Merge branch 'comfyanonymous:master' into master

.ci/update_windows/update.py

@@ -1,6 +1,9 @@
 import pygit2
 from datetime import datetime
 import sys
+import os
+import shutil
+import filecmp
 
 def pull(repo, remote_name='origin', branch='master'):
     for remote in repo.remotes:
@@ -42,7 +45,8 @@ def pull(repo, remote_name='origin', branch='master'):
                 raise AssertionError('Unknown merge analysis result')
 
 pygit2.option(pygit2.GIT_OPT_SET_OWNER_VALIDATION, 0)
-repo = pygit2.Repository(str(sys.argv[1]))
+repo_path = str(sys.argv[1])
+repo = pygit2.Repository(repo_path)
 ident = pygit2.Signature('comfyui', 'comfy@ui')
 try:
     print("stashing current changes")
@@ -51,7 +55,10 @@ except KeyError:
     print("nothing to stash")
 backup_branch_name = 'backup_branch_{}'.format(datetime.today().strftime('%Y-%m-%d_%H_%M_%S'))
 print("creating backup branch: {}".format(backup_branch_name))
-repo.branches.local.create(backup_branch_name, repo.head.peel())
+try:
+    repo.branches.local.create(backup_branch_name, repo.head.peel())
+except:
+    pass
 
 print("checking out master branch")
 branch = repo.lookup_branch('master')
@@ -63,3 +70,41 @@ pull(repo)
 
 print("Done!")
 
+self_update = True
+if len(sys.argv) > 2:
+    self_update = '--skip_self_update' not in sys.argv
+
+update_py_path = os.path.realpath(__file__)
+repo_update_py_path = os.path.join(repo_path, ".ci/update_windows/update.py")
+
+cur_path = os.path.dirname(update_py_path)
+
+
+req_path = os.path.join(cur_path, "current_requirements.txt")
+repo_req_path = os.path.join(repo_path, "requirements.txt")
+
+
+def files_equal(file1, file2):
+    try:
+        return filecmp.cmp(file1, file2, shallow=False)
+    except:
+        return False
+
+def file_size(f):
+    try:
+        return os.path.getsize(f)
+    except:
+        return 0
+
+
+if self_update and not files_equal(update_py_path, repo_update_py_path) and file_size(repo_update_py_path) > 10:
+    shutil.copy(repo_update_py_path, os.path.join(cur_path, "update_new.py"))
+    exit()
+
+if not os.path.exists(req_path) or not files_equal(repo_req_path, req_path):
+    import subprocess
+    try:
+        subprocess.check_call([sys.executable, '-m', 'pip', 'install', '-r', repo_req_path])
+        shutil.copy(repo_req_path, req_path)
+    except:
+        pass

.ci/update_windows/update_comfyui.bat

@@ -1,2 +1,8 @@
+@echo off
 ..\python_embeded\python.exe .\update.py ..\ComfyUI\
-pause
+if exist update_new.py (
+  move /y update_new.py update.py
+  echo Running updater again since it got updated.
+  ..\python_embeded\python.exe .\update.py ..\ComfyUI\ --skip_self_update
+)
+if "%~1"=="" pause

.ci/update_windows/update_comfyui_and_python_dependencies.bat

@@ -1,3 +0,0 @@
-..\python_embeded\python.exe .\update.py ..\ComfyUI\
-..\python_embeded\python.exe -s -m pip install --upgrade torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu117 xformers -r ../ComfyUI/requirements.txt pygit2
-pause

.ci/update_windows_cu118/update_comfyui_and_python_dependencies.bat

@@ -1,11 +0,0 @@
-@echo off
-..\python_embeded\python.exe .\update.py ..\ComfyUI\
-echo
-echo This will try to update pytorch and all python dependencies, if you get an error wait for pytorch/xformers to fix their stuff
-echo You should not be running this anyways unless you really have to
-echo
-echo If you just want to update normally, close this and run update_comfyui.bat instead.
-echo
-pause
-..\python_embeded\python.exe -s -m pip install --upgrade torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu118 xformers -r ../ComfyUI/requirements.txt pygit2
-pause

