# coding=utf-8 # Copyright 2026 HuggingFace Inc. # # 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 CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer from diffusers import DDPMWuerstchenScheduler, StableCascadeCombinedPipeline from diffusers.models import StableCascadeUNet from diffusers.pipelines.deprecated.wuerstchen import PaellaVQModel from ...testing_utils import enable_full_determinism, require_torch_accelerator, torch_device from ..test_pipelines_common import PipelineTesterMixin enable_full_determinism() class StableCascadeCombinedPipelineFastTests(PipelineTesterMixin, unittest.TestCase): pipeline_class = StableCascadeCombinedPipeline params = ["prompt"] batch_params = ["prompt", "negative_prompt"] required_optional_params = [ "generator", "height", "width", "latents", "prior_guidance_scale", "decoder_guidance_scale", "negative_prompt", "num_inference_steps", "return_dict", "prior_num_inference_steps", "output_type", ] test_xformers_attention = True @property def text_embedder_hidden_size(self): return 32 @property def dummy_prior(self): torch.manual_seed(0) model_kwargs = { "conditioning_dim": 128, "block_out_channels": (128, 128), "num_attention_heads": (2, 2), "down_num_layers_per_block": (1, 1), "up_num_layers_per_block": (1, 1), "clip_image_in_channels": 768, "switch_level": (False,), "clip_text_in_channels": self.text_embedder_hidden_size, "clip_text_pooled_in_channels": self.text_embedder_hidden_size, } model = StableCascadeUNet(**model_kwargs) return model.eval() @property def dummy_tokenizer(self): tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") return tokenizer @property def dummy_text_encoder(self): torch.manual_seed(0) config = CLIPTextConfig( bos_token_id=0, eos_token_id=2, projection_dim=self.text_embedder_hidden_size, hidden_size=self.text_embedder_hidden_size, intermediate_size=37, layer_norm_eps=1e-05, num_attention_heads=4, num_hidden_layers=5, pad_token_id=1, vocab_size=1000, ) return CLIPTextModelWithProjection(config).eval() @property def dummy_vqgan(self): torch.manual_seed(0) model_kwargs = { "bottleneck_blocks": 1, "num_vq_embeddings": 2, } model = PaellaVQModel(**model_kwargs) return model.eval() @property def dummy_decoder(self): torch.manual_seed(0) model_kwargs = { "in_channels": 4, "out_channels": 4, "conditioning_dim": 128, "block_out_channels": (16, 32, 64, 128), "num_attention_heads": (-1, -1, 1, 2), "down_num_layers_per_block": (1, 1, 1, 1), "up_num_layers_per_block": (1, 1, 1, 1), "down_blocks_repeat_mappers": (1, 1, 1, 1), "up_blocks_repeat_mappers": (3, 3, 2, 2), "block_types_per_layer": ( ("SDCascadeResBlock", "SDCascadeTimestepBlock"), ("SDCascadeResBlock", "SDCascadeTimestepBlock"), ("SDCascadeResBlock", "SDCascadeTimestepBlock", "SDCascadeAttnBlock"), ("SDCascadeResBlock", "SDCascadeTimestepBlock", "SDCascadeAttnBlock"), ), "switch_level": None, "clip_text_pooled_in_channels": 32, "dropout": (0.1, 0.1, 0.1, 0.1), } model = StableCascadeUNet(**model_kwargs) return model.eval() def get_dummy_components(self): prior = self.dummy_prior scheduler = DDPMWuerstchenScheduler() tokenizer = self.dummy_tokenizer text_encoder = self.dummy_text_encoder decoder = self.dummy_decoder vqgan = self.dummy_vqgan prior_text_encoder = self.dummy_text_encoder prior_tokenizer = self.dummy_tokenizer components = { "text_encoder": text_encoder, "tokenizer": tokenizer, "decoder": decoder, "scheduler": scheduler, "vqgan": vqgan, "prior_text_encoder": prior_text_encoder, "prior_tokenizer": prior_tokenizer, "prior_prior": prior, "prior_scheduler": scheduler, "prior_feature_extractor": None, "prior_image_encoder": None, } 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": "horse", "generator": generator, "prior_guidance_scale": 4.0, "decoder_guidance_scale": 4.0, "num_inference_steps": 2, "prior_num_inference_steps": 2, "output_type": "np", "height": 128, "width": 128, } return inputs def test_stable_cascade(self): device = "cpu" components = self.get_dummy_components() pipe = self.pipeline_class(**components) pipe = pipe.to(device) pipe.set_progress_bar_config(disable=None) output = pipe(**self.get_dummy_inputs(device)) image = output.images image_from_tuple = pipe(**self.get_dummy_inputs(device), return_dict=False)[0] image_slice = image[0, -3:, -3:, -1] image_from_tuple_slice = image_from_tuple[-3:, -3:, -1] assert image.shape == (1, 128, 128, 3) expected_slice = np.array([0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2, ( f" expected_slice {expected_slice}, but got {image_slice.flatten()}" ) assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2, ( f" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}" ) @require_torch_accelerator def test_offloads(self): pipes = [] components = self.get_dummy_components() sd_pipe = self.pipeline_class(**components).to(torch_device) pipes.append(sd_pipe) components = self.get_dummy_components() sd_pipe = self.pipeline_class(**components) sd_pipe.enable_sequential_cpu_offload(device=torch_device) pipes.append(sd_pipe) components = self.get_dummy_components() sd_pipe = self.pipeline_class(**components) sd_pipe.enable_model_cpu_offload(device=torch_device) pipes.append(sd_pipe) image_slices = [] for pipe in pipes: inputs = self.get_dummy_inputs(torch_device) image = pipe(**inputs).images image_slices.append(image[0, -3:, -3:, -1].flatten()) assert np.abs(image_slices[0] - image_slices[1]).max() < 1e-3 assert np.abs(image_slices[0] - image_slices[2]).max() < 1e-3 def test_inference_batch_single_identical(self): super().test_inference_batch_single_identical(expected_max_diff=2e-2) @unittest.skip(reason="fp16 not supported") def test_float16_inference(self): super().test_float16_inference() @unittest.skip(reason="no callback test for combined pipeline") def test_callback_inputs(self): super().test_callback_inputs()