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91
.github/workflows/cla.yml
vendored
Normal file
91
.github/workflows/cla.yml
vendored
Normal file
@ -0,0 +1,91 @@
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name: CLA Assistant
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on:
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issue_comment:
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types: [created]
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pull_request_target:
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types: [opened, synchronize, closed]
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permissions:
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actions: write
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contents: read # 'read' is enough because signatures live in a REMOTE repo
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pull-requests: write
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statuses: write
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jobs:
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cla-assistant:
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runs-on: ubuntu-latest
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steps:
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# The CLA action normally requires every commit author in a PR to sign.
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# We only want the PR author to sign, so we allowlist all other committers
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# by computing them from the PR's commits and excluding the PR author.
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- name: Build author-only allowlist
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id: allowlist
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if: >
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github.event_name == 'pull_request_target' ||
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(github.event_name == 'issue_comment' && github.event.issue.pull_request && (
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github.event.comment.body == 'recheck' ||
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github.event.comment.body == 'I have read and agree to the Contributor License Agreement'
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))
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env:
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GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
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PR_NUMBER: ${{ github.event.pull_request.number || github.event.issue.number }}
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PR_AUTHOR: ${{ github.event.pull_request.user.login || github.event.issue.user.login }}
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BASE_ALLOWLIST: action@github.com,actions-user,ampagent,claude,comfy-pr-bot,GitHub Action,github-actions,github-actions[bot],Glary Bot,Glary-Bot,*[bot]
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run: |
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others=$(gh api "repos/${{ github.repository }}/pulls/${PR_NUMBER}/commits" --paginate \
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--jq '.[] | (.author.login // empty), (.committer.login // empty)' \
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| sort -u | grep -vix "${PR_AUTHOR}" | paste -sd, -)
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if [ -n "$others" ]; then
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echo "allowlist=${BASE_ALLOWLIST},${others}" >> "$GITHUB_OUTPUT"
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else
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echo "allowlist=${BASE_ALLOWLIST}" >> "$GITHUB_OUTPUT"
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fi
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- name: CLA Assistant
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# Run on PR events, on "recheck" comment, or when someone posts the exact signing phrase.
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# IMPORTANT: this phrase must match `custom-pr-sign-comment` below.
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if: >
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github.event_name == 'pull_request_target' ||
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(github.event_name == 'issue_comment' && github.event.issue.pull_request && (
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github.event.comment.body == 'recheck' ||
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github.event.comment.body == 'I have read and agree to the Contributor License Agreement'
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))
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uses: contributor-assistant/github-action@ca4a40a7d1004f18d9960b404b97e5f30a505a08 # v2.6.1
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env:
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GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
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# PAT required to write to the centralized signatures repo.
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PERSONAL_ACCESS_TOKEN: ${{ secrets.PERSONAL_ACCESS_TOKEN }}
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with:
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# Where the CLA document lives (shown to contributors)
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path-to-document: https://github.com/Comfy-Org/comfy-cla/blob/main/comfyui_icla.md
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# Centralized signature storage
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remote-organization-name: comfy-org
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remote-repository-name: comfy-cla
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path-to-signatures: signatures/cla.json
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branch: main
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# Only the PR author must sign: bots plus every non-author committer
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# are allowlisted via the "Build author-only allowlist" step above.
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# *[bot] is a catch-all for any GitHub App bot account.
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allowlist: ${{ steps.allowlist.outputs.allowlist }}
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# Custom PR comment messages
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custom-notsigned-prcomment: |
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🎉 Thank you for your contribution, we really appreciate it! 🎉
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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:
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- Confirm that you own your contribution.
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- Keep the right to reuse your own code.
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- Grant us a copyright license to include and share it within our projects.
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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.
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✍ **To sign, please post a new comment on this PR with exactly the following text:** ✍
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custom-pr-sign-comment: I have read and agree to the Contributor License Agreement
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custom-allsigned-prcomment: |
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✅ All contributors have signed the CLA. Thank you! This PR is ready to be merged.
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@ -5,6 +5,8 @@ Qwen3-VL (layers 0,3,...,33,35), giving a 4096*13 = 53248-dim feature per token.
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"""
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import os
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import torch
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import numbers
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from transformers import Qwen2Tokenizer
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@ -32,14 +34,23 @@ class Ideogram4Tokenizer(sd1_clip.SD1Tokenizer):
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self.llama_template = "<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n"
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def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, **kwargs):
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def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, prevent_empty_text=False, **kwargs):
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skip_template = False
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if text.startswith('<|im_start|>'):
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skip_template = True
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if prevent_empty_text and text == '':
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text = ' '
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if skip_template:
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llama_text = text
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elif llama_template is None:
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llama_text = self.llama_template.format(text)
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else:
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llama_text = llama_template.format(text)
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return super().tokenize_with_weights(llama_text, return_word_ids=return_word_ids, disable_weights=True, **kwargs)
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if llama_template is None:
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llama_text = self.llama_template.format(text)
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else:
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llama_text = llama_template.format(text)
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tokens = super().tokenize_with_weights(llama_text, return_word_ids=return_word_ids, disable_weights=True, **kwargs)
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return tokens
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# Qwen3-VL-8B = 5e6 (vs plain Qwen3-8B's 1e6)
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@ -61,10 +72,54 @@ class Ideogram4TEModel(sd1_clip.SD1ClipModel):
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def __init__(self, device="cpu", dtype=None, model_options={}):
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super().__init__(device=device, dtype=dtype, name="qwen3vl_8b", clip_model=Qwen3VL8BModel, model_options=model_options)
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def encode_token_weights(self, token_weight_pairs):
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def encode_token_weights(self, token_weight_pairs, template_end=0):
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out, pooled, extra = super().encode_token_weights(token_weight_pairs)
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b, n, seq, h = out.shape # (B, n_taps=13, seq, 4096) stacked in ascending layer order.
