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https://github.com/comfyanonymous/ComfyUI.git
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Merge branch 'add_sample_sigmas' into hooks_part2
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commit
db2d7ad9ba
58
.github/workflows/update-frontend.yml
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.github/workflows/update-frontend.yml
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@ -0,0 +1,58 @@
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name: Update Frontend Release
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on:
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workflow_dispatch:
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inputs:
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version:
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description: "Frontend version to update to (e.g., 1.0.0)"
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required: true
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type: string
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jobs:
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update-frontend:
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runs-on: ubuntu-latest
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permissions:
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contents: write
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pull-requests: write
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steps:
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- name: Checkout ComfyUI
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uses: actions/checkout@v4
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- uses: actions/setup-python@v4
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with:
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python-version: '3.10'
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- name: Install requirements
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run: |
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python -m pip install --upgrade pip
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pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
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pip install -r requirements.txt
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pip install wait-for-it
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# Frontend asset will be downloaded to ComfyUI/web_custom_versions/Comfy-Org_ComfyUI_frontend/{version}
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- name: Start ComfyUI server
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run: |
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python main.py --cpu --front-end-version Comfy-Org/ComfyUI_frontend@${{ github.event.inputs.version }} 2>&1 | tee console_output.log &
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wait-for-it --service 127.0.0.1:8188 -t 30
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- name: Configure Git
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run: |
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git config --global user.name "GitHub Action"
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git config --global user.email "action@github.com"
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# Replace existing frontend content with the new version and remove .js.map files
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# See https://github.com/Comfy-Org/ComfyUI_frontend/issues/2145 for why we remove .js.map files
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- name: Update frontend content
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run: |
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rm -rf web/
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cp -r web_custom_versions/Comfy-Org_ComfyUI_frontend/${{ github.event.inputs.version }} web/
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rm web/**/*.js.map
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- name: Create Pull Request
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uses: peter-evans/create-pull-request@v7
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with:
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token: ${{ secrets.PR_BOT_PAT }}
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commit-message: "Update frontend to v${{ github.event.inputs.version }}"
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title: "Frontend Update: v${{ github.event.inputs.version }}"
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body: |
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Automated PR to update frontend content to version ${{ github.event.inputs.version }}
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This PR was created automatically by the frontend update workflow.
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branch: release-${{ github.event.inputs.version }}
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base: master
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labels: Frontend,dependencies
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@ -17,7 +17,7 @@
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/app/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata
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# Frontend assets
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/web/ @huchenlei @webfiltered @pythongosssss
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/web/ @huchenlei @webfiltered @pythongosssss @yoland68 @robinjhuang
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# Extra nodes
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/comfy_extras/ @yoland68 @robinjhuang @huchenlei @pythongosssss @ltdrdata @Kosinkadink
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@ -492,7 +492,7 @@ class HookKeyframeGroup:
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return False
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if curr_t == self._curr_t:
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return False
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max_sigma = torch.max(transformer_options["sigmas"])
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max_sigma = torch.max(transformer_options["sample_sigmas"])
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prev_index = self._current_index
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prev_strength = self._current_strength
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# if met guaranteed steps, look for next keyframe in case need to switch
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@ -1128,10 +1128,6 @@ def unload_all_models():
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free_memory(1e30, get_torch_device())
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def resolve_lowvram_weight(weight, model, key): #TODO: remove
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logging.warning("The comfy.model_management.resolve_lowvram_weight function will be removed soon, please stop using it.")
