From 87f7d111437e1dad2a25d4653c57886f8f058cd3 Mon Sep 17 00:00:00 2001 From: "Wang, Yi" Date: Wed, 17 Dec 2025 16:14:08 +0800 Subject: [PATCH 1/2] extend TorchAoTest::test_model_memory_usage to other platform (#12768) * extend TorchAoTest::test_model_memory_usage to other platform Signe-off-by: Wang, Yi * add some comments Signed-off-by: Wang, Yi --------- Signed-off-by: Wang, Yi --- tests/quantization/torchao/test_torchao.py | 20 ++++++++++++++++++-- 1 file changed, 18 insertions(+), 2 deletions(-) diff --git a/tests/quantization/torchao/test_torchao.py b/tests/quantization/torchao/test_torchao.py index 38997de17b..e6bfc2530a 100644 --- a/tests/quantization/torchao/test_torchao.py +++ b/tests/quantization/torchao/test_torchao.py @@ -35,6 +35,7 @@ from diffusers.models.attention_processor import Attention from diffusers.quantizers import PipelineQuantizationConfig from ...testing_utils import ( + Expectations, backend_empty_cache, backend_synchronize, enable_full_determinism, @@ -497,8 +498,23 @@ class TorchAoTest(unittest.TestCase): def test_model_memory_usage(self): model_id = "hf-internal-testing/tiny-flux-pipe" - expected_memory_saving_ratio = 2.0 - + expected_memory_saving_ratios = Expectations( + { + # XPU: For this tiny model, per-tensor overheads (alignment, fragmentation, metadata) become visible. + # While XPU doesn't have the large fixed cuBLAS workspace of A100, these small overheads prevent reaching the ideal 2.0 ratio. + # Observed ~1.27x (158k vs 124k) for model size. + # The runtime memory overhead is ~88k for both bf16 and int8wo. Adding this to model size: (158k+88k)/(124k+88k) ≈ 1.15. + ("xpu", None): 1.15, + # On Ampere, the cuBLAS kernels used for matrix multiplication often allocate a fixed-size workspace. + # Since the tiny-flux model weights are likely smaller than or comparable to this workspace, the total memory is dominated by the workspace. + ("cuda", 8): 1.02, + # On Hopper, TorchAO utilizes newer, highly optimized kernels (via Triton or CUTLASS 3.x) that are designed to be workspace-free or use negligible extra memory. + # Additionally, Triton kernels often handle unaligned memory better, avoiding the padding overhead seen on other backends for tiny tensors. + # This allows it to achieve the near-ideal 2.0x compression ratio. + ("cuda", 9): 2.0, + } + ) + expected_memory_saving_ratio = expected_memory_saving_ratios.get_expectation() inputs = self.get_dummy_tensor_inputs(device=torch_device) transformer_bf16 = self.get_dummy_components(None, model_id=model_id)["transformer"] From f9c1e612fb85dd971beeba77c3ddc0826e2146a4 Mon Sep 17 00:00:00 2001 From: naykun Date: Wed, 17 Dec 2025 19:27:57 +0800 Subject: [PATCH 2/2] Qwen Image Layered Support (#12853) * [qwen-image] qwen image layered support * [qwen-image] update doc * [qwen-image] fix pr comments * Apply style fixes * make fix-copies --------- Co-authored-by: github-actions[bot] Co-authored-by: Sayak Paul --- src/diffusers/__init__.py | 2 + .../autoencoders/autoencoder_kl_qwenimage.py | 11 +- .../transformers/transformer_qwenimage.py | 143 ++- src/diffusers/pipelines/__init__.py | 2 + src/diffusers/pipelines/qwenimage/__init__.py | 2 + .../qwenimage/pipeline_qwenimage_layered.py | 905 ++++++++++++++++++ .../dummy_torch_and_transformers_objects.py | 15 + 7 files changed, 1070 insertions(+), 10 deletions(-) create mode 100644 src/diffusers/pipelines/qwenimage/pipeline_qwenimage_layered.py diff --git a/src/diffusers/__init__.py b/src/diffusers/__init__.py index 86f196f9be..aec7efd1ff 100644 --- a/src/diffusers/__init__.py +++ b/src/diffusers/__init__.py @@ -564,6 +564,7 @@ else: "QwenImageEditPlusPipeline", "QwenImageImg2ImgPipeline", "QwenImageInpaintPipeline", + "QwenImageLayeredPipeline", "QwenImagePipeline", "ReduxImageEncoder", "SanaControlNetPipeline", @@ -1272,6 +1273,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT: QwenImageEditPlusPipeline, QwenImageImg2ImgPipeline, QwenImageInpaintPipeline, + QwenImageLayeredPipeline, QwenImagePipeline, ReduxImageEncoder, SanaControlNetPipeline, diff --git a/src/diffusers/models/autoencoders/autoencoder_kl_qwenimage.py b/src/diffusers/models/autoencoders/autoencoder_kl_qwenimage.py index 618801dfb6..7f7266146e 100644 --- a/src/diffusers/models/autoencoders/autoencoder_kl_qwenimage.py +++ b/src/diffusers/models/autoencoders/autoencoder_kl_qwenimage.py @@ -394,6 +394,7 @@ class QwenImageEncoder3d(nn.Module): attn_scales=[], temperal_downsample=[True, True, False], dropout=0.0, + input_channels=3, non_linearity: str = "silu", ): super().__init__() @@ -410,7 +411,7 @@ class QwenImageEncoder3d(nn.Module): scale = 1.0 # init block - self.conv_in = QwenImageCausalConv3d(3, dims[0], 3, padding=1) + self.conv_in = QwenImageCausalConv3d(input_channels, dims[0], 3, padding=1) # downsample blocks self.down_blocks = nn.ModuleList([]) @@ -570,6 +571,7 @@ class QwenImageDecoder3d(nn.Module): attn_scales=[], temperal_upsample=[False, True, True], dropout=0.0, + input_channels=3, non_linearity: str = "silu", ): super().__init__() @@ -621,7 +623,7 @@ class QwenImageDecoder3d(nn.Module): # output blocks self.norm_out = QwenImageRMS_norm(out_dim, images=False) - self.conv_out = QwenImageCausalConv3d(out_dim, 3, 3, padding=1) + self.conv_out = QwenImageCausalConv3d(out_dim, input_channels, 3, padding=1) self.gradient_checkpointing = False @@ -684,6 +686,7 @@ class AutoencoderKLQwenImage(ModelMixin, AutoencoderMixin, ConfigMixin, FromOrig attn_scales: List[float] = [], temperal_downsample: List[bool] = [False, True, True], dropout: float = 0.0, + input_channels: int = 3, latents_mean: List[float] = [-0.7571, -0.7089, -0.9113, 0.1075, -0.1745, 0.9653, -0.1517, 1.5508, 0.4134, -0.0715, 0.5517, -0.3632, -0.1922, -0.9497, 0.2503, -0.2921], latents_std: List[float] = [2.8184, 1.4541, 2.3275, 2.6558, 1.2196, 1.7708, 2.6052, 2.0743, 3.2687, 2.1526, 2.8652, 1.5579, 1.6382, 1.1253, 2.8251, 1.9160], ) -> None: @@ -695,13 +698,13 @@ class AutoencoderKLQwenImage(ModelMixin, AutoencoderMixin, ConfigMixin, FromOrig self.temperal_upsample = temperal_downsample[::-1] self.encoder = QwenImageEncoder3d( - base_dim, z_dim * 2, dim_mult, num_res_blocks, attn_scales, self.temperal_downsample, dropout + base_dim, z_dim * 2, dim_mult, num_res_blocks, attn_scales, self.temperal_downsample, dropout, input_channels ) self.quant_conv = QwenImageCausalConv3d(z_dim * 2, z_dim * 2, 1) self.post_quant_conv = QwenImageCausalConv3d(z_dim, z_dim, 1) self.decoder = QwenImageDecoder3d( - base_dim, z_dim, dim_mult, num_res_blocks, attn_scales, self.temperal_upsample, dropout + base_dim, z_dim, dim_mult, num_res_blocks, attn_scales, self.temperal_upsample, dropout, input_channels ) self.spatial_compression_ratio = 2 ** len(self.temperal_downsample) diff --git a/src/diffusers/models/transformers/transformer_qwenimage.py b/src/diffusers/models/transformers/transformer_qwenimage.py index 3adfcdb147..1229bab169 100644 --- a/src/diffusers/models/transformers/transformer_qwenimage.py +++ b/src/diffusers/models/transformers/transformer_qwenimage.py @@ -143,17 +143,26 @@ def apply_rotary_emb_qwen( class QwenTimestepProjEmbeddings(nn.Module): - def __init__(self, embedding_dim): + def __init__(self, embedding_dim, use_additional_t_cond=False): super().