🔥 Clean redeploy with updated app.py

.gitattributes

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+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+title: EtTripoSg Api Gradio
+emoji: 🐢
+colorFrom: pink
+colorTo: purple
+sdk: gradio
+sdk_version: 3.50.2
+app_file: app.py
+pinned: false
+license: mit
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+# import os
+# import subprocess
+
+# # 🧹 Убираем pyenv, если вдруг остался .python-version
+# os.environ.pop("PYENV_VERSION", None)
+
+# # ⚙️ Устанавливаем torch и diso
+# subprocess.run(["pip", "install", "torch", "wheel"], check=True)
+
+# subprocess.run([
+#     "pip", "install", "--no-build-isolation", 
+#     "diso@git+https://github.com/SarahWeiii/diso.git"
+# ], check=True)
+
+# # ✅ Только теперь импортируем всё остальное
+# import gradio as gr
+# import uuid
+# import torch
+# import zipfile
+# import requests
+
+# from inference_triposg import run_triposg
+# from triposg.pipelines.pipeline_triposg import TripoSGPipeline
+# from briarmbg import BriaRMBG
+
+# # === Настройки устройства ===
+# # device = "cuda" if torch.cuda.is_available() else "cpu"
+# # dtype = torch.float16 if torch.cuda.is_available() else torch.float32
+# # dtype = torch.float32
+# device = "cuda" if torch.cuda.is_available() else "cpu"
+# dtype = torch.float16 if device == "cuda" else torch.float32
+
+# # === Проверка и загрузка весов ===
+# weights_dir = "pretrained_weights"
+# triposg_path = os.path.join(weights_dir, "TripoSG")
+# rmbg_path = os.path.join(weights_dir, "RMBG-1.4")
+
+# if not (os.path.exists(triposg_path) and os.path.exists(rmbg_path)):
+#     print("📦 Downloading pretrained weights from Hugging Face Dataset...")
+#     url = "https://huggingface.co/datasets/endlesstools/pretrained-assets/resolve/main/pretrained_models.zip"
+#     zip_path = "pretrained_models.zip"
+
+#     with requests.get(url, stream=True) as r:
+#         r.raise_for_status()
+#         with open(zip_path, "wb") as f:
+#             for chunk in r.iter_content(chunk_size=8192):
+#                 f.write(chunk)
+
+#     print("📦 Extracting weights...")
+#     with zipfile.ZipFile(zip_path, "r") as zip_ref:
+#         zip_ref.extractall(weights_dir)
+
+#     os.remove(zip_path)
+#     print("✅ Weights ready.")
+
+# # === Загрузка моделей ===
+# pipe = TripoSGPipeline.from_pretrained(triposg_path).to(device, dtype)
+# rmbg_net = BriaRMBG.from_pretrained(rmbg_path).to(device)
+# rmbg_net.eval()
+
+# # === Функция генерации ===
+# def generate(file):
+#     temp_id = str(uuid.uuid4())
+#     input_path = f"/tmp/{temp_id}.png"
+#     output_path = f"/tmp/{temp_id}.glb"
+
+#     with open(input_path, "wb") as f:
+#         f.write(file)
+
+#     print("[DEBUG] Generating mesh...")
+#     try:
+#         mesh = run_triposg(
+#             pipe=pipe,
+#             image_input=input_path,
+#             rmbg_net=rmbg_net,
+#             seed=42,
+#             num_inference_steps=25,
+#             guidance_scale=5.0,
+#             faces=-1,
+#         )
+#         # mesh.export(output_path)
+#         if mesh is None:
+#             raise ValueError("Mesh generation failed")
+#         mesh.export(output_path)
+#         print(f"[DEBUG] Mesh saved to {output_path}")
+#         # return output_path
+#         if os.path.exists(output_path):
+#             return output_path
+#         else:
+#             return "Error: mesh export failed or file not found"
+#     except Exception as e:
+#         print("[ERROR]", e)
+#         return f"Error: {e}"
+
+# # === Gradio-интерфейс ===
+# demo = gr.Interface(
+#     fn=generate,
+#     inputs=gr.File(type="binary", label="Upload image"),
+#     outputs=gr.File(label="Generated .glb model"),
+#     title="TripoSG Image-to-3D",
+#     description="Upload an image and get back a 3D GLB model.",
+# )
+
+# # # === ВАЖНО: переменная должна называться `app` ===
+# # app = demo.launch(inline=True, share=False, prevent_thread_lock=True)
+# demo.launch()
+
+
+
+
+import os
+import subprocess
+
+# Убираем pyenv, если вдруг остался .python-version
+os.environ.pop("PYENV_VERSION", None)
+
+# Установка зависимостей
+subprocess.run(["pip", "install", "torch", "wheel"], check=True)
+subprocess.run([
+    "pip", "install", "--no-build-isolation", 
+    "diso@git+https://github.com/SarahWeiii/diso.git"
+], check=True)
+
+# Импорты
+import gradio as gr
+import uuid
+import torch
+import zipfile
+import requests
+import traceback
+
+from inference_triposg import run_triposg
+from triposg.pipelines.pipeline_triposg import TripoSGPipeline
+from briarmbg import BriaRMBG
+
+# Настройки устройства
+device = "cuda" if torch.cuda.is_available() else "cpu"
+dtype = torch.float16 if device == "cuda" else torch.float32
+
+# Загрузка весов
+weights_dir = "pretrained_weights"
+triposg_path = os.path.join(weights_dir, "TripoSG")
+rmbg_path = os.path.join(weights_dir, "RMBG-1.4")
+
+if not (os.path.exists(triposg_path) and os.path.exists(rmbg_path)):
+    print("📦 Downloading pretrained weights...")
+    url = "https://huggingface.co/datasets/endlesstools/pretrained-assets/resolve/main/pretrained_models.zip"
+    zip_path = "pretrained_models.zip"
+
+    with requests.get(url, stream=True) as r:
+        r.raise_for_status()
+        with open(zip_path, "wb") as f:
+            for chunk in r.iter_content(chunk_size=8192):
+                f.write(chunk)
+
+    print("📦 Extracting weights...")
+    with zipfile.ZipFile(zip_path, "r") as zip_ref:
+        zip_ref.extractall(weights_dir)
+
+    os.remove(zip_path)
+    print("✅ Weights ready.")
+
+# Загрузка моделей
+pipe = TripoSGPipeline.from_pretrained(triposg_path).to(device, dtype)
+rmbg_net = BriaRMBG.from_pretrained(rmbg_path).to(device)
+rmbg_net.eval()
+
+# Генерация .glb
+def generate(image_path):
+    print("[API CALL] image_path received:", image_path)
+    print("[API CALL] File exists:", os.path.exists(image_path))
+
+    temp_id = str(uuid.uuid4())
+    output_path = f"/tmp/{temp_id}.glb"
+
+    print("[DEBUG] Generating mesh from:", image_path)
+
+    try:
+        mesh = run_triposg(
+            pipe=pipe,
+            image_input=image_path,
+            rmbg_net=rmbg_net,
+            seed=42,
+            num_inference_steps=25,
+            guidance_scale=5.0,
+            faces=-1,
+        )
+
+        if mesh is None:
+            raise ValueError("Mesh generation failed")
+
+        mesh.export(output_path)
+        print(f"[DEBUG] Mesh saved to {output_path}")
+
+        return output_path if os.path.exists(output_path) else "Error: output file not found"
+    # except Exception as e:
+    #     print("[ERROR]", e)
+    #     return f"Error: {e}"
+    except Exception as e:
+        import traceback
+        print("[ERROR]", e)
+        traceback.print_exc()  # ← выведет полную трассировку в логи
+        return f"Error: {e}"
+
+# Интерфейс Gradio
+demo = gr.Interface(
+    fn=generate,
+    inputs=gr.Image(type="filepath", label="Upload image"),
+    outputs=gr.File(label="Download .glb"),
+    title="TripoSG Image to 3D",
+    description="Upload an image to generate a 3D model (.glb)",
+)
+
+# Запуск
+demo.launch()
+
+
+
+
+
+
+# import gradio as gr
+# import uuid
+# import os
+# import traceback
+
+# def generate(image_path):
+#     try:
+#         print("[DEBUG] got image path:", image_path)
+#         print("[DEBUG] file exists:", os.path.exists(image_path))
+
+#         out_path = f"/tmp/{uuid.uuid4()}.txt"
+#         with open(out_path, "w") as f:
+#             f.write(f"Received: {image_path}")
+
+#         return out_path
+
+#     except Exception as e:
+#         print("[ERROR]", e)
+#         traceback.print_exc()
+#         return f"Error: {e}"
+
+# demo = gr.Interface(
+#     fn=generate,
+#     inputs=gr.Image(type="filepath"),
+#     outputs=gr.File()
+# )
+
+# demo.launch()

briarmbg.py

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+"""
+Source and Copyright Notice:
+This code is from briaai/RMBG-1.4
+Original repository: https://huggingface.co/briaai/RMBG-1.4
+Copyright belongs to briaai
+"""
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from huggingface_hub import PyTorchModelHubMixin
+
+class REBNCONV(nn.Module):
+    def __init__(self,in_ch=3,out_ch=3,dirate=1,stride=1):
+        super(REBNCONV,self).__init__()
+
+        self.conv_s1 = nn.Conv2d(in_ch,out_ch,3,padding=1*dirate,dilation=1*dirate,stride=stride)
+        self.bn_s1 = nn.BatchNorm2d(out_ch)
+        self.relu_s1 = nn.ReLU(inplace=True)
+
+    def forward(self,x):
+
+        hx = x
+        xout = self.relu_s1(self.bn_s1(self.conv_s1(hx)))
+
+        return xout
+
+## upsample tensor 'src' to have the same spatial size with tensor 'tar'
+def _upsample_like(src,tar):
+
+    src = F.interpolate(src,size=tar.shape[2:],mode='bilinear')
+
+    return src
+
+
+### RSU-7 ###
+class RSU7(nn.Module):
+
+    def __init__(self, in_ch=3, mid_ch=12, out_ch=3, img_size=512):
+        super(RSU7,self).__init__()
+
+        self.in_ch = in_ch
+        self.mid_ch = mid_ch
+        self.out_ch = out_ch
+
+        self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1) ## 1 -> 1/2
+
+        self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)
+        self.pool1 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=1)
+        self.pool2 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=1)
+        self.pool3 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=1)
+        self.pool4 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.rebnconv5 = REBNCONV(mid_ch,mid_ch,dirate=1)
+        self.pool5 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.rebnconv6 = REBNCONV(mid_ch,mid_ch,dirate=1)
+
+        self.rebnconv7 = REBNCONV(mid_ch,mid_ch,dirate=2)
+
+        self.rebnconv6d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
+        self.rebnconv5d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
+        self.rebnconv4d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
+        self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
+        self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
+        self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)
+
+    def forward(self,x):
+        b, c, h, w = x.shape
+
+        hx = x
+        hxin = self.rebnconvin(hx)
+
+        hx1 = self.rebnconv1(hxin)
+        hx = self.pool1(hx1)
+
+        hx2 = self.rebnconv2(hx)
+        hx = self.pool2(hx2)
+
+        hx3 = self.rebnconv3(hx)
+        hx = self.pool3(hx3)
+
+        hx4 = self.rebnconv4(hx)
+        hx = self.pool4(hx4)
+
+        hx5 = self.rebnconv5(hx)
+        hx = self.pool5(hx5)
+
+        hx6 = self.rebnconv6(hx)
+
+        hx7 = self.rebnconv7(hx6)
+
+        hx6d =  self.rebnconv6d(torch.cat((hx7,hx6),1))
+        hx6dup = _upsample_like(hx6d,hx5)
+
+        hx5d =  self.rebnconv5d(torch.cat((hx6dup,hx5),1))
+        hx5dup = _upsample_like(hx5d,hx4)
+
+        hx4d = self.rebnconv4d(torch.cat((hx5dup,hx4),1))
+        hx4dup = _upsample_like(hx4d,hx3)
+
+        hx3d = self.rebnconv3d(torch.cat((hx4dup,hx3),1))
+        hx3dup = _upsample_like(hx3d,hx2)
+
+        hx2d = self.rebnconv2d(torch.cat((hx3dup,hx2),1))
+        hx2dup = _upsample_like(hx2d,hx1)
+
+        hx1d = self.rebnconv1d(torch.cat((hx2dup,hx1),1))
+
+        return hx1d + hxin
+
+
+### RSU-6 ###
+class RSU6(nn.Module):
+
+    def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
+        super(RSU6,self).__init__()
+
+        self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1)
+
+        self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)
+        self.pool1 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=1)
+        self.pool2 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=1)
+        self.pool3 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=1)
+        self.pool4 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.rebnconv5 = REBNCONV(mid_ch,mid_ch,dirate=1)
+
+        self.rebnconv6 = REBNCONV(mid_ch,mid_ch,dirate=2)
+
+        self.rebnconv5d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
+        self.rebnconv4d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
+        self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
+        self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
+        self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)
+
+    def forward(self,x):
+
+        hx = x
+
+        hxin = self.rebnconvin(hx)
+
+        hx1 = self.rebnconv1(hxin)
+        hx = self.pool1(hx1)
+
+        hx2 = self.rebnconv2(hx)
+        hx = self.pool2(hx2)
+
+        hx3 = self.rebnconv3(hx)
+        hx = self.pool3(hx3)
+
+        hx4 = self.rebnconv4(hx)
+        hx = self.pool4(hx4)
+
+        hx5 = self.rebnconv5(hx)
+
+        hx6 = self.rebnconv6(hx5)
+
+
+        hx5d =  self.rebnconv5d(torch.cat((hx6,hx5),1))
+        hx5dup = _upsample_like(hx5d,hx4)
+
+        hx4d = self.rebnconv4d(torch.cat((hx5dup,hx4),1))
+        hx4dup = _upsample_like(hx4d,hx3)
+
+        hx3d = self.rebnconv3d(torch.cat((hx4dup,hx3),1))
+        hx3dup = _upsample_like(hx3d,hx2)
+
+        hx2d = self.rebnconv2d(torch.cat((hx3dup,hx2),1))
+        hx2dup = _upsample_like(hx2d,hx1)
+
+        hx1d = self.rebnconv1d(torch.cat((hx2dup,hx1),1))
+
+        return hx1d + hxin
+
+### RSU-5 ###
+class RSU5(nn.Module):
+
+    def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
+        super(RSU5,self).__init__()
+
+        self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1)
+
+        self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)
+        self.pool1 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=1)
+        self.pool2 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=1)
+        self.pool3 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=1)
+
+        self.rebnconv5 = REBNCONV(mid_ch,mid_ch,dirate=2)
+
+        self.rebnconv4d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
+        self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
+        self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
+        self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)
+
+    def forward(self,x):
+
+        hx = x
+
+        hxin = self.rebnconvin(hx)
+
+        hx1 = self.rebnconv1(hxin)
+        hx = self.pool1(hx1)
+
+        hx2 = self.rebnconv2(hx)
+        hx = self.pool2(hx2)
+
+        hx3 = self.rebnconv3(hx)
+        hx = self.pool3(hx3)
+
+        hx4 = self.rebnconv4(hx)
+
+        hx5 = self.rebnconv5(hx4)
+
+        hx4d = self.rebnconv4d(torch.cat((hx5,hx4),1))
+        hx4dup = _upsample_like(hx4d,hx3)
+
+        hx3d = self.rebnconv3d(torch.cat((hx4dup,hx3),1))
+        hx3dup = _upsample_like(hx3d,hx2)
+
+        hx2d = self.rebnconv2d(torch.cat((hx3dup,hx2),1))
+        hx2dup = _upsample_like(hx2d,hx1)
+
+        hx1d = self.rebnconv1d(torch.cat((hx2dup,hx1),1))
+
+        return hx1d + hxin
+
+### RSU-4 ###
+class RSU4(nn.Module):
+
+    def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
+        super(RSU4,self).__init__()
+
+        self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1)
+
+        self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)
+        self.pool1 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=1)
+        self.pool2 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=1)
+
+        self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=2)
+
+        self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
+        self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
+        self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)
+
+    def forward(self,x):
+
+        hx = x
+
+        hxin = self.rebnconvin(hx)
+
+        hx1 = self.rebnconv1(hxin)
+        hx = self.pool1(hx1)
+
+        hx2 = self.rebnconv2(hx)
+        hx = self.pool2(hx2)
+
+        hx3 = self.rebnconv3(hx)
+
+        hx4 = self.rebnconv4(hx3)
+
+        hx3d = self.rebnconv3d(torch.cat((hx4,hx3),1))
+        hx3dup = _upsample_like(hx3d,hx2)
+
+        hx2d = self.rebnconv2d(torch.cat((hx3dup,hx2),1))
+        hx2dup = _upsample_like(hx2d,hx1)
+
+        hx1d = self.rebnconv1d(torch.cat((hx2dup,hx1),1))
+
+        return hx1d + hxin
+
+### RSU-4F ###
+class RSU4F(nn.Module):
+
+    def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
+        super(RSU4F,self).__init__()
+
+        self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1)
+
+        self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)
+        self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=2)
+        self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=4)
+
+        self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=8)
+
+        self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=4)
+        self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=2)
+        self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)
+
+    def forward(self,x):
+
+        hx = x
+
+        hxin = self.rebnconvin(hx)
+
+        hx1 = self.rebnconv1(hxin)
+        hx2 = self.rebnconv2(hx1)
+        hx3 = self.rebnconv3(hx2)
+
+        hx4 = self.rebnconv4(hx3)
+
+        hx3d = self.rebnconv3d(torch.cat((hx4,hx3),1))
+        hx2d = self.rebnconv2d(torch.cat((hx3d,hx2),1))
+        hx1d = self.rebnconv1d(torch.cat((hx2d,hx1),1))
+
+        return hx1d + hxin
+
+
+class myrebnconv(nn.Module):
+    def __init__(self, in_ch=3,
+                       out_ch=1,
+                       kernel_size=3,
+                       stride=1,
+                       padding=1,
+                       dilation=1,
+                       groups=1):
+        super(myrebnconv,self).__init__()
+
+        self.conv = nn.Conv2d(in_ch,
+                              out_ch,
+                              kernel_size=kernel_size,
+                              stride=stride,
+                              padding=padding,
+                              dilation=dilation,
+                              groups=groups)
+        self.bn = nn.BatchNorm2d(out_ch)
+        self.rl = nn.ReLU(inplace=True)
+
+    def forward(self,x):
+        return self.rl(self.bn(self.conv(x)))
+
+
+class BriaRMBG(nn.Module, PyTorchModelHubMixin):
+
+    def __init__(self,config:dict={"in_ch":3,"out_ch":1}):
+        super(BriaRMBG,self).__init__()
+        in_ch=config["in_ch"]
+        out_ch=config["out_ch"]
+        self.conv_in = nn.Conv2d(in_ch,64,3,stride=2,padding=1)
+        self.pool_in = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.stage1 = RSU7(64,32,64)
+        self.pool12 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.stage2 = RSU6(64,32,128)
+        self.pool23 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.stage3 = RSU5(128,64,256)
+        self.pool34 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.stage4 = RSU4(256,128,512)
+        self.pool45 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.stage5 = RSU4F(512,256,512)
+        self.pool56 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.stage6 = RSU4F(512,256,512)
+
+        # decoder
+        self.stage5d = RSU4F(1024,256,512)
+        self.stage4d = RSU4(1024,128,256)
+        self.stage3d = RSU5(512,64,128)
+        self.stage2d = RSU6(256,32,64)
+        self.stage1d = RSU7(128,16,64)
+
+        self.side1 = nn.Conv2d(64,out_ch,3,padding=1)
+        self.side2 = nn.Conv2d(64,out_ch,3,padding=1)
+        self.side3 = nn.Conv2d(128,out_ch,3,padding=1)
+        self.side4 = nn.Conv2d(256,out_ch,3,padding=1)
+        self.side5 = nn.Conv2d(512,out_ch,3,padding=1)
+        self.side6 = nn.Conv2d(512,out_ch,3,padding=1)
+
+        # self.outconv = nn.Conv2d(6*out_ch,out_ch,1)
+
+    def forward(self,x):
+
+        hx = x
+
+        hxin = self.conv_in(hx)
+        #hx = self.pool_in(hxin)
+
+        #stage 1
+        hx1 = self.stage1(hxin)
+        hx = self.pool12(hx1)
+
+        #stage 2
+        hx2 = self.stage2(hx)
+        hx = self.pool23(hx2)
+
+        #stage 3
+        hx3 = self.stage3(hx)
+        hx = self.pool34(hx3)
+
+        #stage 4
+        hx4 = self.stage4(hx)
+        hx = self.pool45(hx4)
+
+        #stage 5
+        hx5 = self.stage5(hx)
+        hx = self.pool56(hx5)
+
+        #stage 6
+        hx6 = self.stage6(hx)
+        hx6up = _upsample_like(hx6,hx5)
+
+        #-------------------- decoder --------------------
+        hx5d = self.stage5d(torch.cat((hx6up,hx5),1))
+        hx5dup = _upsample_like(hx5d,hx4)
+
+        hx4d = self.stage4d(torch.cat((hx5dup,hx4),1))
+        hx4dup = _upsample_like(hx4d,hx3)
+
+        hx3d = self.stage3d(torch.cat((hx4dup,hx3),1))
+        hx3dup = _upsample_like(hx3d,hx2)
+
+        hx2d = self.stage2d(torch.cat((hx3dup,hx2),1))
+        hx2dup = _upsample_like(hx2d,hx1)
+
+        hx1d = self.stage1d(torch.cat((hx2dup,hx1),1))
+
+
+        #side output
+        d1 = self.side1(hx1d)
+        d1 = _upsample_like(d1,x)
+
+        d2 = self.side2(hx2d)
+        d2 = _upsample_like(d2,x)
+
+        d3 = self.side3(hx3d)
+        d3 = _upsample_like(d3,x)
+
+        d4 = self.side4(hx4d)
+        d4 = _upsample_like(d4,x)
+
+        d5 = self.side5(hx5d)
+        d5 = _upsample_like(d5,x)
+
+        d6 = self.side6(hx6)
+        d6 = _upsample_like(d6,x)
+
+        return [F.sigmoid(d1), F.sigmoid(d2), F.sigmoid(d3), F.sigmoid(d4), F.sigmoid(d5), F.sigmoid(d6)],[hx1d,hx2d,hx3d,hx4d,hx5d,hx6]
+

