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Bernini-R Workflow · ComfyUI-BerniniR Wrapper
Perfil de ejecución
Descripción de la fuente
Bernini-R Workflow · ComfyUI-BerniniR Wrapper
A node pack for the Bernini-R (1.3B / 14B) video generation model — VRAM optimization · multi-mode guidance · context window · differential-diffusion masking · segmented prompt control github: xiaolibai-sys/ComfyUI-BerniniRWrapper
Overview
This workflow is built on the ComfyUI-BerniniR Wrapper node pack. A single graph combines VRAM optimization, a multi-mode guidance family, temporal context windows, differential-diffusion masking, and segmented prompt control — letting long videos run on consumer GPUs / low-RAM machines while still allowing fine-grained per-segment content control.
1 · VRAM & Loading Optimization
● Block Swap Per-layer DiT weight swapping between GPU and CPU with pin_memory and asynchronous prefetch, drastically cutting peak VRAM so the 14B model runs on limited memory.
Comentario generado por IA
Explicación generada por IA basada en los detalles de la fuente y la configuración. Las sugerencias están claramente identificadas.
This ComfyUI workflow creates image-to-video clips from an image and text prompts, with prompts assignable to frame ranges and masks for selected regions; it outputs video.
Choose among seven guidance modes—CFG, APG, RAAG, S2, Z2, STG_A, and STG_R—with cosine, linear, or piecewise per-step strength schedules.
For long videos, the workflow pairs FreeNoise noise shuffling with overlapping temporal windows to support motion and semantic coherence across segments.
A dual expert sampler switches between high-noise and low-noise models at a selected step; NAG adds a negative-prompt attention path.
Enter segmented prompts as `1-32(index1): prompt1 ; 33-64(index2): prompt2`; each index maps to a window in BerniniR_SegmentSchedule.
Differential-diffusion masks can freeze or anneal selected regions for inpainting, reference-image preservation, or regional conditioning.
The workflow is built on the ComfyUI-BerniniR Wrapper and uses the Wan Video 2.2 I2V-A14B base model.
The workflow file lists these model files: `Bernini-R_LightX2V_high_noise.safetensors`, `Bernini-R_LightX2V_low_noise.safetensors`, `wan_2.2_vae.safetensors`, `wan_umt5-xxl_bf16_fixed.safetensors`, `wan2.2_bernini_low_noise_fp8_scaled.safetensors`, and `Wan22_Bernini_HIGH_fp8_e4m
Automatic windowing means you do not need to configure a Context Window manually.
Tip: Choose Full (eager) loading when RAM is ample. On memory-constrained systems, streaming loads weights from disk on demand and uses less peak memory, but starts more slowly.
Sugerencia · sin verificar
GGUF quantized weights are not supported; the workflow loads standard safetensors checkpoints.
For storyboard or multi-scene shots with large semantic jumps, consider an explicit Context Window or separate batches; shared local context is weaker across those transitions.
Sugerencia · sin verificar
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Fuentes
1 fuenteExtractos de fuentes
2 extractosSource context: 164 downloads · Type Workflows · Base model Wan Video 2.2 I2V-A14B
● Streaming Loader Optimized for low-RAM devices — weights are streamed from disk on demand instead of loading the whole model into memory at once.
● Automatic Model Offload Lazy-loading strategy: weights are loaded only when used and released when idle, avoiding two copies of the same model weights in memory and keeping the memory peak low.
2 · Guidance
A seven-mode guidance family is included: CFG, APG, RAAG, S2, Z2, STG_A, STG_R — covering classifier-free, projected, ratio-aware, stochastic self-, zero-cost zigzag, and spatiotemporal skip guidance. A per-step guidance strength schedule (cosine / linear / piecewise) is also supported. See the separate guidance documentation for details.
3 · Context & Noise Control
● Context Window Paired with FreeNoise noise shuffling, long videos are split into overlapping temporal windows to keep motion and semantics coherent across segments and avoid long-sequence degradation.
4 · Conditioning & Masking
● Differential Diffusion Mask A differential-diffusion mask mechanism supporting freeze / anneal on selected regions — useful for inpainting, reference-image preservation, or regional conditioning.
● NAG (Normalized Attention Guidance) Injects a negative-prompt attention path to enhance detail.
5 · Sampler
● Dual Expert Sampler Switches between high-noise and low-noise expert models by split step, balancing early structure formation with late detail convergence.
6 · Segmented Prompt Control
● BerniniR SegmentSchedule Generates differing prompts between windows for per-segment semantic choreography.
Prompt Format
1-32(index1): prompt1 ; 33-64(index2): prompt2
Each segment is tagged with a frame range + index, separated by semicolons; the index maps to a window in SegmentSchedule.
Recommendations
● Auto windowing The workflow windows automatically — no manual Context Window configuration needed.
● Recommended: continuous motion control Windows share local context, ideal for character action, camera moves, and other strongly continuous tasks.
● Use Full load when RAM is ample If your machine has enough RAM, prefer the Full (eager) load mode over the streaming loader — weights stay resident in memory so startup and generation begin faster; the streaming loader only pays off on memory-constrained systems.
Note: storyboard / multi-scene shots perform worse — because local context is shared, large semantic jumps between segments are weak. For those, use an explicit Context Window or separate batches.
Limitations
● No GGUF support The pack loads standard safetensors checkpoints; GGUF quantized weights are not supported, so GGUF-based quantized / CPU-offload paths cannot be used.
● Streaming loader is slower Reading weights from disk on demand makes initial load and generation startup slower than eagerly loading the full model into RAM. It trades speed for a lower peak memory footprint on low-RAM machines.
Remaining hardware or download-size estimates are not available yet.
Requisitos
Bernini-R_LightX2V_high_noise.safetensors
Archivo sin verificarCheckpoint · Hugging Face · rzgar/Bernini-R-LightX2V-4step-loras · Bernini R LightX2V 4step Loras
Bernini-R_LightX2V_low_noise.safetensors
Archivo sin verificarSource context: 48 downloads · Type Workflows · Base model Wan Video 2.2 I2V-A14B
Checkpoint · Hugging Face · rzgar/Bernini-R-LightX2V-4step-loras · Bernini R LightX2V 4step Loras
VAE · 1.31 GB · SAFETENSORS · fp8 · pruned · Civitai · 1817671
wan_umt5-xxl_bf16_fixed.safetensors
No resueltoEmbedding · Unknown
wan2.2_bernini_low_noise_fp8_scaled.safetensors
No resueltoCheckpoint · Unknown
Checkpoint · Hugging Face
