Initial release: ComfyUI nodes for STAR video super-resolution
Two-node package wrapping the STAR (ICCV 2025) diffusion-based video upscaling pipeline: - STAR Model Loader: loads UNet+ControlNet, OpenCLIP text encoder, and temporal VAE with auto-download from HuggingFace - STAR Video Super-Resolution: runs the full diffusion pipeline with configurable upscale factor, guidance, solver mode, chunking, and color correction Includes three VRAM offload modes (disabled/model/aggressive) to support GPUs from 12GB to 40GB+. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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__pycache__/
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*.pyc
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*.pyo
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.claude/
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[submodule "STAR"]
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path = STAR
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url = https://github.com/NJU-PCALab/STAR.git
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README.md
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# ComfyUI-STAR
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ComfyUI custom nodes for [STAR (Spatial-Temporal Augmentation with Text-to-Video Models for Real-World Video Super-Resolution)](https://github.com/NJU-PCALab/STAR) — a diffusion-based video upscaling model (ICCV 2025).
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## Features
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- **Diffusion-based 4x video super-resolution** with temporal coherence
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- **Two model variants**: `light_deg.pt` (light degradation) and `heavy_deg.pt` (heavy degradation)
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- **Auto-download**: all models (UNet checkpoint, OpenCLIP text encoder, temporal VAE) download automatically on first use
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- **VRAM offloading**: three modes to fit GPUs from 12GB to 40GB+
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- **Long video support**: sliding-window chunking with 50% overlap
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- **Color correction**: AdaIN and wavelet-based post-processing
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## Installation
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### ComfyUI Manager
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Search for `ComfyUI-STAR` in ComfyUI Manager and install.
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### Manual
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```bash
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cd ComfyUI/custom_nodes
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git clone --recursive git@192.168.1.1:Ethanfel/Comfyui-STAR.git
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cd Comfyui-STAR
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pip install -r requirements.txt
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```
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> The `--recursive` flag clones the STAR submodule. If you forgot it, run `git submodule update --init` afterwards.
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## Nodes
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### STAR Model Loader
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Loads the STAR model components (UNet+ControlNet, OpenCLIP text encoder, temporal VAE).
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| Input | Description |
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|-------|-------------|
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| **model_name** | `light_deg.pt` for mildly degraded video, `heavy_deg.pt` for heavily degraded video. Auto-downloaded from HuggingFace on first use. |
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| **precision** | `fp16` (recommended), `bf16`, or `fp32`. |
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| **offload** | `disabled` (~39GB VRAM), `model` (~16GB — swaps components to CPU when idle), `aggressive` (~12GB — model offload + single-frame VAE decode). |
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### STAR Video Super-Resolution
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Runs the STAR diffusion pipeline on an image batch.
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| Input | Description |
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|-------|-------------|
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| **star_model** | Connect from STAR Model Loader. |
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| **images** | Input video frames (IMAGE batch). |
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| **upscale** | Upscale factor (1–8, default 4). |
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| **steps** | Denoising steps (1–100, default 15). Ignored in `fast` mode. |
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| **guide_scale** | Classifier-free guidance scale (1–20, default 7.5). |
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| **prompt** | Text prompt. Leave empty for STAR's built-in quality prompt. |
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| **solver_mode** | `fast` (optimized 15-step schedule) or `normal` (uniform schedule). |
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| **max_chunk_len** | Max frames per chunk (4–128, default 32). Lower = less VRAM for long videos. |
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| **seed** | Random seed for reproducibility. |
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| **color_fix** | `adain` (match color stats), `wavelet` (preserve low-frequency color), or `none`. |
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## VRAM Requirements
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| Offload Mode | Approximate VRAM | Notes |
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|---|---|---|
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| disabled | ~39 GB | Fastest — everything on GPU |
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| model | ~16 GB | Components swap to CPU between stages |
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| aggressive | ~12 GB | Model offload + frame-by-frame VAE decode |
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Reducing `max_chunk_len` further lowers VRAM usage for long videos at the cost of slightly more processing time.