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out = out.permute(0, 2, 3, 1).reshape(b, seq, h * n) # (B, seq, 4096*13). permute -> (B, seq, H, taps).
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if template_end != 0:
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tok_pairs = token_weight_pairs["qwen3vl_8b"][0]
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suffix_start = -1
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if template_end == -1:
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for i in range(len(tok_pairs) - 2):
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t0 = tok_pairs[i][0]
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t1 = tok_pairs[i + 1][0]
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t2 = tok_pairs[i + 2][0]
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if not torch.is_tensor(t0) and isinstance(t0, numbers.Integral) and \
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not torch.is_tensor(t1) and isinstance(t1, numbers.Integral) and \
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not torch.is_tensor(t2) and isinstance(t2, numbers.Integral):
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if t0 == 151644 and t1 == 872 and t2 == 198:
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template_end = i + 3
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break
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if template_end == -1:
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template_end = 0
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# Scan backward for the <|im_end|> token 151643 to determine suffix_start
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for i in range(len(tok_pairs) - 1, -1, -1):
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t = tok_pairs[i][0]
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if not torch.is_tensor(t) and isinstance(t, numbers.Integral):
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if t == 151643:
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suffix_start = i
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break
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# If template_end resolves to greater than 0:
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if template_end > 0:
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out = out[:, template_end:]
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if "attention_mask" in extra and extra["attention_mask"] is not None:
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extra["attention_mask"] = extra["attention_mask"][:, template_end:]
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# If suffix_start is located and we are doing stripping:
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if suffix_start >= 0:
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suffix_len = len(tok_pairs) - suffix_start
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out = out[:, :-suffix_len]
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if "attention_mask" in extra and extra["attention_mask"] is not None:
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extra["attention_mask"] = extra["attention_mask"][:, :-suffix_len]
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if "attention_mask" in extra and extra["attention_mask"] is not None:
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if extra["attention_mask"].sum() == torch.numel(extra["attention_mask"]):
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extra.pop("attention_mask")
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return out, pooled, extra
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@ -281,11 +281,18 @@ class VideoFromFile(VideoInput):
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video_done = False
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audio_done = True
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if len(container.streams.audio):
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audio_stream = container.streams.audio[-1]
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# Use the last decodable audio stream. Streams FFmpeg has no decoder for have no codec context,
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# and decoding their packets crashes the process. (e.g. APAC spatial-audio track in iPhone)
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audio_stream = next(
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(s for s in reversed(container.streams.audio) if s.codec_context is not None),
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None,
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)
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if audio_stream is not None:
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streams += [audio_stream]
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resampler = av.audio.resampler.AudioResampler(format='fltp')
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audio_done = False
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elif len(container.streams.audio):
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logging.warning("No decodable audio stream found in video; ignoring audio.")
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for packet in container.demux(*streams):
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if video_done and audio_done:
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@ -457,10 +464,13 @@ class VideoFromFile(VideoInput):
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else:
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output_container.metadata[key] = json.dumps(value)
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# Add streams to the new container
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# Add streams to the new container. Streams with no codec context cannot be used as an output template.
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stream_map = {}
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for stream in streams:
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if isinstance(stream, (av.VideoStream, av.AudioStream, SubtitleStream)):
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if stream.codec_context is None:
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logging.warning("Skipping %s stream %d with unsupported codec", stream.type, stream.index)
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continue
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out_stream = output_container.add_stream_from_template(template=stream, opaque=True)
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stream_map[stream] = out_stream
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@ -158,7 +158,14 @@ async def upload_video_to_comfyapi(
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# Convert VideoInput to BytesIO using specified container/codec
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video_bytes_io = BytesIO()
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video.save_to(video_bytes_io, format=container, codec=codec)
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try:
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video.save_to(video_bytes_io, format=container, codec=codec)
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except Exception as e:
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raise ValueError(
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f"Could not convert the input video to {container.value.upper()} for upload; "
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f"the file may be corrupted or use an unsupported codec. "
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f"Try re-exporting it as MP4 (H.264). Original error: {e}"
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) from e
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video_bytes_io.seek(0)
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return await upload_file_to_comfyapi(cls, video_bytes_io, filename, upload_mime_type, wait_label)
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Loading…
Reference in New Issue
Block a user