ones(num_diffusion_timesteps, dtype=np.float64) + elif beta_schedule == "jsd": # 1/T, 1/(T-1), 1/(T-2), ..., 1 + betas = 1.0 / np.linspace( + num_diffusion_timesteps, 1, num_diffusion_timesteps, dtype=np.float64 + ) + else: + raise NotImplementedError(beta_schedule) + assert_shape(betas, (num_diffusion_timesteps,)) + return betas + + +def get_named_beta_schedule(schedule_name, num_diffusion_timesteps, beta_start=0.0001, beta_end=0.02): + """ + Get a pre-defined beta schedule for the given name. + The beta schedule library consists of beta schedules which remain similar + in the limit of num_diffusion_timesteps. + Beta schedules may be added, but should not be removed or changed once + they are committed to maintain backwards compatibility. + """ + if schedule_name == "linear": + # Linear schedule from Ho et al, extended to work for any number of + # diffusion steps. + scale = 1000 / num_diffusion_timesteps + return get_beta_schedule( + "linear", + beta_start=scale * beta_start, # DDPM + beta_end=scale * beta_end, # DDPM + num_diffusion_timesteps=num_diffusion_timesteps, # DDPM + ) + elif schedule_name == "scaled_linear": + return get_beta_schedule( + "quad", + beta_start=beta_start, # StableDiffusion, should be 0.00085 + beta_end=beta_end, # StableDiffusion, should be 0.012 + num_diffusion_timesteps=num_diffusion_timesteps, # StableDiffusion + ) + elif schedule_name == "squaredcos_cap_v2": + return betas_for_alpha_bar( + num_diffusion_timesteps, + lambda t: math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2, + ) + else: + raise NotImplementedError(f"unknown beta schedule: {schedule_name}") + + +def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999): + """ + Create a beta schedule that discretizes the given alpha_t_bar function, + which defines the cumulative product of (1-beta) over time from t = [0,1]. + + :param num_diffusion_timesteps: the number of betas to produce. + :param alpha_bar: a lambda that takes an argument t from 0 to 1 and + produces the cumulative product of (1-beta) up to that + part of the diffusion process. + :param max_beta: the maximum beta to use; use values lower than 1 to + prevent singularities. + """ + betas = [] + for i in range(num_diffusion_timesteps): + t1 = i / num_diffusion_timesteps + t2 = (i + 1) / num_diffusion_timesteps + betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta)) + return np.array(betas) + +class GaussianDiffusion: + """ + Utilities for training and sampling diffusion models. + + Ported directly from here, and then adapted over time to further experimentation. + https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/diffusion_utils_2.py#L42 + + :param betas: a 1-D numpy array of betas for each diffusion timestep, + starting at T and going to 1. + :param model_mean_type: a ModelMeanType determining what the model outputs. + :param model_var_type: a ModelVarType determining how variance is output. + :param loss_type: a LossType determining the loss function to use. + :param rescale_timesteps: if True, pass floating point timesteps into the + model so that they are always scaled like in the + original paper (0 to 1000). + """ + + def __init__( + self, + *, + betas, + model_mean_type, + model_var_type, + loss_type, + rescale_timesteps=False, + mse_loss_weight_type='constant', + noise_offset=0.0, + ): + self.model_mean_type = model_mean_type + self.model_var_type = model_var_type + self.loss_type = loss_type + self.rescale_timesteps = rescale_timesteps + + self.mse_loss_weight_type = mse_loss_weight_type + self.noise_offset = noise_offset + + # Use float64 for accuracy. + betas = np.array(betas, dtype=np.float64) + alphas = 1.0 - betas + alphas_cumprod = np.cumprod(alphas, axis=0) + + self.betas = betas + assert len(betas.shape) == 1, "betas must be 1-D" + assert (betas > 0).all() and (betas <= 1).all() + + self.num_timesteps = int(betas.shape[0]) + + self.alphas_cumprod = alphas_cumprod + self.alphas_cumprod_prev = np.append(1.0, self.alphas_cumprod[:-1]) + self.alphas_cumprod_next = np.append(self.alphas_cumprod[1:], 0.0) + assert_shape(self.alphas_cumprod_prev, (self.num_timesteps,)) + + # calculations for diffusion q(x_t | x_{t-1}) and others + self.sqrt_alphas_cumprod = np.sqrt(self.alphas_cumprod) + self.sqrt_one_minus_alphas_cumprod = np.sqrt(1.0 - self.alphas_cumprod) + self.log_one_minus_alphas_cumprod = np.log(1.0 - self.alphas_cumprod) + self.sqrt_recip_alphas_cumprod = np.sqrt(1.0 / self.alphas_cumprod) + self.sqrt_recipm1_alphas_cumprod = np.sqrt(1.0 / self.alphas_cumprod - 1) + + # calculations for posterior q(x_{t-1} | x_t, x_0) + self.posterior_variance = ( + betas * (1.0 - self.alphas_cumprod_prev) / (1.0 - self.alphas_cumprod) + ) + # log calculation clipped because the posterior variance is 0 at the + # beginning of the diffusion chain. + self.posterior_log_variance_clipped = np.log( + np.append(self.posterior_variance[1], self.posterior_variance[1:]) + ) if len(self.posterior_variance) > 1 else np.array([]) + + self.posterior_mean_coef1 = ( + betas * np.sqrt(self.alphas_cumprod_prev) / (1.0 - self.alphas_cumprod) + ) + self.posterior_mean_coef2 = ( + (1.0 - self.alphas_cumprod_prev) + * np.sqrt(alphas) + / (1.0 - self.alphas_cumprod) + ) + + self.sampler = { + "ddpm": self.p_sample_loop, + "ddim": self.ddim_sample_loop, + "plms": self.plms_sample_loop, + } + + def q_mean_variance(self, x_start, t): + """ + Get the distribution q(x_t | x_0). + + :param x_start: the [N x C x ...] tensor of noiseless inputs. + :param t: the number of diffusion steps (minus 1). Here, 0 means one step. + :return: A tuple (mean, variance, log_variance), all of x_start's shape. + """ + mean = ( + _extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start + ) + variance = _extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape) + log_variance = _extract_into_tensor( + self.log_one_minus_alphas_cumprod, t, x_start.shape + ) + return mean, variance, log_variance + + def q_sample(self, x_start, t, noise=None): + """ + Diffuse the data for a given number of diffusion steps. + + In other words, sample from q(x_t | x_0). + + :param x_start: the initial data batch. + :param t: the number of diffusion steps (minus 1). Here, 0 means one step. + :param noise: if specified, the split-out normal noise. + :return: A noisy version of x_start. + """ + if noise is None: + noise = th.randn_like(x_start) + assert_shape(noise, x_start) + return ( + _extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start + + _extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) + * noise + ) + + def q_posterior_mean_variance(self, x_start, x_t, t): + """ + Compute the mean and variance of the diffusion posterior: + + q(x_{t-1} | x_t, x_0) + + """ + assert_shape(x_start, x_t) + posterior_mean = ( + _extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start + + _extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t + ) + posterior_variance = _extract_into_tensor(self.posterior_variance, t, x_t.shape) + posterior_log_variance_clipped = _extract_into_tensor( + self.posterior_log_variance_clipped, t, x_t.shape + ) + assert_shape( + posterior_mean.shape[:1], + posterior_variance.shape[:1], + posterior_log_variance_clipped.shape[:1], + x_start.shape[:1], + ) + return posterior_mean, posterior_variance, posterior_log_variance_clipped + + def p_mean_variance( + self, model, x, t, clip_denoised=True, denoised_fn=None, model_kwargs=None, + model_var_type=None, + ): + """ + Apply the model to get p(x_{t-1} | x_t), as well as a prediction of + the initial x, x_0. + + :param model: the model, which takes a signal and a batch of timesteps + as input. + :param x: the [N x C x ...] tensor at time t. + :param t: a 1-D Tensor of timesteps. + :param clip_denoised: if True, clip the denoised signal into [-1, 1]. + :param denoised_fn: if not None, a function which applies to the + x_start prediction before it is used to sample. Applies before + clip_denoised. + :param model_kwargs: if not None, a dict of extra keyword arguments to + pass to the model. This can be used for conditioning. + :param model_var_type: if not None, overlap the default self.model_var_type. + It is useful when training with learned var but sampling with fixed var. + :return: a dict with the following keys: + - 'mean': the model mean output. + - 'variance': the model variance output. + - 'log_variance': the log of 'variance'. + - 'pred_xstart': the prediction for x_0. + """ + if model_kwargs is None: + model_kwargs = {} + + if model_var_type is None: + model_var_type = self.model_var_type + + B, C = x.shape[:2] + assert_shape(t, (B,)) + out_dict = model(x, t, **model_kwargs) + model_output = out_dict['x'] + + if len(out_dict) > 1: + extra = {k: v for k, v in out_dict.items() if k != 'x'} + else: + extra = None + + # self.model_var_type corresponds to model output + if self.model_var_type in [ModelVarType.LEARNED, ModelVarType.LEARNED_RANGE]: + assert_shape(model_output, (B, C * 2, *x.shape[2:])) + model_output, model_var_values = th.split(model_output, C, dim=1) + + # model_var_type corresponds to reverse diffusion process + if model_var_type in [ModelVarType.LEARNED, ModelVarType.LEARNED_RANGE]: + if model_var_type == ModelVarType.LEARNED: + model_log_variance = model_var_values + model_variance = th.exp(model_log_variance) + else: + min_log = _extract_into_tensor( + self.posterior_log_variance_clipped, t, x.shape + ) + max_log = _extract_into_tensor(np.log(self.betas), t, x.shape) + # The model_var_values is [-1, 1] for [min_var, max_var]. + frac = (model_var_values + 1) / 2 + model_log_variance = frac * max_log + (1 - frac) * min_log + model_variance = th.exp(model_log_variance) + else: + model_variance, model_log_variance = { + # for fixedlarge, we set the initial (log-)variance like so + # to get a better decoder log likelihood. + ModelVarType.FIXED_LARGE: ( + np.append(self.posterior_variance[1], self.betas[1:]), + np.log(np.append(self.posterior_variance[1], self.betas[1:])), + ), + ModelVarType.FIXED_SMALL: ( + self.posterior_variance, + self.posterior_log_variance_clipped, + ), + }[model_var_type] + model_variance = _extract_into_tensor(model_variance, t, x.shape) + model_log_variance = _extract_into_tensor(model_log_variance, t, x.shape) + + def process_xstart(x): + if denoised_fn is not None: + x = denoised_fn(x) + if clip_denoised: + return x.clamp(-1, 1) + return x + + if self.model_mean_type == ModelMeanType.PREVIOUS_X: + pred_xstart = process_xstart( + self._predict_xstart_from_xprev(x_t=x, t=t, xprev=model_output) + ) + model_mean = model_output + elif self.model_mean_type in [ + ModelMeanType.START_X, + ModelMeanType.EPSILON, + ModelMeanType.VELOCITY + ]: + if self.model_mean_type == ModelMeanType.START_X: + pred_xstart = process_xstart(model_output) + elif self.model_mean_type == ModelMeanType.EPSILON: + pred_xstart = process_xstart( + self._predict_xstart_from_eps(x_t=x, t=t, eps=model_output) + ) + else: + pred_xstart = process_xstart( + self._predict_xstart_from_v(x_t=x, t=t, v=model_output) + ) + model_mean, _, _ = self.q_posterior_mean_variance( + x_start=pred_xstart, x_t=x, t=t + ) + else: + raise NotImplementedError(self.model_mean_type) + + assert_shape(model_mean, model_log_variance, pred_xstart, x) + return { + "mean": model_mean, + "variance": model_variance, + "log_variance": model_log_variance, + "pred_xstart": pred_xstart, + "extra": extra, + } + + def _predict_xstart_from_eps(self, x_t, t, eps): + assert_shape(x_t, eps) + return ( + _extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t + - _extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * eps + ) + + def _predict_xstart_from_v(self, x_t, t, v): + assert_shape(x_t, v) + return ( + _extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * x_t + - _extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * v + ) + + def _predict_xstart_from_xprev(self, x_t, t, xprev): + assert_shape(x_t, xprev) + return ( # (xprev - coef2*x_t) / coef1 + _extract_into_tensor(1.0 / self.posterior_mean_coef1, t, x_t.shape) * xprev + - _extract_into_tensor( + self.posterior_mean_coef2 / self.posterior_mean_coef1, t, x_t.shape + ) + * x_t + ) + + def _predict_eps_from_xstart(self, x_t, t, pred_xstart): + return ( + _extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t + - pred_xstart + ) / _extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) + + def _velocity_from_xstart_and_noise(self, x_start, t, noise): + return ( + _extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * noise + - _extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) + * x_start + ) + + def _vb_terms_bpd( + self, model, x_start, x_t, t, clip_denoised=True, model_kwargs=None + ): + """ + Get a term for the variational lower-bound. + + The resulting units are bits (rather than nats, as one might expect). + This allows for comparison to other papers. + + :return: a dict with the following keys: + - 'output': a shape [N] tensor of NLLs or KLs. + - 'pred_xstart': the x_0 predictions. + """ + true_mean, _, true_log_variance_clipped = self.q_posterior_mean_variance( + x_start=x_start, x_t=x_t, t=t + ) + out = self.p_mean_variance( + model, x_t, t, clip_denoised=clip_denoised, model_kwargs=model_kwargs + ) + kl = normal_kl( + true_mean, true_log_variance_clipped, out["mean"], out["log_variance"] + ) + kl = mean_flat(kl) / np.log(2.0) + + decoder_nll = -discretized_gaussian_log_likelihood( + x_start, means=out["mean"], log_scales=0.5 * out["log_variance"] + ) + assert_shape(decoder_nll, x_start) + decoder_nll = mean_flat(decoder_nll) / np.log(2.0) + + # At the first timestep return the decoder NLL, + # otherwise return KL(q(x_{t-1}|x_t,x_0) || p(x_{t-1}|x_t)) + output = th.where((t == 0), decoder_nll, kl) + return {"output": output, "pred_xstart": out["pred_xstart"], "extra": out["extra"]} + + def training_losses(self, model, x_start, model_kwargs=None, controlnet=None, noise=None): + """ + Compute training losses for a single timestep. + + :param model: the model to evaluate loss on. + :param x_start: the [N x C x ...] tensor of inputs. + :param model_kwargs: if not None, a dict of extra keyword arguments to + pass to the model. This can be used for conditioning. + :param noise: if specified, the specific Gaussian noise to try to remove. + :return: a dict with the key "loss" containing a tensor of shape [N]. + Some mean or variance settings may also have other keys. + """ + if model_kwargs is None: + model_kwargs = {} + # Time steps + t = th.randint(0, self.num_timesteps, (x_start.shape[0],), device=x_start.device) + # Noise + if noise is None: + noise = th.randn_like(x_start) + if self.noise_offset > 0: + # Add channel wise noise offset + # https://www.crosslabs.org/blog/diffusion-with-offset-noise + noise = noise + self.noise_offset * th.randn(*x_start.shape[:2], 1, 1, device=x_start.device) + x_t = self.q_sample(x_start, t, noise=noise) + + terms = {} + + if self.mse_loss_weight_type == 'constant': + mse_loss_weight = th.ones_like(t) + elif self.mse_loss_weight_type.startswith("min_snr_"): + alpha = _extract_into_tensor(self.sqrt_alphas_cumprod, t, t.shape) + sigma = _extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, t.shape) + snr = (alpha / sigma) ** 2 + + k = float(self.mse_loss_weight_type.split('min_snr_')[-1]) + # min{snr, k} + mse_loss_weight = th.stack([snr, k * th.ones_like(t)], dim=1).min(dim=1)[0] / snr + else: + raise ValueError(self.mse_loss_weight_type) + + if self.loss_type == LossType.KL or self.loss_type == LossType.RESCALED_KL: + out_dict = self._vb_terms_bpd( + model=model, + x_start=x_start, + x_t=x_t, + t=t, + clip_denoised=False, + model_kwargs=model_kwargs, + ) + terms["loss"] = out_dict["output"] + if self.loss_type == LossType.RESCALED_KL: + terms["loss"] *= self.num_timesteps + extra = out_dict["extra"] + elif self.loss_type == LossType.MSE or self.loss_type == LossType.RESCALED_MSE: + if controlnet != None: + controls = controlnet(x_t, t, **model_kwargs) + model_kwargs.pop('condition') + model_kwargs.update(controls) + out_dict = model(x_t, t, **model_kwargs) + model_output = out_dict['x'] + extra = {k: v for k, v in out_dict.items() if k != 'x'} + + if self.model_var_type in [ + ModelVarType.LEARNED, + ModelVarType.LEARNED_RANGE, + ]: + B, C = x_t.shape[:2] + assert_shape(model_output, (B, C * 2, *x_t.shape[2:])) + model_output, model_var_values = th.split(model_output, C, dim=1) + # Learn the variance using the variational bound, but don't let + # it affect our mean prediction. + frozen_out = th.cat([model_output.detach(), model_var_values], dim=1) + terms["vb"] = self._vb_terms_bpd( + model=lambda *args, r=frozen_out: dict(x=r), + x_start=x_start, + x_t=x_t, + t=t, + clip_denoised=False, + )["output"] + if self.loss_type == LossType.RESCALED_MSE: + # Divide by 1000 for equivalence with initial implementation. + # Without a factor of 1/1000, the VB term hurts the MSE term. + terms["vb"] *= self.num_timesteps / 1000.0 + + if self.model_mean_type == ModelMeanType.VELOCITY: + target = self._velocity_from_xstart_and_noise(x_start, t, noise) + else: + target = { + # ModelMeanType.PREVIOUS_X: self.q_posterior_mean_variance( + # x_start=x_start, x_t=x_t, t=t + # )[0], + ModelMeanType.START_X: x_start, + ModelMeanType.EPSILON: noise, + }[self.model_mean_type] + assert_shape(model_output, target, x_start) + raw_mse = mean_flat((target - model_output) ** 2).detach() + terms["mse"] = mse_loss_weight * mean_flat((target - model_output) ** 2) + if "vb" in terms: + terms["loss"] = terms["mse"] + terms["vb"] + terms["raw_loss"] = raw_mse + terms["vb"].detach() + else: + terms["loss"] = terms["mse"] + terms["raw_loss"] = raw_mse + else: + raise NotImplementedError(self.loss_type) + + terms.update(extra) + return terms + + def condition_mean(self, cond_fn, p_mean_var, x, t, model_kwargs=None): + """ + Compute the mean for the previous step, given a function cond_fn that + computes the gradient of a conditional log probability with respect to + x. In particular, cond_fn computes grad(log(p(y|x))), and we want to + condition on y. + + This uses the conditioning strategy from Sohl-Dickstein et al. (2015). + """ + gradient = cond_fn(x, t, **model_kwargs) + new_mean = ( + p_mean_var["mean"].float() + p_mean_var["variance"] * gradient.float() + ) + return new_mean + + def condition_score(self, cond_fn, p_mean_var, x, t, model_kwargs=None): + """ + Compute what the p_mean_variance output would have been, should the + model's score function be conditioned by cond_fn. + See condition_mean() for details on cond_fn. + Unlike condition_mean(), this instead uses the conditioning strategy + from Song et al (2020). + """ + alpha_bar = _extract_into_tensor(self.alphas_cumprod, t, x.shape) + + eps = self._predict_eps_from_xstart(x, t, p_mean_var["pred_xstart"]) + eps = eps - (1 - alpha_bar).sqrt() * cond_fn( + x, t, **model_kwargs + ) + + out = p_mean_var.copy() + out["pred_xstart"] = self._predict_xstart_from_eps(x, t, eps) + out["mean"], _, _ = self.q_posterior_mean_variance( + x_start=out["pred_xstart"], x_t=x, t=t + ) + return out + + def p_sample( + self, + model, + x, + t, + clip_denoised=True, + denoised_fn=None, + cond_fn=None, + model_kwargs=None, + model_var_type=None, + **kwargs, + ): + """ + Sample x_{t-1} from the model at the given timestep. + + :param model: the model to sample from. + :param x: the current tensor at x_{t-1}. + :param t: the value of t, starting at 0 for the first diffusion step. + :param clip_denoised: if True, clip the x_start prediction to [-1, 1]. + :param denoised_fn: if not None, a function which applies to the + x_start prediction before it is used to sample. + :param cond_fn: if not None, this is a gradient function that acts + similarly to the model. + :param model_kwargs: if not None, a dict of extra keyword arguments to + pass to the model. This can be used for conditioning. + :param model_var_type: if not None, overlap the default self.model_var_type. + It is useful when training with learned var but sampling with fixed var. + :return: a dict containing the following keys: + - 'sample': a random sample from the model. + - 'pred_xstart': a prediction of x_0. + """ + out = self.p_mean_variance( + model, + x, + t, + clip_denoised=clip_denoised, + denoised_fn=denoised_fn, + model_kwargs=model_kwargs, + model_var_type=model_var_type, + ) + noise = th.randn_like(x) + nonzero_mask = ( + (t != 0).float().view(-1, *([1] * (len(x.shape) - 1))) + ) # no noise when t == 0 + if cond_fn is not None: + out["mean"] = self.condition_mean( + cond_fn, out, x, t, model_kwargs=model_kwargs + ) + + sample = out["mean"] + nonzero_mask * th.exp(0.5 * out["log_variance"]) * noise + + return {"sample": sample, "pred_xstart": out["pred_xstart"]} + + def p_sample_loop( + self, + model, + shape, + noise=None, + clip_denoised=True, + denoised_fn=None, + cond_fn=None, + model_kwargs=None, + model_var_type=None, + device=None, + progress=False, + progress_leave=True, + **kwargs, + ): + """ + Generate samples from the model. + + :param model: the model module. + :param shape: the shape of the samples, (N, C, H, W). + :param noise: if specified, the noise from the encoder to sample. + Should be of the same shape as `shape`. + :param clip_denoised: if True, clip x_start predictions to [-1, 1]. + :param denoised_fn: if not None, a function which applies to the + x_start prediction before it is used to sample. + :param cond_fn: if not None, this is a gradient function that acts + similarly to the model. + :param model_kwargs: if not None, a dict of extra keyword arguments to + pass to the model. This can be used for conditioning. + :param model_var_type: if not None, overlap the default self.model_var_type. + It is useful when training with learned var but sampling with fixed var. + :param device: if specified, the device to