Instructions to use lsmpp/kontextrefiner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use lsmpp/kontextrefiner with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("lsmpp/kontextrefiner", 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: 587 Bytes
1b7e5b9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | #!/bin/bash
# run
# accelerate config
# check with
# accelerate env
export MODEL_DIR="PixArt-alpha/PixArt-XL-2-512x512"
export OUTPUT_DIR="output/pixart-controlnet-hf-diffusers-test"
accelerate launch ./train_pixart_controlnet_hf.py --mixed_precision="fp16" \
--pretrained_model_name_or_path=$MODEL_DIR \
--output_dir=$OUTPUT_DIR \
--dataset_name=fusing/fill50k \
--resolution=512 \
--learning_rate=1e-5 \
--train_batch_size=1 \
--gradient_accumulation_steps=4 \
--report_to="wandb" \
--seed=42 \
--dataloader_num_workers=8
# --lr_scheduler="cosine" --lr_warmup_steps=0 \
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