add LGM

lgm/config.json

@@ -0,0 +1,4 @@
+{
+    "_class_name": "LGM",
+    "_diffusers_version": "0.25.0"
+}

lgm/diffusion_pytorch_model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:79e5160e1fc45559515579a7e41ffc22606cf41c3ed8581b09dae9b4ce437099
+size 830126192

lgm/lgm.py

@@ -0,0 +1,808 @@
+import os
+import warnings
+from functools import partial
+from typing import Literal, Tuple
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from diff_gaussian_rasterization import (
+    GaussianRasterizationSettings,
+    GaussianRasterizer,
+)
+from diffusers import ConfigMixin, ModelMixin
+from torch import Tensor, nn
+
+
+def look_at(campos):
+    forward_vector = -campos / np.linalg.norm(campos, axis=-1)
+    up_vector = np.array([0, 1, 0], dtype=np.float32)
+    right_vector = np.cross(up_vector, forward_vector)
+    up_vector = np.cross(forward_vector, right_vector)
+    R = np.stack([right_vector, up_vector, forward_vector], axis=-1)
+    return R
+
+
+def orbit_camera(elevation, azimuth, radius=1):
+    elevation = np.deg2rad(elevation)
+    azimuth = np.deg2rad(azimuth)
+    x = radius * np.cos(elevation) * np.sin(azimuth)
+    y = -radius * np.sin(elevation)
+    z = radius * np.cos(elevation) * np.cos(azimuth)
+    campos = np.array([x, y, z])
+    T = np.eye(4, dtype=np.float32)
+    T[:3, :3] = look_at(campos)
+    T[:3, 3] = campos
+    return T
+
+
+def get_rays(pose, h, w, fovy, opengl=True):
+    x, y = torch.meshgrid(
+        torch.arange(w, device=pose.device),
+        torch.arange(h, device=pose.device),
+        indexing="xy",
+    )
+    x = x.flatten()
+    y = y.flatten()
+
+    cx = w * 0.5
+    cy = h * 0.5
+
+    focal = h * 0.5 / np.tan(0.5 * np.deg2rad(fovy))
+
+    camera_dirs = F.pad(
+        torch.stack(
+            [
+                (x - cx + 0.5) / focal,
+                (y - cy + 0.5) / focal * (-1.0 if opengl else 1.0),
+            ],
+            dim=-1,
+        ),
+        (0, 1),
+        value=(-1.0 if opengl else 1.0),
+    )
+
+    rays_d = camera_dirs @ pose[:3, :3].transpose(0, 1)
+    rays_o = pose[:3, 3].unsqueeze(0).expand_as(rays_d)
+
+    rays_o = rays_o.view(h, w, 3)
+    rays_d = F.normalize(rays_d, dim=-1).view(h, w, 3)
+
+    return rays_o, rays_d
+
+
+class GaussianRenderer:
+    def __init__(self, fovy, output_size):
+        self.output_size = output_size
+
+        self.bg_color = torch.tensor([1, 1, 1], dtype=torch.float32, device="cuda")
+
+        zfar = 2.5
+        znear = 0.1
+        self.tan_half_fov = np.tan(0.5 * np.deg2rad(fovy))
+        self.proj_matrix = torch.zeros(4, 4, dtype=torch.float32)
+        self.proj_matrix[0, 0] = 1 / self.tan_half_fov
+        self.proj_matrix[1, 1] = 1 / self.tan_half_fov
+        self.proj_matrix[2, 2] = (zfar + znear) / (zfar - znear)
+        self.proj_matrix[3, 2] = -(zfar * znear) / (zfar - znear)
+        self.proj_matrix[2, 3] = 1
+
+    def render(
+        self,
+        gaussians,
+        cam_view,
+        cam_view_proj,
+        cam_pos,
+        bg_color=None,
+        scale_modifier=1,
+    ):
+        device = gaussians.device
+        B, V = cam_view.shape[:2]
+
+        images = []
+        alphas = []
+        for b in range(B):
+
+            means3D = gaussians[b, :, 0:3].contiguous().float()
+            opacity = gaussians[b, :, 3:4].contiguous().float()
+            scales = gaussians[b, :, 4:7].contiguous().float()
+            rotations = gaussians[b, :, 7:11].contiguous().float()
+            rgbs = gaussians[b, :, 11:].contiguous().float()
+
+            for v in range(V):
+                view_matrix = cam_view[b, v].float()
+                view_proj_matrix = cam_view_proj[b, v].float()
+                campos = cam_pos[b, v].float()
+
+                raster_settings = GaussianRasterizationSettings(
+                    image_height=self.output_size,
+                    image_width=self.output_size,
+                    tanfovx=self.tan_half_fov,
+                    tanfovy=self.tan_half_fov,
+                    bg=self.bg_color if bg_color is None else bg_color,
+                    scale_modifier=scale_modifier,
+                    viewmatrix=view_matrix,