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## Model Weights
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Models are stored in `ComfyUI/models/star/` and auto-downloaded on first use:
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| Model | Use Case | Source |
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|-------|----------|--------|
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| `light_deg.pt` | Low-res video from the web, mild compression | [HuggingFace](https://huggingface.co/SherryX/STAR/resolve/main/I2VGen-XL-based/light_deg.pt) |
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| `heavy_deg.pt` | Heavily compressed/degraded video | [HuggingFace](https://huggingface.co/SherryX/STAR/resolve/main/I2VGen-XL-based/heavy_deg.pt) |
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The OpenCLIP text encoder and SVD temporal VAE are downloaded automatically by their respective libraries on first load.
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## Credits
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- [STAR](https://github.com/NJU-PCALab/STAR) by Rui Xie, Yinhong Liu et al. (Nanjing University) — ICCV 2025
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- Based on [I2VGen-XL](https://github.com/ali-vilab/VGen) and [VEnhancer](https://github.com/Vchitect/VEnhancer)
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## License
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This wrapper is MIT licensed. The STAR model weights follow their respective licenses (MIT for I2VGen-XL-based models).
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1
STAR
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STAR
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Submodule STAR added at 69b8bc53e4
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__init__.py
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__init__.py
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from .nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
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__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]
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install.py
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install.py
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import os
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import subprocess
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import sys
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req_file = os.path.join(os.path.dirname(os.path.realpath(__file__)), "requirements.txt")
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subprocess.check_call([sys.executable, "-m", "pip", "install", "-r", req_file])
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star_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "STAR")
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if not os.path.isdir(star_dir):
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subprocess.check_call(["git", "clone", "https://github.com/NJU-PCALab/STAR.git", star_dir])
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nodes.py
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import os
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import sys
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import torch
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import folder_paths
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import comfy.model_management as mm
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# Register the "star" model folder so users can drop .pt weights there.
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star_model_dir = os.path.join(folder_paths.models_dir, "star")
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os.makedirs(star_model_dir, exist_ok=True)
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folder_paths.folder_names_and_paths["star"] = (
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[star_model_dir],
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folder_paths.supported_pt_extensions,
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)
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# Put the cloned STAR repo on sys.path so its internal imports work.
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STAR_REPO = os.path.join(os.path.dirname(os.path.realpath(__file__)), "STAR")
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if STAR_REPO not in sys.path:
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sys.path.insert(0, STAR_REPO)
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# Known models on HuggingFace that can be auto-downloaded.
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HF_REPO = "SherryX/STAR"
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HF_MODELS = {
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"light_deg.pt": "I2VGen-XL-based/light_deg.pt",
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"heavy_deg.pt": "I2VGen-XL-based/heavy_deg.pt",
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}
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def _get_model_list():
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"""Return the union of files already on disk + known downloadable models."""
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on_disk = set(folder_paths.get_filename_list("star"))
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available = set(HF_MODELS.keys())
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return sorted(on_disk | available)
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def _ensure_model(model_name: str) -> str:
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"""Return the local path to model_name, downloading from HF if needed."""
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local = folder_paths.get_full_path("star", model_name)
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if local is not None:
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return local
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if model_name not in HF_MODELS:
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raise FileNotFoundError(
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f"Model '{model_name}' not found in {star_model_dir} and is not a known downloadable model."
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)
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from huggingface_hub import hf_hub_download
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print(f"[STAR] Downloading {model_name} from HuggingFace ({HF_REPO})...")
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path = hf_hub_download(
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repo_id=HF_REPO,
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filename=HF_MODELS[model_name],
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local_dir=star_model_dir,
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)
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# hf_hub_download may place the file in a subdirectory; symlink into the
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# star folder root so folder_paths can find it next time.
