Merge 76719afe7c into 439bd807f8

.github/workflows/cla.yml

@@ -1,91 +0,0 @@
-name: CLA Assistant
-
-on:
-  issue_comment:
-    types: [created]
-  pull_request_target:
-    types: [opened, synchronize, closed]
-
-permissions:
-  actions: write
-  contents: read # 'read' is enough because signatures live in a REMOTE repo
-  pull-requests: write
-  statuses: write
-
-jobs:
-  cla-assistant:
-    runs-on: ubuntu-latest
-    steps:
-      # The CLA action normally requires every commit author in a PR to sign.
-      # We only want the PR author to sign, so we allowlist all other committers
-      # by computing them from the PR's commits and excluding the PR author.
-      - name: Build author-only allowlist
-        id: allowlist
-        if: >
-          github.event_name == 'pull_request_target' ||
-          (github.event_name == 'issue_comment' && github.event.issue.pull_request && (
-            github.event.comment.body == 'recheck' ||
-            github.event.comment.body == 'I have read and agree to the Contributor License Agreement'
-          ))
-        env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          PR_NUMBER: ${{ github.event.pull_request.number || github.event.issue.number }}
-          PR_AUTHOR: ${{ github.event.pull_request.user.login || github.event.issue.user.login }}
-          BASE_ALLOWLIST: action@github.com,actions-user,ampagent,claude,comfy-pr-bot,GitHub Action,github-actions,github-actions[bot],Glary Bot,Glary-Bot,*[bot]
-        run: |
-          others=$(gh api "repos/${{ github.repository }}/pulls/${PR_NUMBER}/commits" --paginate \
-            --jq '.[] | (.author.login // empty), (.committer.login // empty)' \
-            | sort -u | grep -vix "${PR_AUTHOR}" | paste -sd, -)
-          if [ -n "$others" ]; then
-            echo "allowlist=${BASE_ALLOWLIST},${others}" >> "$GITHUB_OUTPUT"
-          else
-            echo "allowlist=${BASE_ALLOWLIST}" >> "$GITHUB_OUTPUT"
-          fi
-
-      - name: CLA Assistant
-        # Run on PR events, on "recheck" comment, or when someone posts the exact signing phrase.
-        # IMPORTANT: this phrase must match `custom-pr-sign-comment` below.
-        if: >
-          github.event_name == 'pull_request_target' ||
-          (github.event_name == 'issue_comment' && github.event.issue.pull_request && (
-            github.event.comment.body == 'recheck' ||
-            github.event.comment.body == 'I have read and agree to the Contributor License Agreement'
-          ))
-        uses: contributor-assistant/github-action@ca4a40a7d1004f18d9960b404b97e5f30a505a08 # v2.6.1
-        env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          # PAT required to write to the centralized signatures repo.
-          PERSONAL_ACCESS_TOKEN: ${{ secrets.PERSONAL_ACCESS_TOKEN }}
-        with:
-          # Where the CLA document lives (shown to contributors)
-          path-to-document: https://github.com/Comfy-Org/comfy-cla/blob/main/comfyui_icla.md
-
-          # Centralized signature storage
-          remote-organization-name: comfy-org
-          remote-repository-name: comfy-cla
-          path-to-signatures: signatures/cla.json
-          branch: main
-
-          # Only the PR author must sign: bots plus every non-author committer
-          # are allowlisted via the "Build author-only allowlist" step above.
-          # *[bot] is a catch-all for any GitHub App bot account.
-          allowlist: ${{ steps.allowlist.outputs.allowlist }}
-
-          # Custom PR comment messages
-          custom-notsigned-prcomment: |
-            🎉 Thank you for your contribution, we really appreciate it! 🎉
-
-            Like many open source projects, we require contributors to sign our [Contributor License Agreement (CLA)](https://github.com/Comfy-Org/comfy-cla/blob/main/comfyui_icla.md). A CLA makes the ownership of contributions explicit, so contributors and the project share a clear understanding of how the code can be used. By signing, you:
-
-            - Confirm that you own your contribution.
-            - Keep the right to reuse your own code.
-            - Grant us a copyright license to include and share it within our projects.
-
-            CLAs are standard practice across major open source projects including those under the Apache Software Foundation and the Linux Foundation. Ours is based on the Apache Software Foundation's CLA. Most importantly, it would enable us to relicense the project under a more permissive license in the future, giving the project and its community greater flexibility.
-
-            ✍ **To sign, please post a new comment on this PR with exactly the following text:** ✍
-
-          custom-pr-sign-comment: I have read and agree to the Contributor License Agreement
-
-          custom-allsigned-prcomment: |
-            ✅ All contributors have signed the CLA. Thank you! This PR is ready to be merged.