create the samples on. + If not specified, use a model parameter's device. + :param progress: if True, show a tqdm progress bar. + :return: a non-differentiable batch of samples. + """ + final = None + for sample in self.p_sample_loop_progressive( + model, + shape, + noise=noise, + clip_denoised=clip_denoised, + denoised_fn=denoised_fn, + cond_fn=cond_fn, + model_kwargs=model_kwargs, + model_var_type=model_var_type, + device=device, + progress=progress, + progress_leave=progress_leave, + **kwargs, + ): + final = sample + return final["sample"] + + def p_sample_loop_progressive( + self, + model, + shape, + noise=None, + clip_denoised=True, + denoised_fn=None, + cond_fn=None, + model_kwargs=None, + model_var_type=None, + device=None, + progress=False, + progress_leave=True, + **kwargs, + ): + """ + Generate samples from the model and yield intermediate samples from + each timestep of diffusion. + + Arguments are the same as p_sample_loop(). + Returns a generator over dicts, where each dict is the return value of + p_sample(). + """ + if device is None: + device = next(model.parameters()).device + assert isinstance(shape, (tuple, list)) + if noise is not None: + img = noise + else: + img = th.randn(*shape, device=device) + indices = list(range(self.num_timesteps))[::-1] + + if progress: + # Lazy import so that we don't depend on tqdm. + from tqdm.auto import tqdm + + indices = tqdm(indices, leave=progress_leave) + + for i in indices: + t = th.tensor([i] * shape[0], device=device) + with th.no_grad(): + out = self.p_sample( + model, + img, + t, + clip_denoised=clip_denoised, + denoised_fn=denoised_fn, + cond_fn=cond_fn, + model_kwargs=model_kwargs, + model_var_type=model_var_type, + **kwargs, + ) + yield out + img = out["sample"] + + def ddim_sample( + self, + model, + x, + t, + clip_denoised=True, + denoised_fn=None, + cond_fn=None, + model_kwargs=None, + eta=0.0, + ): + """ + Sample x_{t-1} from the model using DDIM. + + Same usage as p_sample(). + """ + out = self.p_mean_variance( + model, + x, + t, + clip_denoised=clip_denoised, + denoised_fn=denoised_fn, + model_kwargs=model_kwargs, + ) + if cond_fn is not None: + out = self.condition_score(cond_fn, out, x, t, model_kwargs=model_kwargs) + + # Usually our model outputs epsilon, but we re-derive it + # in case we used x_start or x_prev prediction. + eps = self._predict_eps_from_xstart(x, t, out["pred_xstart"]) + + alpha_bar = _extract_into_tensor(self.alphas_cumprod, t, x.shape) + alpha_bar_prev = _extract_into_tensor(self.alphas_cumprod_prev, t, x.shape) + sigma = ( + eta + * th.sqrt((1 - alpha_bar_prev) / (1 - alpha_bar)) + * th.sqrt(1 - alpha_bar / alpha_bar_prev) + ) + # Equation 12. + noise = th.randn_like(x) + mean_pred = ( + out["pred_xstart"] * th.sqrt(alpha_bar_prev) + + th.sqrt(1 - alpha_bar_prev - sigma ** 2) * eps + ) + nonzero_mask = ( + (t != 0).float().view(-1, *([1] * (len(x.shape) - 1))) + ) # no noise when t == 0 + sample = mean_pred + nonzero_mask * sigma * noise + return {"sample": sample, "pred_xstart": out["pred_xstart"]} + + def ddim_reverse_sample( + self, + model, + x, + t, + clip_denoised=True, + denoised_fn=None, + cond_fn=None, + model_kwargs=None, + eta=0.0, + ): + """ + Sample x_{t+1} from the model using DDIM reverse ODE. + """ + assert eta == 0.0, "Reverse ODE only for deterministic path" + out = self.p_mean_variance( + model, + x, + t, + clip_denoised=clip_denoised, + denoised_fn=denoised_fn, + model_kwargs=model_kwargs, + ) + if cond_fn is not None: + out = self.condition_score(cond_fn, out, x, t, model_kwargs=model_kwargs) + # Usually our model outputs epsilon, but we re-derive it + # in case we used x_start or x_prev prediction. + eps = ( + _extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x.shape) * x + - out["pred_xstart"] + ) / _extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x.shape) + alpha_bar_next = _extract_into_tensor(self.alphas_cumprod_next, t, x.shape) + + # Equation 12. reversed + mean_pred = ( + out["pred_xstart"] * th.sqrt(alpha_bar_next) + + th.sqrt(1 - alpha_bar_next) * eps + ) + + return {"sample": mean_pred, "pred_xstart": out["pred_xstart"]} + + def ddim_sample_loop( + self, + model, + shape, + noise=None, + clip_denoised=True, + denoised_fn=None, + cond_fn=None, + model_kwargs=None, + device=None, + progress=False, + progress_leave=True, + eta=0.0, + ): + """ + Generate samples from the model using DDIM. + + Same usage as p_sample_loop(). + """ + final = None + for sample in self.ddim_sample_loop_progressive( + model, + shape, + noise=noise, + clip_denoised=clip_denoised, + denoised_fn=denoised_fn, + cond_fn=cond_fn, + model_kwargs=model_kwargs, + device=device, + progress=progress, + progress_leave=progress_leave, + eta=eta, + ): + final = sample + return final["sample"] + + def ddim_sample_loop_progressive( + self, + model, + shape, + noise=None, + clip_denoised=True, + denoised_fn=None, + cond_fn=None, + model_kwargs=None, + device=None, + progress=False, + progress_leave=True, + eta=0.0, + ): + """ + Use DDIM to sample from the model and yield intermediate samples from + each timestep of DDIM. + + Same usage as p_sample_loop_progressive(). + """ + if device is None: + device = next(model.parameters()).device + assert isinstance(shape, (tuple, list)) + if noise is not None: + img = noise + else: + img = th.randn(*shape, device=device) + indices = list(range(self.num_timesteps))[::-1] + + if progress: + # Lazy import so that we don't depend on tqdm. + from tqdm.auto import tqdm + + indices = tqdm(indices, leave=progress_leave) + + for i in indices: + t = th.tensor([i] * shape[0], device=device) + with th.no_grad(): + out = self.ddim_sample( + model, + img, + t, + clip_denoised=clip_denoised, + denoised_fn=denoised_fn, + cond_fn=cond_fn, + model_kwargs=model_kwargs, + eta=eta, + ) + yield out + img = out["sample"] + + def _prior_bpd(self, x_start): + """ + Get the prior KL term for the variational lower-bound, measured in + bits-per-dim. + + This term can't be optimized, as it only depends on the encoder. + + :param x_start: the [N x C x ...] tensor of inputs. + :return: a batch of [N] KL values (in bits), one per batch element. + """ + batch_size = x_start.shape[0] + t = th.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device) + qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t) + kl_prior = normal_kl( + mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0 + ) + return mean_flat(kl_prior) / np.log(2.0) + + def calc_bpd_loop(self, model, x_start, clip_denoised=True, model_kwargs=None): + """ + Compute the entire variational lower-bound, measured in bits-per-dim, + as well as other related quantities. + + :param model: the model to evaluate loss on. + :param x_start: the [N x C x ...] tensor of inputs. + :param clip_denoised: if True, clip denoised samples. + :param model_kwargs: if not None, a dict of extra keyword arguments to + pass to the model. This can be used for conditioning. + + :return: a dict containing the following keys: + - total_bpd: the total variational lower-bound, per batch element. + - prior_bpd: the prior term in the lower-bound. + - vb: an [N x T] tensor of terms in the lower-bound. + - xstart_mse: an [N x T] tensor of x_0 MSEs for each timestep. + - mse: an [N x T] tensor of epsilon MSEs for each timestep. + """ + device = x_start.device + batch_size = x_start.shape[0] + + vb = [] + xstart_mse = [] + mse = [] + for t in list(range(self.num_timesteps))[::-1]: + t_batch = th.tensor([t] * batch_size, device=device) + noise = th.randn_like(x_start) + x_t = self.q_sample(x_start=x_start, t=t_batch, noise=noise) + # Calculate VLB term at the current timestep + with th.no_grad(): + out = self._vb_terms_bpd( + model, + x_start=x_start, + x_t=x_t, + t=t_batch, + clip_denoised=clip_denoised, + model_kwargs=model_kwargs, + ) + vb.append(out["output"]) + xstart_mse.append(mean_flat((out["pred_xstart"] - x_start) ** 2)) + eps = self._predict_eps_from_xstart(x_t, t_batch, out["pred_xstart"]) + mse.append(mean_flat((eps - noise) ** 2)) + + vb = th.stack(vb, dim=1) + xstart_mse = th.stack(xstart_mse, dim=1) + mse = th.stack(mse, dim=1) + + prior_bpd = self._prior_bpd(x_start) + total_bpd = vb.sum(dim=1) + prior_bpd + return { + "total_bpd": total_bpd, + "prior_bpd": prior_bpd, + "vb": vb, + "xstart_mse": xstart_mse, + "mse": mse, + } + + def get_eps( + self, + model, + x, + t, + model_kwargs, + cond_fn=None, + ): + model_output = model(x, t, **model_kwargs)['x'] + if isinstance(model_output, tuple): + model_output, _ = model_output + eps = model_output[:, :4] + if cond_fn is not None: + alpha_bar = _extract_into_tensor_lerp(self.alphas_cumprod, t, x.shape) + eps = eps - th.sqrt(1 - alpha_bar) * cond_fn(x, t, **model_kwargs) + return eps + + def eps_to_pred_xstart( + self, + x, + eps, + t, + ): + alpha_bar = _extract_into_tensor_lerp(self.alphas_cumprod, t, x.shape) + return (x - eps * th.sqrt(1 - alpha_bar)) / th.sqrt(alpha_bar) + + def pndm_transfer( + self, + x, + eps, + t_1, + t_2, + ): + pred_xstart = self.eps_to_pred_xstart(x, eps, t_1) + alpha_bar_prev = _extract_into_tensor_lerp(self.alphas_cumprod, t_2, x.shape) + return pred_xstart * th.sqrt(alpha_bar_prev) + th.sqrt(1 - alpha_bar_prev) * eps + + def prk_sample_loop( + self, + model, + shape, + noise=None, + clip_denoised=True, + denoised_fn=None, + cond_fn=None, + model_kwargs=None, + device=None, + progress=False, + ): + """ + Generate samples from the model using PRK. + + Same usage as p_sample_loop(). + """ + final = None + for sample in self.prk_sample_loop_progressive( + model, + shape, + noise=noise, + clip_denoised=clip_denoised, + denoised_fn=denoised_fn, + cond_fn=cond_fn, + model_kwargs=model_kwargs, + device=device, + progress=progress, + ): + final = sample + return final["sample"] + + def prk_sample_loop_progressive( + self, + model, + shape, + noise=None, + clip_denoised=True, + denoised_fn=None, + cond_fn=None, + model_kwargs=None, + device=None, + progress=False, + ): + """ + Use PRK to sample from the model and yield intermediate samples from + each timestep of PRK. + + Same usage as p_sample_loop_progressive(). + """ + if device is None: + device = next(model.parameters()).device + assert isinstance(shape, (tuple, list)) + if noise is not None: + img = noise + else: + img = th.randn(*shape, device=device) + indices = list(range(self.num_timesteps))[::-1][1:-1] + + if progress: + # Lazy import so that we don't depend on tqdm. + from tqdm.auto import tqdm + + indices = tqdm(indices, leave=False) + + for i in indices: + t = th.tensor([i] * shape[0], device=device) + with th.no_grad(): + out = self.prk_sample( + model, + img, + t, + clip_denoised=clip_denoised, + denoised_fn=denoised_fn, + cond_fn=cond_fn, + model_kwargs=model_kwargs, + ) + yield out + img = out["sample"] + + def prk_sample( + self, + model, + x, + t, + clip_denoised=True, + denoised_fn=None, + cond_fn=None, + model_kwargs=None, + ): + """ + Sample x_{t-1} from the model using fourth-order Pseudo Runge-Kutta + (https://openreview.net/forum?id=PlKWVd2yBkY). + + Same usage as p_sample(). + """ + if model_kwargs is None: + model_kwargs = {} + + def process_xstart(x): + if denoised_fn is not None: + x = denoised_fn(x) + if clip_denoised: + return x.clamp(-1, 1) + return x + + eps_1 = self.get_eps(model, x, t, model_kwargs, cond_fn) + x_1 = self.pndm_transfer(x, eps_1, t, t - 0.5) + eps_2 = self.get_eps(model, x_1, t - 0.5, model_kwargs, cond_fn) + x_2 = self.pndm_transfer(x, eps_2, t, t - 0.5) + eps_3 = self.get_eps(model, x_2, t - 0.5, model_kwargs, cond_fn) + x_3 = self.pndm_transfer(x, eps_3, t, t - 1) + eps_4 = self.get_eps(model, x_3, t - 1, model_kwargs, cond_fn) + eps_prime = (eps_1 + 2 * eps_2 + 2 * eps_3 + eps_4) / 6 + + sample = self.pndm_transfer(x, eps_prime, t, t - 1) + pred_xstart = self.eps_to_pred_xstart(x, eps_prime, t) + pred_xstart = process_xstart(pred_xstart) + return {"sample": sample, "pred_xstart": pred_xstart, "eps": eps_prime} + + def plms_sample_loop( + self, + model, + shape, + noise=None, + clip_denoised=True, + denoised_fn=None, + cond_fn=None, + model_kwargs=None, + device=None, + progress=False, + progress_leave=True, + ): + """ + Generate samples from the model using PLMS. + + Same usage as p_sample_loop(). + """ + assert self.model_mean_type == ModelMeanType.EPSILON, \ + 'plms_sample only support model_mean_type == ModelMeanType.EPSILON' + final = None + for sample in self.plms_sample_loop_progressive( + model, + shape, + noise=noise, + clip_denoised=clip_denoised, + denoised_fn=denoised_fn, + cond_fn=cond_fn, + model_kwargs=model_kwargs, + device=device, + progress=progress, + progress_leave=progress_leave, + ): + final = sample + return final["sample"] + + def plms_sample_loop_progressive( + self, + model, + shape, + noise=None, + clip_denoised=True, + denoised_fn=None, + cond_fn=None, + model_kwargs=None, + device=None, + progress=False, + progress_leave=True, + ): + """ + Use PLMS to sample from the model and yield intermediate samples from + each timestep of PLMS. + + Same usage as p_sample_loop_progressive(). + """ + if device is None: + device = next(model.parameters()).device + assert isinstance(shape, (tuple, list)) + if noise is not None: + img = noise + else: + img = th.randn(*shape, device=device) + indices = list(range(self.num_timesteps))[::-1][1:-1] + + if progress: + # Lazy import so that we don't depend on tqdm. + from tqdm.auto import tqdm + + indices = tqdm(indices, leave=progress_leave) + + old_eps = [] + + for i in indices: + t = th.tensor([i] * shape[0], device=device) + with th.no_grad(): + if len(old_eps) < 3: + out = self.prk_sample( + model, + img, + t, + clip_denoised=clip_denoised, + denoised_fn=denoised_fn, + cond_fn=cond_fn, + model_kwargs=model_kwargs, + ) + else: + out = self.plms_sample( + model, + img, + old_eps, + t, + clip_denoised=clip_denoised, + denoised_fn=denoised_fn, + cond_fn=cond_fn, + model_kwargs=model_kwargs, + ) + old_eps.pop(0) + old_eps.append(out["eps"]) + yield out + img = out["sample"] + + def plms_sample( + self, + model, + x, + old_eps, + t, + clip_denoised=True, + denoised_fn=None, + cond_fn=None, + model_kwargs=None, + ): + """ + Sample x_{t-1} from the model using fourth-order Pseudo Linear Multistep + (https://openreview.net/forum?id=PlKWVd2yBkY). + """ + if model_kwargs is None: + model_kwargs = {} + + def process_xstart(x): + if denoised_fn is not None: + x = denoised_fn(x) + if clip_denoised: + return x.clamp(-1, 1) + return x + + eps = self.get_eps(model, x, t, model_kwargs, cond_fn) + eps_prime = (55 * eps - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24 + + sample = self.pndm_transfer(x, eps_prime, t, t - 1) + pred_xstart = self.eps_to_pred_xstart(x, eps, t) + pred_xstart = process_xstart(pred_xstart) + return {"sample": sample, "pred_xstart": pred_xstart, "eps": eps} + + +def _extract_into_tensor(arr, timesteps, broadcast_shape): + """ + Extract values from a 1-D numpy array for a batch of indices. + + :param arr: the 1-D numpy array. + :param timesteps: a tensor of indices into the array to extract. + :param broadcast_shape: a larger shape of K dimensions with the batch + dimension equal to the length of timesteps. + :return: a tensor of shape [batch_size, 1, ...] where the shape has K dims. + """ + res = th.from_numpy(arr).to(device=timesteps.device)[timesteps].float() + while len(res.shape) < len(broadcast_shape): + res = res[..., None] + return res.expand(broadcast_shape) + + +def _extract_into_tensor_lerp(arr, timesteps, broadcast_shape): + """ + Extract values from arr with fractional time steps + """ + timesteps = timesteps.float() + frac = timesteps.frac() + while len(frac.shape) < len(broadcast_shape): + frac = frac[..., None] + res_1 = _extract_into_tensor(arr, timesteps.floor().long(), broadcast_shape) + res_2 = _extract_into_tensor(arr, timesteps.ceil().long(), broadcast_shape) + return th.lerp(res_1, res_2, frac) diff --git a/custom_nodes/comfyui-hydit_min/hydit/diffusion/respace.py b/custom_nodes/comfyui-hydit_min/hydit/diffusion/respace.py new file mode 100644 index 000000000..1e2bf7c0a --- /dev/null +++ b/custom_nodes/comfyui-hydit_min/hydit/diffusion/respace.py @@ -0,0 +1,144 @@ +import numpy as np +import torch as th + +from .gaussian_diffusion import GaussianDiffusion + + +def space_timesteps(num_timesteps, section_counts): + """ + Create a list of timesteps to use from an original diffusion process, + given the number of timesteps we want to take from equally-sized portions + of the original process. + For example, if there's 300 timesteps and the section counts are [10,15,20] + then the first 100 timesteps are strided to be 10 timesteps, the second 100 + are strided to be 15 timesteps, and the final 100 are strided to be 20. + If the stride is a string starting with "ddim", then the fixed striding + from the DDIM paper is used, and only one section is allowed. + :param num_timesteps: the number of diffusion steps in the original + process to divide up. + :param section_counts: either a list of numbers, or a string containing + comma-separated numbers, indicating the step count + per section. As a special case, use "ddimN" where N + is a number of steps to use the striding from the + DDIM paper. + :return: a set of diffusion steps from the original process to use. + """ + if isinstance(section_counts, str): + if section_counts.startswith("ddim"): + desired_count = int(section_counts[len("ddim") :]) + for i in range(1, num_timesteps): + if len(range(0, num_timesteps, i)) == desired_count: + return set(range(0, num_timesteps, i)) + raise ValueError( + f"cannot create exactly {num_timesteps} steps with an integer stride" + ) + section_counts = [int(x) for x in section_counts.split(",")] + size_per = num_timesteps // len(section_counts) + extra = num_timesteps % len(section_counts) + start_idx = 0 + all_steps = [] + for i, section_count in enumerate(section_counts): + size = size_per + (1 if i < extra else 0) + if size < section_count: + raise ValueError( + f"cannot divide section of {size} steps into {section_count}" + ) + if section_count <= 1: + frac_stride = 1 + else: + frac_stride = (size - 1) / (section_count - 1) + cur_idx = 0.0 + taken_steps = [] + for _ in range(section_count): + taken_steps.append(start_idx + round(cur_idx)) + cur_idx += frac_stride + all_steps += taken_steps + start_idx += size + return set(all_steps) + + +class SpacedDiffusion(GaussianDiffusion): + """ + Improved DDPM + + A diffusion process which can skip steps in a base diffusion process. + :param use_timesteps: a collection (sequence or set) of timesteps from the + original diffusion process to retain. + :param kwargs: the kwargs to create the base diffusion process. + """ + + def __init__(self, use_timesteps, **kwargs): + self.use_timesteps = set(use_timesteps) + self.timestep_map = [] + self.original_num_steps = len(kwargs["betas"]) + + base_diffusion = GaussianDiffusion(**kwargs) # pylint: disable=missing-kwoa + last_alpha_cumprod = 1.0 + new_betas = [] + for i, alpha_cumprod in enumerate(base_diffusion.alphas_cumprod): + if i in self.use_timesteps: + new_betas.append(1 - alpha_cumprod / last_alpha_cumprod) + last_alpha_cumprod = alpha_cumprod + self.timestep_map.append(i) + kwargs["betas"] = np.array(new_betas) + super().