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279 lines
10 KiB
Python
279 lines
10 KiB
Python
# ref:
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# - https://github.com/p1atdev/LECO/blob/main/train_lora.py
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import time
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from collections import OrderedDict
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import os
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from typing import Optional
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from safetensors.torch import load_file, save_file
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from tqdm import tqdm
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from toolkit.config_modules import SliderConfig
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from toolkit.layers import ReductionKernel
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from toolkit.paths import REPOS_ROOT
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import sys
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from toolkit.stable_diffusion_model import PromptEmbeds
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sys.path.append(REPOS_ROOT)
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sys.path.append(os.path.join(REPOS_ROOT, 'leco'))
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from toolkit.train_tools import get_torch_dtype, apply_noise_offset
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import gc
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from toolkit import train_tools
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import torch
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from leco import train_util, model_util
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from .BaseSDTrainProcess import BaseSDTrainProcess, StableDiffusion
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def flush():
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torch.cuda.empty_cache()
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gc.collect()
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class RescaleConfig:
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def __init__(
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self,
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**kwargs
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):
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self.from_resolution = kwargs.get('from_resolution', 512)
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self.scale = kwargs.get('scale', 0.5)
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self.prompt_file = kwargs.get('prompt_file', None)
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self.prompt_tensors = kwargs.get('prompt_tensors', None)
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self.to_resolution = kwargs.get('to_resolution', int(self.from_resolution * self.scale))
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if self.prompt_file is None:
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raise ValueError("prompt_file is required")
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class PromptEmbedsCache:
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prompts: dict[str, PromptEmbeds] = {}
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def __setitem__(self, __name: str, __value: PromptEmbeds) -> None:
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self.prompts[__name] = __value
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def __getitem__(self, __name: str) -> Optional[PromptEmbeds]:
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if __name in self.prompts:
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return self.prompts[__name]
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else:
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return None
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class TrainSDRescaleProcess(BaseSDTrainProcess):
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def __init__(self, process_id: int, job, config: OrderedDict):
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super().__init__(process_id, job, config)
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self.step_num = 0
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self.start_step = 0
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self.device = self.get_conf('device', self.job.device)
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self.device_torch = torch.device(self.device)
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self.prompt_cache = PromptEmbedsCache()
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self.rescale_config = RescaleConfig(**self.get_conf('rescale', required=True))
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self.reduce_size_fn = ReductionKernel(
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in_channels=4,
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kernel_size=int(self.rescale_config.from_resolution // self.rescale_config.to_resolution),
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dtype=get_torch_dtype(self.train_config.dtype),
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device=self.device_torch,
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)
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self.prompt_txt_list = []
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def before_model_load(self):
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pass
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def hook_before_train_loop(self):
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self.print(f"Loading prompt file from {self.rescale_config.prompt_file}")
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# read line by line from file
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with open(self.rescale_config.prompt_file, 'r') as f:
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self.prompt_txt_list = f.readlines()
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# clean empty lines
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self.prompt_txt_list = [line.strip() for line in self.prompt_txt_list if len(line.strip()) > 0]
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self.print(f"Loaded {len(self.prompt_txt_list)} prompts. Encoding them..")
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cache = PromptEmbedsCache()
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# get encoded latents for our prompts
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with torch.no_grad():
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if self.rescale_config.prompt_tensors is not None:
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# check to see if it exists
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if os.path.exists(self.rescale_config.prompt_tensors):
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# load it.
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self.print(f"Loading prompt tensors from {self.rescale_config.prompt_tensors}")
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prompt_tensors = load_file(self.rescale_config.prompt_tensors, device='cpu')
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# add them to the cache
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for prompt_txt, prompt_tensor in prompt_tensors.items():
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if prompt_txt.startswith("te:"):
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prompt = prompt_txt[3:]
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# text_embeds
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text_embeds = prompt_tensor
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pooled_embeds = None
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# find pool embeds
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if f"pe:{prompt}" in prompt_tensors:
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pooled_embeds = prompt_tensors[f"pe:{prompt}"]
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# make it
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prompt_embeds = PromptEmbeds([text_embeds, pooled_embeds])
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cache[prompt] = prompt_embeds.to(device='cpu', dtype=torch.float32)
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if len(cache.prompts) == 0:
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print("Prompt tensors not found. Encoding prompts..")
