Remove omegaconf dependency and some ci changes.

.ci/setup_windows_zip_nightly_pytorch.ps1

@@ -5,11 +5,12 @@ cd python_embeded
 Add-Content -Path .\python310._pth -Value 'import site'
 Invoke-WebRequest -Uri https://bootstrap.pypa.io/get-pip.py -OutFile get-pip.py
 .\python.exe get-pip.py
-.\python.exe -s -m pip install torch torchvision torchaudio --pre --extra-index-url https://download.pytorch.org/whl/nightly/cu118 -r ../ComfyUI/requirements.txt pygit2
+python -m pip wheel torch torchvision torchaudio --pre --extra-index-url https://download.pytorch.org/whl/nightly/cu118 -r ../ComfyUI/requirements.txt pygit2 -w ../temp_wheel_dir
+ls ../temp_wheel_dir
+.\python.exe -s -m pip install --pre (get-item ..\temp_wheel_dir\*)
 "../ComfyUI`n" + (Get-Content .\python310._pth -Raw) | Set-Content .\python310._pth
 cd ..
 
-
 mkdir ComfyUI_windows_portable
 mv python_embeded ComfyUI_windows_portable_nightly_pytorch
 mv ComfyUI_copy ComfyUI_windows_portable_nightly_pytorch/ComfyUI

.github/workflows/windows_release.yml

@@ -25,10 +25,11 @@ jobs:
             .\setup_windows_zip.ps1
             ls
 
-        - uses: "marvinpinto/action-automatic-releases@latest"
+        - name: Upload binaries to release
+          uses: svenstaro/upload-release-action@v2
           with:
-                repo_token: "${{ secrets.GITHUB_TOKEN }}"
-                automatic_release_tag: "latest"
-                prerelease: true
-                title: "ComfyUI Standalone Portable Windows Build (For NVIDIA or CPU only)"
-                files: ComfyUI_windows_portable_nvidia_or_cpu.7z
+                repo_token: ${{ secrets.GITHUB_TOKEN }}
+                file: ComfyUI_windows_portable_nvidia_or_cpu.7z
+                tag: "latest"
+                overwrite: true
+

.github/workflows/windows_release_nightly_pytorch.yml

@@ -17,7 +17,9 @@ jobs:
         - uses: actions/checkout@v3
           with:
             fetch-depth: 0
-
+        - uses: actions/setup-python@v4
+          with:
+            python-version: '3.10.9'
         - run: |
             cd ..
             cp ComfyUI/.ci/setup_windows_zip_nightly_pytorch.ps1 ./

comfy/cldm/cldm.py

@@ -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

comfy/ldm/models/diffusion/ddpm.py

@@ -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:

comfy/ldm/modules/diffusionmodules/openaimodel.py

@@ -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

comfy/sd.py

@@ -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)

requirements.txt

 torch
 torchdiffeq
 torchsde
-omegaconf
 einops
 open-clip-torch
 transformers
@@ -9,3 +8,4 @@ safetensors
 pytorch_lightning
 aiohttp
 accelerate
+pyyaml