__init__() self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0, scale=1000) self.timestep_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim) + self.use_additional_t_cond = use_additional_t_cond + if use_additional_t_cond: + self.addition_t_embedding = nn.Embedding(2, embedding_dim) - def forward(self, timestep, hidden_states): + def forward(self, timestep, hidden_states, addition_t_cond=None): timesteps_proj = self.time_proj(timestep) timesteps_emb = self.timestep_embedder(timesteps_proj.to(dtype=hidden_states.dtype)) # (N, D) conditioning = timesteps_emb + if self.use_additional_t_cond: + if addition_t_cond is None: + raise ValueError("When additional_t_cond is True, addition_t_cond must be provided.") + addition_t_emb = self.addition_t_embedding(addition_t_cond) + addition_t_emb = addition_t_emb.to(dtype=hidden_states.dtype) + conditioning = conditioning + addition_t_emb return conditioning @@ -259,6 +268,120 @@ class QwenEmbedRope(nn.Module): return freqs.clone().contiguous() +class QwenEmbedLayer3DRope(nn.Module): + def __init__(self, theta: int, axes_dim: List[int], scale_rope=False): + super().__init__() + self.theta = theta + self.axes_dim = axes_dim + pos_index = torch.arange(4096) + neg_index = torch.arange(4096).flip(0) * -1 - 1 + self.pos_freqs = torch.cat( + [ + self.rope_params(pos_index, self.axes_dim[0], self.theta), + self.rope_params(pos_index, self.axes_dim[1], self.theta), + self.rope_params(pos_index, self.axes_dim[2], self.theta), + ], + dim=1, + ) + self.neg_freqs = torch.cat( + [ + self.rope_params(neg_index, self.axes_dim[0], self.theta), + self.rope_params(neg_index, self.axes_dim[1], self.theta), + self.rope_params(neg_index, self.axes_dim[2], self.theta), + ], + dim=1, + ) + + self.scale_rope = scale_rope + + def rope_params(self, index, dim, theta=10000): + """ + Args: + index: [0, 1, 2, 3] 1D Tensor representing the position index of the token + """ + assert dim % 2 == 0 + freqs = torch.outer(index, 1.0 / torch.pow(theta, torch.arange(0, dim, 2).to(torch.float32).div(dim))) + freqs = torch.polar(torch.ones_like(freqs), freqs) + return freqs + + def forward(self, video_fhw, txt_seq_lens, device): + """ + Args: video_fhw: [frame, height, width] a list of 3 integers representing the shape of the video Args: + txt_length: [bs] a list of 1 integers representing the length of the text + """ + if self.pos_freqs.device != device: + self.pos_freqs = self.pos_freqs.to(device) + self.neg_freqs = self.neg_freqs.to(device) + + if isinstance(video_fhw, list): + video_fhw = video_fhw[0] + if not isinstance(video_fhw, list): + video_fhw = [video_fhw] + + vid_freqs = [] + max_vid_index = 0 + layer_num = len(video_fhw) - 1 + for idx, fhw in enumerate(video_fhw): + frame, height, width = fhw + if idx != layer_num: + video_freq = self._compute_video_freqs(frame, height, width, idx) + else: + ### For the condition image, we set the layer index to -1 + video_freq = self._compute_condition_freqs(frame, height, width) + video_freq = video_freq.to(device) + vid_freqs.append(video_freq) + + if self.scale_rope: + max_vid_index = max(height // 2, width // 2, max_vid_index) + else: + max_vid_index = max(height, width, max_vid_index) + + max_vid_index = max(max_vid_index, layer_num) + max_len = max(txt_seq_lens) + txt_freqs = self.pos_freqs[max_vid_index : max_vid_index + max_len, ...] + vid_freqs = torch.cat(vid_freqs, dim=0) + + return vid_freqs, txt_freqs + + @functools.lru_cache(maxsize=None) + def _compute_video_freqs(self, frame, height, width, idx=0): + seq_lens = frame * height * width + freqs_pos = self.pos_freqs.split([x // 2 for x in self.axes_dim], dim=1) + freqs_neg = self.neg_freqs.split([x // 2 for x in self.axes_dim], dim=1) + + freqs_frame = freqs_pos[0][idx : idx + frame].view(frame, 1, 1, -1).expand(frame, height, width, -1) + if self.scale_rope: + freqs_height = torch.cat([freqs_neg[1][-(height - height // 2) :], freqs_pos[1][: height // 2]], dim=0) + freqs_height = freqs_height.view(1, height, 1, -1).expand(frame, height, width, -1) + freqs_width = torch.cat([freqs_neg[2][-(width - width // 2) :], freqs_pos[2][: width // 2]], dim=0) + freqs_width = freqs_width.view(1, 1, width, -1).expand(frame, height, width, -1) + else: + freqs_height = freqs_pos[1][:height].view(1, height, 1, -1).expand(frame, height, width, -1) + freqs_width = freqs_pos[2][:width].view(1, 1, width, -1).expand(frame, height, width, -1) + + freqs = torch.cat([freqs_frame, freqs_height, freqs_width], dim=-1).reshape(seq_lens, -1) + return freqs.clone().contiguous() + + @functools.lru_cache(maxsize=None) + def _compute_condition_freqs(self, frame, height, width): + seq_lens = frame * height * width + freqs_pos = self.pos_freqs.split([x // 2 for x in self.axes_dim], dim=1) + freqs_neg = self.neg_freqs.split([x // 2 for x in self.axes_dim], dim=1) + + freqs_frame = freqs_neg[0][-1:].view(frame, 1, 1, -1).expand(frame, height, width, -1) + if self.scale_rope: + freqs_height = torch.cat([freqs_neg[1][-(height - height // 2) :], freqs_pos[1][: height // 2]], dim=0) + freqs_height = freqs_height.view(1, height, 1, -1).expand(frame, height, width, -1) + freqs_width = torch.cat([freqs_neg[2][-(width - width // 2) :], freqs_pos[2][: width // 2]], dim=0) + freqs_width = freqs_width.view(1, 1, width, -1).expand(frame, height, width, -1) + else: + freqs_height = freqs_pos[1][:height].view(1, height, 1, -1).expand(frame, height, width, -1) + freqs_width = freqs_pos[2][:width].view(1, 1, width, -1).expand(frame, height, width, -1) + + freqs = torch.cat([freqs_frame, freqs_height, freqs_width], dim=-1).reshape(seq_lens, -1) + return freqs.clone().contiguous() + + class QwenDoubleStreamAttnProcessor2_0: """ Attention processor for Qwen double-stream architecture, matching DoubleStreamLayerMegatron logic. This processor @@ -578,14 +701,21 @@ class QwenImageTransformer2DModel( guidance_embeds: bool = False, # TODO: this should probably be removed axes_dims_rope: Tuple[int, int, int] = (16, 56, 56), zero_cond_t: bool = False, + use_additional_t_cond: bool = False, + use_layer3d_rope: bool = False, ): super().