CLAUDE.md

@@ -0,0 +1 @@
+AGENTS.md

comfy_extras/nodes_seedvr.py

@@ -425,34 +425,29 @@ class SeedVR2TemporalChunk(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="SeedVR2TemporalChunk",
-            display_name="Split SeedVR2 Latent",
+            display_name="Chunk SeedVR2 Latent",
             category="model/latent/batch",
-            description="Split a SeedVR2 video latent into overlapping temporal chunks small enough to sample one at a time within VRAM, wiring latents outputs to both Apply SeedVR2 Conditioning and the sampler latent input before recombining with Merge SeedVR2 Latents.",
-            search_aliases=["seedvr2", "split", "chunk", "temporal", "video upscale", "rebatch"],
+            description="Split a SeedVR2 video latent into overlapping temporal chunks small enough to sample one at a time within VRAM, wiring latent_chunks to both Apply SeedVR2 Conditioning and the sampler latent input before recombining with Merge SeedVR2 Latent Chunks.",
+            search_aliases=["seedvr2", "chunk", "temporal", "video upscale", "rebatch"],
             inputs=[
                 io.Latent.Input("latent", tooltip="The VAE-encoded SeedVR2 latent to split."),
+                io.Int.Input("frames_per_chunk", default=21, min=1, max=16384, step=4,
+                             tooltip="Pixel frames per temporal chunk (4n+1: 1, 5, 9, 13, ...)."),
                 io.Int.Input("temporal_overlap", default=0, min=0, max=16384,
                              tooltip="Latent frames shared between adjacent chunks and crossfaded at merge; 0 = no overlap."),
-                io.DynamicCombo.Input("chunking_mode",
-                                      tooltip="manual = use frames_per_chunk exactly; auto = predict the largest chunk that fits free VRAM.",
-                                      options=[
-                                          io.DynamicCombo.Option("auto", []),
-                                          io.DynamicCombo.Option("manual", [
-                                              io.Int.Input("frames_per_chunk", default=21, min=1, max=16384, step=4,
-                                                           tooltip="Pixel frames per temporal chunk (4n+1: 1, 5, 9, 13, ...)."),
-                                          ]),
-                                      ]),
+                io.Combo.Input("chunking_mode", options=["auto", "manual"], default="manual",
+                               tooltip="manual = use frames_per_chunk exactly; auto = predict the largest chunk that fits free VRAM."),
             ],
             outputs=[
-                io.Latent.Output(display_name="latents", is_output_list=True,
+                io.Latent.Output(display_name="latent_chunks", is_output_list=True,
                                  tooltip="The temporal chunks in sequence order."),
                 io.Int.Output(display_name="temporal_overlap",
-                              tooltip="The effective latent-frame overlap between adjacent chunks, for Merge SeedVR2 Latents."),
+                              tooltip="The effective latent-frame overlap between adjacent chunks, for Merge SeedVR2 Latent Chunks."),
             ],
         )
 
     @classmethod
-    def execute(cls, latent, temporal_overlap, chunking_mode) -> io.NodeOutput:
+    def execute(cls, latent, frames_per_chunk, temporal_overlap, chunking_mode) -> io.NodeOutput:
         samples = latent["samples"]
         if samples.ndim != 5:
             raise ValueError(
@@ -468,16 +463,15 @@ class SeedVR2TemporalChunk(io.ComfyNode):
             raise ValueError(
                 f"SeedVR2TemporalChunk: temporal_overlap must be >= 0; got {temporal_overlap}."
             )
-        mode = chunking_mode["chunking_mode"]
-        if mode not in ("auto", "manual"):
+        if chunking_mode not in ("auto", "manual"):
             raise ValueError(
                 f"SeedVR2TemporalChunk: chunking_mode must be 'auto' or 'manual'; "
-                f"got {mode!r}."
+                f"got {chunking_mode!r}."
             )
         t_latent = samples.shape[2]
         t_pixel = 4 * (t_latent - 1) + 1
 