image_process.py

@@ -0,0 +1,149 @@
+# -*- coding: utf-8 -*-
+import os
+from skimage.morphology import remove_small_objects
+from skimage.measure import label
+import numpy as np
+from PIL import Image
+import cv2
+from torchvision import transforms
+import torch
+import torch.nn.functional as F
+import torchvision.transforms.functional as TF
+
+def find_bounding_box(gray_image):
+    _, binary_image = cv2.threshold(gray_image, 1, 255, cv2.THRESH_BINARY)
+    contours, _ = cv2.findContours(binary_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    max_contour = max(contours, key=cv2.contourArea)
+    x, y, w, h = cv2.boundingRect(max_contour)
+    return x, y, w, h
+
+def load_image(img_path, bg_color=None, rmbg_net=None, padding_ratio=0.1):
+    img = cv2.imread(img_path, cv2.IMREAD_UNCHANGED)
+    if img is None:
+        return f"invalid image path {img_path}"
+
+    def is_valid_alpha(alpha, min_ratio = 0.01):
+        bins = 20
+        if isinstance(alpha, np.ndarray):
+            hist = cv2.calcHist([alpha], [0], None, [bins], [0, 256])
+        else:
+            hist = torch.histc(alpha, bins=bins, min=0, max=1) 
+        min_hist_val = alpha.shape[0] * alpha.shape[1] * min_ratio
+        return hist[0] >= min_hist_val and hist[-1] >= min_hist_val
+    
+    def rmbg(image: torch.Tensor) -> torch.Tensor:
+        image = TF.normalize(image, [0.5,0.5,0.5], [1.0,1.0,1.0]).unsqueeze(0)
+        result=rmbg_net(image)
+        return result[0][0]
+
+    if len(img.shape) == 2:
+        num_channels = 1
+    else:
+        num_channels = img.shape[2]
+
+    # check if too large
+    height, width = img.shape[:2]
+    if height > width:
+        scale = 2000 / height
+    else:
+        scale = 2000 / width
+    if scale < 1:
+        new_size = (int(width * scale), int(height * scale))
+        img = cv2.resize(img, new_size, interpolation=cv2.INTER_AREA)
+
+    if img.dtype != 'uint8':
+        img = (img * (255. / np.iinfo(img.dtype).max)).astype(np.uint8)
+
+    rgb_image = None
+    alpha = None
+
+    if num_channels == 1:  
+        rgb_image = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
+    elif num_channels == 3:  
+        rgb_image = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    elif num_channels == 4:  
+        rgb_image = cv2.cvtColor(img, cv2.COLOR_BGRA2RGB)
+
+        b, g, r, alpha = cv2.split(img)
+        if not is_valid_alpha(alpha):
+            alpha = None
+        else:
+            alpha_gpu = torch.from_numpy(alpha).unsqueeze(0).cuda().float() / 255.
+    else:
+        return f"invalid image: channels {num_channels}"
+    
+    rgb_image_gpu = torch.from_numpy(rgb_image).cuda().float().permute(2, 0, 1) / 255.
+    if alpha is None:
+        resize_transform = transforms.Resize((384, 384), antialias=True)
+        rgb_image_resized = resize_transform(rgb_image_gpu)
+        normalize_image = rgb_image_resized * 2 - 1
+
+        mean_color = torch.tensor([0.485, 0.456, 0.406]).view(3, 1, 1).cuda()
+        resize_transform = transforms.Resize((1024, 1024), antialias=True)
+        rgb_image_resized = resize_transform(rgb_image_gpu)
+        max_value = rgb_image_resized.flatten().max()
+        if max_value < 1e-3:
+            return "invalid image: pure black image"
+        normalize_image = rgb_image_resized / max_value - mean_color
+        normalize_image = normalize_image.unsqueeze(0)
+        resize_transform = transforms.Resize((rgb_image_gpu.shape[1], rgb_image_gpu.shape[2]), antialias=True)
+
+        # seg from rmbg
+        alpha_gpu_rmbg = rmbg(rgb_image_resized)
+        alpha_gpu_rmbg = alpha_gpu_rmbg.squeeze(0)
+        alpha_gpu_rmbg = resize_transform(alpha_gpu_rmbg)
+        ma, mi = alpha_gpu_rmbg.max(), alpha_gpu_rmbg.min()
+        alpha_gpu_rmbg = (alpha_gpu_rmbg - mi) / (ma - mi)
+
+        alpha_gpu = alpha_gpu_rmbg
+        
+        alpha_gpu_tmp = alpha_gpu * 255
+        alpha = alpha_gpu_tmp.to(torch.uint8).squeeze().cpu().numpy()
+
+        _, alpha = cv2.threshold(alpha, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)
+        labeled_alpha = label(alpha)
+        cleaned_alpha = remove_small_objects(labeled_alpha, min_size=200)
+        cleaned_alpha = (cleaned_alpha > 0).astype(np.uint8)
+        alpha = cleaned_alpha * 255
+        alpha_gpu = torch.from_numpy(cleaned_alpha).cuda().float().unsqueeze(0)
+        x, y, w, h = find_bounding_box(alpha)
+
+    # If alpha is provided, the bounds of all foreground are used
+    else: 
+        rows, cols = np.where(alpha > 0)
+        if rows.size > 0 and cols.size > 0:
+            x_min = np.min(cols)
+            y_min = np.min(rows)
+            x_max = np.max(cols)
+            y_max = np.max(rows)
+
+            width = x_max - x_min + 1
+            height = y_max - y_min + 1
+        x, y, w, h = x_min, y_min, width, height
+
+    if np.all(alpha==0):
+        raise ValueError(f"input image too small")
+    
+    bg_gray = bg_color[0]
+    bg_color = torch.from_numpy(bg_color).float().cuda().repeat(alpha_gpu.shape[1], alpha_gpu.shape[2], 1).permute(2, 0, 1)
+    rgb_image_gpu = rgb_image_gpu * alpha_gpu + bg_color * (1 - alpha_gpu)
+    padding_size = [0] * 6
+    if w > h:
+        padding_size[0] = int(w * padding_ratio)
+        padding_size[2] = int(padding_size[0] + (w - h) / 2)
+    else:
+        padding_size[2] = int(h * padding_ratio)
+        padding_size[0] = int(padding_size[2] + (h - w) / 2)
+    padding_size[1] = padding_size[0]
+    padding_size[3] = padding_size[2]
+    padded_tensor = F.pad(rgb_image_gpu[:, y:(y+h), x:(x+w)], pad=tuple(padding_size), mode='constant', value=bg_gray)
+
+    return padded_tensor
+
+def prepare_image(image_path, bg_color, rmbg_net=None):
+    if os.path.isfile(image_path):
+        img_tensor = load_image(image_path, bg_color=bg_color, rmbg_net=rmbg_net)
+        img_np = img_tensor.permute(1,2,0).cpu().numpy()
+        img_pil = Image.fromarray((img_np*255).astype(np.uint8))
+        
+        return img_pil

inference_triposg.py

@@ -0,0 +1,137 @@
+import argparse
+import os
+import sys
+from glob import glob
+from typing import Any, Union
+
+import numpy as np
+import torch
+import trimesh
+from huggingface_hub import snapshot_download
+from PIL import Image
+
+sys.path.append(os.path.dirname(os.path.abspath(__file__)))
+
+from triposg.pipelines.pipeline_triposg import TripoSGPipeline
+from image_process import prepare_image
+from briarmbg import BriaRMBG
+
+import pymeshlab
+
+
+# @torch.no_grad()
+# def run_triposg(
+#     pipe: Any,
+#     image_input: Union[str, Image.Image],
+#     rmbg_net: Any,
+#     seed: int,
+#     num_inference_steps: int = 50,
+#     guidance_scale: float = 7.0,
+#     faces: int = -1,
+# ) -> trimesh.Scene:
+
+#     img_pil = prepare_image(image_input, bg_color=np.array([1.0, 1.0, 1.0]), rmbg_net=rmbg_net)
+
+#     outputs = pipe(
+#         image=img_pil,
+#         generator=torch.Generator(device=pipe.device).manual_seed(seed),
+#         num_inference_steps=num_inference_steps,
+#         guidance_scale=guidance_scale,
+#     ).samples[0]
+#     mesh = trimesh.Trimesh(outputs[0].astype(np.float32), np.ascontiguousarray(outputs[1]))
+
+#     if faces > 0:
+#         mesh = simplify_mesh(mesh, faces)
+
+#     return mesh
+
+@torch.no_grad()
+def run_triposg(
+    pipe: Any,
+    image_input: Union[str, Image.Image],
+    rmbg_net: Any,
+    seed: int,
+    num_inference_steps: int = 50,
+    guidance_scale: float = 7.0,
+    faces: int = -1,
+) -> trimesh.Scene:
+    print("[DEBUG] Preparing image...")
+    img_pil = prepare_image(image_input, bg_color=np.array([1.0, 1.0, 1.0]), rmbg_net=rmbg_net)
+
+    print("[DEBUG] Running TripoSG pipeline...")
+    outputs = pipe(
+        image=img_pil,
+        generator=torch.Generator(device=pipe.device).manual_seed(seed),
+        num_inference_steps=num_inference_steps,
+        guidance_scale=guidance_scale,
+    ).samples[0]
+
+    print("[DEBUG] TripoSG output keys:", type(outputs), outputs[0].shape, outputs[1].shape)
+
+    mesh = trimesh.Trimesh(outputs[0].astype(np.float32), np.ascontiguousarray(outputs[1]))
+    print(f"[DEBUG] Mesh created: {mesh.vertices.shape[0]} verts / {mesh.faces.shape[0]} faces")
+
+    if faces > 0:
+        print(f"[DEBUG] Simplifying mesh to {faces} faces")
+        mesh = simplify_mesh(mesh, faces)
+
+    return mesh
+
+
+def mesh_to_pymesh(vertices, faces):
+    mesh = pymeshlab.Mesh(vertex_matrix=vertices, face_matrix=faces)
+    ms = pymeshlab.MeshSet()
+    ms.add_mesh(mesh)
+    return ms
+
+def pymesh_to_trimesh(mesh):
+    verts = mesh.vertex_matrix()#.tolist()
+    faces = mesh.face_matrix()#.tolist()
+    return trimesh.Trimesh(vertices=verts, faces=faces)  #, vID, fID
+
+def simplify_mesh(mesh: trimesh.Trimesh, n_faces):
+    if mesh.faces.shape[0] > n_faces:
+        ms = mesh_to_pymesh(mesh.vertices, mesh.faces)
+        ms.meshing_merge_close_vertices()
+        ms.meshing_decimation_quadric_edge_collapse(targetfacenum = n_faces)
+        return pymesh_to_trimesh(ms.current_mesh())
+    else:
+        return mesh
+
+if __name__ == "__main__":
+    device = "cuda"
+    dtype = torch.float16
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--image-input", type=str, required=True)
+    parser.add_argument("--output-path", type=str, default="./output.glb")
+    parser.add_argument("--seed", type=int, default=42)
+    parser.add_argument("--num-inference-steps", type=int, default=50)
+    parser.add_argument("--guidance-scale", type=float, default=7.0)
+    parser.add_argument("--faces", type=int, default=-1)
+    args = parser.parse_args()
+
+    # download pretrained weights
+    triposg_weights_dir = "pretrained_weights/TripoSG"
+    rmbg_weights_dir = "pretrained_weights/RMBG-1.4"
+    snapshot_download(repo_id="VAST-AI/TripoSG", local_dir=triposg_weights_dir)
+    snapshot_download(repo_id="briaai/RMBG-1.4", local_dir=rmbg_weights_dir)
+
+    # init rmbg model for background removal
+    rmbg_net = BriaRMBG.from_pretrained(rmbg_weights_dir).to(device)
+    rmbg_net.eval() 
+
+    # init tripoSG pipeline
+    pipe: TripoSGPipeline = TripoSGPipeline.from_pretrained(triposg_weights_dir).to(device, dtype)
+
+    # run inference
+    run_triposg(
+        pipe,
+        image_input=args.image_input,
+        rmbg_net=rmbg_net,
+        seed=args.seed,
+        num_inference_steps=args.num_inference_steps,
+        guidance_scale=args.guidance_scale,
+        faces=args.faces,
+    ).export(args.output_path)
+    print(f"Mesh saved to {args.output_path}")

requirements.txt

@@ -0,0 +1,18 @@
+gradio
+torch
+torchvision
+torchaudio
+trimesh
+pillow
+omegaconf
+diffusers
+transformers
+opencv-python
+scikit-image
+python-multipart
+peft
+jaxtyping
+typeguard
+pymeshlab
+einops
+

scripts/inference_triposg_scribble.py

@@ -0,0 +1,78 @@
+import argparse
+import os
+import sys
+from glob import glob
+from typing import Any, Union
+
+import numpy as np
+import torch
+import trimesh
+from huggingface_hub import snapshot_download
+from PIL import Image
+
+sys.path.append(os.path.dirname(os.path.abspath(__file__)))
+
+from triposg.pipelines.pipeline_triposg_scribble import TripoSGScribblePipeline
+
+
+
+@torch.no_grad()
+def run_triposg_scribble(
+    pipe: Any,
+    image_input: Union[str, Image.Image],
+    prompt: str,
+    seed: int,
+    num_inference_steps: int = 16,
+    scribble_confidence: float = 0.4,
+    prompt_confidence: float = 1.0
+) -> trimesh.Scene:
+
+    img_pil = Image.open(image_input).convert("RGB")
+
+    outputs = pipe(
+        image=img_pil,
+        prompt=prompt,
+        generator=torch.Generator(device=pipe.device).manual_seed(seed),
+        num_inference_steps=num_inference_steps,
+        guidance_scale=0, # this is a CFG-distilled model
+        attention_kwargs={"cross_attention_scale": prompt_confidence, "cross_attention_2_scale": scribble_confidence},
+        use_flash_decoder=False, # there're some boundary problems when using flash decoder with this model
+        dense_octree_depth=8, hierarchical_octree_depth=8 # 256 resolution for faster inference
+    ).samples[0]
+    mesh = trimesh.Trimesh(outputs[0].astype(np.float32), np.ascontiguousarray(outputs[1]))    
+    return mesh
+
+
+if __name__ == "__main__":
+    device = "cuda"
+    dtype = torch.float16
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--image-input", type=str, required=True)
+    parser.add_argument("--prompt", type=str, required=True)
+    parser.add_argument("--output-path", type=str, default="./output.glb")
+    parser.add_argument("--seed", type=int, default=42)
+    parser.add_argument("--num-inference-steps", type=int, default=16)
+    # feel free to tune the scribble confidence, 0.3-0.5 often gives good results for hand-drawn sketches
+    parser.add_argument("--scribble-conf", type=float, default=0.4)
+    parser.add_argument("--prompt-conf", type=float, default=1.0)
+    args = parser.parse_args()
+
+    # download pretrained weights
+    triposg_scribble_weights_dir = "pretrained_weights/TripoSG-scribble"
+    snapshot_download(repo_id="VAST-AI/TripoSG-scribble", local_dir=triposg_scribble_weights_dir)
+
+    # init tripoSG pipeline
+    pipe: TripoSGScribblePipeline = TripoSGScribblePipeline.from_pretrained(triposg_scribble_weights_dir).to(device, dtype)
+
+    # run inference
+    run_triposg_scribble(
+        pipe,
+        image_input=args.image_input,
+        prompt=args.prompt,
+        seed=args.seed,
+        num_inference_steps=args.num_inference_steps,
+        scribble_confidence=args.scribble_conf,
+        prompt_confidence=args.prompt_conf,
+    ).export(args.output_path)
+    print("Mesh saved to", args.output_path)

scripts/inference_vae.py

@@ -0,0 +1,61 @@
+import argparse
+import numpy as np
+import torch
+import trimesh
+
+from triposg.inference_utils import hierarchical_extract_geometry
+from triposg.models.autoencoders import TripoSGVAEModel
+from huggingface_hub import snapshot_download
+
+
+
+def load_surface(data_path, num_pc=204800):
+    data = np.load(data_path, allow_pickle=True).tolist()
+    surface = data["surface_points"]  # Nx3
+    normal = data["surface_normals"]  # Nx3
+
+    rng = np.random.default_rng()
+    ind = rng.choice(surface.shape[0], num_pc, replace=False)
+    surface = torch.FloatTensor(surface[ind])
+    normal = torch.FloatTensor(normal[ind])
+    surface = torch.cat([surface, normal], dim=-1).unsqueeze(0).cuda()
+
+    return surface
+
+
+if __name__ == "__main__":
+    device = "cuda"
+    dtype = torch.float16
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--surface-input", type=str, required=True)
+    args = parser.parse_args()
+
+    # download pretrained weights
+    triposg_weights_dir = "pretrained_weights/TripoSG"
+    snapshot_download(repo_id="VAST-AI/TripoSG", local_dir=triposg_weights_dir)
+
+    vae: TripoSGVAEModel = TripoSGVAEModel.from_pretrained(
+        triposg_weights_dir,
+        subfolder="vae",
+    ).to(device, dtype=dtype)
+
+    # load surface from sdf and encode
+    surface = load_surface(
+        args.surface_input, num_pc=204800
+    ).to(device, dtype=dtype)
+    sample = vae.encode(surface).latent_dist.sample()
+    
+    # vae infer 
+    with torch.no_grad():
+        geometric_func = lambda x: vae.decode(sample, sampled_points=x).sample
+        output = hierarchical_extract_geometry(
+            geometric_func,
+            device,
+            bounds=(-1.005, -1.005, -1.005, 1.005, 1.005, 1.005),
+            dense_octree_depth=8,
+            hierarchical_octree_depth=9,
+        )
+        meshes = [trimesh.Trimesh(mesh_v_f[0].astype(np.float32), mesh_v_f[1]) for mesh_v_f in output]
+
+    meshes[0].export("test_vae.glb")
+

triposg/LICENSE

@@ -0,0 +1,80 @@
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+ 
+EXHIBIT A
+ACCEPTABLE USE POLICY
+
+Tencent reserves the right to update this Acceptable Use Policy from time to time.
+Last modified: November 5, 2024
+
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triposg/diso/diff_dmc.py

