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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 numpy as np
import pytest
import torch
from transformers import AutoTokenizer, T5EncoderModel
from diffusers import AutoencoderKLMochi, FlowMatchEulerDiscreteScheduler, MochiPipeline, MochiTransformer3DModel
from diffusers.utils.testing_utils import (
floats_tensor,
is_peft_available,
is_torch_version,
require_peft_backend,
skip_mps,
torch_device,
)
if is_peft_available():
pass
sys.path.append(".")
from utils import PeftLoraLoaderMixinTests, check_if_lora_correctly_set # noqa: E402
@require_peft_backend
@skip_mps
class MochiLoRATests(unittest.TestCase, PeftLoraLoaderMixinTests):
pipeline_class = MochiPipeline
scheduler_cls = FlowMatchEulerDiscreteScheduler
scheduler_classes = [FlowMatchEulerDiscreteScheduler]
scheduler_kwargs = {}
transformer_kwargs = {
"patch_size": 2,
"num_attention_heads": 2,
"attention_head_dim": 8,
"num_layers": 2,
"pooled_projection_dim": 16,
"in_channels": 12,
"out_channels": None,
"qk_norm": "rms_norm",
"text_embed_dim": 32,
"time_embed_dim": 4,
"activation_fn": "swiglu",
"max_sequence_length": 16,
}
transformer_cls = MochiTransformer3DModel
vae_kwargs = {
"latent_channels": 12,
"out_channels": 3,
"encoder_block_out_channels": (32, 32, 32, 32),
"decoder_block_out_channels": (32, 32, 32, 32),
"layers_per_block": (1, 1, 1, 1, 1),
}
vae_cls = AutoencoderKLMochi
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, 7, 16, 16, 3)
def get_dummy_inputs(self, with_generator=True):
batch_size = 1
sequence_length = 16
num_channels = 4
num_frames = 7
num_latent_frames = 3
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
@pytest.mark.xfail(
condition=torch.device(torch_device).type == "cpu" and is_torch_version(">=", "2.5"),
reason="Test currently fails on CPU and PyTorch 2.5.1 but not on PyTorch 2.4.1.",
strict=True,
)
def test_lora_fuse_nan(self):
for scheduler_cls in self.scheduler_classes:
components, text_lora_config, denoiser_lora_config = self.get_dummy_components(scheduler_cls)
pipe = self.pipeline_class(**components)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
_, _, inputs = self.get_dummy_inputs(with_generator=False)
pipe.transformer.add_adapter(denoiser_lora_config, "adapter-1")
self.assertTrue(check_if_lora_correctly_set(pipe.transformer), "Lora not correctly set in denoiser")
# corrupt one LoRA weight with `inf` values
with torch.no_grad():
pipe.transformer.transformer_blocks[0].attn1.to_q.lora_A["adapter-1"].weight += float("inf")
# with `safe_fusing=True` we should see an Error
with self.assertRaises(ValueError):
pipe.fuse_lora(components=self.pipeline_class._lora_loadable_modules, safe_fusing=True)
# without we should not see an error, but every image will be black
pipe.fuse_lora(components=self.pipeline_class._lora_loadable_modules, safe_fusing=False)
out = pipe(
"test", num_inference_steps=2, max_sequence_length=inputs["max_sequence_length"], output_type="np"
)[0]
self.assertTrue(np.isnan(out).all())
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 Mochi.")
def test_simple_inference_with_text_denoiser_block_scale(self):
pass
@unittest.skip("Not supported in Mochi.")
def test_simple_inference_with_text_denoiser_block_scale_for_all_dict_options(self):
pass
@unittest.skip("Not supported in Mochi.")
def test_modify_padding_mode(self):
pass
@unittest.skip("Text encoder LoRA is not supported in Mochi.")
def test_simple_inference_with_partial_text_lora(self):
pass
@unittest.skip("Text encoder LoRA is not supported in Mochi.")
def test_simple_inference_with_text_lora(self):
pass
@unittest.skip("Text encoder LoRA is not supported in Mochi.")
def test_simple_inference_with_text_lora_and_scale(self):
pass
@unittest.skip("Text encoder LoRA is not supported in Mochi.")
def test_simple_inference_with_text_lora_fused(self):
pass
@unittest.skip("Text encoder LoRA is not supported in Mochi.")
def test_simple_inference_with_text_lora_save_load(self):
pass
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