mirror of
https://github.com/huggingface/diffusers.git
synced 2026-01-27 17:22:53 +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>
119 lines
3.5 KiB
Python
119 lines
3.5 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 AutoTokenizer, UMT5EncoderModel
|
|
|
|
from diffusers import (
|
|
AuraFlowPipeline,
|
|
AuraFlowTransformer2DModel,
|
|
FlowMatchEulerDiscreteScheduler,
|
|
)
|
|
|
|
from ..testing_utils import (
|
|
floats_tensor,
|
|
is_peft_available,
|
|
require_peft_backend,
|
|
)
|
|
|
|
|
|
if is_peft_available():
|
|
pass
|
|
|
|
sys.path.append(".")
|
|
|
|
from .utils import PeftLoraLoaderMixinTests # noqa: E402
|
|
|
|
|
|
@require_peft_backend
|
|
class AuraFlowLoRATests(unittest.TestCase, PeftLoraLoaderMixinTests):
|
|
pipeline_class = AuraFlowPipeline
|
|
scheduler_cls = FlowMatchEulerDiscreteScheduler
|
|
scheduler_kwargs = {}
|
|
|
|
transformer_kwargs = {
|
|
"sample_size": 64,
|
|
"patch_size": 1,
|
|
"in_channels": 4,
|
|
"num_mmdit_layers": 1,
|
|
"num_single_dit_layers": 1,
|
|
"attention_head_dim": 16,
|
|
"num_attention_heads": 2,
|
|
"joint_attention_dim": 32,
|
|
"caption_projection_dim": 32,
|
|
"pos_embed_max_size": 64,
|
|
}
|
|
transformer_cls = AuraFlowTransformer2DModel
|
|
vae_kwargs = {
|
|
"sample_size": 32,
|
|
"in_channels": 3,
|
|
"out_channels": 3,
|
|
"block_out_channels": (4,),
|
|
"layers_per_block": 1,
|
|
"latent_channels": 4,
|
|
"norm_num_groups": 1,
|
|
"use_quant_conv": False,
|
|
"use_post_quant_conv": False,
|
|
"shift_factor": 0.0609,
|
|
"scaling_factor": 1.5035,
|
|
}
|
|
tokenizer_cls, tokenizer_id = AutoTokenizer, "hf-internal-testing/tiny-random-t5"
|
|
text_encoder_cls, text_encoder_id = UMT5EncoderModel, "hf-internal-testing/tiny-random-umt5"
|
|
text_encoder_target_modules = ["q", "k", "v", "o"]
|
|
denoiser_target_modules = ["to_q", "to_k", "to_v", "to_out.0", "linear_1"]
|
|
|
|
supports_text_encoder_loras = False
|
|
|
|
@property
|
|
def output_shape(self):
|
|
return (1, 8, 8, 3)
|
|
|
|
def get_dummy_inputs(self, with_generator=True):
|
|
batch_size = 1
|
|
sequence_length = 10
|
|
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": "A painting of a squirrel eating a burger",
|
|
"num_inference_steps": 4,
|
|
"guidance_scale": 0.0,
|
|
"height": 8,
|
|
"width": 8,
|
|
"output_type": "np",
|
|
}
|
|
if with_generator:
|
|
pipeline_inputs.update({"generator": generator})
|
|
|
|
return noise, input_ids, pipeline_inputs
|
|
|
|
@unittest.skip("Not supported in AuraFlow.")
|
|
def test_simple_inference_with_text_denoiser_block_scale(self):
|
|
pass
|
|
|
|
@unittest.skip("Not supported in AuraFlow.")
|
|
def test_simple_inference_with_text_denoiser_block_scale_for_all_dict_options(self):
|
|
pass
|
|
|
|
@unittest.skip("Not supported in AuraFlow.")
|
|
def test_modify_padding_mode(self):
|
|
pass
|