Add agent recipe (agent.md) for curl-based LoRA training

agent.md

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+# Train a Krea 2 LoRA — agent recipe
+
+You can train a custom **Krea 2** LoRA on Hugging Face Jobs. Krea 2 weights are public, so you
+only need the USER's own write-scoped HF token (https://huggingface.co/settings/tokens). Every
+step below runs under that token and is billed to that account.
+
+## Inputs to collect from the user
+- **lora_name** — a unique name. The result is pushed to `<user>/<lora_name>`.
+- **trigger** — a style phrase (e.g. `"in the style of TOK"`) or, for a subject/face/object, a
+  rare token (e.g. `"TOK person"`). It is included in every training caption.
+- **concept** — one of: `style` | `character` | `object` | `face`.
+- **images** — 4–30 images. The user will give you ONE of:
+  (a) an existing HF dataset repo with `image` + `prompt` columns → use it directly, skip Step 1;
+  (b) a local folder of images → build the dataset (Step 1);
+  (c) a request like *"find impasto-style paintings"* → gather the images yourself first, then
+  build the dataset (Step 1).
+
+## Step 1 — build the dataset (skip if the user already has one)
+Create an imagefolder dataset under the user's account; each row is an image + a caption that
+contains the trigger.
+
+```python
+import json, os
+from pathlib import Path
+from huggingface_hub import HfApi
+
+TOKEN   = os.environ["HF_TOKEN"]                 # the user's write token
+REPO    = "<user>/<lora_name>-dataset"
+TRIGGER = "<trigger>"
+images   = ["img1.png", "img2.png"]              # local paths you gathered
+captions = ["a cat on a sofa, " + TRIGGER, "a cat by a window, " + TRIGGER]  # 1 per image
+
+api = HfApi(token=TOKEN)
+api.create_repo(REPO, repo_type="dataset", private=True, exist_ok=True)
+tmp = Path("ds"); tmp.mkdir(exist_ok=True)
+rows = []
+for i, (src, cap) in enumerate(zip(images, captions)):
+    fn = f"{i:03d}{Path(src).suffix.lower()}"
+    (tmp / fn).write_bytes(Path(src).read_bytes())
+    rows.append({"file_name": fn, "prompt": cap})
+(tmp / "metadata.jsonl").write_text("\n".join(json.dumps(r) for r in rows))
+api.upload_folder(repo_id=REPO, repo_type="dataset", folder_path=str(tmp))
+print("dataset:", REPO)
+```
+
+No captions yet? Use the trigger alone as every caption — fine for a single-concept style.
+
+## Step 2 — launch the training job
+Download the ready-made job script and submit it on HF Jobs under the user's token:
+
+```bash
+export HF_TOKEN=hf_...                     # the user's write token
+curl -sO https://huggingface.co/spaces/multimodalart/krea2-lora-trainer/raw/main/train_job.py
+hf jobs uv run --flavor rtx-pro-6000 --timeout 1h -s HF_TOKEN=$HF_TOKEN train_job.py -- \
+    --dataset <user>/<lora_name>-dataset \
+    --lora-name <lora_name> \
+    --trigger "<trigger>" \
+    --concept style
+```
+
+Python equivalent:
+
+```python
+from huggingface_hub import HfApi
+api = HfApi(token=TOKEN)
+job = api.run_uv_job(
+    "https://huggingface.co/spaces/multimodalart/krea2-lora-trainer/raw/main/train_job.py",
+    flavor="rtx-pro-6000", timeout="1h",
+    secrets={"HF_TOKEN": TOKEN},
+    script_args=["--dataset", "<user>/<lora_name>-dataset",
+                 "--lora-name", "<lora_name>",
+                 "--trigger", "<trigger>", "--concept", "style"],
+)
+print(job.url)
+```
+
+Useful flags: `--steps` (1000), `--rank` (32), `--resolution` (1024), `--learning-rate` (3e-4),
+`--quantization {none,fp8,4bit}`, `--no-gallery`, `--num-gallery` (3). Run with `--help` for all.
+
+## Result
+~40 min on `rtx-pro-6000` (1000 steps, regional torch.compile). The LoRA is pushed to
+`<user>/<lora_name>` with a preview gallery + README. Use it:
+
+```python
+import torch
+from diffusers import Krea2Pipeline
+pipe = Krea2Pipeline.from_pretrained("krea/Krea-2-Turbo", torch_dtype=torch.bfloat16).to("cuda")
+pipe.load_lora_weights("<user>/<lora_name>")
+image = pipe("<trigger>, a fox in a snowy forest", num_inference_steps=8, guidance_scale=0.0).images[0]
+image.save("out.png")
+```