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from diffusers import (
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AutoencoderKLLTXVideo ,
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FlowMatchEulerDiscreteScheduler ,
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+ LTXConditionPipeline ,
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LTXLatentUpsamplePipeline ,
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LTXPipeline ,
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LTXVideoTransformer3DModel ,
@@ -464,7 +465,7 @@ def get_args():
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for param in text_encoder .parameters ():
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param .data = param .data .contiguous ()
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- if args .version == "0.9.5" :
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+ if args .version in [ "0.9.5" , "0.9.7" ] :
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scheduler = FlowMatchEulerDiscreteScheduler (use_dynamic_shifting = False )
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else :
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scheduler = FlowMatchEulerDiscreteScheduler (
@@ -488,23 +489,23 @@ def get_args():
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output_path .as_posix (), safe_serialization = True , variant = variant , max_shard_size = "5GB"
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)
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elif args .version in ["0.9.7" ]:
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- # pipe = LTXPipeline (
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- # scheduler=scheduler,
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- # vae=vae,
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- # text_encoder=text_encoder,
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- # tokenizer=tokenizer,
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- # transformer=transformer,
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- # )
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+ pipe = LTXConditionPipeline (
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+ scheduler = scheduler ,
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+ vae = vae ,
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+ text_encoder = text_encoder ,
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+ tokenizer = tokenizer ,
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+ transformer = transformer ,
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+ )
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pipe_upsample = LTXLatentUpsamplePipeline (
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vae = vae ,
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latent_upsampler = latent_upsampler ,
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)
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- # pipe.save_pretrained(
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- # (output_path / "ltx_pipeline").as_posix(),
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- # safe_serialization=True,
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- # variant=variant,
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- # max_shard_size="5GB",
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- # )
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+ pipe .save_pretrained (
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+ (output_path / "ltx_pipeline" ).as_posix (),
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+ safe_serialization = True ,
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+ variant = variant ,
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+ max_shard_size = "5GB" ,
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+ )
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pipe_upsample .save_pretrained (
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(output_path / "ltx_upsample_pipeline" ).as_posix (),
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safe_serialization = True ,
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