Add Modal training and inpainting model scaffold

training/README-INPAINTING.md

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+# Clover Image Tiny Inpainting
+
+The inpainting model keeps Clover’s text encoder, VAE, scheduler, and safety
+checker, and replaces only the U-Net with a 9-channel fine-tuned version.
+
+The U-Net input is ordered as:
+
+```text
+[noisy_latent (4), mask (1), masked_image_latent (4)]
+```
+
+White mask pixels are regenerated; black pixels are preserved. Masks are
+sampled procedurally during training, so the dataset only needs image/caption
+pairs. The pinned default dataset is `prithivMLmods/Caption3o-Opt` at revision
+`17e893f785fcd3f5d6fc4a5d65a914b9f7b1ff5b` (Apache-2.0, `image` + `caption`).
+
+## Modal
+
+The local Modal CLI is already configured for the `guccichungus69` profile.
+Use that profile before running the commands below:
+
+```bash
+modal profile activate guccichungus69
+modal run modal_inpaint.py --smoke --steps 2
+modal run modal_inpaint.py --steps 4000
+```
+
+The job uses one A10G and writes the finished Diffusers pipeline to the
+`clover-image-tiny-inpaint-output` Volume. No Hub token is required for the
+default public dataset. The estimated compute ceiling for the default four-hour
+job is about $4 at $1/hour; the actual charge depends on runtime.
+
+Download a completed output directory with:
+
+```bash
+modal volume get clover-image-tiny-inpaint-output \
+  clover-image-tiny-inpaint \
+  ./artifacts/clover-image-tiny-inpaint
+```
+
+If a job stops before writing its final pipeline, the Volume retains any files
+that were committed. The current trainer saves only at the end, so use a
+separate `--output-name` for retries.
+
+## Local dry run
+
+The same trainer can run locally when the model and dataset are available:
+
+```bash
+python inpainting/train.py \
+  --pretrained_model_name_or_path . \
+  --dataset_name prithivMLmods/Caption3o-Opt \
+  --dataset_revision 17e893f785fcd3f5d6fc4a5d65a914b9f7b1ff5b \
+  --max_train_samples 4 \
+  --max_train_steps 2 \
+  --output_dir /tmp/clover-image-tiny-inpaint-smoke
+```
+
+The complete model is large enough that Modal is the intended training
+environment. The current repo’s Apple Core ML environment is for conversion,
+not PyTorch training.