Inference: Integrate fp8 kernels

ltx_video/inference.py

@@ -182,6 +182,28 @@ def seed_everething(seed: int):
         torch.mps.manual_seed(seed)
 
 
+def create_transformer(ckpt_path: str, precision: str) -> Transformer3DModel:
+    if precision == "float8_e4m3fn":
+        try:
+            from q8_kernels.integration.patch_transformer import (
+                patch_diffusers_transformer as patch_transformer_for_q8_kernels,
+            )
+
+            transformer = Transformer3DModel.from_pretrained(
+                ckpt_path, dtype=torch.float8_e4m3fn
+            )
+            patch_transformer_for_q8_kernels(transformer)
+            return transformer
+        except ImportError:
+            raise ValueError(
+                "Q8-Kernels not found. To use FP8 checkpoint, please install Q8 kernels from https://github.com/Lightricks/LTXVideo-Q8-Kernels"
+            )
+    elif precision == "bfloat16":
+        return Transformer3DModel.from_pretrained(ckpt_path).to(torch.bfloat16)
+    else:
+        return Transformer3DModel.from_pretrained(ckpt_path)
+
+
 def create_ltx_video_pipeline(
     ckpt_path: str,
     precision: str,
@@ -204,7 +226,7 @@ def create_ltx_video_pipeline(
         allowed_inference_steps = configs.get("allowed_inference_steps", None)
 
     vae = CausalVideoAutoencoder.from_pretrained(ckpt_path)
-    transformer = Transformer3DModel.from_pretrained(ckpt_path)
+    transformer = create_transformer(ckpt_path, precision)
 
     # Use constructor if sampler is specified, otherwise use from_pretrained
     if sampler == "from_checkpoint" or not sampler:
@@ -247,8 +269,6 @@ def create_ltx_video_pipeline(
         prompt_enhancer_llm_tokenizer = None
 
     vae = vae.to(torch.bfloat16)
-    if precision == "bfloat16" and transformer.dtype != torch.bfloat16:
-        transformer = transformer.to(torch.bfloat16)
     text_encoder = text_encoder.to(torch.bfloat16)
 
     # Use submodels for the pipeline