+                    projmatrix=view_proj_matrix,
+                    sh_degree=0,
+                    campos=campos,
+                    prefiltered=False,
+                    debug=False,
+                )
+
+                rasterizer = GaussianRasterizer(raster_settings=raster_settings)
+
+                rendered_image, _, _, rendered_alpha = rasterizer(
+                    means3D=means3D,
+                    means2D=torch.zeros_like(
+                        means3D, dtype=torch.float32, device=device
+                    ),
+                    shs=None,
+                    colors_precomp=rgbs,
+                    opacities=opacity,
+                    scales=scales,
+                    rotations=rotations,
+                    cov3D_precomp=None,
+                )
+
+                rendered_image = rendered_image.clamp(0, 1)
+
+                images.append(rendered_image)
+                alphas.append(rendered_alpha)
+
+        images = torch.stack(images, dim=0).view(
+            B, V, 3, self.output_size, self.output_size
+        )
+        alphas = torch.stack(alphas, dim=0).view(
+            B, V, 1, self.output_size, self.output_size
+        )
+
+        return {"image": images, "alpha": alphas}
+
+    def save_ply(self, gaussians, path):
+        assert gaussians.shape[0] == 1, "only support batch size 1"
+
+        from plyfile import PlyData, PlyElement
+
+        means3D = gaussians[0, :, 0:3].contiguous().float()
+        opacity = gaussians[0, :, 3:4].contiguous().float()
+        scales = gaussians[0, :, 4:7].contiguous().float()
+        rotations = gaussians[0, :, 7:11].contiguous().float()
+        shs = gaussians[0, :, 11:].unsqueeze(1).contiguous().float()
+
+        mask = opacity.squeeze(-1) >= 0.005
+        means3D = means3D[mask]
+        opacity = opacity[mask]
+        scales = scales[mask]
+        rotations = rotations[mask]
+        shs = shs[mask]
+
+        opacity = opacity.clamp(1e-6, 1 - 1e-6)
+        opacity = torch.log(opacity / (1 - opacity))
+        scales = torch.log(scales + 1e-8)
+        shs = (shs - 0.5) / 0.28209479177387814
+
+        xyzs = means3D.detach().cpu().numpy()
+        f_dc = (
+            shs.detach().transpose(1, 2).flatten(start_dim=1).contiguous().cpu().numpy()
+        )
+        opacities = opacity.detach().cpu().numpy()
+        scales = scales.detach().cpu().numpy()
+        rotations = rotations.detach().cpu().numpy()
+
+        h = ["x", "y", "z"]
+        for i in range(f_dc.shape[1]):
+            h.append("f_dc_{}".format(i))
+        h.append("opacity")
+        for i in range(scales.shape[1]):
+            h.append("scale_{}".format(i))
+        for i in range(rotations.shape[1]):
+            h.append("rot_{}".format(i))
+
+        dtype_full = [(attribute, "f4") for attribute in h]
+
+        elements = np.empty(xyzs.shape[0], dtype=dtype_full)
+        attributes = np.concatenate((xyzs, f_dc, opacities, scales, rotations), axis=1)
+        elements[:] = list(map(tuple, attributes))
+        el = PlyElement.describe(elements, "vertex")
+
+        PlyData([el]).write(path)
+
+
+class LGM(ModelMixin, ConfigMixin):
+    def __init__(self):
+        super().__init__()
+
+        self.input_size = 256
+        self.splat_size = 128
+        self.output_size = 512
+        self.radius = 1.5
+        self.fovy = 49.1
+
+        self.unet = UNet(
+            9,
+            14,
+            down_channels=(64, 128, 256, 512, 1024, 1024),
+            down_attention=(False, False, False, True, True, True),
+            mid_attention=True,
+            up_channels=(1024, 1024, 512, 256, 128),
+            up_attention=(True, True, True, False, False),
+        )
+
+        self.conv = nn.Conv2d(14, 14, kernel_size=1)
+        self.gs = GaussianRenderer(self.fovy, self.output_size)
+
+        self.pos_act = lambda x: x.clamp(-1, 1)
+        self.scale_act = lambda x: 0.1 * F.softplus(x)
+        self.opacity_act = lambda x: torch.sigmoid(x)
+        self.rot_act = F.normalize
+        self.rgb_act = lambda x: 0.5 * torch.tanh(x) + 0.5
+
+    def prepare_default_rays(self, device, elevation=0):
+        cam_poses = np.stack(
+            [
