initial commit

.gitattributes

@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,11 +1,12 @@
 ---
-title: Finegrain Product Placement Lora
-emoji: 🦀
-colorFrom: pink
-colorTo: blue
+title: Finegrain Product Placement LoRA
+emoji: 📚
+colorFrom: blue
+colorTo: yellow
 sdk: gradio
 sdk_version: 5.45.0
-app_file: app.py
+python_version: 3.10
+app_file: src/app.py
 pinned: false
 license: mit
 short_description: Flux Kontext extended with product placement capabilities

pyproject.toml

@@ -0,0 +1,55 @@
+[project]
+name = "finegrain-product-placement-lora"
+version = "0.1.0"
+description = "Flux Kontext extended with product placement capabilities"
+authors = [
+    { name = "Cédric Deltheil", email = "cedric@lagon.tech" },
+    { name = "Pierre Chapuis", email = "pierre@lagon.tech" },
+]
+dependencies = [
+    "gradio>=5.35.0",
+    "spaces>=0.37.1",
+    "pillow>=11.3.0",
+    "gradio-image-annotation>=0.4.0",
+    "finegrain_toolbox @ git+ssh://git@github.com/finegrain-ai/finegrain-toolbox",
+    "finegrain @ git+https://github.com/finegrain-ai/finegrain-python@py310#subdirectory=finegrain",
+]
+readme = "README.md"
+requires-python = ">= 3.10"
+classifiers = ["Private :: Do Not Upload"]
+
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[tool.hatch.metadata]
+allow-direct-references = true
+
+[tool.hatch.build.targets.wheel]
+packages = ["src"]
+
+[tool.ruff]
+line-length = 120
+target-version = "py310"
+
+[tool.ruff.lint]
+select = [
+  "E",   # pycodestyle errors
+  "W",   # pycodestyle warnings
+  "F",   # pyflakes
+  "UP",  # pyupgrade
+  "A",   # flake8-builtins
+  "B",   # flake8-bugbear
+  "Q",   # flake8-quotes
+  "I",   # isort
+]
+
+[tool.pyright]
+include = ["src"]
+exclude = ["**/__pycache__"]
+
+[dependency-groups]
+dev = [
+    "pyright>=1.1.404",
+    "ruff>=0.12.11",
+]

requirements.txt

@@ -0,0 +1,6 @@
+gradio>=5.35.0
+spaces>=0.37.1
+pillow>=11.3.0
+gradio-image-annotation>=0.4.0
+git+https://github.com/finegrain-ai/finegrain-toolbox#cf3cc389efa6eaf3e9387805981f4cefe39b3b00
+git+https://github.com/finegrain-ai/finegrain-python@py310#subdirectory=finegrain

