# 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 AutoTokenizer, GlmConfig, GlmForCausalLM from diffusers import AutoencoderKL, CogView4Pipeline, CogView4Transformer2DModel, FlowMatchEulerDiscreteScheduler 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 CogView4PipelineFastTests(PipelineTesterMixin, unittest.TestCase): pipeline_class = CogView4Pipeline 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 def get_dummy_components(self): torch.manual_seed(0) transformer = CogView4Transformer2DModel( patch_size=2, in_channels=4, num_layers=2, attention_head_dim=4, num_attention_heads=4, out_channels=4, text_embed_dim=32, time_embed_dim=8, condition_dim=4, ) torch.manual_seed(0) vae = AutoencoderKL( block_out_channels=[32, 64], in_channels=3, out_channels=3, down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"], up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"], latent_channels=4, sample_size=128, ) torch.manual_seed(0) scheduler = FlowMatchEulerDiscreteScheduler( base_shift=0.25, max_shift=0.75, base_image_seq_len=256, use_dynamic_shifting=True, time_shift_type="linear", ) torch.manual_seed(0) text_encoder_config = GlmConfig( hidden_size=32, intermediate_size=8, num_hidden_layers=2, num_attention_heads=4, head_dim=8 ) text_encoder = GlmForCausalLM(text_encoder_config) # TODO(aryan): change this to THUDM/CogView4 once released tokenizer = AutoTokenizer.from_pretrained("THUDM/glm-4-9b-chat", trust_remote_code=True) components = { "transformer": transformer, "vae": vae, "scheduler": scheduler, "text_encoder": text_encoder, "tokenizer": tokenizer, } 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", "generator": generator, "num_inference_steps": 2, "guidance_scale": 6.0, "height": 16, "width": 16, "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)[0] generated_image = image[0] self.assertEqual(generated_image.shape, (3, 16, 16)) expected_image = torch.randn(3, 16, 16) max_diff = np.abs(generated_image - expected_image).max() self.assertLessEqual(max_diff, 1e10) def test_callback_inputs(self): sig = inspect.signature(self.pipeline_class.__call__) has_callback_tensor_inputs = "callback_on_step_end_tensor_inputs" in sig.parameters has_callback_step_end = "callback_on_step_end" in sig.parameters if not (has_callback_tensor_inputs and has_callback_step_end): return components = self.get_dummy_components() pipe = self.pipeline_class(**components) pipe = pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) self.assertTrue( hasattr(pipe, "_callback_tensor_inputs"), f" {self.pipeline_class} should have `_callback_tensor_inputs` that defines a list of tensor variables its callback function can use as inputs", ) def callback_inputs_subset(pipe, i, t, callback_kwargs): # iterate over callback args for tensor_name, tensor_value in callback_kwargs.items(): # check that we're only passing in allowed tensor inputs assert tensor_name in pipe._callback_tensor_inputs return callback_kwargs def callback_inputs_all(pipe, i, t, callback_kwargs): for tensor_name in pipe._callback_tensor_inputs: assert tensor_name in callback_kwargs # iterate over callback args for tensor_name, tensor_value in callback_kwargs.items(): # check that we're only passing in allowed tensor inputs assert tensor_name in pipe._callback_tensor_inputs return callback_kwargs inputs = self.get_dummy_inputs(torch_device) # Test passing in a subset inputs["callback_on_step_end"] = callback_inputs_subset inputs["callback_on_step_end_tensor_inputs"] = ["latents"] output = pipe(**inputs)[0] # Test passing in a everything inputs["callback_on_step_end"] = callback_inputs_all inputs["callback_on_step_end_tensor_inputs"] = pipe._callback_tensor_inputs output = pipe(**inputs)[0] def callback_inputs_change_tensor(pipe, i, t, callback_kwargs): is_last = i == (pipe.num_timesteps - 1) if is_last: callback_kwargs["latents"] = torch.zeros_like(callback_kwargs["latents"]) return callback_kwargs inputs["callback_on_step_end"] = callback_inputs_change_tensor inputs["callback_on_step_end_tensor_inputs"] = pipe._callback_tensor_inputs output = pipe(**inputs)[0] assert output.abs().sum() < 1e10 def test_inference_batch_single_identical(self): self._test_inference_batch_single_identical(batch_size=3, expected_max_diff=1e-3) def test_attention_slicing_forward_pass( self, test_max_difference=True, test_mean_pixel_difference=True, expected_max_diff=1e-3 ): 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", )