Merge branch 'main' into ltx2_lora

Author: prishajain1

src/maxdiffusion/checkpointing/ltx2_checkpointer.py

@@ -79,19 +79,19 @@ def load_ltx2_configs_from_orbax(self, step: Optional[int]) -> Tuple[Optional[di
     return restored_checkpoint, step
 
   def load_checkpoint(
-      self, step=None, vae_only=False, load_transformer=True
+      self, step=None, vae_only=False, load_transformer=True, load_upsampler=False
   ) -> Tuple[LTX2Pipeline, Optional[dict], Optional[int]]:
     restored_checkpoint, step = self.load_ltx2_configs_from_orbax(step)
     opt_state = None
 
     if restored_checkpoint:
       max_logging.log("Loading LTX2 pipeline from checkpoint")
-      pipeline = LTX2Pipeline.from_checkpoint(self.config, restored_checkpoint, vae_only, load_transformer)
+      pipeline = LTX2Pipeline.from_checkpoint(self.config, restored_checkpoint, vae_only, load_transformer, load_upsampler)
       if "opt_state" in restored_checkpoint.ltx2_state.keys():
         opt_state = restored_checkpoint.ltx2_state["opt_state"]
     else:
       max_logging.log("No checkpoint found, loading pipeline from pretrained hub")
-      pipeline = LTX2Pipeline.from_pretrained(self.config, vae_only, load_transformer)
+      pipeline = LTX2Pipeline.from_pretrained(self.config, vae_only, load_transformer, load_upsampler)
 
     return pipeline, opt_state, step
 

src/maxdiffusion/configs/ltx2_video.yml

@@ -123,3 +123,12 @@ lora_config: {
   rank: [32]
 }
 
+# LTX-2 Latent Upsampler
+run_latent_upsampler: False
+upsampler_model_path: "Lightricks/LTX-2"
+upsampler_spatial_patch_size: 1
+upsampler_temporal_patch_size: 1
+upsampler_adain_factor: 0.0
+upsampler_tone_map_compression_ratio: 0.0
+upsampler_rational_spatial_scale: 2.0
+upsampler_output_type: "pil"

src/maxdiffusion/generate_ltx2.py

@@ -82,7 +82,6 @@ def get_git_commit_hash():
 
 
 def call_pipeline(config, pipeline, prompt, negative_prompt):
-  # Set default generation arguments
   generator = jax.random.key(config.seed) if hasattr(config, "seed") else jax.random.key(0)
   guidance_scale = config.guidance_scale if hasattr(config, "guidance_scale") else 3.0
 
@@ -100,6 +99,7 @@ def call_pipeline(config, pipeline, prompt, negative_prompt):
       decode_noise_scale=getattr(config, "decode_noise_scale", None),
       max_sequence_length=getattr(config, "max_sequence_length", 1024),
       dtype=jnp.bfloat16 if getattr(config, "activations_dtype", "bfloat16") == "bfloat16" else jnp.float32,
+      output_type=getattr(config, "upsampler_output_type", "pil"),
   )
   return out
 
@@ -115,9 +115,11 @@ def run(config, pipeline=None, filename_prefix="", commit_hash=None):
     else:
       max_logging.log("Could not retrieve Git commit hash.")
 
+  checkpoint_loader = LTX2Checkpointer(config=config)
   if pipeline is None:
-    checkpoint_loader = LTX2Checkpointer(config=config)
-    pipeline, _, _ = checkpoint_loader.load_checkpoint()
+    # Use the config flag to determine if the upsampler should be loaded
+    run_latent_upsampler = getattr(config, "run_latent_upsampler", False)
+    pipeline, _, _ = checkpoint_loader.load_checkpoint(load_upsampler=run_latent_upsampler)
 
     # If LoRA is specified, inject layers and load weights.
     if (
@@ -161,6 +163,7 @@ def run(config, pipeline=None, filename_prefix="", commit_hash=None):
   )
 
   out = call_pipeline(config, pipeline, prompt, negative_prompt)
+
   # out should have .frames and .audio
   videos = out.frames if hasattr(out, "frames") else out[0]
   audios = out.audio if hasattr(out, "audio") else None
@@ -169,6 +172,8 @@ def run(config, pipeline=None, filename_prefix="", commit_hash=None):
   max_logging.log(f"model name: {getattr(config, 'model_name', 'ltx-video')}")
   max_logging.log(f"model path: {config.pretrained_model_name_or_path}")
   max_logging.log(f"model type: {getattr(config, 'model_type', 'T2V')}")
+  if getattr(config, "run_latent_upsampler", False):
+    max_logging.log(f"upsampler model path: {config.upsampler_model_path}")
   max_logging.log(f"hardware: {jax.devices()[0].platform}")
   max_logging.log(f"number of devices: {jax.device_count()}")
   max_logging.log(f"per_device_batch_size: {config.per_device_batch_size}")