> ## 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.
# RewardForcing
> Streaming pipeline and autoregressive video diffusion model trained with Rewarded Distribution Matching
# RewardForcing Pipeline
[RewardForcing](https://reward-forcing.github.io/) is a streaming pipeline and autoregressive video diffusion model from ZJU, Ant Group, SIAS-ZJU, HUST and SJTU.
The model is trained with Rewarded Distribution Matching Distillation using Wan2.1 1.3b as the base model.
### At a Glance
| | |
| :----------------- | :----------------------------- |
| **Base Model** | Wan2.1 1.3B |
| **Estimated VRAM** | \~20GB |
| **Training** | Rewarded Distribution Matching |
| **LoRA Support** | 1.3B LoRAs |
| **VACE Support** | Yes |
| **T2V / V2V** | Yes / Yes |
***
## Examples
The following examples include timeline JSON files with the prompts used so you can try them as well.
### Ink Drop
Download timeline
### Melting Ice
Download timeline
***
## Resolution
The generation will be faster for smaller resolutions resulting in smoother video. The visual quality will be better at **832x480** which is the resolution that the model was trained on, but you may need a more powerful GPU in order to achieve a higher FPS.
***
## Seed
The seed parameter in the UI can be used to reproduce generations. If you like the generation for a certain seed value and sequence of prompts you can re-use that value later with those same prompts to reproduce the generation.
***
## Prompting
**Subject and Background/Setting Anchors**
The model works better if you include a subject (who/what) and background/setting (where) in each prompt. If you want continuity in the next scene then you can continue referencing the same subject and/or background/setting.
For example:
```
"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."
```
**Cinematic Long Takes**
The model works better for scene transitions that involve long cinematic long takes and works less well with rapid shot-by-shot transitions or fast cutscenes.
**Long, Detailed Prompts**
The model works better with long, detailed prompts. A helpful technique to extend prompts is to take a base prompt and then ask a LLM chatbot (eg. ChatGPT, Claude, Gemini, etc.) to write a more detailed version.
If your base prompt is:
```
"A cartoon dog jumping and then running."
```
Then, the extended prompt could be:
```
"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."
```
***
## Offline Generation
A test [script](https://github.com/daydreamlive/scope/blob/main/src/scope/core/pipelines/reward_forcing/test.py) can be used for offline generation.
If the model weights are not downloaded yet:
```bash theme={null}
# Run from scope directory
uv run download_models --pipeline reward_forcing
```
Then:
```bash theme={null}
# Run from scope directory
uv run -m scope.core.pipelines.reward_forcing.test
```
This will create an `output.mp4` file in the `reward_forcing` directory.
***
## See Also
### Other Pipelines
Real-time streaming from the original StreamDiffusion creators
Smooth prompt transitions and extended generation from Nvidia
14B model for highest quality generation
Memory bank for long-context consistency