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Documentation Index

Fetch the complete documentation index at: https://docs.daydream.live/llms.txt

Use this file to discover all available pages before exploring further.

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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.

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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.
PipelineBase ModelCreatorEstimated VRAM*
StreamDiffusion V2Wan2.1 1.3BStreamDiffusion team~20GB
LongLiveWan2.1 1.3BNvidia, MIT, HKUST, HKU, THU~20GB
Krea RealtimeWan2.1 14BKrea~32GB
RewardForcingWan2.1 1.3BZJU, Ant Group, SIAS-ZJU, HUST, SJTU~20GB
MemFlowWan2.1 1.3BKling~20GB
LTX 2.3Lightricks DiT 22B (FP8)Lightricks~22GB
*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.

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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.
FeatureStreamDiffusion V2LongLiveKrea RealtimeRewardForcingMemFlowLTX 2.3
Text-to-Video (T2V)YesYesYesYesYesYes
Image-to-Video (I2V)NoNoNoNoNoYes
Video-to-Video (V2V)YesYesLimited*YesYesVia IC-LoRA
Synchronized AudioNoNoNoNoNoYes
LoRA Support1.3B LoRAs1.3B LoRAs14B LoRAs1.3B LoRAs1.3B LoRAsLTX 2.3 LoRAs
VACE SupportYesYesYesYesYesNo
Memory BankNoNoNoNoYesNo
*Krea Realtime’s regular V2V mode (latent initialization) has known quality issues. Use VACE V2V (visual conditioning with input video) for better results.

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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)

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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.

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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.

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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.

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Prompting

These techniques apply to all pipelines and significantly improve output quality.

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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. 
"A 3D animated scene. A panda walks along a path towards the camera in a park on a spring day."

"A 3D animated scene. A panda halts along a path in a park on a spring day."

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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.

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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: 
"A cartoon dog jumping and then running."
Expanded prompt: 
"A cartoon dog with big expressive eyes and floppy ears suddenly leaps into the frame, tail wagging, and then sprints joyfully toward the camera. Its oversized paws pound playfully on the ground, tongue hanging out in excitement. The animation style is colorful, smooth, and bouncy, with exaggerated motion to emphasize energy and fun. The background blurs slightly with speed lines, giving a lively, comic-style effect as if the dog is about to jump right into the viewer."

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See Also

VAE Types

Configure VAE for quality/speed tradeoffs

System Requirements

Hardware requirements for each pipeline

Pipeline Architecture

Technical details for node developers