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ca32b0e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 | # Copyright 2025 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import sys
import unittest
import torch
from transformers import AutoTokenizer, T5EncoderModel
from diffusers import (
AutoencoderKLLTXVideo,
FlowMatchEulerDiscreteScheduler,
LTXPipeline,
LTXVideoTransformer3DModel,
)
from diffusers.utils.testing_utils import floats_tensor, require_peft_backend
sys.path.append(".")
from utils import PeftLoraLoaderMixinTests # noqa: E402
@require_peft_backend
class LTXVideoLoRATests(unittest.TestCase, PeftLoraLoaderMixinTests):
pipeline_class = LTXPipeline
scheduler_cls = FlowMatchEulerDiscreteScheduler
scheduler_classes = [FlowMatchEulerDiscreteScheduler]
scheduler_kwargs = {}
transformer_kwargs = {
"in_channels": 8,
"out_channels": 8,
"patch_size": 1,
"patch_size_t": 1,
"num_attention_heads": 4,
"attention_head_dim": 8,
"cross_attention_dim": 32,
"num_layers": 1,
"caption_channels": 32,
}
transformer_cls = LTXVideoTransformer3DModel
vae_kwargs = {
"in_channels": 3,
"out_channels": 3,
"latent_channels": 8,
"block_out_channels": (8, 8, 8, 8),
"decoder_block_out_channels": (8, 8, 8, 8),
"layers_per_block": (1, 1, 1, 1, 1),
"decoder_layers_per_block": (1, 1, 1, 1, 1),
"spatio_temporal_scaling": (True, True, False, False),
"decoder_spatio_temporal_scaling": (True, True, False, False),
"decoder_inject_noise": (False, False, False, False, False),
"upsample_residual": (False, False, False, False),
"upsample_factor": (1, 1, 1, 1),
"timestep_conditioning": False,
"patch_size": 1,
"patch_size_t": 1,
"encoder_causal": True,
"decoder_causal": False,
}
vae_cls = AutoencoderKLLTXVideo
tokenizer_cls, tokenizer_id = AutoTokenizer, "hf-internal-testing/tiny-random-t5"
text_encoder_cls, text_encoder_id = T5EncoderModel, "hf-internal-testing/tiny-random-t5"
text_encoder_target_modules = ["q", "k", "v", "o"]
@property
def output_shape(self):
return (1, 9, 32, 32, 3)
def get_dummy_inputs(self, with_generator=True):
batch_size = 1
sequence_length = 16
num_channels = 8
num_frames = 9
num_latent_frames = 3 # (num_frames - 1) // temporal_compression_ratio + 1
latent_height = 8
latent_width = 8
generator = torch.manual_seed(0)
noise = floats_tensor((batch_size, num_latent_frames, num_channels, latent_height, latent_width))
input_ids = torch.randint(1, sequence_length, size=(batch_size, sequence_length), generator=generator)
pipeline_inputs = {
"prompt": "dance monkey",
"num_frames": num_frames,
"num_inference_steps": 4,
"guidance_scale": 6.0,
"height": 32,
"width": 32,
"max_sequence_length": sequence_length,
"output_type": "np",
}
if with_generator:
pipeline_inputs.update({"generator": generator})
return noise, input_ids, pipeline_inputs
def test_simple_inference_with_text_lora_denoiser_fused_multi(self):
super().test_simple_inference_with_text_lora_denoiser_fused_multi(expected_atol=9e-3)
def test_simple_inference_with_text_denoiser_lora_unfused(self):
super().test_simple_inference_with_text_denoiser_lora_unfused(expected_atol=9e-3)
@unittest.skip("Not supported in LTXVideo.")
def test_simple_inference_with_text_denoiser_block_scale(self):
pass
@unittest.skip("Not supported in LTXVideo.")
def test_simple_inference_with_text_denoiser_block_scale_for_all_dict_options(self):
pass
@unittest.skip("Not supported in LTXVideo.")
def test_modify_padding_mode(self):
pass
@unittest.skip("Text encoder LoRA is not supported in LTXVideo.")
def test_simple_inference_with_partial_text_lora(self):
pass
@unittest.skip("Text encoder LoRA is not supported in LTXVideo.")
def test_simple_inference_with_text_lora(self):
pass
@unittest.skip("Text encoder LoRA is not supported in LTXVideo.")
def test_simple_inference_with_text_lora_and_scale(self):
pass
@unittest.skip("Text encoder LoRA is not supported in LTXVideo.")
def test_simple_inference_with_text_lora_fused(self):
pass
@unittest.skip("Text encoder LoRA is not supported in LTXVideo.")
def test_simple_inference_with_text_lora_save_load(self):
pass
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