mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2026-01-15 00:30:55 +08:00
708 lines
26 KiB
Python
708 lines
26 KiB
Python
import datetime
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import json
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import logging
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import os
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import numpy as np
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import safetensors
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import torch
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from PIL import Image, ImageDraw, ImageFont
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from PIL.PngImagePlugin import PngInfo
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import torch.utils.checkpoint
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import tqdm
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import comfy.samplers
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import comfy.sd
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import comfy.utils
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import comfy.model_management
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import comfy_extras.nodes_custom_sampler
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import folder_paths
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import node_helpers
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from comfy.cli_args import args
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from comfy.comfy_types.node_typing import IO
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from comfy.weight_adapter import adapters, adapter_maps
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def make_batch_extra_option_dict(d, indicies, full_size=None):
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new_dict = {}
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for k, v in d.items():
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newv = v
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if isinstance(v, dict):
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newv = make_batch_extra_option_dict(v, indicies, full_size=full_size)
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elif isinstance(v, torch.Tensor):
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if full_size is None or v.size(0) == full_size:
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newv = v[indicies]
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elif isinstance(v, (list, tuple)) and len(v) == full_size:
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newv = [v[i] for i in indicies]
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new_dict[k] = newv
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return new_dict
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def process_cond_list(d, prefix=""):
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if hasattr(d, "__iter__") and not hasattr(d, "items"):
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for index, item in enumerate(d):
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process_cond_list(item, f"{prefix}.{index}")
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return d
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elif hasattr(d, "items"):
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for k, v in list(d.items()):
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if isinstance(v, dict):
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process_cond_list(v, f"{prefix}.{k}")
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elif isinstance(v, torch.Tensor):
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d[k] = v.clone()
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elif isinstance(v, (list, tuple)):
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for index, item in enumerate(v):
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process_cond_list(item, f"{prefix}.{k}.{index}")
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return d
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class TrainSampler(comfy.samplers.Sampler):
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def __init__(self, loss_fn, optimizer, loss_callback=None, batch_size=1, grad_acc=1, total_steps=1, seed=0, training_dtype=torch.bfloat16, real_dataset=None):
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self.loss_fn = loss_fn
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self.optimizer = optimizer
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self.loss_callback = loss_callback
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self.batch_size = batch_size
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self.total_steps = total_steps
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self.grad_acc = grad_acc
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self.seed = seed
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self.training_dtype = training_dtype
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self.real_dataset: list[torch.Tensor] | None = real_dataset
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def fwd_bwd(self, model_wrap, batch_sigmas, batch_noise, batch_latent, cond, indicies, extra_args, dataset_size):
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xt = model_wrap.inner_model.model_sampling.noise_scaling(
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batch_sigmas,
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batch_noise,
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batch_latent,
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False
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)
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x0 = model_wrap.inner_model.model_sampling.noise_scaling(
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torch.zeros_like(batch_sigmas),
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torch.zeros_like(batch_noise),
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batch_latent,
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False
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)
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model_wrap.conds["positive"] = [
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cond[i] for i in indicies
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]
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batch_extra_args = make_batch_extra_option_dict(extra_args, indicies, full_size=dataset_size)
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with torch.autocast(xt.device.type, dtype=self.training_dtype):
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x0_pred = model_wrap(xt, batch_sigmas, **batch_extra_args)
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loss = self.loss_fn(x0_pred, x0)
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loss.backward()
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return loss
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def sample(self, model_wrap, sigmas, extra_args, callback, noise, latent_image=None, denoise_mask=None, disable_pbar=False):
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model_wrap.conds = process_cond_list(model_wrap.conds)
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cond = model_wrap.conds["positive"]
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dataset_size = sigmas.size(0)
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torch.cuda.empty_cache()
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for i in (pbar:=tqdm.trange(self.total_steps, desc="Training LoRA", smoothing=0.01, disable=not comfy.utils.PROGRESS_BAR_ENABLED)):
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noisegen = comfy_extras.nodes_custom_sampler.Noise_RandomNoise(self.seed + i * 1000)
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indicies = torch.randperm(dataset_size)[:self.batch_size].tolist()
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if self.real_dataset is None:
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batch_latent = torch.stack([latent_image[i] for i in indicies])
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batch_noise = noisegen.generate_noise({"samples": batch_latent}).to(batch_latent.device)
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batch_sigmas = [
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model_wrap.inner_model.model_sampling.percent_to_sigma(
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torch.rand((1,)).item()
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) for _ in range(min(self.batch_size, dataset_size))
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]
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batch_sigmas = torch.tensor(batch_sigmas).to(batch_latent.device)
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loss = self.fwd_bwd(model_wrap, batch_sigmas, batch_noise, batch_latent, cond, indicies, extra_args, dataset_size)
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if self.loss_callback:
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self.loss_callback(loss.item())
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pbar.set_postfix({"loss": f"{loss.item():.4f}"})
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else:
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total_loss = 0
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for index in indicies:
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single_latent = self.real_dataset[index].to(latent_image)
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batch_noise = noisegen.generate_noise({"samples": single_latent}).to(single_latent.device)
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batch_sigmas = model_wrap.inner_model.model_sampling.percent_to_sigma(
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torch.rand((1,)).item()
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)
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batch_sigmas = torch.tensor([batch_sigmas]).to(single_latent.device)
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loss = self.fwd_bwd(model_wrap, batch_sigmas, batch_noise, single_latent, cond, [index], extra_args, dataset_size)
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total_loss += loss.item()
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total_loss /= len(indicies)
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if self.loss_callback:
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self.loss_callback(loss.item())
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pbar.set_postfix({"loss": f"{total_loss/(index+1):.4f}"})
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if (i+1) % self.grad_acc == 0:
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self.optimizer.step()
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self.optimizer.zero_grad()
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torch.cuda.empty_cache()
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return torch.zeros_like(latent_image)
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class BiasDiff(torch.nn.Module):
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def __init__(self, bias):
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super().__init__()
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self.bias = bias
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def __call__(self, b):
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org_dtype = b.dtype
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return (b.to(self.bias) + self.bias).to(org_dtype)
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def passive_memory_usage(self):
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return self.bias.nelement() * self.bias.element_size()
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def move_to(self, device):
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self.to(device=device)
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return self.passive_memory_usage()
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def draw_loss_graph(loss_map, steps):
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width, height = 500, 300
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img = Image.new("RGB", (width, height), "white")
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draw = ImageDraw.Draw(img)
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min_loss, max_loss = min(loss_map.values()), max(loss_map.values())
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scaled_loss = [(l - min_loss) / (max_loss - min_loss) for l in loss_map.values()]
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prev_point = (0, height - int(scaled_loss[0] * height))
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for i, l in enumerate(scaled_loss[1:], start=1):
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x = int(i / (steps - 1) * width)
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y = height - int(l * height)
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draw.line([prev_point, (x, y)], fill="blue", width=2)
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prev_point = (x, y)
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return img
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def find_all_highest_child_module_with_forward(model: torch.nn.Module, result = None, name = None):
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if result is None:
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result = []
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elif hasattr(model, "forward") and not isinstance(model, (torch.nn.ModuleList, torch.nn.Sequential, torch.nn.ModuleDict)):
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result.append(model)
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logging.debug(f"Found module with forward: {name} ({model.__class__.__name__})")
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return result
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name = name or "root"
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for next_name, child in model.named_children():
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find_all_highest_child_module_with_forward(child, result, f"{name}.{next_name}")
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return result
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def patch(m):
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if not hasattr(m, "forward"):
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return
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org_forward = m.forward
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def fwd(args, kwargs):
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return org_forward(*args, **kwargs)
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def checkpointing_fwd(*args, **kwargs):
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return torch.utils.checkpoint.checkpoint(
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fwd, args, kwargs, use_reentrant=False
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)
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m.org_forward = org_forward
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m.forward = checkpointing_fwd
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def unpatch(m):
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if hasattr(m, "org_forward"):
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m.forward = m.org_forward
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del m.org_forward
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class TrainLoraNode:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"model": (IO.MODEL, {"tooltip": "The model to train the LoRA on."}),
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"latents": (
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"LATENT",
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{
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"tooltip": "The Latents to use for training, serve as dataset/input of the model."