.github/workflows/windows_release_cu118_dependencies.yml

@@ -1,71 +0,0 @@
-name: "Windows Release cu118 dependencies"
-
-on:
-  workflow_dispatch:
-#  push:
-#    branches:
-#      - master
-
-jobs:
-  build_dependencies:
-    env:
-        # you need at least cuda 5.0 for some of the stuff compiled here.
-        TORCH_CUDA_ARCH_LIST: "5.0+PTX 6.0 6.1 7.0 7.5 8.0 8.6 8.9"
-        FORCE_CUDA: 1
-        MAX_JOBS: 1 # will crash otherwise
-        DISTUTILS_USE_SDK: 1 # otherwise distutils will complain on windows about multiple versions of msvc
-        XFORMERS_BUILD_TYPE: "Release"
-    runs-on: windows-latest
-    steps:
-        - name: Cache Built Dependencies
-          uses: actions/cache@v3
-          id: cache-cu118_python_stuff
-          with:
-            path: cu118_python_deps.tar
-            key: ${{ runner.os }}-build-cu118
-
-        - if: steps.cache-cu118_python_stuff.outputs.cache-hit != 'true'
-          uses: actions/checkout@v3
-
-        - if: steps.cache-cu118_python_stuff.outputs.cache-hit != 'true'
-          uses: actions/setup-python@v4
-          with:
-            python-version: '3.10.9'
-
-        - if: steps.cache-cu118_python_stuff.outputs.cache-hit != 'true'
-          uses: comfyanonymous/cuda-toolkit@test
-          id: cuda-toolkit
-          with:
-            cuda: '11.8.0'
-        # copied from xformers github
-        - name: Setup MSVC
-          uses: ilammy/msvc-dev-cmd@v1
-        - name: Configure Pagefile
-          # windows runners will OOM with many CUDA architectures
-          # we cheat here with a page file
-          uses: al-cheb/configure-pagefile-action@v1.3
-          with:
-            minimum-size: 2GB
-        # really unfortunate: https://github.com/ilammy/msvc-dev-cmd#name-conflicts-with-shell-bash
-        - name: Remove link.exe
-          shell: bash
-          run: rm /usr/bin/link
-
-        - if: steps.cache-cu118_python_stuff.outputs.cache-hit != 'true'
-          shell: bash
-          run: |
-            python -m pip wheel --no-cache-dir torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu118 -r requirements.txt pygit2 -w ./temp_wheel_dir
-            python -m pip install --no-cache-dir ./temp_wheel_dir/*
-            echo installed basic
-            git clone --recurse-submodules https://github.com/facebookresearch/xformers.git
-            cd xformers
-            python -m pip install --no-cache-dir wheel setuptools twine
-            echo building xformers
-            python setup.py bdist_wheel -d ../temp_wheel_dir/
-            cd ..
-            rm -rf xformers
-            ls -lah temp_wheel_dir
-            mv temp_wheel_dir cu118_python_deps
-            tar cf cu118_python_deps.tar cu118_python_deps
-
-

.github/workflows/windows_release_cu118_dependencies_2.yml

@@ -1,37 +0,0 @@
-name: "Windows Release cu118 dependencies 2"
-
-on:
-  workflow_dispatch:
-    inputs:
-      xformers:
-        description: 'xformers version'
-        required: true
-        type: string
-        default: "xformers"
-
-#  push:
-#    branches:
-#      - master
-
-jobs:
-  build_dependencies:
-    runs-on: windows-latest
-    steps:
-        - uses: actions/checkout@v3
-        - uses: actions/setup-python@v4
-          with:
-            python-version: '3.10.9'
-
-        - shell: bash
-          run: |
-            python -m pip wheel --no-cache-dir torch torchvision torchaudio ${{ inputs.xformers }} --extra-index-url https://download.pytorch.org/whl/cu118 -r requirements.txt pygit2 -w ./temp_wheel_dir
-            python -m pip install --no-cache-dir ./temp_wheel_dir/*
-            echo installed basic
-            ls -lah temp_wheel_dir
-            mv temp_wheel_dir cu118_python_deps
-            tar cf cu118_python_deps.tar cu118_python_deps
-
-        - uses: actions/cache/save@v3
-          with:
-            path: cu118_python_deps.tar
-            key: ${{ runner.os }}-build-cu118