+                orbit_camera(elevation, 0, radius=self.radius),
+                orbit_camera(elevation, 90, radius=self.radius),
+                orbit_camera(elevation, 180, radius=self.radius),
+                orbit_camera(elevation, 270, radius=self.radius),
+            ],
+            axis=0,
+        )
+        cam_poses = torch.from_numpy(cam_poses)
+
+        rays_embeddings = []
+        for i in range(cam_poses.shape[0]):
+            rays_o, rays_d = get_rays(
+                cam_poses[i], self.input_size, self.input_size, self.fovy
+            )
+            rays_plucker = torch.cat(
+                [torch.cross(rays_o, rays_d, dim=-1), rays_d], dim=-1
+            )
+            rays_embeddings.append(rays_plucker)
+
+        rays_embeddings = (
+            torch.stack(rays_embeddings, dim=0)
+            .permute(0, 3, 1, 2)
+            .contiguous()
+            .to(device)
+        )
+
+        return rays_embeddings
+
+    def forward(self, images):
+        B, V, C, H, W = images.shape
+        images = images.view(B * V, C, H, W)
+
+        x = self.unet(images)
+        x = self.conv(x)
+
+        x = x.reshape(B, 4, 14, self.splat_size, self.splat_size)
+
+        x = x.permute(0, 1, 3, 4, 2).reshape(B, -1, 14)
+
+        pos = self.pos_act(x[..., 0:3])
+        opacity = self.opacity_act(x[..., 3:4])
+        scale = self.scale_act(x[..., 4:7])
+        rotation = self.rot_act(x[..., 7:11])
+        rgbs = self.rgb_act(x[..., 11:])
+
+        q = torch.tensor([0, 0, 1, 0], dtype=pos.dtype, device=pos.device)
+        R = torch.tensor(
+            [
+                [-1, 0, 0],
+                [0, -1, 0],
+                [0, 0, 1],
+            ],
+            dtype=pos.dtype,
+            device=pos.device,
+        )
+
+        pos = torch.matmul(pos, R.T)
+
+        def multiply_quat(q1, q2):
+            w1, x1, y1, z1 = q1.unbind(-1)
+            w2, x2, y2, z2 = q2.unbind(-1)
+            w = w1 * w2 - x1 * x2 - y1 * y2 - z1 * z2
+            x = w1 * x2 + x1 * w2 + y1 * z2 - z1 * y2
+            y = w1 * y2 + y1 * w2 + z1 * x2 - x1 * z2
+            z = w1 * z2 + z1 * w2 + x1 * y2 - y1 * x2
+            return torch.stack([w, x, y, z], dim=-1)
+
+        for i in range(B):
+            rotation[i, :] = multiply_quat(q, rotation[i, :])
+
+        gaussians = torch.cat([pos, opacity, scale, rotation, rgbs], dim=-1)
+
+        return gaussians
+
+
+# =============================================================================
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+# References:
+#   https://github.com/facebookresearch/dino/blob/master/vision_transformer.py
+#   https://github.com/rwightman/pytorch-image-models/tree/master/timm/models/vision_transformer.py
+# =============================================================================
+XFORMERS_ENABLED = os.environ.get("XFORMERS_DISABLED") is None
+try:
+    if XFORMERS_ENABLED:
+        from xformers.ops import memory_efficient_attention, unbind
+
+        XFORMERS_AVAILABLE = True
+        warnings.warn("xFormers is available (Attention)")
+    else:
+        warnings.warn("xFormers is disabled (Attention)")
+        raise ImportError
+except ImportError:
+    XFORMERS_AVAILABLE = False
+    warnings.warn("xFormers is not available (Attention)")
+
+
+class Attention(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int = 8,
+        qkv_bias: bool = False,
+        proj_bias: bool = True,
+        attn_drop: float = 0.0,
+        proj_drop: float = 0.0,
+    ) -> None:
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = head_dim**-0.5
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim, bias=proj_bias)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+    def forward(self, x: Tensor) -> Tensor:
+        B, N, C = x.shape
+        qkv = (
+            self.qkv(x)
+            .reshape(B, N, 3, self.num_heads, C // self.num_heads)
+            .permute(2, 0, 3, 1, 4)
+        )
+
+        q, k, v = qkv[0] * self.scale, qkv[1], qkv[2]
+        attn = q @ k.transpose(-2, -1)
+
+        attn = attn.softmax(dim=-1)
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+
+class MemEffAttention(Attention):
+    def forward(self, x: Tensor, attn_bias=None) -> Tensor:
+        if not XFORMERS_AVAILABLE:
+            if attn_bias is not None:
+                raise AssertionError("xFormers is required for using nested tensors")
+            return super().forward(x)
+
+        B, N, C = x.shape
+        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads)
+
+        q, k, v = unbind(qkv, 2)
+
+        x = memory_efficient_attention(q, k, v, attn_bias=attn_bias)
+        x = x.reshape([B, N, C])
+
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+
+class CrossAttention(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        dim_q: int,
+        dim_k: int,
+        dim_v: int,
+        num_heads: int = 8,
+        qkv_bias: bool = False,
+        proj_bias: bool = True,
+        attn_drop: float = 0.0,
+        proj_drop: float = 0.0,
+    ) -> None:
+        super().__init__()
+        self.dim = dim
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = head_dim**-0.5
+
+        self.to_q = nn.Linear(dim_q, dim, bias=qkv_bias)
+        self.to_k = nn.Linear(dim_k, dim, bias=qkv_bias)
+        self.to_v = nn.Linear(dim_v, dim, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim, bias=proj_bias)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+    def forward(self, q: Tensor, k: Tensor, v: Tensor) -> Tensor:
+        B, N, _ = q.shape
+        M = k.shape[1]
+
+        q = self.scale * self.to_q(q).reshape(
+            B, N, self.num_heads, self.dim // self.num_heads
+        ).permute(0, 2, 1, 3)
+        k = (
+            self.to_k(k)
+            .reshape(B, M, self.num_heads, self.dim // self.num_heads)
+            .permute(0, 2, 1, 3)
+        )
+        v = (
+            self.to_v(v)
+            .reshape(B, M, self.num_heads, self.dim // self.num_heads)
+            .permute(0, 2, 1, 3)
+        )
+
+        attn = q @ k.transpose(-2, -1)
+
+        attn = attn.softmax(dim=-1)
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B, N, -1)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+
+class MemEffCrossAttention(CrossAttention):
+    def forward(self, q: Tensor, k: Tensor, v: Tensor, attn_bias=None) -> Tensor:
+        if not XFORMERS_AVAILABLE:
+            if attn_bias is not None:
+                raise AssertionError("xFormers is required for using nested tensors")
+            return super().forward(q, k, v)
+
+        B, N, _ = q.shape
+        M = k.shape[1]
+
+        q = self.scale * self.to_q(q).reshape(
+            B, N, self.num_heads, self.dim // self.num_heads
+        )
+        k = self.to_k(k).reshape(B, M, self.num_heads, self.dim // self.num_heads)
+        v = self.to_v(v).reshape(B, M, self.num_heads, self.dim // self.num_heads)
+
+        x = memory_efficient_attention(q, k, v, attn_bias=attn_bias)
+        x = x.reshape(B, N, -1)
+
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+
+# =============================================================================
+# End of xFormers
+
+
+class MVAttention(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int = 8,
+        qkv_bias: bool = False,
+        proj_bias: bool = True,
+        attn_drop: float = 0.0,
+        proj_drop: float = 0.0,
+        groups: int = 32,
+        eps: float = 1e-5,
+        residual: bool = True,
+        skip_scale: float = 1,
+        num_frames: int = 4,
+    ):
+        super().__init__()
+
+        self.residual = residual
+        self.skip_scale = skip_scale
+        self.num_frames = num_frames
+
+        self.norm = nn.GroupNorm(
+            num_groups=groups, num_channels=dim, eps=eps, affine=True
+        )
+        self.attn = MemEffAttention(
+            dim, num_heads, qkv_bias, proj_bias, attn_drop, proj_drop
+        )
+
+    def forward(self, x):
+        BV, C, H, W = x.shape
+        B = BV // self.num_frames
+
+        res = x
+        x = self.norm(x)
+
+        x = (
+            x.reshape(B, self.num_frames, C, H, W)
+            .permute(0, 1, 3, 4, 2)
+            .reshape(B, -1, C)
+        )
+        x = self.attn(x)
+        x = (
+            x.reshape(B, self.num_frames, H, W, C)
+            .permute(0, 1, 4, 2, 3)
+            .reshape(BV, C, H, W)
+        )
+
+        if self.residual:
+            x = (x + res) * self.skip_scale
+        return x
+
+
+class ResnetBlock(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        resample: Literal["default", "up", "down"] = "default",
+        groups: int = 32,
+        eps: float = 1e-5,
+        skip_scale: float = 1,
+    ):
+        super().__init__()
+
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.skip_scale = skip_scale
+
+        self.norm1 = nn.GroupNorm(
+            num_groups=groups, num_channels=in_channels, eps=eps, affine=True
+        )
+        self.conv1 = nn.Conv2d(
+            in_channels, out_channels, kernel_size=3, stride=1, padding=1
+        )
+
+        self.norm2 = nn.GroupNorm(
+            num_groups=groups, num_channels=out_channels, eps=eps, affine=True
+        )
+        self.conv2 = nn.Conv2d(
+            out_channels, out_channels, kernel_size=3, stride=1, padding=1
+        )
+
+        self.act = F.silu
+
+        self.resample = None
+        if resample == "up":
+            self.resample = partial(F.interpolate, scale_factor=2.0, mode="nearest")
+        elif resample == "down":
+            self.resample = nn.AvgPool2d(kernel_size=2, stride=2)
+
+        self.shortcut = nn.Identity()
+        if self.in_channels != self.out_channels:
+            self.shortcut = nn.Conv2d(
+                in_channels, out_channels, kernel_size=1, bias=True
+            )
+
+    def forward(self, x):
+        res = x
+        x = self.norm1(x)
+        x = self.act(x)
+        if self.resample:
+            res = self.resample(res)
+            x = self.resample(x)
+        x = self.conv1(x)
+        x = self.norm2(x)
+        x = self.act(x)
+        x = self.conv2(x)
+        x = (x + self.shortcut(res)) * self.skip_scale
+        return x
+
+
+class DownBlock(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        num_layers: int = 1,
+        downsample: bool = True,
+        attention: bool = True,
+        attention_heads: int = 16,
+        skip_scale: float = 1,
+    ):
+        super().__init__()
+
+        nets = []
+        attns = []
+        for i in range(num_layers):
+            in_channels = in_channels if i == 0 else out_channels
+            nets.append(ResnetBlock(in_channels, out_channels, skip_scale=skip_scale))
+            if attention:
+                attns.append(
+                    MVAttention(out_channels, attention_heads, skip_scale=skip_scale)
+                )
+            else:
+                attns.append(None)
+        self.nets = nn.ModuleList(nets)
+        self.attns = nn.ModuleList(attns)
+
+        self.downsample = None
+        if downsample:
+            self.downsample = nn.Conv2d(
+                out_channels, out_channels, kernel_size=3, stride=2, padding=1
+            )
+
+    def forward(self, x):
+        xs = []
+        for attn, net in zip(self.attns, self.nets):
+            x = net(x)
+            if attn:
+                x = attn(x)
+            xs.append(x)
+        if self.downsample:
+            x = self.downsample(x)
+            xs.append(x)
+        return x, xs
+
+
+class MidBlock(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        num_layers: int = 1,
+        attention: bool = True,
+        attention_heads: int = 16,
+        skip_scale: float = 1,
+    ):
+        super().__init__()
+
+        nets = []
+        attns = []
+        nets.append(ResnetBlock(in_channels, in_channels, skip_scale=skip_scale))
+        for _ in range(num_layers):
+            nets.append(ResnetBlock(in_channels, in_channels, skip_scale=skip_scale))
+            if attention:
+                attns.append(
+                    MVAttention(in_channels, attention_heads, skip_scale=skip_scale)
+                )
+            else:
+                attns.append(None)
+        self.nets = nn.ModuleList(nets)
+        self.attns = nn.ModuleList(attns)
+
+    def forward(self, x):
+        x = self.nets[0](x)
+        for attn, net in zip(self.attns, self.nets[1:]):
+            if attn:
+                x = attn(x)
+            x = net(x)
+        return x
+
+
+class UpBlock(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        prev_out_channels: int,