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},
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),
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"positive": (
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IO.CONDITIONING,
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{"tooltip": "The positive conditioning to use for training."},
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),
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"batch_size": (
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IO.INT,
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{
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"default": 1,
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"min": 1,
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"max": 10000,
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"step": 1,
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"tooltip": "The batch size to use for training.",
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},
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),
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"grad_accumulation_steps": (
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IO.INT,
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{
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"default": 1,
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"min": 1,
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"max": 1024,
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"step": 1,
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"tooltip": "The number of gradient accumulation steps to use for training.",
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}
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),
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"steps": (
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IO.INT,
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{
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"default": 16,
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"min": 1,
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"max": 100000,
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"tooltip": "The number of steps to train the LoRA for.",
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},
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),
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"learning_rate": (
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IO.FLOAT,
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{
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"default": 0.0005,
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"min": 0.0000001,
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"max": 1.0,
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"step": 0.000001,
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"tooltip": "The learning rate to use for training.",
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},
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),
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"rank": (
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IO.INT,
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{
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"default": 8,
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"min": 1,
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"max": 128,
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"tooltip": "The rank of the LoRA layers.",
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},
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),
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"optimizer": (
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["AdamW", "Adam", "SGD", "RMSprop"],
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{
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"default": "AdamW",
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"tooltip": "The optimizer to use for training.",
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},
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),
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"loss_function": (
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["MSE", "L1", "Huber", "SmoothL1"],
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{
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"default": "MSE",
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"tooltip": "The loss function to use for training.",
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},
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),
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"seed": (
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IO.INT,
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{
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"default": 0,
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"min": 0,
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"max": 0xFFFFFFFFFFFFFFFF,
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"tooltip": "The seed to use for training (used in generator for LoRA weight initialization and noise sampling)",
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},
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),
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"training_dtype": (
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["bf16", "fp32"],
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{"default": "bf16", "tooltip": "The dtype to use for training."},
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),
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"lora_dtype": (
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["bf16", "fp32"],
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{"default": "bf16", "tooltip": "The dtype to use for lora."},
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),
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"algorithm": (
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list(adapter_maps.keys()),
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{"default": list(adapter_maps.keys())[0], "tooltip": "The algorithm to use for training."},
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),
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"gradient_checkpointing": (
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IO.BOOLEAN,
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{
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"default": True,
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"tooltip": "Use gradient checkpointing for training.",
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}
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),
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"existing_lora": (
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folder_paths.get_filename_list("loras") + ["[None]"],
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{
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"default": "[None]",
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"tooltip": "The existing LoRA to append to. Set to None for new LoRA.",
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},
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),
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},
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}
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RETURN_TYPES = (IO.MODEL, IO.LORA_MODEL, IO.LOSS_MAP, IO.INT)
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RETURN_NAMES = ("model_with_lora", "lora", "loss", "steps")
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FUNCTION = "train"
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CATEGORY = "training"
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EXPERIMENTAL = True
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def train(
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self,
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model,
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latents,
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positive,
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batch_size,
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steps,
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grad_accumulation_steps,
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learning_rate,
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rank,
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optimizer,
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loss_function,
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seed,
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training_dtype,
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lora_dtype,
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algorithm,
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gradient_checkpointing,
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existing_lora,
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):
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mp = model.clone()
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dtype = node_helpers.string_to_torch_dtype(training_dtype)
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lora_dtype = node_helpers.string_to_torch_dtype(lora_dtype)
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mp.set_model_compute_dtype(dtype)
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# latents here can be list of different size latent or one large batch
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latents = latents["samples"]
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if isinstance(latents, list):
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all_shapes = set()
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latents = [t.to(dtype) for t in latents]
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for latent in latents:
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all_shapes.add(latent.shape)
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logging.info(f"Latent shapes: {all_shapes}")
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if len(all_shapes) > 1:
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multi_res = True
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else:
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multi_res = False
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latents = torch.cat(latents, dim=0)
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num_images = len(latents)
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elif isinstance(latents, list):
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latents = latents["samples"].to(dtype)
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num_images = latents.shape[0]
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else:
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logging.error(f"Invalid latents type: {type(latents)}")
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logging.info(f"Total Images: {num_images}, Total Captions: {len(positive)}")
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if len(positive) == 1 and num_images > 1:
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positive = positive * num_images
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elif len(positive) != num_images:
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raise ValueError(
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f"Number of positive conditions ({len(positive)}) does not match number of images ({num_images})."