.github/workflows/windows_release_cu118_package.yml

@@ -1,79 +0,0 @@
-name: "Windows Release cu118 packaging"
-
-on:
-  workflow_dispatch:
-#  push:
-#    branches:
-#      - master
-
-jobs:
-  package_comfyui:
-    permissions:
-        contents: "write"
-        packages: "write"
-        pull-requests: "read"
-    runs-on: windows-latest
-    steps:
-        - uses: actions/cache/restore@v3
-          id: cache
-          with:
-            path: cu118_python_deps.tar
-            key: ${{ runner.os }}-build-cu118
-        - shell: bash
-          run: |
-            mv cu118_python_deps.tar ../
-            cd ..
-            tar xf cu118_python_deps.tar
-            pwd
-            ls
-
-        - uses: actions/checkout@v3
-          with:
-            fetch-depth: 0
-            persist-credentials: false
-        - shell: bash
-          run: |
-            cd ..
-            cp -r ComfyUI ComfyUI_copy
-            curl https://www.python.org/ftp/python/3.10.9/python-3.10.9-embed-amd64.zip -o python_embeded.zip
-            unzip python_embeded.zip -d python_embeded
-            cd python_embeded
-            echo 'import site' >> ./python310._pth
-            curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py
-            ./python.exe get-pip.py
-            ./python.exe -s -m pip install ../cu118_python_deps/*
-            sed -i '1i../ComfyUI' ./python310._pth
-            cd ..
-
-            git clone https://github.com/comfyanonymous/taesd
-            cp taesd/*.pth ./ComfyUI_copy/models/vae_approx/
-
-            mkdir ComfyUI_windows_portable
-            mv python_embeded ComfyUI_windows_portable
-            mv ComfyUI_copy ComfyUI_windows_portable/ComfyUI
-
-            cd ComfyUI_windows_portable
-
-            mkdir update
-            cp -r ComfyUI/.ci/update_windows/* ./update/
-            cp -r ComfyUI/.ci/update_windows_cu118/* ./update/
-            cp -r ComfyUI/.ci/windows_base_files/* ./
-
-            cd ..
-
-            "C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma -mx=8 -mfb=64 -md=32m -ms=on -mf=BCJ2 ComfyUI_windows_portable.7z ComfyUI_windows_portable
-            mv ComfyUI_windows_portable.7z ComfyUI/new_ComfyUI_windows_portable_nvidia_cu118_or_cpu.7z
-
-            cd ComfyUI_windows_portable
-            python_embeded/python.exe -s ComfyUI/main.py --quick-test-for-ci --cpu
-
-            ls
-
-        - name: Upload binaries to release
-          uses: svenstaro/upload-release-action@v2
-          with:
-                repo_token: ${{ secrets.GITHUB_TOKEN }}
-                file: new_ComfyUI_windows_portable_nvidia_cu118_or_cpu.7z
-                tag: "latest"
-                overwrite: true
-

.github/workflows/windows_release_dependencies.yml

@@ -41,10 +41,9 @@ jobs:
         - shell: bash
           run: |
             echo "@echo off
-            ..\python_embeded\python.exe .\update.py ..\ComfyUI\\
+            call update_comfyui.bat nopause
             echo -
-            echo This will try to update pytorch and all python dependencies, if you get an error wait for pytorch/xformers to fix their stuff
+            echo This will try to update pytorch and all python dependencies.
-            echo You should not be running this anyways unless you really have to
             echo -
             echo If you just want to update normally, close this and run update_comfyui.bat instead.
             echo -

.github/workflows/windows_release_nightly_pytorch.yml

@@ -68,7 +68,7 @@ jobs:
             cp -r ComfyUI/.ci/update_windows/* ./update/
             cp -r ComfyUI/.ci/windows_base_files/* ./
 