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dest = os.path.join(star_model_dir, model_name)
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if not os.path.exists(dest):
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os.symlink(path, dest)
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return dest
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class STARModelLoader:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"model_name": (_get_model_list(), {
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"tooltip": "STAR checkpoint to load. light_deg for mildly degraded video, heavy_deg for heavily degraded video. Auto-downloaded from HuggingFace on first use.",
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}),
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"precision": (["fp16", "bf16", "fp32"], {
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"default": "fp16",
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"tooltip": "Weight precision. fp16 is recommended (fastest, lowest VRAM). bf16 for newer GPUs. fp32 for maximum quality at 2x VRAM cost.",
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}),
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"offload": (["disabled", "model", "aggressive"], {
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"default": "disabled",
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"tooltip": "disabled: all on GPU (~39GB). model: swap UNet/VAE/CLIP to CPU when idle (~16GB). aggressive: model offload + single-frame VAE decode (~12GB).",
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}),
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}
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}
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RETURN_TYPES = ("STAR_MODEL",)
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RETURN_NAMES = ("star_model",)
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FUNCTION = "load_model"
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CATEGORY = "STAR"
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DESCRIPTION = "Loads the STAR video super-resolution model (UNet+ControlNet, OpenCLIP text encoder, temporal VAE). All components are auto-downloaded on first use."
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def load_model(self, model_name, precision, offload="disabled"):
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device = mm.get_torch_device()
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dtype_map = {"fp16": torch.float16, "bf16": torch.bfloat16, "fp32": torch.float32}
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dtype = dtype_map[precision]
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# Where to park models when not in use.
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keep_on = device if offload == "disabled" else "cpu"
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model_path = _ensure_model(model_name)
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# ---- Text encoder (OpenCLIP ViT-H-14) ----
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from video_to_video.modules.embedder import FrozenOpenCLIPEmbedder
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text_encoder = FrozenOpenCLIPEmbedder(
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device=device, pretrained="laion2b_s32b_b79k"
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)
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text_encoder.model.to(device)
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# Pre-compute the negative prompt embedding used during sampling.
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from video_to_video.utils.config import cfg
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negative_y = text_encoder(cfg.negative_prompt).detach()
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# Park text encoder after pre-computing embeddings.
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text_encoder.model.to(keep_on)
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# ---- UNet + ControlNet ----
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from video_to_video.modules.unet_v2v import ControlledV2VUNet
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generator = ControlledV2VUNet()
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load_dict = torch.load(model_path, map_location="cpu", weights_only=False)
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if "state_dict" in load_dict:
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load_dict = load_dict["state_dict"]
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generator.load_state_dict(load_dict, strict=False)
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del load_dict
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generator = generator.to(device=keep_on, dtype=dtype)
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generator.eval()
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# ---- Noise schedule + diffusion helper ----
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from video_to_video.diffusion.schedules_sdedit import noise_schedule
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from video_to_video.diffusion.diffusion_sdedit import GaussianDiffusion
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sigmas = noise_schedule(
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schedule="logsnr_cosine_interp",
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n=1000,
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zero_terminal_snr=True,
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scale_min=2.0,
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scale_max=4.0,
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)
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diffusion = GaussianDiffusion(sigmas=sigmas)
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# ---- Temporal VAE (from HuggingFace diffusers) ----
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from diffusers import AutoencoderKLTemporalDecoder
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vae = AutoencoderKLTemporalDecoder.from_pretrained(
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"stabilityai/stable-video-diffusion-img2vid",
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subfolder="vae",
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variant="fp16",
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)
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vae.eval()
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vae.requires_grad_(False)
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vae.to(keep_on)
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torch.cuda.empty_cache()
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star_model = {
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"text_encoder": text_encoder,
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"generator": generator,
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"diffusion": diffusion,
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"vae": vae,
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"negative_y": negative_y,
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"device": device,
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"dtype": dtype,
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"offload": offload,
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}
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return (star_model,)
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class STARVideoSuperResolution:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"star_model": ("STAR_MODEL", {
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"tooltip": "Connect from STAR Model Loader.",
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}),
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"images": ("IMAGE", {
