Fibo edit pipeline

docs/source/en/api/pipelines/bria_fibo_edit.md

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+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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+
+# Bria Fibo Edit
+
+Fibo Edit is an 8B parameter image-to-image model that introduces a new paradigm of structured control, operating on JSON inputs paired with source images to enable deterministic and repeatable editing workflows.
+Featuring native masking for granular precision, it moves beyond simple prompt-based diffusion to offer explicit, interpretable control optimized for production environments.
+Its lightweight architecture is designed for deep customization, empowering researchers to build specialized "Edit" models for domain-specific tasks while delivering top-tier aesthetic quality
+
+## Usage
+_As the model is gated, before using it with diffusers you first need to go to the [Bria Fibo Hugging Face page](https://huggingface.co/briaai/Fibo-Edit), fill in the form and accept the gate. Once you are in, you need to login so that your system knows you’ve accepted the gate._
+
+Use the command below to log in:
+
+```bash
+hf auth login
+```
+
+
+## BriaFiboEditPipeline
+
+[[autodoc]] BriaFiboEditPipeline
+	- all
+	- __call__

examples/dreambooth/README_fibo_edit.md

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+# DreamBooth LoRA training example for Bria Fibo Edit
+
+[DreamBooth](https://huggingface.co/papers/2208.12242) is a method to personalize text2image models given just a few images of a subject.
+
+The `train_dreambooth_fibo_edit.py` script shows how to implement LoRA fine-tuning for [Bria Fibo Edit](https://huggingface.co/briaai/Fibo-edit), an image editing model.
+
+## Running locally with PyTorch
+
+### Installing the dependencies
+
+Before running the scripts, make sure to install the library's training dependencies:
+
+```bash
+git clone https://github.com/huggingface/diffusers
+cd diffusers
+pip install -e .
+```
+
+Then cd in the `examples/dreambooth` folder and run:
+```bash
+pip install -r requirements_fibo_edit.txt
+```
+
+And initialize an [Accelerate](https://github.com/huggingface/accelerate/) environment:
+
+```bash
+accelerate config default
+```
+
+### Dataset format
+
+The training script expects a dataset with the following columns:
+- `input_image`: Source image (before editing)
+- `image`: Target image (after editing)
+- `caption`: Edit instruction in JSON format
+
+You can use a HuggingFace dataset via `--dataset_name` or a local directory via `--instance_data_dir`.
+
+### Training
+
+```bash
+export MODEL_NAME="briaai/Fibo-edit"
+export DATASET_NAME="your-dataset"
+export OUTPUT_DIR="fibo-edit-dreambooth-lora"
+
+accelerate launch train_dreambooth_fibo_edit.py \
+  --pretrained_model_name_or_path=$MODEL_NAME \
+  --dataset_name=$DATASET_NAME \
+  --output_dir=$OUTPUT_DIR \
+  --mixed_precision="bf16" \
+  --train_batch_size=1 \
+  --gradient_accumulation_steps=4 \
+  --learning_rate=1e-4 \
+  --lr_scheduler="cosine_with_warmup" \
+  --lr_warmup_steps=100 \
+  --max_train_steps=1500 \
+  --lora_rank=128 \
+  --checkpointing_steps=250 \
+  --seed=10
+```
+
+### Key arguments
+
+| Argument | Default | Description |
+|----------|---------|-------------|
+| `--lora_rank` | 128 | LoRA rank for fine-tuning |
+| `--learning_rate` | 1e-4 | Initial learning rate |
+| `--lr_scheduler` | cosine_with_warmup | Learning rate scheduler |
+| `--optimizer` | AdamW | Optimizer (AdamW or prodigy) |
+| `--gradient_checkpointing` | 1 | Enable gradient checkpointing to save memory |
+| `--mixed_precision` | bf16 | Mixed precision training mode |
+
+### Resume from checkpoint
+
+To resume training from a checkpoint:
+
+```bash
+accelerate launch train_dreambooth_fibo_edit.py \
+  ... \
+  --resume_from_checkpoint="latest"
+```
+
+Or specify a specific checkpoint path:
+
+```bash
+--resume_from_checkpoint="/path/to/checkpoint_500"
+```

examples/dreambooth/requirements_fibo_edit.txt

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+accelerate>=0.31.0
+torchvision
+transformers>=4.41.2
+peft>=0.11.1
+ujson
+Pillow
+tqdm