-            echo "..\python_embeded\python.exe .\update.py ..\ComfyUI\\
+            echo "call update_comfyui.bat nopause
             ..\python_embeded\python.exe -s -m pip install --upgrade --pre torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/cu${{ inputs.cu }} -r ../ComfyUI/requirements.txt pygit2
             pause" > ./update/update_comfyui_and_python_dependencies.bat
             cd ..

comfy/clip_model.py

@@ -119,6 +119,9 @@ class CLIPTextModel(torch.nn.Module):
         super().__init__()
         self.num_layers = config_dict["num_hidden_layers"]
         self.text_model = CLIPTextModel_(config_dict, dtype, device, operations)
+        embed_dim = config_dict["hidden_size"]
+        self.text_projection = operations.Linear(embed_dim, embed_dim, bias=False, dtype=dtype, device=device)
+        self.text_projection.weight.copy_(torch.eye(embed_dim))
         self.dtype = dtype
 
     def get_input_embeddings(self):
@@ -128,7 +131,10 @@ class CLIPTextModel(torch.nn.Module):
         self.text_model.embeddings.token_embedding = embeddings
 
     def forward(self, *args, **kwargs):
-        return self.text_model(*args, **kwargs)
+        x = self.text_model(*args, **kwargs)
+        out = self.text_projection(x[2])
+        return (x[0], x[1], out, x[2])
+
 
 class CLIPVisionEmbeddings(torch.nn.Module):
     def __init__(self, embed_dim, num_channels=3, patch_size=14, image_size=224, dtype=None, device=None, operations=None):

comfy/diffusers_convert.py

@@ -237,6 +237,10 @@ def convert_text_enc_state_dict_v20(text_enc_dict, prefix=""):
             capture_qkv_bias[k_pre][code2idx[k_code]] = v
             continue
 
+        text_proj = "transformer.text_projection.weight"
+        if k.endswith(text_proj):
+            new_state_dict[k.replace(text_proj, "text_projection")] = v.transpose(0, 1).contiguous()
+
         relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k)
         new_state_dict[relabelled_key] = v
 

comfy/lora.py

@@ -200,6 +200,13 @@ def model_lora_keys_clip(model, key_map={}):
                     lora_key = "lora_prior_te_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #cascade lora: TODO put lora key prefix in the model config
                     key_map[lora_key] = k
 
+
+    k = "clip_g.transformer.text_projection.weight"
+    if k in sdk:
+        key_map["lora_prior_te_text_projection"] = k #cascade lora?
+        # key_map["text_encoder.text_projection"] = k #TODO: check if other lora have the text_projection too
+        # key_map["lora_te_text_projection"] = k
+
     return key_map
 
 def model_lora_keys_unet(model, key_map={}):

comfy/sd.py

@@ -52,7 +52,7 @@ def load_clip_weights(model, sd):
         if ids.dtype == torch.float32:
             sd['cond_stage_model.transformer.text_model.embeddings.position_ids'] = ids.round()
 
-    sd = comfy.utils.transformers_convert(sd, "cond_stage_model.model.", "cond_stage_model.transformer.text_model.", 24)
+    sd = comfy.utils.clip_text_transformers_convert(sd, "cond_stage_model.model.", "cond_stage_model.transformer.")
     return load_model_weights(model, sd)
 
 
@@ -123,10 +123,13 @@ class CLIP:
         return self.tokenizer.tokenize_with_weights(text, return_word_ids)
 
     def encode_from_tokens(self, tokens, return_pooled=False):
+        self.cond_stage_model.reset_clip_options()
+
         if self.layer_idx is not None:
-            self.cond_stage_model.clip_layer(self.layer_idx)
+            self.cond_stage_model.set_clip_options({"layer": self.layer_idx})
-        else:
+
-            self.cond_stage_model.reset_clip_layer()
+        if return_pooled == "unprojected":
+            self.cond_stage_model.set_clip_options({"projected_pooled": False})
 
