# Copyright 2026 The HuggingFace Team. # # 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 unittest import numpy as np import torch from transformers import Qwen2Tokenizer, Qwen3VLConfig, Qwen3VLModel from diffusers import ( AutoencoderKLQwenImage, FlowMatchEulerDiscreteScheduler, Krea2Pipeline, Krea2Transformer2DModel, ) from ...testing_utils import enable_full_determinism, torch_device from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS from ..test_pipelines_common import PipelineTesterMixin, to_np enable_full_determinism() class Krea2PipelineFastTests(PipelineTesterMixin, unittest.TestCase): pipeline_class = Krea2Pipeline params = TEXT_TO_IMAGE_PARAMS - {"cross_attention_kwargs"} batch_params = TEXT_TO_IMAGE_BATCH_PARAMS image_params = TEXT_TO_IMAGE_IMAGE_PARAMS image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS required_optional_params = frozenset( [ "num_inference_steps", "generator", "latents", "return_dict", "callback_on_step_end", "callback_on_step_end_tensor_inputs", ] ) test_xformers_attention = False test_layerwise_casting = True test_group_offloading = True def get_dummy_components(self): torch.manual_seed(0) transformer = Krea2Transformer2DModel( in_channels=16, num_layers=2, attention_head_dim=8, num_attention_heads=4, num_key_value_heads=2, intermediate_size=32, timestep_embed_dim=8, text_hidden_dim=16, num_text_layers=3, text_num_attention_heads=2, text_num_key_value_heads=1, text_intermediate_size=16, num_layerwise_text_blocks=1, num_refiner_text_blocks=1, axes_dims_rope=(4, 2, 2), rope_theta=1000.0, ) torch.manual_seed(0) z_dim = 4 vae = AutoencoderKLQwenImage( base_dim=z_dim * 6, z_dim=z_dim, dim_mult=[1, 2, 4], num_res_blocks=1, temperal_downsample=[False, True], # fmt: off latents_mean=[0.0] * 4, latents_std=[1.0] * 4, # fmt: on ) torch.manual_seed(0) scheduler = FlowMatchEulerDiscreteScheduler( use_dynamic_shifting=True, base_shift=0.5, max_shift=1.15, base_image_seq_len=256, max_image_seq_len=6400, ) torch.manual_seed(0) config = Qwen3VLConfig( text_config={ "hidden_size": 16, "intermediate_size": 16, "num_hidden_layers": 2, "num_attention_heads": 2, "num_key_value_heads": 2, "head_dim": 8, }, vision_config={ "depth": 2, "hidden_size": 16, "intermediate_size": 16, "num_heads": 2, "out_hidden_size": 16, }, vocab_size=152064, ) text_encoder = Qwen3VLModel(config).eval() tokenizer = Qwen2Tokenizer.from_pretrained("hf-internal-testing/tiny-random-Qwen2VLForConditionalGeneration") components = { "transformer": transformer, "vae": vae, "scheduler": scheduler, "text_encoder": text_encoder, "tokenizer": tokenizer, "text_encoder_select_layers": (0, 1, 2), } return components def get_dummy_inputs(self, device, seed=0): if str(device).startswith("mps"): generator = torch.manual_seed(seed) else: generator = torch.Generator(device=device).manual_seed(seed) inputs = { "prompt": "dance monkey", "negative_prompt": "bad quality", "generator": generator, "num_inference_steps": 2, "guidance_scale": 3.0, "height": 32, "width": 32, "max_sequence_length": 16, "output_type": "pt", } return inputs def test_inference(self): device = "cpu" components = self.get_dummy_components() pipe = self.pipeline_class(**components) pipe.to(device) pipe.set_progress_bar_config(disable=None) inputs = self.get_dummy_inputs(device) image = pipe(**inputs).images generated_image = image[0] self.assertEqual(generated_image.shape, (3, 32, 32)) # fmt: off expected_slice = torch.tensor([0.5649, 0.6510, 0.5885, 0.4954, 0.5551, 0.5973, 0.6043, 0.6009, 0.4307, 0.4733, 0.6145, 0.5121, 0.4431, 0.5144, 0.4427, 0.5011]) # fmt: on generated_slice = generated_image.flatten() generated_slice = torch.cat([generated_slice[:8], generated_slice[-8:]]) self.assertTrue(torch.allclose(generated_slice, expected_slice, atol=5e-3)) def test_inference_batch_single_identical(self): self._test_inference_batch_single_identical(batch_size=3, expected_max_diff=1e-1) def test_components_function(self): # Same as the common test, but `text_encoder_select_layers` is a config value (a tuple), not a module, so it # is excluded from `pipe.components`. init_components = self.get_dummy_components() init_components = {k: v for k, v in init_components.items() if not isinstance(v, (str, int, float, tuple))} pipe = self.pipeline_class(**init_components) self.assertTrue(hasattr(pipe, "components")) self.assertTrue(set(pipe.components.keys()) == set(init_components.keys())) def test_encode_prompt_works_in_isolation(self): # Krea 2 enables classifier-free guidance whenever `guidance_scale > 0` and then encodes the (default empty) # negative prompt, which needs the tokenizer. The isolation pipeline carries no tokenizer, so run without # guidance; the common test already forwards only the positive `encode_prompt` outputs. original_get_dummy_inputs = self.get_dummy_inputs def get_dummy_inputs_without_guidance(device, seed=0): inputs = original_get_dummy_inputs(device, seed) inputs["guidance_scale"] = 0.0 return inputs self.get_dummy_inputs = get_dummy_inputs_without_guidance try: super().test_encode_prompt_works_in_isolation() finally: self.get_dummy_inputs = original_get_dummy_inputs def test_attention_slicing_forward_pass( self, test_max_difference=True, test_mean_pixel_difference=True, expected_max_diff=1e-3 ): # Same as the qwenimage override: the common helper assumes channel-last outputs for the mean-pixel check, # which does not hold for `output_type="pt"`; compare max difference only. if not self.test_attention_slicing: return components = self.get_dummy_components() pipe = self.pipeline_class(**components) for component in pipe.components.values(): if hasattr(component, "set_default_attn_processor"): component.set_default_attn_processor() pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) generator_device = "cpu" inputs = self.get_dummy_inputs(generator_device) output_without_slicing = pipe(**inputs)[0] pipe.enable_attention_slicing(slice_size=1) inputs = self.get_dummy_inputs(generator_device) output_with_slicing1 = pipe(**inputs)[0] pipe.enable_attention_slicing(slice_size=2) inputs = self.get_dummy_inputs(generator_device) output_with_slicing2 = pipe(**inputs)[0] if test_max_difference: max_diff1 = np.abs(to_np(output_with_slicing1) - to_np(output_without_slicing)).max() max_diff2 = np.abs(to_np(output_with_slicing2) - to_np(output_without_slicing)).max() self.assertLess( max(max_diff1, max_diff2), expected_max_diff, "Attention slicing should not affect the inference results", )