__init__() self.out_channels = out_channels or in_channels self.inner_dim = num_attention_heads * attention_head_dim - self.pos_embed = QwenEmbedRope(theta=10000, axes_dim=list(axes_dims_rope), scale_rope=True) + if not use_layer3d_rope: + self.pos_embed = QwenEmbedRope(theta=10000, axes_dim=list(axes_dims_rope), scale_rope=True) + else: + self.pos_embed = QwenEmbedLayer3DRope(theta=10000, axes_dim=list(axes_dims_rope), scale_rope=True) - self.time_text_embed = QwenTimestepProjEmbeddings(embedding_dim=self.inner_dim) + self.time_text_embed = QwenTimestepProjEmbeddings( + embedding_dim=self.inner_dim, use_additional_t_cond=use_additional_t_cond + ) self.txt_norm = RMSNorm(joint_attention_dim, eps=1e-6) @@ -621,6 +751,7 @@ class QwenImageTransformer2DModel( guidance: torch.Tensor = None, # TODO: this should probably be removed attention_kwargs: Optional[Dict[str, Any]] = None, controlnet_block_samples=None, + additional_t_cond=None, return_dict: bool = True, ) -> Union[torch.Tensor, Transformer2DModelOutput]: """ @@ -683,9 +814,9 @@ class QwenImageTransformer2DModel( guidance = guidance.to(hidden_states.dtype) * 1000 temb = ( - self.time_text_embed(timestep, hidden_states) + self.time_text_embed(timestep, hidden_states, additional_t_cond) if guidance is None - else self.time_text_embed(timestep, guidance, hidden_states) + else self.time_text_embed(timestep, guidance, hidden_states, additional_t_cond) ) image_rotary_emb = self.pos_embed(img_shapes, txt_seq_lens, device=hidden_states.device) diff --git a/src/diffusers/pipelines/__init__.py b/src/diffusers/pipelines/__init__.py index ff5cd829ce..a2a374906b 100644 --- a/src/diffusers/pipelines/__init__.py +++ b/src/diffusers/pipelines/__init__.py @@ -422,6 +422,7 @@ else: "QwenImageEditInpaintPipeline", "QwenImageControlNetInpaintPipeline", "QwenImageControlNetPipeline", + "QwenImageLayeredPipeline", ] _import_structure["chronoedit"] = ["ChronoEditPipeline"] try: @@ -764,6 +765,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT: QwenImageEditPlusPipeline, QwenImageImg2ImgPipeline, QwenImageInpaintPipeline, + QwenImageLayeredPipeline, QwenImagePipeline, ) from .sana import ( diff --git a/src/diffusers/pipelines/qwenimage/__init__.py b/src/diffusers/pipelines/qwenimage/__init__.py index 2400632ba2..3f43d0ebb0 100644 --- a/src/diffusers/pipelines/qwenimage/__init__.py +++ b/src/diffusers/pipelines/qwenimage/__init__.py @@ -31,6 +31,7 @@ else: _import_structure["pipeline_qwenimage_edit_plus"] = ["QwenImageEditPlusPipeline"] _import_structure["pipeline_qwenimage_img2img"] = ["QwenImageImg2ImgPipeline"] _import_structure["pipeline_qwenimage_inpaint"] = ["QwenImageInpaintPipeline"] + _import_structure["pipeline_qwenimage_layered"] = ["QwenImageLayeredPipeline"] if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT: try: @@ -47,6 +48,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT: from .pipeline_qwenimage_edit_plus import QwenImageEditPlusPipeline from .pipeline_qwenimage_img2img import QwenImageImg2ImgPipeline from .pipeline_qwenimage_inpaint import QwenImageInpaintPipeline + from .pipeline_qwenimage_layered import QwenImageLayeredPipeline else: import sys diff --git a/src/diffusers/pipelines/qwenimage/pipeline_qwenimage_layered.py b/src/diffusers/pipelines/qwenimage/pipeline_qwenimage_layered.py new file mode 100644 index 0000000000..7bb12c26ba --- /dev/null +++ b/src/diffusers/pipelines/qwenimage/pipeline_qwenimage_layered.py @@ -0,0 +1,905 @@ +# Copyright 2025 Qwen-Image Team and The HuggingFace Team. All rights reserved. +# +# 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 math +from typing import Any, Callable, Dict, List, Optional, Union + +import numpy as np +import torch +from transformers import Qwen2_5_VLForConditionalGeneration, Qwen2Tokenizer, Qwen2VLProcessor + +from ...image_processor import PipelineImageInput, VaeImageProcessor +from ...loaders import QwenImageLoraLoaderMixin +from ...models import AutoencoderKLQwenImage, QwenImageTransformer2DModel +from ...schedulers import FlowMatchEulerDiscreteScheduler +from ...utils import is_torch_xla_available, logging, replace_example_docstring +from ...utils.torch_utils import randn_tensor +from ..pipeline_utils import DiffusionPipeline +from .pipeline_output import QwenImagePipelineOutput + + +if is_torch_xla_available(): + import torch_xla.core.xla_model as xm + + XLA_AVAILABLE = True +else: + XLA_AVAILABLE = False + + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + +EXAMPLE_DOC_STRING = """ + Examples: + ```py + >>> import torch + >>> from PIL import Image + >>> from diffusers import QwenImageLayeredPipeline + >>> from diffusers.utils import load_image + + >>> pipe = QwenImageLayeredPipeline.from_pretrained("Qwen/Qwen-Image-Layered", torch_dtype=torch.bfloat16) + >>> pipe.to("cuda") + >>> image = load_image( + ... "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/yarn-art-pikachu.png" + ... ).convert("RGBA") + >>> prompt = "" + >>> # Depending on the variant being used, the pipeline call will slightly vary. + >>> # Refer to the pipeline documentation for more details. + >>> images = pipe( + ... image, + ... prompt, + ... num_inference_steps=50, + ... true_cfg_scale=4.0, + ... layers=4, + ... resolution=640, + ... cfg_normalize=False, + ... use_en_prompt=True, + ... ).images[0] + >>> for i, image in enumerate(images): + ... image.save(f"{i}.out.png") + ``` +""" + + +# Copied from diffusers.pipelines.qwenimage.pipeline_qwenimage.calculate_shift +def calculate_shift( + image_seq_len, + base_seq_len: int = 256, + max_seq_len: int = 4096, + base_shift: float = 0.5, + max_shift: float = 1.15, +): + m = (max_shift - base_shift) / (max_seq_len - base_seq_len) + b = base_shift - m * base_seq_len + mu = image_seq_len * m + b + return mu + + +# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps +def retrieve_timesteps( + scheduler, + num_inference_steps: Optional[int] = None, + device: Optional[Union[str, torch.device]] = None, + timesteps: Optional[List[int]] = None, + sigmas: Optional[List[float]] = None, + **kwargs, +): + r""" + Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles + custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`. + + Args: + scheduler (`SchedulerMixin`): + The scheduler to get timesteps from. + num_inference_steps (`int`): + The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps` + must be `None`. + device (`str` or `torch.device`, *optional*): + The device to which the timesteps should be moved to. If `None`, the timesteps are not moved. + timesteps (`List[int]`, *optional*): + Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed, + `num_inference_steps` and `sigmas` must be `None`. + sigmas (`List[float]`, *optional*): + Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed, + `num_inference_steps` and `timesteps` must be `None`. + + Returns: + `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the + second element is the number of inference steps. + """ + if timesteps is not None and sigmas is not None: + raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values") + if timesteps is not None: + accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys()) + if not accepts_timesteps: + raise ValueError( + f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" + f" timestep schedules. Please check whether you are using the correct scheduler." + ) + scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs) + timesteps = scheduler.timesteps + num_inference_steps = len(timesteps) + elif sigmas is not None: + accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys()) + if not accept_sigmas: + raise ValueError( + f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" + f" sigmas schedules. Please check whether you are using the correct scheduler." + ) + scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs) + timesteps = scheduler.timesteps + num_inference_steps = len(timesteps) + else: + scheduler.set_timesteps(num_inference_steps, device=device, **kwargs) + timesteps = scheduler.timesteps + return timesteps, num_inference_steps + + +# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents +def retrieve_latents( + encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = "sample" +): + if hasattr(encoder_output, "latent_dist") and sample_mode == "sample": + return encoder_output.latent_dist.sample(generator) + elif hasattr(encoder_output, "latent_dist") and sample_mode == "argmax": + return encoder_output.latent_dist.mode() + elif hasattr(encoder_output, "latents"): + return encoder_output.latents + else: + raise AttributeError("Could not access latents of provided encoder_output") + + +# Copied from diffusers.pipelines.qwenimage.pipeline_qwenimage_edit_plus.calculate_dimensions +def calculate_dimensions(target_area, ratio): + width = math.sqrt(target_area * ratio) + height = width / ratio + + width = round(width / 32) * 32 + height = round(height / 32) * 32 + + return width, height + + +class QwenImageLayeredPipeline(DiffusionPipeline, QwenImageLoraLoaderMixin): + r""" + The Qwen-Image-Layered pipeline for image decomposing. + + Args: + transformer ([`QwenImageTransformer2DModel`]): + Conditional Transformer (MMDiT) architecture to denoise the encoded image latents. + scheduler ([`FlowMatchEulerDiscreteScheduler`]): + A scheduler to be used in combination with `transformer` to denoise the encoded image latents. + vae ([`AutoencoderKL`]): + Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. + text_encoder ([`Qwen2.5-VL-7B-Instruct`]): + [Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct), specifically the + [Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) variant. + tokenizer (`QwenTokenizer`): + Tokenizer of class + [CLIPTokenizer](https://huggingface.co/docs/transformers/en/model_doc/clip#transformers.CLIPTokenizer). + """ + + model_cpu_offload_seq = "text_encoder->transformer->vae" + _callback_tensor_inputs = ["latents", "prompt_embeds"] + + def __init__( + self, + scheduler: FlowMatchEulerDiscreteScheduler, + vae: AutoencoderKLQwenImage, + text_encoder: Qwen2_5_VLForConditionalGeneration, + tokenizer: Qwen2Tokenizer, + processor: Qwen2VLProcessor, + transformer: QwenImageTransformer2DModel, + ): + super().__init__() + + self.register_modules( + vae=vae, + text_encoder=text_encoder, + tokenizer=tokenizer, + processor=processor, + transformer=transformer, + scheduler=scheduler, + ) + self.vae_scale_factor = 2 ** len(self.vae.temperal_downsample) if getattr(self, "vae", None) else 8 + self.latent_channels = self.vae.config.z_dim if getattr(self, "vae", None) else 16 + # QwenImage latents are turned into 2x2 patches and packed. This means the latent width and height has to be divisible + # by the patch size. So the vae scale factor is multiplied by the patch size to account for this + self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor * 2) + self.vl_processor = processor + self.tokenizer_max_length = 1024 + + self.prompt_template_encode = "<|im_start|>system\nDescribe the image by detailing the color, shape, size, texture, quantity, text, spatial relationships of the objects and background:<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n" + self.prompt_template_encode_start_idx = 34 + self.image_caption_prompt_cn = """<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n# 图像标注器\n你是一个专业的图像标注器。请基于输入图像，撰写图注:\n1. +使用自然、描述性的语言撰写图注，不要使用结构化形式或富文本形式。\n2. 通过加入以下内容，丰富图注细节：\n - 对象的属性：如数量、颜色、形状、大小、位置、材质、状态、动作等\n - +对象间的视觉关系：如空间关系、功能关系、动作关系、从属关系、比较关系、因果关系等\n - 环境细节：例如天气、光照、颜色、纹理、气氛等\n - 文字内容：识别图像中清晰可见的文字，不做翻译和解释，用引号在图注中强调\n3. +保持真实性与准确性：\n - 不要使用笼统的描述\n - +描述图像中所有可见的信息，但不要加入没有在图像中出现的内容\n<|vision_start|><|image_pad|><|vision_end|><|im_end|>\n<|im_start|>assistant\n""" + self.image_caption_prompt_en = """<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n# Image Annotator\nYou are a professional +image annotator. Please write an image caption based on the input image:\n1. Write the caption using natural, +descriptive language without structured formats or rich text.