Dataset Preparation

Before fine-tuning, prepare your dataset as a ZIP file. Then upload it to a public URL or via the Segmind data upload endpoint.

Upload Endpoints

• Flux Dev → Upload here
• Flux Kontext → Upload here
• Fast Flux → Upload here
• Flux Pro → Upload here

⚠️ Use public or private upload depending on your model.

Pipeline-Specific Guidelines

🔹 Flux Dev

• Upload 10–20 images in a ZIP.
• Select a trigger_word → model learns to associate this word with your subject/style.
• Captions: Auto-generated or custom .txt per image.

• Example: img_0.jpg → img_0.txt.
• Image resolution: ~1024×1024 (larger images will be resized).
• Style LoRAs: Use images highlighting distinctive features, keep style consistent.
• Character LoRAs: Show subject in different settings/expressions.

• Avoid different haircuts, ages, or excessive hand-face overlaps.

📌 Reference Dataset: Coming soon.

🔹 Flux Pro

• At least 5 high-quality images.
• Supported: JPG, JPEG, PNG, WebP.
• Optional .txt files with same name as images.

• Example: sample.jpg → sample.txt.
• Package all into a single ZIP.

📌 Reference Dataset: Coming soon.

🔹 Fast Flux

• Upload 10–20 images in a ZIP.
• Select a trigger_word.
• Captions: Auto-generated or custom .txt per image.

• Example: img_0.jpg → img_0.txt.
• Image resolution: ~1024×1024.
• Style LoRAs: Use varied subjects, keep style consistent.
• Character LoRAs: Avoid hair/age variations & hand-face overlaps.

📌 Reference Dataset: Coming soon.

🔹 Flux Kontext

• Paired images (INDEX_start.ext and INDEX_end.ext).
• INDEX.txt optional (edit instructions).
• Use zero-padded indexes (01, 02, …).

📌 Reference Dataset: Kontext Fine-Tune Samples

Updated on: 29/10/2025