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7aa6af1138
* update
* update
* update
* update
* update
* merge main
* Revert "merge main"
This reverts commit 65efbcead5.
131 lines
4.1 KiB
Python
131 lines
4.1 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 Qwen2_5_VLForConditionalGeneration, Qwen2Tokenizer
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from diffusers import (
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AutoencoderKLQwenImage,
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FlowMatchEulerDiscreteScheduler,
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QwenImagePipeline,
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QwenImageTransformer2DModel,
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)
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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 QwenImageLoRATests(unittest.TestCase, PeftLoraLoaderMixinTests):
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pipeline_class = QwenImagePipeline
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scheduler_cls = FlowMatchEulerDiscreteScheduler
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scheduler_classes = [FlowMatchEulerDiscreteScheduler]
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scheduler_kwargs = {}
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transformer_kwargs = {
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"patch_size": 2,
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"in_channels": 16,
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"out_channels": 4,
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"num_layers": 2,
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"attention_head_dim": 16,
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"num_attention_heads": 3,
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"joint_attention_dim": 16,
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"guidance_embeds": False,
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"axes_dims_rope": (8, 4, 4),
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}
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transformer_cls = QwenImageTransformer2DModel
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z_dim = 4
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vae_kwargs = {
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"base_dim": z_dim * 6,
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"z_dim": z_dim,
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"dim_mult": [1, 2, 4],
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"num_res_blocks": 1,
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"temperal_downsample": [False, True],
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"latents_mean": [0.0] * 4,
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"latents_std": [1.0] * 4,
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}
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vae_cls = AutoencoderKLQwenImage
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tokenizer_cls, tokenizer_id = Qwen2Tokenizer, "hf-internal-testing/tiny-random-Qwen25VLForCondGen"
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text_encoder_cls, text_encoder_id = (
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Qwen2_5_VLForConditionalGeneration,
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"hf-internal-testing/tiny-random-Qwen25VLForCondGen",
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)
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denoiser_target_modules = ["to_q", "to_k", "to_v", "to_out.0"]
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@property
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def output_shape(self):
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return (1, 8, 8, 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 = 10
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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": "A painting of a squirrel eating a burger",
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"num_inference_steps": 4,
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"guidance_scale": 0.0,
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"height": 8,
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"width": 8,
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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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@unittest.skip("Not supported in Qwen Image.")
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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 Qwen Image.")
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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 Qwen Image.")
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def test_modify_padding_mode(self):
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pass
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@unittest.skip("Text encoder LoRA is not supported in Qwen Image.")
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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 Qwen Image.")
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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 Qwen Image.")
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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 Qwen Image.")
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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 Qwen Image.")
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def test_simple_inference_with_text_lora_save_load(self):
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pass
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