Spaces:
Running
on
Zero
Running
on
Zero
Commit
·
fd49e19
1
Parent(s):
34d9a52
restructure code
Browse files
app.py
CHANGED
@@ -154,61 +154,66 @@ def preprocess(input_image, do_remove_background):
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return input_image
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def pipeline_callback(
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latents = callback_kwargs["latents"]
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image = pipe.vae.decode(latents / pipe.vae.config.scaling_factor, return_dict=False)[0] # type: ignore[attr-defined]
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image = pipe.image_processor.postprocess(image, output_type="np").squeeze() # type: ignore[attr-defined]
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return callback_kwargs
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def generate_mvs(input_image, sample_steps, sample_seed):
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seed_everything(sample_seed)
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output_queue
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global model
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if IS_FLEXICUBES:
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model.init_flexicubes_geometry(device, use_renderer=False)
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@@ -238,9 +243,8 @@ def _make3d(output_queue: SimpleQueue, images: Image.Image):
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vertices, faces, vertex_colors = mesh_out
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(
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"log",
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"mesh",
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rr.Mesh3D(
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vertex_positions=vertices,
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@@ -249,7 +253,8 @@ def _make3d(output_queue: SimpleQueue, images: Image.Image):
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),
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)
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)
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def generate_blueprint() -> rrb.Blueprint:
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return rrb.Blueprint(
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@@ -258,49 +263,56 @@ def generate_blueprint() -> rrb.Blueprint:
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rrb.Grid(
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rrb.Spatial2DView(origin="z123image"),
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rrb.Spatial2DView(origin="preprocessed_image"),
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rrb.Spatial2DView(origin="mvs
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rrb.TensorView(origin="
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),
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column_shares=[1, 1],
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),
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collapse_panels=True,
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)
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@spaces.GPU
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@rr.thread_local_stream("InstantMesh")
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def log_to_rr(input_image, do_remove_background, sample_steps, sample_seed):
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stream = rr.binary_stream()
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blueprint = generate_blueprint()
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rr.send_blueprint(blueprint)
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yield stream.read()
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for msg in generate_mvs(preprocessed_image, sample_steps, sample_seed):
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if msg[0] == "z123_image":
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z123_image = msg[1]
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break
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-
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entity_path = msg
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entity = msg[2]
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rr.log(entity_path, entity)
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yield stream.read()
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yield stream.read()
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for msg in make3d(z123_image):
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if msg[0] == "log":
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rr.log(msg[1], msg[2])
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yield stream.read()
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if msg[0] == "mesh":
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mesh = msg[1]
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# return mesh
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_HEADER_ = '''
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return input_image
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def pipeline_callback(log_queue: SimpleQueue, pipe: Any, step_index: int, timestep: float, callback_kwargs: dict[str, Any]) -> dict[str, Any]:
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latents = callback_kwargs["latents"]
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image = pipe.vae.decode(latents / pipe.vae.config.scaling_factor, return_dict=False)[0] # type: ignore[attr-defined]
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image = pipe.image_processor.postprocess(image, output_type="np").squeeze() # type: ignore[attr-defined]
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log_queue.put(("mvs", rr.Image(image)))
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log_queue.put(("latents", rr.Tensor(latents.squeeze())))
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return callback_kwargs
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def generate_mvs(log_queue, input_image, sample_steps, sample_seed):
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seed_everything(sample_seed)
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return pipeline(
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input_image,
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num_inference_steps=sample_steps,
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callback_on_step_end=lambda *args, **kwargs: pipeline_callback(log_queue, *args, **kwargs),
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).images[0]
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# def thread_target(output_queue, input_image, sample_steps):
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# z123_image = pipeline(
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# input_image,
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# num_inference_steps=sample_steps,
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# callback_on_step_end=lambda *args, **kwargs: pipeline_callback(output_queue, *args, **kwargs),
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# ).images[0]
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# log_queue.put(("z123_image", z123_image))
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# output_queue = SimpleQueue()
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# z123_thread = threading.Thread(
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# target=thread_target,
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# args=
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# [
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# output_queue,
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# input_image,
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# sample_steps,
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# ]
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# )
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# z123_thread.start()
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# while True:
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# msg = output_queue.get()
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# yield msg
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# if msg[0] == "z123_image":
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# break
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# z123_thread.join()
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# def make3d(images: Image.Image):
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# output_queue = SimpleQueue()
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# handle = threading.Thread(target=_make3d, args=[output_queue, images])
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# handle.start()
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# while True:
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# msg = output_queue.get()
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# yield msg
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# if msg[0] == "mesh":
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# break
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# handle.join()
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def make3d(log_queue, images: Image.Image):
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global model
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if IS_FLEXICUBES:
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model.init_flexicubes_geometry(device, use_renderer=False)
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vertices, faces, vertex_colors = mesh_out
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log_queue.put(
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(
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"mesh",
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rr.Mesh3D(
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vertex_positions=vertices,
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),
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)
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)
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return mesh_out
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def generate_blueprint() -> rrb.Blueprint:
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return rrb.Blueprint(
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rrb.Grid(
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rrb.Spatial2DView(origin="z123image"),
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rrb.Spatial2DView(origin="preprocessed_image"),
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rrb.Spatial2DView(origin="mvs"),
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rrb.TensorView(origin="latents", ),
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),
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column_shares=[1, 1],
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),
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collapse_panels=True,
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)
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def compute(log_queue, input_image, do_remove_background, sample_steps, sample_seed):
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preprocessed_image = preprocess(input_image, do_remove_background)
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log_queue.put(("preprocessed_image", rr.Image(preprocessed_image)))
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# rr.log("preprocessed_image", rr.Image(preprocessed_image))
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z123_image = generate_mvs(log_queue, preprocessed_image, sample_steps, sample_seed)
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log_queue.put(("z123image", rr.Image(z123_image)))
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# rr.log("z123image", rr.Image(z123_image))
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mesh_out = make3d(log_queue, z123_image)
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log_queue.put("done")
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@spaces.GPU
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@rr.thread_local_stream("InstantMesh")
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def log_to_rr(input_image, do_remove_background, sample_steps, sample_seed):
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log_queue = SimpleQueue()
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stream = rr.binary_stream()
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blueprint = generate_blueprint()
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rr.send_blueprint(blueprint)
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yield stream.read()
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handle = threading.Thread(target=compute, args=[log_queue, input_image, do_remove_background, sample_steps, sample_seed])
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handle.start()
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while True:
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msg = log_queue.get()
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if msg == "done":
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break
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else:
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entity_path, entity = msg
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rr.log(entity_path, entity)
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yield stream.read()
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handle.join()
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# return mesh
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_HEADER_ = '''
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