AbstractPhil / sdxl-interpolated

• Requires a custom training notebook that will be provided soon.

• Distilling SDXL using T5 attention masking for the sake of teaching SDXL; CLIP_L and CLIP_G to expect the T5 attention mask.

• Additional finetuning required, additional interpolation required, addistional distillation required for full cohesion.

• Ongoing training effort interpolating the T5 into SDXL using teacher/student process.

• -config = {

• "epochs": 10,

• "batch_size": 64,

• "learning_rate": 1e-6, # Lower learning rate for stability

• "save_interval_steps": 10, # Save checkpoint every 10 training steps

• "test_save_interval_steps": 10, # Save test images every 10 training steps

• "checkpoint_dir": "./checkpoints", # Full diffusers checkpoint folder

• "compact_model_dir": "./compact_model", # For final compact model (not used for caching)

• "baseline_test_dir": "./baseline_test", # For baseline images & captions

• "cache_dir": "./cache", # Folder for caching T5 outputs and teacher features

• "num_generated_captions": 128, # Number of captions to generate for training

• "model_id": "stabilityai/stable-diffusion-xl-base-1.0",

• "model_name": "my_interpolative_distillation", # Folder name for checkpoints

• "seed": 420,

• "device": torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu"),

• "inference_steps": 50,

• "height": 1024,

• "width": 1024,

• "guidance_scale": 7.5,

• "inference_interval": 10,

• "max_caption_length": 512,

• Batch size for teacher feature caching (set very low to reduce VRAM usage)

• "cache_teacher_batch_size": 64,

-}