# 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 parameterized import parameterized

from transformers import AutoTokenizer, T5EncoderModel

from diffusers import (

AutoencoderKLCogVideoX,

CogVideoXDPMScheduler,

CogVideoXPipeline,

CogVideoXTransformer3DModel,

)

from ..testing_utils import (

floats_tensor,

require_peft_backend,

require_torch_accelerator,

)

sys.path.append(".")

from .utils import PeftLoraLoaderMixinTests # noqa: E402

@require_peft_backend

class CogVideoXLoRATests(unittest.TestCase, PeftLoraLoaderMixinTests):

pipeline_class = CogVideoXPipeline

scheduler_cls = CogVideoXDPMScheduler

scheduler_kwargs = {"timestep_spacing": "trailing"}

transformer_kwargs = {

"num_attention_heads": 4,

"attention_head_dim": 8,

"in_channels": 4,

"out_channels": 4,

"time_embed_dim": 2,

"text_embed_dim": 32,

"num_layers": 1,

"sample_width": 16,

"sample_height": 16,

"sample_frames": 9,

"patch_size": 2,

"temporal_compression_ratio": 4,

"max_text_seq_length": 16,

}

transformer_cls = CogVideoXTransformer3DModel

vae_kwargs = {

"in_channels": 3,

"out_channels": 3,

"down_block_types": (

"CogVideoXDownBlock3D",

"CogVideoXDownBlock3D",

"CogVideoXDownBlock3D",

"CogVideoXDownBlock3D",

),

"up_block_types": (

"CogVideoXUpBlock3D",

"CogVideoXUpBlock3D",

"CogVideoXUpBlock3D",

"CogVideoXUpBlock3D",

),

"block_out_channels": (8, 8, 8, 8),

"latent_channels": 4,

"layers_per_block": 1,

"norm_num_groups": 2,

"temporal_compression_ratio": 4,

}

vae_cls = AutoencoderKLCogVideoX

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"]

supports_text_encoder_loras = False

@property

def output_shape(self):

return (1, 9, 16, 16, 3)

def get_dummy_inputs(self, with_generator=True):

batch_size = 1

sequence_length = 16

num_channels = 4

num_frames = 9

num_latent_frames = 3 # (num_frames - 1) // temporal_compression_ratio + 1

sizes = (2, 2)

generator = torch.manual_seed(0)

noise = floats_tensor((batch_size, num_latent_frames, num_channels) + sizes)

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,

# Cannot reduce because convolution kernel becomes bigger than sample

"height": 16,

"width": 16,

"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)

def test_lora_scale_kwargs_match_fusion(self):

super().test_lora_scale_kwargs_match_fusion(expected_atol=9e-3, expected_rtol=9e-3)

@parameterized.expand([("block_level", True), ("leaf_level", False)])

@require_torch_accelerator

def test_group_offloading_inference_denoiser(self, offload_type, use_stream):

# TODO: We don't run the (leaf_level, True) test here that is enabled for other models.

# The reason for this can be found here: https://github.com/huggingface/diffusers/pull/11804#issuecomment-3013325338

super()._test_group_offloading_inference_denoiser(offload_type, use_stream)

@unittest.skip("Not supported in CogVideoX.")

def test_simple_inference_with_text_denoiser_block_scale(self):

pass

@unittest.skip("Not supported in CogVideoX.")

def test_simple_inference_with_text_denoiser_block_scale_for_all_dict_options(self):

pass

@unittest.skip("Not supported in CogVideoX.")

def test_modify_padding_mode(self):

pass

@unittest.skip("Not supported in CogVideoX.")

def test_simple_inference_with_text_denoiser_multi_adapter_block_lora(self):

pass