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neutral = ""
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# encode neutral
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cache[neutral] = self.sd.encode_prompt(neutral)
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for prompt in tqdm(self.prompt_txt_list, desc="Encoding prompts", leave=False):
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# build the cache
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if cache[prompt] is None:
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cache[prompt] = self.sd.encode_prompt(prompt).to(device="cpu", dtype=torch.float32)
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if self.rescale_config.prompt_tensors:
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print(f"Saving prompt tensors to {self.rescale_config.prompt_tensors}")
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state_dict = {}
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for prompt_txt, prompt_embeds in cache.prompts.items():
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state_dict[f"te:{prompt_txt}"] = prompt_embeds.text_embeds.to("cpu", dtype=get_torch_dtype('fp16'))
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if prompt_embeds.pooled_embeds is not None:
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state_dict[f"pe:{prompt_txt}"] = prompt_embeds.pooled_embeds.to("cpu", dtype=get_torch_dtype('fp16'))
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save_file(state_dict, self.rescale_config.prompt_tensors)
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self.print("Encoding complete.")
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# move to cpu to save vram
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# We don't need text encoder anymore, but keep it on cpu for sampling
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# if text encoder is list
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if isinstance(self.sd.text_encoder, list):
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for encoder in self.sd.text_encoder:
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encoder.to("cpu")
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else:
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self.sd.text_encoder.to("cpu")
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self.prompt_cache = cache
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flush()
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# end hook_before_train_loop
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def hook_train_loop(self):
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dtype = get_torch_dtype(self.train_config.dtype)
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# get random encoded prompt from cache
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prompt_txt = self.prompt_txt_list[
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torch.randint(0, len(self.prompt_txt_list), (1,)).item()
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]
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prompt = self.prompt_cache[prompt_txt].to(device=self.device_torch, dtype=dtype)
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neutral = self.prompt_cache[""].to(device=self.device_torch, dtype=dtype)
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if prompt is None:
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raise ValueError(f"Prompt {prompt_txt} is not in cache")
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prompt_batch = train_tools.concat_prompt_embeddings(
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prompt,
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neutral,
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self.train_config.batch_size,
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)
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noise_scheduler = self.sd.noise_scheduler
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optimizer = self.optimizer
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lr_scheduler = self.lr_scheduler
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loss_function = torch.nn.MSELoss()
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def get_noise_pred(p, n, gs, cts, dn):
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return self.predict_noise(
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latents=dn,
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text_embeddings=train_tools.concat_prompt_embeddings(
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p, # unconditional
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n, # positive
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self.train_config.batch_size,
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),
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timestep=cts,
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guidance_scale=gs,
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)
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with torch.no_grad():
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self.sd.noise_scheduler.set_timesteps(
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self.train_config.max_denoising_steps, device=self.device_torch
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)
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self.optimizer.zero_grad()
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# # ger a random number of steps
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timesteps_to = torch.randint(
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1, self.train_config.max_denoising_steps, (1,)
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).item()
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# get noise
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noise = self.get_latent_noise(
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pixel_height=self.rescale_config.from_resolution,
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pixel_width=self.rescale_config.from_resolution,
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).to(self.device_torch, dtype=dtype)
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# get latents
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latents = noise * self.sd.noise_scheduler.init_noise_sigma
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latents = latents.to(self.device_torch, dtype=dtype)
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#
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# # predict without network
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# assert self.network.is_active is False
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# denoised_latents = self.diffuse_some_steps(
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# latents, # pass simple noise latents
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# prompt_batch,
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# start_timesteps=0,
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# total_timesteps=timesteps_to,
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# guidance_scale=3,
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# )
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# noise_scheduler.set_timesteps(1000)
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#
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# current_timestep = noise_scheduler.timesteps[
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# int(timesteps_to * 1000 / self.train_config.max_denoising_steps)
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# ]
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current_timestep = 0
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denoised_latents = latents
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# get noise prediction at full scale
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from_prediction = get_noise_pred(
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prompt, neutral, 1, current_timestep, denoised_latents
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)
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reduced_from_prediction = self.reduce_size_fn(from_prediction).to("cpu", dtype=torch.float32)
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# get noise prediction at reduced scale
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to_denoised_latents = self.reduce_size_fn(denoised_latents)
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# start gradient
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optimizer.zero_grad()
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self.network.multiplier = 1.0
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with self.network:
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assert self.network.is_active is True
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to_prediction = get_noise_pred(
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prompt, neutral, 1, current_timestep, to_denoised_latents
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).to("cpu", dtype=torch.float32)
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reduced_from_prediction.requires_grad = False
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from_prediction.requires_grad = False
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loss = loss_function(
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reduced_from_prediction,
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to_prediction,
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)
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loss_float = loss.item()
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loss = loss.to(self.device_torch)
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loss.backward()
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optimizer.step()
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lr_scheduler.step()
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del (
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reduced_from_prediction,
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from_prediction,
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to_denoised_latents,
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to_prediction,
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latents,
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)
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flush()
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# reset network
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self.network.multiplier = 1.0
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loss_dict = OrderedDict(
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{'loss': loss_float},
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)
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return loss_dict
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# end hook_train_loop
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