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"tooltip": "Input video frames (IMAGE batch). Can come from LoadImage, VHS LoadVideo, etc.",
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}),
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"upscale": ("INT", {
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"default": 4, "min": 1, "max": 8,
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"tooltip": "Upscale factor applied to the input resolution. 4x is the default. Higher values need more VRAM.",
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}),
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"steps": ("INT", {
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"default": 15, "min": 1, "max": 100,
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"tooltip": "Number of denoising steps. Ignored in 'fast' solver mode (hardcoded 15). More steps = better quality but slower.",
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}),
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"guide_scale": ("FLOAT", {
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"default": 7.5, "min": 1.0, "max": 20.0, "step": 0.5,
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"tooltip": "Classifier-free guidance scale. Higher values follow the prompt more strongly. 7.5 is a good default.",
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}),
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"prompt": ("STRING", {
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"default": "", "multiline": True,
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"tooltip": "Text prompt describing the desired output. Leave empty to use STAR's built-in quality prompt.",
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}),
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"solver_mode": (["fast", "normal"], {
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"default": "fast",
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"tooltip": "fast: optimized 15-step schedule (4 coarse + 11 fine). normal: uniform schedule using the steps parameter.",
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}),
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"max_chunk_len": ("INT", {
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"default": 32, "min": 4, "max": 128,
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"tooltip": "Max frames processed at once. Lower values reduce VRAM usage for long videos. Chunks overlap by 50%.",
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}),
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"seed": ("INT", {
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"default": 0, "min": 0, "max": 0xFFFFFFFFFFFFFFFF,
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"tooltip": "Random seed for reproducible results.",
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}),
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"color_fix": (["adain", "wavelet", "none"], {
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"default": "adain",
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"tooltip": "Post-processing color correction. adain: match color stats from input. wavelet: preserve input low-frequency color. none: no correction.",
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}),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("images",)
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FUNCTION = "upscale_video"
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CATEGORY = "STAR"
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DESCRIPTION = "Upscale video frames using STAR diffusion-based super-resolution."
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def upscale_video(
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self,
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star_model,
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images,
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upscale,
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steps,
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guide_scale,
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prompt,
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solver_mode,
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max_chunk_len,
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seed,
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color_fix,
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):
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from .star_pipeline import run_star_inference
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result = run_star_inference(
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star_model=star_model,
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images=images,
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upscale=upscale,
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steps=steps,
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guide_scale=guide_scale,
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prompt=prompt,
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solver_mode=solver_mode,
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max_chunk_len=max_chunk_len,
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seed=seed,
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color_fix=color_fix,
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)
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return (result,)
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NODE_CLASS_MAPPINGS = {
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"STARModelLoader": STARModelLoader,
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"STARVideoSuperResolution": STARVideoSuperResolution,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"STARModelLoader": "STAR Model Loader",
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"STARVideoSuperResolution": "STAR Video Super-Resolution",
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}
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6
requirements.txt
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requirements.txt
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easydict
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einops
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open-clip-torch
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torchsde
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diffusers>=0.25.0
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huggingface_hub
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star_pipeline.py
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star_pipeline.py
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import torch
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import torch.nn.functional as F
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import torch.amp
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from einops import rearrange
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import comfy.utils
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import comfy.model_management as mm
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from video_to_video.video_to_video_model import pad_to_fit, make_chunks
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from video_to_video.utils.config import cfg
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# ---------------------------------------------------------------------------
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# Tensor format conversions
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# ---------------------------------------------------------------------------
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def comfyui_to_star_frames(images: torch.Tensor) -> torch.Tensor:
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"""Convert ComfyUI IMAGE batch to STAR input format.
