ComfyUI-FL-DiffVSR

ComfyUI-FL-DiffVSR
★ 20

视频超分时序一致扩散模型自动下载
基于扩散模型的视频超分节点，提供4×无闪烁时序一致放大，支持文本引导、分块降显存与HuggingFace自动下载模型
💡 对视频逐帧进行4倍无闪烁超分并可用文本对结果进行引导
🍴 2 Forks💻 Python🔄 2026-01-24
📦
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https://pan.quark.cn/s/e00a65475347
📦 requirements.txt
#
FL
DiffVSR
Requirements
#
Core
dependencies
for
Stream-DiffVSR
video
super-resolution
#
Core
ML
frameworks
torch>=2.0.0
torchvision>=0.15.0
#
Diffusers
and
transformers
diffusers>=0.21.0
transformers>=4.30.0
#
Model
loading
safetensors>=0.3.0
huggingface_hub>=0.16.0
accelerate>=0.20.0
#
Image
processing
Pillow>=9.0.0
numpy>=1.20.0
#
Optional:
Memory
efficient
attention
(recommended)
xformers>=0.0.20
#
Tensor
operations
einops>=0.6.0
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📄 README

FL DiffVSR

Diffusion-based video super-resolution nodes for ComfyUI powered by Stream-DiffVSR. Upscale videos 4x with temporal coherence for smooth, artifact-free results.

[](https://arxiv.org/abs/2512.23709)

[](https://www.patreon.com/Machinedelusions)

Features

  • 4x Video Upscaling – Upscale video frames to 4x resolution with high fidelity
  • Temporal Coherence – Maintains consistency across frames for flicker-free results
  • Diffusion-Based – Leverages diffusion models for superior detail reconstruction
  • Text Guidance – Optional prompt support for guided upscaling
  • Memory Efficient – Chunked processing and xformers support for lower VRAM usage
  • Automatic Downloads – Models download automatically from HuggingFace on first use

    • Nodes

    | Node | Description |

    |——|————-|

    | FL DiffVSR Load Model | Downloads and loads Stream-DiffVSR model from HuggingFace |

    | FL DiffVSR Upscale | Upscales video frames with temporal coherence |

    Installation

    ComfyUI Manager

    Search for “FL DiffVSR” and install.

    Manual

    cd ComfyUI/custom_nodes
git clone https://github.com/filliptm/ComfyUI-FL-DiffVSR.git
cd ComfyUI-FL-DiffVSR
pip install -r requirements.txt

    Quick Start

  • Add FL DiffVSR Load Model node and configure precision/device settings
  • Connect to FL DiffVSR Upscale node
  • Feed your video frames as an IMAGE batch
  • Adjust inference steps (4 recommended for speed/quality balance)
  • Generate upscaled frames

    • Parameters

    Model Loader

    | Parameter | Options | Description |

    |———–|———|————-|

    | precision | auto, fp32, fp16, bf16 | Model precision (auto selects fp16 for GPU) |

    | device | auto, cuda, cpu | Target device for inference |

    | enable_xformers | true/false | Enable memory-efficient attention |

    Upscaler

    | Parameter | Default | Description |

    |———–|———|————-|

    | inference_steps | 4 | Denoising steps (higher = better quality, slower) |

    | guidance_scale | 0.0 | CFG scale (0 = no guidance) |

    | chunk_size | 8 | Frames per batch (lower = less VRAM) |

    | prompt | “” | Optional text guidance |

    | negative_prompt | “” | Optional negative prompt |

    | seed | -1 | Random seed (-1 for random) |

    Requirements

  • Python 3.10+
  • 8GB VRAM minimum (16GB+ recommended for larger videos)
  • NVIDIA GPU recommended (CPU supported but slow)

    • Model

    The Stream-DiffVSR model downloads automatically to ComfyUI/models/stream_diffvsr/ on first use (~2GB).

    | Model | Source | Size |

    |——-|——–|——|

    | Stream-DiffVSR | Jamichsu/Stream-DiffVSR | ~2GB |

    License

    Apache 2.0