# Copyright 2025 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 inspect import unittest import numpy as np import torch from transformers import Qwen3VLConfig, Qwen3VLForConditionalGeneration, Qwen3VLProcessor from diffusers import ( AutoencoderKLQwenImage, FlowMatchEulerDiscreteScheduler, NucleusMoEImagePipeline, NucleusMoEImageTransformer2DModel, ) from diffusers.utils.source_code_parsing_utils import ReturnNameVisitor 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 NucleusMoEImagePipelineFastTests(PipelineTesterMixin, unittest.TestCase): pipeline_class = NucleusMoEImagePipeline 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 = NucleusMoEImageTransformer2DModel( patch_size=2, in_channels=16, out_channels=4, num_layers=2, attention_head_dim=16, num_attention_heads=4, joint_attention_dim=16, axes_dims_rope=(8, 4, 4), moe_enabled=False, capacity_factors=[8.0, 8.0], ) 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] * z_dim, latents_std=[1.0] * z_dim, # fmt: on ) torch.manual_seed(0) scheduler = FlowMatchEulerDiscreteScheduler() torch.manual_seed(0) config = Qwen3VLConfig( text_config={ "hidden_size": 16, "intermediate_size": 16, "num_hidden_layers": 8, "num_attention_heads": 2, "num_key_value_heads": 2, "rope_scaling": { "mrope_section": [1, 1, 2], "rope_type": "default", "type": "default", }, "rope_theta": 1000000.0, "vocab_size": 151936, "head_dim": 8, }, vision_config={ "depth": 2, "hidden_size": 16, "intermediate_size": 16, "num_heads": 2, "out_channels": 16, }, ) text_encoder = Qwen3VLForConditionalGeneration(config).eval() processor = Qwen3VLProcessor.from_pretrained("hf-internal-testing/tiny-random-Qwen2VLForConditionalGeneration") components = { "transformer": transformer, "vae": vae, "scheduler": scheduler, "text_encoder": text_encoder, "processor": processor, } 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": "A cat sitting on a mat", "negative_prompt": "bad quality", "generator": generator, "num_inference_steps": 2, "return_index": -1, "guidance_scale": 1.0, "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)) def test_inference_batch_single_identical(self): self._test_inference_batch_single_identical(batch_size=3, expected_max_diff=1e-1) def test_true_cfg(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) inputs["guidance_scale"] = 4.0 inputs["negative_prompt"] = "low quality" image = pipe(**inputs).images self.assertEqual(image[0].shape, (3, 32, 32)) def test_prompt_embeds(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) prompt_embeds, prompt_embeds_mask = pipe.encode_prompt( prompt=inputs["prompt"], device=device, max_sequence_length=inputs["max_sequence_length"], ) inputs_with_embeds = self.get_dummy_inputs(device) inputs_with_embeds.pop("prompt") inputs_with_embeds["prompt_embeds"] = prompt_embeds inputs_with_embeds["prompt_embeds_mask"] = prompt_embeds_mask image = pipe(**inputs_with_embeds).images self.assertEqual(image[0].shape, (3, 32, 32)) def test_attention_slicing_forward_pass( self, test_max_difference=True, test_mean_pixel_difference=True, expected_max_diff=1e-3 ): # PipelineTesterMixin compares outputs with assert_mean_pixel_difference, which assumes HWC numpy/PIL layout. # With output_type="pt", tensors are CHW; numpy_to_pil then fails. Match QwenImage: only assert max diff. 