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
Release calibrated Image 2.1 Turbo FP8 transformer, accelerated SM120 runtime, quality evidence and real-time demo
ffc54ec verified Download source/package_release.py from ProCreations/Image-2.1-Turbo-Calibrated-FP8: direct link, hf CLI and curl.
- Browser
- Download file 3.36 kB
-
https://huggingface.co/ProCreations/Image-2.1-Turbo-Calibrated-FP8/resolve/main/source/package_release.py
- Command line
-
hf download hf://ProCreations/Image-2.1-Turbo-Calibrated-FP8/source/package_release.py
-
curl -L -o package_release.py https://huggingface.co/ProCreations/Image-2.1-Turbo-Calibrated-FP8/resolve/main/source/package_release.py
3.36 kB
| """Assemble a self-contained public release directory (code + reports) next to its transformer. | |
| usage: package_release.py fp8|nvfp4 | |
| Copies runtime/CLI modules to the release root and reproducibility sources to source/, writes | |
| LICENSE/Notice/requirements. README.md is written separately from measured results. Does not publish. | |
| """ | |
| import json, shutil, sys | |
| from pathlib import Path | |
| from common import ROOT, BASE | |
| fmt = sys.argv[1] | |
| SRC = ROOT / 'src' | |
| OUT = ROOT / fmt / 'release' | |
| RUNTIME = {'fp8': ['fp8_runtime.py'], 'nvfp4': ['nvfp4_runtime.py', 'dynamic_scale.py', 'fp8_runtime.py']}[fmt] | |
| COMMON = ['acceleration.py', 'vae_fast.py', 'transparency.py', 'prompt_cache.py'] | |
| SOURCE = ['fast_decode.py', 'verify_video.py', 'final_round.sh', 'speed_round3.sh', 'assemble_mixed.py', 'hybrid.py', 'test_split.py', 'micro_attn.py', 'micro_vae.py', 'micro_tune.py', 'common.py', 'prompts.py', 'smoke.py', 'verify_base.py', 'calibrate.py', 'hessians.py', 'hessian_store.py', 'gptq.py', | |
| 'weight_quant.py', 'quantize_fp8.py', 'quantize_nvfp4.py', 'evaluate.py', 'metrics.py', 'benchmark.py', 'speedlab.py', | |
| 'video.py', 'package_release.py', 'test_gptq.py', 'test_vae.py'] | |
| for f in RUNTIME + COMMON: | |
| if (SRC / f).exists(): | |
| shutil.copy(SRC / f, OUT / f) | |
| gen = (SRC / 'generate_template.py').read_text() | |
| fp8_revision = json.loads((ROOT / 'fp8' / 'publication.json').read_text())['revision'] if fmt == 'nvfp4' else 'not-used-by-fp8-release' | |
| gen = (gen.replace('RUNTIME_MODULE', RUNTIME[0][:-3]).replace('RUNTIME_NAME', fmt.upper()) | |
| .replace('DEFAULT_FP8_FIRST_STEPS', '1').replace('FP8_REVISION_PLACEHOLDER', fp8_revision)) | |
| (OUT / 'generate.py').write_text(gen) | |
| (OUT / 'source').mkdir(exist_ok=True) | |
| for f in SOURCE: | |
| if (SRC / f).exists(): | |
| shutil.copy(SRC / f, OUT / 'source' / f) | |
| shutil.copy(BASE / 'LICENSE', OUT / 'LICENSE') | |
| (OUT / 'Notice').write_text( | |
| 'Qwen is licensed under the Qwen RESEARCH LICENSE AGREEMENT, Copyright (c) 2026 Hangzhou Tongyi Laboratory Technology Co., Ltd. ' | |
| 'All Rights Reserved.\n\nBuilt with Qwen.\nProCreations modified the Qwen-Image-2.1-Turbo transformer weights through calibrated ' | |
| f'{fmt.upper()} quantization on 2026-10-09. The custom runtime, acceleration and calibration/evaluation scripts were added by ' | |
| 'ProCreations. Non-commercial research and evaluation only under the included upstream license.\n') | |
| req = ['torch==2.14.0 # CUDA 13.0 build: pip install torch==2.14.0 --index-url https://download.pytorch.org/whl/cu130', 'torchvision==0.29.0', 'transformers==5.17.0', 'accelerate==1.15.0', 'triton==3.8.0', 'safetensors==0.8.0', | |
| 'huggingface-hub', 'pillow==12.3.0', 'sentencepiece==0.2.2', 'diffusers==0.41.0'] | |
| if fmt == 'nvfp4': | |
| req.append('flashinfer-python==0.7.1') | |
| (OUT / 'requirements.txt').write_text('\n'.join(req) + '\n# Optional, faster attention (--attention sage/auto): SageAttention 2.2.0 built from source (tested commit d1a57a5;\n# Torch 2.14 needs a C++20 host compiler), e.g. pip install --no-build-isolation git+https://github.com/thu-ml/SageAttention.git@d1a57a546c3d395b1ffcbeecc66d81db76f3b4b5\n') | |
| (OUT / 'requirements-evaluation.txt').write_text('-r requirements.txt\nlpips==0.1.4\nscikit-image==0.26.0\nimageio-ffmpeg\n') | |
| shutil.copy(ROOT / 'base_integrity.json', OUT / 'reports' / 'base_integrity.json') | |
| print('PACKAGED', OUT) | |