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return weight
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#TODO: might be cleaner to put this somewhere else
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import threading
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@ -849,7 +849,7 @@ class CFGGuider:
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self.conds = process_conds(self.inner_model, noise, self.conds, device, latent_image, denoise_mask, seed)
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extra_model_options = comfy.model_patcher.create_model_options_clone(self.model_options)
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extra_model_options.setdefault("transformer_options", {})["sigmas"] = sigmas
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extra_model_options.setdefault("transformer_options", {})["sample_sigmas"] = sigmas
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extra_args = {"model_options": extra_model_options, "seed": seed}
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executor = comfy.patcher_extension.WrapperExecutor.new_class_executor(
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22
nodes.py
22
nodes.py
@ -913,6 +913,9 @@ class CLIPLoader:
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def INPUT_TYPES(s):
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return {"required": { "clip_name": (folder_paths.get_filename_list("text_encoders"), ),
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"type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio", "mochi", "ltxv", "pixart"], ),
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},
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"optional": {
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"device": (["default", "cpu"], {"advanced": True}),
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}}
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RETURN_TYPES = ("CLIP",)
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FUNCTION = "load_clip"
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@ -921,7 +924,7 @@ class CLIPLoader:
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DESCRIPTION = "[Recipes]\n\nstable_diffusion: clip-l\nstable_cascade: clip-g\nsd3: t5 / clip-g / clip-l\nstable_audio: t5\nmochi: t5"
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def load_clip(self, clip_name, type="stable_diffusion"):
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def load_clip(self, clip_name, type="stable_diffusion", device="default"):
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if type == "stable_cascade":
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clip_type = comfy.sd.CLIPType.STABLE_CASCADE
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elif type == "sd3":
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@ -937,8 +940,12 @@ class CLIPLoader:
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else:
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clip_type = comfy.sd.CLIPType.STABLE_DIFFUSION
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model_options = {}
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if device == "cpu":
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model_options["load_device"] = model_options["offload_device"] = torch.device("cpu")
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clip_path = folder_paths.get_full_path_or_raise("text_encoders", clip_name)
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clip = comfy.sd.load_clip(ckpt_paths=[clip_path], embedding_directory=folder_paths.get_folder_paths("embeddings"), clip_type=clip_type)
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clip = comfy.sd.load_clip(ckpt_paths=[clip_path], embedding_directory=folder_paths.get_folder_paths("embeddings"), clip_type=clip_type, model_options=model_options)
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return (clip,)
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class DualCLIPLoader:
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@ -947,6 +954,9 @@ class DualCLIPLoader:
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return {"required": { "clip_name1": (folder_paths.get_filename_list("text_encoders"), ),
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"clip_name2": (folder_paths.get_filename_list("text_encoders"), ),
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"type": (["sdxl", "sd3", "flux", "hunyuan_video"], ),
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},
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"optional": {
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"device": (["default", "cpu"], {"advanced": True}),
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}}
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RETURN_TYPES = ("CLIP",)
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FUNCTION = "load_clip"
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@ -955,7 +965,7 @@ class DualCLIPLoader:
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DESCRIPTION = "[Recipes]\n\nsdxl: clip-l, clip-g\nsd3: clip-l, clip-g / clip-l, t5 / clip-g, t5\nflux: clip-l, t5"
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def load_clip(self, clip_name1, clip_name2, type):
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def load_clip(self, clip_name1, clip_name2, type, device="default"):
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clip_path1 = folder_paths.get_full_path_or_raise("text_encoders", clip_name1)
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clip_path2 = folder_paths.get_full_path_or_raise("text_encoders", clip_name2)
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if type == "sdxl":
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@ -967,7 +977,11 @@ class DualCLIPLoader:
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elif type == "hunyuan_video":
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clip_type = comfy.sd.CLIPType.HUNYUAN_VIDEO
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clip = comfy.sd.load_clip(ckpt_paths=[clip_path1, clip_path2], embedding_directory=folder_paths.get_folder_paths("embeddings"), clip_type=clip_type)
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model_options = {}
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if device == "cpu":
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model_options["load_device"] = model_options["offload_device"] = torch.device("cpu")
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clip = comfy.sd.load_clip(ckpt_paths=[clip_path1, clip_path2], embedding_directory=folder_paths.get_folder_paths("embeddings"), clip_type=clip_type, model_options=model_options)
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return (clip,)
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class CLIPVisionLoader:
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