mirror of
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139 lines
4.5 KiB
Python
139 lines
4.5 KiB
Python
# coding=utf-8
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# Copyright 2025 HuggingFace Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import sys
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import unittest
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import torch
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from transformers import Gemma2Model, GemmaTokenizer
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from diffusers import AutoencoderDC, FlowMatchEulerDiscreteScheduler, SanaPipeline, SanaTransformer2DModel
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from ..testing_utils import floats_tensor, require_peft_backend
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sys.path.append(".")
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from .utils import PeftLoraLoaderMixinTests # noqa: E402
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@require_peft_backend
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class SanaLoRATests(unittest.TestCase, PeftLoraLoaderMixinTests):
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pipeline_class = SanaPipeline
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scheduler_cls = FlowMatchEulerDiscreteScheduler
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scheduler_kwargs = {"shift": 7.0}
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transformer_kwargs = {
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"patch_size": 1,
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"in_channels": 4,
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"out_channels": 4,
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"num_layers": 1,
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"num_attention_heads": 2,
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"attention_head_dim": 4,
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"num_cross_attention_heads": 2,
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"cross_attention_head_dim": 4,
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"cross_attention_dim": 8,
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"caption_channels": 8,
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"sample_size": 32,
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}
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transformer_cls = SanaTransformer2DModel
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vae_kwargs = {
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"in_channels": 3,
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"latent_channels": 4,
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"attention_head_dim": 2,
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"encoder_block_types": (
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"ResBlock",
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"EfficientViTBlock",
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),
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"decoder_block_types": (
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"ResBlock",
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"EfficientViTBlock",
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),
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"encoder_block_out_channels": (8, 8),
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"decoder_block_out_channels": (8, 8),
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"encoder_qkv_multiscales": ((), (5,)),
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"decoder_qkv_multiscales": ((), (5,)),
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"encoder_layers_per_block": (1, 1),
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"decoder_layers_per_block": [1, 1],
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"downsample_block_type": "conv",
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"upsample_block_type": "interpolate",
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"decoder_norm_types": "rms_norm",
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"decoder_act_fns": "silu",
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"scaling_factor": 0.41407,
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}
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vae_cls = AutoencoderDC
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tokenizer_cls, tokenizer_id = GemmaTokenizer, "hf-internal-testing/dummy-gemma"
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text_encoder_cls, text_encoder_id = Gemma2Model, "hf-internal-testing/dummy-gemma-for-diffusers"
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@property
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def output_shape(self):
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return (1, 32, 32, 3)
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def get_dummy_inputs(self, with_generator=True):
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batch_size = 1
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sequence_length = 16
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num_channels = 4
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sizes = (32, 32)
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generator = torch.manual_seed(0)
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noise = floats_tensor((batch_size, num_channels) + sizes)
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input_ids = torch.randint(1, sequence_length, size=(batch_size, sequence_length), generator=generator)
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pipeline_inputs = {
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"prompt": "",
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"negative_prompt": "",
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"num_inference_steps": 4,
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"guidance_scale": 4.5,
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"height": 32,
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"width": 32,
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"max_sequence_length": sequence_length,
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"output_type": "np",
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"complex_human_instruction": None,
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}
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if with_generator:
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pipeline_inputs.update({"generator": generator})
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return noise, input_ids, pipeline_inputs
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@unittest.skip("Not supported in SANA.")
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def test_modify_padding_mode(self):
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pass
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@unittest.skip("Not supported in SANA.")
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def test_simple_inference_with_text_denoiser_block_scale(self):
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pass
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@unittest.skip("Not supported in SANA.")
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def test_simple_inference_with_text_denoiser_block_scale_for_all_dict_options(self):
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pass
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@unittest.skip("Text encoder LoRA is not supported in SANA.")
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def test_simple_inference_with_partial_text_lora(self):
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pass
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@unittest.skip("Text encoder LoRA is not supported in SANA.")
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def test_simple_inference_with_text_lora(self):
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pass
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@unittest.skip("Text encoder LoRA is not supported in SANA.")
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def test_simple_inference_with_text_lora_and_scale(self):
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pass
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@unittest.skip("Text encoder LoRA is not supported in SANA.")
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def test_simple_inference_with_text_lora_fused(self):
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pass
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@unittest.skip("Text encoder LoRA is not supported in SANA.")
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def test_simple_inference_with_text_lora_save_load(self):
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pass
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