__init__(**kwargs) + + def p_mean_variance( + self, model, *args, **kwargs + ): # pylint: disable=signature-differs + return super().p_mean_variance(self._wrap_model(model), *args, **kwargs) + + def training_losses( + self, model, controlnet=None, *args, **kwargs + ): # pylint: disable=signature-differs + if controlnet != None: + return super().training_losses(self._wrap_model(model), controlnet=self._wrap_model(controlnet), *args, **kwargs) + else: + return super().training_losses(self._wrap_model(model), *args, **kwargs) + + def condition_mean(self, cond_fn, *args, **kwargs): + return super().condition_mean(self._wrap_model(cond_fn), *args, **kwargs) + + def condition_score(self, cond_fn, *args, **kwargs): + return super().condition_score(self._wrap_model(cond_fn), *args, **kwargs) + + def get_eps(self, model, *args, **kwargs): + return super().get_eps(self._wrap_model(model), *args, **kwargs) + + def _wrap_model(self, model): + if isinstance(model, _WrappedModel): + return model + return _WrappedModel( + model, self.timestep_map, self.original_num_steps + ) + + def _scale_timesteps(self, t): + # Scaling is done by the wrapped model. + return t + + +class _WrappedModel: + """ + Improved DDPM + + When using a subsequent timesteps (e.g., 250), we must wrap the model + for mapping the timesteps 1-250 with step 1 to 1-1000 with step 4 + """ + def __init__(self, model, timestep_map, original_num_steps): + self.model = model + self.timestep_map = timestep_map + # self.rescale_timesteps = rescale_timesteps + self.original_num_steps = original_num_steps + + def __call__(self, x, ts, **kwargs): + """ + Here we must make a interpolation because `ts` maybe a float (e.g., 4.5) + in the PLMS/PNDM sampler. + """ + ts = ts.float() + frac = ts.frac() + map_tensor = th.tensor(self.timestep_map, device=ts.device, dtype=ts.dtype) + new_ts_1 = map_tensor[ts.floor().long()] + new_ts_2 = map_tensor[ts.ceil().long()] + new_ts = th.lerp(new_ts_1, new_ts_2, frac) + return self.model(x, new_ts, **kwargs) diff --git a/custom_nodes/comfyui-hydit_min/hydit/ds_config.py b/custom_nodes/comfyui-hydit_min/hydit/ds_config.py new file mode 100644 index 000000000..d108a6de4 --- /dev/null +++ b/custom_nodes/comfyui-hydit_min/hydit/ds_config.py @@ -0,0 +1,140 @@ +# -*- coding: utf-8 -*- +import os + +def deepspeed_config_from_args(args, global_batch_size): + if args.use_zero_stage == 2: + deepspeed_config = { + "train_batch_size": global_batch_size, + "train_micro_batch_size_per_gpu": args.batch_size, + "gradient_accumulation_steps": args.grad_accu_steps, + "steps_per_print": args.log_every, + "optimizer": { + "type": "AdamW", + "params": { + "lr": args.lr, + "betas": [ + 0.9, + 0.999 + ], + "eps": 1e-08, + "weight_decay": args.weight_decay + } + }, + + "zero_optimization": { + "stage": 2, + "reduce_scatter": False, + "reduce_bucket_size": 1e9, + }, + + "gradient_clipping": 1.0, + "prescale_gradients": True, + + "fp16": { + "enabled": args.use_fp16, + "loss_scale": 0, + "loss_scale_window": 500, + "hysteresis": 2, + "min_loss_scale": 1e-3, + "initial_scale_power": 15 + }, + + "bf16": { + "enabled": False + }, + + "wall_clock_breakdown": False + } + + elif args.use_zero_stage == 3: + deepspeed_config = { + "train_batch_size": args.global_batch_size, + # "train_micro_batch_size_per_gpu": args.batch_size, + "gradient_accumulation_steps": args.grad_accu_steps, + "steps_per_print": args.log_every, + + "optimizer": { + "type": "AdamW", + "params": { + "lr": args.lr, + "betas": [ + 0.9, + 0.999 + ], + "eps": 1e-08, + "weight_decay": args.weight_decay + } + }, + + "zero_optimization": { + "stage": 3, + "allgather_partitions": True, + "overlap_comm": True, + "reduce_scatter": True, + "contiguous_gradients": True, + "stage3_prefetch_bucket_size": 5e8, + "stage3_max_live_parameters" : 6e8, + "reduce_bucket_size": 1.2e9, + "sub_group_size": 1e9, + "sub_group_buffer_num": 10, + "pipeline_optimizer": True, + "max_contigous_event_size": 0, + "cache_sub_group_rate": 0.0, + "prefetch_cache_sub_group_rate": 1.0, + "max_contigous_params_size": -1, + "max_param_reduce_events": 0, + "stage3_param_persistence_threshold": 9e9, + "is_communication_time_profiling": False, + "save_large_model_multi_slice": True, + "use_fused_op_with_grad_norm_overflow": False, + "offload_optimizer": { + "device": "cpu", + "pin_memory": True + }, + "offload_param": { + "device": "cpu", + "pin_memory": True + } + }, + + "gradient_clipping": 1.0, + "prescale_gradients": False, + + "fp16": { + "enabled": True, + "loss_scale": 0, + "loss_scale_window": 500, + "hysteresis": 2, + "min_loss_scale": 1, + "initial_scale_power": 15 + }, + + "bf16": { + "enabled": False + }, + + "wall_clock_breakdown": False, + "mem_chunk": { + "default_chunk_size": 536870911, + "use_fake_dist": False, + "client": { + "mem_tracer": { + "use_async_mem_monitor": True, + "warmup_gpu_chunk_mem_ratio": 0.8, + "overall_gpu_mem_ratio": 0.8, + "overall_cpu_mem_ratio": 1.0, + "margin_use_ratio": 0.8, + "use_fake_dist": False + }, + "opts": { + "with_mem_cache": True, + "with_async_move": True + } + } + } + } + else: + raise ValueError + return deepspeed_config + + diff --git a/custom_nodes/comfyui-hydit_min/hydit/inference_comfyui.py b/custom_nodes/comfyui-hydit_min/hydit/inference_comfyui.py new file mode 100644 index 000000000..254d89a82 --- /dev/null +++ b/custom_nodes/comfyui-hydit_min/hydit/inference_comfyui.py @@ -0,0 +1,510 @@ +import random +import time +from pathlib import Path + +import numpy as np +import torch + +# For reproducibility +# torch.backends.cudnn.benchmark = False +# torch.backends.cudnn.deterministic = True + +from diffusers import schedulers +from diffusers.models import AutoencoderKL +from loguru import logger +from transformers import BertModel, BertTokenizer +from transformers.modeling_utils import logger as tf_logger + +from .constants import SAMPLER_FACTORY, NEGATIVE_PROMPT, TRT_MAX_WIDTH, TRT_MAX_HEIGHT, TRT_MAX_BATCH_SIZE +from .diffusion.pipeline import StableDiffusionPipeline +from .modules.models import HunYuanDiT, HUNYUAN_DIT_CONFIG +from .modules.posemb_layers import get_2d_rotary_pos_embed, get_fill_resize_and_crop +from .modules.text_encoder import MT5Embedder +from .utils.tools import set_seeds +from peft import LoraConfig + + +class Resolution: + def __init__(self, width, height): + self.width = width + self.height = height + + def __str__(self): + return f'{self.height}x{self.width}' + + +class ResolutionGroup: + def __init__(self): + self.data = [ + Resolution(1024, 1024), # 1:1 + Resolution(1280, 1280), # 1:1 + Resolution(1024, 768), # 4:3 + Resolution(1152, 864), # 4:3 + Resolution(1280, 960), # 4:3 + Resolution(768, 1024), # 3:4 + Resolution(864, 1152), # 3:4 + Resolution(960, 1280), # 3:4 + Resolution(1280, 768), # 16:9 + Resolution(768, 1280), # 9:16 + ] + self.supported_sizes = set([(r.width, r.height) for r in self.data]) + + def is_valid(self, width, height): + return (width, height) in self.supported_sizes + + +STANDARD_RATIO = np.array([ + 1.0, # 1:1 + 4.0 / 3.0, # 4:3 + 3.0 / 4.0, # 3:4 + 16.0 / 9.0, # 16:9 + 9.0 / 16.0, # 9:16 +]) +STANDARD_SHAPE = [ + [(1024, 1024), (1280, 1280)], # 1:1 + [(1280, 960)], # 4:3 + [(960, 1280)], # 3:4 + [(1280, 768)], # 16:9 + [(768, 1280)], # 9:16 +] +STANDARD_AREA = [ + np.array([w * h for w, h in shapes]) + for shapes in STANDARD_SHAPE +] + + +def get_standard_shape(target_width, target_height): + """ + Map image size to standard size. + """ + target_ratio = target_width / target_height + closest_ratio_idx = np.argmin(np.abs(STANDARD_RATIO - target_ratio)) + closest_area_idx = np.argmin(np.abs(STANDARD_AREA[closest_ratio_idx] - target_width * target_height)) + width, height = STANDARD_SHAPE[closest_ratio_idx][closest_area_idx] + return width, height + + +def _to_tuple(val): + if isinstance(val, (list, tuple)): + if len(val) == 1: + val = [val[0], val[0]] + elif len(val) == 2: + val = tuple(val) + else: + raise ValueError(f"Invalid value: {val}") + elif isinstance(val, (int, float)): + val = (val, val) + else: + raise ValueError(f"Invalid value: {val}") + return val + + +def get_pipeline(args, vae, text_encoder, tokenizer, model, device, rank, + embedder_t5, infer_mode, sampler=None): + """ + Get scheduler and pipeline for sampling. The sampler and pipeline are both + based on diffusers and make some modifications. + + Returns + ------- + pipeline: StableDiffusionPipeline + sampler_name: str + """ + sampler = sampler or args.sampler + + # Load sampler from factory + kwargs = SAMPLER_FACTORY[sampler]['kwargs'] + scheduler = SAMPLER_FACTORY[sampler]['scheduler'] + + # Update sampler according to the arguments + kwargs['beta_schedule'] = args.noise_schedule + kwargs['beta_start'] = args.beta_start + kwargs['beta_end'] = args.beta_end + kwargs['prediction_type'] = args.predict_type + + # Build scheduler according to the sampler. + scheduler_class = getattr(schedulers, scheduler) + scheduler = scheduler_class(**kwargs) + #print(scheduler) + #assert(0) + + # Set timesteps for inference steps. + scheduler.set_timesteps(args.infer_steps, device) + + # Only enable progress bar for rank 0 + progress_bar_config = {} if rank == 0 else {'disable': True} + + pipeline = StableDiffusionPipeline(vae=vae, + text_encoder=text_encoder, + tokenizer=tokenizer, + unet=model, + scheduler=scheduler, + feature_extractor=None, + safety_checker=None, + requires_safety_checker=False, + progress_bar_config=progress_bar_config, + embedder_t5=embedder_t5, + infer_mode=infer_mode, + ) + + pipeline = pipeline.to(device) + + return pipeline, sampler + + +class End2End(object): + def __init__(self, args, models_root_path, MODEL_PATH = None, VAE_PATH = None, LOAR_PATH = None): + self.args = args + + # Check arguments + t2i_root_path = Path(models_root_path) / "t2i" + self.root = t2i_root_path + logger.info(f"Got text-to-image model root path: {t2i_root_path}") + + # Set device and disable gradient + self.device = "cuda" if torch.cuda.is_available() else "cpu" + torch.set_grad_enabled(False) + # Disable BertModel logging checkpoint info + tf_logger.setLevel('ERROR') + + # ======================================================================== + model_dir = self.root / "model" + + # ======================================================================== + logger.info(f"Loading CLIP Text Encoder...") + text_encoder_path = self.root / "clip_text_encoder" + self.clip_text_encoder = BertModel.from_pretrained(str(text_encoder_path), False, revision=None).to(self.device) + logger.info(f"Loading CLIP Text Encoder finished") + #print(self.clip_text_encoder) + + # ======================================================================== + logger.info(f"Loading CLIP Tokenizer...") + tokenizer_path = self.root / "tokenizer" + self.tokenizer = BertTokenizer.from_pretrained(str(tokenizer_path)) + logger.info(f"Loading CLIP Tokenizer finished") + #print(self.tokenizer) + #assert(0) + + # ======================================================================== + logger.info(f"Loading T5 Text Encoder and T5 Tokenizer...") + t5_text_encoder_path = self.root / 'mt5' + embedder_t5 = MT5Embedder(t5_text_encoder_path, torch_dtype=torch.float16, max_length=256) + self.embedder_t5 = embedder_t5 + self.embedder_t5.model.to(self.device) # Only move encoder to device + logger.info(f"Loading t5_text_encoder and t5_tokenizer finished") + + # ======================================================================== + logger.info(f"Loading VAE...") + if VAE_PATH: + vae_path = VAE_PATH + self.vae = AutoencoderKL.from_single_file(str(vae_path)).to(self.device) + else: + vae_path = self.root / "sdxl-vae-fp16-fix" + self.vae = AutoencoderKL.from_pretrained(str(vae_path)).to(self.device) + + + logger.info(f"Loading VAE finished") + + # ======================================================================== + # Create model structure and load the checkpoint + logger.info(f"Building HunYuan-DiT model...") + #print(self.args.model) + #print(self.args) + model_config = HUNYUAN_DIT_CONFIG[self.args.model] + self.patch_size = model_config['patch_size'] + self.head_size = model_config['hidden_size'] // model_config['num_heads'] + self.resolutions, self.freqs_cis_img = self.standard_shapes() # Used for TensorRT models + self.image_size = _to_tuple(self.args.image_size) + latent_size = (self.image_size[0] // 8, self.image_size[1] // 8) + + self.infer_mode = self.args.infer_mode + if self.infer_mode in ['fa', 'torch']: + if MODEL_PATH: + model_path = Path(MODEL_PATH) + else: + model_path = model_dir / f"pytorch_model_{self.args.load_key}.pt" + + if not model_path.exists(): + raise ValueError(f"model_path not exists: {model_path}") + # Build model structure + self.model = HunYuanDiT(self.args, + input_size=latent_size, + **model_config, + log_fn=logger.info, + ).half().to(self.device) # Force to use fp16 + # Load model checkpoint + logger.info(f"Loading model checkpoint {model_path}...") + #self.load_torch_weights() + state_dict = torch.load(model_path, map_location=lambda storage, loc: storage) + self.model.load_state_dict(state_dict) + if LOAR_PATH: + self.model.load_adapter(LOAR_PATH) + self.model.merge_and_unload() + + self.model.eval() + elif self.infer_mode == 'trt': + raise NotImplementedError("TensorRT model is not supported yet.") + else: + raise ValueError(f"Unknown infer_mode: {self.infer_mode}") + + # ======================================================================== + # Build inference pipeline. We use a customized StableDiffusionPipeline. + logger.info(f"Loading inference pipeline...") + #self.pipeline, self.sampler = self.load_sampler() + logger.info(f'Loading pipeline finished') + + # ======================================================================== + self.default_negative_prompt = NEGATIVE_PROMPT + logger.info("==================================================") + logger.info(f" Model is ready. ") + logger.info("==================================================") + + def load_torch_weights(self): + load_key = self.args.load_key + if self.args.dit_weight is not None: + dit_weight = Path(self.args.dit_weight) + if dit_weight.is_dir(): + files = list(dit_weight.glob("*.pt")) + if len(files) == 0: + raise ValueError(f"No model weights found in {dit_weight}") + if str(files[0]).startswith('pytorch_model_'): + model_path = dit_weight / f"pytorch_model_{load_key}.pt" + bare_model = True + elif any(str(f).endswith('_model_states.pt') for f in files): + files = [f for f in files if str(f).endswith('_model_states.pt')] + model_path = files[0] + if len(files) > 1: + logger.warning(f"Multiple model weights found in {dit_weight}, using {model_path}") + bare_model = False + else: + raise ValueError(f"Invalid model path: {dit_weight} with unrecognized weight format: " + f"{list(map(str, files))}. When given a directory as --dit-weight, only " + f"`pytorch_model_*.pt`(provided by HunyuanDiT official) and " + f"`*_model_states.pt`(saved by deepspeed) can be parsed. If you want to load a " + f"specific weight file, please provide the full path to the file.") + elif dit_weight.is_file(): + model_path = dit_weight + bare_model = 'unknown' + else: + raise ValueError(f"Invalid model path: {dit_weight}") + else: + model_dir = self.root / "model" + model_path = model_dir / f"pytorch_model_{load_key}.pt" + bare_model = True + + if not model_path.exists(): + raise ValueError(f"model_path not exists: {model_path}") + logger.info(f"Loading torch model {model_path}...") + if model_path.suffix == '.safetensors': + raise NotImplementedError(f"Loading safetensors is not supported yet.") + else: + # Assume it's a single weight file in the *.pt format. + state_dict = torch.load(model_path, map_location=lambda storage, loc: storage) + + if bare_model == 'unknown' and ('ema' in state_dict or 'module' in state_dict): + bare_model = False + if bare_model is False: + if load_key in state_dict: + state_dict = state_dict[load_key] + else: + raise KeyError(f"Missing key: `{load_key}` in the checkpoint: {model_path}. The keys in the checkpoint " + f"are: {list(state_dict.keys())}.") + + if 'style_embedder.weight' in state_dict and not hasattr(self.model, 'style_embedder'): + raise ValueError(f"You might be attempting to load the weights of HunYuanDiT version <= 1.1. You need " + f"to set `--use-style-cond --size-cond 1024 1024 --beta-end 0.03` to adapt to these weights." + f"Alternatively, you can use weights of version >= 1.2, which no longer depend on " + f"these two parameters.") + if 'style_embedder.weight' not in state_dict and hasattr(self.model, 'style_embedder'): + raise ValueError(f"You might be attempting to load the weights of HunYuanDiT version >= 1.2. You need " + f"to remove `--use-style-cond` and `--size-cond 1024 1024` to adapt to these weights.") + # Don't set strict=False. Always explicitly check the state_dict. + self.model.load_state_dict(state_dict, strict=True) + + def load_sampler(self, sampler=None): + pipeline, sampler = get_pipeline(self.args, + self.vae, + self.clip_text_encoder, + self.tokenizer, + self.model, + device=self.device, + rank=0, + embedder_t5=self.embedder_t5, + infer_mode=self.infer_mode, + sampler=sampler, + ) + return pipeline, sampler + + def calc_rope(self, height, width): + th = height // 8 // self.patch_size + tw = width // 8 // self.patch_size + base_size = 512 // 8 // self.patch_size + + start, stop = get_fill_resize_and_crop((th, tw), base_size) + sub_args = [start, stop, (th, tw)] + #import pdb + #pdb.set_trace() + rope = get_2d_rotary_pos_embed(self.head_size, *sub_args) + return rope + + def standard_shapes(self): + resolutions = ResolutionGroup() + freqs_cis_img = {} + for reso in resolutions.data: + freqs_cis_img[str(reso)] = self.calc_rope(reso.height, reso.width) + return resolutions, freqs_cis_img + + def predict(self, + user_prompt, + image, + height=1024, + width=1024, + seed=None, + enhanced_prompt=None, + negative_prompt=None, + infer_steps=100, + guidance_scale=6, + batch_size=1, + src_size_cond=None, + sampler=None, + control_weight = 1.0, + use_style_cond=False, + ): + # ======================================================================== + # Arguments: seed + # ======================================================================== + if seed is None: + seed = random.randint(0, 1_000_000) + if not isinstance(seed, int): + raise TypeError(f"`seed` must be an integer, but got {type(seed)}") + generator = set_seeds(seed, device=self.device) + + # ======================================================================== + # Arguments: target_width, target_height + # ======================================================================== + if width <= 0 or height <= 0: + raise ValueError(f"`height` and `width` must be positive integers, got height={height}, width={width}") + logger.info(f"Input (height, width) = ({height}, {width})") + if self.infer_mode in ['fa', 'torch']: + # We must force height and width to align to 16 and to be an integer. + target_height = int((height // 16) * 16) + target_width = int((width // 16) * 16) + logger.info(f"Align to 16: (height, width) = ({target_height}, {target_width})") + elif self.infer_mode == 'trt': + target_width, target_height = get_standard_shape(width, height) + logger.info(f"Align to standard shape: (height, width) = ({target_height}, {target_width})") + else: + raise ValueError(f"Unknown infer_mode: {self.infer_mode}") + + # ======================================================================== + # Arguments: prompt, new_prompt, negative_prompt + # ======================================================================== + if not isinstance(user_prompt, str): + raise TypeError(f"`user_prompt` must be a string, but got {type(user_prompt)}") + user_prompt = user_prompt.strip() + prompt = user_prompt + + if enhanced_prompt is not None: + if not isinstance(enhanced_prompt, str): + raise TypeError(f"`enhanced_prompt` must be a string, but got {type(enhanced_prompt)}") + enhanced_prompt = enhanced_prompt.strip() + prompt = enhanced_prompt + + # negative prompt + if negative_prompt is None or negative_prompt == '': + negative_prompt = self.default_negative_prompt + if not isinstance(negative_prompt, str): + raise TypeError(f"`negative_prompt` must be a string, but got {type(negative_prompt)}") + + # ======================================================================== + # Arguments: style. (A fixed argument. Don't Change it.) + # ======================================================================== + if use_style_cond: + # Only for hydit <= 1.1 + style = torch.as_tensor([0, 0] * batch_size, device=self.device) + else: + style = None + + # ======================================================================== + # Inner arguments: image_meta_size (Please refer to SDXL.) + # ======================================================================== + + if src_size_cond is None: + size_cond = None + image_meta_size = None + else: + # Only for hydit <= 1.1 + if isinstance(src_size_cond, int): + src_size_cond = [src_size_cond, src_size_cond] + if not isinstance(src_size_cond, (list, tuple)): + raise TypeError(f"`src_size_cond` must be a list or tuple, but got {type(src_size_cond)}") + if len(src_size_cond) != 2: + raise ValueError(f"`src_size_cond` must be a tuple of 2 integers, but got {len(src_size_cond)}") + size_cond = list(src_size_cond) + [target_width, target_height, 0, 0] + image_meta_size = torch.as_tensor([size_cond] * 2 * batch_size, device=self.device) + + # ======================================================================== + start_time = time.time() + logger.debug(f""" + prompt: {user_prompt} + enhanced prompt: {enhanced_prompt} + seed: {seed} + (height, width): {(target_height, target_width)} + negative_prompt: {negative_prompt} + batch_size: {batch_size} + guidance_scale: {guidance_scale} + infer_steps: {infer_steps} + image_meta_size: {size_cond} + """) + reso = f'{target_height}x{target_width}' + if reso in self.freqs_cis_img: + freqs_cis_img = self.freqs_cis_img[reso] + else: + freqs_cis_img = self.calc_rope(target_height, target_width) + + #if sampler is not None and sampler != self.sampler: + # self.pipeline, self.sampler = self.load_sampler(sampler) + + if isinstance(image, torch.Tensor): + samples = self.pipeline( + height=target_height, + width=target_width, + prompt=prompt, + negative_prompt=negative_prompt, + num_images_per_prompt=batch_size, + guidance_scale=guidance_scale, + num_inference_steps=infer_steps, + image_meta_size=image_meta_size, + style=style, + return_dict=False, + generator=generator, + freqs_cis_img=freqs_cis_img, + use_fp16=self.args.use_fp16, + learn_sigma=self.args.learn_sigma, + image=image, + control_weight=control_weight, + )[0] + else: + samples = self.pipeline( + height=target_height, + width=target_width, + prompt=prompt, + negative_prompt=negative_prompt, + num_images_per_prompt=batch_size, + guidance_scale=guidance_scale, + num_inference_steps=infer_steps, + image_meta_size=image_meta_size, + style=style, + return_dict=False, + generator=generator, + freqs_cis_img=freqs_cis_img, + use_fp16=self.args.use_fp16, + learn_sigma=self.args.learn_sigma, + )[0] + gen_time = time.time() - start_time + logger.debug(f"Success, time: {gen_time}") + + return { + 'images': samples, + 'seed': seed, + } diff --git a/custom_nodes/comfyui-hydit_min/hydit/lr_scheduler.py b/custom_nodes/comfyui-hydit_min/hydit/lr_scheduler.py new file mode 100644 index 000000000..027c7e6b4 --- /dev/null +++ b/custom_nodes/comfyui-hydit_min/hydit/lr_scheduler.py @@ -0,0 +1,761 @@ +""" +Implementation of learning rate schedules. + +Taken and modified from PyTorch v1.0.1 source +https://github.com/pytorch/pytorch/blob/v1.1.0/torch/optim/lr_scheduler.py +""" + +import argparse +from torch.optim import Optimizer +import math + +LR_SCHEDULE = 'lr_schedule' +LR_RANGE_TEST = 'LRRangeTest' +ONE_CYCLE = 'OneCycle' +WARMUP_LR = 'WarmupLR' +WARMUP_DECAY_LR = 'WarmupDecayLR' +VALID_LR_SCHEDULES = [LR_RANGE_TEST, ONE_CYCLE, WARMUP_LR, WARMUP_DECAY_LR] + +LR_RANGE_TEST_MIN_LR = 'lr_range_test_min_lr' +LR_RANGE_TEST_STEP_RATE = 'lr_range_test_step_rate' +LR_RANGE_TEST_STEP_SIZE = 'lr_range_test_step_size' +LR_RANGE_TEST_STAIRCASE = 'lr_range_test_staircase' + +EDGE_VALUE = 'edge_value' +MID_VALUE = 'mid_value' + +CYCLE_FIRST_STEP_SIZE = 'cycle_first_step_size' +CYCLE_FIRST_STAIR_COUNT = 