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: 622 Bytes
9264c1c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | #!/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}"
: "${MASK_DIR:?Set MASK_DIR to a directory of same-named mask videos}"
GPU_1STEP="${GPU_1STEP:-0}"
GPU_2STEP="${GPU_2STEP:-1}"
GPU="$GPU_1STEP" OUTPUT_DIR="${OUTPUT_DIR_1STEP:-$ROOT/outputs/dmd_1step}" \
bash "$ROOT/scripts/infer_1step.sh" > "$ROOT/infer_1step.log" 2>&1 &
pid1=$!
GPU="$GPU_2STEP" OUTPUT_DIR="${OUTPUT_DIR_2STEP:-$ROOT/outputs/dmd_2step}" \
bash "$ROOT/scripts/infer_2step.sh" > "$ROOT/infer_2step.log" 2>&1 &
pid2=$!
wait "$pid1"
wait "$pid2"
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