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SkillsMP has collected 8 skills from tsiendragon/qwen-image-finetune. Open a skill to review its source and details.
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Step 1 of 8. Start here to fine-tune or personalize Qwen-Image-Edit with LoRA on an Intel AI PC (Core Ultra processor) Windows laptop — even if the user doesn't use those exact terms, whenever they want to train Qwen-Image-Edit on their own dataset, mention…
Step 2 of 8 of the Qwen-Image-Edit AI PC fine-tuning walkthrough. Use after qwen-image-edit-aipc-finetune-01-preflight. Prepare, structure, and validate a training dataset (image directory, parquet, or CSV), understand the control/target edit-pair paradigm,…
Step 3 of 8 of the Qwen-Image-Edit AI PC fine-tuning walkthrough. Use after qwen-image-edit-aipc-finetune-02-dataset. Create the conda environment, install torch+xpu matched to the installed oneAPI, apply the two required requirements.txt fixes, install…
Step 4 of 8 of the Qwen-Image-Edit AI PC fine-tuning walkthrough. Use after qwen-image-edit-aipc-finetune-03-env-setup. Auto-detect the trainer from the model's model_index.json, run the hardware probe, generate a recommended NF4 QLoRA training YAML matched…
Step 5 of 8 of the Qwen-Image-Edit AI PC fine-tuning walkthrough. Use after qwen-image-edit-aipc-finetune-04-config. Apply all required XPU/NF4 source patches to the qwen-image-finetune clone: device_utils.py, residual CUDA/flash-attention fixes, the…
Step 6 of 8 of the Qwen-Image-Edit AI PC fine-tuning walkthrough. Use after qwen-image-edit-aipc-finetune-05-adaptation. This is a CONDITIONAL step: perform the one-time NF4 pre-quantization of the transformer (DiT) and, on 32 GB machines, the text_encoder,…
Step 7 of 8 of the Qwen-Image-Edit AI PC fine-tuning walkthrough. Use after qwen-image-edit-aipc-finetune-06-quantize. Run the cache-first two-phase training: the cache phase (one-time per dataset) then the fit phase, including a small smoke-test run to catch…
Step 8 of 8 (final) of the Qwen-Image-Edit AI PC fine-tuning walkthrough. Use after qwen-image-edit-aipc-finetune-07-training. Validate that the trained LoRA learned the intended edit: optional in-training TensorBoard sampling, post-training base-vs-LoRA…