+        out_channels: int,
+        num_layers: int = 1,
+        upsample: bool = True,
+        attention: bool = True,
+        attention_heads: int = 16,
+        skip_scale: float = 1,
+    ):
+        super().__init__()
+
+        nets = []
+        attns = []
+        for i in range(num_layers):
+            cin = in_channels if i == 0 else out_channels
+            cskip = prev_out_channels if (i == num_layers - 1) else out_channels
+
+            nets.append(ResnetBlock(cin + cskip, out_channels, skip_scale=skip_scale))
+            if attention:
+                attns.append(
+                    MVAttention(out_channels, attention_heads, skip_scale=skip_scale)
+                )
+            else:
+                attns.append(None)
+        self.nets = nn.ModuleList(nets)
+        self.attns = nn.ModuleList(attns)
+
+        self.upsample = None
+        if upsample:
+            self.upsample = nn.Conv2d(
+                out_channels, out_channels, kernel_size=3, stride=1, padding=1
+            )
+
+    def forward(self, x, xs):
+        for attn, net in zip(self.attns, self.nets):
+            res_x = xs[-1]
+            xs = xs[:-1]
+            x = torch.cat([x, res_x], dim=1)
+            x = net(x)
+            if attn:
+                x = attn(x)
+        if self.upsample:
+            x = F.interpolate(x, scale_factor=2.0, mode="nearest")
+            x = self.upsample(x)
+        return x
+
+
+class UNet(nn.Module):
+    def __init__(
+        self,
+        in_channels: int = 9,
+        out_channels: int = 14,
+        down_channels: Tuple[int, ...] = (64, 128, 256, 512, 1024, 1024),
+        down_attention: Tuple[bool, ...] = (False, False, False, True, True, True),
+        mid_attention: bool = True,
+        up_channels: Tuple[int, ...] = (1024, 1024, 512, 256, 128),
+        up_attention: Tuple[bool, ...] = (True, True, True, False, False),
+        layers_per_block: int = 2,
+        skip_scale: float = np.sqrt(0.5),
+    ):
+        super().__init__()
+
+        self.conv_in = nn.Conv2d(
+            in_channels, down_channels[0], kernel_size=3, stride=1, padding=1
+        )
+
+        down_blocks = []
+        cout = down_channels[0]
+        for i in range(len(down_channels)):
+            cin = cout
+            cout = down_channels[i]
+
+            down_blocks.append(
+                DownBlock(
+                    cin,
+                    cout,
+                    num_layers=layers_per_block,
+                    downsample=(i != len(down_channels) - 1),
+                    attention=down_attention[i],
+                    skip_scale=skip_scale,
+                )
+            )
+        self.down_blocks = nn.ModuleList(down_blocks)
+
+        self.mid_block = MidBlock(
+            down_channels[-1], attention=mid_attention, skip_scale=skip_scale
+        )
+
+        up_blocks = []
+        cout = up_channels[0]
+        for i in range(len(up_channels)):
+            cin = cout
+            cout = up_channels[i]
+            cskip = down_channels[max(-2 - i, -len(down_channels))]
+
+            up_blocks.append(
+                UpBlock(
+                    cin,
+                    cskip,
+                    cout,
+                    num_layers=layers_per_block + 1,
+                    upsample=(i != len(up_channels) - 1),
+                    attention=up_attention[i],
+                    skip_scale=skip_scale,
+                )
+            )
+        self.up_blocks = nn.ModuleList(up_blocks)
+        self.norm_out = nn.GroupNorm(
+            num_channels=up_channels[-1], num_groups=32, eps=1e-5
+        )
+        self.conv_out = nn.Conv2d(
+            up_channels[-1], out_channels, kernel_size=3, stride=1, padding=1
+        )
+
+    def forward(self, x):
+        x = self.conv_in(x)
+        xss = [x]
+        for block in self.down_blocks:
+            x, xs = block(x)
+            xss.extend(xs)
+        x = self.mid_block(x)
+        for block in self.up_blocks:
+            xs = xss[-len(block.nets) :]
+            xss = xss[: -len(block.nets)]
+            x = block(x, xs)
+        x = self.norm_out(x)
+        x = F.silu(x)
+        x = self.conv_out(x)
+        return x

