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finegrain-product-placement-lora

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Pierre Chapuis commited on Sep 16

Commit

400ee27

unverified ·

1 Parent(s): 801ec70

drop feature using Finegrain API to cutout

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Files changed (4) hide show

pyproject.toml +0 -1
requirements.txt +0 -1
src/app.py +13 -63
uv.lock +26 -199

pyproject.toml CHANGED Viewed

@@ -12,7 +12,6 @@ dependencies = [
     "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"

     "pillow>=11.3.0",
     "gradio-image-annotation>=0.4.0",
     "finegrain_toolbox @ git+ssh://git@github.com/finegrain-ai/finegrain-toolbox",
 ]
 readme = "README.md"
 requires-python = ">= 3.10"

requirements.txt CHANGED Viewed

@@ -3,4 +3,3 @@ 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

 pillow>=11.3.0
 gradio-image-annotation>=0.4.0
 git+https://github.com/finegrain-ai/finegrain-toolbox#cf3cc389efa6eaf3e9387805981f4cefe39b3b00

src/app.py CHANGED Viewed

@@ -1,13 +1,9 @@
-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
@@ -15,13 +11,11 @@ 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)
@@ -56,16 +50,6 @@ 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:
@@ -104,51 +88,16 @@ def _process(
     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)
@@ -165,6 +114,12 @@ The LoRA was trained using EditNet, our before / after image editing dataset.
 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> |
@@ -200,11 +155,6 @@ with gr.Blocks() as demo:
                     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():
@@ -235,7 +185,7 @@ with gr.Blocks() as demo:
     run_btn.click(
         fn=process,
-        inputs=[scene, reference, seed, cut_out_reference],
         outputs=[output_image, output_reference, output_scene],
     )

 from pathlib import Path
+from typing import Any, cast
 import gradio as gr
 import spaces
 import torch
 from finegrain_toolbox.flux import Model
 from finegrain_toolbox.flux.prompt import prompt_with_embeds
 from finegrain_toolbox.processors import product_placement
 from huggingface_hub import hf_hub_download
 from PIL import Image
 from safetensors.torch import load_file
 # initialize on CPU then move to GPU (Zero GPU)
 DEVICE_CPU = torch.device("cpu")
 DTYPE = torch.bfloat16
 model = Model.from_pretrained("black-forest-labs/FLUX.1-Kontext-dev", device=DEVICE_CPU, dtype=DTYPE)
 prompt = prompt.to(device=DEVICE, dtype=DTYPE)
 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:
     return (before_after, result.reference, result.scene)
 def process(
     scene: dict[str, Any],
     reference: Image.Image,
     seed: int = 1234,
 ) -> tuple[tuple[Image.Image, Image.Image], Image.Image, Image.Image]:
+    assert reference.mode == "RGBA"
+    extrema = cast(tuple[tuple[int, int], ...], reference.getextrema())
+    if extrema[reference.mode.index("A")][0] == 255:
+        raise gr.Error("The reference must be a cutout.", duration=5)
     return _process(scene, reference, seed)
 Just draw a box to set where the subject should be blended, and at what size.
 </p>
+<p>
+<em>The reference must be a cutout, i.e. have a transparent background.</em>
+If you do not have a cutout available you can create one
+<a href="https://huggingface.co/spaces/finegrain/finegrain-object-cutter">here</a>.
+</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> |
                     step=1,
                     label="Seed",
                 )
             with gr.Row():
                 run_btn = gr.ClearButton(value="Blend", interactive=False)
         with gr.Column():
     run_btn.click(
         fn=process,
+        inputs=[scene, reference, seed],
         outputs=[output_image, output_reference, output_scene],
     )

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