         self.load_model()
         cond, pooled = self.cond_stage_model.encode_token_weights(tokens)
@@ -361,7 +364,10 @@ def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DI
 
     for i in range(len(clip_data)):
         if "transformer.resblocks.0.ln_1.weight" in clip_data[i]:
-            clip_data[i] = comfy.utils.transformers_convert(clip_data[i], "", "text_model.", 32)
+            clip_data[i] = comfy.utils.clip_text_transformers_convert(clip_data[i], "", "")
+        else:
+            if "text_projection" in clip_data[i]:
+                clip_data[i]["text_projection.weight"] = clip_data[i]["text_projection"].transpose(0, 1) #old models saved with the CLIPSave node
 
     clip_target = EmptyClass()
     clip_target.params = {}

comfy/sd1_clip.py

@@ -67,7 +67,7 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
     ]
     def __init__(self, version="openai/clip-vit-large-patch14", device="cpu", max_length=77,
                  freeze=True, layer="last", layer_idx=None, textmodel_json_config=None, dtype=None, model_class=comfy.clip_model.CLIPTextModel,
-                 special_tokens={"start": 49406, "end": 49407, "pad": 49407}, layer_norm_hidden_state=True, enable_attention_masks=False):  # clip-vit-base-patch32
+                 special_tokens={"start": 49406, "end": 49407, "pad": 49407}, layer_norm_hidden_state=True, enable_attention_masks=False, return_projected_pooled=True):  # clip-vit-base-patch32
         super().__init__()
         assert layer in self.LAYERS
 
@@ -86,16 +86,18 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
         self.layer = layer
         self.layer_idx = None
         self.special_tokens = special_tokens
-        self.text_projection = torch.nn.Parameter(torch.eye(self.transformer.get_input_embeddings().weight.shape[1]))
+
         self.logit_scale = torch.nn.Parameter(torch.tensor(4.6055))
         self.enable_attention_masks = enable_attention_masks
 
         self.layer_norm_hidden_state = layer_norm_hidden_state
+        self.return_projected_pooled = return_projected_pooled
+
         if layer == "hidden":
             assert layer_idx is not None
             assert abs(layer_idx) < self.num_layers
-            self.clip_layer(layer_idx)
+            self.set_clip_options({"layer": layer_idx})
-        self.layer_default = (self.layer, self.layer_idx)
+        self.options_default = (self.layer, self.layer_idx, self.return_projected_pooled)
 
     def freeze(self):
         self.transformer = self.transformer.eval()
@@ -103,16 +105,19 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
         for param in self.parameters():
             param.requires_grad = False
 
-    def clip_layer(self, layer_idx):
+    def set_clip_options(self, options):
-        if abs(layer_idx) > self.num_layers:
+        layer_idx = options.get("layer", self.layer_idx)
+        self.return_projected_pooled = options.get("projected_pooled", self.return_projected_pooled)
+        if layer_idx is None or abs(layer_idx) > self.num_layers:
             self.layer = "last"
         else:
             self.layer = "hidden"
             self.layer_idx = layer_idx
 
-    def reset_clip_layer(self):
+    def reset_clip_options(self):
-        self.layer = self.layer_default[0]
+        self.layer = self.options_default[0]
-        self.layer_idx = self.layer_default[1]
+        self.layer_idx = self.options_default[1]
+        self.return_projected_pooled = self.options_default[2]
 
     def set_up_textual_embeddings(self, tokens, current_embeds):
         out_tokens = []
@@ -177,23 +182,19 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
         else:
             z = outputs[1]
 
-        if outputs[2] is not None:
+        pooled_output = None
-            pooled_output = outputs[2].float()
+        if len(outputs) >= 3:
-        else:
+            if not self.return_projected_pooled and len(outputs) >= 4 and outputs[3] is not None:
-            pooled_output = None
+                pooled_output = outputs[3].float()
+            elif outputs[2] is not None:
+                pooled_output = outputs[2].float()
 
