mirror of
https://github.com/huggingface/diffusers.git
synced 2026-01-29 07:22:12 +03:00
f1a93c765f
* Improve incorrect LoRA format error message * Add flag in PeftLoraLoaderMixinTests to disable text encoder LoRA tests * Apply changes to LTX2LoraTests * Further improve incorrect LoRA format error msg following review --------- Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
125 lines
4.1 KiB
Python
125 lines
4.1 KiB
Python
# coding=utf-8
|
|
# 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()
|