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)
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with torch.inference_mode(False):
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lora_sd = {}
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generator = torch.Generator()
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generator.manual_seed(seed)
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# Load existing LoRA weights if provided
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existing_weights = {}
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existing_steps = 0
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if existing_lora != "[None]":
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lora_path = folder_paths.get_full_path_or_raise("loras", existing_lora)
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# Extract steps from filename like "trained_lora_10_steps_20250225_203716"
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existing_steps = int(existing_lora.split("_steps_")[0].split("_")[-1])
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if lora_path:
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existing_weights = comfy.utils.load_torch_file(lora_path)
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all_weight_adapters = []
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for n, m in mp.model.named_modules():
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if hasattr(m, "weight_function"):
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if m.weight is not None:
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key = "{}.weight".format(n)
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shape = m.weight.shape
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if len(shape) >= 2:
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alpha = float(existing_weights.get(f"{key}.alpha", 1.0))
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dora_scale = existing_weights.get(
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f"{key}.dora_scale", None
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)
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for adapter_cls in adapters:
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existing_adapter = adapter_cls.load(
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n, existing_weights, alpha, dora_scale
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)
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if existing_adapter is not None:
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break
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else:
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existing_adapter = None
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adapter_cls = adapter_maps[algorithm]
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if existing_adapter is not None:
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train_adapter = existing_adapter.to_train().to(lora_dtype)
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else:
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# Use LoRA with alpha=1.0 by default
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train_adapter = adapter_cls.create_train(
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m.weight, rank=rank, alpha=1.0
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).to(lora_dtype)
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for name, parameter in train_adapter.named_parameters():
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lora_sd[f"{n}.{name}"] = parameter
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mp.add_weight_wrapper(key, train_adapter)
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all_weight_adapters.append(train_adapter)
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else:
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diff = torch.nn.Parameter(
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torch.zeros(
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m.weight.shape, dtype=lora_dtype, requires_grad=True
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)
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)
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diff_module = BiasDiff(diff)
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mp.add_weight_wrapper(key, BiasDiff(diff))
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all_weight_adapters.append(diff_module)
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lora_sd["{}.diff".format(n)] = diff
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if hasattr(m, "bias") and m.bias is not None:
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key = "{}.bias".format(n)
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bias = torch.nn.Parameter(
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torch.zeros(m.bias.shape, dtype=lora_dtype, requires_grad=True)
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)
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bias_module = BiasDiff(bias)
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lora_sd["{}.diff_b".format(n)] = bias
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mp.add_weight_wrapper(key, BiasDiff(bias))
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all_weight_adapters.append(bias_module)
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if optimizer == "Adam":
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optimizer = torch.optim.Adam(lora_sd.values(), lr=learning_rate)
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elif optimizer == "AdamW":
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optimizer = torch.optim.AdamW(lora_sd.values(), lr=learning_rate)
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elif optimizer == "SGD":
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optimizer = torch.optim.SGD(lora_sd.values(), lr=learning_rate)