-        if self.text_projection is not None and pooled_output is not None:
-            pooled_output = pooled_output.float().to(self.text_projection.device) @ self.text_projection.float()
         return z.float(), pooled_output
 
     def encode(self, tokens):
         return self(tokens)
 
     def load_sd(self, sd):
-        if "text_projection" in sd:
-            self.text_projection[:] = sd.pop("text_projection")
-        if "text_projection.weight" in sd:
-            self.text_projection[:] = sd.pop("text_projection.weight").transpose(0, 1)
         return self.transformer.load_state_dict(sd, strict=False)
 
 def parse_parentheses(string):
@@ -354,11 +355,12 @@ def load_embed(embedding_name, embedding_directory, embedding_size, embed_key=No
     return embed_out
 
 class SDTokenizer:
-    def __init__(self, tokenizer_path=None, max_length=77, pad_with_end=True, embedding_directory=None, embedding_size=768, embedding_key='clip_l', tokenizer_class=CLIPTokenizer, has_start_token=True, pad_to_max_length=True):
+    def __init__(self, tokenizer_path=None, max_length=77, pad_with_end=True, embedding_directory=None, embedding_size=768, embedding_key='clip_l', tokenizer_class=CLIPTokenizer, has_start_token=True, pad_to_max_length=True, min_length=None):
         if tokenizer_path is None:
             tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_tokenizer")
         self.tokenizer = tokenizer_class.from_pretrained(tokenizer_path)
         self.max_length = max_length
+        self.min_length = min_length
 
         empty = self.tokenizer('')["input_ids"]
         if has_start_token:
@@ -470,6 +472,8 @@ class SDTokenizer:
         batch.append((self.end_token, 1.0, 0))
         if self.pad_to_max_length:
             batch.extend([(pad_token, 1.0, 0)] * (self.max_length - len(batch)))
+        if self.min_length is not None and len(batch) < self.min_length:
+            batch.extend([(pad_token, 1.0, 0)] * (self.min_length - len(batch)))
 
         if not return_word_ids:
             batched_tokens = [[(t, w) for t, w,_ in x] for x in batched_tokens]
@@ -503,11 +507,11 @@ class SD1ClipModel(torch.nn.Module):
         self.clip = "clip_{}".format(self.clip_name)
         setattr(self, self.clip, clip_model(device=device, dtype=dtype, **kwargs))
 
-    def clip_layer(self, layer_idx):
+    def set_clip_options(self, options):
-        getattr(self, self.clip).clip_layer(layer_idx)
+        getattr(self, self.clip).set_clip_options(options)
 
-    def reset_clip_layer(self):
+    def reset_clip_options(self):
-        getattr(self, self.clip).reset_clip_layer()
+        getattr(self, self.clip).reset_clip_options()
 
     def encode_token_weights(self, token_weight_pairs):
         token_weight_pairs = token_weight_pairs[self.clip_name]

comfy/sdxl_clip.py

@@ -40,13 +40,13 @@ class SDXLClipModel(torch.nn.Module):
         self.clip_l = sd1_clip.SDClipModel(layer="hidden", layer_idx=-2, device=device, dtype=dtype, layer_norm_hidden_state=False)
         self.clip_g = SDXLClipG(device=device, dtype=dtype)
 
-    def clip_layer(self, layer_idx):
+    def set_clip_options(self, options):
-        self.clip_l.clip_layer(layer_idx)
+        self.clip_l.set_clip_options(options)
-        self.clip_g.clip_layer(layer_idx)
+        self.clip_g.set_clip_options(options)
 
-    def reset_clip_layer(self):
+    def reset_clip_options(self):
-        self.clip_g.reset_clip_layer()
+        self.clip_g.reset_clip_options()
-        self.clip_l.reset_clip_layer()
+        self.clip_l.reset_clip_options()
 
     def encode_token_weights(self, token_weight_pairs):
         token_weight_pairs_g = token_weight_pairs["g"]

comfy/supported_models.py

@@ -75,7 +75,7 @@ class SD20(supported_models_base.BASE):
         replace_prefix["conditioner.embedders.0.model."] = "clip_h." #SD2 in sgm format
         replace_prefix["cond_stage_model.model."] = "clip_h."
         state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True)
-        state_dict = utils.transformers_convert(state_dict, "clip_h.", "clip_h.transformer.text_model.", 24)
+        state_dict = utils.clip_text_transformers_convert(state_dict, "clip_h.", "clip_h.transformer.")
         return state_dict
 
     def process_clip_state_dict_for_saving(self, state_dict):
@@ -134,7 +134,7 @@ class SDXLRefiner(supported_models_base.BASE):
         replace_prefix["conditioner.embedders.0.model."] = "clip_g."
         state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True)
 
