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https://github.com/huggingface/diffusers.git
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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>
126 lines
3.8 KiB
Python
126 lines
3.8 KiB
Python
# 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 AutoTokenizer, T5EncoderModel
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from diffusers import (
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AutoencoderKLWan,
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FlowMatchEulerDiscreteScheduler,
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WanPipeline,
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WanTransformer3DModel,
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)
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from ..testing_utils import (
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floats_tensor,
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require_peft_backend,
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skip_mps,
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)
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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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@skip_mps
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class WanLoRATests(unittest.TestCase, PeftLoraLoaderMixinTests):
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pipeline_class = WanPipeline
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scheduler_cls = FlowMatchEulerDiscreteScheduler
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scheduler_kwargs = {}
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transformer_kwargs = {
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"patch_size": (1, 2, 2),
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"num_attention_heads": 2,
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"attention_head_dim": 12,
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"in_channels": 16,
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"out_channels": 16,
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"text_dim": 32,
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"freq_dim": 256,
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"ffn_dim": 32,
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"num_layers": 2,
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"cross_attn_norm": True,
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"qk_norm": "rms_norm_across_heads",
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"rope_max_seq_len": 32,
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}
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transformer_cls = WanTransformer3DModel
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vae_kwargs = {
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"base_dim": 3,
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"z_dim": 16,
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"dim_mult": [1, 1, 1, 1],
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"num_res_blocks": 1,
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"temperal_downsample": [False, True, True],
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}
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vae_cls = AutoencoderKLWan
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has_two_text_encoders = True
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tokenizer_cls, tokenizer_id = AutoTokenizer, "hf-internal-testing/tiny-random-t5"
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text_encoder_cls, text_encoder_id = T5EncoderModel, "hf-internal-testing/tiny-random-t5"
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text_encoder_target_modules = ["q", "k", "v", "o"]
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supports_text_encoder_loras = False
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@property
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def output_shape(self):
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return (1, 9, 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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num_frames = 9
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num_latent_frames = 3 # (num_frames - 1) // temporal_compression_ratio + 1
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sizes = (4, 4)
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generator = torch.manual_seed(0)
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noise = floats_tensor((batch_size, num_latent_frames, 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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"num_frames": num_frames,
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"num_inference_steps": 1,
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"guidance_scale": 6.0,
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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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}
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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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def test_simple_inference_with_text_lora_denoiser_fused_multi(self):
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super().test_simple_inference_with_text_lora_denoiser_fused_multi(expected_atol=9e-3)
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def test_simple_inference_with_text_denoiser_lora_unfused(self):
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super().test_simple_inference_with_text_denoiser_lora_unfused(expected_atol=9e-3)
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@unittest.skip("Not supported in Wan.")
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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 Wan.")
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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("Not supported in Wan.")
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def test_modify_padding_mode(self):
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pass
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