Instructions to use mignonjia/LongLive-1.0-Stage-1-Diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use mignonjia/LongLive-1.0-Stage-1-Diffusers with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("mignonjia/LongLive-1.0-Stage-1-Diffusers", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
LongLive 1.0 Stage 1 Diffusers
Diffusers-style LongLive stage-1 checkpoint converted from:
/mnt/weka/home/hao.zhang/mhuo/FastVideo/LongLive/longlive_models/models/longlive_base.pt
The model uses the causal Wan 2.1 T2V 1.3B layout. The LongLive weights replace
the transformer checkpoint; scheduler, tokenizer, text encoder, and VAE follow
wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers.
Recommended runtime kwargs:
model_kwargs:
timestep_shift: 5.0
local_attn_size: 12
sink_size: 3
FastVideo training config equivalent:
models:
student:
_target_: fastvideo.train.models.wan.WanCausalModel
init_from: mignonjia/LongLive-1.0-Stage-1-Diffusers
trainable: true
pipeline:
flow_shift: 5
dit_config:
local_attn_size: 12
sink_size: 3
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