Lightricks
MAC - MPS Support
The README specifies CUDA as a prerequisite. Is there any plans to add MPS support for Apple Silicon?
When I download the node in ComfyUI on macOS, it is still incompatible.
Device: MAC Studio M3 ULTRA 256GB VRAM
It looks like https://github.com/Lightricks/ComfyUI-LTXVideo/issues/338 is having issue with gemma loader too
I don't have time to test this but you can try updating from line 433 in the gemma_encoder.py file with:
# fork_rng requires explicit device list for CUDA; MPS uses CPU RNG state
device_type = self.model.device.type
devices = [self.model.device] if device_type == "cuda" else []
with torch.inference_mode(), torch.random.fork_rng(devices=devices):
torch.manual_seed(seed)
if device_type == "mps":
torch.mps.manual_seed(seed)
outputs = self.model.generate(
**model_inputs,
max_new_tokens=max_new_tokens,
do_sample=True,
temperature=0.7,
)
generated_ids = outputs[0][len(model_inputs.input_ids[0]) :]
enhanced_prompt = self.processor.tokenizer.decode(
generated_ids, skip_special_tokens=True
)
return enhanced_prompt
And if the bfloat16 dtype is giving an error you can search for clip_dtype = torch.bfloat16 around line 598 and change to clip_dtype = torch.float16. Then update the ltxv_gemma_clip function at line 495 with:
def ltxv_gemma_clip(encoder_path, ltxv_path, processor=None, dtype=None):
class _LTXVGemmaTextEncoderModel(LTXVGemmaTextEncoderModel):
def __init__(self, device="cpu", dtype=dtype, model_options={}):
if dtype is None:
dtype = torch.bfloat16
gemma_model = Gemma3ForConditionalGeneration.from_pretrained(
encoder_path,
local_files_only=True,
torch_dtype=dtype,
)
feature_extractor_linear = load_proj_matrix_from_ltxv(
ltxv_path,
"text_embedding_projection.",
)
if feature_extractor_linear is None:
feature_extractor_linear = load_proj_matrix_from_checkpoint(
encoder_path / "proj_linear.safetensors"
)
embeddings_connector = load_video_embeddings_connector(
ltxv_path, dtype=dtype
)
audio_embeddings_connector = load_audio_embeddings_connector(
ltxv_path, dtype=dtype
)
super().__init__(
model=gemma_model,
feature_extractor_linear=feature_extractor_linear,
embeddings_connector=embeddings_connector,
audio_embeddings_connector=audio_embeddings_connector,
processor=processor,
dtype=dtype,
device=device,
)
return _LTXVGemmaTextEncoderModel
Good luck!
