Claude
e4111cf74a
Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01BzJLJWVu7RZnbRqm3nYAnX
203 lines
12 KiB
Python
203 lines
12 KiB
Python
"""LTX 2.3 pipelines, as loadable presets.
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Built from ComfyUI's own LTX nodes plus FunUI's, the chain the stock templates use:
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conditioning carries the frame rate, and the sampler runs the distilled
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schedule written out as numbers (`project_sigmas`: the official anchors, never replaced).
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Four separate files, as most people keep them: the diffusion model, the video VAE, the audio VAE and the text
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encoder(s), each in its own loader (FunUI's own, like the H3 pipeline).
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Every `frame_rate` and `fps` is a role at `project.video`, so the project's FPS is the one number
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the conditioning, the sound's length and the saved video agree on.
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"""
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#: The distilled 8-step schedule, from the official ComfyUI LTX 2.x templates.
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DISTILLED_SIGMAS = "1.0, 0.99375, 0.9875, 0.98125, 0.975, 0.909375, 0.725, 0.421875, 0.0"
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def _base(image_to_video: bool, as_guide: bool = False, ic_lora: bool = False):
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video = {"at": "project.video", "input": "frame_rate", "label": "FPS", "drives": "fps"}
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latent_source = ["latent", 0]
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model_source, positive_source, negative_source = ["modifiers", 0], ["conditioning", 0], ["conditioning", 1]
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slots = [
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{"id": "model", "group": "Loaders", "node": "FunUIDiffusionModelLoader",
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"inputs": {"weight_dtype": "default", "compute_dtype": "default", "attention": "default"}},
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{"id": "vae", "group": "Loaders", "node": "FunUIVAELoader", "inputs": {"dtype": "bfloat16"}},
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{"id": "audio_vae", "group": "Loaders", "node": "FunUIVAELoader", "inputs": {"dtype": "bfloat16"}},
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{"id": "clip", "group": "Loaders", "node": "FunUICLIPLoader", "inputs": {"type": "ltxv"}},
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*_ic_lora(ic_lora),
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{"id": "positive", "group": "Preparation", "node": "CLIPTextEncode",
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"roles": [{"at": "generation.prompt", "input": "text", "label": "Prompt"}],
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"inputs": {"clip": ["clip", 0], "text": ""}},
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{"id": "negative", "group": "Preparation", "node": "CLIPTextEncode",
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"roles": [{"at": "project.negative", "input": "text", "label": "Negative prompt"}],
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"inputs": {"clip": ["clip", 0], "text": ""}},
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{"id": "conditioning", "group": "Preparation", "node": "LTXVConditioning",
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"roles": [video],
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"inputs": {"positive": ["positive", 0], "negative": ["negative", 0], "frame_rate": 25.0}},
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{"id": "latent", "group": "Preparation", "node": "FunUIEmptyLatent",
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"roles": [{"at": "project.video", "input": "width", "label": "Width"},
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{"at": "project.video", "input": "height", "label": "Height"},
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{"at": "project.video", "input": "length", "label": "Length", "drives": "frames"},
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video],
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"inputs": {"model": ["model", 0], "audio_vae": ["audio_vae", 0],
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"width": 768, "height": 512, "length": 121, "frame_rate": 25.0, "batch_size": 1}},
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]
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if image_to_video:
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slots += [
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{"id": "source_image", "group": "Reference media", "node": "FunUILoadMedia",
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# video_frames: a video picked as the start is fed whole (prepared as Video input says), not as its first frame.
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"roles": [{"at": "assets.source_image", "input": "media_id", "video_frames": True}],
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"inputs": {"media_id": ""}},
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{"id": "split", "group": "Preparation", "node": "LTXVSeparateAVLatent",
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"inputs": {"av_latent": ["latent", 0]}},
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]
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if as_guide:
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# The picture steers through guide attention at frame 0 and is NOT the clip's first frame:
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# the latent stays empty, so the opening is free to move. Strength is the slot's own number.