'cycle_first_stair_count' +CYCLE_SECOND_STEP_SIZE = 'cycle_second_step_size' +CYCLE_SECOND_STAIR_COUNT = 'cycle_second_stair_count' +DECAY_STEP_SIZE = 'decay_step_size' + +CYCLE_MIN_LR = 'cycle_min_lr' +CYCLE_MAX_LR = 'cycle_max_lr' +DECAY_LR_RATE = 'decay_lr_rate' + +CYCLE_MIN_MOM = 'cycle_min_mom' +CYCLE_MAX_MOM = 'cycle_max_mom' +DECAY_MOM_RATE = 'decay_mom_rate' + +WARMUP_MIN_LR = 'warmup_min_lr' +WARMUP_MAX_LR = 'warmup_max_lr' +WARMUP_NUM_STEPS = 'warmup_num_steps' +WARMUP_TYPE = 'warmup_type' +WARMUP_LOG_RATE = 'log' +WARMUP_LINEAR_RATE = 'linear' + +TOTAL_NUM_STEPS = 'total_num_steps' + + +def add_tuning_arguments(parser): + group = parser.add_argument_group('Convergence Tuning', 'Convergence tuning configurations') + + # LR scheduler + group.add_argument('--lr_schedule', type=str, default=None, help='LR schedule for training.') + + # Learning rate range test + group.add_argument("--lr_range_test_min_lr", type=float, default=0.001, help='Starting lr value.') + group.add_argument("--lr_range_test_step_rate", type=float, default=1.0, help='scaling rate for LR range test.') + group.add_argument("--lr_range_test_step_size", type=int, default=1000, help='training steps per LR change.') + group.add_argument("--lr_range_test_staircase", + type=bool, + default=False, + help='use staircase scaling for LR range test.') + + # OneCycle schedule + group.add_argument("--cycle_first_step_size", + type=int, + default=1000, + help='size of first step of 1Cycle schedule (training steps).') + group.add_argument("--cycle_first_stair_count", + type=int, + default=-1, + help='first stair count for 1Cycle schedule.') + group.add_argument("--cycle_second_step_size", + type=int, + default=-1, + help='size of second step of 1Cycle schedule (default first_step_size).') + group.add_argument("--cycle_second_stair_count", + type=int, + default=-1, + help='second stair count for 1Cycle schedule.') + group.add_argument("--decay_step_size", + type=int, + default=1000, + help='size of intervals for applying post cycle decay (training steps).') + + # 1Cycle LR + group.add_argument("--cycle_min_lr", type=float, default=0.01, help='1Cycle LR lower bound.') + group.add_argument("--cycle_max_lr", type=float, default=0.1, help='1Cycle LR upper bound.') + group.add_argument("--decay_lr_rate", type=float, default=0.0, help='post cycle LR decay rate.') + + # 1Cycle Momentum + group.add_argument('--cycle_momentum', default=False, action='store_true', help='Enable 1Cycle momentum schedule.') + group.add_argument("--cycle_min_mom", type=float, default=0.8, help='1Cycle momentum lower bound.') + group.add_argument("--cycle_max_mom", type=float, default=0.9, help='1Cycle momentum upper bound.') + group.add_argument("--decay_mom_rate", type=float, default=0.0, help='post cycle momentum decay rate.') + + # Warmup LR + group.add_argument('--warmup_min_lr', type=float, default=0, help='WarmupLR minimum/initial LR value') + group.add_argument('--warmup_max_lr', type=float, default=0.001, help='WarmupLR maximum LR value.') + group.add_argument('--warmup_num_steps', type=int, default=1000, help='WarmupLR step count for LR warmup.') + group.add_argument('--warmup_type', + type=str, + default=WARMUP_LOG_RATE, + help='WarmupLR increasing function during warmup') + return parser + + +def parse_arguments(): + parser = argparse.ArgumentParser() + parser = add_tuning_arguments(parser) + + lr_sched_args, unknown_args = parser.parse_known_args() + return lr_sched_args, unknown_args + + +def override_lr_range_test_params(args, params): + if hasattr(args, LR_RANGE_TEST_MIN_LR) and args.lr_range_test_min_lr is not None: + params[LR_RANGE_TEST_MIN_LR] = args.lr_range_test_min_lr + + if hasattr(args, LR_RANGE_TEST_STEP_RATE) and args.lr_range_test_step_rate is not None: + params[LR_RANGE_TEST_STEP_RATE] = args.lr_range_test_step_rate + + if hasattr(args, LR_RANGE_TEST_STEP_SIZE) and args.lr_range_test_step_size is not None: + params[LR_RANGE_TEST_STEP_SIZE] = args.lr_range_test_step_size + + if hasattr(args, LR_RANGE_TEST_STAIRCASE) and args.lr_range_test_staircase is not None: + params[LR_RANGE_TEST_STAIRCASE] = args.lr_range_test_staircase + + +def override_1cycle_params(args, params): + if hasattr(args, CYCLE_FIRST_STEP_SIZE) and args.cycle_first_step_size is not None: + params[CYCLE_FIRST_STEP_SIZE] = args.cycle_first_step_size + + if hasattr(args, CYCLE_FIRST_STAIR_COUNT) and args.cycle_first_stair_count is not None: + params[CYCLE_FIRST_STAIR_COUNT] = args.cycle_first_stair_count + + if hasattr(args, CYCLE_SECOND_STEP_SIZE) and args.cycle_second_step_size is not None: + params[CYCLE_SECOND_STEP_SIZE] = args.cycle_second_step_size + + if hasattr(args, CYCLE_SECOND_STAIR_COUNT) and args.cycle_second_stair_count is not None: + params[CYCLE_SECOND_STAIR_COUNT] = args.cycle_second_stair_count + + if hasattr(args, DECAY_STEP_SIZE) and args.decay_step_size is not None: + params[DECAY_STEP_SIZE] = args.decay_step_size + + # 1Cycle LR params + if hasattr(args, CYCLE_MIN_LR) and args.cycle_min_lr is not None: + params[CYCLE_MIN_LR] = args.cycle_min_lr + + if hasattr(args, CYCLE_MAX_LR) and args.cycle_max_lr is not None: + params[CYCLE_MAX_LR] = args.cycle_max_lr + + if hasattr(args, DECAY_LR_RATE) and args.decay_lr_rate is not None: + params[DECAY_LR_RATE] = args.decay_lr_rate + + # 1Cycle MOM params + if hasattr(args, CYCLE_MIN_MOM) and args.cycle_min_mom is not None: + params[CYCLE_MIN_MOM] = args.cycle_min_mom + + if hasattr(args, CYCLE_MAX_MOM) and args.cycle_max_mom is not None: + params[CYCLE_MAX_MOM] = args.cycle_max_mom + + if hasattr(args, DECAY_MOM_RATE) and args.decay_mom_rate is not None: + params[DECAY_MOM_RATE] = args.decay_mom_rate + + +def override_warmupLR_params(args, params): + if hasattr(args, WARMUP_MIN_LR) and args.warmup_min_lr is not None: + params[WARMUP_MIN_LR] = args.warmup_min_lr + + if hasattr(args, WARMUP_MAX_LR) and args.warmup_max_lr is not None: + params[WARMUP_MAX_LR] = args.warmup_max_lr + + if hasattr(args, WARMUP_NUM_STEPS) and args.warmup_num_steps is not None: + params[WARMUP_NUM_STEPS] = args.warmup_num_steps + + if hasattr(args, WARMUP_TYPE) and args.warmup_type is not None: + params[WARMUP_TYPE] = args.warmup_type + + +def override_params(args, params): + # LR range test params + override_lr_range_test_params(args, params) + + # 1Cycle params + override_1cycle_params(args, params) + + # WarmupLR params + override_warmupLR_params(args, params) + + +def get_config_from_args(args): + if not hasattr(args, LR_SCHEDULE) or args.lr_schedule is None: + return None, '--{} not specified on command line'.format(LR_SCHEDULE) + + if not args.lr_schedule in VALID_LR_SCHEDULES: + return None, '{} is not supported LR schedule'.format(args.lr_schedule) + + config = {} + config['type'] = args.lr_schedule + config['params'] = {} + + if args.lr_schedule == LR_RANGE_TEST: + override_lr_range_test_params(args, config['params']) + elif args.lr_schedule == ONE_CYCLE: + override_1cycle_params(args, config['params']) + else: + override_warmupLR_params(args, config['params']) + + return config, None + + +def get_lr_from_config(config): + if not 'type' in config: + return None, 'LR schedule type not defined in config' + + if not 'params' in config: + return None, 'LR schedule params not defined in config' + + lr_schedule = config['type'] + lr_params = config['params'] + + if not lr_schedule in VALID_LR_SCHEDULES: + return None, '{} is not a valid LR schedule'.format(lr_schedule) + + if lr_schedule == LR_RANGE_TEST: + return lr_params[LR_RANGE_TEST_MIN_LR], '' + if lr_schedule == ONE_CYCLE: + return lr_params[CYCLE_MAX_LR], '' + # Warmup LR + return lr_params[WARMUP_MAX_LR], '' + + +""" +Only optimizers that are subclass of torch.optim.Optimizer are supported. So check the passed optimizer and wrapped +optimizer to see if requirement is satisfied. +TODO: Looking under the hood to examine the wrapped optimizer is a hack that requires a better long-term fix. +""" + + +def get_torch_optimizer(optimizer): + if isinstance(optimizer, Optimizer): + return optimizer + + if hasattr(optimizer, 'optimizer') and isinstance(optimizer.optimizer, Optimizer): + return optimizer.optimizer + + raise TypeError('{} is not a subclass of torch.optim.Optimizer'.format(type(optimizer).__name__)) + + +class LRRangeTest(object): + """Sets the learning rate of each parameter group according to + learning rate range test (LRRT) policy. The policy increases learning + rate starting from a base value with a constant frequency, as detailed in + the paper `A disciplined approach to neural network hyper-parameters: Part1`_. + + LRRT policy is used for finding maximum LR that trains a model without divergence, and can be used to + configure the LR boundaries for Cyclic LR schedules. + + LRRT changes the learning rate after every batch. + `step` should be called after a batch has been used for training. + + Args: + optimizer (Optimizer): Wrapped optimizer. + lr_range_test_min_lr (float or list): Initial learning rate which is the + lower boundary in the range test for each parameter group. + lr_range_test_step_size (int): Interval of training steps to increase learning rate. Default: 2000 + lr_range_test_step_rate (float): Scaling rate for range test. Default: 1.0 + lr_range_test_staircase (bool): Scale in staircase fashion, rather than continuous. Default: False. + last_batch_iteration (int): The index of the last batch. This parameter is used when + resuming a training job. Since `step()` should be invoked after each + batch instead of after each epoch, this number represents the total + number of *batches* computed, not the total number of epochs computed. + When last_batch_iteration=-1, the schedule is started from the beginning. + Default: -1 + + Example: + >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9) + >>> scheduler = LRRangeTest(optimizer) + >>> data_loader = torch.utils.data.DataLoader(...) + >>> for epoch in range(10): + >>> for batch in data_loader: + >>> train_batch(...) + >>> scheduler.step() + + _A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay: + https://arxiv.org/abs/1803.09820 +""" + + def __init__(self, + optimizer: Optimizer, + lr_range_test_min_lr: float = 1e-3, + lr_range_test_step_size: int = 2000, + lr_range_test_step_rate: float = 1.0, + lr_range_test_staircase: bool = False, + last_batch_iteration: int = -1): + + self.optimizer = get_torch_optimizer(optimizer) + + if isinstance(lr_range_test_min_lr, list) or isinstance(lr_range_test_min_lr, tuple): + if len(lr_range_test_min_lr) != len(self.optimizer.param_groups): + raise ValueError("expected {} lr_range_test_min_lr, got {}".format(len(self.optimizer.param_groups), + len(lr_range_test_min_lr))) + self.min_lr = list(lr_range_test_min_lr) + else: + self.min_lr = [lr_range_test_min_lr] * len(self.optimizer.param_groups) + + self.step_size = lr_range_test_step_size + self.step_rate = lr_range_test_step_rate + self.last_batch_iteration = last_batch_iteration + self.staircase = lr_range_test_staircase + self.interval_fn = self._staircase_interval if lr_range_test_staircase else self._continuous_interval + + if last_batch_iteration == -1: + self._update_optimizer(self.min_lr) + + def _staircase_interval(self): + return math.floor(float(self.last_batch_iteration + 1) / self.step_size) + + def _continuous_interval(self): + return float(self.last_batch_iteration + 1) / self.step_size + + def _get_increase(self): + return (1 + self.step_rate * self.interval_fn()) + + def get_lr(self): + lr_increase = self._get_increase() + return [lr_range_test_min_lr * lr_increase for lr_range_test_min_lr in self.min_lr] + + def get_last_lr(self): + """ Return last computed learning rate by current scheduler. + """ + assert getattr(self, '_last_lr', None) is not None, "need to call step() first" + return self._last_lr + + def _update_optimizer(self, group_lrs): + for param_group, lr in zip(self.optimizer.param_groups, group_lrs): + param_group['lr'] = lr + + def step(self, batch_iteration=None): + if batch_iteration is None: + batch_iteration = self.last_batch_iteration + 1 + self.last_batch_iteration = batch_iteration + self._update_optimizer(self.get_lr()) + self._last_lr = [group['lr'] for group in self.optimizer.param_groups] + + def state_dict(self): + return {'last_batch_iteration': self.last_batch_iteration} + + def load_state_dict(self, sd): + self.last_batch_iteration = sd['last_batch_iteration'] + + + +class OneCycle(object): + """Sets the learning rate of each parameter group according to + 1Cycle learning rate policy (1CLR). 1CLR is a variation of the + Cyclical Learning Rate (CLR) policy that involves one cycle followed by + decay. The policy simultaneously cycles the learning rate (and momentum) + between two boundaries with a constant frequency, as detailed in + the paper `A disciplined approach to neural network hyper-parameters`_. + + 1CLR policy changes the learning rate after every batch. + `step` should be called after a batch has been used for training. + + This implementation was adapted from the github repo: `pytorch/pytorch`_ + + Args: + optimizer (Optimizer): Wrapped optimizer. + cycle_min_lr (float or list): Initial learning rate which is the + lower boundary in the cycle for each parameter group. + cycle_max_lr (float or list): Upper learning rate boundaries in the cycle + for each parameter group. Functionally, + it defines the cycle amplitude (cycle_max_lr - cycle_min_lr). + The lr at any cycle is the sum of cycle_min_lr + and some scaling of the amplitude; therefore + cycle_max_lr may not actually be reached depending on + scaling function. + decay_lr_rate(float): Decay rate for learning rate. Default: 0. + cycle_first_step_size (int): Number of training iterations in the + increasing half of a cycle. Default: 2000 + cycle_second_step_size (int): Number of training iterations in the + decreasing half of a cycle. If cycle_second_step_size is None, + it is set to cycle_first_step_size. Default: None + cycle_first_stair_count(int): Number of stairs in first half of cycle phase. This means + lr/mom are changed in staircase fashion. Default 0, means staircase disabled. + cycle_second_stair_count(int): Number of stairs in second half of cycle phase. This means + lr/mom are changed in staircase fashion. Default 0, means staircase disabled. + decay_step_size (int): Intervals for applying decay in decay phase. Default: 0, means no decay. + cycle_momentum (bool): If ``True``, momentum is cycled inversely + to learning rate between 'cycle_min_mom' and 'cycle_max_mom'. + Default: True + cycle_min_mom (float or list): Initial momentum which is the + lower boundary in the cycle for each parameter group. + Default: 0.8 + cycle_max_mom (float or list): Upper momentum boundaries in the cycle + for each parameter group. Functionally, + it defines the cycle amplitude (cycle_max_mom - cycle_min_mom). + The momentum at any cycle is the difference of cycle_max_mom + and some scaling of the amplitude; therefore + cycle_min_mom may not actually be reached depending on + scaling function. Default: 0.9 + decay_mom_rate (float): Decay rate for momentum. Default: 0. + last_batch_iteration (int): The index of the last batch. This parameter is used when + resuming a training job. Since `step()` should be invoked after each + batch instead of after each epoch, this number represents the total + number of *batches* computed, not the total number of epochs computed. + When last_batch_iteration=-1, the schedule is started from the beginning. + Default: -1 + + Example: + >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9) + >>> scheduler = OneCycle(optimizer, 0.0001, 0.0010) + >>> data_loader = torch.utils.data.DataLoader(...) + >>> for epoch in range(10): + >>> for batch in data_loader: + >>> train_batch(...) + >>> scheduler.step() + + + .. _A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay: https://arxiv.org/abs/1803.09820 + """ + + def __init__(self, + optimizer, + cycle_min_lr, + cycle_max_lr, + decay_lr_rate=0., + cycle_first_step_size=2000, + cycle_second_step_size=None, + cycle_first_stair_count=0, + cycle_second_stair_count=None, + decay_step_size=0, + cycle_momentum=True, + cycle_min_mom=0.8, + cycle_max_mom=0.9, + decay_mom_rate=0., + last_batch_iteration=-1): + + self.optimizer = get_torch_optimizer(optimizer) + + # Initialize cycle shape + self._initialize_cycle(cycle_first_step_size, cycle_second_step_size, cycle_first_stair_count, + cycle_second_stair_count, decay_step_size) + + # Initialize cycle lr + self._initialize_lr(self.optimizer, cycle_min_lr, cycle_max_lr, decay_lr_rate, last_batch_iteration) + + # Initialize cyclic momentum + self.cycle_momentum = cycle_momentum + if cycle_momentum: + self._initialize_momentum(self.optimizer, cycle_min_mom, cycle_max_mom, decay_mom_rate, + last_batch_iteration) + + # Initialize batch iteration tracker + self.last_batch_iteration = last_batch_iteration + + # Configure cycle shape + + def _initialize_cycle(self, cycle_first_step_size, cycle_second_step_size, cycle_first_stair_count, + cycle_second_stair_count, decay_step_size): + cycle_first_step_size = float(cycle_first_step_size) + cycle_second_step_size = float( + cycle_second_step_size) if cycle_second_step_size is not None else cycle_first_step_size + + self.total_size = cycle_first_step_size + cycle_second_step_size + self.step_ratio = cycle_first_step_size / self.total_size + self.first_stair_count = cycle_first_stair_count + self.second_stair_count = cycle_first_stair_count if cycle_second_stair_count is None else cycle_second_stair_count + self.decay_step_size = decay_step_size + + if math.isclose(self.decay_step_size, 0): + self.skip_lr_decay = True + self.skip_mom_decay = True + else: + self.skip_lr_decay = False + self.skip_mom_decay = False + + # Configure lr schedule + def _initialize_lr(self, optimizer, cycle_min_lr, cycle_max_lr, decay_lr_rate, last_batch_iteration): + self.min_lrs = [cycle_min_lr] * len(optimizer.param_groups) + if last_batch_iteration == -1: + for lr, group in zip(self.min_lrs, optimizer.param_groups): + group['lr'] = lr + + self.max_lrs = [cycle_max_lr] * len(optimizer.param_groups) + self.decay_lr_rate = decay_lr_rate + + if math.isclose(self.decay_lr_rate, 0): + self.skip_lr_decay = True + + # Configure momentum schedule + def _initialize_momentum(self, optimizer, cycle_min_mom, cycle_max_mom, decay_mom_rate, last_batch_iteration): + if 'betas' not in optimizer.defaults: + optimizer_name = type(optimizer).__name__ + print( + f"cycle_momentum is disabled because optimizer {optimizer_name} does not support momentum, no betas attribute in defaults" + ) + self.cycle_momentum = False + return + + self.decay_mom_rate = decay_mom_rate + self.min_moms = [(cycle_min_mom, 0.99)] * len(optimizer.param_groups) + self.max_moms = [(cycle_max_mom, 0.99)] * len(optimizer.param_groups) + + if last_batch_iteration == -1: + for momentum, group in zip(self.min_moms, optimizer.param_groups): + group['betas'] = momentum + + if math.isclose(self.decay_mom_rate, 0): + self.skip_mom_decay = True + + def _get_scale_factor(self): + batch_iteration = (self.last_batch_iteration + 1) + cycle = math.floor(1 + batch_iteration / self.total_size) + x = 1. + batch_iteration / self.total_size - cycle + if x <= self.step_ratio: + scale_factor = x / self.step_ratio + else: + scale_factor = (x - 1) / (self.step_ratio - 1) + + return scale_factor + + def _get_cycle_mom(self): + scale_factor = self._get_scale_factor() + momentums = [] + for base_betas, max_betas in zip(self.min_moms, self.max_moms): + cycle_min_mom = base_betas[0] + cycle_max_mom = max_betas[0] + base_height = (cycle_max_mom - cycle_min_mom) * scale_factor + momentum = cycle_max_mom - base_height + momentums.append((momentum, base_betas[1])) + return momentums + + def _get_cycle_lr(self): + scale_factor = self._get_scale_factor() + lrs = [] + for cycle_min_lr, cycle_max_lr in zip(self.min_lrs, self.max_lrs): + base_height = (cycle_max_lr - cycle_min_lr) * scale_factor + lr = cycle_min_lr + base_height + lrs.append(lr) + + return lrs + + def _get_decay_mom(self, decay_batch_iteration): + if self.skip_mom_decay: + return self.max_moms + + decay_interval = decay_batch_iteration / self.decay_step_size + mom_decay_factor = (1 + self.decay_mom_rate * decay_interval) + momentums = [(beta0 * mom_decay_factor, beta1) for beta0, beta1 in self.max_moms] + + return momentums + + def _get_decay_lr(self, decay_batch_iteration): + """Calculates the learning rate at batch index. This function is used + after the cycle completes and post cycle decaying of lr/mom is enabled. + This function treats `self.last_batch_iteration` as the last batch index. + """ + if self.skip_lr_decay: + return self.min_lrs + + decay_interval = decay_batch_iteration / self.decay_step_size + lr_decay_factor = (1 + self.decay_lr_rate * decay_interval) + lrs = [cycle_min_lr / lr_decay_factor for cycle_min_lr in self.min_lrs] + + return lrs + + def get_lr(self): + """Calculates the learning rate at batch index. This function treats + `self.last_batch_iteration` as the last batch index. + """ + if self.last_batch_iteration < self.total_size: + return self._get_cycle_lr() + return self._get_decay_lr(self.last_batch_iteration - self.total_size + 1) + + def get_mom(self): + """Calculates the momentum at batch index. This function treats + `self.last_batch_iteration` as the last batch index. + """ + if not self.cycle_momentum: + return None + + if self.last_batch_iteration < self.total_size: + return self._get_cycle_mom() + return self._get_decay_mom(self.last_batch_iteration - self.total_size + 1) + + def get_last_lr(self): + """ Return last computed learning rate by current scheduler. + """ + assert getattr(self, '_last_lr', None) is not None, "need to call step() first" + return self._last_lr + + def step(self, batch_iteration=None): + """ Updates the optimizer with the learning rate for the last batch index. + `self.last_batch_iteration` is treated as the last batch index. + + If self.cycle_momentum is true, also updates optimizer momentum. + """ + if batch_iteration is None: + batch_iteration = self.last_batch_iteration + 1 + + self.last_batch_iteration = batch_iteration + for param_group, lr in zip(self.optimizer.param_groups, self.get_lr()): + param_group['lr'] = lr + self._last_lr = [group['lr'] for group in self.optimizer.param_groups] + + if self.cycle_momentum: + momentums = self.get_mom() + for param_group, momentum in zip(self.optimizer.param_groups, momentums): + param_group['betas'] = momentum + + def state_dict(self): + return {'last_batch_iteration': self.last_batch_iteration} + + def load_state_dict(self, sd): + self.last_batch_iteration = sd['last_batch_iteration'] + + + +class WarmupLR(object): + """Increase the learning rate of each parameter group from min lr to max lr + over warmup_num_steps steps, and then fix at max lr. + + Args: + optimizer (Optimizer): Wrapped optimizer. + warmup_min_lr (float or list): minimum learning rate. Default: 0 + warmup_max_lr (float or list): maximum learning rate. Default: 0.001 + warmup_num_steps (int): number of steps to warm up from min_lr to max_lr. Default: 1000 + warmup_type {‘log’, ‘linear’}: increasing function from min_lr to max_lr during warmup. Default: log + last_batch_iteration (int): The index of the last batch. Default: -1. + Example: + >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9) + >>> scheduler = WarmupLR(optimizer) + >>> data_loader = torch.utils.data.DataLoader(...) + >>> for epoch in range(10): + >>> for batch in data_loader: + >>> train_batch(...) + >>> scheduler.step() + + """ + + def __init__(self, + optimizer: Optimizer, + warmup_min_lr: float = 0.0, + warmup_max_lr: float = 0.001, + warmup_num_steps: int = 1000, + warmup_type: str = WARMUP_LOG_RATE, + last_batch_iteration: int = -1): + + self.optimizer = get_torch_optimizer(optimizer) + + self.min_lrs = self._format_param(self.optimizer, warmup_min_lr, "min_lr") + self.max_lrs = self._format_param(self.optimizer, warmup_max_lr, "max_lr") + self.delta_lrs = [big - small for big, small in zip(self.max_lrs, self.min_lrs)] + self.warmup_num_steps = max(2, warmup_num_steps) + # Currently only support linear and log function + if warmup_type not in {WARMUP_LOG_RATE, WARMUP_LINEAR_RATE}: + print(f"Using unknown warmup_type: {warmup_type}. The increasing function " + f"is set to default (log)") + warmup_type = WARMUP_LOG_RATE + self.warmup_type = warmup_type + self.inverse_log_warm_up = 1.0 / math.log(self.warmup_num_steps) + self.last_batch_iteration = last_batch_iteration + + def get_lr(self): + if self.last_batch_iteration < 0: + print("Attempting to get learning rate from scheduler before it has started") + return [0.0] + gamma = self._get_gamma() + return [min_lr + (delta_lr * gamma) for min_lr, delta_lr in zip(self.min_lrs, self.delta_lrs)] + + def get_last_lr(self): + """ Return last computed learning rate by current scheduler. + """ + assert getattr(self, '_last_lr', None) is not None, "need to call step() first" + return self._last_lr + + def step(self, last_batch_iteration=None): + if last_batch_iteration is None: + last_batch_iteration = self.last_batch_iteration + 1 + self.last_batch_iteration = last_batch_iteration + for param_group, lr in zip(self.optimizer.param_groups, self.get_lr()): + param_group['lr'] = lr + self._last_lr = [group['lr'] for group in self.optimizer.param_groups] + + def state_dict(self): + return {'last_batch_iteration': self.last_batch_iteration} + + def load_state_dict(self, sd): + self.last_batch_iteration = sd['last_batch_iteration'] + + def _get_gamma(self): + if self.last_batch_iteration < self.warmup_num_steps: + if self.warmup_type == WARMUP_LOG_RATE: + return self.inverse_log_warm_up * math.log(self.last_batch_iteration + 1) + elif self.warmup_type == WARMUP_LINEAR_RATE: + return self.last_batch_iteration / self.warmup_num_steps + return 1.0 + + def _format_param(self, optimizer, param_value, param_name): + if isinstance(param_value, list) or isinstance(param_value, tuple): + if len(param_value) != len(optimizer.param_groups): + raise ValueError("expected {} value for {}, got {}".format(len(optimizer.param_groups), param_name, + FileNotFoundError(param_value))) + return list(param_value) + return [param_value] * len(optimizer.param_groups) + + + +class WarmupDecayLR(WarmupLR): + """Increase the learning rate of each parameter group from min lr to max lr + over warmup_num_steps steps, and then decay at linear rate over the remaining training steps. + + Args: + optimizer (Optimizer): Wrapped optimizer. + total_num_steps (int): total number of training steps + warmup_min_lr (float or list): minimum learning rate. Default: 0 + warmup_max_lr (float or list): maximum learning rate. Default: 0.001 + warmup_num_steps (int): number of steps to warm up from min_lr to max_lr. Default: 1000 + warmup_type {‘log’, ‘linear’}: increasing function from min_lr to max_lr during warmup. Default: log + last_batch_iteration (int): The index of the last batch. Default: -1. + Example: + >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9) + >>> scheduler = WarmupDecayLR(optimizer, 1000000) + >>> data_loader = torch.utils.data.DataLoader(...) + >>> for epoch in range(10): + >>> for batch in data_loader: + >>> train_batch(...) + >>> scheduler.step() + + """ + + def __init__(self, + optimizer: Optimizer, + total_num_steps: int, + warmup_min_lr: float = 0.0, + warmup_max_lr: float = 0.001, + warmup_num_steps: int = 1000, + warmup_type: str = WARMUP_LOG_RATE, + last_batch_iteration: int = -1): + + self.total_num_steps = total_num_steps + super(WarmupDecayLR, self).__init__(optimizer, warmup_min_lr, warmup_max_lr, warmup_num_steps, warmup_type, + last_batch_iteration) + if self.total_num_steps < self.warmup_num_steps: + print('total_num_steps {} is less than warmup_num_steps {}'.format( + total_num_steps, warmup_num_steps)) + + def _get_gamma(self): + if self.last_batch_iteration < self.warmup_num_steps: + if self.warmup_type == WARMUP_LOG_RATE: + return self.inverse_log_warm_up * math.log(self.last_batch_iteration + 1) + elif self.warmup_type == WARMUP_LINEAR_RATE: + return self.last_batch_iteration / self.warmup_num_steps + return max( + 0.0, + float(self.total_num_steps - self.last_batch_iteration) / + float(max(1.0, self.total_num_steps - self.warmup_num_steps))) diff --git a/custom_nodes/comfyui-hydit_min/hydit/modules/__init__.py b/custom_nodes/comfyui-hydit_min/hydit/modules/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/custom_nodes/comfyui-hydit_min/hydit/modules/attn_layers.py b/custom_nodes/comfyui-hydit_min/hydit/modules/attn_layers.py new file mode 100644 index 000000000..712e783ee --- /dev/null +++ b/custom_nodes/comfyui-hydit_min/hydit/modules/attn_layers.py @@ -0,0 +1,401 @@ +import torch +import torch.nn as nn +from typing import Tuple, Union, Optional +from comfy.ldm.modules.attention import optimized_attention + +try: + import flash_attn + if hasattr(flash_attn, '__version__') and int(flash_attn.__version__[0]) == 2: + from flash_attn.flash_attn_interface import flash_attn_kvpacked_func + from flash_attn.modules.mha import FlashSelfAttention, FlashCrossAttention + else: + from flash_attn.flash_attn_interface import flash_attn_unpadded_kvpacked_func + from flash_attn.modules.mha import FlashSelfAttention, FlashCrossAttention +except Exception as e: + print(f'flash_attn import failed: {e}') + + +def reshape_for_broadcast(freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]], x: torch.Tensor, head_first=False): + """ + Reshape frequency tensor for broadcasting it with another tensor. + + This function reshapes the frequency tensor to have the same shape as the target tensor 'x' + for the purpose of broadcasting the frequency tensor during element-wise operations. + + Args: + freqs_cis (Union[torch.Tensor, Tuple[torch.Tensor]]): Frequency tensor to be reshaped. + x (torch.Tensor): Target tensor for broadcasting compatibility. + head_first (bool): head dimension first (except batch dim) or not. + + Returns: + torch.Tensor: Reshaped frequency tensor. + + Raises: + AssertionError: If the frequency tensor doesn't match the expected shape. + AssertionError: If the target tensor 'x' doesn't have the expected number of dimensions. + """ + ndim = x.ndim + assert 0 <= 1 < ndim + + if isinstance(freqs_cis, tuple): + # freqs_cis: (cos, sin) in real space + if head_first: + assert freqs_cis[0].shape == (x.shape[-2], x.shape[-1]), f'freqs_cis shape {freqs_cis[0].shape} does not match x shape {x.shape}' + shape = [d if i == ndim - 2 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)] + else: + assert freqs_cis[0].shape == (x.shape[1], x.shape[-1]), f'freqs_cis shape {freqs_cis[0].shape} does not match x shape {x.shape}' + shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)] + return freqs_cis[0].view(*shape), freqs_cis[1].view(*shape) + else: + # freqs_cis: values in complex space + if head_first: + assert freqs_cis.shape == (x.shape[-2], x.shape[-1]), f'freqs_cis shape {freqs_cis.shape} does not match x shape {x.shape}' + shape = [d if i == ndim - 2 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)] + else: + assert freqs_cis.shape == (x.shape[1], x.shape[-1]), f'freqs_cis shape {freqs_cis.shape} does not match x shape {x.shape}' + shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)] + return freqs_cis.view(*shape) + + +def rotate_half(x): + x_real, x_imag = x.float().reshape(*x.shape[:-1], -1, 2).unbind(-1) # [B, S, H, D//2] + return torch.stack([-x_imag, x_real], dim=-1).flatten(3) + + +def apply_rotary_emb( + xq: torch.Tensor, + xk: Optional[torch.Tensor], + freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]], + head_first: bool = False, +) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Apply rotary embeddings to input tensors using the given frequency tensor. + + This function applies rotary embeddings to the given query 'xq' and key 'xk' tensors using the provided + frequency tensor 'freqs_cis'. The input tensors are reshaped as complex numbers, and the frequency tensor + is reshaped for broadcasting compatibility. The resulting tensors contain rotary embeddings and are + returned as real tensors. + + Args: + xq (torch.Tensor): Query tensor to apply rotary embeddings. [B, S, H, D] + xk (torch.Tensor): Key tensor to apply rotary embeddings. [B, S, H, D] + freqs_cis (Union[torch.Tensor, Tuple[torch.Tensor]]): Precomputed frequency tensor for complex exponentials. + head_first (bool): head dimension first (except batch dim) or not. + + Returns: + Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor and key tensor with rotary embeddings. + + """ + xk_out = None + if isinstance(freqs_cis, tuple): + cos, sin = reshape_for_broadcast(freqs_cis, xq, head_first) # [S, D] + cos, sin = cos.to(xq.device), sin.to(xq.device) + xq_out = (xq.float() * cos + rotate_half(xq.float()) * sin).type_as(xq) + if xk is not None: + xk_out = (xk.float() * cos + rotate_half(xk.float()) * sin).type_as(xk) + else: + xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2)) # [B, S, H, D//2] + freqs_cis = reshape_for_broadcast(freqs_cis, xq_, head_first).to(xq.device) # [S, D//2] --> [1, S, 1, D//2] + xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3).type_as(xq) + if xk is not None: + xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2)) # [B, S, H, D//2] + xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3).type_as(xk) + + return xq_out, xk_out + + +class FlashSelfMHAModified(nn.Module): + """ + Use QK Normalization. + """ + def __init__(self, + dim, + num_heads, + qkv_bias=True, + qk_norm=False, + attn_drop=0.0, + proj_drop=0.0, + device=None, + dtype=None, + norm_layer=nn.LayerNorm, + ): + factory_kwargs = {'device': device, 'dtype': dtype} + super().__init__() + self.dim = dim + self.num_heads = num_heads + assert self.dim % num_heads == 0, "self.kdim must be divisible by num_heads" + self.head_dim = self.dim // num_heads + assert self.head_dim % 8 == 0 and self.head_dim <= 128, "Only support head_dim <= 128 and divisible by 8" + + self.Wqkv = nn.Linear(dim, 3 * dim, bias=qkv_bias, **factory_kwargs) + # TODO: eps should be 1 / 65530 if using fp16 + self.q_norm = norm_layer(self.head_dim, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity() + self.k_norm = norm_layer(self.head_dim, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity() + self.inner_attn = FlashSelfAttention(attention_dropout=attn_drop) + self.out_proj = nn.Linear(dim, dim, bias=qkv_bias, **factory_kwargs) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, x, freqs_cis_img=None): + """ + Parameters + ---------- + x: torch.Tensor + (batch, seqlen, hidden_dim) (where hidden_dim = num heads * head dim) + freqs_cis_img: torch.Tensor + (batch, hidden_dim // 2), RoPE for image + """ + b, s, d = x.shape + + qkv = self.Wqkv(x) + qkv = qkv.view(b, s, 3, self.num_heads, self.head_dim) # [b, s, 3, h, d] + q, k, v = qkv.unbind(dim=2) # [b, s, h, d] + q = self.q_norm(q).half() # [b, s, h, d] + k = self.k_norm(k).half() + + # Apply RoPE if needed + if freqs_cis_img is not None: + qq, kk = apply_rotary_emb(q, k, freqs_cis_img) + assert qq.shape == q.shape and kk.shape == k.shape, f'qq: {qq.shape}, q: {q.shape}, kk: {kk.shape}, k: {k.shape}' + q, k = qq, kk + + qkv = torch.stack([q, k, v], dim=2) # [b, s, 3, h, d] + context = self.inner_attn(qkv) + out = self.out_proj(context.view(b, s, d)) + out = self.proj_drop(out) + + out_tuple = (out,) + + return out_tuple + + +class FlashCrossMHAModified(nn.Module): + """ + Use QK Normalization. + """ + def __init__(self, + qdim, + kdim, + num_heads, + qkv_bias=True, + qk_norm=False, + attn_drop=0.0, + proj_drop=0.0, + device=None, + dtype=None, + norm_layer=nn.LayerNorm, + ): + factory_kwargs = {'device': device, 'dtype': dtype} + super().__init__() + self.qdim = qdim + self.kdim = kdim + self.num_heads = num_heads + assert self.qdim % num_heads == 0, "self.qdim must be divisible by num_heads" + self.head_dim = self.qdim // num_heads + assert self.head_dim % 8 == 0 and self.head_dim <= 128, "Only support head_dim <= 128 and divisible by 8" + + self.scale = self.head_dim ** -0.5 + + self.q_proj = nn.Linear(qdim, qdim, bias=qkv_bias, **factory_kwargs) + self.kv_proj = nn.Linear(kdim, 2 * qdim, bias=qkv_bias, **factory_kwargs) + + # TODO: eps should be 1 / 65530 if using fp16 + self.q_norm = norm_layer(self.head_dim, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity() + self.k_norm = norm_layer(self.head_dim, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity() + + self.inner_attn = FlashCrossAttention(attention_dropout=attn_drop) + self.out_proj = nn.Linear(qdim, qdim, bias=qkv_bias, **factory_kwargs) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, x, y, freqs_cis_img=None): + """ + Parameters + ---------- + x: torch.Tensor + (batch, seqlen1, hidden_dim) (where hidden_dim = num_heads * head_dim) + y: torch.Tensor + (batch, seqlen2, hidden_dim2) + freqs_cis_img: torch.Tensor + (batch, hidden_dim // num_heads), RoPE for image + """ + b, s1, _ = x.shape # [b, s1, D] + _, s2, _ = y.shape # [b, s2, 1024] + + q = self.q_proj(x).view(b, s1, self.num_heads, self.head_dim) # [b, s1, h, d] + kv = self.kv_proj(y).view(b, s2, 2, self.num_heads, self.head_dim) # [b, s2, 2, h, d] + k, v = kv.unbind(dim=2) # [b, s2, h, d] + q = self.q_norm(q).half() # [b, s1, h, d] + k = self.k_norm(k).half() # [b, s2, h, d] + + # Apply RoPE if needed + if freqs_cis_img is not None: + qq, _ = apply_rotary_emb(q, None, freqs_cis_img) + assert qq.shape == q.shape, f'qq: {qq.shape}, q: {q.shape}' + q = qq # [b, s1, h, d] + kv = torch.stack([k, v], dim=2) # [b, s1, 2, h, d] + context = self.inner_attn(q, kv) # [b, s1, h, d] + context = context.view(b, s1, -1) # [b, s1, D] + + out = self.out_proj(context) + out = self.proj_drop(out) + + out_tuple = (out,) + + return out_tuple + + +class CrossAttention(nn.Module): + """ + Use QK Normalization. + """ + def __init__(self, + qdim, + kdim, + num_heads, + qkv_bias=True, + qk_norm=False, + attn_drop=0.0, + proj_drop=0.0, + device=None, + dtype=None, + norm_layer=nn.LayerNorm, + ): + factory_kwargs = {'device': device, 'dtype': dtype} + super().__init__() + self.qdim = qdim + self.kdim = kdim + self.num_heads = num_heads + assert self.qdim % num_heads == 0, "self.qdim must be divisible by num_heads" + self.head_dim = self.qdim // num_heads + assert self.head_dim % 8 == 0 and self.head_dim <= 128, "Only support head_dim <= 128 and divisible by 8" + self.scale = self.head_dim ** -0.5 + + self.q_proj = nn.Linear(qdim, qdim, bias=qkv_bias, **factory_kwargs) + self.kv_proj = nn.Linear(kdim, 2 * qdim, bias=qkv_bias, **factory_kwargs) + + # TODO: eps should be 1 / 65530 if using fp16 + self.q_norm = norm_layer(self.head_dim, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity() + self.k_norm = norm_layer(self.head_dim, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity() + self.attn_drop = nn.Dropout(attn_drop) + self.out_proj = nn.Linear(qdim, qdim, bias=qkv_bias, **factory_kwargs) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, x, y, freqs_cis_img=None): + """ + Parameters + ---------- + x: torch.Tensor + (batch, seqlen1, hidden_dim) (where hidden_dim = num heads * head dim) + y: torch.Tensor + (batch, seqlen2, hidden_dim2) + freqs_cis_img: torch.Tensor + (batch, hidden_dim // 2), RoPE for image + """ + b, s1, c = x.shape # [b, s1, D] + _, s2, c = y.shape # [b, s2, 1024] + + q = self.q_proj(x).view(b, s1, self.num_heads, self.head_dim) # [b, s1, h, d] + kv = self.kv_proj(y).view(b, s2, 2, self.num_heads, self.head_dim) # [b, s2, 2, h, d] + k, v = kv.unbind(dim=2) # [b, s, h, d] + q = self.q_norm(q) + k = self.k_norm(k) + + # Apply RoPE if needed + if freqs_cis_img is not None: + qq, _ = apply_rotary_emb(q, None, freqs_cis_img) + assert qq.shape == q.shape, f'qq: {qq.shape}, q: {q.shape}' + q = qq + + + q = q.transpose(-2, -3).contiguous() # q -> B, L1, H, C - B, H, L1, C + k = k.transpose(-2, -3).contiguous() # k -> B, L2, H, C - B, H, C, L2 + v = v.transpose(-2, -3).contiguous() + + """ + q = q * self.scale + attn = q @ k.transpose(-2, -1) # attn -> B, H, L1, L2 + attn = attn.softmax(dim=-1) # attn -> B, H, L1, L2 + attn = self.attn_drop(attn) + x = attn @ v # v -> B, L2, H, C - B, H, L2, C x-> B, H, L1, C + + context = x.transpose(1, 2) # context -> B, H, L1, C - B, L1, H, C + context = context.contiguous().view(b, s1, -1) + """ + + + context = optimized_attention(q, k, v, self.num_heads, skip_reshape=True) + + + + out = self.out_proj(context) # context.reshape - B, L1, -1 + out = self.proj_drop(out) + + out_tuple = (out,) + + return out_tuple + + +class Attention(nn.Module): + """ + We rename some layer names to align with flash attention + """ + def __init__(self, dim, num_heads, qkv_bias=True, qk_norm=False, attn_drop=0., proj_drop=0., + norm_layer=nn.LayerNorm, + ): + super().__init__() + self.dim = dim + self.num_heads = num_heads + assert self.dim % num_heads == 0, 'dim should be divisible by num_heads' + self.head_dim = self.dim // num_heads + # This assertion is aligned with flash attention + assert self.head_dim % 8 == 0 and self.head_dim <= 128, "Only support head_dim <= 128 and divisible by 8" + self.scale = self.head_dim ** -0.5 + + # qkv --> Wqkv + self.Wqkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + # TODO: eps should be 1 / 65530 if using fp16 + self.q_norm = norm_layer(self.head_dim, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity() + self.k_norm = norm_layer(self.head_dim, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity() + self.attn_drop = nn.Dropout(attn_drop) + self.out_proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, x, freqs_cis_img=None): + B, N, C = x.shape + qkv = self.Wqkv(x).reshape(B, N, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4) # [3, b, h, s, d] + q, k, v = qkv.unbind(0) # [b, h, s, d] + q = self.q_norm(q) # [b, h, s, d] + k = self.k_norm(k) # [b, h, s, d] + + # Apply RoPE if needed + if freqs_cis_img is not None: + qq, kk = apply_rotary_emb(q, k, freqs_cis_img, head_first=True) + assert qq.shape == q.shape and kk.shape == k.shape, \ + f'qq: {qq.shape}, q: {q.shape}, kk: {kk.shape}, k: {k.shape}' + q, k = qq, kk + + x = optimized_attention(q, k, v, self.num_heads, skip_reshape=True) + """ + import pdb + pdb.set_trace() + + q = q * self.scale + attn = q @ k.transpose(-2, -1) # [b, h, s, d] @ [b, h, d, s] + attn = attn.softmax(dim=-1) # [b, h, s, s] + attn = self.attn_drop(attn) + x = attn @ v # [b, h, s, d] + + x = x.transpose(1, 2).reshape(B, N, C) # [b, s, h, d] + + x_comfyui = optimized_attention(q, k, v, self.num_heads, skip_reshape=True) + """ + + + + + x = self.out_proj(x) + x = self.proj_drop(x) + + + out_tuple = (x,) + + return out_tuple diff --git a/custom_nodes/comfyui-hydit_min/hydit/modules/attn_layers.py.bak b/custom_nodes/comfyui-hydit_min/hydit/modules/attn_layers.py.bak new file mode 100644 index 000000000..4308af932 --- /dev/null +++ b/custom_nodes/comfyui-hydit_min/hydit/modules/attn_layers.py.bak @@ -0,0 +1,377 @@ +import torch +import torch.nn as nn +from typing import Tuple, Union, Optional + +try: + import flash_attn + if hasattr(flash_attn, '__version__') and int(flash_attn.