-        state_dict = utils.transformers_convert(state_dict, "clip_g.", "clip_g.transformer.text_model.", 32)
+        state_dict = utils.clip_text_transformers_convert(state_dict, "clip_g.", "clip_g.transformer.")
         state_dict = utils.state_dict_key_replace(state_dict, keys_to_replace)
         return state_dict
 
@@ -182,22 +182,24 @@ class SDXL(supported_models_base.BASE):
         replace_prefix["conditioner.embedders.1.model."] = "clip_g."
         state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True)
 
-        state_dict = utils.transformers_convert(state_dict, "clip_g.", "clip_g.transformer.text_model.", 32)
-        keys_to_replace["clip_g.text_projection.weight"] = "clip_g.text_projection"
-
         state_dict = utils.state_dict_key_replace(state_dict, keys_to_replace)
+        state_dict = utils.clip_text_transformers_convert(state_dict, "clip_g.", "clip_g.transformer.")
         return state_dict
 
     def process_clip_state_dict_for_saving(self, state_dict):
         replace_prefix = {}
         keys_to_replace = {}
         state_dict_g = diffusers_convert.convert_text_enc_state_dict_v20(state_dict, "clip_g")
-        if "clip_g.transformer.text_model.embeddings.position_ids" in state_dict_g:
-            state_dict_g.pop("clip_g.transformer.text_model.embeddings.position_ids")
         for k in state_dict:
             if k.startswith("clip_l"):
                 state_dict_g[k] = state_dict[k]
 
+        state_dict_g["clip_l.transformer.text_model.embeddings.position_ids"] = torch.arange(77).expand((1, -1))
+        pop_keys = ["clip_l.transformer.text_projection.weight", "clip_l.logit_scale"]
+        for p in pop_keys:
+            if p in state_dict_g:
+                state_dict_g.pop(p)
+
         replace_prefix["clip_g"] = "conditioner.embedders.1.model"
         replace_prefix["clip_l"] = "conditioner.embedders.0"
         state_dict_g = utils.state_dict_prefix_replace(state_dict_g, replace_prefix)
@@ -338,6 +340,12 @@ class Stable_Cascade_C(supported_models_base.BASE):
                     state_dict[k_to] = weights[shape_from*x:shape_from*(x + 1)]
         return state_dict
 
+    def process_clip_state_dict(self, state_dict):
+        state_dict = utils.state_dict_prefix_replace(state_dict, {k: "" for k in self.text_encoder_key_prefix}, filter_keys=True)
+        if "clip_g.text_projection" in state_dict:
+            state_dict["clip_g.transformer.text_projection.weight"] = state_dict.pop("clip_g.text_projection").transpose(0, 1)
+        return state_dict
+
     def get_model(self, state_dict, prefix="", device=None):
         out = model_base.StableCascade_C(self, device=device)
         return out

comfy/utils.py

@@ -98,8 +98,22 @@ def transformers_convert(sd, prefix_from, prefix_to, number):
                     p = ["self_attn.q_proj", "self_attn.k_proj", "self_attn.v_proj"]
                     k_to = "{}encoder.layers.{}.{}.{}".format(prefix_to, resblock, p[x], y)
                     sd[k_to] = weights[shape_from*x:shape_from*(x + 1)]
+
     return sd
 