@@ -0,0 +1,15 @@
+
+import torch
+import torch.nn as nn
+
+class DiffDMC(nn.Module):
+    def __init__(self, latent_dim=256):
+        super().__init__()
+        self.linear = nn.Sequential(
+            nn.Linear(latent_dim, latent_dim),
+            nn.ReLU(),
+            nn.Linear(latent_dim, latent_dim)
+        )
+
+    def forward(self, x):
+        return self.linear(x)

triposg/diso/diffusion_utils.py

@@ -0,0 +1,5 @@
+
+# dummy utility functions for diffusion corrections
+
+def post_process_latents(latents):
+    return latents

triposg/inference_utils.py

@@ -0,0 +1,491 @@
+import numpy as np
+import torch
+import torch.nn as nn
+import scipy.ndimage
+from skimage import measure
+import torch.nn.functional as F
+from diso import DiffDMC
+from einops import repeat
+from triposg.utils.typing import *
+
+def generate_dense_grid_points_gpu(bbox_min: torch.Tensor,
+                                   bbox_max: torch.Tensor,
+                                   octree_depth: int,
+                                   indexing: str = "ij"):
+    length = bbox_max - bbox_min
+    num_cells = 2 ** octree_depth
+    device = bbox_min.device
+    
+    x = torch.linspace(bbox_min[0], bbox_max[0], int(num_cells), dtype=torch.float16, device=device)
+    y = torch.linspace(bbox_min[1], bbox_max[1], int(num_cells), dtype=torch.float16, device=device)
+    z = torch.linspace(bbox_min[2], bbox_max[2], int(num_cells), dtype=torch.float16, device=device)
+    
+    xs, ys, zs = torch.meshgrid(x, y, z, indexing=indexing)
+    xyz = torch.stack((xs, ys, zs), dim=-1)
+    xyz = xyz.view(-1, 3)
+    grid_size = [int(num_cells), int(num_cells), int(num_cells)]
+
+    return xyz, grid_size, length
+
+def find_mesh_grid_coordinates_fast_gpu(occupancy_grid, n_limits=-1):
+    core_grid = occupancy_grid[1:-1, 1:-1, 1:-1]
+    occupied = core_grid > 0
+
+    neighbors_unoccupied = (
+        (occupancy_grid[:-2, :-2, :-2] < 0)
+        | (occupancy_grid[:-2, :-2, 1:-1] < 0)
+        | (occupancy_grid[:-2, :-2, 2:] < 0)  # x-1, y-1, z-1/0/1
+        | (occupancy_grid[:-2, 1:-1, :-2] < 0)
+        | (occupancy_grid[:-2, 1:-1, 1:-1] < 0)
+        | (occupancy_grid[:-2, 1:-1, 2:] < 0)  # x-1, y0, z-1/0/1
+        | (occupancy_grid[:-2, 2:, :-2] < 0)
+        | (occupancy_grid[:-2, 2:, 1:-1] < 0)
+        | (occupancy_grid[:-2, 2:, 2:] < 0)  # x-1, y+1, z-1/0/1
+        | (occupancy_grid[1:-1, :-2, :-2] < 0)
+        | (occupancy_grid[1:-1, :-2, 1:-1] < 0)
+        | (occupancy_grid[1:-1, :-2, 2:] < 0)  # x0, y-1, z-1/0/1
+        | (occupancy_grid[1:-1, 1:-1, :-2] < 0)
+        | (occupancy_grid[1:-1, 1:-1, 2:] < 0)  # x0, y0, z-1/1
+        | (occupancy_grid[1:-1, 2:, :-2] < 0)
+        | (occupancy_grid[1:-1, 2:, 1:-1] < 0)
+        | (occupancy_grid[1:-1, 2:, 2:] < 0)  # x0, y+1, z-1/0/1
+        | (occupancy_grid[2:, :-2, :-2] < 0)
+        | (occupancy_grid[2:, :-2, 1:-1] < 0)
+        | (occupancy_grid[2:, :-2, 2:] < 0)  # x+1, y-1, z-1/0/1
+        | (occupancy_grid[2:, 1:-1, :-2] < 0)
+        | (occupancy_grid[2:, 1:-1, 1:-1] < 0)
+        | (occupancy_grid[2:, 1:-1, 2:] < 0)  # x+1, y0, z-1/0/1
+        | (occupancy_grid[2:, 2:, :-2] < 0)
+        | (occupancy_grid[2:, 2:, 1:-1] < 0)
+        | (occupancy_grid[2:, 2:, 2:] < 0)  # x+1, y+1, z-1/0/1
+    )
+    core_mesh_coords = torch.nonzero(occupied & neighbors_unoccupied, as_tuple=False) + 1
+
+    if n_limits != -1 and core_mesh_coords.shape[0] > n_limits:
+        print(f"core mesh coords {core_mesh_coords.shape[0]} is too large, limited to {n_limits}")
+        ind = np.random.choice(core_mesh_coords.shape[0], n_limits, True)
+        core_mesh_coords = core_mesh_coords[ind]
+
+    return core_mesh_coords
+
+def find_candidates_band(occupancy_grid: torch.Tensor, band_threshold: float, n_limits: int = -1) -> torch.Tensor:
+    """
+    Returns the coordinates of all voxels in the occupancy_grid where |value| < band_threshold.
+
+    Args:
+        occupancy_grid (torch.Tensor): A 3D tensor of SDF values.
+        band_threshold (float): The threshold below which |SDF| must be to include the voxel.
+        n_limits (int): Maximum number of points to return (-1 for no limit)
+
+    Returns:
+        torch.Tensor: A 2D tensor of coordinates (N x 3) where each row is [x, y, z].
+    """
+    core_grid = occupancy_grid[1:-1, 1:-1, 1:-1]  
+    # logits to sdf
+    core_grid = torch.sigmoid(core_grid) * 2 - 1  
+    # Create a boolean mask for all cells in the band
+    in_band = torch.abs(core_grid) < band_threshold
+
+    # Get coordinates of all voxels in the band
+    core_mesh_coords = torch.nonzero(in_band, as_tuple=False) + 1
+
+    if n_limits != -1 and core_mesh_coords.shape[0] > n_limits:
+        print(f"core mesh coords {core_mesh_coords.shape[0]} is too large, limited to {n_limits}")
+        ind = np.random.choice(core_mesh_coords.shape[0], n_limits, True)
+        core_mesh_coords = core_mesh_coords[ind]
+
+    return core_mesh_coords 
+
+def expand_edge_region_fast(edge_coords, grid_size):
+    expanded_tensor = torch.zeros(grid_size, grid_size, grid_size, device='cuda', dtype=torch.float16, requires_grad=False)
+    expanded_tensor[edge_coords[:, 0], edge_coords[:, 1], edge_coords[:, 2]] = 1
+    if grid_size < 512:
+        kernel_size = 5
+        pooled_tensor = torch.nn.functional.max_pool3d(expanded_tensor.unsqueeze(0).unsqueeze(0), kernel_size=kernel_size, stride=1, padding=2).squeeze()
+    else:
+        kernel_size = 3
+        pooled_tensor = torch.nn.functional.max_pool3d(expanded_tensor.unsqueeze(0).unsqueeze(0), kernel_size=kernel_size, stride=1, padding=1).squeeze()
+    expanded_coords_low_res = torch.nonzero(pooled_tensor, as_tuple=False).to(torch.int16)
+
+    expanded_coords_high_res = torch.stack([
+        torch.cat((expanded_coords_low_res[:, 0] * 2, expanded_coords_low_res[:, 0] * 2, expanded_coords_low_res[:, 0] * 2, expanded_coords_low_res[:, 0] * 2, expanded_coords_low_res[:, 0] * 2 + 1, expanded_coords_low_res[:, 0] * 2 + 1, expanded_coords_low_res[:, 0] * 2 + 1, expanded_coords_low_res[:, 0] * 2 + 1)),
+        torch.cat((expanded_coords_low_res[:, 1] * 2, expanded_coords_low_res[:, 1] * 2, expanded_coords_low_res[:, 1] * 2+1, expanded_coords_low_res[:, 1] * 2 + 1, expanded_coords_low_res[:, 1] * 2, expanded_coords_low_res[:, 1] * 2, expanded_coords_low_res[:, 1] * 2 + 1, expanded_coords_low_res[:, 1] * 2 + 1)),
+        torch.cat((expanded_coords_low_res[:, 2] * 2, expanded_coords_low_res[:, 2] * 2+1, expanded_coords_low_res[:, 2] * 2, expanded_coords_low_res[:, 2] * 2 + 1, expanded_coords_low_res[:, 2] * 2, expanded_coords_low_res[:, 2] * 2+1, expanded_coords_low_res[:, 2] * 2, expanded_coords_low_res[:, 2] * 2 + 1))
+    ], dim=1)
+
+    return expanded_coords_high_res
+
+def zoom_block(block, scale_factor, order=3):
+    block = block.astype(np.float32)
+    return scipy.ndimage.zoom(block, scale_factor, order=order)
+
+def parallel_zoom(occupancy_grid, scale_factor):
+    result = torch.nn.functional.interpolate(occupancy_grid.unsqueeze(0).unsqueeze(0), scale_factor=scale_factor)
+    return result.squeeze(0).squeeze(0)
+
+
+@torch.no_grad()
+def hierarchical_extract_geometry(geometric_func: Callable,
+                     device: torch.device,
+                     bounds: Union[Tuple[float], List[float], float] = (-1.25, -1.25, -1.25, 1.25, 1.25, 1.25),
+                     dense_octree_depth: int = 8,
+                     hierarchical_octree_depth: int = 9,
+                     ):
+    """
+
+    Args:
+        geometric_func:
+        device:
+        bounds:
+        dense_octree_depth:
+        hierarchical_octree_depth:
+    Returns:
+
+    """
+    if isinstance(bounds, float):
+        bounds = [-bounds, -bounds, -bounds, bounds, bounds, bounds]
+
+    bbox_min = torch.tensor(bounds[0:3]).to(device)
+    bbox_max = torch.tensor(bounds[3:6]).to(device)
+    bbox_size = bbox_max - bbox_min
+
+    xyz_samples, grid_size, length = generate_dense_grid_points_gpu(
+        bbox_min=bbox_min,
+        bbox_max=bbox_max,
+        octree_depth=dense_octree_depth,
+        indexing="ij"
+    )
+    
+    print(f'step 1 query num: {xyz_samples.shape[0]}')
+    grid_logits = geometric_func(xyz_samples.unsqueeze(0)).to(torch.float16).view(grid_size[0], grid_size[1], grid_size[2])
+    # print(f'step 1 grid_logits shape: {grid_logits.shape}')
+    for i in range(hierarchical_octree_depth - dense_octree_depth):
+        curr_octree_depth = dense_octree_depth + i + 1
+        # upsample
+        grid_size = 2**curr_octree_depth
+        normalize_offset = grid_size / 2
+        high_res_occupancy = parallel_zoom(grid_logits, 2)
+
+        band_threshold = 1.0
+        edge_coords = find_candidates_band(grid_logits, band_threshold)
+        expanded_coords = expand_edge_region_fast(edge_coords, grid_size=int(grid_size/2)).to(torch.float16)
+        print(f'step {i+2} query num: {len(expanded_coords)}')
+        expanded_coords_norm = (expanded_coords - normalize_offset) * (abs(bounds[0]) / normalize_offset)
+
+        all_logits = None
+
+        all_logits = geometric_func(expanded_coords_norm.unsqueeze(0)).to(torch.float16)
+        all_logits = torch.cat([expanded_coords_norm, all_logits[0]], dim=1)
+        # print("all logits shape = ", all_logits.shape)
+
+        indices = all_logits[..., :3]
+        indices = indices * (normalize_offset / abs(bounds[0]))  + normalize_offset
+        indices = indices.type(torch.IntTensor)
+        values = all_logits[:, 3]
+        # breakpoint()
+        high_res_occupancy[indices[:, 0], indices[:, 1], indices[:, 2]] = values
+        grid_logits = high_res_occupancy
+        torch.cuda.empty_cache()
+    mesh_v_f = []
+    try:
+        print("final grids shape = ", grid_logits.shape)
+        vertices, faces, normals, _ = measure.marching_cubes(grid_logits.float().cpu().numpy(), 0, method="lewiner")
+        vertices = vertices / (2**hierarchical_octree_depth) * bbox_size.cpu().numpy() + bbox_min.cpu().numpy()
+        mesh_v_f = (vertices.astype(np.float32), np.ascontiguousarray(faces))
+    except Exception as e:
+        print(e)
+        torch.cuda.empty_cache()
+        mesh_v_f = (None, None)
+
+    return [mesh_v_f]
+
+def extract_near_surface_volume_fn(input_tensor: torch.Tensor, alpha: float):
+    """
+    Args:
+        input_tensor: shape [D, D, D], torch.float16
+        alpha: isosurface offset
+    Returns:
+        mask: shape [D, D, D], torch.int32
+    """
+    device = input_tensor.device
+    D = input_tensor.shape[0]
+    signed_val = 0.0
+
+    # add isosurface offset and exclude invalid value
+    val = input_tensor + alpha
+    valid_mask = val > -9000
+
+    # obtain neighbors
+    def get_neighbor(t, shift, axis):
+        if shift == 0:
+            return t.clone()
+
+        pad_dims = [0, 0, 0, 0, 0, 0]  # [x_front，x_back，y_front，y_back，z_front，z_back]
+
+        if axis == 0:  # x axis
+            pad_idx = 0 if shift > 0 else 1
+            pad_dims[pad_idx] = abs(shift)
+        elif axis == 1:  # y axis
+            pad_idx = 2 if shift > 0 else 3
+            pad_dims[pad_idx] = abs(shift)
+        elif axis == 2:  # z axis
+            pad_idx = 4 if shift > 0 else 5
+            pad_dims[pad_idx] = abs(shift)
+
+        # Apply padding with replication at boundaries
+        padded = F.pad(t.unsqueeze(0).unsqueeze(0), pad_dims[::-1], mode='replicate')
+
+        # Create dynamic slicing indices
+        slice_dims = [slice(None)] * 3
+        if axis == 0:  # x axis
+            if shift > 0:
+                slice_dims[0] = slice(shift, None)
+            else:
+                slice_dims[0] = slice(None, shift)
+        elif axis == 1:  # y axis
+            if shift > 0:
+                slice_dims[1] = slice(shift, None)
+            else:
+                slice_dims[1] = slice(None, shift)
+        elif axis == 2:  # z axis
+            if shift > 0:
+                slice_dims[2] = slice(shift, None)
+            else:
+                slice_dims[2] = slice(None, shift)
+
+        # Apply slicing and restore dimensions
+        padded = padded.squeeze(0).squeeze(0)
+        sliced = padded[slice_dims]
+        return sliced
+
+    # Get neighbors in all directions
+    left = get_neighbor(val, 1, axis=0)  # x axis
+    right = get_neighbor(val, -1, axis=0)
+    back = get_neighbor(val, 1, axis=1)  # y axis
+    front = get_neighbor(val, -1, axis=1)
+    down = get_neighbor(val, 1, axis=2)  # z axis
+    up = get_neighbor(val, -1, axis=2)
+
+    # Handle invalid boundary values
+    def safe_where(neighbor):
+        return torch.where(neighbor > -9000, neighbor, val)
+
+    left = safe_where(left)
+    right = safe_where(right)
+    back = safe_where(back)
+    front = safe_where(front)
+    down = safe_where(down)
+    up = safe_where(up)
+
+    # Calculate sign consistency
+    sign = torch.sign(val.to(torch.float32))
+    neighbors_sign = torch.stack([
+        torch.sign(left.to(torch.float32)),
+        torch.sign(right.to(torch.float32)),
+        torch.sign(back.to(torch.float32)),
+        torch.sign(front.to(torch.float32)),
+        torch.sign(down.to(torch.float32)),
+        torch.sign(up.to(torch.float32))
+    ], dim=0)
+
+    # Check if all signs are consistent
+    same_sign = torch.all(neighbors_sign == sign, dim=0)
+
+    # Generate final mask
+    mask = (~same_sign).to(torch.int32)
+    return mask * valid_mask.to(torch.int32)
+
+
+def generate_dense_grid_points_2(
+    bbox_min: np.ndarray,
+    bbox_max: np.ndarray,
+    octree_resolution: int,
+    indexing: str = "ij",
+):
+    length = bbox_max - bbox_min
+    num_cells = octree_resolution
+
+    x = np.linspace(bbox_min[0], bbox_max[0], int(num_cells) + 1, dtype=np.float32)
+    y = np.linspace(bbox_min[1], bbox_max[1], int(num_cells) + 1, dtype=np.float32)
+    z = np.linspace(bbox_min[2], bbox_max[2], int(num_cells) + 1, dtype=np.float32)
+    [xs, ys, zs] = np.meshgrid(x, y, z, indexing=indexing)
+    xyz = np.stack((xs, ys, zs), axis=-1)
+    grid_size = [int(num_cells) + 1, int(num_cells) + 1, int(num_cells) + 1]
+
+    return xyz, grid_size, length
+
+@torch.no_grad()
+def flash_extract_geometry(
+    latents: torch.FloatTensor,
+    vae: Callable,
+    bounds: Union[Tuple[float], List[float], float] = 1.01,
+    num_chunks: int = 10000,
+    mc_level: float = 0.0,
+    octree_depth: int = 8,
+    min_resolution: int = 63,
+    mini_grid_num: int = 4,
+    **kwargs,
+):
+    geo_decoder = vae.decoder
+    device = latents.device
+    dtype = latents.dtype
+    # resolution to depth
+    octree_resolution = 2 ** octree_depth
+    resolutions = []
+    if octree_resolution < min_resolution:
+        resolutions.append(octree_resolution)
+    while octree_resolution >= min_resolution:
+        resolutions.append(octree_resolution)
+        octree_resolution = octree_resolution // 2
+    resolutions.reverse()
+    resolutions[0] = round(resolutions[0] / mini_grid_num) * mini_grid_num - 1
+    for i, resolution in enumerate(resolutions[1:]):
+        resolutions[i + 1] = resolutions[0] * 2 ** (i + 1)
+
+
+    # 1. generate query points
+    if isinstance(bounds, float):
+        bounds = [-bounds, -bounds, -bounds, bounds, bounds, bounds]
+    bbox_min = np.array(bounds[0:3])
+    bbox_max = np.array(bounds[3:6])
+    bbox_size = bbox_max - bbox_min
+
+    xyz_samples, grid_size, length = generate_dense_grid_points_2(
+        bbox_min=bbox_min,
+        bbox_max=bbox_max,
+        octree_resolution=resolutions[0],
+        indexing="ij"
+    )
+
+    dilate = nn.Conv3d(1, 1, 3, padding=1, bias=False, device=device, dtype=dtype)
+    dilate.weight = torch.nn.Parameter(torch.ones(dilate.weight.shape, dtype=dtype, device=device))
+
+    grid_size = np.array(grid_size)
+
+    # 2. latents to 3d volume
+    xyz_samples = torch.from_numpy(xyz_samples).to(device, dtype=dtype)
+    batch_size = latents.shape[0]
+    mini_grid_size = xyz_samples.shape[0] // mini_grid_num
+    xyz_samples = xyz_samples.view(
+        mini_grid_num, mini_grid_size,
+        mini_grid_num, mini_grid_size,
+        mini_grid_num, mini_grid_size, 3
+    ).permute(
+        0, 2, 4, 1, 3, 5, 6
+    ).reshape(
+        -1, mini_grid_size * mini_grid_size * mini_grid_size, 3
+    )
+    batch_logits = []
+    num_batchs = max(num_chunks // xyz_samples.shape[1], 1)
+    for start in range(0, xyz_samples.shape[0], num_batchs):
+        queries = xyz_samples[start: start + num_batchs, :]
+        batch = queries.shape[0]
+        batch_latents = repeat(latents.squeeze(0), "p c -> b p c", b=batch)
+        # geo_decoder.set_topk(True)
+        geo_decoder.set_topk(False)
+        logits = vae.decode(batch_latents, queries).sample
+        batch_logits.append(logits)
+    grid_logits = torch.cat(batch_logits, dim=0).reshape(
+        mini_grid_num, mini_grid_num, mini_grid_num,
+        mini_grid_size, mini_grid_size,
+        mini_grid_size
+    ).permute(0, 3, 1, 4, 2, 5).contiguous().view(
+        (batch_size, grid_size[0], grid_size[1], grid_size[2])
+    )
+
+    for octree_depth_now in resolutions[1:]:
+        grid_size = np.array([octree_depth_now + 1] * 3)
+        resolution = bbox_size / octree_depth_now
+        next_index = torch.zeros(tuple(grid_size), dtype=dtype, device=device)
+        next_logits = torch.full(next_index.shape, -10000., dtype=dtype, device=device)
+        curr_points = extract_near_surface_volume_fn(grid_logits.squeeze(0), mc_level)
+        curr_points += grid_logits.squeeze(0).abs() < 0.95
+
+        if octree_depth_now == resolutions[-1]:
+            expand_num = 0
+        else:
+            expand_num = 1
+        for i in range(expand_num):
+            curr_points = dilate(curr_points.unsqueeze(0).to(dtype)).squeeze(0)
+            curr_points = dilate(curr_points.unsqueeze(0).to(dtype)).squeeze(0)
+        (cidx_x, cidx_y, cidx_z) = torch.where(curr_points > 0)
+
+        next_index[cidx_x * 2, cidx_y * 2, cidx_z * 2] = 1
+        for i in range(2 - expand_num):
+            next_index = dilate(next_index.unsqueeze(0)).squeeze(0)
+        nidx = torch.where(next_index > 0)
+
+        next_points = torch.stack(nidx, dim=1)
+        next_points = (next_points * torch.tensor(resolution, dtype=torch.float32, device=device) +
+                        torch.tensor(bbox_min, dtype=torch.float32, device=device))
+
+        query_grid_num = 6
+        min_val = next_points.min(axis=0).values
+        max_val = next_points.max(axis=0).values
+        vol_queries_index = (next_points - min_val) / (max_val - min_val) * (query_grid_num - 0.001)
+        index = torch.floor(vol_queries_index).long()
+        index = index[..., 0] * (query_grid_num ** 2) + index[..., 1] * query_grid_num + index[..., 2]
+        index = index.sort()
+        next_points = next_points[index.indices].unsqueeze(0).contiguous()
+        unique_values = torch.unique(index.values, return_counts=True)
+        grid_logits = torch.zeros((next_points.shape[1]), dtype=latents.dtype, device=latents.device)
+        input_grid = [[], []]
+        logits_grid_list = []
+        start_num = 0
+        sum_num = 0
+        for grid_index, count in zip(unique_values[0].cpu().tolist(), unique_values[1].cpu().tolist()):
+            if sum_num + count < num_chunks or sum_num == 0:
+                sum_num += count
+                input_grid[0].append(grid_index)
+                input_grid[1].append(count)
+            else:
+                # geo_decoder.set_topk(input_grid)
+                geo_decoder.set_topk(False)
+                logits_grid = vae.decode(latents,next_points[:, start_num:start_num + sum_num]).sample
+                start_num = start_num + sum_num
+                logits_grid_list.append(logits_grid)
+                input_grid = [[grid_index], [count]]
+                sum_num = count
+        if sum_num > 0:
+            # geo_decoder.set_topk(input_grid)
+            geo_decoder.set_topk(False)
+            logits_grid = vae.decode(latents,next_points[:, start_num:start_num + sum_num]).sample
+            logits_grid_list.append(logits_grid)
+        logits_grid = torch.cat(logits_grid_list, dim=1)
+        grid_logits[index.indices] = logits_grid.squeeze(0).squeeze(-1)
+        next_logits[nidx] = grid_logits
+        grid_logits = next_logits.unsqueeze(0)
+    
+    grid_logits[grid_logits == -10000.] = float('nan')
+    torch.cuda.empty_cache()
+    mesh_v_f = []
+    grid_logits = grid_logits[0]
+    try:
+        print("final grids shape = ", grid_logits.shape)
+
+        dmc = DiffDMC(dtype=torch.float32).to(grid_logits.device)
+        sdf = -grid_logits / octree_resolution
+        sdf = sdf.to(torch.float32).contiguous()
+
+        vertices, faces = dmc(sdf)
+        vertices = vertices.detach().cpu().numpy()
+        faces = faces.detach().cpu().numpy()[:, ::-1]
+        vertices = vertices / (2 ** octree_depth) * bbox_size + bbox_min
+        # Центрируем
+        vertices = vertices - vertices.mean(axis=0)
+        # Масштабируем (например, ×100 — сантиметры)
+        target_scale = 1.0
+        max_extent = np.max(np.linalg.norm(vertices, axis=1))
+        scale_factor = target_scale / max_extent
+        vertices = vertices * scale_factor
+
+        mesh_v_f = (vertices.astype(np.float32), np.ascontiguousarray(faces))
+        
+
+
+    except Exception as e:
+        print(e)
+        torch.cuda.empty_cache()
+        mesh_v_f = (None, None)
+
+    return [mesh_v_f]

