stable-diffusion-webui/modules/sd_models_config.py
2023-01-28 08:30:17 +03:00

71 lines
2.5 KiB
Python

import re
import os
from modules import shared, paths
sd_configs_path = shared.sd_configs_path
sd_repo_configs_path = os.path.join(paths.paths['Stable Diffusion'], "configs", "stable-diffusion")
config_default = shared.sd_default_config
config_sd2 = os.path.join(sd_repo_configs_path, "v2-inference.yaml")
config_sd2v = os.path.join(sd_repo_configs_path, "v2-inference-v.yaml")
config_sd2_inpainting = os.path.join(sd_repo_configs_path, "v2-inpainting-inference.yaml")
config_depth_model = os.path.join(sd_repo_configs_path, "v2-midas-inference.yaml")
config_inpainting = os.path.join(sd_configs_path, "v1-inpainting-inference.yaml")
config_instruct_pix2pix = os.path.join(sd_configs_path, "instruct-pix2pix.yaml")
config_alt_diffusion = os.path.join(sd_configs_path, "alt-diffusion-inference.yaml")
re_parametrization_v = re.compile(r'-v\b')
def guess_model_config_from_state_dict(sd, filename):
fn = os.path.basename(filename)
sd2_cond_proj_weight = sd.get('cond_stage_model.model.transformer.resblocks.0.attn.in_proj_weight', None)
diffusion_model_input = sd.get('model.diffusion_model.input_blocks.0.0.weight', None)
if sd.get('depth_model.model.pretrained.act_postprocess3.0.project.0.bias', None) is not None:
return config_depth_model
if sd2_cond_proj_weight is not None and sd2_cond_proj_weight.shape[1] == 1024:
if diffusion_model_input.shape[1] == 9:
return config_sd2_inpainting
elif re.search(re_parametrization_v, fn):
return config_sd2v
else:
return config_sd2
if diffusion_model_input is not None:
if diffusion_model_input.shape[1] == 9:
return config_inpainting
if diffusion_model_input.shape[1] == 8:
return config_instruct_pix2pix
if sd.get('cond_stage_model.roberta.embeddings.word_embeddings.weight', None) is not None:
return config_alt_diffusion
return config_default
def find_checkpoint_config(state_dict, info):
if info is None:
return guess_model_config_from_state_dict(state_dict, "")
config = find_checkpoint_config_near_filename(info)
if config is not None:
return config
return guess_model_config_from_state_dict(state_dict, info.filename)
def find_checkpoint_config_near_filename(info):
if info is None:
return None
config = os.path.splitext(info.filename)[0] + ".yaml"
if os.path.exists(config):
return config
return None