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#231·LTX-Video

[BUG]FP8Linear.forward() argument mismatch in LTX-Video inference

Author: ighoshsubhoCreated Jul 25, 2025Updated Mar 7, 2026

Encountering a TypeError during LTX-Video inference where FP8Linear.forward() receives 5 positional arguments but only accepts 2-4. This appears to be related to the q8_kernels integration with the diffusers pipeline.

Error Details

TypeError: FP8Linear.forward() takes from 2 to 4 positional arguments but 5 were given

Full Stack Trace

python
/workspace/LTX-Video/env/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm
Padded dimensions: 960x1280x81
Loading checkpoint shards: 100%|██████████| 2/2 [00:00<00:00, 55.41it/s]
/workspace/LTX-Video/env/lib/python3.11/site-packages/torch/functional.py:554: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /pytorch/aten/src/ATen/native/TensorShape.cpp:4314.)
  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
  0%|          | 0/7 [00:00<?, ?it/s]
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[1], line 8
      5 WIDTH = 1280
      6 NUM_FRAMES = 81
----> 8 infer(
      9     InferenceConfig(
     10         pipeline_config="configs/ltxv-13b-0.9.8-distilled-fp8.yaml",
     11         prompt=PROMPT,
     12         height=HEIGHT,
     13         width=WIDTH,
     14         num_frames=NUM_FRAMES,
     15         output_path="/workspace/output.mp4",
     16     )
     17 )

File /workspace/LTX-Video/ltx_video/inference.py:569, in infer(config)
    560 sample = {
    561     "prompt": config.prompt,
    562     "prompt_attention_mask": None,
    563     "negative_prompt": config.negative_prompt,
    564     "negative_prompt_attention_mask": None,
    565 }
    567 generator = torch.Generator(device=device).manual_seed(config.seed)
--> 569 images = pipeline(
    570     **pipeline_config,
    571     skip_layer_strategy=skip_layer_strategy,
    572     generator=generator,
    573     output_type="pt",
    574     callback_on_step_end=None,
    575     height=height_padded,
    576     width=width_padded,
    577     num_frames=num_frames_padded,
    578     frame_rate=config.frame_rate,
    579     **sample,
    580     media_items=media_item,
    581     conditioning_items=conditioning_items,
    582     is_video=True,
    583     vae_per_channel_normalize=True,
    584     image_cond_noise_scale=config.image_cond_noise_scale,
    585     mixed_precision=(precision == "mixed_precision"),
    586     offload_to_cpu=offload_to_cpu,
    587     device=device,
    588     enhance_prompt=enhance_prompt,
    589 ).images

File /workspace/LTX-Video/ltx_video/pipelines/pipeline_ltx_video.py:1865, in LTXMultiScalePipeline.__call__(self, downscale_factor, first_pass, second_pass, *args, **kwargs)
   1863 kwargs["height"] = downscaled_height
   1864 kwargs.update(**first_pass)
-> 1865 result = self.video_pipeline(*args, **kwargs)
   1866 latents = result.images
   1868 upsampled_latents = self._upsample_latents(self.latent_upsampler, latents)

File /workspace/LTX-Video/env/lib/python3.11/site-packages/torch/utils/_contextlib.py:116, in context_decorator.<locals>.decorate_context(*args, **kwargs)
    113 @functools.wraps(func)
    114 def decorate_context(*args, **kwargs):
    115     with ctx_factory():
--> 116         return func(*args, **kwargs)

File /workspace/LTX-Video/ltx_video/pipelines/pipeline_ltx_video.py:1206, in LTXVideoPipeline.__call__(self, height, width, num_frames, frame_rate, prompt, negative_prompt, num_inference_steps, skip_initial_inference_steps, skip_final_inference_steps, timesteps, guidance_scale, cfg_star_rescale, skip_layer_strategy, skip_block_list, stg_scale, rescaling_scale, guidance_timesteps, num_images_per_prompt, eta, generator, latents, prompt_embeds, prompt_attention_mask, negative_prompt_embeds, negative_prompt_attention_mask, output_type, return_dict, callback_on_step_end, conditioning_items, decode_timestep, decode_noise_scale, mixed_precision, offload_to_cpu, enhance_prompt, text_encoder_max_tokens, stochastic_sampling, media_items, tone_map_compression_ratio, **kwargs)
   1204 # predict noise model_output
   1205 with context_manager:
-> 1206     noise_pred = self.transformer(
   1207         latent_model_input.to(self.transformer.dtype),
   1208         indices_grid=fractional_coords,
   1209         encoder_hidden_states=prompt_embeds_batch[indices].to(
   1210             self.transformer.dtype
   1211         ),
   1212         encoder_attention_mask=prompt_attention_mask_batch[indices],
   1213         timestep=current_timestep,
   1214         skip_layer_mask=skip_layer_mask,
   1215         skip_layer_strategy=skip_layer_strategy,
   1216         return_dict=False,
   1217     )[0]
   1219 # perform guidance
   1220 if do_spatio_temporal_guidance:

File /workspace/LTX-Video/env/lib/python3.11/site-packages/torch/nn/modules/module.py:1751, in Module._wrapped_call_impl(self, *args, **kwargs)
   1749     return self._compiled_call_impl(*args, **kwargs)  # type: ignore[misc]
   1750 else:
-> 1751     return self._call_impl(*args, **kwargs)

File /workspace/LTX-Video/env/lib/python3.11/site-packages/torch/nn/modules/module.py:1762, in Module._call_impl(self, *args, **kwargs)
   1757 # If we don't have any hooks, we want to skip the rest of the logic in
   1758 # this function, and just call forward.
   1759 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
   1760         or _global_backward_pre_hooks or _global_backward_hooks
   1761         or _global_forward_hooks or _global_forward_pre_hooks):
-> 1762     return forward_call(*args, **kwargs)

File /workspace/LTX-Video/ltx_video/models/transformers/transformer3d.py:478, in Transformer3DModel.forward(self, hidden_states, indices_grid, encoder_hidden_states, timestep, class_labels, cross_attention_kwargs, attention_mask, encoder_attention_mask, skip_layer_mask, skip_layer_strategy, return_dict)
    459         hidden_states = torch.utils.checkpoint.checkpoint(
    460             create_custom_forward(block),
    461             hidden_states,
   (...)    475             **ckpt_kwargs,
    476         )
    477     else:
--> 478         hidden_states = block(
    479             hidden_states,
    480             freqs_cis=freqs_cis,
    481             attention_mask=attention_mask,
    482             encoder_hidden_states=encoder_hidden_states,
    483             encoder_attention_mask=encoder_attention_mask,
    484             timestep=timestep,
    485             cross_attention_kwargs=cross_attention_kwargs,
    486             class_labels=class_labels,
    487             skip_layer_mask=(
    488                 skip_layer_mask[block_idx]
    489                 if skip_layer_mask is not None
    490                 else None
    491             ),
    492             skip_layer_strategy=skip_layer_strategy,
    493         )

File /workspace/LTX-Video/env/lib/python3.11/site-packages/torch/nn/modules/module.py:1751, in Module._wrapped_call_impl(self, *args, **kwargs)
   1749     return self._compiled_call_impl(*args, **kwargs)  # type: ignore[misc]
   1750 else:
-> 1751     return self._call_impl(*args, **kwargs)

File /workspace/LTX-Video/env/lib/python3.11/site-packages/torch/nn/modules/module.py:1762, in Module._call_impl(self, *args, **kwargs)
   1757 # If we don't have any hooks, we want to skip the rest of the logic in
   1758 # this function, and just call forward.
   1759 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
   1760         or _global_backward_pre_hooks or _global_backward_hooks
   1761         or _global_forward_hooks or _global_forward_pre_hooks):
-> 1762     return forward_call(*args, **kwargs)

File /workspace/LTX-Video/env/lib/python3.11/site-packages/q8_kernels/integration/diffusers.py:380, in create_forwards.<locals>.fused_forward(self, hidden_states, freqs_cis, attention_mask, encoder_hidden_states, encoder_attention_mask, timestep, cross_attention_kwargs, class_labels, sharding_mesh, skip_layer_mask, skip_layer_strategy)
    375 # 1. Prepare GLIGEN inputs
    376 cross_attention_kwargs = (
    377     cross_attention_kwargs.copy() if cross_attention_kwargs is not None else {}
    378 )
--> 380 attn_output = self.attn1(
    381     norm_hidden_states,
    382     norm_hidden_states_scales,
    383     freqs_cis=freqs_cis,
    384     encoder_hidden_states=(
    385         encoder_hidden_states if self.only_cross_attention else None
    386     ),
    387     attention_mask=attention_mask,
    388     skip_layer_mask=skip_layer_mask,
    389     skip_layer_strategy=skip_layer_strategy,
    390     **cross_attention_kwargs,
    391 )
    392 if gate_msa is not None:
    393     attn_output = gate_msa * attn_output

File /workspace/LTX-Video/env/lib/python3.11/site-packages/torch/nn/modules/module.py:1751, in Module._wrapped_call_impl(self, *args, **kwargs)
   1749     return self._compiled_call_impl(*args, **kwargs)  # type: ignore[misc]
   1750 else:
-> 1751     return self._call_impl(*args, **kwargs)