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elif optimizer == "RMSprop":
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optimizer = torch.optim.RMSprop(lora_sd.values(), lr=learning_rate)
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# Setup loss function based on selection
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if loss_function == "MSE":
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criterion = torch.nn.MSELoss()
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elif loss_function == "L1":
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criterion = torch.nn.L1Loss()
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elif loss_function == "Huber":
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criterion = torch.nn.HuberLoss()
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elif loss_function == "SmoothL1":
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criterion = torch.nn.SmoothL1Loss()
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# setup models
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if gradient_checkpointing:
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for m in find_all_highest_child_module_with_forward(mp.model.diffusion_model):
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patch(m)
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mp.model.requires_grad_(False)
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comfy.model_management.load_models_gpu([mp], memory_required=1e20, force_full_load=True)
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# Setup sampler and guider like in test script
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loss_map = {"loss": []}
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def loss_callback(loss):
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loss_map["loss"].append(loss)
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train_sampler = TrainSampler(
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criterion,
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optimizer,
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loss_callback=loss_callback,
|
|
batch_size=batch_size,
|
|
grad_acc=grad_accumulation_steps,
|
|
total_steps=steps*grad_accumulation_steps,
|
|
seed=seed,
|
|
training_dtype=dtype,
|
|
real_dataset=latents if multi_res else None
|
|
)
|
|
guider = comfy_extras.nodes_custom_sampler.Guider_Basic(mp)
|
|
guider.set_conds(positive) # Set conditioning from input
|
|
|
|
# Training loop
|
|
try:
|
|
# Generate dummy sigmas and noise
|
|
sigmas = torch.tensor(range(num_images))
|
|
noise = comfy_extras.nodes_custom_sampler.Noise_RandomNoise(seed)
|
|
if multi_res:
|
|
# use first latent as dummy latent if multi_res
|
|
latents = latents[0].repeat(num_images, 1, 1, 1)
|
|
guider.sample(
|
|
noise.generate_noise({"samples": latents}),
|
|
latents,
|
|
train_sampler,
|
|
sigmas,
|
|
seed=noise.seed
|
|
)
|
|
finally:
|
|
for m in mp.model.modules():
|
|
unpatch(m)
|
|
del train_sampler, optimizer
|
|
|
|
for adapter in all_weight_adapters:
|
|
adapter.requires_grad_(False)
|
|
|
|
for param in lora_sd:
|
|
lora_sd[param] = lora_sd[param].to(lora_dtype)
|
|
|
|
return (mp, lora_sd, loss_map, steps + existing_steps)
|
|
|
|
|
|
class LoraModelLoader:
|
|
def __init__(self):
|
|
self.loaded_lora = None
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"model": ("MODEL", {"tooltip": "The diffusion model the LoRA will be applied to."}),
|
|
"lora": (IO.LORA_MODEL, {"tooltip": "The LoRA model to apply to the diffusion model."}),
|
|
"strength_model": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01, "tooltip": "How strongly to modify the diffusion model. This value can be negative."}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("MODEL",)
|
|
OUTPUT_TOOLTIPS = ("The modified diffusion model.",)
|
|
FUNCTION = "load_lora_model"
|
|
|
|
CATEGORY = "loaders"
|
|
DESCRIPTION = "Load Trained LoRA weights from Train LoRA node."
|
|
EXPERIMENTAL = True
|
|
|
|
def load_lora_model(self, model, lora, strength_model):
|
|
if strength_model == 0:
|
|
return (model, )
|
|
|
|
model_lora, _ = comfy.sd.load_lora_for_models(model, None, lora, strength_model, 0)
|
|
return (model_lora, )
|
|
|
|
|
|
class SaveLoRA:
|
|
def __init__(self):
|
|
self.output_dir = folder_paths.get_output_directory()
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"lora": (
|
|
IO.LORA_MODEL,
|
|
{
|
|
"tooltip": "The LoRA model to save. Do not use the model with LoRA layers."
|
|
},
|
|
),
|
|
"prefix": (
|
|
"STRING",
|
|
{
|
|
"default": "loras/ComfyUI_trained_lora",
|
|
"tooltip": "The prefix to use for the saved LoRA file.",
|
|
},
|
|
),
|
|
},
|
|
"optional": {
|
|
"steps": (
|
|
IO.INT,
|
|
{
|
|
"forceInput": True,
|
|
"tooltip": "Optional: The number of steps to LoRA has been trained for, used to name the saved file.",
|
|
},
|
|
),
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ()
|
|
FUNCTION = "save"
|
|
CATEGORY = "loaders"
|
|
EXPERIMENTAL = True
|
|
OUTPUT_NODE = True
|
|
|
|
def save(self, lora, prefix, steps=None):
|
|
full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(prefix, self.output_dir)
|
|
if steps is None:
|
|
output_checkpoint = f"{filename}_{counter:05}_.safetensors"
|
|
else:
|
|
output_checkpoint = f"{filename}_{steps}_steps_{counter:05}_.safetensors"
|
|
output_checkpoint = os.path.join(full_output_folder, output_checkpoint)
|
|
safetensors.torch.save_file(lora, output_checkpoint)
|
|
return {}
|
|
|
|
|
|
class LossGraphNode:
|
|
def __init__(self):
|
|
self.output_dir = folder_paths.get_temp_directory()
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"loss": (IO.LOSS_MAP, {"default": {}}),
|
|
"filename_prefix": (IO.STRING, {"default": "loss_graph"}),
|
|
},
|
|
"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
|
|
}
|
|
|
|
RETURN_TYPES = ()
|
|
FUNCTION = "plot_loss"
|
|
OUTPUT_NODE = True
|
|
CATEGORY = "training"
|
|
EXPERIMENTAL = True
|
|
DESCRIPTION = "Plots the loss graph and saves it to the output directory."
