update

common.py

@@ -41,7 +41,7 @@ from embodied_gen.data.differentiable_render import entrypoint as render_api
 from embodied_gen.data.utils import trellis_preprocess, zip_files
 from embodied_gen.models.delight_model import DelightingModel
 from embodied_gen.models.gs_model import GaussianOperator
-from embodied_gen.models.sam3d import Sam3dInference
+# from embodied_gen.models.sam3d import Sam3dInference
 from embodied_gen.models.segment_model import (
     BMGG14Remover,
     RembgRemover,
@@ -92,13 +92,13 @@ if os.getenv("GRADIO_APP").startswith("imageto3d"):
     RBG_REMOVER = RembgRemover()
     RBG14_REMOVER = BMGG14Remover()
     SAM_PREDICTOR = SAMPredictor(model_type="vit_h", device="cpu")
-    if "sam3d" in os.getenv("GRADIO_APP"):
-        PIPELINE = Sam3dInference()
-    else:
-        PIPELINE = TrellisImageTo3DPipeline.from_pretrained(
-            "microsoft/TRELLIS-image-large"
-        )
-        # PIPELINE.cuda()
+    # if "sam3d" in os.getenv("GRADIO_APP"):
+    #     PIPELINE = Sam3dInference()
+    # else:
+    PIPELINE = TrellisImageTo3DPipeline.from_pretrained(
+        "microsoft/TRELLIS-image-large"
+    )
+    # PIPELINE.cuda()
     SEG_CHECKER = ImageSegChecker(GPT_CLIENT)
     GEO_CHECKER = MeshGeoChecker(GPT_CLIENT)
     AESTHETIC_CHECKER = ImageAestheticChecker()
@@ -287,32 +287,32 @@ def image_to_3d(
         seg_image = Image.fromarray(seg_image)
 
     logger.info("Start generating 3D representation from image...")
-    if isinstance(PIPELINE, Sam3dInference):
-        outputs = PIPELINE.run(
-            seg_image,
-            seed=seed,
-            stage1_inference_steps=ss_sampling_steps,
-            stage2_inference_steps=slat_sampling_steps,
-        )
-    else:
-        PIPELINE.cuda()
-        seg_image = trellis_preprocess(seg_image)
-        outputs = PIPELINE.run(
-            seg_image,
-            seed=seed,
-            formats=["gaussian", "mesh"],
-            preprocess_image=False,
-            sparse_structure_sampler_params={
-                "steps": ss_sampling_steps,
-                "cfg_strength": ss_guidance_strength,
-            },
-            slat_sampler_params={
-                "steps": slat_sampling_steps,
-                "cfg_strength": slat_guidance_strength,
-            },
-        )
-        # Set back to cpu for memory saving.
-        PIPELINE.cpu()
+    # if isinstance(PIPELINE, Sam3dInference):
+    #     outputs = PIPELINE.run(
+    #         seg_image,
+    #         seed=seed,
+    #         stage1_inference_steps=ss_sampling_steps,
+    #         stage2_inference_steps=slat_sampling_steps,
+    #     )
+    # else:
+    PIPELINE.cuda()
+    seg_image = trellis_preprocess(seg_image)
+    outputs = PIPELINE.run(
+        seg_image,
+        seed=seed,
+        formats=["gaussian", "mesh"],
+        preprocess_image=False,
+        sparse_structure_sampler_params={
+            "steps": ss_sampling_steps,
+            "cfg_strength": ss_guidance_strength,
+        },
+        slat_sampler_params={
+            "steps": slat_sampling_steps,
+            "cfg_strength": slat_guidance_strength,
+        },
+    )
+    # Set back to cpu for memory saving.
+    PIPELINE.cpu()
 
     gs_model = outputs["gaussian"][0]
     mesh_model = outputs["mesh"][0]

requirements.txt

@@ -61,7 +61,7 @@ MoGe@git+https://github.com/microsoft/MoGe.git@a8c3734
 
 
 # git+https://github.com/facebookresearch/pytorch3d.git@stable
-https://huggingface.co/xinjjj/RoboAssetGen/resolve/main/wheel_cu121/pytorch3d-0.7.8-cp310-cp310-linux_x86_64.whl
+# https://huggingface.co/xinjjj/RoboAssetGen/resolve/main/wheel_cu121/pytorch3d-0.7.8-cp310-cp310-linux_x86_64.whl
 # git+https://github.com/nerfstudio-project/gsplat.git@v1.5.3
 https://github.com/nerfstudio-project/gsplat/releases/download/v1.5.0/gsplat-1.5.0+pt24cu121-cp310-cp310-linux_x86_64.whl
 # flash-attn==2.7.0.post2