Pipelines Supported in Scope
Scope supports six video pipelines. Five are autoregressive video diffusion pipelines built on Wan2.1 base models using the Self-Forcing training methodology (except RewardForcing, which uses Rewarded Distribution Matching). The sixth, LTX 2.3, is a joint audio-video DiT model from Lightricks that ships as an installable plugin rather than a built-in pipeline. Each pipeline has its own strengths. Some optimize for speed, others for quality, long-context consistency, or synchronized audio. The comparison tables below summarize the key differences to help you choose.Quick Comparison
Four pipelines use the smaller Wan2.1 1.3B model, Krea Realtime uses the larger Wan2.1 14B model for higher quality output, and LTX 2.3 uses a 22B DiT from Lightricks.
*Estimated runtime VRAM usage. A 24GB GPU (e.g. RTX 4090) is the minimum commercially available card for 1.3B pipelines and LTX 2.3. See System Requirements for minimum hardware specs.
Feature Support
All five Wan2.1-based pipelines support Text-to-Video (T2V), Video-to-Video (V2V), LoRA adapters, and VACE conditioning. MemFlow adds a memory bank for long-context consistency. LTX 2.3 is the only pipeline that generates synchronized audio alongside video, but it does not support VACE.Pipeline Details
StreamDiffusion V2
Real-time streaming from the original StreamDiffusion creators
LongLive
Smooth prompt transitions and extended generation from Nvidia
Krea Realtime
14B model for highest quality generation
RewardForcing
Reward-matched training for improved output quality
MemFlow
Memory bank for long-context consistency
LTX 2.3
Joint audio-video generation with ID-LoRA and IC-LoRA control (plugin)
Shared Parameters
The Wan2.1-based pipelines share these common parameters. LTX 2.3 uses a different set of constraints (32-pixel resolution multiples and 8×K+1 frame counts), documented on its reference page.Resolution
Generation is faster at smaller resolutions, resulting in smoother video. The visual quality is best at 832x480, which is the training resolution for most pipelines. You may need a more powerful GPU to maintain high FPS at this resolution.Seed
The seed parameter enables reproducible generations. If you find a seed value that produces good results with a specific prompt sequence, save it to reproduce that generation later.Prompting
These techniques apply to all pipelines and significantly improve output quality.Subject and Background Anchors
Include a clear subject (who/what) and background/setting (where) in each prompt. For scene continuity, reference the same subject and/or setting across prompts.Cinematic Long Takes
The models work better with long cinematic takes rather than rapid shot-by-shot transitions. Avoid quick cutscenes, rapid scene changes, and jump cuts. Instead, let scenes flow naturally with gradual transitions.Long, Detailed Prompts
All pipelines perform better with detailed prompts. A helpful technique is to expand a base prompt using an LLM (ChatGPT, Claude, etc.). Base prompt:See Also
VAE Types
Configure VAE for quality/speed tradeoffs
System Requirements
Hardware requirements for each pipeline
Pipeline Architecture
Technical details for node developers