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ComfyUI: [N, H, W, 3] float32 in [0, 1]
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STAR: [N, 3, H, W] float32 in [-1, 1]
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"""
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t = images.permute(0, 3, 1, 2) # [N,3,H,W]
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t = t * 2.0 - 1.0
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return t
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def star_output_to_comfyui(video: torch.Tensor) -> torch.Tensor:
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"""Convert STAR output to ComfyUI IMAGE batch.
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STAR output: [1, 3, F, H, W] float32 in [-1, 1]
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ComfyUI: [F, H, W, 3] float32 in [0, 1]
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"""
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v = video.squeeze(0) # [3, F, H, W]
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v = v.permute(1, 2, 3, 0) # [F, H, W, 3]
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v = (v + 1.0) / 2.0
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v = v.clamp(0.0, 1.0)
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return v
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# ---------------------------------------------------------------------------
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# VAE helpers (mirror VideoToVideo_sr methods)
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# ---------------------------------------------------------------------------
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|
||||
def vae_encode(vae, t, chunk_size=1):
|
||||
"""Encode [B, F, C, H, W] video tensor to latent space."""
|
||||
num_f = t.shape[1]
|
||||
t = rearrange(t, "b f c h w -> (b f) c h w")
|
||||
z_list = []
|
||||
for ind in range(0, t.shape[0], chunk_size):
|
||||
z_list.append(vae.encode(t[ind : ind + chunk_size]).latent_dist.sample())
|
||||
z = torch.cat(z_list, dim=0)
|
||||
z = rearrange(z, "(b f) c h w -> b c f h w", f=num_f)
|
||||
return z * vae.config.scaling_factor
|
||||
|
||||
|
||||
def vae_decode_chunk(vae, z, chunk_size=3):
|
||||
"""Decode latent [B, C, F, H, W] back to pixel frames."""
|
||||
z = rearrange(z, "b c f h w -> (b f) c h w")
|
||||
video = []
|
||||
for ind in range(0, z.shape[0], chunk_size):
|
||||
chunk = z[ind : ind + chunk_size]
|
||||
num_f = chunk.shape[0]
|
||||
decoded = vae.decode(chunk / vae.config.scaling_factor, num_frames=num_f).sample
|
||||
video.append(decoded)
|
||||
video = torch.cat(video)
|
||||
return video
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Color correction wrappers
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def apply_color_fix(output_frames, input_frames_star, method):
|
||||
"""Apply colour correction to the upscaled output.
|
||||
|
||||
output_frames: [F, H, W, 3] float [0, 1] (ComfyUI format)
|
||||
input_frames_star: [F, 3, H, W] float [-1, 1] (STAR format)
|
||||
method: "adain" | "wavelet" | "none"
|
||||
"""
|
||||
if method == "none":
|
||||
return output_frames
|
||||
|
||||
from video_super_resolution.color_fix import adain_color_fix, wavelet_color_fix
|
||||
|
||||
# Resize input to match output spatial size for stats transfer
|
||||
_, h_out, w_out, _ = output_frames.shape
|
||||
source = F.interpolate(
|
||||
input_frames_star, size=(h_out, w_out), mode="bilinear", align_corners=False
|
||||
)
|
||||
|
||||
# The color_fix functions expect:
|
||||
# target: [T, H, W, C] in [0, 255]
|
||||
# source: [T, C, H, W] in [-1, 1]
|
||||
target = output_frames * 255.0
|
||||
|
||||
if method == "adain":
|
||||
result = adain_color_fix(target, source)
|
||||
else:
|
||||
result = wavelet_color_fix(target, source)
|
||||
|
||||
return (result / 255.0).clamp(0.0, 1.0)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Progress-bar integration via trange monkey-patch
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def _make_progress_trange(pbar, total_steps):
|
||||
"""Return a drop-in replacement for tqdm.auto.trange that drives *pbar*."""