__version__[0]) == 2: + from flash_attn.flash_attn_interface import flash_attn_kvpacked_func + from flash_attn.modules.mha import FlashSelfAttention, FlashCrossAttention + else: + from flash_attn.flash_attn_interface import flash_attn_unpadded_kvpacked_func + from flash_attn.modules.mha import FlashSelfAttention, FlashCrossAttention +except Exception as e: + print(f'flash_attn import failed: {e}') + + +def reshape_for_broadcast(freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]], x: torch.Tensor, head_first=False): + """ + Reshape frequency tensor for broadcasting it with another tensor. + + This function reshapes the frequency tensor to have the same shape as the target tensor 'x' + for the purpose of broadcasting the frequency tensor during element-wise operations. + + Args: + freqs_cis (Union[torch.Tensor, Tuple[torch.Tensor]]): Frequency tensor to be reshaped. + x (torch.Tensor): Target tensor for broadcasting compatibility. + head_first (bool): head dimension first (except batch dim) or not. + + Returns: + torch.Tensor: Reshaped frequency tensor. + + Raises: + AssertionError: If the frequency tensor doesn't match the expected shape. + AssertionError: If the target tensor 'x' doesn't have the expected number of dimensions. + """ + ndim = x.ndim + assert 0 <= 1 < ndim + + if isinstance(freqs_cis, tuple): + # freqs_cis: (cos, sin) in real space + if head_first: + assert freqs_cis[0].shape == (x.shape[-2], x.shape[-1]), f'freqs_cis shape {freqs_cis[0].shape} does not match x shape {x.shape}' + shape = [d if i == ndim - 2 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)] + else: + assert freqs_cis[0].shape == (x.shape[1], x.shape[-1]), f'freqs_cis shape {freqs_cis[0].shape} does not match x shape {x.shape}' + shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)] + return freqs_cis[0].view(*shape), freqs_cis[1].view(*shape) + else: + # freqs_cis: values in complex space + if head_first: + assert freqs_cis.shape == (x.shape[-2], x.shape[-1]), f'freqs_cis shape {freqs_cis.shape} does not match x shape {x.shape}' + shape = [d if i == ndim - 2 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)] + else: + assert freqs_cis.shape == (x.shape[1], x.shape[-1]), f'freqs_cis shape {freqs_cis.shape} does not match x shape {x.shape}' + shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)] + return freqs_cis.view(*shape) + + +def rotate_half(x): + x_real, x_imag = x.float().reshape(*x.shape[:-1], -1, 2).unbind(-1) # [B, S, H, D//2] + return torch.stack([-x_imag, x_real], dim=-1).flatten(3) + + +def apply_rotary_emb( + xq: torch.Tensor, + xk: Optional[torch.Tensor], + freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]], + head_first: bool = False, +) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Apply rotary embeddings to input tensors using the given frequency tensor. + + This function applies rotary embeddings to the given query 'xq' and key 'xk' tensors using the provided + frequency tensor 'freqs_cis'. The input tensors are reshaped as complex numbers, and the frequency tensor + is reshaped for broadcasting compatibility. The resulting tensors contain rotary embeddings and are + returned as real tensors. + + Args: + xq (torch.Tensor): Query tensor to apply rotary embeddings. [B, S, H, D] + xk (torch.Tensor): Key tensor to apply rotary embeddings. [B, S, H, D] + freqs_cis (Union[torch.Tensor, Tuple[torch.Tensor]]): Precomputed frequency tensor for complex exponentials. + head_first (bool): head dimension first (except batch dim) or not. + + Returns: + Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor and key tensor with rotary embeddings. + + """ + xk_out = None + if isinstance(freqs_cis, tuple): + cos, sin = reshape_for_broadcast(freqs_cis, xq, head_first) # [S, D] + cos, sin = cos.to(xq.device), sin.to(xq.device) + xq_out = (xq.float() * cos + rotate_half(xq.float()) * sin).type_as(xq) + if xk is not None: + xk_out = (xk.float() * cos + rotate_half(xk.float()) * sin).type_as(xk) + else: + xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2)) # [B, S, H, D//2] + freqs_cis = reshape_for_broadcast(freqs_cis, xq_, head_first).to(xq.device) # [S, D//2] --> [1, S, 1, D//2] + xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3).type_as(xq) + if xk is not None: + xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2)) # [B, S, H, D//2] + xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3).type_as(xk) + + return xq_out, xk_out + + +class FlashSelfMHAModified(nn.Module): + """ + Use QK Normalization. + """ + def __init__(self, + dim, + num_heads, + qkv_bias=True, + qk_norm=False, + attn_drop=0.0, + proj_drop=0.0, + device=None, + dtype=None, + norm_layer=nn.LayerNorm, + ): + factory_kwargs = {'device': device, 'dtype': dtype} + super().__init__() + self.dim = dim + self.num_heads = num_heads + assert self.dim % num_heads == 0, "self.kdim must be divisible by num_heads" + self.head_dim = self.dim // num_heads + assert self.head_dim % 8 == 0 and self.head_dim <= 128, "Only support head_dim <= 128 and divisible by 8" + + self.Wqkv = nn.Linear(dim, 3 * dim, bias=qkv_bias, **factory_kwargs) + # TODO: eps should be 1 / 65530 if using fp16 + self.q_norm = norm_layer(self.head_dim, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity() + self.k_norm = norm_layer(self.head_dim, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity() + self.inner_attn = FlashSelfAttention(attention_dropout=attn_drop) + self.out_proj = nn.Linear(dim, dim, bias=qkv_bias, **factory_kwargs) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, x, freqs_cis_img=None): + """ + Parameters + ---------- + x: torch.Tensor + (batch, seqlen, hidden_dim) (where hidden_dim = num heads * head dim) + freqs_cis_img: torch.Tensor + (batch, hidden_dim // 2), RoPE for image + """ + b, s, d = x.shape + + qkv = self.Wqkv(x) + qkv = qkv.view(b, s, 3, self.num_heads, self.head_dim) # [b, s, 3, h, d] + q, k, v = qkv.unbind(dim=2) # [b, s, h, d] + q = self.q_norm(q).half() # [b, s, h, d] + k = self.k_norm(k).half() + + # Apply RoPE if needed + if freqs_cis_img is not None: + qq, kk = apply_rotary_emb(q, k, freqs_cis_img) + assert qq.shape == q.shape and kk.shape == k.shape, f'qq: {qq.shape}, q: {q.shape}, kk: {kk.shape}, k: {k.shape}' + q, k = qq, kk + + qkv = torch.stack([q, k, v], dim=2) # [b, s, 3, h, d] + context = self.inner_attn(qkv) + out = self.out_proj(context.view(b, s, d)) + out = self.proj_drop(out) + + out_tuple = (out,) + + return out_tuple + + +class FlashCrossMHAModified(nn.Module): + """ + Use QK Normalization. + """ + def __init__(self, + qdim, + kdim, + num_heads, + qkv_bias=True, + qk_norm=False, + attn_drop=0.0, + proj_drop=0.0, + device=None, + dtype=None, + norm_layer=nn.LayerNorm, + ): + factory_kwargs = {'device': device, 'dtype': dtype} + super().__init__() + self.qdim = qdim + self.kdim = kdim + self.num_heads = num_heads + assert self.qdim % num_heads == 0, "self.qdim must be divisible by num_heads" + self.head_dim = self.qdim // num_heads + assert self.head_dim % 8 == 0 and self.head_dim <= 128, "Only support head_dim <= 128 and divisible by 8" + + self.scale = self.head_dim ** -0.5 + + self.q_proj = nn.Linear(qdim, qdim, bias=qkv_bias, **factory_kwargs) + self.kv_proj = nn.Linear(kdim, 2 * qdim, bias=qkv_bias, **factory_kwargs) + + # TODO: eps should be 1 / 65530 if using fp16 + self.q_norm = norm_layer(self.head_dim, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity() + self.k_norm = norm_layer(self.head_dim, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity() + + self.inner_attn = FlashCrossAttention(attention_dropout=attn_drop) + self.out_proj = nn.Linear(qdim, qdim, bias=qkv_bias, **factory_kwargs) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, x, y, freqs_cis_img=None): + """ + Parameters + ---------- + x: torch.Tensor + (batch, seqlen1, hidden_dim) (where hidden_dim = num_heads * head_dim) + y: torch.Tensor + (batch, seqlen2, hidden_dim2) + freqs_cis_img: torch.Tensor + (batch, hidden_dim // num_heads), RoPE for image + """ + b, s1, _ = x.shape # [b, s1, D] + _, s2, _ = y.shape # [b, s2, 1024] + + q = self.q_proj(x).view(b, s1, self.num_heads, self.head_dim) # [b, s1, h, d] + kv = self.kv_proj(y).view(b, s2, 2, self.num_heads, self.head_dim) # [b, s2, 2, h, d] + k, v = kv.unbind(dim=2) # [b, s2, h, d] + q = self.q_norm(q).half() # [b, s1, h, d] + k = self.k_norm(k).half() # [b, s2, h, d] + + # Apply RoPE if needed + if freqs_cis_img is not None: + qq, _ = apply_rotary_emb(q, None, freqs_cis_img) + assert qq.shape == q.shape, f'qq: {qq.shape}, q: {q.shape}' + q = qq # [b, s1, h, d] + kv = torch.stack([k, v], dim=2) # [b, s1, 2, h, d] + context = self.inner_attn(q, kv) # [b, s1, h, d] + context = context.view(b, s1, -1) # [b, s1, D] + + out = self.out_proj(context) + out = self.proj_drop(out) + + out_tuple = (out,) + + return out_tuple + + +class CrossAttention(nn.Module): + """ + Use QK Normalization. + """ + def __init__(self, + qdim, + kdim, + num_heads, + qkv_bias=True, + qk_norm=False, + attn_drop=0.0, + proj_drop=0.0, + device=None, + dtype=None, + norm_layer=nn.LayerNorm, + ): + factory_kwargs = {'device': device, 'dtype': dtype} + super().__init__() + self.qdim = qdim + self.kdim = kdim + self.num_heads = num_heads + assert self.qdim % num_heads == 0, "self.qdim must be divisible by num_heads" + self.head_dim = self.qdim // num_heads + assert self.head_dim % 8 == 0 and self.head_dim <= 128, "Only support head_dim <= 128 and divisible by 8" + self.scale = self.head_dim ** -0.5 + + self.q_proj = nn.Linear(qdim, qdim, bias=qkv_bias, **factory_kwargs) + self.kv_proj = nn.Linear(kdim, 2 * qdim, bias=qkv_bias, **factory_kwargs) + + # TODO: eps should be 1 / 65530 if using fp16 + self.q_norm = norm_layer(self.head_dim, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity() + self.k_norm = norm_layer(self.head_dim, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity() + self.attn_drop = nn.Dropout(attn_drop) + self.out_proj = nn.Linear(qdim, qdim, bias=qkv_bias, **factory_kwargs) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, x, y, freqs_cis_img=None): + """ + Parameters + ---------- + x: torch.Tensor + (batch, seqlen1, hidden_dim) (where hidden_dim = num heads * head dim) + y: torch.Tensor + (batch, seqlen2, hidden_dim2) + freqs_cis_img: torch.Tensor + (batch, hidden_dim // 2), RoPE for image + """ + b, s1, c = x.shape # [b, s1, D] + _, s2, c = y.shape # [b, s2, 1024] + + q = self.q_proj(x).view(b, s1, self.num_heads, self.head_dim) # [b, s1, h, d] + kv = self.kv_proj(y).view(b, s2, 2, self.num_heads, self.head_dim) # [b, s2, 2, h, d] + k, v = kv.unbind(dim=2) # [b, s, h, d] + q = self.q_norm(q) + k = self.k_norm(k) + + # Apply RoPE if needed + if freqs_cis_img is not None: + qq, _ = apply_rotary_emb(q, None, freqs_cis_img) + assert qq.shape == q.shape, f'qq: {qq.shape}, q: {q.shape}' + q = qq + + q = q * self.scale + q = q.transpose(-2, -3).contiguous() # q -> B, L1, H, C - B, H, L1, C + k = k.permute(0, 2, 3, 1).contiguous() # k -> B, L2, H, C - B, H, C, L2 + attn = q @ k # attn -> B, H, L1, L2 + attn = attn.softmax(dim=-1) # attn -> B, H, L1, L2 + attn = self.attn_drop(attn) + x = attn @ v.transpose(-2, -3) # v -> B, L2, H, C - B, H, L2, C x-> B, H, L1, C + context = x.transpose(1, 2) # context -> B, H, L1, C - B, L1, H, C + + context = context.contiguous().view(b, s1, -1) + + out = self.out_proj(context) # context.reshape - B, L1, -1 + out = self.proj_drop(out) + + out_tuple = (out,) + + return out_tuple + + +class Attention(nn.Module): + """ + We rename some layer names to align with flash attention + """ + def __init__(self, dim, num_heads, qkv_bias=True, qk_norm=False, attn_drop=0., proj_drop=0., + norm_layer=nn.LayerNorm, + ): + super().__init__() + self.dim = dim + self.num_heads = num_heads + assert self.dim % num_heads == 0, 'dim should be divisible by num_heads' + self.head_dim = self.dim // num_heads + # This assertion is aligned with flash attention + assert self.head_dim % 8 == 0 and self.head_dim <= 128, "Only support head_dim <= 128 and divisible by 8" + self.scale = self.head_dim ** -0.5 + + # qkv --> Wqkv + self.Wqkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + # TODO: eps should be 1 / 65530 if using fp16 + self.q_norm = norm_layer(self.head_dim, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity() + self.k_norm = norm_layer(self.head_dim, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity() + self.attn_drop = nn.Dropout(attn_drop) + self.out_proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, x, freqs_cis_img=None): + B, N, C = x.shape + qkv = self.Wqkv(x).reshape(B, N, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4) # [3, b, h, s, d] + q, k, v = qkv.unbind(0) # [b, h, s, d] + q = self.q_norm(q) # [b, h, s, d] + k = self.k_norm(k) # [b, h, s, d] + + # Apply RoPE if needed + if freqs_cis_img is not None: + qq, kk = apply_rotary_emb(q, k, freqs_cis_img, head_first=True) + assert qq.shape == q.shape and kk.shape == k.shape, \ + f'qq: {qq.shape}, q: {q.shape}, kk: {kk.shape}, k: {k.shape}' + q, k = qq, kk + + q = q * self.scale + attn = q @ k.transpose(-2, -1) # [b, h, s, d] @ [b, h, d, s] + attn = attn.softmax(dim=-1) # [b, h, s, s] + attn = self.attn_drop(attn) + x = attn @ v # [b, h, s, d] + + x = x.transpose(1, 2).reshape(B, N, C) # [b, s, h, d] + x = self.out_proj(x) + x = self.proj_drop(x) + + out_tuple = (x,) + + return out_tuple diff --git a/custom_nodes/comfyui-hydit_min/hydit/modules/ema.py b/custom_nodes/comfyui-hydit_min/hydit/modules/ema.py new file mode 100644 index 000000000..acc31ee29 --- /dev/null +++ b/custom_nodes/comfyui-hydit_min/hydit/modules/ema.py @@ -0,0 +1,127 @@ +from collections import OrderedDict +from copy import deepcopy + +import torch +from deepspeed.utils import instrument_w_nvtx +from pathlib import Path + +def requires_grad(model, flag=True): + """ + Set requires_grad flag for all parameters in a model. + """ + for p in model.parameters(): + p.requires_grad = flag + + +class EMA(object): + def __init__(self, args, model, device, logger): + if args.ema_dtype == 'fp32': + self.warmup = args.ema_warmup + self.update_after_step = 0 + self.max_value = args.ema_decay if args.ema_decay is not None else 0.9999 + self.inv_gamma = 1.0 + self.power = args.ema_warmup_power if args.ema_warmup_power is not None else 2 / 3 + self.min_value = 0.0 + else: + self.warmup = args.ema_warmup + self.update_after_step = 0 + self.max_value = args.ema_decay if args.ema_decay is not None else 0.992 + self.inv_gamma = 1.0 + self.power = args.ema_warmup_power if args.ema_warmup_power is not None else 0.446249 + # 0.446249 == math.log(1 - 0.992) / math.log(50000) + self.min_value = 0.0 + + self.ema_reset_decay = args.ema_reset_decay + self.decay_steps = 0 + + if args.ema_dtype == 'none': + ema_dtype = 'fp16' if args.use_fp16 else 'fp32' + else: + ema_dtype = args.ema_dtype + + # 由于module.half()和module.float()会发生inplace类型修改，因此需要先copy后修改类型 + self.ema_model = deepcopy(model) + if ema_dtype == 'fp16': + self.ema_model = self.ema_model.half().to(device) + elif ema_dtype == 'fp32': + self.ema_model = self.ema_model.float().to(device) + else: + raise ValueError(f"Unknown EMA dtype {ema_dtype}.") + + requires_grad(self.ema_model, False) + + logger.info(f" Using EMA with date type {args.ema_dtype} " + f"(decay={args.ema_decay}, warmup={args.ema_warmup}, warmup_power={args.ema_warmup_power}, " + f"reset_decay={args.ema_reset_decay}).") + + def get_decay(self): + """ + @crowsonkb's notes on EMA Warmup: + If gamma=1 and power=1, implements a simple average. gamma=1, power=2/3 are good values for models you plan + to train for a million or more steps (reaches decay factor 0.999 at 31.6K steps, 0.9999 at 1M steps), + gamma=1, power=3/4 for models you plan to train for less (reaches decay factor 0.999 at 10K steps, 0.9999 + at 215.4k steps). + + @jarvizhang's notes on EMA max_value when enabling FP16: + If using FP16 for EMA, max_value=0.995 is better (Don't larger than 0.999, unless you know + what you are doing). This is because FP16 has less precision than FP32, so the EMA value can + be pushed out of the range of FP16. + + gamma=1, power=0.446249 are good values for models (reaches decay factor 0.99 at 30K steps, + 0.992 at 50K steps). + """ + if self.warmup: + step = max(0, self.decay_steps - self.update_after_step - 1) + value = 1 - (1 + step / self.inv_gamma) ** -self.power + + if step <= 0: + return 0.0 + + return max(self.min_value, min(value, self.max_value)) + else: + return self.max_value + + @torch.no_grad() + @instrument_w_nvtx + def update(self, model, step, decay=None): + """ + Step the EMA model towards the current model. + + Parameters + ---------- + model: nn.Module + The current model + step: int + The current training step. This is used to determine the decay factor. If you want to control + the decay, you can pass in a custom step instead. + For example, if you want to restart the EMA decay, you can pass in step=0 at start and increase + step by step. + decay: float + The decay factor. If None, will be determined by the current step. + """ + if decay is None: + if self.ema_reset_decay: + self.decay_steps += 1 + else: + self.decay_steps = step + decay = self.get_decay() + + ema_params = OrderedDict(self.ema_model.named_parameters()) + model_params = OrderedDict(model.named_parameters()) + for name, param in model_params.items(): + # TODO: Consider applying only to params that require_grad to avoid small numerical changes of pos_embed + ema_params[name].mul_(decay).add_(param.data, alpha=1 - decay) + + return None + + def state_dict(self, *args, **kwargs): + return self.ema_model.state_dict(*args, **kwargs) + + def load_state_dict(self, *args, **kwargs): + return self.ema_model.load_state_dict(*args, **kwargs) + + def train(self): + self.ema_model.train() + + def eval(self): + self.ema_model.eval() diff --git a/custom_nodes/comfyui-hydit_min/hydit/modules/embedders.py b/custom_nodes/comfyui-hydit_min/hydit/modules/embedders.py new file mode 100644 index 000000000..9fe08cba2 --- /dev/null +++ b/custom_nodes/comfyui-hydit_min/hydit/modules/embedders.py @@ -0,0 +1,111 @@ +import math +import torch +import torch.nn as nn +from einops import repeat + +from timm.models.layers import to_2tuple + + +class PatchEmbed(nn.Module): + """ 2D Image to Patch Embedding + + Image to Patch Embedding using Conv2d + + A convolution based approach to patchifying a 2D image w/ embedding projection. + + Based on the impl in https://github.com/google-research/vision_transformer + + Hacked together by / Copyright 2020 Ross Wightman + + Remove the _assert function in forward function to be compatible with multi-resolution images. + """ + def __init__( + self, + img_size=224, + patch_size=16, + in_chans=3, + embed_dim=768, + norm_layer=None, + flatten=True, + bias=True, + ): + super().__init__() + if isinstance(img_size, int): + img_size = to_2tuple(img_size) + elif isinstance(img_size, (tuple, list)) and len(img_size) == 2: + img_size = tuple(img_size) + else: + raise ValueError(f"img_size must be int or tuple/list of length 2. Got {img_size}") + patch_size = to_2tuple(patch_size) + self.img_size = img_size + self.patch_size = patch_size + self.grid_size = (img_size[0] // patch_size[0], img_size[1] // patch_size[1]) + self.num_patches = self.grid_size[0] * self.grid_size[1] + self.flatten = flatten + + self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias) + self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity() + + def update_image_size(self, img_size): + self.img_size = img_size + self.grid_size = (img_size[0] // self.patch_size[0], img_size[1] // self.patch_size[1]) + self.num_patches = self.grid_size[0] * self.grid_size[1] + + def forward(self, x): + # B, C, H, W = x.shape + # _assert(H == self.img_size[0], f"Input image height ({H}) doesn't match model ({self.img_size[0]}).") + # _assert(W == self.img_size[1], f"Input image width ({W}) doesn't match model ({self.img_size[1]}).") + x = self.proj(x) + if self.flatten: + x = x.flatten(2).transpose(1, 2) # BCHW -> BNC + x = self.norm(x) + return x + + +def timestep_embedding(t, dim, max_period=10000, repeat_only=False): + """ + Create sinusoidal timestep embeddings. + :param t: a 1-D Tensor of N indices, one per batch element. + These may be fractional. + :param dim: the dimension of the output. + :param max_period: controls the minimum frequency of the embeddings. + :return: an (N, D) Tensor of positional embeddings. + """ + # https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py + if not repeat_only: + half = dim // 2 + freqs = torch.exp( + -math.log(max_period) + * torch.arange(start=0, end=half, dtype=torch.float32) + / half + ).to(device=t.device) # size: [dim/2], 一个指数衰减的曲线 + args = t[:, None].float() * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat( + [embedding, torch.zeros_like(embedding[:, :1])], dim=-1 + ) + else: + embedding = repeat(t, "b -> b d", d=dim) + return embedding + + +class TimestepEmbedder(nn.Module): + """ + Embeds scalar timesteps into vector representations. + """ + def __init__(self, hidden_size, frequency_embedding_size=256, out_size=None): + super().__init__() + if out_size is None: + out_size = hidden_size + self.mlp = nn.Sequential( + nn.Linear(frequency_embedding_size, hidden_size, bias=True), + nn.SiLU(), + nn.Linear(hidden_size, out_size, bias=True), + ) + self.frequency_embedding_size = frequency_embedding_size + + def forward(self, t): + t_freq = timestep_embedding(t, self.frequency_embedding_size).type(self.mlp[0].weight.dtype) + t_emb = self.mlp(t_freq) + return t_emb diff --git a/custom_nodes/comfyui-hydit_min/hydit/modules/fp16_layers.py b/custom_nodes/comfyui-hydit_min/hydit/modules/fp16_layers.py new file mode 100644 index 000000000..681281104 --- /dev/null +++ b/custom_nodes/comfyui-hydit_min/hydit/modules/fp16_layers.py @@ -0,0 +1,86 @@ +import torch +from torch.autograd import Variable +from torch.nn.parameter import Parameter + + +_FLOAT_TYPES = (torch.FloatTensor, torch.cuda.FloatTensor) +_HALF_TYPES = (torch.HalfTensor, torch.cuda.HalfTensor) + + +def conversion_helper(val, conversion): + """Apply conversion to val. Recursively apply conversion if `val` + #is a nested tuple/list structure.""" + if isinstance(val, dict): + res_dict = {} + for k, v in val.items(): + if k!