model_index.json

@@ -1,33 +1,13 @@
 {
-  "_class_name": "MVDreamPipeline",
-  "_diffusers_version": "0.25.0",
-  "feature_extractor": [
-    "transformers",
-    "CLIPImageProcessor"
-  ],
-  "image_encoder": [
-    "transformers",
-    "CLIPVisionModel"
-  ],
-  "requires_safety_checker": false,
-  "scheduler": [
-    "diffusers",
-    "DDIMScheduler"
-  ],
-  "text_encoder": [
-    "transformers",
-    "CLIPTextModel"
-  ],
-  "tokenizer": [
-    "transformers",
-    "CLIPTokenizer"
-  ],
-  "unet": [
-    "mv_unet",
-    "MultiViewUNetModel"
-  ],
-  "vae": [
-    "diffusers",
-    "AutoencoderKL"
-  ]
+    "_class_name": "LGMFullPipeline",
+    "_diffusers_version": "0.25.0",
+    "feature_extractor": ["transformers", "CLIPImageProcessor"],
+    "image_encoder": ["transformers", "CLIPVisionModel"],
+    "requires_safety_checker": false,
+    "scheduler": ["diffusers", "DDIMScheduler"],
+    "text_encoder": ["transformers", "CLIPTextModel"],
+    "tokenizer": ["transformers", "CLIPTokenizer"],
+    "unet": ["mv_unet", "MultiViewUNetModel"],
+    "vae": ["diffusers", "AutoencoderKL"],
+    "lgm": ["lgm", "LGM"]
 }

pipeline.py

@@ -7,6 +7,7 @@ import numpy as np
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
+import torchvision.transforms.functional as TF
 
 # require xformers!
 import xformers
@@ -1031,7 +1032,7 @@ class MultiViewUNetModel(ModelMixin, ConfigMixin):
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 
-class MVDreamPipeline(DiffusionPipeline):
+class LGMFullPipeline(DiffusionPipeline):
 
     _optional_components = ["feature_extractor", "image_encoder"]
 
@@ -1045,6 +1046,7 @@ class MVDreamPipeline(DiffusionPipeline):
         # imagedream variant
         feature_extractor: CLIPImageProcessor,
         image_encoder: CLIPVisionModel,
+        lgm,
         requires_safety_checker: bool = False,
     ):
         super().__init__()
@@ -1080,6 +1082,11 @@ class MVDreamPipeline(DiffusionPipeline):
             new_config["clip_sample"] = False
             scheduler._internal_dict = FrozenDict(new_config)
 
+        self.imagenet_default_mean = (0.485, 0.456, 0.406)
+        self.imagenet_default_std = (0.229, 0.224, 0.225)
+
+        lgm = lgm.half().cuda()
+
         self.register_modules(
             vae=vae,
             unet=unet,
@@ -1088,10 +1095,14 @@ class MVDreamPipeline(DiffusionPipeline):
             text_encoder=text_encoder,
             feature_extractor=feature_extractor,
             image_encoder=image_encoder,
+            lgm=lgm,
         )
         self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
         self.register_to_config(requires_safety_checker=requires_safety_checker)
 
+    def save_ply(self, gaussians, path):
+        self.lgm.gs.save_ply(gaussians, path)
+
     def enable_vae_slicing(self):
         r"""
         Enable sliced VAE decoding.
@@ -1589,4 +1600,21 @@ class MVDreamPipeline(DiffusionPipeline):
         if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
             self.final_offload_hook.offload()
 
-        return image
+        images = np.stack([image[1], image[2], image[3], image[0]], axis=0)
+        images = torch.from_numpy(images).permute(0, 3, 1, 2).float().cuda()
+        images = F.interpolate(
+            images,
+            size=(256, 256),
+            mode="bilinear",
+            align_corners=False,
+        )
+        images = TF.normalize(
+            images, self.imagenet_default_mean, self.imagenet_default_std
+        )
+
+        rays_embeddings = self.lgm.prepare_default_rays("cuda", elevation=0)
+        images = torch.cat([images, rays_embeddings], dim=1).unsqueeze(0)
+        images = images.half().cuda()
+
+        result = self.lgm(images)
+        return result