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", ) def test_encode_prompt_works_in_isolation(self, extra_required_param_value_dict=None, atol=1e-4, rtol=1e-4): # PipelineTesterMixin only keeps components whose keys contain "text" or "tokenizer"; this pipeline also # needs `processor` for encode_prompt (apply_chat_template). Mirror the mixin with that key included. if not hasattr(self.pipeline_class, "encode_prompt"): return components = self.get_dummy_components() for key in components: if "text_encoder" in key and hasattr(components[key], "eval"): components[key].eval() def _is_text_stack_component(k): return "text" in k or "tokenizer" in k or k == "processor" components_with_text_encoders = {} for k in components: if _is_text_stack_component(k): components_with_text_encoders[k] = components[k] else: components_with_text_encoders[k] = None pipe_with_just_text_encoder = self.pipeline_class(**components_with_text_encoders) pipe_with_just_text_encoder = pipe_with_just_text_encoder.to(torch_device) inputs = self.get_dummy_inputs(torch_device) encode_prompt_signature = inspect.signature(pipe_with_just_text_encoder.encode_prompt) encode_prompt_parameters = list(encode_prompt_signature.parameters.values()) required_params = [] for param in encode_prompt_parameters: if param.name == "self" or param.name == "kwargs": continue if param.default is inspect.Parameter.empty: required_params.append(param.name) encode_prompt_param_names = [p.name for p in encode_prompt_parameters if p.name != "self"] input_keys = list(inputs.keys()) encode_prompt_inputs = {k: inputs.pop(k) for k in input_keys if k in encode_prompt_param_names} pipe_call_signature = inspect.signature(pipe_with_just_text_encoder.__call__) pipe_call_parameters = pipe_call_signature.parameters for required_param_name in required_params: if required_param_name not in encode_prompt_inputs: pipe_call_param = pipe_call_parameters.get(required_param_name, None) if pipe_call_param is not None and pipe_call_param.default is not inspect.Parameter.empty: encode_prompt_inputs[required_param_name] = pipe_call_param.default elif extra_required_param_value_dict is not None and isinstance(extra_required_param_value_dict, dict): encode_prompt_inputs[required_param_name] = extra_required_param_value_dict[required_param_name] else: raise ValueError( f"Required parameter '{required_param_name}' in " f"encode_prompt has no default in either encode_prompt or __call__." ) with torch.no_grad(): encoded_prompt_outputs = pipe_with_just_text_encoder.encode_prompt(**encode_prompt_inputs) ast_visitor = ReturnNameVisitor() encode_prompt_tree = ast_visitor.get_ast_tree(cls=self.pipeline_class) ast_visitor.visit(encode_prompt_tree) prompt_embed_kwargs = ast_visitor.return_names prompt_embeds_kwargs = dict(zip(prompt_embed_kwargs, encoded_prompt_outputs)) adapted_prompt_embeds_kwargs = { k: prompt_embeds_kwargs.pop(k) for k in list(prompt_embeds_kwargs.keys()) if k in pipe_call_parameters } components_with_text_encoders = {} for k in components: if _is_text_stack_component(k): components_with_text_encoders[k] = None else: components_with_text_encoders[k] = components[k] pipe_without_text_encoders = self.pipeline_class(**components_with_text_encoders).to(torch_device) pipe_without_tes_inputs = {**inputs, **adapted_prompt_embeds_kwargs} if ( pipe_call_parameters.get("negative_prompt", None) is not None and pipe_call_parameters.get("negative_prompt").default is not None ): pipe_without_tes_inputs.update({"negative_prompt": None}) if ( pipe_call_parameters.get("prompt", None) is not None and pipe_call_parameters.get("prompt").default is inspect.Parameter.empty and pipe_call_parameters.get("prompt_embeds", None) is not None and pipe_call_parameters.get("prompt_embeds").default is None ): pipe_without_tes_inputs.update({"prompt": None}) pipe_out = pipe_without_text_encoders(**pipe_without_tes_inputs)[0] full_pipe = self.pipeline_class(**components).to(torch_device) inputs = self.get_dummy_inputs(torch_device) pipe_out_2 = full_pipe(**inputs)[0] if isinstance(pipe_out, np.ndarray) and isinstance(pipe_out_2, np.ndarray): self.assertTrue(np.allclose(pipe_out, pipe_out_2, atol=atol, rtol=rtol)) elif isinstance(pipe_out, torch.Tensor) and isinstance(pipe_out_2, torch.Tensor): self.assertTrue(torch.allclose(pipe_out, pipe_out_2, atol=atol, rtol=rtol))