Instructions to use kimi000/quiet-cascade-58 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use kimi000/quiet-cascade-58 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("kimi000/quiet-cascade-58", 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
FLUX.2 Klein Base 4B AlphaGRPO checkpoint
This is a complete native Diffusers Flux2KleinPipeline. The EMA LoRA is
already merged into the transformer, so FAR and PEFT are not required for
inference.
Source experiment: flux2_klein_base_4b_diffusionnft_dvreward_prompt_rubric_v4_3_1_unified_geneval_only_upgrade_4align4text2adaptive2aesthetic_75pct_16prompts_group14_7train_1dvreward_tp1_2node_512px_20step_cfg4_cw
Source checkpoint: step_500.pt
Training profile: 512px, 20 rollout steps, CFG 4, AlphaGRPO DVReward.
python demo.py --prompt "A red cube beside a blue glass sphere."
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Model tree for kimi000/quiet-cascade-58
Base model
black-forest-labs/FLUX.2-klein-base-4B