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| import sys |
| import unittest |
|
|
| import torch |
| from transformers import Qwen2_5_VLForConditionalGeneration, Qwen2Tokenizer |
|
|
| from diffusers import ( |
| AutoencoderKLQwenImage, |
| FlowMatchEulerDiscreteScheduler, |
| QwenImagePipeline, |
| QwenImageTransformer2DModel, |
| ) |
|
|
| from ..testing_utils import floats_tensor, require_peft_backend |
|
|
|
|
| sys.path.append(".") |
|
|
| from .utils import PeftLoraLoaderMixinTests |
|
|
|
|
| @require_peft_backend |
| class QwenImageLoRATests(unittest.TestCase, PeftLoraLoaderMixinTests): |
| pipeline_class = QwenImagePipeline |
| scheduler_cls = FlowMatchEulerDiscreteScheduler |
| scheduler_classes = [FlowMatchEulerDiscreteScheduler] |
| scheduler_kwargs = {} |
|
|
| transformer_kwargs = { |
| "patch_size": 2, |
| "in_channels": 16, |
| "out_channels": 4, |
| "num_layers": 2, |
| "attention_head_dim": 16, |
| "num_attention_heads": 3, |
| "joint_attention_dim": 16, |
| "guidance_embeds": False, |
| "axes_dims_rope": (8, 4, 4), |
| } |
| transformer_cls = QwenImageTransformer2DModel |
| z_dim = 4 |
| vae_kwargs = { |
| "base_dim": z_dim * 6, |
| "z_dim": z_dim, |
| "dim_mult": [1, 2, 4], |
| "num_res_blocks": 1, |
| "temperal_downsample": [False, True], |
| "latents_mean": [0.0] * 4, |
| "latents_std": [1.0] * 4, |
| } |
| vae_cls = AutoencoderKLQwenImage |
| tokenizer_cls, tokenizer_id = Qwen2Tokenizer, "hf-internal-testing/tiny-random-Qwen25VLForCondGen" |
| text_encoder_cls, text_encoder_id = ( |
| Qwen2_5_VLForConditionalGeneration, |
| "hf-internal-testing/tiny-random-Qwen25VLForCondGen", |
| ) |
| denoiser_target_modules = ["to_q", "to_k", "to_v", "to_out.0"] |
|
|
| @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 Qwen Image.") |
| def test_simple_inference_with_text_denoiser_block_scale(self): |
| pass |
|
|
| @unittest.skip("Not supported in Qwen Image.") |
| def test_simple_inference_with_text_denoiser_block_scale_for_all_dict_options(self): |
| pass |
|
|
| @unittest.skip("Not supported in Qwen Image.") |
| def test_modify_padding_mode(self): |
| pass |
|
|
| @unittest.skip("Text encoder LoRA is not supported in Qwen Image.") |
| def test_simple_inference_with_partial_text_lora(self): |
| pass |
|
|
| @unittest.skip("Text encoder LoRA is not supported in Qwen Image.") |
| def test_simple_inference_with_text_lora(self): |
| pass |
|
|
| @unittest.skip("Text encoder LoRA is not supported in Qwen Image.") |
| def test_simple_inference_with_text_lora_and_scale(self): |
| pass |
|
|
| @unittest.skip("Text encoder LoRA is not supported in Qwen Image.") |
| def test_simple_inference_with_text_lora_fused(self): |
| pass |
|
|
| @unittest.skip("Text encoder LoRA is not supported in Qwen Image.") |
| def test_simple_inference_with_text_lora_save_load(self): |
| pass |
|
|