src/app.py

@@ -0,0 +1,290 @@
+import io
+import os
+from functools import cache, lru_cache
+from pathlib import Path
+from typing import Any
+
+import gradio as gr
+import spaces
+import torch
+from finegrain import CutoutResultWithImage, EditorAPIContext, ErrorResult
+from finegrain_toolbox.flux import Model
+from finegrain_toolbox.flux.prompt import prompt_with_embeds
+from finegrain_toolbox.processors import product_placement
+from gradio_image_annotation import image_annotator
+from huggingface_hub import hf_hub_download
+from PIL import Image
+from safetensors.torch import load_file
+from typing_extensions import TypeIs
+
+# initialize on CPU then move to GPU (Zero GPU)
+
+DEVICE_CPU = torch.device("cpu")
+DTYPE = torch.bfloat16
+FG_API_KEY = os.getenv("FG_API_KEY")
+
+model = Model.from_pretrained("black-forest-labs/FLUX.1-Kontext-dev", device=DEVICE_CPU, dtype=DTYPE)
+
+lora_path = Path(
+    hf_hub_download(
+        repo_id="finegrain/finegrain-product-placement-lora",
+        filename="finegrain-placement-v1-rank8.safetensors",
+    )
+)
+
+prompt_path = Path(
+    hf_hub_download(
+        repo_id="finegrain/finegrain-product-placement-lora",
+        filename="addinbox-prompt.safetensors",
+    )
+)
+
+prompt_st = load_file(prompt_path, device="cpu")
+
+prompt = prompt_with_embeds(
+    text="Add this in the box",
+    clip_prompt_embeds=prompt_st["clip"],
+    t5_prompt_embeds=prompt_st["t5"],
+)
+
+model.transformer.load_lora_adapter(lora_path, adapter_name="placement")
+model.transformer.fuse_lora()
+model.transformer.unload_lora()
+
+DEVICE = torch.device("cuda")
+model = model.to(device=DEVICE, dtype=DTYPE)
+prompt = prompt.to(device=DEVICE, dtype=DTYPE)
+
+
+@cache
+def _ctx() -> EditorAPIContext:
+    assert FG_API_KEY is not None
+    return EditorAPIContext(
+        api_key=FG_API_KEY,
+        user_agent="fg-hf-placement",
+        priority="low",
+    )
+
+
+def on_change(scene: dict[str, Any] | None, reference: Image.Image | None) -> tuple[dict[str, Any], str]:
+    bbox_str = ""
+    if scene is not None and isinstance(scene.get("boxes"), list) and len(scene.get("boxes", [])) == 1:
+        assert scene is not None
+        box = scene["boxes"][0]
+        bbox_str = f"({box['xmin']}, {box['ymin']}, {box['xmax']}, {box['ymax']})"
+    return (gr.update(interactive=reference is not None and bbox_str != ""), bbox_str)
+
+
+@spaces.GPU(duration=120)
+def _process(
+    scene: dict[str, Any],
+    reference: Image.Image,
+    seed: int = 1234,
+) -> tuple[tuple[Image.Image, Image.Image], Image.Image, Image.Image]:
+    assert isinstance(scene_image := scene["image"], Image.Image)
+    assert isinstance(boxes := scene["boxes"], list)
+    assert len(boxes) == 1
+    assert isinstance(box := boxes[0], dict)
+    bbox = tuple(box[k] for k in ["xmin", "ymin", "xmax", "ymax"])
+
+    result = product_placement.process(
+        model=model,
+        scene=scene_image,
+        reference=reference,
+        bbox=bbox,
+        prompt=prompt,
+        seed=seed,
+        max_short_size=1024,
+        max_long_size=2048,
+    )
+
+    output = result.output
+
+    before_after = (scene_image.resize(output.size), output)
+    return (before_after, result.reference, result.scene)
+
+
+def _is_error(result: Any) -> TypeIs[ErrorResult]:
+    if isinstance(result, ErrorResult):
+        raise RuntimeError(result.error)
+    return False
+
+
+@lru_cache(maxsize=32)
+def _cutout_reference(image_bytes: bytes) -> Image.Image:
+    async def _process(ctx: EditorAPIContext, image_bytes: bytes) -> Image.Image:
+        st_input = await ctx.call_async.upload_image(image_bytes)
+        name_r = await ctx.call_async.infer_product_name(st_input)
+        assert not _is_error(name_r)
+        bbox_r = await ctx.call_async.infer_bbox(st_input, product_name=name_r.is_product)
+        assert not _is_error(bbox_r)
+        mask_r = await ctx.call_async.segment(st_input, bbox=bbox_r.bbox)
+        assert not _is_error(mask_r)
+        cutout_r = await ctx.call_async.cutout(st_input, mask_r.state_id, with_image=True)
+        assert not _is_error(cutout_r)
+        assert isinstance(cutout_r, CutoutResultWithImage)
+        return Image.open(io.BytesIO(cutout_r.image))
+
+    api_ctx = _ctx()
+    try:
+        cutout = api_ctx.run_one_sync(_process, image_bytes)
+    except AssertionError:
+        api_ctx.reset()
+        cutout = api_ctx.run_one_sync(_process, image_bytes)
+
+    return cutout
+
+
+def cutout_reference(reference: Image.Image) -> Image.Image:
+    buf = io.BytesIO()
+    reference.save(buf, format="PNG")
+    return _cutout_reference(buf.getvalue())
+
+
+def process(
+    scene: dict[str, Any],
+    reference: Image.Image,
+    seed: int = 1234,
+    cut_out_reference: bool = False,