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slots += [
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{"id": "anchor", "group": "Preparation", "node": "LTXVAddGuide",
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"inputs": {"positive": ["conditioning", 0], "negative": ["conditioning", 1], "vae": ["vae", 0],
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"latent": ["split", 0], "image": ["source_image", 1], "frame_idx": 0, "strength": 1.0 if ic_lora else 0.35,
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**({"iclora_parameters": ["ic_params", 0]} if ic_lora else {})}},
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{"id": "join", "group": "Preparation", "node": "LTXVConcatAVLatent",
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"inputs": {"video_latent": ["anchor", 2], "audio_latent": ["split", 1]}},
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]
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guided = (["anchor", 0], ["anchor", 1])
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else:
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slots += [
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{"id": "anchor", "group": "Preparation", "node": "LTXVImgToVideoInplace",
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"inputs": {"vae": ["vae", 0], "image": ["source_image", 1], "latent": ["split", 0],
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"strength": 1.0, "bypass": False}},
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{"id": "join", "group": "Preparation", "node": "LTXVConcatAVLatent",
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"inputs": {"video_latent": ["anchor", 0], "audio_latent": ["split", 1]}},
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]
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guided = (["conditioning", 0], ["conditioning", 1])
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latent_source = ["join", 0]
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positive_source, negative_source = guided
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slots += [
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{"id": "settings", "group": "Preparation", "node": "FunUIModifierSettings", "inputs": {"settings": "{}"}},
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{"id": "modifiers", "group": "Preparation", "node": "FunUILoadModifiers",
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"inputs": {"model": ["ic_lora", 0] if ic_lora else ["model", 0], "settings": ["settings", 0]}},
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]
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if image_to_video:
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slots += [
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# A reference face carried into the clip. The picture is the scene's first reference
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# (assets.reference_1): with none picked this node passes everything through untouched.
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{"id": "identity", "group": "Preparation", "node": "FunUIIdentityTransfer",
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"inputs": {"model": ["modifiers", 0], "vae": ["vae", 0], "positive": positive_source,
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"negative": negative_source, "latent": latent_source, "source_id": 2.0,
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"phase_scale": 1.0, "id_strength": 1.0, "arcface_mode": "auto_adjust"}},
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{"id": "identity_source", "group": "Reference media", "node": "FunUILoadMedia",
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"roles": [{"at": "assets.reference_1", "input": "media_id",
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"wireTo": {"slot": "identity", "input": "image"}}],
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"inputs": {"media_id": ""}},
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]
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model_source, positive_source, negative_source = ["identity", 0], ["identity", 1], ["identity", 2]
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slots += [
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{"id": "sigmas", "group": "Sampling", "node": "ManualSigmas", "inputs": {"sigmas": DISTILLED_SIGMAS}},
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{"id": "sampler", "group": "Sampling", "node": "FunUISampler",
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"roles": [{"at": "generation.sampling", "input": "sampler_name", "label": "Sampler"},
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{"at": "generation.seed", "input": "seed", "label": "Seed"}],
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"inputs": {"model": model_source, "positive": positive_source, "negative": negative_source,
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"latent": latent_source, "settings": ["settings", 0], "sigmas": ["sigmas", 0],
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"seed": 0, "steps": 8, "cfg": 1.0, "sampler_name": "euler", "scheduler": "normal",
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"denoise": 1.0}},
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*_uncrop(as_guide),
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{"id": "decode", "group": "Render", "node": "FunUIDecode", "inputs": {
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"samples": ["rejoin", 0] if as_guide else ["sampler", 0], "vae": ["vae", 0], "model": ["model", 0], "audio_vae": ["audio_vae", 0]}},
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{"id": "save", "group": "Render", "node": "FunUISaveVideo",
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"roles": [{"at": "project.video", "input": "fps", "label": "FPS", "drives": "fps"}],
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"inputs": {"images": ["decode", 0], "audio": ["decode", 1], "fps": 25.0, "filename_prefix": "FunUI"}},
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]
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return slots
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def _direct_video_to_video():
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"""Plain video-to-video, no IC-LoRA: the picked video's frames are encoded and sampled again from part-way down the
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schedule (the tail of the distilled sigmas), so the result keeps the video's layout and motion and changes what
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the prompt and the noise ask for. The later the schedule starts, the more of the video survives.
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The size and length are the video's own (Video input sets them); the sound starts empty and is made with the picture.
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"""
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slots = _base(False)
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at = next(i for i, x in enumerate(slots) if x["id"] == "latent")
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fps = {"at": "project.video", "input": "frame_rate", "label": "FPS", "drives": "fps"}
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slots[at:at + 1] = [
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# video_frames: the whole video is fed, not its first frame (see inputs.js).
