Instructions to use ProCreations/Image-2.1-Calibrated-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ProCreations/Image-2.1-Calibrated-FP8 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ProCreations/Image-2.1-Calibrated-FP8", 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
- Local Apps Settings
- Draw Things
- DiffusionBee
File size: 994 Bytes
f642d1f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | import hashlib,json,time
from pathlib import Path
from huggingface_hub import HfApi
root=Path('/home/user/models/qwen-image-2.1-b3179ad')
info=HfApi().model_info('Qwen/Qwen-Image-2.1',revision='b3179ad355be050328e483a9dfdd9e60cd62adfa',files_metadata=True)
rows=[]
for f in info.siblings:
p=root/f.rfilename
assert p.exists(),f.rfilename
h=hashlib.sha256()
with p.open('rb') as stream:
while b:=stream.read(8<<20):h.update(b)
digest=h.hexdigest()
if f.size is not None:assert p.stat().st_size==f.size
if f.lfs:assert digest==f.lfs.sha256,(f.rfilename,digest)
rows.append({'file':f.rfilename,'bytes':p.stat().st_size,'sha256':digest,'upstream_lfs_sha256_matched':bool(f.lfs)})
receipt={'model':info.id,'revision':info.sha,'files':rows,'total_bytes':sum(r['bytes'] for r in rows),'verified_at':time.time()}
out=Path(__file__).parent/'base_integrity.json';out.write_text(json.dumps(receipt,indent=2))
print('BASE_VERIFIED',len(rows),receipt['total_bytes'])
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