Instructions to use Kry4ta1/Effecteraser-VOR-Inference with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Kry4ta1/Effecteraser-VOR-Inference with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Kry4ta1/Effecteraser-VOR-Inference", 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
File size: 750 Bytes
9264c1c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | #!/usr/bin/env bash
set -euo pipefail
ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
: "${INPUT_DIR:?Set INPUT_DIR to a directory of input videos}"
: "${MASK_DIR:?Set MASK_DIR to a directory of same-named mask videos}"
OUTPUT_DIR="${OUTPUT_DIR:-$ROOT/outputs/dmd_2step}"
GPU="${GPU:-0}"
PYTHON_BIN="${PYTHON_BIN:-python}"
cd "$ROOT/src"
CUDA_VISIBLE_DEVICES="$GPU" "$PYTHON_BIN" -u infer.py \
--model_name "$ROOT/checkpoints/dmd_2step" \
--common_model_name "$ROOT/checkpoints/common" \
--config_path "$ROOT/configs/wan2.1/wan_civitai.yaml" \
--lightvae_path "$ROOT/checkpoints/common/lightvae.pth" \
--skip_lora --dmd_steps 2 --guidance_scale 1.0 \
--input_dir "$INPUT_DIR" --input_mask_dir "$MASK_DIR" --save_dir "$OUTPUT_DIR"
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