Instructions to use BryanW/43.wm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use BryanW/43.wm with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("BryanW/43.wm", 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
Evaluations
GenEval
1. Sample prompt images
python ./evaluations/geneval/sample.py \
--height 1024 --width 1024 \
--guidance_scale 7 --num_inference_steps 25 \
--ckpt /path/to/URSA-1.7B-IBQ1024 \
--prompt_size 4 --outdir ./samples/geneval/URSA-1.7B-IBQ1024
2. Evaluation
=./samples/geneval/URSA-1.7B-IBQ1024
Please refer GenEval evaluation guide.
DPG-Bench
1. Sample prompt images
python evaluations/dpgbench/sample.py \
--height 1024 --width 1024 \
--guidance_scale 7 --num_inference_steps 25 \
--ckpt ./checkpoints/URSA-1.7B-IBQ1024 \
--prompt_size 4 --outdir samples/dpgbench/URSA-1.7B-IBQ1024
2. Evaluation
=./samples/dpgbench/URSA-1.7B-IBQ1024
Please refer DPG-Bench evaluation guide.
VBench
1. Sample prompt videos
python evaluations/vbench/sample.py \
--num_frames 49 --height 320 --width 512 \
--guidance_scale 7 --num_inference_steps 50 --motion_score 9 \
--ckpt ./checkpoints/URSA-1.7B-FSQ320 \
--prompt_size 1 --outdir ./samples/vbench/URSA-1.7B-FSQ320
2. Evaluation
=./samples/vbench/URSA-1.7B-FSQ320
Please refer VBench evaluation guide.