EveryDream2trainer/utils/unet_utils.py

"""
Copyright [2022] Victor C Hall

Licensed under the GNU Affero General Public License;
You may not use this code except in compliance with the License.
You may obtain a copy of the License at

    https://www.gnu.org/licenses/agpl-3.0.en.html

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
import os
import json
import logging

import torch
from colorama import Fore, Style

def enforce_zero_terminal_snr(betas):
    # from https://arxiv.org/pdf/2305.08891.pdf
    alphas = 1 - betas
    alphas_bar = alphas.cumprod(0)
    alphas_bar_sqrt = alphas_bar.sqrt()

    alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone()
    alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone()
    alphas_bar_sqrt -= alphas_bar_sqrt_T
    alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T)
    
    alphas_bar = alphas_bar_sqrt ** 2
    alphas = alphas_bar[1:] / alphas_bar[:-1]
    alphas = torch.cat([alphas_bar[0:1], alphas])
    betas = 1 - alphas
    return betas

def get_attn_yaml(ckpt_path):
    """
    Analyze the checkpoint to determine the attention head type and yaml to use for inference
    """
    unet_cfg_path = os.path.join(ckpt_path, "unet", "config.json")
    with open(unet_cfg_path, "r") as f:
        unet_cfg = json.load(f)

    scheduler_cfg_path = os.path.join(ckpt_path, "scheduler", "scheduler_config.json")
    with open(scheduler_cfg_path, "r") as f:
        scheduler_cfg = json.load(f)

    is_sd1attn = unet_cfg["attention_head_dim"] == [8, 8, 8, 8]
    is_sd1attn = unet_cfg["attention_head_dim"] == 8 or is_sd1attn

    if 'prediction_type' not in scheduler_cfg:
        logging.warn(f"Model has no prediction_type, assuming epsilon")
        prediction_type = "epsilon"
    else:
        prediction_type = scheduler_cfg["prediction_type"]

    logging.info(f" unet attention_head_dim: {unet_cfg['attention_head_dim']}")

    yaml = ''
    if prediction_type in ["v_prediction","v-prediction"] and not is_sd1attn:
        yaml = "v2-inference-v.yaml"
    elif prediction_type == "epsilon" and not is_sd1attn:
        yaml = "v2-inference.yaml"
    elif prediction_type == "epsilon" and is_sd1attn:
        yaml = "v1-inference.yaml"
    else:
        logging.warning(f"{Fore.YELLOW}Unknown model format for: {prediction_type} and attention_head_dim {unet_cfg['attention_head_dim']}{Style.RESET_ALL}")
        yaml = "v1-inference.yaml" # HACK: for now this means no yaml is saved together with .ckpt files during checkpointing


    logging.info(f"Inferred yaml: {yaml}, attn: {'sd1' if is_sd1attn else 'sd2'}, prediction_type: {prediction_type}")

    return is_sd1attn, yaml