-        if mode == "auto":
+        if chunking_mode == "auto":
             free_gb = comfy.model_management.get_free_memory(
                 comfy.model_management.get_torch_device()) / (1024 ** 3)
             mpx_per_frame = (samples.shape[0] * samples.shape[3] * samples.shape[4]) * (BYTEDANCE_VAE_SPATIAL_DOWNSAMPLE ** 2) / 1e6
@@ -488,13 +482,11 @@ class SeedVR2TemporalChunk(io.ComfyNode):
                 "SeedVR2TemporalChunk auto: free=%.2fGiB, %.2fMpx -> frames_per_chunk=%d (t_pixel=%d).",
                 free_gb, mpx_per_frame, frames_per_chunk, t_pixel,
             )
-        else:
-            frames_per_chunk = chunking_mode["frames_per_chunk"]
-            if frames_per_chunk < 1 or (frames_per_chunk - 1) % 4 != 0:
-                raise ValueError(
-                    f"SeedVR2TemporalChunk: frames_per_chunk must be a 4n+1 pixel-frame count "
-                    f"(1, 5, 9, 13, 17, 21, ...); got {frames_per_chunk}."
-                )
+        elif frames_per_chunk < 1 or (frames_per_chunk - 1) % 4 != 0:
+            raise ValueError(
+                f"SeedVR2TemporalChunk: frames_per_chunk must be a 4n+1 pixel-frame count "
+                f"(1, 5, 9, 13, 17, 21, ...); got {frames_per_chunk}."
+            )
 
         if t_pixel <= frames_per_chunk:
             return io.NodeOutput([latent], 0)
@@ -519,15 +511,15 @@ class SeedVR2TemporalMerge(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="SeedVR2TemporalMerge",
-            display_name="Merge SeedVR2 Latents",
+            display_name="Merge SeedVR2 Latent Chunks",
             category="model/latent/batch",
             is_input_list=True,
-            description="Recombine sampled SeedVR2 latent temporal chunks into one latent, crossfading each overlap with a Hann window sized by the temporal_overlap wired from Split SeedVR2 Latent.",
+            description="Recombine sampled SeedVR2 temporal chunks into one latent, crossfading each overlap with a Hann window sized by the temporal_overlap wired from Chunk SeedVR2 Latent.",
             search_aliases=["seedvr2", "merge", "temporal", "hann", "crossfade"],
             inputs=[
-                io.Latent.Input("latents", tooltip="The sampled temporal chunks in sequence order."),
+                io.Latent.Input("latent_chunks", tooltip="The sampled temporal chunks in sequence order."),
                 io.Int.Input("temporal_overlap", default=0, min=0, max=16384, force_input=True,
-                             tooltip="The temporal_overlap output of Split SeedVR2 Latent. 0 = plain concatenation."),
+                             tooltip="The temporal_overlap output of Chunk SeedVR2 Latent. 0 = plain concatenation."),
             ],
             outputs=[
                 io.Latent.Output(display_name="latent", tooltip="The recombined full-length latent."),
@@ -535,13 +527,13 @@ class SeedVR2TemporalMerge(io.ComfyNode):
         )
 
     @classmethod
-    def execute(cls, latents, temporal_overlap) -> io.NodeOutput:
+    def execute(cls, latent_chunks, temporal_overlap) -> io.NodeOutput:
         temporal_overlap = temporal_overlap[0]
         if temporal_overlap < 0:
             raise ValueError(
                 f"SeedVR2TemporalMerge: temporal_overlap must be >= 0; got {temporal_overlap}."
             )
-        chunks = [entry["samples"] for entry in latents]
+        chunks = [entry["samples"] for entry in latent_chunks]
         first = chunks[0]
         if first.ndim != 5:
             raise ValueError(
@@ -560,7 +552,7 @@ class SeedVR2TemporalMerge(io.ComfyNode):
                     f"chunk 0 has {first.shape[2]}; only the final chunk may be shorter."
                 )
 
-        out = latents[0].copy()
+        out = latent_chunks[0].copy()
         out.pop("noise_mask", None)
 
         if len(chunks) == 1:

tests-unit/comfy_extras_test/test_seedvr2_temporal_chunk.py

@@ -17,10 +17,7 @@ def _latent(t_latent, h=8, w=8, b=1):
     return {"samples": torch.randn(b, SEEDVR2_LATENT_CHANNELS, t_latent, h, w, generator=g)}
 
 def _split(latent, frames_per_chunk, temporal_overlap, chunking_mode="manual"):
-    combo = {"chunking_mode": chunking_mode}
-    if chunking_mode != "auto":
-        combo["frames_per_chunk"] = frames_per_chunk
-    return SeedVR2TemporalChunk.execute(latent, temporal_overlap, combo).args
+    return SeedVR2TemporalChunk.execute(latent, frames_per_chunk, temporal_overlap, chunking_mode).args
 
 def _merge(chunks, temporal_overlap):
     return SeedVR2TemporalMerge.execute(chunks, [temporal_overlap]).args[0]