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# 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 Gemma2Model, GemmaTokenizer
from diffusers import AutoencoderDC, FlowMatchEulerDiscreteScheduler, SanaPipeline, SanaTransformer2DModel
from ..testing_utils import IS_GITHUB_ACTIONS, floats_tensor, require_peft_backend
sys.path.append(".")
from .utils import PeftLoraLoaderMixinTests # noqa: E402
@require_peft_backend
class SanaLoRATests(unittest.TestCase, PeftLoraLoaderMixinTests):
pipeline_class = SanaPipeline
scheduler_cls = FlowMatchEulerDiscreteScheduler
scheduler_kwargs = {"shift": 7.0}
transformer_kwargs = {
"patch_size": 1,
"in_channels": 4,
"out_channels": 4,
"num_layers": 1,
"num_attention_heads": 2,
"attention_head_dim": 4,
"num_cross_attention_heads": 2,
"cross_attention_head_dim": 4,
"cross_attention_dim": 8,
"caption_channels": 8,
"sample_size": 32,
}
transformer_cls = SanaTransformer2DModel
vae_kwargs = {
"in_channels": 3,
"latent_channels": 4,
"attention_head_dim": 2,
"encoder_block_types": (
"ResBlock",
"EfficientViTBlock",
),
"decoder_block_types": (
"ResBlock",
"EfficientViTBlock",
),
"encoder_block_out_channels": (8, 8),
"decoder_block_out_channels": (8, 8),
"encoder_qkv_multiscales": ((), (5,)),
"decoder_qkv_multiscales": ((), (5,)),
"encoder_layers_per_block": (1, 1),
"decoder_layers_per_block": [1, 1],
"downsample_block_type": "conv",
"upsample_block_type": "interpolate",
"decoder_norm_types": "rms_norm",
"decoder_act_fns": "silu",
"scaling_factor": 0.41407,
}
vae_cls = AutoencoderDC
tokenizer_cls, tokenizer_id = GemmaTokenizer, "hf-internal-testing/dummy-gemma"
text_encoder_cls, text_encoder_id = Gemma2Model, "hf-internal-testing/dummy-gemma-for-diffusers"
supports_text_encoder_loras = False
@property
def output_shape(self):
return (1, 32, 32, 3)
def get_dummy_inputs(self, with_generator=True):
batch_size = 1
sequence_length = 16
num_channels = 4
sizes = (32, 32)
generator = torch.manual_seed(0)
noise = floats_tensor((batch_size, num_channels) + sizes)
input_ids = torch.randint(1, sequence_length, size=(batch_size, sequence_length), generator=generator)
pipeline_inputs = {
"prompt": "",
"negative_prompt": "",
"num_inference_steps": 4,
"guidance_scale": 4.5,
"height": 32,
"width": 32,
"max_sequence_length": sequence_length,
"output_type": "np",
"complex_human_instruction": None,
}
if with_generator:
pipeline_inputs.update({"generator": generator})
return noise, input_ids, pipeline_inputs
@unittest.skip("Not supported in SANA.")
def test_modify_padding_mode(self):
pass
@unittest.skip("Not supported in SANA.")
def test_simple_inference_with_text_denoiser_block_scale(self):
pass
@unittest.skip("Not supported in SANA.")
def test_simple_inference_with_text_denoiser_block_scale_for_all_dict_options(self):
pass
@unittest.skipIf(IS_GITHUB_ACTIONS, reason="Skipping test inside GitHub Actions environment")
def test_layerwise_casting_inference_denoiser(self):
return super().test_layerwise_casting_inference_denoiser()
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