|
||||
from tqdm.auto import trange as _real_trange
|
||||
|
||||
def _progress_trange(*args, **kwargs):
|
||||
kwargs["disable"] = True # silence console output
|
||||
for val in _real_trange(*args, **kwargs):
|
||||
yield val
|
||||
pbar.update(1)
|
||||
|
||||
return _progress_trange
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Main inference entry point
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def _move(module, device):
|
||||
"""Move a nn.Module to device and free source memory."""
|
||||
module.to(device)
|
||||
if device == "cpu" or (isinstance(device, torch.device) and device.type == "cpu"):
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
|
||||
def run_star_inference(
|
||||
star_model: dict,
|
||||
images: torch.Tensor,
|
||||
upscale: int = 4,
|
||||
steps: int = 15,
|
||||
guide_scale: float = 7.5,
|
||||
prompt: str = "",
|
||||
solver_mode: str = "fast",
|
||||
max_chunk_len: int = 32,
|
||||
seed: int = 0,
|
||||
color_fix: str = "adain",
|
||||
) -> torch.Tensor:
|
||||
"""Run STAR video super-resolution and return ComfyUI IMAGE batch."""
|
||||
|
||||
device = star_model["device"]
|
||||
dtype = star_model["dtype"]
|
||||
text_encoder = star_model["text_encoder"]
|
||||
generator = star_model["generator"]
|
||||
diffusion = star_model["diffusion"]
|
||||
vae = star_model["vae"]
|
||||
negative_y = star_model["negative_y"]
|
||||
offload = star_model.get("offload", "disabled")
|
||||
|
||||
# In aggressive mode use smaller VAE chunks to cut peak VRAM.
|
||||
vae_enc_chunk = 1
|
||||
vae_dec_chunk = 3
|
||||
if offload == "aggressive":
|
||||
vae_dec_chunk = 1
|
||||
|
||||
total_noise_levels = 1000
|
||||
|
||||
# -- Convert ComfyUI frames to STAR format --
|
||||
video_data = comfyui_to_star_frames(images) # [F, 3, H, W]
|
||||
|
||||
# Keep a copy at input resolution (on CPU) for colour correction later
|
||||
input_frames_star = video_data.clone().cpu()
|
||||
|
||||
frames_num, _, orig_h, orig_w = video_data.shape
|
||||
target_h = orig_h * upscale
|
||||
target_w = orig_w * upscale
|
||||
|
||||
# -- Bilinear upscale to target resolution --
|
||||
video_data = F.interpolate(video_data, size=(target_h, target_w), mode="bilinear", align_corners=False)
|
||||
_, _, h, w = video_data.shape
|
||||
|
||||
# -- Pad to model-friendly resolution --
|
||||
padding = pad_to_fit(h, w)
|
||||
video_data = F.pad(video_data, padding, "constant", 1)
|
||||
|
||||
video_data = video_data.unsqueeze(0).to(device) # [1, F, 3, H_pad, W_pad]
|
||||
|
||||
# ---- Stage 1: Text encoding ----
|
||||
if offload != "disabled":
|
||||
text_encoder.model.to(device)
|
||||
text_encoder.device = device
|
||||
text = prompt if prompt.strip() else cfg.positive_prompt
|
||||
y = text_encoder(text).detach()
|
||||
if offload != "disabled":
|
||||
text_encoder.model.to("cpu")
|
||||
text_encoder.device = "cpu"
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
# -- Diffusion sampling (autocast needed for fp16 VAE / UNet) --
|
||||
with torch.amp.autocast("cuda"):
|
||||
# ---- Stage 2: VAE encode ----
|
||||
if offload != "disabled":
|
||||
_move(vae, device)
|
||||
video_data_feature = vae_encode(vae, video_data, chunk_size=vae_enc_chunk)
|
||||
if offload != "disabled":
|
||||
_move(vae, "cpu")
