rzgar/Bernini-v2-ComfyUI

Bernini v2 is a video editor: t2v / i2v / v2v / r2v / rv2v / ads2v. The source media (reference images / source video) are first-class inputs. The pipeline has two stages:

1. Semantic planning: a fine-tuned Qwen2.5-VL (mllm) + connector + vit_decoder (MaskGIT-style loop) turn the text + source visual tokens into diff_mllm_contexts (the novel part Bernini-R lacks).
2. Rendering: two co-trained Wan2.2 DiTs (high-noise / low-noise, switched at 0.875) with the Bernini vae_txt_vit_wapg guidance (4-way CFG with APG projection), VAE-encoded source media as context_latents, Wan2.1 VAE decode.

ComfyUI Custom node

Extract the ZIP file in ComfyUI/custom_nodes

Task is inferred from which media inputs are connected: (none) = t2v, source_video = v2v, source_video + reference_images = rv2v, reference_images only = r2v, source_video + reference_video = ads2v.

Recommended settings

• CFG: 3
• Steps: 16, 23 or 50
• Sampler: UniPC / DPMPP_2M / EULER

Model files

• FP8_scaled

Component	File
high-noise DiT	wan2.2_bernini_v2_high_noise_nvfp4.safetensors
low-noise DiT	wan2.2_bernini_v2_low_noise_nvfp4.safetensors
T5 (UMT5-XXL)	umt5_xxl_fp8_e4m3fn_scaled.safetensors
VAE (Wan2.1)	Wan2_1_VAE_fp32.safetensors

Place the planner files anywhere under models/text_encoders/ - folder names don't matters:

Component	File	Where
planner MLLM	bernini_mllm_fp8_scaled.safetensors	text_encoders/Bernini/
connector	connector.safetensors	text_encoders/Bernini/
vit_decoder	vit_decoder_bf16.safetensors	text_encoders/Bernini/
mask_tokens	mask_tokens.safetensors	text_encoders/Bernini/
planner tokenizer + processor configs	mllm folder	text_encoders/Bernini/mllm

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