Fibo Edit

pyproject.toml

@@ -87,6 +87,7 @@ mflux-generate-kontext = "mflux.models.flux.cli.flux_generate_kontext:main"
 mflux-generate-qwen = "mflux.models.qwen.cli.qwen_image_generate:main"
 mflux-generate-qwen-edit = "mflux.models.qwen.cli.qwen_image_edit_generate:main"
 mflux-generate-fibo = "mflux.models.fibo.cli.fibo_generate:main"
+mflux-generate-fibo-edit = "mflux.models.fibo.cli.fibo_edit_generate:main"
 mflux-generate-z-image = "mflux.models.z_image.cli.z_image_generate:main"
 mflux-generate-z-image-turbo = "mflux.models.z_image.cli.z_image_turbo_generate:main"
 mflux-refine-fibo = "mflux.models.fibo_vlm.cli.fibo_refine:main"

src/mflux/cli/defaults/defaults.py

@@ -19,6 +19,7 @@
     "dev-krea",
     "qwen",
     "fibo",
+    "fibo-edit",
     "z-image",
     "z-image-turbo",
     "flux2-klein-4b",
@@ -34,6 +35,7 @@
     "qwen-image": 20,
     "qwen-image-edit": 20,
     "fibo": 20,
+    "fibo-edit": 20,
     "z-image": 50,
     "z-image-turbo": 9,
     "flux2-klein-4b": 4,

src/mflux/models/common/config/model_config.py

@@ -137,6 +137,11 @@ def qwen_image_edit() -> "ModelConfig":
     def fibo() -> "ModelConfig":
         return AVAILABLE_MODELS["fibo"]
 
+    @staticmethod
+    @lru_cache
+    def fibo_edit() -> "ModelConfig":
+        return AVAILABLE_MODELS["fibo-edit"]
+
     @staticmethod
     @lru_cache
     def z_image_turbo() -> "ModelConfig":
@@ -453,8 +458,20 @@ def from_name(
         supports_guidance=True,
         requires_sigma_shift=False,
     ),
-    "z-image": ModelConfig(
+    "fibo-edit": ModelConfig(
         priority=18,
+        aliases=["fibo-edit", "fiboedit"],
+        model_name="briaai/Fibo-Edit",
+        base_model=None,
+        controlnet_model=None,
+        custom_transformer_model=None,
+        num_train_steps=1000,
+        max_sequence_length=512,
+        supports_guidance=True,
+        requires_sigma_shift=False,
+    ),
+    "z-image": ModelConfig(
+        priority=19,
         aliases=["z-image", "zimage"],
         model_name="Tongyi-MAI/Z-Image",
         base_model=None,
@@ -466,7 +483,7 @@ def from_name(
         requires_sigma_shift=True,
     ),
     "z-image-turbo": ModelConfig(
-        priority=19,
+        priority=20,
         aliases=["z-image-turbo", "zimage-turbo"],
         model_name="Tongyi-MAI/Z-Image-Turbo",
         base_model=None,
@@ -478,7 +495,7 @@ def from_name(
         requires_sigma_shift=True,
     ),
     "seedvr2-3b": ModelConfig(
-        priority=20,
+        priority=21,
         aliases=["seedvr2-3b", "seedvr2"],
         model_name="numz/SeedVR2_comfyUI",
         base_model=None,

src/mflux/models/fibo/README.md

@@ -13,7 +13,7 @@ Most text-to-image models excel at imagination—but not control. FIBO is traine
 - **Strong prompt adherence**: High alignment on PRISM-style evaluations
 - **Enterprise-grade**: 100% licensed data with governance, repeatability, and legal clarity
 
-## The three modes: Generate, Refine, and Inspire
+## The four modes: Generate, Edit, Refine, and Inspire
 
 ### Generate
 While the actual prompt input to FIBO is a structured JSON file, the generate command provides an interface to input pure text prompts. These are then expanded into structured JSON prompts using FIBO's Vision-Language Model (VLM) before being passed to the diffusion model for image generation.
@@ -259,6 +259,35 @@ image.save("owl_white.png")
 
 It is worth noting that refine does not work the same way as other editing techniques like Flux Kontext or Qwen Image Edit. Instead of modifying an existing image, it modifies the underlying **structured prompt** to produce a new image.
 