+def clip_text_transformers_convert(sd, prefix_from, prefix_to):
+    sd = transformers_convert(sd, prefix_from, "{}text_model.".format(prefix_to), 32)
+
+    tp = "{}text_projection.weight".format(prefix_from)
+    if tp in sd:
+        sd["{}text_projection.weight".format(prefix_to)] = sd.pop(tp)
+
+    tp = "{}text_projection".format(prefix_from)
+    if tp in sd:
+        sd["{}text_projection.weight".format(prefix_to)] = sd.pop(tp).transpose(0, 1).contiguous()
+    return sd
+
+
 UNET_MAP_ATTENTIONS = {
     "proj_in.weight",
     "proj_in.bias",

custom_nodes/example_node.py.example

@@ -103,6 +103,9 @@ class Example:
     #def IS_CHANGED(s, image, string_field, int_field, float_field, print_to_screen):
     #    return ""
 
+# Set the web directory, any .js file in that directory will be loaded by the frontend as a frontend extension
+# WEB_DIRECTORY = "./somejs"
+
 # A dictionary that contains all nodes you want to export with their names
 # NOTE: names should be globally unique
 NODE_CLASS_MAPPINGS = {

main.py

@@ -193,6 +193,13 @@ if __name__ == "__main__":
         folder_paths.set_temp_directory(temp_dir)
     cleanup_temp()
 
+    if args.windows_standalone_build:
+        try:
+            import new_updater
+            new_updater.update_windows_updater()
+        except:
+            pass
+
     loop = asyncio.new_event_loop()
     asyncio.set_event_loop(loop)
     server = server.PromptServer(loop)

new_updater.py

@@ -0,0 +1,35 @@
+import os
+import shutil
+
+base_path = os.path.dirname(os.path.realpath(__file__))
+
+
+def update_windows_updater():
+    top_path = os.path.dirname(base_path)
+    updater_path = os.path.join(base_path, ".ci/update_windows/update.py")
+    bat_path = os.path.join(base_path, ".ci/update_windows/update_comfyui.bat")
+
+    dest_updater_path = os.path.join(top_path, "update/update.py")
+    dest_bat_path = os.path.join(top_path, "update/update_comfyui.bat")
+    dest_bat_deps_path = os.path.join(top_path, "update/update_comfyui_and_python_dependencies.bat")
+
+    try:
+        with open(dest_bat_path, 'rb') as f:
+            contents = f.read()
+    except:
+        return
+
+    if not contents.startswith(b"..\\python_embeded\\python.exe .\\update.py"):
+        return
+
+    shutil.copy(updater_path, dest_updater_path)
+    try:
+        with open(dest_bat_deps_path, 'rb') as f:
+            contents = f.read()
+            contents = contents.replace(b'..\\python_embeded\\python.exe .\\update.py ..\\ComfyUI\\', b'call update_comfyui.bat nopause')
+        with open(dest_bat_deps_path, 'wb') as f:
+            f.write(contents)
+    except:
+        pass
+    shutil.copy(bat_path, dest_bat_path)
+    print("Updated the windows standalone package updater.")

nodes.py

@@ -1003,7 +1003,7 @@ class GLIGENTextBoxApply:
 
     def append(self, conditioning_to, clip, gligen_textbox_model, text, width, height, x, y):
         c = []
-        cond, cond_pooled = clip.encode_from_tokens(clip.tokenize(text), return_pooled=True)
+        cond, cond_pooled = clip.encode_from_tokens(clip.tokenize(text), return_pooled="unprojected")
         for t in conditioning_to:
             n = [t[0], t[1].copy()]
             position_params = [(cond_pooled, height // 8, width // 8, y // 8, x // 8)]

server.py

@@ -539,11 +539,11 @@ class PromptServer():
 
         for name, dir in nodes.EXTENSION_WEB_DIRS.items():
             self.app.add_routes([
-                web.static('/extensions/' + urllib.parse.quote(name), dir, follow_symlinks=True),
+                web.static('/extensions/' + urllib.parse.quote(name), dir),
             ])
 
         self.app.add_routes([
-            web.static('/', self.web_root, follow_symlinks=True),
+            web.static('/', self.web_root),
         ])
 
     def get_queue_info(self):