\n2. Enrich caption details by including: \n - Object +attributes, such as quantity, color, shape, size, material, state, position, actions, and so on\n - Vision Relations +between objects, such as spatial relations, functional relations, possessive relations, attachment relations, action +relations, comparative relations, causal relations, and so on\n - Environmental details, such as weather, lighting, +colors, textures, atmosphere, and so on\n - Identify the text clearly visible in the image, without translation or +explanation, and highlight it in the caption with quotation marks\n3. Maintain authenticity and accuracy:\n - Avoid +generalizations\n - Describe all visible information in the image, while do not add information not explicitly shown in +the image\n<|vision_start|><|image_pad|><|vision_end|><|im_end|>\n<|im_start|>assistant\n""" + self.default_sample_size = 128 + + # Copied from diffusers.pipelines.qwenimage.pipeline_qwenimage.QwenImagePipeline._extract_masked_hidden + def _extract_masked_hidden(self, hidden_states: torch.Tensor, mask: torch.Tensor): + bool_mask = mask.bool() + valid_lengths = bool_mask.sum(dim=1) + selected = hidden_states[bool_mask] + split_result = torch.split(selected, valid_lengths.tolist(), dim=0) + + return split_result + + def _get_qwen_prompt_embeds( + self, + prompt: Union[str, List[str]] = None, + device: Optional[torch.device] = None, + dtype: Optional[torch.dtype] = None, + ): + device = device or self._execution_device + dtype = dtype or self.text_encoder.dtype + + prompt = [prompt] if isinstance(prompt, str) else prompt + + template = self.prompt_template_encode + drop_idx = self.prompt_template_encode_start_idx + txt = [template.format(e) for e in prompt] + txt_tokens = self.tokenizer( + txt, + padding=True, + return_tensors="pt", + ).to(device) + encoder_hidden_states = self.text_encoder( + input_ids=txt_tokens.input_ids, + attention_mask=txt_tokens.attention_mask, + output_hidden_states=True, + ) + hidden_states = encoder_hidden_states.hidden_states[-1] + split_hidden_states = self._extract_masked_hidden(hidden_states, txt_tokens.attention_mask) + split_hidden_states = [e[drop_idx:] for e in split_hidden_states] + attn_mask_list = [torch.ones(e.size(0), dtype=torch.long, device=e.device) for e in split_hidden_states] + max_seq_len = max([e.size(0) for e in split_hidden_states]) + prompt_embeds = torch.stack( + [torch.cat([u, u.new_zeros(max_seq_len - u.size(0), u.size(1))]) for u in split_hidden_states] + ) + encoder_attention_mask = torch.stack( + [torch.cat([u, u.new_zeros(max_seq_len - u.size(0))]) for u in attn_mask_list] + ) + + prompt_embeds = prompt_embeds.to(dtype=dtype, device=device) + + return prompt_embeds, encoder_attention_mask + + # Copied from diffusers.pipelines.qwenimage.pipeline_qwenimage.QwenImagePipeline.encode_prompt + def encode_prompt( + self, + prompt: Union[str, List[str]], + device: Optional[torch.device] = None, + num_images_per_prompt: int = 1, + prompt_embeds: Optional[torch.Tensor] = None, + prompt_embeds_mask: Optional[torch.Tensor] = None, + max_sequence_length: int = 1024, + ): + r""" + + Args: + prompt (`str` or `List[str]`, *optional*): + prompt to be encoded + device: (`torch.device`): + torch device + num_images_per_prompt (`int`): + number of images that should be generated per prompt + prompt_embeds (`torch.Tensor`, *optional*): + Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not + provided, text embeddings will be generated from `prompt` input argument. + """ + device = device or self._execution_device + + prompt = [prompt] if isinstance(prompt, str) else prompt + batch_size = len(prompt) if prompt_embeds is None else prompt_embeds.shape[0] + + if prompt_embeds is None: + prompt_embeds, prompt_embeds_mask = self._get_qwen_prompt_embeds(prompt, device) + + prompt_embeds = prompt_embeds[:, :max_sequence_length] + prompt_embeds_mask = prompt_embeds_mask[:, :max_sequence_length] + + _, seq_len, _ = prompt_embeds.shape + prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) + prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) + prompt_embeds_mask = prompt_embeds_mask.repeat(1, num_images_per_prompt, 1) + prompt_embeds_mask = prompt_embeds_mask.view(batch_size * num_images_per_prompt, seq_len) + + return prompt_embeds, prompt_embeds_mask + + def get_image_caption(self, prompt_image, use_en_prompt=True, device=None): + if use_en_prompt: + prompt = self.image_caption_prompt_en + else: + prompt = self.image_caption_prompt_cn + model_inputs = self.vl_processor( + text=prompt, + images=prompt_image, + padding=True, + return_tensors="pt", + ).to(device) + generated_ids = self.text_encoder.generate(**model_inputs, max_new_tokens=512) + generated_ids_trimmed = [ + out_ids[len(in_ids) :] for in_ids, out_ids in zip(model_inputs.input_ids, generated_ids) + ] + output_text = self.vl_processor.batch_decode( + generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False + )[0] + return output_text.strip() + + def check_inputs( + self, + height, + width, + negative_prompt=None, + prompt_embeds=None, + negative_prompt_embeds=None, + prompt_embeds_mask=None, + negative_prompt_embeds_mask=None, + callback_on_step_end_tensor_inputs=None, + max_sequence_length=None, + ): + if height % (self.vae_scale_factor * 2) != 0 or width % (self.vae_scale_factor * 2) != 0: + logger.warning( + f"`height` and `width` have to be divisible by {self.vae_scale_factor * 2} but are {height} and {width}. Dimensions will be resized accordingly" + ) + + if callback_on_step_end_tensor_inputs is not None and not all( + k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs + ): + raise ValueError( + f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}" + ) + + if negative_prompt is not None and negative_prompt_embeds is not None: + raise ValueError( + f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" + f" {negative_prompt_embeds}. Please make sure to only forward one of the two." + ) + + if prompt_embeds is not None and prompt_embeds_mask is None: + raise ValueError( + "If `prompt_embeds` are provided, `prompt_embeds_mask` also have to be passed. Make sure to generate `prompt_embeds_mask` from the same text encoder that was used to generate `prompt_embeds`." + ) + if negative_prompt_embeds is not None and negative_prompt_embeds_mask is None: + raise ValueError( + "If `negative_prompt_embeds` are provided, `negative_prompt_embeds_mask` also have to be passed. Make sure to generate `negative_prompt_embeds_mask` from the same text encoder that was used to generate `negative_prompt_embeds`." + ) + + if max_sequence_length is not None and max_sequence_length > 1024: + raise ValueError(f"`max_sequence_length` cannot be greater than 1024 but is {max_sequence_length}") + + @staticmethod + def _pack_latents(latents, batch_size, num_channels_latents, height, width, layers): + latents = latents.view(batch_size, layers, num_channels_latents, height // 2, 2, width // 2, 2) + latents = latents.permute(0, 1, 3, 5, 2, 4, 6) + latents = latents.reshape(batch_size, layers * (height // 2) * (width // 2), num_channels_latents * 4) + + return latents + + @staticmethod + def _unpack_latents(latents, height, width, layers, vae_scale_factor): + batch_size, num_patches, channels = latents.shape + + # VAE applies 8x compression on images but we must also account for packing which requires + # latent height and width to be divisible by 2. + height = 2 * (int(height) // (vae_scale_factor * 2)) + width = 2 * (int(width) // (vae_scale_factor * 2)) + + latents = latents.view(batch_size, layers + 1, height // 2, width // 2, channels // 4, 2, 2) + latents = latents.permute(0, 1, 4, 2, 5, 3, 6) + + latents = latents.reshape(batch_size, layers + 1, channels // (2 * 2), height, width) + latents = latents.permute(0, 2, 1, 3, 4) # (b, c, f, h, w) + + return latents + + # Copied from diffusers.pipelines.qwenimage.pipeline_qwenimage_edit.QwenImageEditPipeline._encode_vae_image + def _encode_vae_image(self, image: torch.Tensor, generator: torch.Generator): + if isinstance(generator, list): + image_latents = [ + retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i], sample_mode="argmax") + for i in range(image.shape[0]) + ] + image_latents = torch.cat(image_latents, dim=0) + else: + image_latents = retrieve_latents(self.vae.encode(image), generator=generator, sample_mode="argmax") + latents_mean = ( + torch.tensor(self.vae.config.latents_mean) + .view(1, self.latent_channels, 1, 1, 1) + .to(image_latents.device, image_latents.dtype) + ) + latents_std = ( + torch.tensor(self.vae.config.latents_std) + .view(1, self.latent_channels, 1, 1, 1) + .to(image_latents.device, image_latents.dtype) + ) + image_latents = (image_latents - latents_mean) / latents_std + + return image_latents + + def prepare_latents( + self, + image, + batch_size, + num_channels_latents, + height, + width, + layers, + dtype, + device, + generator, + latents=None, + ): + # VAE applies 8x compression on images but we must also account for packing which requires + # latent height and width to be divisible by 2. + height = 2 * (int(height) // (self.vae_scale_factor * 2)) + width = 2 * (int(width) // (self.vae_scale_factor * 2)) + + shape = ( + batch_size, + layers + 1, + num_channels_latents, + height, + width, + ) ### the generated first image is combined image + + image_latents = None + if image is not None: + image = image.to(device=device, dtype=dtype) + if image.shape[1] != self.latent_channels: + image_latents = self._encode_vae_image(image=image, generator=generator) + else: + image_latents = image + if batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] == 0: + # expand init_latents for batch_size + additional_image_per_prompt = batch_size // image_latents.shape[0] + image_latents = torch.cat([image_latents] * additional_image_per_prompt, dim=0) + elif batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] != 0: + raise ValueError( + f"Cannot duplicate `image` of batch size {image_latents.shape[0]} to {batch_size} text prompts." + ) + else: + image_latents = torch.cat([image_latents], dim=0) + + image_latent_height, image_latent_width = image_latents.shape[3:] + image_latents = image_latents.permute(0, 2, 1, 3, 4) # (b, c, f, h, w) -> (b, f, c, h, w) + image_latents = self._pack_latents( + image_latents, batch_size, num_channels_latents, image_latent_height, image_latent_width, 1 + ) + + if isinstance(generator, list) and len(generator) != batch_size: + raise ValueError( + f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" + f" size of {batch_size}. Make sure the batch size matches the length of the generators." + ) + if latents is None: + latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) + latents = self._pack_latents(latents, batch_size, num_channels_latents, height, width, layers + 1) + else: + latents = latents.to(device=device, dtype=dtype) + + return latents, image_latents + + @property + def guidance_scale(self): + return self._guidance_scale + + @property + def attention_kwargs(self): + return self._attention_kwargs + + @property + def num_timesteps(self): + return self._num_timesteps + + @property + def current_timestep(self): + return self._current_timestep + + @property + def interrupt(self): + return self._interrupt + + @torch.no_grad() + @replace_example_docstring(EXAMPLE_DOC_STRING) + def __call__( + self, + image: Optional[PipelineImageInput] = None, + prompt: Union[str, List[str]] = None, + negative_prompt: Union[str, List[str]] = None, + true_cfg_scale: float = 4.0, + layers: Optional[int] = 4, + num_inference_steps: int = 50, + sigmas: Optional[List[float]] = None, + guidance_scale: Optional[float] = None, + num_images_per_prompt: int = 1, + generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, + latents: Optional[torch.Tensor] = None, + prompt_embeds: Optional[torch.Tensor] = None, + prompt_embeds_mask: Optional[torch.Tensor] = None, + negative_prompt_embeds: Optional[torch.Tensor] = None, + negative_prompt_embeds_mask: Optional[torch.Tensor] = None, + output_type: Optional[str] = "pil", + return_dict: bool = True, + attention_kwargs: Optional[Dict[str, Any]] = None, + callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None, + callback_on_step_end_tensor_inputs: List[str] = ["latents"], + max_sequence_length: int = 512, + resolution: int = 640, + cfg_normalize: bool = False, + use_en_prompt: bool = False, + ): + r""" + Function invoked when calling the pipeline for generation. + + Args: + image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`): + `Image`, numpy array or tensor representing an image batch to be used as the starting point. For both + numpy array and pytorch tensor, the expected value range is between `[0, 1]` If it's a tensor or a list + or tensors, the expected shape should be `(B, C, H, W)` or `(C, H, W)`. If it is a numpy array or a + list of arrays, the expected shape should be `(B, H, W, C)` or `(H, W, C)` It can also accept image + latents as `image`, but if passing latents directly it is not encoded again. + prompt (`str` or `List[str]`, *optional*): + The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. + instead. + negative_prompt (`str` or `List[str]`, *optional*): + The prompt or prompts not to guide the image generation. If not defined, one has to pass + `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `true_cfg_scale` is + not greater than `1`). + true_cfg_scale (`float`, *optional*, defaults to 1.0): + true_cfg_scale (`float`, *optional*, defaults to 1.0): Guidance scale as defined in [Classifier-Free + Diffusion Guidance](https://huggingface.co/papers/2207.12598). `true_cfg_scale` is defined as `w` of + equation 