= 'cos_cis_img' and k != 'sin_cis_img': + res_dict[k] = conversion_helper(v, conversion) + else: + res_dict[k] = v + return res_dict + if not isinstance(val, (tuple, list)): + return conversion(val) + rtn = [conversion_helper(v, conversion) for v in val] + if isinstance(val, tuple): + rtn = tuple(rtn) + return rtn + + +def fp32_to_float16(val, float16_convertor): + """Convert fp32 `val` to fp16/bf16""" + def half_conversion(val): + val_typecheck = val + if isinstance(val_typecheck, (Parameter, Variable)): + val_typecheck = val.data + if isinstance(val_typecheck, _FLOAT_TYPES): + val = float16_convertor(val) + return val + return conversion_helper(val, half_conversion) + + +def float16_to_fp32(val): + """Convert fp16/bf16 `val` to fp32""" + def float_conversion(val): + val_typecheck = val + if isinstance(val_typecheck, (Parameter, Variable)): + val_typecheck = val.data + if isinstance(val_typecheck, (_HALF_TYPES,)): + val = val.float() + return val + return conversion_helper(val, float_conversion) + + +class Float16Module(torch.nn.Module): + + def __init__(self, module, args): + super(Float16Module, self).__init__() + + self.add_module('module', module.half()) + + def float16_convertor(val): + return val.half() + + self.float16_convertor = float16_convertor + + self.config = self.module.config + self.dtype = torch.float16 + + def forward(self, *inputs, **kwargs): + inputs = fp32_to_float16(inputs, self.float16_convertor) + kwargs = fp32_to_float16(kwargs, self.float16_convertor) + outputs = self.module(*inputs, **kwargs) + outputs = float16_to_fp32(outputs) + return outputs + + def state_dict(self, destination=None, prefix='', keep_vars=False): + return self.module.state_dict(destination, prefix, keep_vars) + + + def state_dict_for_save_checkpoint(self, destination=None, prefix='', + keep_vars=False): + return self.module.state_dict_for_save_checkpoint(destination, prefix, + keep_vars) + + def load_state_dict(self, state_dict, strict=True): + self.module.load_state_dict(state_dict, strict=strict) + diff --git a/custom_nodes/comfyui-hydit_min/hydit/modules/models.py b/custom_nodes/comfyui-hydit_min/hydit/modules/models.py new file mode 100644 index 000000000..688c01e31 --- /dev/null +++ b/custom_nodes/comfyui-hydit_min/hydit/modules/models.py @@ -0,0 +1,556 @@ +from typing import Any + +import torch +import torch.nn as nn +import torch.nn.functional as F +#from diffusers.configuration_utils import ConfigMixin, register_to_config +#from diffusers.models import ModelMixin +#from peft.utils import ( +# ModulesToSaveWrapper, +# _get_submodules, +#) +from timm.models.vision_transformer import Mlp +from torch.utils import checkpoint +from tqdm import tqdm +#from transformers.integrations import PeftAdapterMixin + +from .attn_layers import Attention, CrossAttention +from .embedders import TimestepEmbedder, PatchEmbed, timestep_embedding +from .norm_layers import RMSNorm +from .poolers import AttentionPool +#from .posemb_layers import get_2d_rotary_pos_embed, get_fill_resize_and_crop + + +def modulate(x, shift, scale): + return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) + + +class FP32_Layernorm(nn.LayerNorm): + def forward(self, inputs: torch.Tensor) -> torch.Tensor: + origin_dtype = inputs.dtype + return F.layer_norm(inputs.float(), self.normalized_shape, self.weight.float(), self.bias.float(), + self.eps).to(origin_dtype) + + +class FP32_SiLU(nn.SiLU): + def forward(self, inputs: torch.Tensor) -> torch.Tensor: + return torch.nn.functional.silu(inputs.float(), inplace=False).to(inputs.dtype) + + +class HunYuanDiTBlock(nn.Module): + """ + A HunYuanDiT block with `add` conditioning. + """ + def __init__(self, + hidden_size, + c_emb_size, + num_heads, + mlp_ratio=4.0, + text_states_dim=1024, + use_flash_attn=False, + qk_norm=False, + norm_type="layer", + skip=False, + ): + super().__init__() + self.use_flash_attn = use_flash_attn + use_ele_affine = True + + if norm_type == "layer": + norm_layer = FP32_Layernorm + elif norm_type == "rms": + norm_layer = RMSNorm + else: + raise ValueError(f"Unknown norm_type: {norm_type}") + + # ========================= Self-Attention ========================= + self.norm1 = norm_layer(hidden_size, elementwise_affine=use_ele_affine, eps=1e-6) + if use_flash_attn: + self.attn1 = FlashSelfMHAModified(hidden_size, num_heads=num_heads, qkv_bias=True, qk_norm=qk_norm) + else: + self.attn1 = Attention(hidden_size, num_heads=num_heads, qkv_bias=True, qk_norm=qk_norm) + + # ========================= FFN ========================= + self.norm2 = norm_layer(hidden_size, elementwise_affine=use_ele_affine, eps=1e-6) + mlp_hidden_dim = int(hidden_size * mlp_ratio) + approx_gelu = lambda: nn.GELU(approximate="tanh") + self.mlp = Mlp(in_features=hidden_size, hidden_features=mlp_hidden_dim, act_layer=approx_gelu, drop=0) + + # ========================= Add ========================= + # Simply use add like SDXL. + self.default_modulation = nn.Sequential( + FP32_SiLU(), + nn.Linear(c_emb_size, hidden_size, bias=True) + ) + + # ========================= Cross-Attention ========================= + if use_flash_attn: + self.attn2 = FlashCrossMHAModified(hidden_size, text_states_dim, num_heads=num_heads, qkv_bias=True, + qk_norm=qk_norm) + else: + self.attn2 = CrossAttention(hidden_size, text_states_dim, num_heads=num_heads, qkv_bias=True, + qk_norm=qk_norm) + self.norm3 = norm_layer(hidden_size, elementwise_affine=True, eps=1e-6) + + # ========================= Skip Connection ========================= + if skip: + self.skip_norm = norm_layer(2 * hidden_size, elementwise_affine=True, eps=1e-6) + self.skip_linear = nn.Linear(2 * hidden_size, hidden_size) + else: + self.skip_linear = None + + self.gradient_checkpointing = False + + def _forward(self, x, c=None, text_states=None, freq_cis_img=None, skip=None): + # Long Skip Connection + if self.skip_linear is not None: + cat = torch.cat([x, skip], dim=-1) + cat = self.skip_norm(cat) + x = self.skip_linear(cat) + + # Self-Attention + shift_msa = self.default_modulation(c).unsqueeze(dim=1) + attn_inputs = ( + self.norm1(x) + shift_msa, freq_cis_img, + ) + x = x + self.attn1(*attn_inputs)[0] + + # Cross-Attention + cross_inputs = ( + self.norm3(x), text_states, freq_cis_img + ) + x = x + self.attn2(*cross_inputs)[0] + + # FFN Layer + mlp_inputs = self.norm2(x) + x = x + self.mlp(mlp_inputs) + + return x + + def forward(self, x, c=None, text_states=None, freq_cis_img=None, skip=None): + if self.gradient_checkpointing and self.training: + return checkpoint.checkpoint(self._forward, x, c, text_states, freq_cis_img, skip) + return self._forward(x, c, text_states, freq_cis_img, skip) + + +class FinalLayer(nn.Module): + """ + The final layer of HunYuanDiT. + """ + def __init__(self, final_hidden_size, c_emb_size, patch_size, out_channels): + super().__init__() + self.norm_final = nn.LayerNorm(final_hidden_size, elementwise_affine=False, eps=1e-6) + self.linear = nn.Linear(final_hidden_size, patch_size * patch_size * out_channels, bias=True) + self.adaLN_modulation = nn.Sequential( + FP32_SiLU(), + nn.Linear(c_emb_size, 2 * final_hidden_size, bias=True) + ) + + def forward(self, x, c): + shift, scale = self.adaLN_modulation(c).chunk(2, dim=1) + x = modulate(self.norm_final(x), shift, scale) + x = self.linear(x) + return x + + +class HunYuanDiT(nn.Module): + """ + HunYuanDiT: Diffusion model with a Transformer backbone. + + Inherit ModelMixin and ConfigMixin to be compatible with the sampler StableDiffusionPipeline of diffusers. + + Inherit PeftAdapterMixin to be compatible with the PEFT training pipeline. + + Parameters + ---------- + args: argparse.Namespace + The arguments parsed by argparse. + input_size: tuple + The size of the input image. + patch_size: int + The size of the patch. + in_channels: int + The number of input channels. + hidden_size: int + The hidden size of the transformer backbone. + depth: int + The number of transformer blocks. + num_heads: int + The number of attention heads. + mlp_ratio: float + The ratio of the hidden size of the MLP in the transformer block. + log_fn: callable + The logging function. + """ + #@register_to_config + def __init__(self, + args: Any, + input_size: tuple = (32, 32), + patch_size: int = 2, + in_channels: int = 4, + hidden_size: int = 1152, + depth: int = 28, + num_heads: int = 16, + mlp_ratio: float = 4.0, + log_fn: callable = print, + **kwargs, + ): + super().__init__() + self.args = args + self.log_fn = log_fn + self.depth = depth + self.learn_sigma = args.learn_sigma + self.in_channels = in_channels + self.out_channels = in_channels * 2 if args.learn_sigma else in_channels + self.patch_size = patch_size + self.num_heads = num_heads + self.hidden_size = hidden_size + self.text_states_dim = args.text_states_dim + self.text_states_dim_t5 = args.text_states_dim_t5 + self.text_len = args.text_len + self.text_len_t5 = args.text_len_t5 + self.norm = args.norm + self.dtype = torch.float16 + #import pdb + #pdb.set_trace() + + use_flash_attn = args.infer_mode == 'fa' or args.use_flash_attn + if use_flash_attn: + log_fn(f" Enable Flash Attention.") + qk_norm = args.qk_norm # See http://arxiv.org/abs/2302.05442 for details. + + self.mlp_t5 = nn.Sequential( + nn.Linear(self.text_states_dim_t5, self.text_states_dim_t5 * 4, bias=True), + FP32_SiLU(), + nn.Linear(self.text_states_dim_t5 * 4, self.text_states_dim, bias=True), + ) + # learnable replace + self.text_embedding_padding = nn.Parameter( + torch.randn(self.text_len + self.text_len_t5, self.text_states_dim, dtype=torch.float32)) + + # Attention pooling + pooler_out_dim = 1024 + self.pooler = AttentionPool(self.text_len_t5, self.text_states_dim_t5, num_heads=8, output_dim=pooler_out_dim) + + # Dimension of the extra input vectors + self.extra_in_dim = pooler_out_dim + + if args.size_cond: + # Image size and crop size conditions + self.extra_in_dim += 6 * 256 + + if args.use_style_cond: + # Here we use a default learned embedder layer for future extension. + self.style_embedder = nn.Embedding(1, hidden_size) + self.extra_in_dim += hidden_size + + # Text embedding for `add` + self.x_embedder = PatchEmbed(input_size, patch_size, in_channels, hidden_size) + self.t_embedder = TimestepEmbedder(hidden_size) + self.extra_embedder = nn.Sequential( + nn.Linear(self.extra_in_dim, hidden_size * 4), + FP32_SiLU(), + nn.Linear(hidden_size * 4, hidden_size, bias=True), + ) + + # Image embedding + num_patches = self.x_embedder.num_patches + log_fn(f" Number of tokens: {num_patches}") + + # HUnYuanDiT Blocks + self.blocks = nn.ModuleList([ + HunYuanDiTBlock(hidden_size=hidden_size, + c_emb_size=hidden_size, + num_heads=num_heads, + mlp_ratio=mlp_ratio, + text_states_dim=self.text_states_dim, + use_flash_attn=use_flash_attn, + qk_norm=qk_norm, + norm_type=self.norm, + skip=layer > depth // 2, + ) + for layer in range(depth) + ]) + + self.final_layer = FinalLayer(hidden_size, hidden_size, patch_size, self.out_channels) + self.unpatchify_channels = self.out_channels + + self.initialize_weights() + + def check_condition_validation(self, image_meta_size, style): + if self.args.size_cond is None and image_meta_size is not None: + raise ValueError(f"When `size_cond` is None, `image_meta_size` should be None, but got " + f"{type(image_meta_size)}. ") + if self.args.size_cond is not None and image_meta_size is None: + raise ValueError(f"When `size_cond` is not None, `image_meta_size` should not be None. ") + if not self.args.use_style_cond and style is not None: + raise ValueError(f"When `use_style_cond` is False, `style` should be None, but got {type(style)}. ") + if self.args.use_style_cond and style is None: + raise ValueError(f"When `use_style_cond` is True, `style` should be not None.") + + def enable_gradient_checkpointing(self): + for block in self.blocks: + block.gradient_checkpointing = True + + def disable_gradient_checkpointing(self): + for block in self.blocks: + block.gradient_checkpointing = False + + def forward(self, + x, + t, + encoder_hidden_states=None, + text_embedding_mask=None, + encoder_hidden_states_t5=None, + text_embedding_mask_t5=None, + image_meta_size=None, + style=None, + cos_cis_img=None, + sin_cis_img=None, + return_dict=True, + controls=None, + ): + """ + Forward pass of the encoder. + + Parameters + ---------- + x: torch.Tensor + (B, D, H, W) + t: torch.Tensor + (B) + encoder_hidden_states: torch.Tensor + CLIP text embedding, (B, L_clip, D) + text_embedding_mask: torch.Tensor + CLIP text embedding mask, (B, L_clip) + encoder_hidden_states_t5: torch.Tensor + T5 text embedding, (B, L_t5, D) + text_embedding_mask_t5: torch.Tensor + T5 text embedding mask, (B, L_t5) + image_meta_size: torch.Tensor + (B, 6) + style: torch.Tensor + (B) + cos_cis_img: torch.Tensor + sin_cis_img: torch.Tensor + return_dict: bool + Whether to return a dictionary. + """ + #import pdb + #pdb.set_trace() + text_states = encoder_hidden_states # 2,77,1024 + text_states_t5 = encoder_hidden_states_t5 # 2,256,2048 + text_states_mask = text_embedding_mask.bool() # 2,77 + text_states_t5_mask = text_embedding_mask_t5.bool() # 2,256 + b_t5, l_t5, c_t5 = text_states_t5.shape + text_states_t5 = self.mlp_t5(text_states_t5.view(-1, c_t5)) + text_states = torch.cat([text_states, text_states_t5.view(b_t5, l_t5, -1)], dim=1) # 2,205，1024 + clip_t5_mask = torch.cat([text_states_mask, text_states_t5_mask], dim=-1) + + clip_t5_mask = clip_t5_mask + text_states = torch.where(clip_t5_mask.unsqueeze(2), text_states, self.text_embedding_padding.to(text_states)) + + _, _, oh, ow = x.shape + th, tw = oh // self.patch_size, ow // self.patch_size + + # ========================= Build time and image embedding ========================= + t = self.t_embedder(t) + x = self.x_embedder(x) + + # Get image RoPE embedding according to `reso`lution. + freqs_cis_img = (cos_cis_img, sin_cis_img) + + # ========================= Concatenate all extra vectors ========================= + # Build text tokens with pooling + extra_vec = self.pooler(encoder_hidden_states_t5) + + self.check_condition_validation(image_meta_size, style) + # Build image meta size tokens if applicable + if image_meta_size is not None: + image_meta_size = timestep_embedding(image_meta_size.view(-1), 256) # [B * 6, 256] + if self.args.use_fp16: + image_meta_size = image_meta_size.half() + image_meta_size = image_meta_size.view(-1, 6 * 256) + extra_vec = torch.cat([extra_vec, image_meta_size], dim=1) # [B, D + 6 * 256] + + # Build style tokens + if style is not None: + style_embedding = self.style_embedder(style) + extra_vec = torch.cat([extra_vec, style_embedding], dim=1) + + # Concatenate all extra vectors + c = t + self.extra_embedder(extra_vec) # [B, D] + + # ========================= Forward pass through HunYuanDiT blocks ========================= + skips = [] + for layer, block in enumerate(self.blocks): + if layer > self.depth // 2: + if controls is not None: + skip = skips.pop() + controls.pop() + else: + skip = skips.pop() + x = block(x, c, text_states, freqs_cis_img, skip) # (N, L, D) + else: + x = block(x, c, text_states, freqs_cis_img) # (N, L, D) + + if layer < (self.depth // 2 - 1): + skips.append(x) + if controls is not None and len(controls) != 0: + raise ValueError("The number of controls is not equal to the number of skip connections.") + + # ========================= Final layer ========================= + x = self.final_layer(x, c) # (N, L, patch_size ** 2 * out_channels) + x = self.unpatchify(x, th, tw) # (N, out_channels, H, W) + + if return_dict: + return {'x': x} + return x + + + def initialize_weights(self): + # Initialize transformer layers: + def _basic_init(module): + if isinstance(module, nn.Linear): + torch.nn.init.xavier_uniform_(module.weight) + if module.bias is not None: + nn.init.constant_(module.bias, 0) + self.apply(_basic_init) + + # Initialize patch_embed like nn.Linear (instead of nn.Conv2d): + w = self.x_embedder.proj.weight.data + nn.init.xavier_uniform_(w.view([w.shape[0], -1])) + nn.init.constant_(self.x_embedder.proj.bias, 0) + + # Initialize label embedding table: + nn.init.normal_(self.extra_embedder[0].weight, std=0.02) + nn.init.normal_(self.extra_embedder[2].weight, std=0.02) + + # Initialize timestep embedding MLP: + nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02) + nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02) + + # Zero-out adaLN modulation layers in HunYuanDiT blocks: + for block in self.blocks: + nn.init.constant_(block.default_modulation[-1].weight, 0) + nn.init.constant_(block.default_modulation[-1].bias, 0) + + # Zero-out output layers: + nn.init.constant_(self.final_layer.adaLN_modulation[-1].weight, 0) + nn.init.constant_(self.final_layer.adaLN_modulation[-1].bias, 0) + nn.init.constant_(self.final_layer.linear.weight, 0) + nn.init.constant_(self.final_layer.linear.bias, 0) + + def unpatchify(self, x, h, w): + """ + x: (N, T, patch_size**2 * C) + imgs: (N, H, W, C) + """ + c = self.unpatchify_channels + p = self.x_embedder.patch_size[0] + # h = w = int(x.shape[1] ** 0.5) + assert h * w == x.shape[1] + + x = x.reshape(shape=(x.shape[0], h, w, p, p, c)) + x = torch.einsum('nhwpqc->nchpwq', x) + imgs = x.reshape(shape=(x.shape[0], c, h * p, w * p)) + return imgs + + def _replace_module(self, parent, child_name, new_module, child) -> None: + setattr(parent, child_name, new_module) + # It's not necessary to set requires_grad here, as that is handled by + # _mark_only_adapters_as_trainable + + # child layer wraps the original module, unpack it + if hasattr(child, "base_layer"): + child = child.get_base_layer() + elif hasattr(child, "quant_linear_module"): + # TODO maybe not necessary to have special treatment? + child = child.quant_linear_module + + if not hasattr(new_module, "base_layer"): + new_module.weight = child.weight + if hasattr(child, "bias"): + new_module.bias = child.bias + + if getattr(child, "state", None) is not None: + if hasattr(new_module, "base_layer"): + new_module.base_layer.state = child.state + else: + new_module.state = child.state + new_module.to(child.weight.device) + + # dispatch to correct device + for name, module in new_module.named_modules(): + # if any(prefix in name for prefix in PREFIXES): + # module.to(child.weight.device) + if "ranknum" in name: + module.to(child.weight.device) + + def merge_and_unload(self, + merge=True, + progressbar: bool = False, + safe_merge: bool = False, + adapter_names = None,): + if merge: + if getattr(self, "quantization_method", None) == "gptq": + raise ValueError("Cannot merge layers when the model is gptq quantized") + + def merge_recursively(module): + # helper function to recursively merge the base_layer of the target + path = [] + layer = module + while hasattr(layer, "base_layer"): + path.append(layer) + layer = layer.base_layer + for layer_before, layer_after in zip(path[:-1], path[1:]): + layer_after.merge(safe_merge=safe_merge, adapter_names=adapter_names) + layer_before.base_layer = layer_after.base_layer + module.merge(safe_merge=safe_merge, adapter_names=adapter_names) + + key_list = [key for key, _ in self.named_modules()] + desc = "Unloading " + ("and merging " if merge else "") + "model" + + for key in tqdm(key_list, disable=not progressbar, desc=desc): + try: + parent, target, target_name = _get_submodules(self, key) + except AttributeError: + continue + + if hasattr(target, "base_layer"): + if merge: + merge_recursively(target) + self._replace_module(parent, target_name, target.get_base_layer(), target) + elif isinstance(target, ModulesToSaveWrapper): + # save any additional trainable modules part of `modules_to_save` + new_module = target.modules_to_save[target.active_adapter] + if hasattr(new_module, "base_layer"): + # check if the module is itself a tuner layer + if merge: + new_module.merge(safe_merge=safe_merge, adapter_names=adapter_names) + new_module = new_module.get_base_layer() + setattr(parent, target_name, new_module) + + + +################################################################################# +# HunYuanDiT Configs # +################################################################################# + +HUNYUAN_DIT_CONFIG = { + 'DiT-g/2': {'depth': 40, 'hidden_size': 1408, 'patch_size': 2, 'num_heads': 16, 'mlp_ratio': 4.3637}, + 'DiT-XL/2': {'depth': 28, 'hidden_size': 1152, 'patch_size': 2, 'num_heads': 16}, +} + + +def DiT_g_2(args, **kwargs): + return HunYuanDiT(args, depth=40, hidden_size=1408, patch_size=2, num_heads=16, mlp_ratio=4.3637, **kwargs) + + +def DiT_XL_2(args, **kwargs): + return HunYuanDiT(args, depth=28, hidden_size=1152, patch_size=2, num_heads=16, **kwargs) + + +HUNYUAN_DIT_MODELS = { + 'DiT-g/2': DiT_g_2, + 'DiT-XL/2': DiT_XL_2, +} \ No newline at end of file diff --git a/custom_nodes/comfyui-hydit_min/hydit/modules/norm_layers.py b/custom_nodes/comfyui-hydit_min/hydit/modules/norm_layers.py new file mode 100644 index 000000000..5204ad9e5 --- /dev/null +++ b/custom_nodes/comfyui-hydit_min/hydit/modules/norm_layers.py @@ -0,0 +1,68 @@ +import torch +import torch.nn as nn + + +class RMSNorm(nn.Module): + def __init__(self, dim: int, elementwise_affine=True, eps: float = 1e-6): + """ + Initialize the RMSNorm normalization layer. + + Args: + dim (int): The dimension of the input tensor. + eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6. + + Attributes: + eps (float): A small value added to the denominator for numerical stability. + weight (nn.Parameter): Learnable scaling parameter. + + """ + super().