up

src/diffusers/pipelines/qwenimage/pipeline_qwenimage_layered.py

@@ -882,24 +882,21 @@ the image\n<|vision_start|><|image_pad|><|vision_end|><|im_end|>\n<|im_start|>as
             latents = latents / latents_std + latents_mean
 
             b, c, f, h, w = latents.shape
-
             latents = latents[:, :, 1:]  # remove the first frame as it is the orgin input
-
             latents = latents.permute(0, 2, 1, 3, 4).view(-1, c, 1, h, w)
 
-            image = self.vae.decode(latents, return_dict=False)[0]  # (b f) c 1 h w
+            img = self.vae.decode(latents, return_dict=False)[0]  # (b f) c 1 h w
+            img = img.squeeze(2)
 
-            image = image.squeeze(2)
+            img = self.image_processor.postprocess(img, output_type=output_type)
-
+            image = []
-            image = self.image_processor.postprocess(image, output_type=output_type)
-            images = []
             for bidx in range(b):
-                images.append(image[bidx * f : (bidx + 1) * f])
+                image.append(img[bidx * f : (bidx + 1) * f])
 
         # Offload all models
         self.maybe_free_model_hooks()
 
         if not return_dict:
-            return (images,)
+            return (image,)
 
-        return QwenImagePipelineOutput(images=images)
+        return QwenImagePipelineOutput(images=image)

tests/pipelines/qwenimage/test_qwenimage_layered.py

@@ -0,0 +1,223 @@
+# 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 unittest
+
+import diffusers
+import numpy as np
+import torch
+from PIL import Image
+from transformers import Qwen2_5_VLConfig, Qwen2_5_VLForConditionalGeneration, Qwen2Tokenizer, Qwen2VLProcessor
+
+from diffusers import (
+    AutoencoderKLQwenImage,
+    FlowMatchEulerDiscreteScheduler,
+    QwenImageLayeredPipeline,
+    QwenImageTransformer2DModel,
+)
+
+from ...testing_utils import enable_full_determinism, torch_device
+from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
+from ..test_pipelines_common import PipelineTesterMixin, to_np
+
+
+enable_full_determinism()
+
+
+class QwenImageLayeredPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
+    pipeline_class = QwenImageLayeredPipeline
+    params = TEXT_TO_IMAGE_PARAMS - {"height", "width", "cross_attention_kwargs"}
+    batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
+    image_params = frozenset(["image"])
+    image_latents_params = frozenset(["latents"])
+    required_optional_params = frozenset(
+        [
+            "num_inference_steps",
+            "generator",
+            "latents",
+            "return_dict",
+            "callback_on_step_end",
+            "callback_on_step_end_tensor_inputs",
+        ]
+    )
+    supports_dduf = False
+    test_xformers_attention = False
+    test_layerwise_casting = True
+    test_group_offloading = True
+
+    def get_dummy_components(self):
+        tiny_ckpt_id = "hf-internal-testing/tiny-random-Qwen2VLForConditionalGeneration"
+
+        torch.manual_seed(0)
+        transformer = QwenImageTransformer2DModel(
+            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),
+        )
+
+        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],
+            latents_mean=[0.0] * z_dim,
+            latents_std=[1.0] * z_dim,
+        )
+
+        torch.manual_seed(0)
+        scheduler = FlowMatchEulerDiscreteScheduler()
+
+        torch.manual_seed(0)
+        config = Qwen2_5_VLConfig(
+            text_config={
+                "hidden_size": 16,
+                "intermediate_size": 16,
+                "num_hidden_layers": 2,
+                "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,
+            },
+            vision_config={
+                "depth": 2,
+                "hidden_size": 16,
+                "intermediate_size": 16,
+                "num_heads": 2,
+                "out_hidden_size": 16,
+            },
+            hidden_size=16,
+            vocab_size=152064,
+            vision_end_token_id=151653,
+            vision_start_token_id=151652,
+            vision_token_id=151654,
+        )
+        text_encoder = Qwen2_5_VLForConditionalGeneration(config)
+        tokenizer = Qwen2Tokenizer.from_pretrained(tiny_ckpt_id)
+        processor = Qwen2VLProcessor.from_pretrained(tiny_ckpt_id)
+
+        components = {
+            "transformer": transformer,
+            "vae": vae,
+            "scheduler": scheduler,
+            "text_encoder": text_encoder,
+            "tokenizer": tokenizer,
+            "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": "dance monkey",
+            "image": Image.new("RGB", (32, 32)),
+            "negative_prompt": "bad quality",
+            "generator": generator,
+            "true_cfg_scale": 1.0,
+            "layers": 2,
+            "num_inference_steps": 2,
+            "max_sequence_length": 16,
+            "resolution": 640,
+            "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)
+        images = pipe(**inputs).images
+        self.assertEqual(len(images), 1)
+
+        generated_layers = images[0]
+        self.assertEqual(generated_layers.shape, (inputs["layers"], 3, 640, 640))
+
+        # fmt: off
+        expected_slice_layer_0 = torch.tensor([0.5752, 0.6324, 0.4913, 0.4421, 0.4917, 0.4923, 0.4790, 0.4299, 0.4029, 0.3506, 0.3302, 0.3352, 0.3579, 0.4422, 0.5086, 0.5961])
+        expected_slice_layer_1 = torch.tensor([0.5103, 0.6606, 0.5652, 0.6512, 0.5900, 0.5814, 0.5873, 0.5083, 0.5058, 0.4131, 0.4321, 0.5300, 0.3507, 0.4826, 0.4745, 0.5426])
+        # fmt: on
+
+        layer_0_slice = torch.cat([generated_layers[0].flatten()[:8], generated_layers[0].flatten()[-8:]])
+        layer_1_slice = torch.cat([generated_layers[1].flatten()[:8], generated_layers[1].flatten()[-8:]])
+
+        self.assertTrue(torch.allclose(layer_0_slice, expected_slice_layer_0, atol=1e-3))
+        self.assertTrue(torch.allclose(layer_1_slice, expected_slice_layer_1, atol=1e-3))
+
+    def test_inference_batch_single_identical(self, batch_size=3, expected_max_diff=1e-1):
+        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)
+
+        inputs = self.get_dummy_inputs(torch_device)
+        inputs["generator"] = self.get_generator(0)
+
+        logger = diffusers.logging.get_logger(pipe.__module__)
+        logger.setLevel(level=diffusers.logging.FATAL)
+
+        batched_inputs = {}
+        batched_inputs.update(inputs)
+
+        for name in self.batch_params:
+            if name not in inputs:
+                continue
+
+            value = inputs[name]
+            if name == "prompt":
+                len_prompt = len(value)
+                batched_inputs[name] = [value[: len_prompt // i] for i in range(1, batch_size + 1)]
+                batched_inputs[name][-1] = 100 * "very long"
+            else:
+                batched_inputs[name] = batch_size * [value]
+
+        if "generator" in inputs:
+            batched_inputs["generator"] = [self.get_generator(i) for i in range(batch_size)]
+
+        if "batch_size" in inputs:
+            batched_inputs["batch_size"] = batch_size
+
+        batched_inputs["num_inference_steps"] = inputs["num_inference_steps"]
+
+        output = pipe(**inputs).images
+        output_batch = pipe(**batched_inputs).images
+
+        self.assertEqual(len(output_batch), batch_size)
+
+        max_diff = np.abs(to_np(output_batch[0][0]) - to_np(output[0][0])).max()
+        self.assertLess(max_diff, expected_max_diff)