Fix installation issues

example.py

@@ -10,7 +10,7 @@
 from trellis.utils import render_utils, postprocessing_utils
 
 # Load a pipeline from a model folder or a Hugging Face model hub.
-pipeline = TrellisImageTo3DPipeline.from_pretrained("microsoft/TRELLIS-image-large")
+pipeline = TrellisImageTo3DPipeline.from_pretrained("jetx/trellis-image-large")
 pipeline.cuda()
 
 # Load an image

trellis/modules/sparse/attention/full_attn.py

@@ -194,7 +194,7 @@ def sparse_scaled_dot_product_attention(*args, **kwargs):
         q = q.unsqueeze(0)
         k = k.unsqueeze(0)
         v = v.unsqueeze(0)
-        mask = xops.fmha.BlockDiagonalMask.from_seqlens(q_seqlen, kv_seqlen)
+        mask = xops.fmha.attn_bias.BlockDiagonalMask.from_seqlens(q_seqlen, kv_seqlen)
         out = xops.memory_efficient_attention(q, k, v, mask)[0]
     elif ATTN == 'flash_attn':
         cu_seqlens_q = torch.cat([torch.tensor([0]), torch.cumsum(torch.tensor(q_seqlen), dim=0)]).int().to(device)

trellis/modules/sparse/attention/serialized_attn.py

@@ -177,7 +177,7 @@ def sparse_serialized_scaled_dot_product_self_attention(
             q = q.unsqueeze(0)                                      # [1, M, H, C]
             k = k.unsqueeze(0)                                      # [1, M, H, C]
             v = v.unsqueeze(0)                                      # [1, M, H, C]
-            mask = xops.fmha.BlockDiagonalMask.from_seqlens(seq_lens)
+            mask = xops.fmha.attn_bias.BlockDiagonalMask.from_seqlens(seq_lens)
             out = xops.memory_efficient_attention(q, k, v, mask)[0] # [M, H, C]
         elif ATTN == 'flash_attn':
             cu_seqlens = torch.cat([torch.tensor([0]), torch.cumsum(torch.tensor(seq_lens), dim=0)], dim=0) \

trellis/modules/sparse/attention/windowed_attn.py

@@ -119,7 +119,7 @@ def sparse_windowed_scaled_dot_product_self_attention(
             q = q.unsqueeze(0)                                      # [1, M, H, C]
             k = k.unsqueeze(0)                                      # [1, M, H, C]
             v = v.unsqueeze(0)                                      # [1, M, H, C]
-            mask = xops.fmha.BlockDiagonalMask.from_seqlens(seq_lens)
+            mask = xops.fmha.attn_bias.BlockDiagonalMask.from_seqlens(seq_lens)
             out = xops.memory_efficient_attention(q, k, v, mask)[0] # [M, H, C]
         elif ATTN == 'flash_attn':
             cu_seqlens = torch.cat([torch.tensor([0]), torch.cumsum(torch.tensor(seq_lens), dim=0)], dim=0) \