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{"id": "source_image", "group": "Reference media", "node": "FunUILoadMedia",
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"roles": [{"at": "assets.source_image", "input": "media_id", "video_frames": True, "video_only": True}],
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"inputs": {"media_id": ""}},
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{"id": "size", "group": "Preparation", "node": "GetImageSize", "inputs": {"image": ["source_image", 1]}},
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{"id": "encode", "group": "Preparation", "node": "VAEEncode", "inputs": {"pixels": ["source_image", 1], "vae": ["vae", 0]}},
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{"id": "audio_latent", "group": "Preparation", "node": "LTXVEmptyLatentAudio",
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"roles": [fps], "inputs": {"frames_number": ["size", 2], "frame_rate": 25, "batch_size": 1, "audio_vae": ["audio_vae", 0]}},
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{"id": "latent", "group": "Preparation", "node": "LTXVConcatAVLatent",
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"inputs": {"video_latent": ["encode", 0], "audio_latent": ["audio_latent", 0]}},
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]
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for x in slots:
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if x["id"] == "sigmas":
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x["inputs"]["sigmas"] = "0.909375, 0.725, 0.421875, 0.0" # the last three steps of the distilled schedule: about 90% re-noised
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return slots
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def _ic_lora(on):
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"""The IC-LoRA of ComfyUI's own LTX 2.3 video-to-video template: the stock LoRA loader (it keeps the file's metadata)
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and the node that reads the guide's downscale factor from it. The picked video then steers through LTXVAddGuide."""
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if not on:
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return []
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return [
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{"id": "ic_lora", "group": "Loaders", "node": "LoraLoaderModelOnly",
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"inputs": {"model": ["model", 0], "lora_name": "", "strength_model": 1.0}},
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{"id": "ic_params", "group": "Loaders", "node": "GetICLoRAParameters", "inputs": {"iclora_model": ["ic_lora", 0]}},
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]
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def _uncrop(as_guide):
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"""A guide sits in the latent as extra frames: take them off again before the picture is decoded."""
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if not as_guide:
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return []
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return [
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{"id": "unjoin", "group": "Render", "node": "LTXVSeparateAVLatent", "inputs": {"av_latent": ["sampler", 0]}},
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{"id": "crop", "group": "Render", "node": "LTXVCropGuides",
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"inputs": {"positive": ["identity", 1], "negative": ["identity", 2], "latent": ["unjoin", 0]}},
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{"id": "rejoin", "group": "Render", "node": "LTXVConcatAVLatent",
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"inputs": {"video_latent": ["crop", 2], "audio_latent": ["unjoin", 1]}},
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]
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#: See minimax_h3/pipeline.py: what to pass through when the node's module did not load.
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OPTIONAL_NODES = {"FunUIIdentityTransfer": {"pass": {"0": "model", "1": "positive", "2": "negative"}}}
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def upgrade(slots, _outputs=None):
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"""A pipeline saved while the picture inputs still read the loader's first output: a start slot that takes whole
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videos feeds them from the frames output now (output 0 is always one frame, so such a pipeline ran on a single frame)."""
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start = next((s for s in slots if s.get("id") == "source_image" and s.get("node") == "FunUILoadMedia"
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and any(r.get("video_frames") for r in s.get("roles") or [])), None)
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if not start:
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return slots
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return [dict(s, inputs={k: (["source_image", 1] if isinstance(v, (list, tuple)) and list(v) == ["source_image", 0] else v)
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for k, v in (s.get("inputs") or {}).items()}) for s in slots]
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def optional_nodes():
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return OPTIONAL_NODES
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def presets():
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return [{"id": "ltx23_text_to_video", "title": "LTX 2.3 · Text to Video (UNTESTED)", "slots": _base(False)},
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{"id": "ltx23_image_to_video", "title": "LTX 2.3 · Image to Video (UNTESTED)", "slots": _base(True)},
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{"id": "ltx23_anchor_guide", "title": "LTX 2.3 · Anchor as guide (UNTESTED)",
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"slots": _base(True, True)},
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{"id": "ltx23_video_to_video", "title": "LTX 2.3 · Video to Video, IC-LoRA (UNTESTED)",
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"slots": _base(True, True, True)},
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{"id": "ltx23_video_to_video_direct", "title": "LTX 2.3 · Video to Video, direct (UNTESTED)",
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"slots": _direct_video_to_video()}]
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PROVIDES = {"pipeline_presets": presets}
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