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Remove omegaconf dependency and some ci changes.

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This commit is contained in:
comfyanonymous
2023-03-13 14:49:18 -04:00
parent 80665081e0
commit 54dbfaf2ec
8 changed files with 26 additions and 22 deletions
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6
comfy/cldm/cldm.py
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@@ -59,9 +59,9 @@ class ControlNet(nn.Module):
if context_dim is not None:
assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
from omegaconf.listconfig import ListConfig
if type(context_dim) == ListConfig:
context_dim = list(context_dim)
# from omegaconf.listconfig import ListConfig
# if type(context_dim) == ListConfig:
# context_dim = list(context_dim)
if num_heads_upsample == -1:
num_heads_upsample = num_heads

5
comfy/ldm/models/diffusion/ddpm.py
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@@ -18,7 +18,6 @@ import itertools
from tqdm import tqdm
from torchvision.utils import make_grid
# from pytorch_lightning.utilities.distributed import rank_zero_only
from omegaconf import ListConfig
from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
from ldm.modules.ema import LitEma
@@ -1124,8 +1123,8 @@ class LatentDiffusion(DDPM):
def get_unconditional_conditioning(self, batch_size, null_label=None):
if null_label is not None:
xc = null_label
if isinstance(xc, ListConfig):
xc = list(xc)
# if isinstance(xc, ListConfig):
# xc = list(xc)
if isinstance(xc, dict) or isinstance(xc, list):
c = self.get_learned_conditioning(xc)
else:

6
comfy/ldm/modules/diffusionmodules/openaimodel.py
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@@ -477,9 +477,9 @@ class UNetModel(nn.Module):
if context_dim is not None:
assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
from omegaconf.listconfig import ListConfig
if type(context_dim) == ListConfig:
context_dim = list(context_dim)
# from omegaconf.listconfig import ListConfig
# if type(context_dim) == ListConfig:
# context_dim = list(context_dim)
if num_heads_upsample == -1:
num_heads_upsample = num_heads

7
comfy/sd.py
View File

@@ -6,7 +6,7 @@ import sd2_clip
import model_management
from .ldm.util import instantiate_from_config
from .ldm.models.autoencoder import AutoencoderKL
from omegaconf import OmegaConf
import yaml
from .cldm import cldm
from .t2i_adapter import adapter
@@ -726,7 +726,8 @@ def load_clip(ckpt_path, embedding_directory=None):
return clip
def load_checkpoint(config_path, ckpt_path, output_vae=True, output_clip=True, embedding_directory=None):
config = OmegaConf.load(config_path)
with open(config_path, 'r') as stream:
config = yaml.safe_load(stream)
model_config_params = config['model']['params']
clip_config = model_config_params['cond_stage_config']
scale_factor = model_config_params['scale_factor']
@@ -750,7 +751,7 @@ def load_checkpoint(config_path, ckpt_path, output_vae=True, output_clip=True, e
w.cond_stage_model = clip.cond_stage_model
load_state_dict_to = [w]
model = instantiate_from_config(config.model)
model = instantiate_from_config(config["model"])
sd = load_torch_file(ckpt_path)
model = load_model_weights(model, sd, verbose=False, load_state_dict_to=load_state_dict_to)
return (ModelPatcher(model), clip, vae)