File size: 5,252 Bytes
2fa003a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 | import os
import torch
from modules import shared, paths, sd_disable_initialization, devices
sd_configs_path = shared.sd_configs_path
sd_repo_configs_path = os.path.join(paths.paths['Stable Diffusion'], "configs", "stable-diffusion")
sd_xl_repo_configs_path = os.path.join(paths.paths['Stable Diffusion XL'], "configs", "inference")
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_sdxl = os.path.join(sd_xl_repo_configs_path, "sd_xl_base.yaml")
config_sdxl_refiner = os.path.join(sd_xl_repo_configs_path, "sd_xl_refiner.yaml")
config_sdxl_inpainting = os.path.join(sd_configs_path, "sd_xl_inpaint.yaml")
config_depth_model = os.path.join(sd_repo_configs_path, "v2-midas-inference.yaml")
config_unclip = os.path.join(sd_repo_configs_path, "v2-1-stable-unclip-l-inference.yaml")
config_unopenclip = os.path.join(sd_repo_configs_path, "v2-1-stable-unclip-h-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")
config_alt_diffusion_m18 = os.path.join(sd_configs_path, "alt-diffusion-m18-inference.yaml")
def is_using_v_parameterization_for_sd2(state_dict):
"""
Detects whether unet in state_dict is using v-parameterization. Returns True if it is. You're welcome.
"""
import ldm.modules.diffusionmodules.openaimodel
device = devices.cpu
with sd_disable_initialization.DisableInitialization():
unet = ldm.modules.diffusionmodules.openaimodel.UNetModel(
use_checkpoint=True,
use_fp16=False,
image_size=32,
in_channels=4,
out_channels=4,
model_channels=320,
attention_resolutions=[4, 2, 1],
num_res_blocks=2,
channel_mult=[1, 2, 4, 4],
num_head_channels=64,
use_spatial_transformer=True,
use_linear_in_transformer=True,
transformer_depth=1,
context_dim=1024,
legacy=False
)
unet.eval()
with torch.no_grad():
unet_sd = {k.replace("model.diffusion_model.", ""): v for k, v in state_dict.items() if "model.diffusion_model." in k}
unet.load_state_dict(unet_sd, strict=True)
unet.to(device=device, dtype=torch.float)
test_cond = torch.ones((1, 2, 1024), device=device) * 0.5
x_test = torch.ones((1, 4, 8, 8), device=device) * 0.5
out = (unet(x_test, torch.asarray([999], device=device), context=test_cond) - x_test).mean().item()
return out < -1
def guess_model_config_from_state_dict(sd, 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)
sd2_variations_weight = sd.get('embedder.model.ln_final.weight', None)
if sd.get('conditioner.embedders.1.model.ln_final.weight', None) is not None:
if diffusion_model_input.shape[1] == 9:
return config_sdxl_inpainting
else:
return config_sdxl
if sd.get('conditioner.embedders.0.model.ln_final.weight', None) is not None:
return config_sdxl_refiner
elif sd.get('depth_model.model.pretrained.act_postprocess3.0.project.0.bias', None) is not None:
return config_depth_model
elif sd2_variations_weight is not None and sd2_variations_weight.shape[0] == 768:
return config_unclip
elif sd2_variations_weight is not None and sd2_variations_weight.shape[0] == 1024:
return config_unopenclip
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 is_using_v_parameterization_for_sd2(sd):
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:
if sd.get('cond_stage_model.transformation.weight').size()[0] == 1024:
return config_alt_diffusion_m18
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 = f"{os.path.splitext(info.filename)[0]}.yaml"
if os.path.exists(config):
return config
return None
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