File /workspace/LTX-Video/env/lib/python3.11/site-packages/torch/nn/modules/module.py:1762, in Module._call_impl(self, *args, **kwargs)
   1757 # If we don't have any hooks, we want to skip the rest of the logic in
   1758 # this function, and just call forward.
   1759 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
   1760         or _global_backward_pre_hooks or _global_backward_hooks
   1761         or _global_forward_hooks or _global_forward_pre_hooks):
-> 1762     return forward_call(*args, **kwargs)

File /workspace/LTX-Video/env/lib/python3.11/site-packages/q8_kernels/integration/diffusers.py:67, in attn_forward(self, hidden_states, hidden_states_scales, freqs_cis, encoder_hidden_states, attention_mask, skip_layer_mask, skip_layer_strategy, **cross_attention_kwargs)
     59     logger.warning(
     60         f"cross_attention_kwargs {unused_kwargs} are not expected by"
     61         f" {self.processor.__class__.__name__} and will be ignored."
     62     )
     63 cross_attention_kwargs = {
     64     k: w for k, w in cross_attention_kwargs.items() if k in attn_parameters
     65 }
---> 67 return self.processor(
     68     self,
     69     hidden_states,
     70     hidden_states_scales,
     71     freqs_cis=freqs_cis,
     72     encoder_hidden_states=encoder_hidden_states,
     73     attention_mask=attention_mask,
     74     skip_layer_mask=skip_layer_mask,
     75     skip_layer_strategy=skip_layer_strategy,
     76     **cross_attention_kwargs,
     77 )

File /workspace/LTX-Video/env/lib/python3.11/site-packages/q8_kernels/integration/diffusers.py:158, in create_attn_processor.<locals>.AttnProcessor3_0.__call__(self, attn, hidden_states, hidden_states_scales, freqs_cis, encoder_hidden_states, attention_mask, temb, skip_layer_mask, skip_layer_strategy, *args, **kwargs)
    155 else:  # if no context provided do self-attention
    156     is_self_attention = True
--> 158     query = attn.to_q(
    159         hidden_states, hidden_states_scales, False, torch.bfloat16
    160     )
    161     query = rms_norm_rope(
    162         query, freqs_cis[0], freqs_cis[1], attn.q_norm.weight
    163     )
    165     key = attn.to_k(
    166         hidden_states, hidden_states_scales, False, torch.bfloat16
    167     )

File /workspace/LTX-Video/env/lib/python3.11/site-packages/torch/nn/modules/module.py:1751, in Module._wrapped_call_impl(self, *args, **kwargs)
   1749     return self._compiled_call_impl(*args, **kwargs)  # type: ignore[misc]
   1750 else:
-> 1751     return self._call_impl(*args, **kwargs)

File /workspace/LTX-Video/env/lib/python3.11/site-packages/torch/nn/modules/module.py:1762, in Module._call_impl(self, *args, **kwargs)
   1757 # If we don't have any hooks, we want to skip the rest of the logic in
   1758 # this function, and just call forward.
   1759 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
   1760         or _global_backward_pre_hooks or _global_backward_hooks
   1761         or _global_forward_hooks or _global_forward_pre_hooks):
-> 1762     return forward_call(*args, **kwargs)

TypeError: FP8Linear.forward() takes from 2 to 4 positional arguments but 5 were given

Reproduction Steps

  1. Set up LTX-Video environment with FP8 distilled model
  2. Configure inference with the following parameters:
    python
    PROMPT = [your_prompt_here]
HEIGHT = 960
WIDTH = 1280
NUM_FRAMES = 81

infer(
    InferenceConfig(
        pipeline_config="configs/ltxv-13b-0.9.8-distilled-fp8.yaml",
        prompt=PROMPT,
        height=HEIGHT,
        width=WIDTH,
        num_frames=NUM_FRAMES,
        output_path="/workspace/output.mp4",
    )
)
  3. Execute inference - error occurs during transformer forward pass

Root Cause Analysis

The error occurs in the q8_kernels integration where attn.to_q() is called with 4 arguments:

python
query = attn.to_q(
    hidden_states, hidden_states_scales, False, torch.bfloat16
)

However, the FP8Linear.forward() method only accepts 2-4 positional arguments, suggesting a signature mismatch between the expected interface and the actual implementation.

Environment Information

  • Model Config: configs/ltxv-13b-0.9.8-distilled-fp8.yaml
  • Python: 3.11
  • PyTorch: 2.7.1+cu126
  • q8_kernels: q8_kernels @ file:///workspace/LTX-Video/LTX-Video-Q8-Kernels/dist/q8_kernels-0.0.5-cp311-cp311-linux_x86_64.whl#sha256=54e6ab58fa9acccfd60316e876e0807ced33df91929f59415d9b57a893f0acb7
  • Platform: Linux workspace environment

Source: Lightricks/LTX-Video

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