mirror of
https://git.datalinker.icu/comfyanonymous/ComfyUI
synced 2026-09-04 01:57:08 +08:00
Updates of training logic
* use seperated dtype for trainable weight * force "training module only" before training * disable gradient after training * ensure same dtype after training
This commit is contained in:
Kohaku-Blueleaf
parent
9c0cf36cd7
commit
5e43ec9031
@ -8,9 +8,12 @@ import safetensors
|
||||
import torch
|
||||
from PIL import Image, ImageDraw, ImageFont
|
||||
from PIL.PngImagePlugin import PngInfo
|
||||
import torch.utils.checkpoint
|
||||
|
||||
import comfy.samplers
|
||||
import comfy.sd
|
||||
import comfy.utils
|
||||
import comfy.model_management
|
||||
import comfy_extras.nodes_custom_sampler
|
||||
import folder_paths
|
||||
import node_helpers
|
||||
@ -37,6 +40,7 @@ class TrainSampler(comfy.samplers.Sampler):
|
||||
)
|
||||
|
||||
# Ensure model is in training mode and computing gradients
|
||||
# x0 pred
|
||||
denoised = model_wrap(noise, sigmas, **extra_args)
|
||||
try:
|
||||
loss = self.loss_fn(denoised, latent.clone())
|
||||
@ -301,9 +305,13 @@ class TrainLoraNode:
|
||||
},
|
||||
),
|
||||
"training_dtype": (
|
||||
["bf16", "fp32"],
|
||||
["bf16", "fp32"],
|
||||
{"default": "bf16", "tooltip": "The dtype to use for training."},
|
||||
),
|
||||
"lora_dtype": (
|
||||
["bf16", "fp32"],
|
||||
{"default": "bf32", "tooltip": "The dtype to use for lora."},
|
||||
),
|
||||
"existing_lora": (
|
||||
folder_paths.get_filename_list("loras") + ["[None]"],
|
||||
{
|
||||
@ -334,6 +342,7 @@ class TrainLoraNode:
|
||||
loss_function,
|
||||
seed,
|
||||
training_dtype,
|
||||
lora_dtype,
|
||||
existing_lora,
|
||||
):
|
||||
num_images = image.shape[0]
|
||||
@ -344,6 +353,7 @@ class TrainLoraNode:
|
||||
encoded = vae.encode(batch_tensor)
|
||||
mp = model.clone()
|
||||
dtype = node_helpers.string_to_torch_dtype(training_dtype)
|
||||
lora_dtype = node_helpers.string_to_torch_dtype(lora_dtype)
|
||||
mp.set_model_compute_dtype(dtype)
|
||||
|
||||
with torch.inference_mode(False):
|
||||
@ -361,46 +371,60 @@ class TrainLoraNode:
|
||||
if lora_path:
|
||||
existing_weights = comfy.utils.load_torch_file(lora_path)
|
||||
|
||||
all_weight_adapters = []
|
||||
for n, m in mp.model.named_modules():
|
||||
if hasattr(m, "weight_function"):
|
||||
if m.weight is not None:
|
||||
key = "{}.weight".format(n)
|
||||
shape = m.weight.shape
|
||||
if len(shape) >= 2:
|
||||
existing_adapter = None
|
||||
alpha = float(existing_weights.get(f"{key}.alpha", 1.0))
|
||||
dora_scale = existing_weights.get(
|
||||
f"{key}.dora_scale", None
|
||||
)
|
||||
for adapter_cls in adapters:
|
||||
existing_adapter = adapter_cls.load(
|
||||
n, existing_weights
|
||||
n, existing_weights, alpha, dora_scale
|
||||
)
|
||||
if existing_adapter is not None:
|
||||
break
|
||||
else:
|
||||
# If no existing adapter found, use LoRA
|
||||
# We will add algo option in the future
|
||||
existing_adapter = None
|
||||
adapter_cls = adapters[0]
|
||||
|
||||
if existing_adapter is not None:
|
||||
train_adapter = existing_adapter.to_train()
|
||||
train_adapter = existing_adapter.to_train().to(lora_dtype)
|
||||
for name, parameter in train_adapter.named_parameters():
|
||||
lora_sd[f"{n}.{name}"] = parameter
|
||||
else:
|
||||
# Use LoRA with alpha=1.0 by default
|
||||
train_adapter = adapter_cls[0].create_train(
|
||||
train_adapter = adapter_cls.create_train(
|
||||
m.weight, rank=rank, alpha=1.0
|
||||
)
|
||||
).to(lora_dtype)
|
||||
|
||||
mp.add_weight_wrapper(key, train_adapter)
|
||||
all_weight_adapters.append(train_adapter)
|
||||
else:
|
||||
diff = torch.nn.Parameter(
|
||||
torch.zeros(
|
||||
m.weight.shape, dtype=dtype, requires_grad=True
|
||||
)
|
||||
)
|
||||
diff_module = BiasDiff(diff)
|
||||
mp.add_weight_wrapper(key, BiasDiff(diff))
|
||||
all_weight_adapters.append(diff_module)
|
||||
lora_sd["{}.diff".format(n)] = diff
|
||||
if hasattr(m, "bias") and m.bias is not None:
|
||||
key = "{}.bias".format(n)
|
||||
bias = torch.nn.Parameter(
|
||||
torch.zeros(m.bias.shape, dtype=dtype, requires_grad=True)
|
||||
)
|
||||
bias_module = BiasDiff(bias)
|
||||
lora_sd["{}.diff_b".format(n)] = bias
|
||||
mp.add_weight_wrapper(key, BiasDiff(bias))
|
||||
all_weight_adapters.append(bias_module)
|
||||
|
||||
if optimizer == "Adam":
|
||||
optimizer = torch.optim.Adam(lora_sd.values(), lr=learning_rate)
|
||||
@ -433,7 +457,11 @@ class TrainLoraNode:
|
||||
|
||||
# yoland: this currently resize to the first image in the dataset
|
||||
|
||||
# setup before training
|
||||
comfy.model_management.load_models_gpu([mp], memory_required=1e20, force_full_load=True)
|
||||
|
||||
# Training loop
|
||||
torch.cuda.empty_cache()
|
||||
for step in range(steps):
|
||||
# Generate random sigma
|
||||
sigma = mp.model.model_sampling.percent_to_sigma(
|
||||
@ -446,6 +474,14 @@ class TrainLoraNode:
|
||||
ss.sample(
|
||||
noise, guider, train_sampler, sigma, {"samples": encoded.clone()}
|
||||
)
|
||||
del ss, train_sampler, optimizer
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
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)
|
||||
|
||||
@ -519,7 +555,7 @@ class LossGraphNode:
|
||||
|
||||
def plot_loss(self, loss, filename_prefix, prompt=None, extra_pnginfo=None):
|
||||
loss_values = loss["loss"]
|
||||
width, height = 500, 300
|
||||
width, height = 800, 480
|
||||
margin = 40
|
||||
|
||||
img = Image.new(
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user