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| # coding=utf-8 | |
| # 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 | |
| from transformers import AutoTokenizer, CLIPTextModelWithProjection, CLIPTokenizer, T5EncoderModel | |
| from diffusers import FlowMatchEulerDiscreteScheduler, SD3Transformer2DModel, StableDiffusion3Pipeline | |
| from diffusers.utils.testing_utils import is_peft_available, require_peft_backend, require_torch_gpu, torch_device | |
| if is_peft_available(): | |
| pass | |
| sys.path.append(".") | |
| from utils import PeftLoraLoaderMixinTests # noqa: E402 | |
| class SD3LoRATests(unittest.TestCase, PeftLoraLoaderMixinTests): | |
| pipeline_class = StableDiffusion3Pipeline | |
| scheduler_cls = FlowMatchEulerDiscreteScheduler | |
| scheduler_kwargs = {} | |
| scheduler_classes = [FlowMatchEulerDiscreteScheduler] | |
| transformer_kwargs = { | |
| "sample_size": 32, | |
| "patch_size": 1, | |
| "in_channels": 4, | |
| "num_layers": 1, | |
| "attention_head_dim": 8, | |
| "num_attention_heads": 4, | |
| "caption_projection_dim": 32, | |
| "joint_attention_dim": 32, | |
| "pooled_projection_dim": 64, | |
| "out_channels": 4, | |
| } | |
| transformer_cls = SD3Transformer2DModel | |
| 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, | |
| } | |
| has_three_text_encoders = True | |
| tokenizer_cls, tokenizer_id = CLIPTokenizer, "hf-internal-testing/tiny-random-clip" | |
| tokenizer_2_cls, tokenizer_2_id = CLIPTokenizer, "hf-internal-testing/tiny-random-clip" | |
| tokenizer_3_cls, tokenizer_3_id = AutoTokenizer, "hf-internal-testing/tiny-random-t5" | |
| text_encoder_cls, text_encoder_id = CLIPTextModelWithProjection, "hf-internal-testing/tiny-sd3-text_encoder" | |
| text_encoder_2_cls, text_encoder_2_id = CLIPTextModelWithProjection, "hf-internal-testing/tiny-sd3-text_encoder-2" | |
| text_encoder_3_cls, text_encoder_3_id = T5EncoderModel, "hf-internal-testing/tiny-random-t5" | |
| def output_shape(self): | |
| return (1, 32, 32, 3) | |
| def test_sd3_lora(self): | |
| """ | |
| Test loading the loras that are saved with the diffusers and peft formats. | |
| Related PR: https://github.com/huggingface/diffusers/pull/8584 | |
| """ | |
| components = self.get_dummy_components() | |
| pipe = self.pipeline_class(**components[0]) | |
| pipe = pipe.to(torch_device) | |
| pipe.set_progress_bar_config(disable=None) | |
| lora_model_id = "hf-internal-testing/tiny-sd3-loras" | |
| lora_filename = "lora_diffusers_format.safetensors" | |
| pipe.load_lora_weights(lora_model_id, weight_name=lora_filename) | |
| pipe.unload_lora_weights() | |
| lora_filename = "lora_peft_format.safetensors" | |
| pipe.load_lora_weights(lora_model_id, weight_name=lora_filename) | |
| def test_simple_inference_with_text_denoiser_block_scale(self): | |
| pass | |
| def test_simple_inference_with_text_denoiser_multi_adapter_block_lora(self): | |
| pass | |
| def test_simple_inference_with_text_denoiser_block_scale_for_all_dict_options(self): | |
| pass | |
| def test_modify_padding_mode(self): | |
| pass | |