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from daggr import FnNode, GradioNode, InferenceNode, Graph
from daggr.state import get_daggr_files_dir
import gradio as gr
import numpy as np
from PIL import Image
from typing import Any
import uuid
def downscale_image_to_file(image: Any, scale: float = 0.25) -> str | None:
pil_img = Image.open(image)
scale_f = max(0.05, min(1.0, float(scale)))
w, h = pil_img.size
new_w = max(1, int(w * scale_f))
new_h = max(1, int(h * scale_f))
resized = pil_img.resize((new_w, new_h), resample=Image.LANCZOS)
out_path = get_daggr_files_dir() / f"{uuid.uuid4()}.png"
resized.save(out_path)
return str(out_path)
background_remover = GradioNode(
"merve/background-removal",
api_name="/image",
run_locally=True,
inputs={
"image": gr.Image(),
},
outputs={
"original_image": None,
"final_image": gr.Image(
label="Final Image"
),
},
)
downscaler = FnNode(
downscale_image_to_file,
name="Downscale image for Inference",
inputs={
"image": background_remover.final_image,
"scale": gr.Slider(
label="Downscale factor",
minimum=0.25,
maximum=0.75,
step=0.05,
value=0.25,
),
},
outputs={
"image": gr.Image(label="Downscaled Image", type="filepath"),
},
)
trellis_3d = GradioNode(
"microsoft/TRELLIS.2",
api_name="/image_to_3d",
inputs={
"image": downscaler.image,
"ss_guidance_strength": 7.5,
"ss_sampling_steps": 12,
},
outputs={
"glb": gr.HTML(label="3D Asset (GLB preview)"),
},
)
sam3d_obj = GradioNode(
"HorizonRobotics/EmbodiedGen-Image-to-3D",
api_name="/extract_3d_representations_v3",
inputs=[
]
)
graph = Graph(
name="Image to 3D Asset Pipeline",
nodes=[background_remover, downscaler, trellis_3d],
)
'''
from gradio_client import Client, file
client = Client("HorizonRobotics/EmbodiedGen-Image-to-3D")
client.predict(
enable_delight=None,
texture_size=true,
api_name="/extract_3d_representations_v3"
)
client.predict(
api_name="/lambda_4"
)
client.predict(
gs_path="/home/user/app/sessions/imageto3d/29hqqc189st/sample_gs_aligned.ply",
mesh_obj_path="/home/user/app/sessions/imageto3d/29hqqc189st/sample.obj",
asset_cat_text="",
height_range_text="",
mass_range_text="",
asset_version_text="",
api_name="/extract_urdf"
)
client.predict(
api_name="/lambda_5"
)
'''
'''
from gradio_client import Client, file
client = Client("HorizonRobotics/EmbodiedGen-Image-to-3D")
client.predict(
api_name="/lambda_2"
)
client.predict(
content=handle_file('https://horizonrobotics-embodiedgen-image-to-3d.hf.space/gradio_api/file=/tmp/gradio/1219da499ed7b9468eca3ab819eb09a47479748a66a61f8608006b92a4a635a7/chairelect.png'),
api_name="/active_btn_by_content"
)
client.predict(
image=handle_file('https://horizonrobotics-embodiedgen-image-to-3d.hf.space/gradio_api/file=/tmp/gradio/1219da499ed7b9468eca3ab819eb09a47479748a66a61f8608006b92a4a635a7/chairelect.png'),
rmbg_tag="rembg",
api_name="/preprocess_image_fn"
)
client.predict(
api_name="/lambda_2"
)
client.predict(
content=handle_file('https://horizonrobotics-embodiedgen-image-to-3d.hf.space/gradio_api/file=/tmp/gradio/f0b1343c3d64f50b7a08ce3027056ba9259d96960e58625a1df07922e4a3a3f4/image.png'),
api_name="/active_btn_by_content"
)
client.predict(
randomize_seed=False,
seed=0,
api_name="/get_seed"
)
client.predict(
image=handle_file('https://horizonrobotics-embodiedgen-image-to-3d.hf.space/gradio_api/file=/tmp/gradio/f0b1343c3d64f50b7a08ce3027056ba9259d96960e58625a1df07922e4a3a3f4/image.png'),
seed=0,
ss_sampling_steps=25,
slat_sampling_steps=25,
raw_image_cache=handle_file('https://horizonrobotics-embodiedgen-image-to-3d.hf.space/gradio_api/file=/tmp/gradio/a7f55099fbfd47c44667d5e3eeee8818bf41ab1a5a70fc9bed2d5ce3c68f7015/image.png'),
ss_guidance_strength=7.5,
slat_guidance_strength=3,
sam_image=None,
api_name="/image_to_3d"
)
client.predict(
enable_delight=None,
texture_size=true,
api_name="/extract_3d_representations_v3"
)
client.predict(
api_name="/lambda_4"
)
client.predict(
gs_path="/home/user/app/sessions/imageto3d/1kxl1n8ek38/sample_gs_aligned.ply",
mesh_obj_path="/home/user/app/sessions/imageto3d/1kxl1n8ek38/sample.obj",
asset_cat_text="chair",
height_range_text="0.5",
mass_range_text="6",
asset_version_text="0.0.1",
api_name="/extract_urdf"
)
client.predict(
gs_path="/home/user/app/sessions/imageto3d/1kxl1n8ek38/sample_gs_aligned.ply",
mesh_obj_path="/home/user/app/sessions/imageto3d/1kxl1n8ek38/sample.obj",
asset_cat_text="chair",
height_range_text="0.5-0.7",
mass_range_text="2.1-3.5",
asset_version_text="v0.0.1",
api_name="/extract_urdf"
)
client.predict(
api_name="/lambda_5"
)
'''
'''
from gradio_client import Client, file
client = Client("prithivMLmods/Z-Image-Turbo-LoRA-DLC")
client.predict(
width=1024,
height=1024,
api_name="/update_selection"
)
client.predict(
prompt="Pull a purple plumb out ya butt",
image_input=None,
image_strength=0.75,
cfg_scale=0,
steps=9,
randomize_seed=None,
seed=true,
width=256386538,
height=1024,
lora_scale=1024,
api_name="/run_lora"
)
'''
if __name__ == "__main__":
graph.launch()
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