Instructions to use ProCreations/Image-2.1-Turbo-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-Turbo-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-Turbo-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: 1,210 Bytes
ffc54ec | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | """Verify the local Qwen-Image-2.1-Turbo snapshot against upstream sizes and LFS SHA-256 (assets excluded)."""
import hashlib, json, time
from huggingface_hub import HfApi
from common import BASE, BASE_REPO, BASE_REVISION, ROOT
info = HfApi().model_info(BASE_REPO, revision=BASE_REVISION, files_metadata=True)
rows = []
for f in info.siblings:
if f.rfilename.startswith('assets/'):
continue
p = BASE / f.rfilename
assert p.exists(), f.rfilename
h = hashlib.sha256()
with p.open('rb') as s:
while b := s.read(8 << 20): h.update(b)
digest = h.hexdigest()
if f.size is not None: assert p.stat().st_size == f.size, f.rfilename
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),
'excluded': 'assets/* preview images (not needed for inference)', 'verified_at': time.time()}
(ROOT / 'base_integrity.json').write_text(json.dumps(receipt, indent=2))
print('BASE_VERIFIED', len(rows), receipt['total_bytes'])
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