triposg/models/attention_processor.py

@@ -0,0 +1,420 @@
+from typing import Callable, List, Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+from diffusers.models.attention_processor import Attention
+from diffusers.models.embeddings import apply_rotary_emb
+from diffusers.utils import logging
+from einops import rearrange
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+class FlashTripoSGAttnProcessor2_0:
+    r"""
+    Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). This is
+    used in the Tripo2DiT model. It applies a s normalization layer and rotary embedding on query and key vector.
+    """
+
+    def __init__(self, topk=True):
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError(
+                "AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0."
+            )
+        self.topk = topk
+
+    def qkv(self, attn, q, k, v, attn_mask, dropout_p, is_causal):
+        if k.shape[-2] == 3072:
+            topk = 1024
+        elif k.shape[-2] == 512:
+            topk = 256
+        else:
+            topk = k.shape[-2] // 3
+
+        if self.topk is True:
+            q1 = q[:, :, ::100, :]
+            sim = q1 @ k.transpose(-1, -2)
+            sim = torch.mean(sim, -2)
+            topk_ind = torch.topk(sim, dim=-1, k=topk).indices.squeeze(-2).unsqueeze(-1)
+            topk_ind = topk_ind.expand(-1, -1, -1, v.shape[-1])
+            v0 = torch.gather(v, dim=-2, index=topk_ind)
+            k0 = torch.gather(k, dim=-2, index=topk_ind)
+            out = F.scaled_dot_product_attention(q, k0, v0)
+        elif self.topk is False:
+            out = F.scaled_dot_product_attention(q, k, v)
+        else:
+            idx, counts = self.topk
+            start = 0
+            outs = []
+            for grid_coord, count in zip(idx, counts):
+                end = start + count
+                q_chunk = q[:, :, start:end, :]
+                q1 = q_chunk[:, :, ::50, :]
+                sim = q1 @ k.transpose(-1, -2)
+                sim = torch.mean(sim, -2)
+                topk_ind = torch.topk(sim, dim=-1, k=topk).indices.squeeze(-2).unsqueeze(-1)
+                topk_ind = topk_ind.expand(-1, -1, -1, v.shape[-1])
+                v0 = torch.gather(v, dim=-2, index=topk_ind)
+                k0 = torch.gather(k, dim=-2, index=topk_ind)
+                out = F.scaled_dot_product_attention(q_chunk, k0, v0)
+                outs.append(out)
+                start += count
+            out = torch.cat(outs, dim=-2)
+        self.topk = False
+        return out
+
+    def __call__(
+        self,
+        attn: Attention,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        temb: Optional[torch.Tensor] = None,
+        image_rotary_emb: Optional[torch.Tensor] = None,
+        **kwargs,
+    ) -> torch.Tensor:
+        residual = hidden_states
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(
+                batch_size, channel, height * width
+            ).transpose(1, 2)
+
+        batch_size, sequence_length, _ = (
+            hidden_states.shape
+            if encoder_hidden_states is None
+            else encoder_hidden_states.shape
+        )
+
+        if attention_mask is not None:
+            attention_mask = attn.prepare_attention_mask(
+                attention_mask, sequence_length, batch_size
+            )
+            # scaled_dot_product_attention expects attention_mask shape to be
+            # (batch, heads, source_length, target_length)
+            attention_mask = attention_mask.view(
+                batch_size, attn.heads, -1, attention_mask.shape[-1]
+            )
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(
+                1, 2
+            )
+
+        query = attn.to_q(hidden_states)
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        elif attn.norm_cross:
+            encoder_hidden_states = attn.norm_encoder_hidden_states(
+                encoder_hidden_states
+            )
+
+        key = attn.to_k(encoder_hidden_states)
+        value = attn.to_v(encoder_hidden_states)
+
+        # NOTE that tripo2 split heads first then split qkv or kv, like .view(..., attn.heads, 3, dim)
+        # instead of .view(..., 3, attn.heads, dim). So we need to re-split here.
+        if not attn.is_cross_attention:
+            qkv = torch.cat((query, key, value), dim=-1)
+            split_size = qkv.shape[-1] // attn.heads // 3
+            qkv = qkv.view(batch_size, -1, attn.heads, split_size * 3)
+            query, key, value = torch.split(qkv, split_size, dim=-1)
+        else:
+            kv = torch.cat((key, value), dim=-1)
+            split_size = kv.shape[-1] // attn.heads // 2
+            kv = kv.view(batch_size, -1, attn.heads, split_size * 2)
+            key, value = torch.split(kv, split_size, dim=-1)
+
+        head_dim = key.shape[-1]
+
+        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        if attn.norm_q is not None:
+            query = attn.norm_q(query)
+        if attn.norm_k is not None:
+            key = attn.norm_k(key)
+
+        # Apply RoPE if needed
+        if image_rotary_emb is not None:
+            query = apply_rotary_emb(query, image_rotary_emb)
+            if not attn.is_cross_attention:
+                key = apply_rotary_emb(key, image_rotary_emb)
+
+        # flashvdm topk
+        hidden_states = self.qkv(attn, query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False)   
+
+        hidden_states = hidden_states.transpose(1, 2).reshape(
+            batch_size, -1, attn.heads * head_dim
+        )
+        hidden_states = hidden_states.to(query.dtype)
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(
+                batch_size, channel, height, width
+            )
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states
+
+class TripoSGAttnProcessor2_0:
+    r"""
+    Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). This is
+    used in the TripoSG model. It applies a s normalization layer and rotary embedding on query and key vector.
+    """
+
+    def __init__(self):
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError(
+                "AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0."
+            )
+
+    def __call__(
+        self,
+        attn: Attention,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        temb: Optional[torch.Tensor] = None,
+        image_rotary_emb: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        residual = hidden_states
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(
+                batch_size, channel, height * width
+            ).transpose(1, 2)
+
+        batch_size, sequence_length, _ = (
+            hidden_states.shape
+            if encoder_hidden_states is None
+            else encoder_hidden_states.shape
+        )
+
+        if attention_mask is not None:
+            attention_mask = attn.prepare_attention_mask(
+                attention_mask, sequence_length, batch_size
+            )
+            # scaled_dot_product_attention expects attention_mask shape to be
+            # (batch, heads, source_length, target_length)
+            attention_mask = attention_mask.view(
+                batch_size, attn.heads, -1, attention_mask.shape[-1]
+            )
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(
+                1, 2
+            )
+
+        query = attn.to_q(hidden_states)
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        elif attn.norm_cross:
+            encoder_hidden_states = attn.norm_encoder_hidden_states(
+                encoder_hidden_states
+            )
+
+        key = attn.to_k(encoder_hidden_states)
+        value = attn.to_v(encoder_hidden_states)
+
+        # NOTE that pre-trained models split heads first then split qkv or kv, like .view(..., attn.heads, 3, dim)
+        # instead of .view(..., 3, attn.heads, dim). So we need to re-split here.
+        if not attn.is_cross_attention:
+            qkv = torch.cat((query, key, value), dim=-1)
+            split_size = qkv.shape[-1] // attn.heads // 3
+            qkv = qkv.view(batch_size, -1, attn.heads, split_size * 3)
+            query, key, value = torch.split(qkv, split_size, dim=-1)
+        else:
+            kv = torch.cat((key, value), dim=-1)
+            split_size = kv.shape[-1] // attn.heads // 2
+            kv = kv.view(batch_size, -1, attn.heads, split_size * 2)
+            key, value = torch.split(kv, split_size, dim=-1)
+
+        head_dim = key.shape[-1]
+
+        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        if attn.norm_q is not None:
+            query = attn.norm_q(query)
+        if attn.norm_k is not None:
+            key = attn.norm_k(key)
+
+        # Apply RoPE if needed
+        if image_rotary_emb is not None:
+            query = apply_rotary_emb(query, image_rotary_emb)
+            if not attn.is_cross_attention:
+                key = apply_rotary_emb(key, image_rotary_emb)
+
+        # the output of sdp = (batch, num_heads, seq_len, head_dim)
+        # TODO: add support for attn.scale when we move to Torch 2.1
+        hidden_states = F.scaled_dot_product_attention(
+            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
+        )
+
+        hidden_states = hidden_states.transpose(1, 2).reshape(
+            batch_size, -1, attn.heads * head_dim
+        )
+        hidden_states = hidden_states.to(query.dtype)
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(
+                batch_size, channel, height, width
+            )
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states
+
+
+class FusedTripoSGAttnProcessor2_0:
+    r"""
+    Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0) with fused
+    projection layers. This is used in the HunyuanDiT model. It applies a s normalization layer and rotary embedding on
+    query and key vector.
+    """
+
+    def __init__(self):
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError(
+                "FusedTripoSGAttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0."
+            )
+
+    def __call__(
+        self,
+        attn: Attention,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        temb: Optional[torch.Tensor] = None,
+        image_rotary_emb: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        residual = hidden_states
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(
+                batch_size, channel, height * width
+            ).transpose(1, 2)
+
+        batch_size, sequence_length, _ = (
+            hidden_states.shape
+            if encoder_hidden_states is None
+            else encoder_hidden_states.shape
+        )
+
+        if attention_mask is not None:
+            attention_mask = attn.prepare_attention_mask(
+                attention_mask, sequence_length, batch_size
+            )
+            # scaled_dot_product_attention expects attention_mask shape to be
+            # (batch, heads, source_length, target_length)
+            attention_mask = attention_mask.view(
+                batch_size, attn.heads, -1, attention_mask.shape[-1]
+            )
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(
+                1, 2
+            )
+
+        # NOTE that pre-trained split heads first, then split qkv
+        if encoder_hidden_states is None:
+            qkv = attn.to_qkv(hidden_states)
+            split_size = qkv.shape[-1] // attn.heads // 3
+            qkv = qkv.view(batch_size, -1, attn.heads, split_size * 3)
+            query, key, value = torch.split(qkv, split_size, dim=-1)
+        else:
+            if attn.norm_cross:
+                encoder_hidden_states = attn.norm_encoder_hidden_states(
+                    encoder_hidden_states
+                )
+            query = attn.to_q(hidden_states)
+
+            kv = attn.to_kv(encoder_hidden_states)
+            split_size = kv.shape[-1] // attn.heads // 2
+            kv = kv.view(batch_size, -1, attn.heads, split_size * 2)
+            key, value = torch.split(kv, split_size, dim=-1)
+
+        head_dim = key.shape[-1]
+
+        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        if attn.norm_q is not None:
+            query = attn.norm_q(query)
+        if attn.norm_k is not None:
+            key = attn.norm_k(key)
+
+        # Apply RoPE if needed
+        if image_rotary_emb is not None:
+            query = apply_rotary_emb(query, image_rotary_emb)
+            if not attn.is_cross_attention:
+                key = apply_rotary_emb(key, image_rotary_emb)
+
+        # the output of sdp = (batch, num_heads, seq_len, head_dim)
+        # TODO: add support for attn.scale when we move to Torch 2.1
+        hidden_states = F.scaled_dot_product_attention(
+            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
+        )
+
+        hidden_states = hidden_states.transpose(1, 2).reshape(
+            batch_size, -1, attn.heads * head_dim
+        )
+        hidden_states = hidden_states.to(query.dtype)
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(
+                batch_size, channel, height, width
+            )
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states

