Instructions to use rzgar/Bernini-v2-ComfyUI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use rzgar/Bernini-v2-ComfyUI with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("rzgar/Bernini-v2-ComfyUI", 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
Quantized and full-precision (fp32) extraction of every component from the sharded bernini/ bundle.
Tensor keys are preserved verbatim (including their original prefixes).
Quantized
Components
LightX2V 4Step
| File | Component | Tensors | Size | Original prefix |
|---|---|---|---|---|
connector.safetensors |
MLP connector (planner→renderer) | 12 | 0.28 GB | connector. |
mask_tokens.safetensors |
mask-token embedding (4096 × 3584) | 1 | 0.06 GB | mask_tokens |
vit_decoder.safetensors |
vision decoder | 140 | 5.75 GB | vit_decoder. |
mllm.safetensors |
Qwen2.5-VL planner (fine-tuned) | 729 | 33.17 GB | mllm. |
t5_text_encoder_fp32.safetensors |
UMT5-XXL text encoder | 243 | 26.92 GB | t5_text_encoder. |
high_noise_fp32.safetensors |
Wan2.2 high-noise DiT | 1095 | 57.15 GB | diff_dec. |
low_noise_fp32.safetensors |
Wan2.2 low-noise DiT | 1095 | 57.15 GB | diff_dec_low. |
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Model tree for rzgar/Bernini-v2-ComfyUI
Base model
ByteDance/Bernini-Diffusers-v2