2. of [Imagen Paper](https://huggingface.co/papers/2205.11487). Classifier-free guidance is + enabled by setting `true_cfg_scale > 1` and a provided `negative_prompt`. Higher guidance scale + encourages to generate images that are closely linked to the text `prompt`, usually at the expense of + lower image quality. + num_inference_steps (`int`, *optional*, defaults to 50): + The number of denoising steps. More denoising steps usually lead to a higher quality image at the + expense of slower inference. + sigmas (`List[float]`, *optional*): + Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in + their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed + will be used. + guidance_scale (`float`, *optional*, defaults to None): + A guidance scale value for guidance distilled models. Unlike the traditional classifier-free guidance + where the guidance scale is applied during inference through noise prediction rescaling, guidance + distilled models take the guidance scale directly as an input parameter during forward pass. Guidance + scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images + that are closely linked to the text `prompt`, usually at the expense of lower image quality. This + parameter in the pipeline is there to support future guidance-distilled models when they come up. It is + ignored when not using guidance distilled models. To enable traditional classifier-free guidance, + please pass `true_cfg_scale > 1.0` and `negative_prompt` (even an empty negative prompt like " " should + enable classifier-free guidance computations). + num_images_per_prompt (`int`, *optional*, defaults to 1): + The number of images to generate per prompt. + generator (`torch.Generator` or `List[torch.Generator]`, *optional*): + One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) + to make generation deterministic. + latents (`torch.Tensor`, *optional*): + Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image + generation. Can be used to tweak the same generation with different prompts. If not provided, a latents + tensor will be generated by sampling using the supplied random `generator`. + prompt_embeds (`torch.Tensor`, *optional*): + Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not + provided, text embeddings will be generated from `prompt` input argument. + negative_prompt_embeds (`torch.Tensor`, *optional*): + Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt + weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input + argument. + output_type (`str`, *optional*, defaults to `"pil"`): + The output format of the generate image. Choose between + [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`~pipelines.qwenimage.QwenImagePipelineOutput`] instead of a plain tuple. + attention_kwargs (`dict`, *optional*): + A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under + `self.processor` in + [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). + callback_on_step_end (`Callable`, *optional*): + A function that calls at the end of each denoising steps during the inference. The function is called + with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, + callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by + `callback_on_step_end_tensor_inputs`. + callback_on_step_end_tensor_inputs (`List`, *optional*): + The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list + will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the + `._callback_tensor_inputs` attribute of your pipeline class. + max_sequence_length (`int` defaults to 512): Maximum sequence length to use with the `prompt`. + resolution (`int`, *optional*, defaults to 640): + using different bucket in (640, 1024) to determin the condition and output resolution + cfg_normalize (`bool`, *optional*, defaults to `False`) + whether enable cfg normalization. + use_en_prompt (`bool`, *optional*, defaults to `False`) + automatic caption language if user does not provide caption + + Examples: + + Returns: + [`~pipelines.qwenimage.QwenImagePipelineOutput`] or `tuple`: + [`~pipelines.qwenimage.QwenImagePipelineOutput`] if `return_dict` is True, otherwise a `tuple`. When + returning a tuple, the first element is a list with the generated images. + """ + image_size = image[0].size if isinstance(image, list) else image.size + assert resolution in [640, 1024], f"resolution must be either 640 or 1024, but got {resolution}" + calculated_width, calculated_height = calculate_dimensions( + resolution * resolution, image_size[0] / image_size[1] + ) + height = calculated_height + width = calculated_width + + multiple_of = self.vae_scale_factor * 2 + width = width // multiple_of * multiple_of + height = height // multiple_of * multiple_of + + # 1. Check inputs. Raise error if not correct + self.check_inputs( + height, + width, + negative_prompt=negative_prompt, + prompt_embeds=prompt_embeds, + negative_prompt_embeds=negative_prompt_embeds, + prompt_embeds_mask=prompt_embeds_mask, + negative_prompt_embeds_mask=negative_prompt_embeds_mask, + callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs, + max_sequence_length=max_sequence_length, + ) + + self._guidance_scale = guidance_scale + self._attention_kwargs = attention_kwargs + self._current_timestep = None + self._interrupt = False + + device = self._execution_device + # 2. Preprocess image + if image is not None and not (isinstance(image, torch.Tensor) and image.size(1) == self.latent_channels): + image = self.image_processor.resize(image, calculated_height, calculated_width) + prompt_image = image + image = self.image_processor.preprocess(image, calculated_height, calculated_width) + image = image.unsqueeze(2) + image = image.to(dtype=self.text_encoder.dtype) + + if prompt is None or prompt == "" or prompt == " ": + prompt = self.get_image_caption(prompt_image, use_en_prompt=use_en_prompt, device=device) + + # 3. Define call parameters + if prompt is not None and isinstance(prompt, str): + batch_size = 1 + elif prompt is not None and isinstance(prompt, list): + batch_size = len(prompt) + else: + batch_size = prompt_embeds.shape[0] + + has_neg_prompt = negative_prompt is not None or ( + negative_prompt_embeds is not None and negative_prompt_embeds_mask is not None + ) + + if true_cfg_scale > 1 and not has_neg_prompt: + logger.warning( + f"true_cfg_scale is passed as {true_cfg_scale}, but classifier-free guidance is not enabled since no negative_prompt is provided." + ) + elif true_cfg_scale <= 1 and has_neg_prompt: + logger.warning( + " negative_prompt