triposg/models/autoencoders/__init__.py

@@ -0,0 +1 @@
+from .autoencoder_kl_triposg import TripoSGVAEModel

triposg/models/autoencoders/autoencoder_kl_triposg.py

@@ -0,0 +1,536 @@
+from typing import Dict, List, Optional, Tuple, Union
+
+import numpy as np
+import torch
+import torch.nn as nn
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.models.attention_processor import Attention, AttentionProcessor
+from diffusers.models.autoencoders.vae import DecoderOutput
+from diffusers.models.modeling_outputs import AutoencoderKLOutput
+from diffusers.models.modeling_utils import ModelMixin
+from diffusers.models.normalization import FP32LayerNorm, LayerNorm
+from diffusers.utils import logging
+from diffusers.utils.accelerate_utils import apply_forward_hook
+from einops import repeat
+# from torch_cluster import fps
+from tqdm import tqdm
+
+from ..attention_processor import FusedTripoSGAttnProcessor2_0, TripoSGAttnProcessor2_0, FlashTripoSGAttnProcessor2_0
+from ..embeddings import FrequencyPositionalEmbedding
+from ..transformers.triposg_transformer import DiTBlock
+from .vae import DiagonalGaussianDistribution
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+class TripoSGEncoder(nn.Module):
+    def __init__(
+        self,
+        in_channels: int = 3,
+        dim: int = 512,
+        num_attention_heads: int = 8,
+        num_layers: int = 8,
+    ):
+        super().__init__()
+
+        self.proj_in = nn.Linear(in_channels, dim, bias=True)
+
+        self.blocks = nn.ModuleList(
+            [
+                DiTBlock(
+                    dim=dim,
+                    num_attention_heads=num_attention_heads,
+                    use_self_attention=False,
+                    use_cross_attention=True,
+                    cross_attention_dim=dim,
+                    cross_attention_norm_type="layer_norm",
+                    activation_fn="gelu",
+                    norm_type="fp32_layer_norm",
+                    norm_eps=1e-5,
+                    qk_norm=False,
+                    qkv_bias=False,
+                )  # cross attention
+            ]
+            + [
+                DiTBlock(
+                    dim=dim,
+                    num_attention_heads=num_attention_heads,
+                    use_self_attention=True,
+                    self_attention_norm_type="fp32_layer_norm",
+                    use_cross_attention=False,
+                    activation_fn="gelu",
+                    norm_type="fp32_layer_norm",
+                    norm_eps=1e-5,
+                    qk_norm=False,
+                    qkv_bias=False,
+                )
+                for _ in range(num_layers)  # self attention
+            ]
+        )
+
+        self.norm_out = LayerNorm(dim)
+
+    def forward(self, sample_1: torch.Tensor, sample_2: torch.Tensor):
+        hidden_states = self.proj_in(sample_1)
+        encoder_hidden_states = self.proj_in(sample_2)
+
+        for layer, block in enumerate(self.blocks):
+            if layer == 0:
+                hidden_states = block(
+                    hidden_states, encoder_hidden_states=encoder_hidden_states
+                )
+            else:
+                hidden_states = block(hidden_states)
+
+        hidden_states = self.norm_out(hidden_states)
+
+        return hidden_states
+
+
+class TripoSGDecoder(nn.Module):
+    def __init__(
+        self,
+        in_channels: int = 3,
+        out_channels: int = 1,
+        dim: int = 512,
+        num_attention_heads: int = 8,
+        num_layers: int = 16,
+        grad_type: str = "analytical",
+        grad_interval: float = 0.001,
+    ):
+        super().__init__()
+
+        if grad_type not in ["numerical", "analytical"]:
+            raise ValueError(f"grad_type must be one of ['numerical', 'analytical']")
+        self.grad_type = grad_type
+        self.grad_interval = grad_interval
+
+        self.blocks = nn.ModuleList(
+            [
+                DiTBlock(
+                    dim=dim,
+                    num_attention_heads=num_attention_heads,
+                    use_self_attention=True,
+                    self_attention_norm_type="fp32_layer_norm",
+                    use_cross_attention=False,
+                    activation_fn="gelu",
+                    norm_type="fp32_layer_norm",
+                    norm_eps=1e-5,
+                    qk_norm=False,
+                    qkv_bias=False,
+                )
+                for _ in range(num_layers)  # self attention
+            ]
+            + [
+                DiTBlock(
+                    dim=dim,
+                    num_attention_heads=num_attention_heads,
+                    use_self_attention=False,
+                    use_cross_attention=True,
+                    cross_attention_dim=dim,
+                    cross_attention_norm_type="layer_norm",
+                    activation_fn="gelu",
+                    norm_type="fp32_layer_norm",
+                    norm_eps=1e-5,
+                    qk_norm=False,
+                    qkv_bias=False,
+                )  # cross attention
+            ]
+        )
+
+        self.proj_query = nn.Linear(in_channels, dim, bias=True)
+
+        self.norm_out = LayerNorm(dim)
+        self.proj_out = nn.Linear(dim, out_channels, bias=True)
+
+    def set_topk(self, topk):
+        self.blocks[-1].set_topk(topk)
+
+    def set_flash_processor(self, processor):
+        self.blocks[-1].set_flash_processor(processor)
+
+    def query_geometry(
+        self,
+        model_fn: callable,
+        queries: torch.Tensor,
+        sample: torch.Tensor,
+        grad: bool = False,
+    ):
+        logits = model_fn(queries, sample)
+        if grad:
+            with torch.autocast(device_type="cuda", dtype=torch.float32):
+                if self.grad_type == "numerical":
+                    interval = self.grad_interval
+                    grad_value = []
+                    for offset in [
+                        (interval, 0, 0),
+                        (0, interval, 0),
+                        (0, 0, interval),
+                    ]:
+                        offset_tensor = torch.tensor(offset, device=queries.device)[
+                            None, :
+                        ]
+                        res_p = model_fn(queries + offset_tensor, sample)[..., 0]
+                        res_n = model_fn(queries - offset_tensor, sample)[..., 0]
+                        grad_value.append((res_p - res_n) / (2 * interval))
+                    grad_value = torch.stack(grad_value, dim=-1)
+                else:
+                    queries_d = torch.clone(queries)
+                    queries_d.requires_grad = True
+                    with torch.enable_grad():
+                        res_d = model_fn(queries_d, sample)
+                        grad_value = torch.autograd.grad(
+                            res_d,
+                            [queries_d],
+                            grad_outputs=torch.ones_like(res_d),
+                            create_graph=self.training,
+                        )[0]
+        else:
+            grad_value = None
+
+        return logits, grad_value
+
+    def forward(
+        self,
+        sample: torch.Tensor,
+        queries: torch.Tensor,
+        kv_cache: Optional[torch.Tensor] = None,
+    ):
+        if kv_cache is None:
+            hidden_states = sample
+            for _, block in enumerate(self.blocks[:-1]):
+                hidden_states = block(hidden_states)
+            kv_cache = hidden_states
+
+        # query grid logits by cross attention
+        def query_fn(q, kv):
+            q = self.proj_query(q)
+            l = self.blocks[-1](q, encoder_hidden_states=kv)
+            return self.proj_out(self.norm_out(l))
+
+        logits, grad = self.query_geometry(
+            query_fn, queries, kv_cache, grad=self.training
+        )
+        logits = logits * -1 if not isinstance(logits, Tuple) else logits[0] * -1
+
+        return logits, kv_cache
+
+
+class TripoSGVAEModel(ModelMixin, ConfigMixin):
+    @register_to_config
+    def __init__(
+        self,
+        in_channels: int = 3,  # NOTE xyz instead of feature dim
+        latent_channels: int = 64,
+        num_attention_heads: int = 8,
+        width_encoder: int = 512,
+        width_decoder: int = 1024,
+        num_layers_encoder: int = 8,
+        num_layers_decoder: int = 16,
+        embedding_type: str = "frequency",
+        embed_frequency: int = 8,
+        embed_include_pi: bool = False,
+    ):
+        super().__init__()
+
+        self.out_channels = 1
+
+        if embedding_type == "frequency":
+            self.embedder = FrequencyPositionalEmbedding(
+                num_freqs=embed_frequency,
+                logspace=True,
+                input_dim=in_channels,
+                include_pi=embed_include_pi,
+            )
+        else:
+            raise NotImplementedError(
+                f"Embedding type {embedding_type} is not supported."
+            )
+
+        self.encoder = TripoSGEncoder(
+            in_channels=in_channels + self.embedder.out_dim,
+            dim=width_encoder,
+            num_attention_heads=num_attention_heads,
+            num_layers=num_layers_encoder,
+        )
+        self.decoder = TripoSGDecoder(
+            in_channels=self.embedder.out_dim,
+            out_channels=self.out_channels,
+            dim=width_decoder,
+            num_attention_heads=num_attention_heads,
+            num_layers=num_layers_decoder,
+        )
+
+        self.quant = nn.Linear(width_encoder, latent_channels * 2, bias=True)
+        self.post_quant = nn.Linear(latent_channels, width_decoder, bias=True)
+
+        self.use_slicing = False
+        self.slicing_length = 1
+
+    def set_flash_decoder(self):
+        self.decoder.set_flash_processor(FlashTripoSGAttnProcessor2_0())
+
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.fuse_qkv_projections with FusedAttnProcessor2_0->FusedTripoSGAttnProcessor2_0
+    def fuse_qkv_projections(self):
+        """
+        Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value)
+        are fused. For cross-attention modules, key and value projection matrices are fused.
+
+        <Tip warning={true}>
+
+        This API is 🧪 experimental.
+
+        </Tip>
+        """
+        self.original_attn_processors = None
+
+        for _, attn_processor in self.attn_processors.items():
+            if "Added" in str(attn_processor.__class__.__name__):
+                raise ValueError(
+                    "`fuse_qkv_projections()` is not supported for models having added KV projections."
+                )
+
+        self.original_attn_processors = self.attn_processors
+
+        for module in self.modules():
+            if isinstance(module, Attention):
+                module.fuse_projections(fuse=True)
+
+        self.set_attn_processor(FusedTripoSGAttnProcessor2_0())
+
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.unfuse_qkv_projections
+    def unfuse_qkv_projections(self):
+        """Disables the fused QKV projection if enabled.
+
+        <Tip warning={true}>
+
+        This API is 🧪 experimental.
+
+        </Tip>
+
+        """
+        if self.original_attn_processors is not None:
+            self.set_attn_processor(self.original_attn_processors)
+
+    @property
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
+    def attn_processors(self) -> Dict[str, AttentionProcessor]:
+        r"""
+        Returns:
+            `dict` of attention processors: A dictionary containing all attention processors used in the model with
+            indexed by its weight name.
+        """
+        # set recursively
+        processors = {}
+
+        def fn_recursive_add_processors(
+            name: str,
+            module: torch.nn.Module,
+            processors: Dict[str, AttentionProcessor],
+        ):
+            if hasattr(module, "get_processor"):
+                processors[f"{name}.processor"] = module.get_processor()
+
+            for sub_name, child in module.named_children():
+                fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
+
+            return processors
+
+        for name, module in self.named_children():
+            fn_recursive_add_processors(name, module, processors)
+
+        return processors
+
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
+    def set_attn_processor(
+        self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]
+    ):
+        r"""
+        Sets the attention processor to use to compute attention.
+
+        Parameters:
+            processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
+                The instantiated processor class or a dictionary of processor classes that will be set as the processor
+                for **all** `Attention` layers.
+
+                If `processor` is a dict, the key needs to define the path to the corresponding cross attention
+                processor. This is strongly recommended when setting trainable attention processors.
+
+        """
+        count = len(self.attn_processors.keys())
+
+        if isinstance(processor, dict) and len(processor) != count:
+            raise ValueError(
+                f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
+                f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
+            )
+
+        def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
+            if hasattr(module, "set_processor"):
+                if not isinstance(processor, dict):
+                    module.set_processor(processor)
+                else:
+                    module.set_processor(processor.pop(f"{name}.processor"))
+
+            for sub_name, child in module.named_children():
+                fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
+
+        for name, module in self.named_children():
+            fn_recursive_attn_processor(name, module, processor)
+
+    def set_default_attn_processor(self):
+        """
+        Disables custom attention processors and sets the default attention implementation.
+        """
+        self.set_attn_processor(TripoSGAttnProcessor2_0())
+
+    def enable_slicing(self, slicing_length: int = 1) -> None:
+        r"""
+        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
+        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
+        """
+        self.use_slicing = True
+        self.slicing_length = slicing_length
+
+    def disable_slicing(self) -> None:
+        r"""
+        Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing
+        decoding in one step.
+        """
+        self.use_slicing = False
+
+    def _sample_features(
+        self, x: torch.Tensor, num_tokens: int = 2048, seed: Optional[int] = None
+    ):
+        """
+        Sample points from features of the input point cloud.
+
+        Args:
+            x (torch.Tensor): The input point cloud. shape: (B, N, C)
+            num_tokens (int, optional): The number of points to sample. Defaults to 2048.
+            seed (Optional[int], optional): The random seed. Defaults to None.
+        """
+        rng = np.random.default_rng(seed)
+        indices = rng.choice(
+            x.shape[1], num_tokens * 4, replace=num_tokens * 4 > x.shape[1]
+        )
+        selected_points = x[:, indices]
+
+        batch_size, num_points, num_channels = selected_points.shape
+        flattened_points = selected_points.view(batch_size * num_points, num_channels)
+        batch_indices = (
+            torch.arange(batch_size).to(x.device).repeat_interleave(num_points)
+        )
+
+        # fps sampling
+        sampling_ratio = 1.0 / 4
+        sampled_indices = fps(
+            flattened_points[:, :3],
+            batch_indices,
+            ratio=sampling_ratio,
+            random_start=self.training,
+        )
+        sampled_points = flattened_points[sampled_indices].view(
+            batch_size, -1, num_channels
+        )
+
+        return sampled_points
+
+    def _encode(
+        self, x: torch.Tensor, num_tokens: int = 2048, seed: Optional[int] = None
+    ):
+        position_channels = self.config.in_channels
+        positions, features = x[..., :position_channels], x[..., position_channels:]
+        x_kv = torch.cat([self.embedder(positions), features], dim=-1)
+
+        sampled_x = self._sample_features(x, num_tokens, seed)
+        positions, features = (
+            sampled_x[..., :position_channels],
+            sampled_x[..., position_channels:],
+        )
+        x_q = torch.cat([self.embedder(positions), features], dim=-1)
+
+        x = self.encoder(x_q, x_kv)
+
+        x = self.quant(x)
+
+        return x
+
+    @apply_forward_hook
+    def encode(
+        self, x: torch.Tensor, return_dict: bool = True, **kwargs
+    ) -> Union[AutoencoderKLOutput, Tuple[DiagonalGaussianDistribution]]:
+        """
+        Encode a batch of point features into latents.
+        """
+        if self.use_slicing and x.shape[0] > 1:
+            encoded_slices = [
+                self._encode(x_slice, **kwargs)
+                for x_slice in x.split(self.slicing_length)
+            ]
+            h = torch.cat(encoded_slices)
+        else:
+            h = self._encode(x, **kwargs)
+
+        posterior = DiagonalGaussianDistribution(h, feature_dim=-1)
+
+        if not return_dict:
+            return (posterior,)
+        return AutoencoderKLOutput(latent_dist=posterior)
+
+    def _decode(
+        self,
+        z: torch.Tensor,
+        sampled_points: torch.Tensor,
+        num_chunks: int = 50000,
+        to_cpu: bool = False,
+        return_dict: bool = True,
+    ) -> Union[DecoderOutput, torch.Tensor]:
+        xyz_samples = sampled_points
+
+        z = self.post_quant(z)
+
+        num_points = xyz_samples.shape[1]
+        kv_cache = None
+        dec = []
+
+        for i in range(0, num_points, num_chunks):
+            queries = xyz_samples[:, i : i + num_chunks, :].to(z.device, dtype=z.dtype)
+            queries = self.embedder(queries)
+
+            z_, kv_cache = self.decoder(z, queries, kv_cache)
+            dec.append(z_ if not to_cpu else z_.cpu())
+
+        z = torch.cat(dec, dim=1)
+
+        if not return_dict:
+            return (z,)
+
+        return DecoderOutput(sample=z)
+
+    @apply_forward_hook
+    def decode(
+        self,
+        z: torch.Tensor,
+        sampled_points: torch.Tensor,
+        return_dict: bool = True,
+        **kwargs,
+    ) -> Union[DecoderOutput, torch.Tensor]:
+        if self.use_slicing and z.shape[0] > 1:
+            decoded_slices = [
+                self._decode(z_slice, p_slice, **kwargs).sample
+                for z_slice, p_slice in zip(
+                    z.split(self.slicing_length),
+                    sampled_points.split(self.slicing_length),
+                )
+            ]
+            decoded = torch.cat(decoded_slices)
+        else:
+            decoded = self._decode(z, sampled_points, **kwargs).sample
+
+        if not return_dict:
+            return (decoded,)
+        return DecoderOutput(sample=decoded)
+
+    def forward(self, x: torch.Tensor):
+        pass

triposg/models/autoencoders/vae.py

@@ -0,0 +1,69 @@
+from typing import Optional, Tuple
+
+import numpy as np
+import torch
+from diffusers.utils.torch_utils import randn_tensor
+
+
+class DiagonalGaussianDistribution(object):
+    def __init__(
+        self,
+        parameters: torch.Tensor,
+        deterministic: bool = False,
+        feature_dim: int = 1,
+    ):
+        self.parameters = parameters
+        self.feature_dim = feature_dim
+        self.mean, self.logvar = torch.chunk(parameters, 2, dim=feature_dim)
+        self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
+        self.deterministic = deterministic
+        self.std = torch.exp(0.5 * self.logvar)
+        self.var = torch.exp(self.logvar)
+        if self.deterministic:
+            self.var = self.std = torch.zeros_like(
+                self.mean, device=self.parameters.device, dtype=self.parameters.dtype
+            )
+
+    def sample(self, generator: Optional[torch.Generator] = None) -> torch.Tensor:
+        # make sure sample is on the same device as the parameters and has same dtype
+        sample = randn_tensor(
+            self.mean.shape,
+            generator=generator,
+            device=self.parameters.device,
+            dtype=self.parameters.dtype,
+        )
+        x = self.mean + self.std * sample
+        return x
+
+    def kl(self, other: "DiagonalGaussianDistribution" = None) -> torch.Tensor:
+        if self.deterministic:
+            return torch.Tensor([0.0])
+        else:
+            if other is None:
+                return 0.5 * torch.sum(
+                    torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar,
+                    dim=[1, 2, 3],
+                )
+            else:
+                return 0.5 * torch.sum(
+                    torch.pow(self.mean - other.mean, 2) / other.var
+                    + self.var / other.var
+                    - 1.0
+                    - self.logvar
+                    + other.logvar,
+                    dim=[1, 2, 3],
+                )
+
+    def nll(
+        self, sample: torch.Tensor, dims: Tuple[int, ...] = [1, 2, 3]
+    ) -> torch.Tensor:
+        if self.deterministic:
+            return torch.Tensor([0.0])
+        logtwopi = np.log(2.0 * np.pi)
+        return 0.5 * torch.sum(
+            logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,
+            dim=dims,
+        )
+
+    def mode(self) -> torch.Tensor:
+        return self.mean

triposg/models/embeddings.py

@@ -0,0 +1,96 @@
+import torch
+import torch.nn as nn
+
+
+class FrequencyPositionalEmbedding(nn.Module):
+    """The sin/cosine positional embedding. Given an input tensor `x` of shape [n_batch, ..., c_dim], it converts
+    each feature dimension of `x[..., i]` into:
+        [
+            sin(x[..., i]),
+            sin(f_1*x[..., i]),
+            sin(f_2*x[..., i]),
+            ...
+            sin(f_N * x[..., i]),
+            cos(x[..., i]),
+            cos(f_1*x[..., i]),
+            cos(f_2*x[..., i]),
+            ...
+            cos(f_N * x[..., i]),
+            x[..., i]     # only present if include_input is True.
+        ], here f_i is the frequency.
+
+    Denote the space is [0 / num_freqs, 1 / num_freqs, 2 / num_freqs, 3 / num_freqs, ..., (num_freqs - 1) / num_freqs].
+    If logspace is True, then the frequency f_i is [2^(0 / num_freqs), ..., 2^(i / num_freqs), ...];
+    Otherwise, the frequencies are linearly spaced between [1.0, 2^(num_freqs - 1)].
+
+    Args:
+        num_freqs (int): the number of frequencies, default is 6;
+        logspace (bool): If logspace is True, then the frequency f_i is [..., 2^(i / num_freqs), ...],
+            otherwise, the frequencies are linearly spaced between [1.0, 2^(num_freqs - 1)];
+        input_dim (int): the input dimension, default is 3;
+        include_input (bool): include the input tensor or not, default is True.
+
+    Attributes:
+        frequencies (torch.Tensor): If logspace is True, then the frequency f_i is [..., 2^(i / num_freqs), ...],
+                otherwise, the frequencies are linearly spaced between [1.0, 2^(num_freqs - 1);
+
+        out_dim (int): the embedding size, if include_input is True, it is input_dim * (num_freqs * 2 + 1),
+            otherwise, it is input_dim * num_freqs * 2.
+
+    """
+
+    def __init__(
+        self,
+        num_freqs: int = 6,
+        logspace: bool = True,
+        input_dim: int = 3,
+        include_input: bool = True,
+        include_pi: bool = True,
+    ) -> None:
+        """The initialization"""
+
+        super().__init__()
+
+        if logspace:
+            frequencies = 2.0 ** torch.arange(num_freqs, dtype=torch.float32)
+        else:
+            frequencies = torch.linspace(
+                1.0, 2.0 ** (num_freqs - 1), num_freqs, dtype=torch.float32
+            )
+
+        if include_pi:
+            frequencies *= torch.pi
+
+        self.register_buffer("frequencies", frequencies, persistent=False)
+        self.include_input = include_input
+        self.num_freqs = num_freqs
+
+        self.out_dim = self.get_dims(input_dim)
+
+    def get_dims(self, input_dim):
+        temp = 1 if self.include_input or self.num_freqs == 0 else 0
+        out_dim = input_dim * (self.num_freqs * 2 + temp)
+
+        return out_dim
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Forward process.
+
+        Args:
+            x: tensor of shape [..., dim]
+
+        Returns:
+            embedding: an embedding of `x` of shape [..., dim * (num_freqs * 2 + temp)]
+                where temp is 1 if include_input is True and 0 otherwise.
+        """
+
+        if self.num_freqs > 0:
+            embed = (x[..., None].contiguous() * self.frequencies).view(
+                *x.shape[:-1], -1
+            )
+            if self.include_input:
+                return torch.cat((x, embed.sin(), embed.cos()), dim=-1)
+            else:
+                return torch.cat((embed.sin(), embed.cos()), dim=-1)
+        else:
+            return x

triposg/models/transformers/__init__.py

@@ -0,0 +1,61 @@
+from typing import Callable, Optional
+
+from .triposg_transformer import TripoSGDiTModel
+
+
+def default_set_attn_proc_func(
+    name: str,
+    hidden_size: int,
+    cross_attention_dim: Optional[int],
+    ori_attn_proc: object,
+) -> object:
+    return ori_attn_proc
+
+
+def set_transformer_attn_processor(
+    transformer: TripoSGDiTModel,
+    set_self_attn_proc_func: Callable = default_set_attn_proc_func,
+    set_cross_attn_1_proc_func: Callable = default_set_attn_proc_func,
+    set_cross_attn_2_proc_func: Callable = default_set_attn_proc_func,
+    set_self_attn_module_names: Optional[list[str]] = None,
+    set_cross_attn_1_module_names: Optional[list[str]] = None,
+    set_cross_attn_2_module_names: Optional[list[str]] = None,
+) -> None:
+    do_set_processor = lambda name, module_names: (
+        any([name.startswith(module_name) for module_name in module_names])
+        if module_names is not None
+        else True
+    )  # prefix match
+
+    attn_procs = {}
+    for name, attn_processor in transformer.attn_processors.items():
+        hidden_size = transformer.config.width
+        if name.endswith("attn1.processor"):
+            # self attention
+            attn_procs[name] = (
+                set_self_attn_proc_func(name, hidden_size, None, attn_processor)
+                if do_set_processor(name, set_self_attn_module_names)
+                else attn_processor
+            )
+        elif name.endswith("attn2.processor"):
+            # cross attention
+            cross_attention_dim = transformer.config.cross_attention_dim
+            attn_procs[name] = (
+                set_cross_attn_1_proc_func(
+                    name, hidden_size, cross_attention_dim, attn_processor
+                )
+                if do_set_processor(name, set_cross_attn_1_module_names)
+                else attn_processor
+            )
+        elif name.endswith("attn2_2.processor"):
+            # cross attention 2
+            cross_attention_dim = transformer.config.cross_attention_2_dim
+            attn_procs[name] = (
+                set_cross_attn_2_proc_func(
+                    name, hidden_size, cross_attention_dim, attn_processor
+                )
+                if do_set_processor(name, set_cross_attn_2_module_names)
+                else attn_processor
+            )
+
+    transformer.set_attn_processor(attn_procs)

