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# Copyright 2024 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 (
AutoencoderKLCogVideoX,
CogVideoXDDIMScheduler,
CogVideoXDPMScheduler,
CogVideoXPipeline,
CogVideoXTransformer3DModel,
)
from diffusers.utils.testing_utils import (
floats_tensor,
require_peft_backend,
)
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"}
scheduler_classes = [CogVideoXDDIMScheduler, CogVideoXDPMScheduler]
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"]
@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)
@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("Text encoder LoRA is not supported in CogVideoX.")
def test_simple_inference_with_partial_text_lora(self):
pass
@unittest.skip("Text encoder LoRA is not supported in CogVideoX.")
def test_simple_inference_with_text_lora(self):
pass
@unittest.skip("Text encoder LoRA is not supported in CogVideoX.")
def test_simple_inference_with_text_lora_and_scale(self):
pass
@unittest.skip("Text encoder LoRA is not supported in CogVideoX.")
def test_simple_inference_with_text_lora_fused(self):
pass
@unittest.skip("Text encoder LoRA is not supported in CogVideoX.")
def test_simple_inference_with_text_lora_save_load(self):
pass
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