|
|
|
|
def plot_loss(self, loss, filename_prefix, prompt=None, extra_pnginfo=None):
|
|
loss_values = loss["loss"]
|
|
width, height = 800, 480
|
|
margin = 40
|
|
|
|
img = Image.new(
|
|
"RGB", (width + margin, height + margin), "white"
|
|
) # Extend canvas
|
|
draw = ImageDraw.Draw(img)
|
|
|
|
min_loss, max_loss = min(loss_values), max(loss_values)
|
|
scaled_loss = [(l - min_loss) / (max_loss - min_loss) for l in loss_values]
|
|
|
|
steps = len(loss_values)
|
|
|
|
prev_point = (margin, height - int(scaled_loss[0] * height))
|
|
for i, l in enumerate(scaled_loss[1:], start=1):
|
|
x = margin + int(i / steps * width) # Scale X properly
|
|
y = height - int(l * height)
|
|
draw.line([prev_point, (x, y)], fill="blue", width=2)
|
|
prev_point = (x, y)
|
|
|
|
draw.line([(margin, 0), (margin, height)], fill="black", width=2) # Y-axis
|
|
draw.line(
|
|
[(margin, height), (width + margin, height)], fill="black", width=2
|
|
) # X-axis
|
|
|
|
font = None
|
|
try:
|
|
font = ImageFont.truetype("arial.ttf", 12)
|
|
except IOError:
|
|
font = ImageFont.load_default()
|
|
|
|
# Add axis labels
|
|
draw.text((5, height // 2), "Loss", font=font, fill="black")
|
|
draw.text((width // 2, height + 10), "Steps", font=font, fill="black")
|
|
|
|
# Add min/max loss values
|
|
draw.text((margin - 30, 0), f"{max_loss:.2f}", font=font, fill="black")
|
|
draw.text(
|
|
(margin - 30, height - 10), f"{min_loss:.2f}", font=font, fill="black"
|
|
)
|
|
|
|
metadata = None
|
|
if not args.disable_metadata:
|
|
metadata = PngInfo()
|
|
if prompt is not None:
|
|
metadata.add_text("prompt", json.dumps(prompt))
|
|
if extra_pnginfo is not None:
|
|
for x in extra_pnginfo:
|
|
metadata.add_text(x, json.dumps(extra_pnginfo[x]))
|
|
|
|
date = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
|
|
img.save(
|
|
os.path.join(self.output_dir, f"{filename_prefix}_{date}.png"),
|
|
pnginfo=metadata,
|
|
)
|
|
return {
|
|
"ui": {
|
|
"images": [
|
|
{
|
|
"filename": f"{filename_prefix}_{date}.png",
|
|
"subfolder": "",
|
|
"type": "temp",
|
|
}
|
|
]
|
|
}
|
|
}
|
|
|
|
|
|
NODE_CLASS_MAPPINGS = {
|
|
"TrainLoraNode": TrainLoraNode,
|
|
"SaveLoRANode": SaveLoRA,
|
|
"LoraModelLoader": LoraModelLoader,
|
|
"LossGraphNode": LossGraphNode,
|
|
}
|
|
|
|
NODE_DISPLAY_NAME_MAPPINGS = {
|
|
"TrainLoraNode": "Train LoRA",
|
|
"SaveLoRANode": "Save LoRA Weights",
|
|
"LoraModelLoader": "Load LoRA Model",
|
|
"LossGraphNode": "Plot Loss Graph",
|
|
}
|