+### Edit
+FIBO Edit supports direct image-conditioned editing using a structured JSON prompt that includes an `edit_instruction` field.
+
+```sh
+mflux-generate-fibo-edit \
+    --image-path owl_original.png \
+    --prompt-file owl_brown.json \
+    --edit-instruction "Make the owl white and add round glasses while keeping composition unchanged." \
+    --width 1024 \
+    --height 560 \
+    --steps 20 \
+    --guidance 4.0 \
+    --seed 42 \
+    --output owl_white_edit.png
+```
+
+Optional localized editing is supported with a mask:
+
+```sh
+mflux-generate-fibo-edit \
+    --image-path owl_original.png \
+    --mask-path owl_mask.png \
+    --prompt-file owl_brown.json \
+    --edit-instruction "Replace only the owl with a white owl wearing glasses." \
+    --steps 20 \
+    --seed 42 \
+    --output owl_masked_edit.png
+```
+
 ### Inspire
 Provide an image instead of text. FIBO's vision-language model extracts a detailed, structured prompt, blends it with your creative intent, and produces related images—ideal for inspiration without overreliance on the original.
 

src/mflux/models/fibo/cli/fibo_edit_generate.py

@@ -0,0 +1,79 @@
+from pathlib import Path
+
+from mflux.callbacks.callback_manager import CallbackManager
+from mflux.cli.defaults import defaults as ui_defaults
+from mflux.cli.parser.parsers import CommandLineParser
+from mflux.models.fibo.latent_creator.fibo_latent_creator import FiboLatentCreator
+from mflux.models.fibo.variants.edit.fibo_edit import FIBOEdit
+from mflux.models.fibo.variants.edit.util import FiboEditUtil
+from mflux.models.fibo.variants.txt2img.util import FiboUtil
+from mflux.utils.dimension_resolver import DimensionResolver
+from mflux.utils.exceptions import PromptFileReadError, StopImageGenerationException
+from mflux.utils.prompt_util import PromptUtil
+
+
+def main():
+    parser = CommandLineParser(description="Generate an edited image using Bria FIBO Edit.")
+    parser.add_general_arguments()
+    parser.add_model_arguments(require_model_arg=False)
+    parser.add_lora_arguments()
+    parser.add_image_generator_arguments(supports_metadata_config=True, supports_dimension_scale_factor=True)
+    parser.add_argument("--image-path", type=Path, required=True, help="Local path to source image for editing.")
+    parser.add_argument("--mask-path", type=Path, default=None, help="Optional mask image path for localized edits.")
+    parser.add_argument(
+        "--edit-instruction",
+        type=str,
+        default=None,
+        help="Optional edit instruction. Used when prompt JSON does not already include `edit_instruction`.",
+    )
+    parser.add_output_arguments()
+    args = parser.parse_args()
+
+    if args.guidance is None:
+        args.guidance = ui_defaults.GUIDANCE_SCALE
+
+    json_prompt = FiboUtil.get_json_prompt(args, quantize=args.quantize)
+    json_prompt = FiboEditUtil.ensure_edit_instruction(json_prompt, edit_instruction=args.edit_instruction)
+
+    fibo_edit = FIBOEdit(
+        quantize=args.quantize,
+        model_path=args.model_path,
+        lora_paths=args.lora_paths,
+        lora_scales=args.lora_scales,
+    )
+
+    memory_saver = CallbackManager.register_callbacks(
+        args=args,
+        model=fibo_edit,
+        latent_creator=FiboLatentCreator,
+    )
+
+    try:
+        width, height = DimensionResolver.resolve(
+            width=args.width,
+            height=args.height,
+            reference_image_path=args.image_path,
+        )
+        for seed in args.seed:
+            image = fibo_edit.generate_image(
+                seed=seed,
+                prompt=json_prompt,
+                image_path=args.image_path,
+                mask_path=args.mask_path,
+                width=width,
+                height=height,
+                guidance=args.guidance,
+                num_inference_steps=args.steps,
+                scheduler="flow_match_euler_discrete",
+                negative_prompt=PromptUtil.read_negative_prompt(args),
+            )
+            image.save(path=args.output.format(seed=seed), export_json_metadata=args.metadata)
+    except (StopImageGenerationException, PromptFileReadError, ValueError) as exc:
+        print(exc)
+    finally:
+        if memory_saver:
+            print(memory_saver.memory_stats())
+
+
+if __name__ == "__main__":
+    main()