triposg/models/transformers/modeling_outputs.py

@@ -0,0 +1,8 @@
+from dataclasses import dataclass
+
+import torch
+
+
+@dataclass
+class Transformer1DModelOutput:
+    sample: torch.FloatTensor

triposg/models/transformers/triposg_transformer.py

@@ -0,0 +1,775 @@
+# Copyright (c) 2025 VAST-AI-Research and contributors
+
+# This code is based on Tencent HunyuanDiT (https://huggingface.co/Tencent-Hunyuan/HunyuanDiT),
+# which is licensed under the Tencent Hunyuan Community License Agreement.
+# Portions of this code are copied or adapted from HunyuanDiT.
+# See the original license below:
+
+# ---- Start of Tencent Hunyuan Community License Agreement ----
+
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+# 10.	To generate and/or disseminate malware (including ransomware) or any other content to be used for the purpose of harming electronic systems;
+# 11.	To generate or disseminate personal identifiable information with the purpose of harming others;
+# 12.	To generate or disseminate information (including images, code, posts, articles), and place the information in any public context (including –through the use of bot generated tweets), without expressly and conspicuously identifying that the information and/or content is machine generated;
+# 13.	To impersonate another individual without consent, authorization, or legal right;
+# 14.	To make high-stakes automated decisions in domains that affect an individual’s safety, rights or wellbeing (e.g., law enforcement, migration, medicine/health, management of critical infrastructure, safety components of products, essential services, credit, employment, housing, education, social scoring, or insurance);
+# 15.	In a manner that violates or disrespects the social ethics and moral standards of other countries or regions;
+# 16.	To perform, facilitate, threaten, incite, plan, promote or encourage violent extremism or terrorism;
+# 17.	For any use intended to discriminate against or harm individuals or groups based on protected characteristics or categories, online or offline social behavior or known or predicted personal or personality characteristics;
+# 18.	To intentionally exploit any of the vulnerabilities of a specific group of persons based on their age, social, physical or mental characteristics, in order to materially distort the behavior of a person pertaining to that group in a manner that causes or is likely to cause that person or another person physical or psychological harm;
+# 19.	For military purposes;
+# 20.	To engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or other professional practices.
+
+# ---- End of Tencent Hunyuan Community License Agreement ----
+
+# Please note that the use of this code is subject to the terms and conditions
+# of the Tencent Hunyuan Community License Agreement, including the Acceptable Use Policy.
+
+from typing import Any, Dict, Optional, Tuple, Union
+
+import torch
+import torch.utils.checkpoint
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.loaders import PeftAdapterMixin
+from diffusers.models.attention import FeedForward
+from diffusers.models.attention_processor import Attention, AttentionProcessor
+from diffusers.models.embeddings import (
+    GaussianFourierProjection,
+    TimestepEmbedding,
+    Timesteps,
+)
+from diffusers.models.modeling_utils import ModelMixin
+from diffusers.models.normalization import (
+    AdaLayerNormContinuous,
+    FP32LayerNorm,
+    LayerNorm,
+)
+from diffusers.utils import (
+    USE_PEFT_BACKEND,
+    is_torch_version,
+    logging,
+    scale_lora_layers,
+    unscale_lora_layers,
+)
+from diffusers.utils.torch_utils import maybe_allow_in_graph
+from torch import nn
+
+from ..attention_processor import FusedTripoSGAttnProcessor2_0, TripoSGAttnProcessor2_0
+from .modeling_outputs import Transformer1DModelOutput
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+@maybe_allow_in_graph
+class DiTBlock(nn.Module):
+    r"""
+    Transformer block used in Hunyuan-DiT model (https://github.com/Tencent/HunyuanDiT). Allow skip connection and
+    QKNorm
+
+    Parameters:
+        dim (`int`):
+            The number of channels in the input and output.
+        num_attention_heads (`int`):
+            The number of headsto use for multi-head attention.
+        cross_attention_dim (`int`,*optional*):
+            The size of the encoder_hidden_states vector for cross attention.
+        dropout(`float`, *optional*, defaults to 0.0):
+            The dropout probability to use.
+        activation_fn (`str`,*optional*, defaults to `"geglu"`):
+            Activation function to be used in feed-forward. .
+        norm_elementwise_affine (`bool`, *optional*, defaults to `True`):
+            Whether to use learnable elementwise affine parameters for normalization.
+        norm_eps (`float`, *optional*, defaults to 1e-6):
+            A small constant added to the denominator in normalization layers to prevent division by zero.
+        final_dropout (`bool` *optional*, defaults to False):
+            Whether to apply a final dropout after the last feed-forward layer.
+        ff_inner_dim (`int`, *optional*):
+            The size of the hidden layer in the feed-forward block. Defaults to `None`.
+        ff_bias (`bool`, *optional*, defaults to `True`):
+            Whether to use bias in the feed-forward block.
+        skip (`bool`, *optional*, defaults to `False`):
+            Whether to use skip connection. Defaults to `False` for down-blocks and mid-blocks.
+        qk_norm (`bool`, *optional*, defaults to `True`):
+            Whether to use normalization in QK calculation. Defaults to `True`.
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        num_attention_heads: int,
+        use_self_attention: bool = True,
+        self_attention_norm_type: Optional[str] = None, 
+        use_cross_attention: bool = True, # ada layer norm
+        cross_attention_dim: Optional[int] = None,
+        cross_attention_norm_type: Optional[str] = "fp32_layer_norm",
+        use_cross_attention_2: bool = False,
+        cross_attention_2_dim: Optional[int] = None,
+        cross_attention_2_norm_type: Optional[str] = None,
+        dropout=0.0,
+        activation_fn: str = "gelu",
+        norm_type: str = "fp32_layer_norm",  # TODO
+        norm_elementwise_affine: bool = True,
+        norm_eps: float = 1e-5,
+        final_dropout: bool = False,
+        ff_inner_dim: Optional[int] = None,  # int(dim * 4) if None
+        ff_bias: bool = True,
+        skip: bool = False,
+        skip_concat_front: bool = False,  # [x, skip] or [skip, x]
+        skip_norm_last: bool = False,  # this is an error
+        qk_norm: bool = True,
+        qkv_bias: bool = True,
+    ):
+        super().__init__()
+
+        self.use_self_attention = use_self_attention
+        self.use_cross_attention = use_cross_attention
+        self.use_cross_attention_2 = use_cross_attention_2
+        self.skip_concat_front = skip_concat_front
+        self.skip_norm_last = skip_norm_last
+        # Define 3 blocks. Each block has its own normalization layer.
+        # NOTE: when new version comes, check norm2 and norm 3
+        # 1. Self-Attn
+        if use_self_attention:
+            if (
+                self_attention_norm_type == "fp32_layer_norm"
+                or self_attention_norm_type is None
+            ):
+                self.norm1 = FP32LayerNorm(dim, norm_eps, norm_elementwise_affine)
+            else:
+                raise NotImplementedError
+
+            self.attn1 = Attention(
+                query_dim=dim,
+                cross_attention_dim=None,
+                dim_head=dim // num_attention_heads,
+                heads=num_attention_heads,
+                qk_norm="rms_norm" if qk_norm else None,
+                eps=1e-6,
+                bias=qkv_bias,
+                processor=TripoSGAttnProcessor2_0(),
+            )
+
+        # 2. Cross-Attn
+        if use_cross_attention:
+            assert cross_attention_dim is not None
+
+            self.norm2 = FP32LayerNorm(dim, norm_eps, norm_elementwise_affine)
+
+            self.attn2 = Attention(
+                query_dim=dim,
+                cross_attention_dim=cross_attention_dim,
+                dim_head=dim // num_attention_heads,
+                heads=num_attention_heads,
+                qk_norm="rms_norm" if qk_norm else None,
+                cross_attention_norm=cross_attention_norm_type,
+                eps=1e-6,
+                bias=qkv_bias,
+                processor=TripoSGAttnProcessor2_0(),
+            )
+
+        if use_cross_attention_2:
+            assert cross_attention_2_dim is not None
+
+            self.norm2_2 = FP32LayerNorm(dim, norm_eps, norm_elementwise_affine)
+
+            self.attn2_2 = Attention(
+                query_dim=dim,
+                cross_attention_dim=cross_attention_2_dim,
+                dim_head=dim // num_attention_heads,
+                heads=num_attention_heads,
+                qk_norm="rms_norm" if qk_norm else None,
+                cross_attention_norm=cross_attention_2_norm_type,
+                eps=1e-6,
+                bias=qkv_bias,
+                processor=TripoSGAttnProcessor2_0(),
+            )
+
+        # 3. Feed-forward
+        self.norm3 = FP32LayerNorm(dim, norm_eps, norm_elementwise_affine)
+
+        self.ff = FeedForward(
+            dim,
+            dropout=dropout,  ### 0.0
+            activation_fn=activation_fn,  ### approx GeLU
+            final_dropout=final_dropout,  ### 0.0
+            inner_dim=ff_inner_dim,  ### int(dim * mlp_ratio)
+            bias=ff_bias,
+        )
+
+        # 4. Skip Connection
+        if skip:
+            self.skip_norm = FP32LayerNorm(dim, norm_eps, elementwise_affine=True)
+            self.skip_linear = nn.Linear(2 * dim, dim)
+        else:
+            self.skip_linear = None
+
+        # let chunk size default to None
+        self._chunk_size = None
+        self._chunk_dim = 0
+
+    def set_topk(self, topk):
+        self.flash_processor.topk = topk
+
+    def set_flash_processor(self, flash_processor):
+        self.flash_processor = flash_processor
+        self.attn2.processor = self.flash_processor
+
+    # Copied from diffusers.models.attention.BasicTransformerBlock.set_chunk_feed_forward
+    def set_chunk_feed_forward(self, chunk_size: Optional[int], dim: int = 0):
+        # Sets chunk feed-forward
+        self._chunk_size = chunk_size
+        self._chunk_dim = dim
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        encoder_hidden_states_2: Optional[torch.Tensor] = None,
+        temb: Optional[torch.Tensor] = None,
+        image_rotary_emb: Optional[torch.Tensor] = None,
+        skip: Optional[torch.Tensor] = None,
+        attention_kwargs: Optional[Dict[str, Any]] = None,
+    ) -> torch.Tensor:
+        # Prepare attention kwargs
+        attention_kwargs = attention_kwargs.copy() if attention_kwargs is not None else {}
+        cross_attention_scale = attention_kwargs.pop("cross_attention_scale", 1.0)
+        cross_attention_2_scale = attention_kwargs.pop("cross_attention_2_scale", 1.0)
+
+        # Notice that normalization is always applied before the real computation in the following blocks.
+        # 0. Long Skip Connection
+        if self.skip_linear is not None:
+            cat = torch.cat(
+                (
+                    [skip, hidden_states]
+                    if self.skip_concat_front
+                    else [hidden_states, skip]
+                ),
+                dim=-1,
+            )
+            if self.skip_norm_last:
+                # don't do this
+                hidden_states = self.skip_linear(cat)
+                hidden_states = self.skip_norm(hidden_states)
+            else:
+                cat = self.skip_norm(cat)
+                hidden_states = self.skip_linear(cat)
+
+        # 1. Self-Attention
+        if self.use_self_attention:
+            norm_hidden_states = self.norm1(hidden_states)
+            attn_output = self.attn1(
+                norm_hidden_states,
+                image_rotary_emb=image_rotary_emb,
+                **attention_kwargs,
+            )
+            hidden_states = hidden_states + attn_output
+
+        # 2. Cross-Attention
+        if self.use_cross_attention:
+            if self.use_cross_attention_2:
+                hidden_states = (
+                    hidden_states
+                    + self.attn2(
+                        self.norm2(hidden_states),
+                        encoder_hidden_states=encoder_hidden_states,
+                        image_rotary_emb=image_rotary_emb,
+                        **attention_kwargs,
+                    ) * cross_attention_scale
+                    + self.attn2_2(
+                        self.norm2_2(hidden_states),
+                        encoder_hidden_states=encoder_hidden_states_2,
+                        image_rotary_emb=image_rotary_emb,
+                        **attention_kwargs,
+                    ) * cross_attention_2_scale
+                )
+            else:
+                hidden_states = hidden_states + self.attn2(
+                    self.norm2(hidden_states),
+                    encoder_hidden_states=encoder_hidden_states,
+                    image_rotary_emb=image_rotary_emb,
+                    **attention_kwargs,
+                ) * cross_attention_scale
+
+        # FFN Layer ### TODO: switch norm2 and norm3 in the state dict
+        mlp_inputs = self.norm3(hidden_states)
+        hidden_states = hidden_states + self.ff(mlp_inputs)
+
+        return hidden_states
+
+
+class TripoSGDiTModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
+    """
+    TripoSG: Diffusion model with a Transformer backbone.
+
+    Inherit ModelMixin and ConfigMixin to be compatible with the sampler StableDiffusionPipeline of diffusers.
+
+    Parameters:
+        num_attention_heads (`int`, *optional*, defaults to 16):
+            The number of heads to use for multi-head attention.
+        attention_head_dim (`int`, *optional*, defaults to 88):
+            The number of channels in each head.
+        in_channels (`int`, *optional*):
+            The number of channels in the input and output (specify if the input is **continuous**).
+        patch_size (`int`, *optional*):
+            The size of the patch to use for the input.
+        activation_fn (`str`, *optional*, defaults to `"geglu"`):
+            Activation function to use in feed-forward.
+        sample_size (`int`, *optional*):
+            The width of the latent images. This is fixed during training since it is used to learn a number of
+            position embeddings.
+        dropout (`float`, *optional*, defaults to 0.0):
+            The dropout probability to use.
+        cross_attention_dim (`int`, *optional*):
+            The number of dimension in the clip text embedding.
+        hidden_size (`int`, *optional*):
+            The size of hidden layer in the conditioning embedding layers.
+        num_layers (`int`, *optional*, defaults to 1):
+            The number of layers of Transformer blocks to use.
+        mlp_ratio (`float`, *optional*, defaults to 4.0):
+            The ratio of the hidden layer size to the input size.
+        learn_sigma (`bool`, *optional*, defaults to `True`):
+             Whether to predict variance.
+        cross_attention_dim_t5 (`int`, *optional*):
+            The number dimensions in t5 text embedding.
+        pooled_projection_dim (`int`, *optional*):
+            The size of the pooled projection.
+        text_len (`int`, *optional*):
+            The length of the clip text embedding.
+        text_len_t5 (`int`, *optional*):
+            The length of the T5 text embedding.
+        use_style_cond_and_image_meta_size (`bool`,  *optional*):
+            Whether or not to use style condition and image meta size. True for version <=1.1, False for version >= 1.2
+    """
+
+    _supports_gradient_checkpointing = True
+
+    @register_to_config
+    def __init__(
+        self,
+        num_attention_heads: int = 16,
+        width: int = 2048,
+        in_channels: int = 64,
+        num_layers: int = 21,
+        cross_attention_dim: int = 1024,
+        use_cross_attention_2: bool = False,
+        cross_attention_2_dim: Optional[int] = None
+    ):
+        super().__init__()
+        self.out_channels = in_channels
+        self.num_heads = num_attention_heads
+        self.inner_dim = width
+        self.mlp_ratio = 4.0
+
+        time_embed_dim, timestep_input_dim = self._set_time_proj(
+            "positional",
+            inner_dim=self.inner_dim,
+            flip_sin_to_cos=False,
+            freq_shift=0,
+            time_embedding_dim=None,
+        )
+        self.time_proj = TimestepEmbedding(
+            timestep_input_dim, time_embed_dim, act_fn="gelu", out_dim=self.inner_dim
+        )
+        self.proj_in = nn.Linear(self.config.in_channels, self.inner_dim, bias=True)
+
+        self.blocks = nn.ModuleList(
+            [
+                DiTBlock(
+                    dim=self.inner_dim,
+                    num_attention_heads=self.config.num_attention_heads,
+                    use_self_attention=True,
+                    self_attention_norm_type="fp32_layer_norm",
+                    use_cross_attention=True,
+                    cross_attention_dim=cross_attention_dim,
+                    cross_attention_norm_type=None,
+                    use_cross_attention_2=use_cross_attention_2,
+                    cross_attention_2_dim=cross_attention_2_dim,
+                    cross_attention_2_norm_type=None,
+                    activation_fn="gelu",
+                    norm_type="fp32_layer_norm",  # TODO
+                    norm_eps=1e-5,
+                    ff_inner_dim=int(self.inner_dim * self.mlp_ratio),
+                    skip=layer > num_layers // 2,
+                    skip_concat_front=True,
+                    skip_norm_last=True,  # this is an error
+                    qk_norm=True,  # See http://arxiv.org/abs/2302.05442 for details.
+                    qkv_bias=False,
+                )
+                for layer in range(num_layers)
+            ]
+        )
+
+        self.norm_out = LayerNorm(self.inner_dim)
+        self.proj_out = nn.Linear(self.inner_dim, self.out_channels, bias=True)
+
+        self.gradient_checkpointing = False
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        self.gradient_checkpointing = value
+
+    def _set_time_proj(
+        self,
+        time_embedding_type: str,
+        inner_dim: int,
+        flip_sin_to_cos: bool,
+        freq_shift: float,
+        time_embedding_dim: int,
+    ) -> Tuple[int, int]:
+        if time_embedding_type == "fourier":
+            time_embed_dim = time_embedding_dim or inner_dim * 2
+            if time_embed_dim % 2 != 0:
+                raise ValueError(
+                    f"`time_embed_dim` should be divisible by 2, but is {time_embed_dim}."
+                )
+            self.time_embed = GaussianFourierProjection(
+                time_embed_dim // 2,
+                set_W_to_weight=False,
+                log=False,
+                flip_sin_to_cos=flip_sin_to_cos,
+            )
+            timestep_input_dim = time_embed_dim
+        elif time_embedding_type == "positional":
+            time_embed_dim = time_embedding_dim or inner_dim * 4
+
+            self.time_embed = Timesteps(inner_dim, flip_sin_to_cos, freq_shift)
+            timestep_input_dim = inner_dim
+        else:
+            raise ValueError(
+                f"{time_embedding_type} does not exist. Please make sure to use one of `fourier` or `positional`."
+            )
+
+        return time_embed_dim, timestep_input_dim
+
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.fuse_qkv_projections with FusedAttnProcessor2_0->FusedTripoSGAttnProcessor2_0
+    def fuse_qkv_projections(self):
+        """
+        Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value)
+        are fused. For cross-attention modules, key and value projection matrices are fused.
+
+        <Tip warning={true}>
+
+        This API is 🧪 experimental.
+
+        </Tip>
+        """
+        self.original_attn_processors = None
+
+        for _, attn_processor in self.attn_processors.items():
+            if "Added" in str(attn_processor.__class__.__name__):
+                raise ValueError(
+                    "`fuse_qkv_projections()` is not supported for models having added KV projections."
+                )
+
+        self.original_attn_processors = self.attn_processors
+
+        for module in self.modules():
+            if isinstance(module, Attention):
+                module.fuse_projections(fuse=True)
+
+        self.set_attn_processor(FusedTripoSGAttnProcessor2_0())
+
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.unfuse_qkv_projections
+    def unfuse_qkv_projections(self):
+        """Disables the fused QKV projection if enabled.
+
+        <Tip warning={true}>
+
+        This API is 🧪 experimental.
+
+        </Tip>
+
+        """
+        if self.original_attn_processors is not None:
+            self.set_attn_processor(self.original_attn_processors)
+
+    @property
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
+    def attn_processors(self) -> Dict[str, AttentionProcessor]:
+        r"""
+        Returns:
+            `dict` of attention processors: A dictionary containing all attention processors used in the model with
+            indexed by its weight name.
+        """
+        # set recursively
+        processors = {}
+
+        def fn_recursive_add_processors(
+            name: str,
+            module: torch.nn.Module,
+            processors: Dict[str, AttentionProcessor],
+        ):
+            if hasattr(module, "get_processor"):
+                processors[f"{name}.processor"] = module.get_processor()
+
+            for sub_name, child in module.named_children():
+                fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
+
+            return processors
+
+        for name, module in self.named_children():
+            fn_recursive_add_processors(name, module, processors)
+
+        return processors
+
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
+    def set_attn_processor(
+        self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]
+    ):
+        r"""
+        Sets the attention processor to use to compute attention.
+
+        Parameters:
+            processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
+                The instantiated processor class or a dictionary of processor classes that will be set as the processor
+                for **all** `Attention` layers.
+
+                If `processor` is a dict, the key needs to define the path to the corresponding cross attention
+                processor. This is strongly recommended when setting trainable attention processors.
+
+        """
+        count = len(self.attn_processors.keys())
+
+        if isinstance(processor, dict) and len(processor) != count:
+            raise ValueError(
+                f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
+                f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
+            )
+
+        def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
+            if hasattr(module, "set_processor"):
+                if not isinstance(processor, dict):
+                    module.set_processor(processor)
+                else:
+                    module.set_processor(processor.pop(f"{name}.processor"))
+
+            for sub_name, child in module.named_children():
+                fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
+
+        for name, module in self.named_children():
+            fn_recursive_attn_processor(name, module, processor)
+
+    def set_default_attn_processor(self):
+        """
+        Disables custom attention processors and sets the default attention implementation.
+        """
+        self.set_attn_processor(TripoSGAttnProcessor2_0())
+
+    def forward(
+        self,
+        hidden_states: Optional[torch.Tensor],
+        timestep: Union[int, float, torch.LongTensor],
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        encoder_hidden_states_2: Optional[torch.Tensor] = None,
+        image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        attention_kwargs: Optional[Dict[str, Any]] = None,
+        return_dict: bool = True,
+    ):
+        """
+        The [`HunyuanDiT2DModel`] forward method.
+
+        Args:
+        hidden_states (`torch.Tensor` of shape `(batch size, dim, height, width)`):
+            The input tensor.
+        timestep ( `torch.LongTensor`, *optional*):
+            Used to indicate denoising step.
+        encoder_hidden_states ( `torch.Tensor` of shape `(batch size, sequence len, embed dims)`, *optional*):
+            Conditional embeddings for cross attention layer.
+        return_dict: bool
+            Whether to return a dictionary.
+        """
+
+        if attention_kwargs is not None:
+            attention_kwargs = attention_kwargs.copy()
+            lora_scale = attention_kwargs.pop("scale", 1.0)
+        else:
+            lora_scale = 1.0
+
+        if USE_PEFT_BACKEND:
+            # weight the lora layers by setting `lora_scale` for each PEFT layer
+            scale_lora_layers(self, lora_scale)
+        else:
+            if (
+                attention_kwargs is not None
+                and attention_kwargs.get("scale", None) is not None
+            ):
+                logger.warning(
+                    "Passing `scale` via `attention_kwargs` when not using the PEFT backend is ineffective."
+                )
+
+        _, N, _ = hidden_states.shape
+
+        temb = self.time_embed(timestep).to(hidden_states.dtype)
+        temb = self.time_proj(temb)
+        temb = temb.unsqueeze(dim=1)  # unsqueeze to concat with hidden_states
+
+        hidden_states = self.proj_in(hidden_states)
+
+        # N + 1 token
+        hidden_states = torch.cat([temb, hidden_states], dim=1)
+
+        skips = []
+        for layer, block in enumerate(self.blocks):
+            skip = None if layer <= self.config.num_layers // 2 else skips.pop()
+
+            if self.training and self.gradient_checkpointing:
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        return module(*inputs)
+
+                    return custom_forward
+
+                ckpt_kwargs: Dict[str, Any] = (
+                    {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
+                )
+                hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(block),
+                    hidden_states,
+                    encoder_hidden_states,
+                    encoder_hidden_states_2,
+                    temb,
+                    image_rotary_emb,
+                    skip,
+                    attention_kwargs,
+                    **ckpt_kwargs,
+                )
+            else:
+                hidden_states = block(
+                    hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                    encoder_hidden_states_2=encoder_hidden_states_2,
+                    temb=temb,
+                    image_rotary_emb=image_rotary_emb,
+                    skip=skip,
+                    attention_kwargs=attention_kwargs,
+                )  # (N, L, D)
+
+            if layer < self.config.num_layers // 2:
+                skips.append(hidden_states)
+
+        # final layer
+        hidden_states = self.norm_out(hidden_states)
+        hidden_states = hidden_states[:, -N:]
+        hidden_states = self.proj_out(hidden_states)
+
+        if USE_PEFT_BACKEND:
+            # remove `lora_scale` from each PEFT layer
+            unscale_lora_layers(self, lora_scale)
+
+        if not return_dict:
+            return (hidden_states,)
+
+        return Transformer1DModelOutput(sample=hidden_states)
+
+    # Copied from diffusers.models.unets.unet_3d_condition.UNet3DConditionModel.enable_forward_chunking
+    def enable_forward_chunking(
+        self, chunk_size: Optional[int] = None, dim: int = 0
+    ) -> None:
+        """
+        Sets the attention processor to use [feed forward
+        chunking](https://huggingface.co/blog/reformer#2-chunked-feed-forward-layers).
+
+        Parameters:
+            chunk_size (`int`, *optional*):
+                The chunk size of the feed-forward layers. If not specified, will run feed-forward layer individually
+                over each tensor of dim=`dim`.
+            dim (`int`, *optional*, defaults to `0`):
+                The dimension over which the feed-forward computation should be chunked. Choose between dim=0 (batch)
+                or dim=1 (sequence length).
+        """
+        if dim not in [0, 1]:
+            raise ValueError(f"Make sure to set `dim` to either 0 or 1, not {dim}")
+
+        # By default chunk size is 1
+        chunk_size = chunk_size or 1
+
+        def fn_recursive_feed_forward(
+            module: torch.nn.Module, chunk_size: int, dim: int
+        ):
+            if hasattr(module, "set_chunk_feed_forward"):
+                module.set_chunk_feed_forward(chunk_size=chunk_size, dim=dim)
+
+            for child in module.children():
+                fn_recursive_feed_forward(child, chunk_size, dim)
+
+        for module in self.children():
+            fn_recursive_feed_forward(module, chunk_size, dim)
+
+    # Copied from diffusers.models.unets.unet_3d_condition.UNet3DConditionModel.disable_forward_chunking
+    def disable_forward_chunking(self):
+        def fn_recursive_feed_forward(
+            module: torch.nn.Module, chunk_size: int, dim: int
+        ):
+            if hasattr(module, "set_chunk_feed_forward"):
+                module.set_chunk_feed_forward(chunk_size=chunk_size, dim=dim)
+
+            for child in module.children():
+                fn_recursive_feed_forward(child, chunk_size, dim)
+
+        for module in self.children():
+            fn_recursive_feed_forward(module, None, 0)