+) -> tuple[tuple[Image.Image, Image.Image], Image.Image, Image.Image]:
+    if cut_out_reference:
+        reference = cutout_reference(reference)
+
+    return _process(scene, reference, seed)
+
+
+TITLE = """
+<h1>Finegrain Product Placement LoRA</h1>
+
+<p>
+🧪 An experiment to extend Flux Kontext with product placement capabilities.
+The LoRA was trained using EditNet, our before / after image editing dataset.
+</p>
+
+<p>
+Just draw a box to set where the subject should be blended, and at what size.
+</p>
+
+<p>
+<a href="https://huggingface.co/finegrain/finegrain-product-placement-lora">Model Card</a> |
+<a href="https://blog.finegrain.ai/posts/product-placement-flux-lora-experiment/">Blog Post</a> |
+<a href="https://finegrain.ai/editnet">EditNet</a>
+</p>
+"""
+
+with gr.Blocks() as demo:
+    gr.HTML(TITLE)
+    with gr.Row():
+        with gr.Column():
+            scene = image_annotator(
+                label="Scene",
+                image_type="pil",
+                disable_edit_boxes=True,
+                show_download_button=False,
+                show_share_button=False,
+                single_box=True,
+                image_mode="RGB",
+            )
+            reference = gr.Image(
+                label="Product Reference",
+                visible=True,
+                interactive=True,
+                type="pil",
+                image_mode="RGBA",
+            )
+            with gr.Accordion("Options", open=False):
+                seed = gr.Slider(
+                    minimum=0,
+                    maximum=10_000,
+                    value=1234,
+                    step=1,
+                    label="Seed",
+                )
+                cut_out_reference = gr.Checkbox(
+                    label="Cut out reference",
+                    value=bool(FG_API_KEY),
+                    interactive=bool(FG_API_KEY),
+                )
+            with gr.Row():
+                run_btn = gr.ClearButton(value="Blend", interactive=False)
+        with gr.Column():
+            output_image = gr.ImageSlider(label="Output Image", show_fullscreen_button=False)
+            with gr.Accordion("Debug", open=False):
+                output_textbox = gr.Textbox(label="Bounding Box", interactive=False)
+                output_reference = gr.Image(
+                    label="Reference",
+                    visible=True,
+                    interactive=False,
+                    type="pil",
+                    image_mode="RGB",
+                )
+                output_scene = gr.Image(
+                    label="Scene",
+                    visible=True,
+                    interactive=False,
+                    type="pil",
+                    image_mode="RGB",
+                )
+
+    run_btn.add(output_image)
+
+    # Watch for changes (scene and reference)
+    # i.e. the user must select a box in the scene and upload a reference image
+    scene.change(fn=on_change, inputs=[scene, reference], outputs=[run_btn, output_textbox])
+    reference.change(fn=on_change, inputs=[scene, reference], outputs=[run_btn, output_textbox])
+
+    run_btn.click(
+        fn=process,
+        inputs=[scene, reference, seed, cut_out_reference],
+        outputs=[output_image, output_reference, output_scene],
+    )
+
+    examples = [
+        [
+            {
+                "image": "examples/sunglasses/scene.jpg",
+                "boxes": [{"xmin": 164, "ymin": 89, "xmax": 379, "ymax": 204}],
+            },
+            "examples/sunglasses/reference.webp",
+        ],
+        [
+            {
+                "image": "examples/kitchen/scene.webp",
+                "boxes": [{"xmin": 165, "ymin": 765, "xmax": 332, "ymax": 883}],
+            },
+            "examples/kitchen/reference.webp",
+        ],
+        [
+            {
+                "image": "examples/glass/scene.webp",
+                "boxes": [{"xmin": 389, "ymin": 509, "xmax": 611, "ymax": 1088}],
+            },
+            "examples/glass/reference.webp",
+        ],
+        [
+            {
+                "image": "examples/chair/scene.webp",
+                "boxes": [{"xmin": 366, "ymin": 389, "xmax": 623, "ymax": 728}],
+            },
+            "examples/chair/reference.webp",
+        ],
+        [
+            {
+                "image": "examples/lantern/scene.webp",
+                "boxes": [{"xmin": 497, "ymin": 690, "xmax": 618, "ymax": 873}],
+            },
+            "examples/lantern/reference.webp",
+        ],
+    ]
+
+    ex = gr.Examples(
+        examples=examples,
+        inputs=[scene, reference],
+        outputs=[output_image, output_reference, output_scene],
+        fn=process,
+        cache_examples=True,
+        cache_mode="eager",
+    )
+
+
+demo.launch(show_api=False, ssr_mode=False)