src/mflux/models/fibo/model/fibo_transformer/transformer.py

@@ -35,20 +35,29 @@ def __call__(
         hidden_states: mx.array,
         encoder_hidden_states: mx.array,
         text_encoder_layers: list[mx.array],
+        conditioning_seq_len: int = 0,
+        conditioning_image_ids: mx.array | None = None,
     ) -> mx.array:
         # 1. Create embeddings
         hidden_states = FiboTransformer._handle_classifier_free_guidance(hidden_states, encoder_hidden_states)
         hidden_states = self.x_embedder(hidden_states)
         encoder_hidden_states = self.context_embedder(encoder_hidden_states)
         time_embeddings = FiboTransformer._compute_time_embeddings(t, config, hidden_states.shape[0], hidden_states.dtype, self.time_embed)  # fmt: off
-        image_rotary_emb = FiboTransformer._compute_rotary_embeddings(encoder_hidden_states, self.pos_embed, config, hidden_states.dtype)  # fmt: off
+        image_rotary_emb = FiboTransformer._compute_rotary_embeddings(
+            encoder_hidden_states=encoder_hidden_states,
+            pos_embed=self.pos_embed,
+            config=config,
+            dtype=hidden_states.dtype,
+            conditioning_image_ids=conditioning_image_ids,
+        )
 
         # 2. Compute attention mask
         attention_mask = FiboTransformer._compute_attention_mask(
             config=config,
             batch_size=hidden_states.shape[0],
             encoder_hidden_states=encoder_hidden_states,
             max_tokens=encoder_hidden_states.shape[1],
+            conditioning_seq_len=conditioning_seq_len,
         )
 
         # 3. Project the fibo-specific text encoder layers
@@ -165,10 +174,15 @@ def _compute_rotary_embeddings(
         pos_embed: FiboEmbedND,
         config: Config,
         dtype: mx.Dtype,
+        conditioning_image_ids: mx.array | None = None,
     ) -> mx.array:
         max_tokens = encoder_hidden_states.shape[1]
         txt_ids = mx.zeros((max_tokens, 3), dtype=dtype)
         img_ids = FiboTransformer._prepare_latent_image_ids(height=config.height, width=config.width, dtype=dtype)
+        if conditioning_image_ids is not None:
+            if conditioning_image_ids.ndim == 3:
+                conditioning_image_ids = conditioning_image_ids[0]
+            img_ids = mx.concatenate([img_ids, conditioning_image_ids.astype(dtype)], axis=0)
 
         if txt_ids.ndim == 3 and txt_ids.shape[0] == 1:
             txt_ids = txt_ids[0]
@@ -212,14 +226,21 @@ def _compute_attention_mask(
         config: Config,
         encoder_hidden_states: mx.array,
         max_tokens: int,
+        conditioning_seq_len: int = 0,
     ) -> mx.array:
         vae_scale_factor = 16
         latent_height = config.height // vae_scale_factor
         latent_width = config.width // vae_scale_factor
         latent_seq_len = latent_height * latent_width
         prompt_attention_mask = mx.ones((batch_size, max_tokens), dtype=mx.float32)
         latent_attention_mask = mx.ones((batch_size, latent_seq_len), dtype=mx.float32)
-        attention_mask_2d = mx.concatenate([prompt_attention_mask, latent_attention_mask], axis=1)
+        if conditioning_seq_len > 0:
+            conditioning_attention_mask = mx.ones((batch_size, conditioning_seq_len), dtype=mx.float32)
+            attention_mask_2d = mx.concatenate(
+                [prompt_attention_mask, latent_attention_mask, conditioning_attention_mask], axis=1
+            )
+        else:
+            attention_mask_2d = mx.concatenate([prompt_attention_mask, latent_attention_mask], axis=1)
         attention_mask = FiboTransformer._prepare_attention_mask(attention_mask_2d)
         attention_mask = attention_mask.astype(encoder_hidden_states.dtype)
         return attention_mask

src/mflux/models/fibo/variants/edit/__init__.py

@@ -0,0 +1,3 @@
+from .fibo_edit import FIBOEdit
+
+__all__ = ["FIBOEdit"]