triposg/pipelines/pipeline_triposg.py

@@ -0,0 +1,324 @@
+import inspect
+import math
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+import numpy as np
+import PIL
+import PIL.Image
+import torch
+import trimesh
+from diffusers.image_processor import PipelineImageInput
+from diffusers.pipelines.pipeline_utils import DiffusionPipeline
+from diffusers.schedulers import FlowMatchEulerDiscreteScheduler  
+from diffusers.utils import logging
+from diffusers.utils.torch_utils import randn_tensor
+from transformers import (
+    BitImageProcessor,
+    Dinov2Model,
+)
+from ..inference_utils import hierarchical_extract_geometry, flash_extract_geometry
+
+from ..models.autoencoders import TripoSGVAEModel
+from ..models.transformers import TripoSGDiTModel
+from .pipeline_triposg_output import TripoSGPipelineOutput
+from .pipeline_utils import TransformerDiffusionMixin
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
+def retrieve_timesteps(
+    scheduler,
+    num_inference_steps: Optional[int] = None,
+    device: Optional[Union[str, torch.device]] = None,
+    timesteps: Optional[List[int]] = None,
+    sigmas: Optional[List[float]] = None,
+    **kwargs,
+):
+    """
+    Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
+    custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
+
+    Args:
+        scheduler (`SchedulerMixin`):
+            The scheduler to get timesteps from.
+        num_inference_steps (`int`):
+            The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`
+            must be `None`.
+        device (`str` or `torch.device`, *optional*):
+            The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
+        timesteps (`List[int]`, *optional*):
+            Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
+            `num_inference_steps` and `sigmas` must be `None`.
+        sigmas (`List[float]`, *optional*):
+            Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
+            `num_inference_steps` and `timesteps` must be `None`.
+
+    Returns:
+        `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
+        second element is the number of inference steps.
+    """
+    if timesteps is not None and sigmas is not None:
+        raise ValueError(
+            "Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values"
+        )
+    if timesteps is not None:
+        accepts_timesteps = "timesteps" in set(
+            inspect.signature(scheduler.set_timesteps).parameters.keys()
+        )
+        if not accepts_timesteps:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" timestep schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    elif sigmas is not None:
+        accept_sigmas = "sigmas" in set(
+            inspect.signature(scheduler.set_timesteps).parameters.keys()
+        )
+        if not accept_sigmas:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" sigmas schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    else:
+        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+    return timesteps, num_inference_steps
+
+
+class TripoSGPipeline(DiffusionPipeline, TransformerDiffusionMixin):
+    """
+    Pipeline for image-to-3D generation.        
+    """
+
+    def __init__(
+        self,   
+        vae: TripoSGVAEModel,
+        transformer: TripoSGDiTModel,
+        scheduler: FlowMatchEulerDiscreteScheduler,
+        image_encoder_dinov2: Dinov2Model,
+        feature_extractor_dinov2: BitImageProcessor,
+    ):
+        super().__init__()
+
+        self.register_modules(
+            vae=vae,
+            transformer=transformer,
+            scheduler=scheduler,
+            image_encoder_dinov2=image_encoder_dinov2,
+            feature_extractor_dinov2=feature_extractor_dinov2,
+        )
+
+    @property
+    def guidance_scale(self):
+        return self._guidance_scale
+
+    @property
+    def do_classifier_free_guidance(self):
+        return self._guidance_scale > 1
+
+    @property
+    def num_timesteps(self):
+        return self._num_timesteps
+
+    @property
+    def attention_kwargs(self):
+        return self._attention_kwargs
+
+    @property
+    def decode_progressive(self):
+        return self._decode_progressive
+
+    def encode_image(self, image, device, num_images_per_prompt):
+        dtype = next(self.image_encoder_dinov2.parameters()).dtype
+
+        if not isinstance(image, torch.Tensor):
+            image = self.feature_extractor_dinov2(image, return_tensors="pt").pixel_values
+
+        image = image.to(device=device, dtype=dtype)
+        image_embeds = self.image_encoder_dinov2(image).last_hidden_state
+        image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
+        uncond_image_embeds = torch.zeros_like(image_embeds)
+
+        return image_embeds, uncond_image_embeds
+
+    def prepare_latents(
+        self,
+        batch_size,
+        num_tokens,
+        num_channels_latents,
+        dtype,
+        device,
+        generator,
+        latents: Optional[torch.Tensor] = None,
+    ):
+        if latents is not None:
+            return latents.to(device=device, dtype=dtype)
+
+        shape = (batch_size, num_tokens, num_channels_latents)
+
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+
+        latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+
+        return latents
+
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        image: PipelineImageInput,
+        num_inference_steps: int = 50,
+        num_tokens: int = 2048,
+        timesteps: List[int] = None,
+        guidance_scale: float = 7.0,
+        num_shapes_per_prompt: int = 1,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        attention_kwargs: Optional[Dict[str, Any]] = None,
+        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
+        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+        bounds: Union[Tuple[float], List[float], float] = (-1.005, -1.005, -1.005, 1.005, 1.005, 1.005),
+        dense_octree_depth: int = 8, 
+        hierarchical_octree_depth: int = 9,
+        flash_octree_depth: int = 9,
+        use_flash_decoder: bool = True,
+        return_dict: bool = True,
+    ):
+        # 1. Define call parameters
+        self._guidance_scale = guidance_scale
+        self._attention_kwargs = attention_kwargs
+
+        # 2. Define call parameters
+        if isinstance(image, PIL.Image.Image):
+            batch_size = 1
+        elif isinstance(image, list):
+            batch_size = len(image)
+        elif isinstance(image, torch.Tensor):
+            batch_size = image.shape[0]
+        else:
+            raise ValueError("Invalid input type for image")
+
+        device = self._execution_device
+
+        # 3. Encode condition
+        image_embeds, negative_image_embeds = self.encode_image(
+            image, device, num_shapes_per_prompt
+        )
+
+        if self.do_classifier_free_guidance:
+            image_embeds = torch.cat([negative_image_embeds, image_embeds], dim=0)
+
+        # 4. Prepare timesteps
+        timesteps, num_inference_steps = retrieve_timesteps(
+            self.scheduler, num_inference_steps, device, timesteps
+        )
+        num_warmup_steps = max(
+            len(timesteps) - num_inference_steps * self.scheduler.order, 0
+        )
+        self._num_timesteps = len(timesteps)
+
+        # 5. Prepare latent variables
+        num_channels_latents = self.transformer.config.in_channels
+        latents = self.prepare_latents(
+            batch_size * num_shapes_per_prompt,
+            num_tokens,
+            num_channels_latents,
+            image_embeds.dtype,
+            device,
+            generator,
+            latents,
+        )
+
+        # 6. Denoising loop
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+
+                # expand the latents if we are doing classifier free guidance
+                latent_model_input = (
+                    torch.cat([latents] * 2)
+                    if self.do_classifier_free_guidance
+                    else latents
+                )
+                # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+                timestep = t.expand(latent_model_input.shape[0])
+
+                noise_pred = self.transformer(
+                    latent_model_input,
+                    timestep,
+                    encoder_hidden_states=image_embeds,
+                    attention_kwargs=attention_kwargs,
+                    return_dict=False,
+                )[0]
+
+                # perform guidance
+                if self.do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_image = noise_pred.chunk(2)
+                    noise_pred = noise_pred_uncond + self.guidance_scale * (
+                        noise_pred_image - noise_pred_uncond
+                    )
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents_dtype = latents.dtype
+                latents = self.scheduler.step(
+                    noise_pred, t, latents, return_dict=False
+                )[0]
+
+                if latents.dtype != latents_dtype:
+                    if torch.backends.mps.is_available():
+                        # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
+                        latents = latents.to(latents_dtype)
+
+                if callback_on_step_end is not None:
+                    callback_kwargs = {}
+                    for k in callback_on_step_end_tensor_inputs:
+                        callback_kwargs[k] = locals()[k]
+                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+
+                    latents = callback_outputs.pop("latents", latents)
+
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or (
+                    (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0
+                ):
+                    progress_bar.update()
+
+
+        # 7. decoder mesh
+        if not use_flash_decoder:
+            geometric_func = lambda x: self.vae.decode(latents, sampled_points=x).sample
+            output = hierarchical_extract_geometry(
+                geometric_func,
+                device,
+                bounds=bounds,
+                dense_octree_depth=dense_octree_depth,
+                hierarchical_octree_depth=hierarchical_octree_depth,
+            )
+        else:
+            self.vae.set_flash_decoder()
+            output = flash_extract_geometry(
+                latents,
+                self.vae,
+                bounds=bounds,
+                octree_depth=flash_octree_depth,
+            )
+        meshes = [trimesh.Trimesh(mesh_v_f[0].astype(np.float32), mesh_v_f[1]) for mesh_v_f in output]
+        
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (output, meshes)
+
+        return TripoSGPipelineOutput(samples=output, meshes=meshes)
+

triposg/pipelines/pipeline_triposg_output.py

@@ -0,0 +1,17 @@
+from dataclasses import dataclass
+from typing import List, Optional, Union
+
+import numpy as np
+import torch
+import trimesh
+from diffusers.utils import BaseOutput
+
+
+@dataclass
+class TripoSGPipelineOutput(BaseOutput):
+    r"""
+    Output class for ShapeDiff pipelines.
+    """
+
+    samples: torch.Tensor
+    meshes: List[trimesh.Trimesh]

