Model reference · open weights
zeroscope_v2_576w is an open-weight video model from cerspense. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.
Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.
What it is
| Maker | cerspense |
|---|---|
| Type | Video models |
| Task | Text→video |
| Runs with | diffusers |
| Released | 2023-06-21 |
| Popularity | 12k downloads / month |
| Licence | Commercial licence needed |
About
A watermark-free Modelscope-based video model optimized for producing high-quality 16:9 compositions and a smooth video output. This model was trained from the original weights using 9,923 clips and 29,769 tagged frames at 24 frames, 576x320 resolution. zeroscope_v2_567w is specifically designed for upscaling with zeroscope_v2_XL using vid2vid in the 1111 text2video extension by kabachuha. Leveraging this model as a preliminary step allows for superior overall compositions at higher resolutions in zeroscope_v2_XL, permitting faster exploration in 576x320 before transitioning to a high-resolution render. See some example outputs that have been upscaled to 1024x576 using zeroscope_v2_XL. (courtesy of dotsimulate)
zeroscope_v2_576w uses 7.9gb of vram when rendering 30 frames at 576x320
For upscaling, it's recommended to use zeroscope_v2_XL via vid2vid in the 1111 extension. It works best at 1024x576 with a denoise strength between 0.66 and 0.85. Remember to use the same prompt that was used to generate the original clip.
Let's first install the libraries required:
$ pip install diffusers transformers accelerate torch
Now, generate a video:
import torch
from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
from diffusers.utils import export_to_video
pipe = DiffusionPipeline.from_pretrained("cerspense/zeroscope_v2_576w", torch_dtype=torch.float16)
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
pipe.enable_model_cpu_offload()
prompt = "Darth Vader is surfing on waves"
video_frames = pipe(prompt, num_inference_steps=40, height=320, width=576, num_frames=24).frames
video_path = export_to_video(video_frames)
Here are some results:
Darth vader is surfing on waves.
alt="Darth vader surfing in waves."
style="width: 576;" />
Lower resolutions or fewer frames could lead to suboptimal output.
Thanks to camenduru, kabachuha, ExponentialML, dotsimulate, VANYA, polyware, tin2tin
From the published model card. Full card on the HuggingFace links in the sidebar.
Using it via the API
Once AxForge deploys zeroscope-v2-576w for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (zeroscope-v2-576w below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/videos/generations \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"zeroscope-v2-576w","prompt":"a drone shot over a forest"}'
Create an account — your API key is available in the console. 5M tokens/month currently included with every new account at launch.