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import lightning as L
from PIL import Image, ImageFilter, ImageDraw
import numpy as np
from transformers import pipeline
import cv2
import torch
import os
from torchvision import transforms as T
try:
import wandb
except ImportError:
wandb = None
from ..flux.condition import Condition
from ..flux.generate import generate
class FaceMoGLECallback(L.Callback):
def __init__(self, run_name, training_config: dict = {}):
self.run_name, self.training_config = run_name, training_config
self.print_every_n_steps = training_config.get("print_every_n_steps", 10)
self.save_interval = training_config.get("save_interval", 1000)
self.sample_interval = training_config.get("sample_interval", 1000)
self.save_path = training_config.get("save_path", "./runs")
self.wandb_config = training_config.get("wandb", None)
self.use_wandb = (
wandb is not None and os.environ.get("WANDB_API_KEY") is not None
)
self.total_steps = 0
def to_tensor(self, x):
return T.ToTensor()(x)
def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx):
gradient_size = 0
max_gradient_size = 0
count = 0
for _, param in pl_module.named_parameters():
if param.grad is not None:
gradient_size += param.grad.norm(2).item()
max_gradient_size = max(max_gradient_size, param.grad.norm(2).item())
count += 1
if count > 0:
gradient_size /= count
self.total_steps += 1
# Print training progress every n steps
if self.use_wandb:
report_dict = {
"steps": batch_idx,
"steps": self.total_steps,
"epoch": trainer.current_epoch,
"gradient_size": gradient_size,
}
loss_value = outputs["loss"].item() * trainer.accumulate_grad_batches
report_dict["loss"] = loss_value
report_dict["t"] = pl_module.last_t
wandb.log(report_dict)
if self.total_steps % self.print_every_n_steps == 0:
print(
f"Epoch: {trainer.current_epoch}, Steps: {self.total_steps}, Batch: {batch_idx}, Loss: {pl_module.log_loss:.4f}, Gradient size: {gradient_size:.4f}, Max gradient size: {max_gradient_size:.4f}"
)
# Save LoRA weights at specified intervals
if self.total_steps % self.save_interval == 0:
print(
f"Epoch: {trainer.current_epoch}, Steps: {self.total_steps} - Saving LoRA weights"
)
pl_module.save_lora(
f"{self.save_path}/{self.run_name}/ckpt/{self.total_steps}"
)
if hasattr(pl_module, "save_moe"):
pl_module.save_moe(
f"{self.save_path}/{self.run_name}/ckpt/{self.total_steps}/moe.pt"
)
# Generate and save a sample image at specified intervals
if self.total_steps % self.sample_interval == 0:
print(
f"Epoch: {trainer.current_epoch}, Steps: {self.total_steps} - Generating a sample"
)
self.generate_a_sample(
trainer,
pl_module,
f"{self.save_path}/{self.run_name}/output",
f"lora_{self.total_steps}",
batch["condition_type"][
0
], # Use the condition type from the current batch
)
@torch.no_grad()
def generate_a_sample(
self,
trainer,
pl_module,
save_path,
file_name,
condition_type="super_resolution",
):
# TODO: change this two variables to parameters
target_size = trainer.training_config["dataset"]["target_size"]
position_scale = trainer.training_config["dataset"].get("position_scale", 1.0)
generator = torch.Generator(device=pl_module.device)
generator.manual_seed(42)
test_list = []
condition_img_path = "data/mmcelebahq/mask/27000.png"
# condition_img = self.deepth_pipe(condition_img)["depth"].convert("RGB")
test_list.append(
(
condition_img_path,
[0, 0],
"She is wearing lipstick. She is attractive and has straight hair.",
{"position_scale": position_scale} if position_scale != 1.0 else {},
)
)
if not os.path.exists(save_path):
os.makedirs(save_path)
for i, (condition_img_path, position_delta, prompt, *others) in enumerate(
test_list
):
global_mask = Image.open(condition_img_path).convert("RGB")
mask_list = [self.to_tensor(global_mask)]
mask = Image.open(condition_img_path)
mask = np.array(mask)
for i in range(19):
local_mask = np.zeros_like(mask)
local_mask[mask == i] = 255
local_mask_rgb = Image.fromarray(local_mask).convert("RGB")
local_mask_tensor = self.to_tensor(local_mask_rgb)
mask_list.append(local_mask_tensor)
condition_img = torch.stack(mask_list, dim=0)
# condition_img = condition_img.unsqueeze(0)
condition = Condition(
condition_type=condition_type,
condition=condition_img,
position_delta=position_delta,
**(others[0] if others else {}),
)
res = generate(
pl_module.flux_pipe,
mogle=pl_module.mogle,
prompt=prompt,
conditions=[condition],
height=target_size,
width=target_size,
generator=generator,
model_config=pl_module.model_config,
default_lora=True,
)
res.images[0].save(
os.path.join(save_path, f"{file_name}_{condition_type}_{i}.jpg")
)
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