计划执行与检查节点
executing-plans
按既定实施计划完成工作,保持过程检查和反馈。
janusflow-workflows【效率与协作】Guides JanusFlow understanding and rectified-flow image generation with diffusers and an SDXL VAE.
独立 GitHub 快照 · 自动分类 · 以原作者文档为准
Use this sub-skill when the task mentions JanusFlow, rectified flow, SDXL VAE, image_gen_tag, or the JanusFlow-1.3B model family.
references/janusflow-reference.md for the JanusFlow-specific API and flow-generation path.references/troubleshooting.md for diffusers, VAE, and attention-mask failures.scripts/janusflow_text_to_image.py for a safe dry-run helper and an optional real model run.For installation and model-family selection, use the root ../../references/installation-and-models.md.
Choose this route when the user asks to:
image_gen_tag, or VAE decode path.image_gen_tag.z tensor and run the ODE-style update loop.Validate the prompt and generation parameters without downloading weights:
python sub-skills/janusflow-workflows/scripts/janusflow_text_to_image.py \
--model-id deepseek-ai/JanusFlow-1.3B \
--prompt "A glass of red wine on a reflective surface."
The default mode prints a plan and does not download model weights. Add --run-model only after you confirm diffusers compatibility, CUDA, and SDXL VAE access.
../multimodal-understanding/SKILL.md for JanusFlow understanding or image+question tasks.../image-generation/SKILL.md for Janus / Janus-Pro autoregressive generation.../demos-and-serving/SKILL.md for Gradio or FastAPI serving.