|
||||
# Free the full-res pixel tensor — only latents needed from here.
|
||||
del video_data
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
t = torch.LongTensor([total_noise_levels - 1]).to(device)
|
||||
noised_lr = diffusion.diffuse(video_data_feature, t)
|
||||
|
||||
model_kwargs = [{"y": y}, {"y": negative_y}, {"hint": video_data_feature}]
|
||||
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
chunk_inds = (
|
||||
make_chunks(frames_num, interp_f_num=0, max_chunk_len=max_chunk_len)
|
||||
if frames_num > max_chunk_len
|
||||
else None
|
||||
)
|
||||
# Need at least 2 chunks; a single chunk causes IndexError in
|
||||
# model_chunk_fn when it accesses chunk_inds[1].
|
||||
if chunk_inds is not None and len(chunk_inds) < 2:
|
||||
chunk_inds = None
|
||||
|
||||
# Monkey-patch trange for progress reporting
|
||||
import video_to_video.diffusion.solvers_sdedit as _solvers_mod
|
||||
_orig_trange = _solvers_mod.trange
|
||||
|
||||
# Calculate actual number of sigma steps for progress bar
|
||||
# (matches logic inside GaussianDiffusion.sample_sr)
|
||||
if solver_mode == "fast":
|
||||
num_sigma_steps = 14 # 4 coarse + 11 fine = 15 sigmas, trange iterates len-1 = 14
|
||||
else:
|
||||
num_sigma_steps = steps
|
||||
pbar = comfy.utils.ProgressBar(num_sigma_steps)
|
||||
_solvers_mod.trange = _make_progress_trange(pbar, num_sigma_steps)
|
||||
|
||||
# ---- Stage 3: Diffusion (UNet) ----
|
||||
if offload != "disabled":
|
||||
_move(generator, device)
|
||||
try:
|
||||
torch.manual_seed(seed)
|
||||
|
||||
gen_vid = diffusion.sample_sr(
|
||||
noise=noised_lr,
|
||||
model=generator,
|
||||
model_kwargs=model_kwargs,
|
||||
guide_scale=guide_scale,
|
||||
guide_rescale=0.2,
|
||||
solver="dpmpp_2m_sde",
|
||||
solver_mode=solver_mode,
|
||||
return_intermediate=None,
|
||||
steps=steps,
|
||||
t_max=total_noise_levels - 1,
|
||||
t_min=0,
|
||||
discretization="trailing",
|
||||
chunk_inds=chunk_inds,
|
||||
)
|
||||
finally:
|
||||
_solvers_mod.trange = _orig_trange
|
||||
if offload != "disabled":
|
||||
_move(generator, "cpu")
|
||||
|
||||
# Free latents that are no longer needed.
|
||||
del noised_lr, video_data_feature, model_kwargs
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
# ---- Stage 4: VAE decode ----
|
||||
if offload != "disabled":
|
||||
_move(vae, device)
|
||||
vid_tensor_gen = vae_decode_chunk(vae, gen_vid, chunk_size=vae_dec_chunk)
|
||||
if offload != "disabled":
|
||||
_move(vae, "cpu")
|
||||
|
||||
# -- Remove padding --
|
||||
w1, w2, h1, h2 = padding
|
||||
vid_tensor_gen = vid_tensor_gen[:, :, h1 : h + h1, w1 : w + w1]
|
||||
|
||||
# -- Reshape to [B, C, F, H, W] then convert to ComfyUI format --
|
||||
gen_video = rearrange(vid_tensor_gen, "(b f) c h w -> b c f h w", b=1)
|
||||
gen_video = gen_video.float().cpu()
|
||||
|
||||
result = star_output_to_comfyui(gen_video) # [F, H, W, 3]
|
||||
|
||||
# -- Color correction --
|
||||
result = apply_color_fix(result, input_frames_star, color_fix)
|
||||
|
||||
torch.cuda.empty_cache()
|
||||
mm.soft_empty_cache()
|
||||
|
||||
return result
|
||||
Reference in New Issue
Block a user