Synced official GitHub structure with Shiv AI branding

ltx_video/inference.py

@@ -38,7 +38,7 @@ from ltx_video.utils.skip_layer_strategy import SkipLayerStrategy
 from ltx_video.models.autoencoders.latent_upsampler import LatentUpsampler
 import ltx_video.pipelines.crf_compressor as crf_compressor
 
-logger = logging.get_logger("LTX-Video")
+logger = logging.get_logger("Shiv-AI-Video-Generator")
 
 
 def get_total_gpu_memory():
@@ -196,7 +196,7 @@ def create_transformer(ckpt_path: str, precision: str) -> Transformer3DModel:
             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"
+                "Q8-Kernels not found. To use FP8 checkpoint, please install Q8 kernels from https://github.com/Shri Ram Nag/LTXVideo-Q8-Kernels"
             )
     elif precision == "bfloat16":
         return Transformer3DModel.from_pretrained(ckpt_path).to(torch.bfloat16)
@@ -392,7 +392,7 @@ def infer(config: InferenceConfig):
     ltxv_model_name_or_path = pipeline_config["checkpoint_path"]
     if not os.path.isfile(ltxv_model_name_or_path):
         ltxv_model_path = hf_hub_download(
-            repo_id="Lightricks/LTX-Video",
+            repo_id="Shri Ram Nag/Shiv-AI-Video-Generator",
             filename=ltxv_model_name_or_path,
             repo_type="model",
         )
@@ -406,7 +406,7 @@ def infer(config: InferenceConfig):
         spatial_upscaler_model_name_or_path
     ):
         spatial_upscaler_model_path = hf_hub_download(
-            repo_id="Lightricks/LTX-Video",
+            repo_id="Shri Ram Nag/Shiv-AI-Video-Generator",
             filename=spatial_upscaler_model_name_or_path,
             repo_type="model",
         )

ltx_video/models/autoencoders/causal_video_autoencoder.py

@@ -78,7 +78,7 @@ class CausalVideoAutoencoder(AutoencoderKLWrapper):
 
             assert config in diffusers_and_ours_config_mapping, (
                 "Provided diffusers checkpoint config for VAE is not suppported. "
-                "We only support diffusers configs found in Lightricks/LTX-Video."
+                "We only support diffusers configs found in Shri Ram Nag/Shiv-AI-Video-Generator."
             )
 
             config = diffusers_and_ours_config_mapping[config]

ltx_video/models/transformers/transformer3d.py

@@ -285,7 +285,7 @@ class Transformer3DModel(ModelMixin, ConfigMixin):
 
             assert config in diffusers_and_ours_config_mapping, (
                 "Provided diffusers checkpoint config for transformer is not suppported. "
-                "We only support diffusers configs found in Lightricks/LTX-Video."
+                "We only support diffusers configs found in Shri Ram Nag/Shiv-AI-Video-Generator."
             )
 
             config = diffusers_and_ours_config_mapping[config]

ltx_video/pipelines/pipeline_ltx_video.py

@@ -212,7 +212,7 @@ class ConditioningItem:
 
 class LTXVideoPipeline(DiffusionPipeline):
     r"""
-    Pipeline for text-to-image generation using LTX-Video.
+    Pipeline for text-to-image generation using Shiv-AI-Video-Generator.
 
     This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
     library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)

pyproject.toml

@@ -5,9 +5,9 @@ build-backend = "setuptools.build_meta"
 [project]
 name = "ltx-video"
 version = "0.1.2"
-description = "A package for LTX-Video model"
+description = "A package for Shiv-AI-Video-Generator model"
 authors = [
-    { name = "LTX-Video Team", email = "ltx-video@lightricks.com" }
+    { name = "Shiv-AI-Video-Generator Team", email = "ltx-video@lightricks.com" }
 ]
 requires-python = ">=3.10"
 readme = "README.md"