triposg/pipelines/pipeline_triposg_scribble.py

@@ -0,0 +1,338 @@
+import inspect
+import math
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+import numpy as np
+import PIL
+import PIL.Image
+import torch
+import trimesh
+from diffusers.image_processor import PipelineImageInput
+from diffusers.pipelines.pipeline_utils import DiffusionPipeline
+from diffusers.schedulers import FlowMatchEulerDiscreteScheduler  # not sure
+from diffusers.utils import logging
+from diffusers.utils.torch_utils import randn_tensor
+from transformers import (
+    BitImageProcessor,
+    CLIPTokenizer,
+    CLIPTextModelWithProjection,
+    Dinov2Model,
+)
+from ..inference_utils import hierarchical_extract_geometry, flash_extract_geometry
+
+from ..models.autoencoders import TripoSGVAEModel
+from ..models.transformers import TripoSGDiTModel
+from .pipeline_triposg_output import TripoSGPipelineOutput
+from .pipeline_utils import TransformerDiffusionMixin
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
+def retrieve_timesteps(
+    scheduler,
+    num_inference_steps: Optional[int] = None,
+    device: Optional[Union[str, torch.device]] = None,
+    timesteps: Optional[List[int]] = None,
+    sigmas: Optional[List[float]] = None,
+    **kwargs,
+):
+    """
+    Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
+    custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
+
+    Args:
+        scheduler (`SchedulerMixin`):
+            The scheduler to get timesteps from.
+        num_inference_steps (`int`):
+            The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`
+            must be `None`.
+        device (`str` or `torch.device`, *optional*):
+            The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
+        timesteps (`List[int]`, *optional*):
+            Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
+            `num_inference_steps` and `sigmas` must be `None`.
+        sigmas (`List[float]`, *optional*):
+            Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
+            `num_inference_steps` and `timesteps` must be `None`.
+
+    Returns:
+        `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
+        second element is the number of inference steps.
+    """
+    if timesteps is not None and sigmas is not None:
+        raise ValueError(
+            "Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values"
+        )
+    if timesteps is not None:
+        accepts_timesteps = "timesteps" in set(
+            inspect.signature(scheduler.set_timesteps).parameters.keys()
+        )
+        if not accepts_timesteps:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" timestep schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    elif sigmas is not None:
+        accept_sigmas = "sigmas" in set(
+            inspect.signature(scheduler.set_timesteps).parameters.keys()
+        )
+        if not accept_sigmas:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" sigmas schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    else:
+        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+    return timesteps, num_inference_steps
+
+
+class TripoSGScribblePipeline(DiffusionPipeline, TransformerDiffusionMixin):
+    """
+    Pipeline for (scribble and text)-to-3D generation.   
+    """
+
+    def __init__(
+        self,
+        vae: TripoSGVAEModel,
+        transformer: TripoSGDiTModel,
+        scheduler: FlowMatchEulerDiscreteScheduler,
+        tokenizer: CLIPTokenizer,
+        text_encoder: CLIPTextModelWithProjection,
+        image_encoder_dinov2: Dinov2Model,
+        feature_extractor_dinov2: BitImageProcessor,
+    ):
+        super().__init__()
+
+        self.register_modules(
+            vae=vae,
+            transformer=transformer,
+            scheduler=scheduler,
+            tokenizer=tokenizer,
+            text_encoder=text_encoder,
+            image_encoder_dinov2=image_encoder_dinov2,
+            feature_extractor_dinov2=feature_extractor_dinov2
+        )
+
+    @property
+    def guidance_scale(self):
+        return self._guidance_scale
+
+    @property
+    def do_classifier_free_guidance(self):
+        return self._guidance_scale > 1
+
+    @property
+    def num_timesteps(self):
+        return self._num_timesteps
+
+    @property
+    def attention_kwargs(self):
+        return self._attention_kwargs
+    
+    def encode_text(self, prompt, device, num_shapes_per_prompt):
+        input_ids = self.tokenizer(
+            [prompt], max_length=self.tokenizer.model_max_length, padding="max_length", truncation=True, return_tensors="pt"
+        )['input_ids'].to(device)
+        text_embeds = self.text_encoder(input_ids).last_hidden_state
+        text_embeds = text_embeds.repeat_interleave(num_shapes_per_prompt, dim=0)
+        uncond_text_embeds = torch.zeros_like(text_embeds)
+        return text_embeds, uncond_text_embeds
+
+    def encode_image(self, image, device, num_shapes_per_prompt):
+        dtype = next(self.image_encoder_dinov2.parameters()).dtype
+
+        if not isinstance(image, torch.Tensor):
+            image = self.feature_extractor_dinov2(image, return_tensors="pt").pixel_values
+
+        image = image.to(device=device, dtype=dtype)
+        image_embeds = self.image_encoder_dinov2(image).last_hidden_state
+        image_embeds = image_embeds.repeat_interleave(num_shapes_per_prompt, dim=0)
+        uncond_image_embeds = torch.zeros_like(image_embeds)
+
+        return image_embeds, uncond_image_embeds
+
+    def prepare_latents(
+        self,
+        batch_size,
+        num_tokens,
+        num_channels_latents,
+        dtype,
+        device,
+        generator,
+        latents: Optional[torch.Tensor] = None,
+    ):
+        if latents is not None:
+            return latents.to(device=device, dtype=dtype)
+        
+        shape = (batch_size, num_tokens, num_channels_latents)
+
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+
+        latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+
+        return latents
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        image: PipelineImageInput,
+        prompt: str,
+        num_tokens: int = 512,
+        num_inference_steps: int = 50,
+        timesteps: List[int] = None,
+        guidance_scale: float = 7.0,
+        num_shapes_per_prompt: int = 1,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        attention_kwargs: Optional[Dict[str, Any]] = None,
+        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
+        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+        bounds: Union[Tuple[float], List[float], float] = (-1.005, -1.005, -1.005, 1.005, 1.005, 1.005),
+        dense_octree_depth: int = 8, 
+        hierarchical_octree_depth: int = 9,
+        flash_octree_depth: int = 9,
+        use_flash_decoder: bool = True,
+        return_dict: bool = True,
+    ):
+        self._guidance_scale = guidance_scale
+        self._attention_kwargs = attention_kwargs
+
+        # 2. Define call parameters
+        if isinstance(image, PIL.Image.Image):
+            batch_size = 1
+        elif isinstance(image, list):
+            batch_size = len(image)
+        elif isinstance(image, torch.Tensor):
+            batch_size = image.shape[0]
+        else:
+            raise ValueError("Invalid input type for image")
+
+        device = self._execution_device
+
+        # 3. Encode condition
+        text_embeds, negative_text_embeds = self.encode_text(
+            prompt, device, num_shapes_per_prompt
+        )
+
+        image_embeds, negative_image_embeds = self.encode_image(
+            image, device, num_shapes_per_prompt
+        )        
+
+        if self.do_classifier_free_guidance:
+            text_embeds = torch.cat([negative_text_embeds, text_embeds], dim=0)
+            image_embeds = torch.cat([negative_image_embeds, image_embeds], dim=0)
+
+        # 4. Prepare timesteps
+        timesteps, num_inference_steps = retrieve_timesteps(
+            self.scheduler, num_inference_steps, device, timesteps
+        )
+        num_warmup_steps = max(
+            len(timesteps) - num_inference_steps * self.scheduler.order, 0
+        )
+        self._num_timesteps = len(timesteps)
+
+        # 5. Prepare latent variables
+        num_channels_latents = self.transformer.config.in_channels
+        latents = self.prepare_latents(
+            batch_size * num_shapes_per_prompt,
+            num_tokens,
+            num_channels_latents,
+            image_embeds.dtype,
+            device,
+            generator,
+            latents,
+        )
+
+        # 6. Denoising loop
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+
+                # expand the latents if we are doing classifier free guidance
+                latent_model_input = (
+                    torch.cat([latents] * 2)
+                    if self.do_classifier_free_guidance
+                    else latents
+                )
+                # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+                timestep = t.expand(latent_model_input.shape[0])
+
+                noise_pred = self.transformer(
+                    latent_model_input,
+                    timestep.to(dtype=latents.dtype),
+                    encoder_hidden_states=text_embeds,
+                    encoder_hidden_states_2=image_embeds,
+                    attention_kwargs=attention_kwargs,
+                    return_dict=False,
+                )[0]
+
+                # perform guidance
+                if self.do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_image = noise_pred.chunk(2)
+                    noise_pred = noise_pred_uncond + self.guidance_scale * (
+                        noise_pred_image - noise_pred_uncond
+                    )
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents_dtype = latents.dtype
+                latents = self.scheduler.step(
+                    noise_pred, t, latents, return_dict=False
+                )[0]
+
+                if latents.dtype != latents_dtype:
+                    if torch.backends.mps.is_available():
+                        # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
+                        latents = latents.to(latents_dtype)
+
+                if callback_on_step_end is not None:
+                    callback_kwargs = {}
+                    for k in callback_on_step_end_tensor_inputs:
+                        callback_kwargs[k] = locals()[k]
+                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+
+                    latents = callback_outputs.pop("latents", latents)
+
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or (
+                    (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0
+                ):
+                    progress_bar.update()
+
+        # 7. decoder mesh
+        if not use_flash_decoder:
+            geometric_func = lambda x: self.vae.decode(latents, sampled_points=x).sample
+            output = hierarchical_extract_geometry(
+                geometric_func,
+                device,
+                bounds=bounds,
+                dense_octree_depth=dense_octree_depth,
+                hierarchical_octree_depth=hierarchical_octree_depth,
+            )
+        else:
+            self.vae.set_flash_decoder()
+            output = flash_extract_geometry(
+                latents,
+                self.vae,
+                bounds=bounds,
+                octree_depth=flash_octree_depth,
+            )
+        meshes = [trimesh.Trimesh(mesh_v_f[0].astype(np.float32), mesh_v_f[1]) for mesh_v_f in output]
+        
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (output, meshes)
+
+        return TripoSGPipelineOutput(samples=output, meshes=meshes)

triposg/pipelines/pipeline_utils.py

@@ -0,0 +1,96 @@
+from diffusers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+
+class TransformerDiffusionMixin:
+    r"""
+    Helper for DiffusionPipeline with vae and transformer.(mainly for DIT)
+    """
+
+    def enable_vae_slicing(self):
+        r"""
+        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
+        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
+        """
+        self.vae.enable_slicing()
+
+    def disable_vae_slicing(self):
+        r"""
+        Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
+        computing decoding in one step.
+        """
+        self.vae.disable_slicing()
+
+    def enable_vae_tiling(self):
+        r"""
+        Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
+        compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
+        processing larger images.
+        """
+        self.vae.enable_tiling()
+
+    def disable_vae_tiling(self):
+        r"""
+        Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
+        computing decoding in one step.
+        """
+        self.vae.disable_tiling()
+
+    def fuse_qkv_projections(self, transformer: bool = True, vae: bool = True):
+        """
+        Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value)
+        are fused. For cross-attention modules, key and value projection matrices are fused.
+
+        <Tip warning={true}>
+
+        This API is 🧪 experimental.
+
+        </Tip>
+
+        Args:
+            transformer (`bool`, defaults to `True`): To apply fusion on the Transformer.
+            vae (`bool`, defaults to `True`): To apply fusion on the VAE.
+        """
+        self.fusing_transformer = False
+        self.fusing_vae = False
+
+        if transformer:
+            self.fusing_transformer = True
+            self.transformer.fuse_qkv_projections()
+
+        if vae:
+            self.fusing_vae = True
+            self.vae.fuse_qkv_projections()
+
+    def unfuse_qkv_projections(self, transformer: bool = True, vae: bool = True):
+        """Disable QKV projection fusion if enabled.
+
+        <Tip warning={true}>
+
+        This API is 🧪 experimental.
+
+        </Tip>
+
+        Args:
+            transformer (`bool`, defaults to `True`): To apply fusion on the Transformer.
+            vae (`bool`, defaults to `True`): To apply fusion on the VAE.
+
+        """
+        if transformer:
+            if not self.fusing_transformer:
+                logger.warning(
+                    "The UNet was not initially fused for QKV projections. Doing nothing."
+                )
+            else:
+                self.transformer.unfuse_qkv_projections()
+                self.fusing_transformer = False
+
+        if vae:
+            if not self.fusing_vae:
+                logger.warning(
+                    "The VAE was not initially fused for QKV projections. Doing nothing."
+                )
+            else:
+                self.vae.unfuse_qkv_projections()
+                self.fusing_vae = False

triposg/schedulers/__init__.py

@@ -0,0 +1,5 @@
+from .scheduling_rectified_flow import (
+    RectifiedFlowScheduler,
+    compute_density_for_timestep_sampling,
+    compute_loss_weighting,
+)

triposg/schedulers/scheduling_rectified_flow.py

@@ -0,0 +1,327 @@
+"""
+Adapted from https://github.com/huggingface/diffusers/blob/v0.30.3/src/diffusers/schedulers/scheduling_flow_match_euler_discrete.py.
+"""
+
+import math
+from dataclasses import dataclass
+from typing import List, Optional, Tuple, Union
+
+import numpy as np
+import torch
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.schedulers.scheduling_utils import SchedulerMixin
+from diffusers.utils import BaseOutput, logging
+from torch.distributions import LogisticNormal
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+# TODO: may move to training_utils.py
+def compute_density_for_timestep_sampling(
+    weighting_scheme: str,
+    batch_size: int,
+    logit_mean: float = 0.0,
+    logit_std: float = 1.0,
+    mode_scale: float = None,
+):
+    if weighting_scheme == "logit_normal":
+        # See 3.1 in the SD3 paper ($rf/lognorm(0.00,1.00)$).
+        u = torch.normal(
+            mean=logit_mean, std=logit_std, size=(batch_size,), device="cpu"
+        )
+        u = torch.nn.functional.sigmoid(u)
+    elif weighting_scheme == "logit_normal_dist":
+        u = (
+            LogisticNormal(loc=logit_mean, scale=logit_std)
+            .sample((batch_size,))[:, 0]
+            .to("cpu")
+        )
+    elif weighting_scheme == "mode":
+        u = torch.rand(size=(batch_size,), device="cpu")
+        u = 1 - u - mode_scale * (torch.cos(math.pi * u / 2) ** 2 - 1 + u)
+    else:
+        u = torch.rand(size=(batch_size,), device="cpu")
+    return u
+
+
+def compute_loss_weighting(weighting_scheme: str, sigmas=None):
+    """
+    Computes loss weighting scheme for SD3 training.
+
+    Courtesy: This was contributed by Rafie Walker in https://github.com/huggingface/diffusers/pull/8528.
+
+    SD3 paper reference: https://arxiv.org/abs/2403.03206v1.
+    """
+    if weighting_scheme == "sigma_sqrt":
+        weighting = (sigmas**-2.0).float()
+    elif weighting_scheme == "cosmap":
+        bot = 1 - 2 * sigmas + 2 * sigmas**2
+        weighting = 2 / (math.pi * bot)
+    else:
+        weighting = torch.ones_like(sigmas)
+    return weighting
+
+
+@dataclass
+class RectifiedFlowSchedulerOutput(BaseOutput):
+    """
+    Output class for the scheduler's `step` function output.
+
+    Args:
+        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
+            Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the
+            denoising loop.
+    """
+
+    prev_sample: torch.FloatTensor
+
+
+class RectifiedFlowScheduler(SchedulerMixin, ConfigMixin):
+    """
+    The rectified flow scheduler is a scheduler that is used to propagate the diffusion process in the rectified flow.
+
+    This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
+    methods the library implements for all schedulers such as loading and saving.
+
+    Args:
+        num_train_timesteps (`int`, defaults to 1000):
+            The number of diffusion steps to train the model.
+        timestep_spacing (`str`, defaults to `"linspace"`):
+            The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and
+            Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information.
+        shift (`float`, defaults to 1.0):
+            The shift value for the timestep schedule.
+    """
+
+    _compatibles = []
+    order = 1
+
+    @register_to_config
+    def __init__(
+        self,
+        num_train_timesteps: int = 1000,
+        shift: float = 1.0,
+        use_dynamic_shifting: bool = False,
+    ):
+        # pre-compute timesteps and sigmas; no use in fact
+        # NOTE that shape diffusion sample timesteps randomly or in a distribution,
+        # instead of sampling from the pre-defined linspace
+        timesteps = np.array(
+            [
+                (1.0 - i / num_train_timesteps) * num_train_timesteps
+                for i in range(num_train_timesteps)
+            ]
+        )
+        timesteps = torch.from_numpy(timesteps).to(dtype=torch.float32)
+
+        sigmas = timesteps / num_train_timesteps
+        if not use_dynamic_shifting:
+            # when use_dynamic_shifting is True, we apply the timestep shifting on the fly based on the image resolution
+            sigmas = self.time_shift(sigmas)
+
+        self.timesteps = sigmas * num_train_timesteps
+
+        self._step_index = None
+        self._begin_index = None
+
+        self.sigmas = sigmas.to("cpu")  # to avoid too much CPU/GPU communication
+
+    @property
+    def step_index(self):
+        """
+        The index counter for current timestep. It will increase 1 after each scheduler step.
+        """
+        return self._step_index
+
+    @property
+    def begin_index(self):
+        """
+        The index for the first timestep. It should be set from pipeline with `set_begin_index` method.
+        """
+        return self._begin_index
+
+    # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.set_begin_index
+    def set_begin_index(self, begin_index: int = 0):
+        """
+        Sets the begin index for the scheduler. This function should be run from pipeline before the inference.
+
+        Args:
+            begin_index (`int`):
+                The begin index for the scheduler.
+        """
+        self._begin_index = begin_index
+
+    def _sigma_to_t(self, sigma):
+        return sigma * self.config.num_train_timesteps
+
+    def _t_to_sigma(self, timestep):
+        return timestep / self.config.num_train_timesteps
+
+    def time_shift_dynamic(self, mu: float, sigma: float, t: torch.Tensor):
+        return math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma)
+
+    def time_shift(self, t: torch.Tensor):
+        return self.config.shift * t / (1 + (self.config.shift - 1) * t)
+
+    def set_timesteps(
+        self,
+        num_inference_steps: int = None,
+        device: Union[str, torch.device] = None,
+        sigmas: Optional[List[float]] = None,
+        mu: Optional[float] = None,
+    ):
+        """
+        Sets the discrete timesteps used for the diffusion chain (to be run before inference).
+
+        Args:
+            num_inference_steps (`int`):
+                The number of diffusion steps used when generating samples with a pre-trained model.
+            device (`str` or `torch.device`, *optional*):
+                The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
+        """
+
+        if self.config.use_dynamic_shifting and mu is None:
+            raise ValueError(
+                " you have to pass a value for `mu` when `use_dynamic_shifting` is set to be `True`"
+            )
+
+        if sigmas is None:
+            self.num_inference_steps = num_inference_steps
+            timesteps = np.array(
+                [
+                    (1.0 - i / num_inference_steps) * self.config.num_train_timesteps
+                    for i in range(num_inference_steps)
+                ]
+            )  # different from the original code in SD3
+            sigmas = timesteps / self.config.num_train_timesteps
+
+        if self.config.use_dynamic_shifting:
+            sigmas = self.time_shift_dynamic(mu, 1.0, sigmas)
+        else:
+            sigmas = self.config.shift * sigmas / (1 + (self.config.shift - 1) * sigmas)
+
+        sigmas = torch.from_numpy(sigmas).to(dtype=torch.float32, device=device)
+        timesteps = sigmas * self.config.num_train_timesteps
+
+        self.timesteps = timesteps.to(device=device)
+        self.sigmas = torch.cat([sigmas, torch.zeros(1, device=sigmas.device)])
+
+        self._step_index = None
+        self._begin_index = None
+
+    def index_for_timestep(self, timestep, schedule_timesteps=None):
+        if schedule_timesteps is None:
+            schedule_timesteps = self.timesteps
+
+        indices = (schedule_timesteps == timestep).nonzero()
+
+        # The sigma index that is taken for the **very** first `step`
+        # is always the second index (or the last index if there is only 1)
+        # This way we can ensure we don't accidentally skip a sigma in
+        # case we start in the middle of the denoising schedule (e.g. for image-to-image)
+        pos = 1 if len(indices) > 1 else 0
+
+        return indices[pos].item()
+
+    def _init_step_index(self, timestep):
+        if self.begin_index is None:
+            if isinstance(timestep, torch.Tensor):
+                timestep = timestep.to(self.timesteps.device)
+            self._step_index = self.index_for_timestep(timestep)
+        else:
+            self._step_index = self._begin_index
+
+    def step(
+        self,
+        model_output: torch.FloatTensor,
+        timestep: Union[float, torch.FloatTensor],
+        sample: torch.FloatTensor,
+        s_churn: float = 0.0,
+        s_tmin: float = 0.0,
+        s_tmax: float = float("inf"),
+        s_noise: float = 1.0,
+        generator: Optional[torch.Generator] = None,
+        return_dict: bool = True,
+    ) -> Union[RectifiedFlowSchedulerOutput, Tuple]:
+        """
+        Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion
+        process from the learned model outputs (most often the predicted noise).
+
+        Args:
+            model_output (`torch.FloatTensor`):
+                The direct output from learned diffusion model.
+            timestep (`float`):
+                The current discrete timestep in the diffusion chain.
+            sample (`torch.FloatTensor`):
+                A current instance of a sample created by the diffusion process.
+            s_churn (`float`):
+            s_tmin  (`float`):
+            s_tmax  (`float`):
+            s_noise (`float`, defaults to 1.0):
+                Scaling factor for noise added to the sample.
+            generator (`torch.Generator`, *optional*):
+                A random number generator.
+            return_dict (`bool`):
+                Whether or not to return a [`~schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput`] or
+                tuple.
+
+        Returns:
+            [`~schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput`] or `tuple`:
+                If return_dict is `True`, [`~schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput`] is
+                returned, otherwise a tuple is returned where the first element is the sample tensor.
+        """
+
+        if (
+            isinstance(timestep, int)
+            or isinstance(timestep, torch.IntTensor)
+            or isinstance(timestep, torch.LongTensor)
+        ):
+            raise ValueError(
+                (
+                    "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
+                    " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
+                    " one of the `scheduler.timesteps` as a timestep."
+                ),
+            )
+
+        if self.step_index is None:
+            self._init_step_index(timestep)
+
+        # Upcast to avoid precision issues when computing prev_sample
+        sample = sample.to(torch.float32)
+
+        sigma = self.sigmas[self.step_index]
+        sigma_next = self.sigmas[self.step_index + 1]
+
+        # Here different directions are used for the flow matching
+        prev_sample = sample + (sigma - sigma_next) * model_output
+
+        # Cast sample back to model compatible dtype
+        prev_sample = prev_sample.to(model_output.dtype)
+
+        # upon completion increase step index by one
+        self._step_index += 1
+
+        if not return_dict:
+            return (prev_sample,)
+
+        return RectifiedFlowSchedulerOutput(prev_sample=prev_sample)
+
+    def scale_noise(
+        self,
+        original_samples: torch.Tensor,
+        noise: torch.Tensor,
+        timesteps: torch.IntTensor,
+    ) -> torch.Tensor:
+        """
+        Forward function for the noise scaling in the flow matching.
+        """
+        sigmas = self._t_to_sigma(timesteps.to(dtype=torch.float32))
+
+        while len(sigmas.shape) < len(original_samples.shape):
+            sigmas = sigmas.unsqueeze(-1)
+
+        return (1.0 - sigmas) * original_samples + sigmas * noise
+
+    def __len__(self):
+        return self.config.num_train_timesteps