__init__() + self.eps = eps + if elementwise_affine: + self.weight = nn.Parameter(torch.ones(dim)) + + def _norm(self, x): + """ + Apply the RMSNorm normalization to the input tensor. + + Args: + x (torch.Tensor): The input tensor. + + Returns: + torch.Tensor: The normalized tensor. + + """ + return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) + + def forward(self, x): + """ + Forward pass through the RMSNorm layer. + + Args: + x (torch.Tensor): The input tensor. + + Returns: + torch.Tensor: The output tensor after applying RMSNorm. + + """ + output = self._norm(x.float()).type_as(x) + if hasattr(self, "weight"): + output = output * self.weight + return output + + +class GroupNorm32(nn.GroupNorm): + def __init__(self, num_groups, num_channels, eps=1e-5, dtype=None): + super().__init__(num_groups=num_groups, num_channels=num_channels, eps=eps, dtype=dtype) + + def forward(self, x): + y = super().forward(x).to(x.dtype) + return y + +def normalization(channels, dtype=None): + """ + Make a standard normalization layer. + :param channels: number of input channels. + :return: an nn.Module for normalization. + """ + return GroupNorm32(num_channels=channels, num_groups=32, dtype=dtype) diff --git a/custom_nodes/comfyui-hydit_min/hydit/modules/poolers.py b/custom_nodes/comfyui-hydit_min/hydit/modules/poolers.py new file mode 100644 index 000000000..a4adcaca5 --- /dev/null +++ b/custom_nodes/comfyui-hydit_min/hydit/modules/poolers.py @@ -0,0 +1,39 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class AttentionPool(nn.Module): + def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None): + super().__init__() + self.positional_embedding = nn.Parameter(torch.randn(spacial_dim + 1, embed_dim) / embed_dim ** 0.5) + self.k_proj = nn.Linear(embed_dim, embed_dim) + self.q_proj = nn.Linear(embed_dim, embed_dim) + self.v_proj = nn.Linear(embed_dim, embed_dim) + self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim) + self.num_heads = num_heads + + def forward(self, x): + x = x.permute(1, 0, 2) # NLC -> LNC + x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0) # (L+1)NC + x = x + self.positional_embedding[:, None, :].to(x.dtype) # (L+1)NC + x, _ = F.multi_head_attention_forward( + query=x[:1], key=x, value=x, + embed_dim_to_check=x.shape[-1], + num_heads=self.num_heads, + q_proj_weight=self.q_proj.weight, + k_proj_weight=self.k_proj.weight, + v_proj_weight=self.v_proj.weight, + in_proj_weight=None, + in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]), + bias_k=None, + bias_v=None, + add_zero_attn=False, + dropout_p=0, + out_proj_weight=self.c_proj.weight, + out_proj_bias=self.c_proj.bias, + use_separate_proj_weight=True, + training=self.training, + need_weights=False + ) + return x.squeeze(0) diff --git a/custom_nodes/comfyui-hydit_min/hydit/modules/posemb_layers.py b/custom_nodes/comfyui-hydit_min/hydit/modules/posemb_layers.py new file mode 100644 index 000000000..dcb41a713 --- /dev/null +++ b/custom_nodes/comfyui-hydit_min/hydit/modules/posemb_layers.py @@ -0,0 +1,224 @@ +import torch +import numpy as np +from typing import Union + + +def _to_tuple(x): + if isinstance(x, int): + return x, x + else: + return x + + +def get_fill_resize_and_crop(src, tgt): + th, tw = _to_tuple(tgt) + h, w = _to_tuple(src) + + tr = th / tw # base resolution + r = h / w # target resolution + + # resize + if r > tr: + resize_height = th + resize_width = int(round(th / h * w)) + else: + resize_width = tw + resize_height = int(round(tw / w * h)) # resize the target resolution down based on the base resolution + + crop_top = int(round((th - resize_height) / 2.0)) + crop_left = int(round((tw - resize_width) / 2.0)) + + return (crop_top, crop_left), (crop_top + resize_height, crop_left + resize_width) + + +def get_meshgrid(start, *args): + if len(args) == 0: + # start is grid_size + num = _to_tuple(start) + start = (0, 0) + stop = num + elif len(args) == 1: + # start is start, args[0] is stop, step is 1 + start = _to_tuple(start) + stop = _to_tuple(args[0]) + num = (stop[0] - start[0], stop[1] - start[1]) + elif len(args) == 2: + # start is start, args[0] is stop, args[1] is num + start = _to_tuple(start) + stop = _to_tuple(args[0]) + num = _to_tuple(args[1]) + else: + raise ValueError(f"len(args) should be 0, 1 or 2, but got {len(args)}") + + grid_h = np.linspace(start[0], stop[0], num[0], endpoint=False, dtype=np.float32) + grid_w = np.linspace(start[1], stop[1], num[1], endpoint=False, dtype=np.float32) + grid = np.meshgrid(grid_w, grid_h) # here w goes first + grid = np.stack(grid, axis=0) # [2, W, H] + return grid + +################################################################################# +# Sine/Cosine Positional Embedding Functions # +################################################################################# +# https://github.com/facebookresearch/mae/blob/main/util/pos_embed.py + +def get_2d_sincos_pos_embed(embed_dim, start, *args, cls_token=False, extra_tokens=0): + """ + grid_size: int of the grid height and width + return: + pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token) + """ + grid = get_meshgrid(start, *args) # [2, H, w] + # grid_h = np.arange(grid_size, dtype=np.float32) + # grid_w = np.arange(grid_size, dtype=np.float32) + # grid = np.meshgrid(grid_w, grid_h) # here w goes first + # grid = np.stack(grid, axis=0) # [2, W, H] + + grid = grid.reshape([2, 1, *grid.shape[1:]]) + pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid) + if cls_token and extra_tokens > 0: + pos_embed = np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0) + return pos_embed + + +def get_2d_sincos_pos_embed_from_grid(embed_dim, grid): + assert embed_dim % 2 == 0 + + # use half of dimensions to encode grid_h + emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2) + emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2) + + emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D) + return emb + + +def get_1d_sincos_pos_embed_from_grid(embed_dim, pos): + """ + embed_dim: output dimension for each position + pos: a list of positions to be encoded: size (W,H) + out: (M, D) + """ + assert embed_dim % 2 == 0 + omega = np.arange(embed_dim // 2, dtype=np.float64) + omega /= embed_dim / 2. + omega = 1. / 10000**omega # (D/2,) + + pos = pos.reshape(-1) # (M,) + out = np.einsum('m,d->md', pos, omega) # (M, D/2), outer product + + emb_sin = np.sin(out) # (M, D/2) + emb_cos = np.cos(out) # (M, D/2) + + emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D) + return emb + + +################################################################################# +# Rotary Positional Embedding Functions # +################################################################################# +# https://github.com/facebookresearch/llama/blob/main/llama/model.py#L443 + +def get_2d_rotary_pos_embed(embed_dim, start, *args, use_real=True): + """ + This is a 2d version of precompute_freqs_cis, which is a RoPE for image tokens with 2d structure. + + Parameters + ---------- + embed_dim: int + embedding dimension size + start: int or tuple of int + If len(args) == 0, start is num; If len(args) == 1, start is start, args[0] is stop, step is 1; + If len(args) == 2, start is start, args[0] is stop, args[1] is num. + use_real: bool + If True, return real part and imaginary part separately. Otherwise, return complex numbers. + + Returns + ------- + pos_embed: torch.Tensor + [HW, D/2] + """ + grid = get_meshgrid(start, *args) # [2, H, w] + grid = grid.reshape([2, 1, *grid.shape[1:]]) # Returns a sampling matrix with the same resolution as the target resolution + pos_embed = get_2d_rotary_pos_embed_from_grid(embed_dim, grid, use_real=use_real) + return pos_embed + + +def get_2d_rotary_pos_embed_from_grid(embed_dim, grid, use_real=False): + assert embed_dim % 4 == 0 + + # use half of dimensions to encode grid_h + emb_h = get_1d_rotary_pos_embed(embed_dim // 2, grid[0].reshape(-1), use_real=use_real) # (H*W, D/4) + emb_w = get_1d_rotary_pos_embed(embed_dim // 2, grid[1].reshape(-1), use_real=use_real) # (H*W, D/4) + + if use_real: + cos = torch.cat([emb_h[0], emb_w[0]], dim=1) # (H*W, D/2) + sin = torch.cat([emb_h[1], emb_w[1]], dim=1) # (H*W, D/2) + return cos, sin + else: + emb = torch.cat([emb_h, emb_w], dim=1) # (H*W, D/2) + return emb + + +def get_1d_rotary_pos_embed(dim: int, pos: Union[np.ndarray, int], theta: float = 10000.0, use_real=False): + """ + Precompute the frequency tensor for complex exponentials (cis) with given dimensions. + + This function calculates a frequency tensor with complex exponentials using the given dimension 'dim' + and the end index 'end'. The 'theta' parameter scales the frequencies. + The returned tensor contains complex values in complex64 data type. + + Args: + dim (int): Dimension of the frequency tensor. + pos (np.ndarray, int): Position indices for the frequency tensor. [S] or scalar + theta (float, optional): Scaling factor for frequency computation. Defaults to 10000.0. + use_real (bool, optional): If True, return real part and imaginary part separately. + Otherwise, return complex numbers. + + Returns: + torch.Tensor: Precomputed frequency tensor with complex exponentials. [S, D/2] + + """ + if isinstance(pos, int): + pos = np.arange(pos) + freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim)) # [D/2] + t = torch.from_numpy(pos).to(freqs.device) # type: ignore # [S] + freqs = torch.outer(t, freqs).float() # type: ignore # [S, D/2] + if use_real: + freqs_cos = freqs.cos().repeat_interleave(2, dim=1) # [S, D] + freqs_sin = freqs.sin().repeat_interleave(2, dim=1) # [S, D] + return freqs_cos, freqs_sin + else: + freqs_cis = torch.polar(torch.ones_like(freqs), freqs) # complex64 # [S, D/2] + return freqs_cis + + + +def calc_sizes(rope_img, patch_size, th, tw): + if rope_img == 'extend': + # Expansion mode + sub_args = [(th, tw)] + elif rope_img.startswith('base'): + # Based on the specified dimensions, other dimensions are obtained through interpolation. + base_size = int(rope_img[4:]) // 8 // patch_size + start, stop = get_fill_resize_and_crop((th, tw), base_size) + sub_args = [start, stop, (th, tw)] + else: + raise ValueError(f"Unknown rope_img: {rope_img}") + return sub_args + + +def init_image_posemb(rope_img, + resolutions, + patch_size, + hidden_size, + num_heads, + log_fn, + rope_real=True, + ): + freqs_cis_img = {} + for reso in resolutions: + th, tw = reso.height // 8 // patch_size, reso.width // 8 // patch_size + sub_args = calc_sizes(rope_img, patch_size, th, tw) + freqs_cis_img[str(reso)] = get_2d_rotary_pos_embed(hidden_size // num_heads, *sub_args, use_real=rope_real) + log_fn(f" Using image RoPE ({rope_img}) ({'real' if rope_real else 'complex'}): {sub_args} | ({reso}) " + f"{freqs_cis_img[str(reso)][0].shape if rope_real else freqs_cis_img[str(reso)].shape}") + return freqs_cis_img diff --git a/custom_nodes/comfyui-hydit_min/hydit/modules/text_encoder.py b/custom_nodes/comfyui-hydit_min/hydit/modules/text_encoder.py new file mode 100644 index 000000000..ef61573c9 --- /dev/null +++ b/custom_nodes/comfyui-hydit_min/hydit/modules/text_encoder.py @@ -0,0 +1,128 @@ +import torch +import torch.nn as nn +from transformers import AutoTokenizer, T5EncoderModel, T5ForConditionalGeneration,T5Config,modeling_utils +import os +import comfy.utils + +class MT5Embedder(nn.Module): + available_models = ["t5-v1_1-xxl"] + + def __init__( + self, + model_dir="t5-v1_1-xxl", + model_kwargs=None, + torch_dtype=None, + use_tokenizer_only=False, + conditional_generation=False, + max_length=128, + ksampler = False + ): + super().__init__() + self.device = "cuda" if torch.cuda.is_available() else "cpu" + self.torch_dtype = torch_dtype or torch.bfloat16 + self.max_length = max_length + if model_kwargs is None: + model_kwargs = { + # "low_cpu_mem_usage": True, + "torch_dtype": self.torch_dtype, + } + model_kwargs["device_map"] = {"shared": self.device, "encoder": self.device} + if not ksampler: + self.tokenizer = AutoTokenizer.from_pretrained(model_dir) + if use_tokenizer_only: + return + """ + if conditional_generation: + self.model = None + self.generation_model = T5ForConditionalGeneration.from_pretrained( + model_dir + ) + return + """ + self.model = T5EncoderModel.from_pretrained(model_dir, **model_kwargs).eval().to(self.torch_dtype) + else: + tokenizer_path = os.path.join( # local + os.path.dirname(os.path.realpath(__file__)), "..", "..", + "tokenizer_mt5", + ) + self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_path) + #self.tokenizer.eval().to(self.device) + if use_tokenizer_only: + return + """ + if conditional_generation: + self.model = None + self.generation_model = T5ForConditionalGeneration.from_pretrained( + model_dir + ) + return + """ + textmodel_json_config = os.path.join( + os.path.dirname(os.path.realpath(__file__)), "..", "..", + f"config_mt5.json" + ) + config = T5Config.from_json_file(textmodel_json_config) + with modeling_utils.no_init_weights(): + self.model = T5EncoderModel(config) + sd = comfy.utils.load_torch_file(model_dir) + self.model.load_state_dict(sd, strict=False) + self.model.eval().to(self.device) + + def get_tokens_and_mask(self, texts): + text_tokens_and_mask = self.tokenizer( + texts, + max_length=self.max_length, + padding="max_length", + truncation=True, + return_attention_mask=True, + add_special_tokens=True, + return_tensors="pt", + ) + tokens = text_tokens_and_mask["input_ids"][0] + mask = text_tokens_and_mask["attention_mask"][0] + # tokens = torch.tensor(tokens).clone().detach() + # mask = torch.tensor(mask, dtype=torch.bool).clone().detach() + return tokens, mask + + def get_text_embeddings(self, texts, attention_mask=True, layer_index=-1): + text_tokens_and_mask = self.tokenizer( + texts, + max_length=self.max_length, + padding="max_length", + truncation=True, + return_attention_mask=True, + add_special_tokens=True, + return_tensors="pt", + ) + + with torch.no_grad(): + outputs = self.model( + input_ids=text_tokens_and_mask["input_ids"].to(self.device), + attention_mask=text_tokens_and_mask["attention_mask"].to(self.device) + if attention_mask + else None, + output_hidden_states=True, + ) + text_encoder_embs = outputs["hidden_states"][layer_index].detach() + + return text_encoder_embs, text_tokens_and_mask["attention_mask"].to(self.device) + + @torch.no_grad() + def __call__(self, tokens, attention_mask, layer_index=-1): + with torch.cuda.amp.autocast(): + outputs = self.model( + input_ids=tokens, + attention_mask=attention_mask, + output_hidden_states=True, + ) + + z = outputs.hidden_states[layer_index].detach() + return z + + def general(self, text: str): + # input_ids = input_ids = torch.tensor([list(text.encode("utf-8"))]) + num_special_tokens + input_ids = self.tokenizer(text, max_length=128).input_ids + print(input_ids) + outputs = self.generation_model(input_ids) + return outputs + diff --git a/custom_nodes/comfyui-hydit_min/hydit/utils/tools.py b/custom_nodes/comfyui-hydit_min/hydit/utils/tools.py new file mode 100644 index 000000000..71d829227 --- /dev/null +++ b/custom_nodes/comfyui-hydit_min/hydit/utils/tools.py @@ -0,0 +1,190 @@ +import random +import logging +from pathlib import Path +import shutil + +import numpy as np +from PIL import Image +import torch +import torch.distributed as dist +from tqdm.auto import tqdm +import math +import torch.nn.functional as F +import os + +def get_trainable_params(model): + params = model.parameters() + params = [p for p in params if p.requires_grad] + return params + + +def set_seeds(seed_list, device=None): + if isinstance(seed_list, (tuple, list)): + seed = sum(seed_list) + else: + seed = seed_list + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + + return torch.Generator(device).manual_seed(seed) + +def get_start_epoch(resume_path, ckpt, steps_per_epoch): + if 'epoch' in ckpt: + start_epoch = ckpt['epoch'] + else: + start_epoch = 0 + if 'steps' in ckpt: + train_steps = ckpt['steps'] + else: + try: + train_steps = int(Path(resume_path).stem) + except: + train_steps = start_epoch * steps_per_epoch + + start_epoch_step = train_steps % steps_per_epoch + 1 + return start_epoch, start_epoch_step, train_steps + +def assert_shape(*args): + if len(args) < 2: + return + cond = True + fail_str = f"{args[0] if isinstance(args[0], (list, tuple)) else args[0].shape}" + for i in range(1, len(args)): + shape1 = args[i] if isinstance(args[i], (list, tuple)) else args[i].shape + shape2 = args[i - 1] if isinstance(args[i - 1], (list, tuple)) else args[i - 1].shape + cond = cond and (shape1 == shape2) + fail_str += f" vs {args[i] if isinstance(args[i], (list, tuple)) else args[i].shape}" + assert cond, fail_str + + +def create_logger(logging_dir=None, logging_file=None, ddp=True): + """ + Create a logger that writes to a log file and stdout. + """ + if not ddp or (ddp and dist.get_rank() == 0): # real logger + if logging_file is not None: + file_handler = [logging.FileHandler(logging_file)] + elif logging_dir is not None: + file_handler = [logging.FileHandler(f"{logging_dir}/log.txt")] + else: + file_handler = [] + logging.basicConfig( + level=logging.INFO, + format='[\033[34m%(asctime)s\033[0m] %(message)s', + datefmt='%Y-%m-%d %H:%M:%S', + handlers=[logging.StreamHandler()] + file_handler + ) + logger = logging.getLogger(__name__) + else: + logger = logging.getLogger(__name__) + logger.addHandler(logging.NullHandler()) + return logger + +def create_exp_folder(args, rank): + if rank == 0: + os.makedirs(args.results_dir, exist_ok=True) + existed_experiments = list(Path(args.results_dir).glob("*dit*")) + if len(existed_experiments) == 0: + experiment_index = 1 + else: + existed_experiments.sort() + print('existed_experiments', existed_experiments) + experiment_index = max([int(x.stem.split('-')[0]) for x in existed_experiments]) + 1 + dist.barrier() + model_string_name = args.task_flag if args.task_flag else args.model.replace("/", "-") + experiment_dir = f"{args.results_dir}/{experiment_index:03d}-{model_string_name}" # Create an experiment folder + checkpoint_dir = f"{experiment_dir}/checkpoints" # Stores saved model checkpoints + if rank == 0: + os.makedirs(checkpoint_dir, exist_ok=True) + logger = create_logger(experiment_dir) + logger.info(f"Experiment directory created at {experiment_dir}") + else: + logger = create_logger() + experiment_dir = "" + + return experiment_dir, checkpoint_dir, logger + + +def model_resume(args, model, ema, logger, len_loader): + """ + Load pretrained weights. + """ + start_epoch = 0 + start_epoch_step = 0 + train_steps = 0 + resume_path = args.resume + if not Path(resume_path).exists(): + raise FileNotFoundError(f" Cannot find checkpoint from {resume_path}") + + logger.info(f" Resume deepspeed={args.resume_deepspeed}, " + f"Resume split={args.resume_split}, " + f"Resume from checkpoint {resume_path}") + # Resume model and ema states (not include optimizer states) from a checkpoint saved by Deepspeed version of DIT. + if args.resume_deepspeed: + assert 'mp_rank_00_model_states.pt' in os.listdir(resume_path), f' Cannot find dp chkpt from {resume_path}' + resume_ckpt = torch.load(os.path.join(resume_path, 'mp_rank_00_model_states.pt'), + map_location=lambda storage, loc: storage) + # Resume main model + if args.ema_to_module: + logger.info(" Resume main model from the ema states.") + model.load_state_dict(resume_ckpt['ema'], strict=args.strict) + else: + logger.info(" Resume main model from the main states.") + model.load_state_dict(resume_ckpt['module'], strict=args.strict) + # Resume EMA model + if args.use_ema: + if args.module_to_ema: + logger.info(" Resume EMA model from the main states.") + ema.load_state_dict(resume_ckpt['module'], strict=args.strict) + else: + logger.info(" Resume EMA model from the EMA states.") + ema.load_state_dict(resume_ckpt['ema'], strict=args.strict) + if not args.reset_loader: + start_epoch, start_epoch_step, train_steps = get_start_epoch(args.resume, resume_ckpt, len_loader) + # Resume model and ema states (not include optimizer states) from two checkpoints separated from DeepSpeed ckpt. + elif args.resume_split: + # Resume main model + if args.ema_to_module: + assert 'pytorch_model_ema.pt' in os.listdir( + resume_path), f' Cannot find pytorch_model_ema.pt from {resume_path}' + logger.info(f" Resume main model from ema states.") + resume_ckpt_ema = torch.load(os.path.join(resume_path, 'pytorch_model_ema.pt'), + map_location=lambda storage, loc: storage) + model.load_state_dict(resume_ckpt_ema, strict=args.strict) + else: + assert 'pytorch_model_module.pt' in os.listdir( + resume_path), f' Cannot find pytorch_model_module.pt from {resume_path}' + logger.info(f" Resume main model from main states.") + resume_ckpt_module = torch.load(os.path.join(resume_path, 'pytorch_model_module.pt'), + map_location=lambda storage, loc: storage) + model.load_state_dict(resume_ckpt_module, strict=args.strict) + # Resume ema model + if args.use_ema: + if args.module_to_ema: + if "resume_ckpt_module" in locals(): + logger.info(f" Resume ema model from main states.") + ema.load_state_dict(resume_ckpt_module, strict=args.strict) + else: + assert 'pytorch_model_module.pt' in os.listdir( + resume_path), f' Cannot find pytorch_model_module.pt from {resume_path}' + logger.info(f" Resume ema model from module states.") + resume_ckpt_module = torch.load(os.path.join(resume_path, 'pytorch_model_module.pt'), + map_location=lambda storage, loc: storage) + ema.load_state_dict(resume_ckpt_module, strict=args.strict) + else: + if "resume_ckpt_ema" in locals(): + logger.info(f" Resume ema model from EMA states.") + ema.load_state_dict(resume_ckpt_ema, strict=args.strict) + else: + assert 'pytorch_model_ema.pt' in os.listdir( + resume_path), f' Cannot find pytorch_model_ema.pt from {resume_path}' + logger.info(f" Resume ema model from EMA states.") + resume_ckpt_ema = torch.load(os.path.join(resume_path, 'pytorch_model_ema.pt'), + map_location=lambda storage, loc: storage) + ema.load_state_dict(resume_ckpt_ema, strict=args.strict) + else: + raise 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