FL PixelGen Training Config

A ComfyUI node in FL/PixelGen/Training with 18 inputs and 1 output.

By filliptm·Created 5 months ago·Updated 4 months ago· 13

config

◄lora_rank8►

◄lora_alpha16►

◄lora_dropout0.10►

◄learning_rate0.0001►

◄epochs100►

◄batch_size1►

◄gradient_accumulation4►

◄save_every10►

◄max_grad_norm1.0►

◄seed42►

◄warmup_steps100►

◄feat_loss_weight0.5►

◄lpips_weight1.0►

◄dino_weight1.0►

◄percept_ratio1.0►

◄noise_gating_threshold0.00►

◄cfg_dropout0.10►

◄output_dir./output/pixelgen►

CategoryFL/PixelGen/Training

Inputs (18)

Name	Type	Default	Description
lora_rank	INT	81–128	Number of low-rank dimensions per adapter. Higher rank = more expressive but slower training and larger files. Start with 8, increase to 16-32 for complex styles.
lora_alpha	INT	161–256	Scaling factor for LoRA weights. Typically set to 2x the rank. Higher values make the LoRA effect stronger during training.
lora_dropout	FLOAT	0.100–0.5	Randomly drops LoRA connections during training to prevent overfitting. 0.1 is a good default; increase to 0.2-0.3 for very small datasets.
learning_rate	FLOAT	0.00010.000001–0.01	How fast the model learns. Too high causes instability, too low learns nothing. 1e-4 is a solid starting point; lower to 5e-5 if loss is noisy.
epochs	INT	1001–10000	Number of full passes through the dataset. More epochs = more training. Watch the loss curve — if it plateaus or rises, you're overfitting.
batch_size	INT	11–8	Images processed in parallel per step. Higher values give smoother gradients but use more VRAM. Effective batch = batch_size x gradient_accumulation.
gradient_accumulation	INT	41–32	Simulates a larger batch by accumulating gradients across multiple steps before updating weights. Increase this instead of batch_size to save VRAM.
save_every	INT	101–1000	Save a checkpoint every N epochs. Lower values give more recovery points but use more disk space. Also triggers validation inference if a prompt is set.
max_grad_norm	FLOAT	1.00.1–10	Clips large gradients to prevent training explosions. 1.0 is standard. Only increase if training is very stable and loss is stalling.
seed	INT	420–18446744073709550000	Random seed for reproducibility. Same seed + same settings = same training run.
warmup_stepsopt	INT	1000–10000	Gradually ramps up the learning rate from 10% to full over this many steps. Prevents early instability. 100 steps is a safe default.
feat_loss_weightopt	FLOAT	0.50–10	Weight for cosine feature alignment between denoiser and DINOv2 features (REPA). Higher values push the model to match DINOv2's semantic understanding.
lpips_weightopt	FLOAT	1.00–10	Weight for LPIPS perceptual loss. Encourages outputs that look perceptually similar to the target. Higher values prioritize visual quality over exact pixel matching.
dino_weightopt	FLOAT	1.00–10	Weight for DINO multi-layer feature matching loss. Guides the model to capture high-level structure and object features from DINOv2.
percept_ratioopt	FLOAT	1.00–10	Global multiplier for all perceptual losses (LPIPS + DINO). Scale this to balance perceptual quality vs flow matching fidelity.
noise_gating_thresholdopt	FLOAT	0.000–1	Only apply perceptual losses when timestep t >= this threshold. Focuses perceptual guidance on less noisy samples where image structure is visible. 0 = always apply.
cfg_dropoutopt	FLOAT	0.100–1	Probability of dropping text conditioning during training. Teaches the model to generate without text guidance, improving classifier-free guidance quality at inference.
output_diropt	STRING	./output/pixelgen	Directory where training checkpoints and validation images are saved.

Outputs (1)

Name	Type	Description
config	PIXELGEN_TRAINING_CONFIG	—