updates.patch

diff --git a/src/diffusers/models/transformers/transformer_ltx.py b/src/diffusers/models/transformers/transformer_ltx.py
index a895340bd..688d109e0 100644
--- a/src/diffusers/models/transformers/transformer_ltx.py
+++ b/src/diffusers/models/transformers/transformer_ltx.py
@@ -260,7 +260,7 @@ class LTXVideoTransformerBlock(nn.Module):
         hidden_states = hidden_states + attn_hidden_states
         norm_hidden_states = self.norm2(hidden_states) * (1 + scale_mlp) + shift_mlp
 
-        ff_output = self.ff(norm_hidden_states)
+        ff_output = self.ff(norm_hidden_states).to(norm_hidden_states.dtype)
         hidden_states = hidden_states + ff_output * gate_mlp
 
         return hidden_states
diff --git a/src/diffusers/pipelines/ltx/pipeline_ltx.py b/src/diffusers/pipelines/ltx/pipeline_ltx.py
index 96d41bb32..6a6e2f761 100644
--- a/src/diffusers/pipelines/ltx/pipeline_ltx.py
+++ b/src/diffusers/pipelines/ltx/pipeline_ltx.py
@@ -30,6 +30,11 @@ from ...video_processor import VideoProcessor
 from ..pipeline_utils import DiffusionPipeline
 from .pipeline_output import LTXPipelineOutput
 
+try:
+    import q8_kernels # noqa
+    from q8_kernels.modules.linear import Q8Linear
+except:
+    Q8Linear = None
 
 if is_torch_xla_available():
     import torch_xla.core.xla_model as xm
@@ -186,11 +191,11 @@ class LTXPipeline(DiffusionPipeline, FromSingleFileMixin, LTXVideoLoraLoaderMixi
             scheduler=scheduler,
         )
 
-        self.vae_spatial_compression_ratio = self.vae.spatial_compression_ratio if hasattr(self, "vae") else 32
-        self.vae_temporal_compression_ratio = self.vae.temporal_compression_ratio if hasattr(self, "vae") else 8
-        self.transformer_spatial_patch_size = self.transformer.config.patch_size if hasattr(self, "transformer") else 1
+        self.vae_spatial_compression_ratio = self.vae.spatial_compression_ratio if getattr(self, "vae", None) is not None else 32
+        self.vae_temporal_compression_ratio = self.vae.temporal_compression_ratio if getattr(self, "vae", None) is not None else 8
+        self.transformer_spatial_patch_size = self.transformer.config.patch_size if getattr(self, "transformer", None) is not None else 1
         self.transformer_temporal_patch_size = (
-            self.transformer.config.patch_size_t if hasattr(self, "transformer") else 1
+            self.transformer.config.patch_size_t if getattr(self, "transformer", None) is not None else 1
         )
 
         self.video_processor = VideoProcessor(vae_scale_factor=self.vae_spatial_compression_ratio)
@@ -649,6 +654,11 @@ class LTXPipeline(DiffusionPipeline, FromSingleFileMixin, LTXVideoLoraLoaderMixi
             prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
             prompt_attention_mask = torch.cat([negative_prompt_attention_mask, prompt_attention_mask], dim=0)
 
+        if Q8Linear is not None and isinstance(self.transformer.transformer_blocks[0].attn1.to_q, Q8Linear):
+            prompt_attention_mask = prompt_attention_mask.to(torch.int64)
+            prompt_attention_mask = prompt_attention_mask.argmin(-1).int().squeeze()
+            prompt_attention_mask[prompt_attention_mask == 0] = max_sequence_length
+
         # 4. Prepare latent variables
         num_channels_latents = self.transformer.config.in_channels
         latents = self.prepare_latents(