Instructions to use kimi000/maple-dawn-64 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use kimi000/maple-dawn-64 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/maple-dawn-64", 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
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Check out the documentation for more information.
Z-Image Step 1500 EMA
Complete native BF16 Diffusers pipeline, with EMA LoRA merged.
Source experiment: zimage_base_diffusionnft_dvreward_version_base_v0_1_seasonal_39family_codex_100pct_16prompts_group14_7train_1dvreward_tp1_2node_512px_20step_cfg4_cw
Source checkpoint: /home/hcai/workspace/code/junyiwu/FAR-RL-agentic-online-prompt-curriculum-clean/experiments/zimage_base_diffusionnft_dvreward_version_base_v0_1_seasonal_39family_codex_100pct_16prompts_group14_7train_1dvreward_tp1_2node_512px_20step_cfg4_cw/runs/zimage-vb-v01-codex-formal1505-policyfix-20261007/code/experiments/zimage_base_diffusionnft_dvreward_version_base_v0_1_seasonal_39family_codex_100pct_16prompts_group14_7train_1dvreward_tp1_2node_512px_20step_cfg4_cw/models/step_1500.pt
Fixed shift 6, dynamic shifting disabled. See export_manifest.json and verification.json for provenance and validation.
Run python demo.py --prompt "A red cube beside a blue sphere".
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