is passed but classifier-free guidance is not enabled since true_cfg_scale <= 1" + ) + + do_true_cfg = true_cfg_scale > 1 and has_neg_prompt + prompt_embeds, prompt_embeds_mask = self.encode_prompt( + prompt=prompt, + prompt_embeds=prompt_embeds, + prompt_embeds_mask=prompt_embeds_mask, + device=device, + num_images_per_prompt=num_images_per_prompt, + max_sequence_length=max_sequence_length, + ) + if do_true_cfg: + negative_prompt_embeds, negative_prompt_embeds_mask = self.encode_prompt( + prompt=negative_prompt, + prompt_embeds=negative_prompt_embeds, + prompt_embeds_mask=negative_prompt_embeds_mask, + device=device, + num_images_per_prompt=num_images_per_prompt, + max_sequence_length=max_sequence_length, + ) + + # 4. Prepare latent variables + num_channels_latents = self.transformer.config.in_channels // 4 + latents, image_latents = self.prepare_latents( + image, + batch_size * num_images_per_prompt, + num_channels_latents, + height, + width, + layers, + prompt_embeds.dtype, + device, + generator, + latents, + ) + img_shapes = [ + [ + *[ + (1, height // self.vae_scale_factor // 2, width // self.vae_scale_factor // 2) + for _ in range(layers + 1) + ], + (1, calculated_height // self.vae_scale_factor // 2, calculated_width // self.vae_scale_factor // 2), + ] + ] * batch_size + + # 5. Prepare timesteps + sigmas = np.linspace(1.0, 0, num_inference_steps + 1)[:-1] if sigmas is None else sigmas + image_seq_len = latents.shape[1] + base_seqlen = 256 * 256 / 16 / 16 + mu = (image_latents.shape[1] / base_seqlen) ** 0.5 + timesteps, num_inference_steps = retrieve_timesteps( + self.scheduler, + num_inference_steps, + device, + sigmas=sigmas, + mu=mu, + ) + num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0) + self._num_timesteps = len(timesteps) + + # handle guidance + if self.transformer.config.guidance_embeds and guidance_scale is None: + raise ValueError("guidance_scale is required for guidance-distilled model.") + elif self.transformer.config.guidance_embeds: + guidance = torch.full([1], guidance_scale, device=device, dtype=torch.float32) + guidance = guidance.expand(latents.shape[0]) + elif not self.transformer.config.guidance_embeds and guidance_scale is not None: + logger.warning( + f"guidance_scale is passed as {guidance_scale}, but ignored since the model is not guidance-distilled." + ) + guidance = None + elif not self.transformer.config.guidance_embeds and guidance_scale is None: + guidance = None + + if self.attention_kwargs is None: + self._attention_kwargs = {} + + txt_seq_lens = prompt_embeds_mask.sum(dim=1).tolist() if prompt_embeds_mask is not None else None + negative_txt_seq_lens = ( + negative_prompt_embeds_mask.sum(dim=1).tolist() if negative_prompt_embeds_mask is not None else None + ) + is_rgb = torch.tensor([0] * batch_size).to(device=device, dtype=torch.long) + # 6. Denoising loop + self.scheduler.set_begin_index(0) + with self.progress_bar(total=num_inference_steps) as progress_bar: + for i, t in enumerate(timesteps): + if self.interrupt: + continue + + self._current_timestep = t + + latent_model_input = latents + if image_latents is not None: + latent_model_input = torch.cat([latents, image_latents], dim=1) + + # broadcast to batch dimension in a way that's compatible with ONNX/Core ML + timestep = t.expand(latents.shape[0]).to(latents.dtype) + with self.transformer.cache_context("cond"): + noise_pred = self.transformer( + hidden_states=latent_model_input, + timestep=timestep / 1000, + guidance=guidance, + encoder_hidden_states_mask=prompt_embeds_mask, + encoder_hidden_states=prompt_embeds, + img_shapes=img_shapes, + txt_seq_lens=txt_seq_lens, + attention_kwargs=self.attention_kwargs, + additional_t_cond=is_rgb, + return_dict=False, + )[0] + noise_pred = noise_pred[:, : latents.size(1)] + + if do_true_cfg: + with self.transformer.cache_context("uncond"): + neg_noise_pred = self.transformer( + hidden_states=latent_model_input, + timestep=timestep / 1000, + guidance=guidance, + encoder_hidden_states_mask=negative_prompt_embeds_mask, + encoder_hidden_states=negative_prompt_embeds, + img_shapes=img_shapes, + txt_seq_lens=negative_txt_seq_lens, + attention_kwargs=self.attention_kwargs, + additional_t_cond=is_rgb, + return_dict=False, + )[0] + neg_noise_pred = neg_noise_pred[:, : latents.size(1)] + comb_pred = neg_noise_pred + true_cfg_scale * (noise_pred - neg_noise_pred) + + if cfg_normalize: + cond_norm = torch.norm(noise_pred, dim=-1, keepdim=True) + noise_norm = torch.norm(comb_pred, dim=-1, keepdim=True) + noise_pred = comb_pred * (cond_norm / noise_norm) + else: + noise_pred = comb_pred + + # compute the previous noisy sample x_t -> x_t-1 + latents_dtype = latents.dtype + latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0] + + if latents.dtype != latents_dtype: + if torch.backends.mps.is_available(): + # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272 + latents = latents.to(latents_dtype) + + if callback_on_step_end is not None: + callback_kwargs = {} + for k in callback_on_step_end_tensor_inputs: + callback_kwargs[k] = locals()[k] + callback_outputs = callback_on_step_end(self, i, t, callback_kwargs) + + latents = callback_outputs.pop("latents", latents) + prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds) + + # call the callback, if provided + if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): + progress_bar.update() + + if XLA_AVAILABLE: + xm.mark_step() + + self._current_timestep = None + if output_type == "latent": + image = latents + else: + latents = self._unpack_latents(latents, height, width, layers, self.vae_scale_factor) + latents = latents.to(self.vae.dtype) + latents_mean = ( + torch.tensor(self.vae.config.latents_mean) + .view(1, self.vae.config.z_dim, 1, 1, 1) + .to(latents.device, latents.dtype) + ) + latents_std = 1.0 / torch.tensor(self.vae.config.latents_std).view(1, self.vae.config.z_dim, 1, 1, 1).to( + latents.device, latents.dtype + ) + 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 + + image = image.squeeze(2) + + image = self.image_processor.postprocess(image, output_type=output_type) + images = [] + for bidx in range(b): + images.append(image[bidx * f : (bidx + 1) * f]) + + # Offload all models + self.maybe_free_model_hooks() + + if not return_dict: + return (images,) + + return QwenImagePipelineOutput(images=images) diff --git a/src/diffusers/utils/dummy_torch_and_transformers_objects.py b/src/diffusers/utils/dummy_torch_and_transformers_objects.py index be7f8f8ce4..cd51d3a567 100644 --- a/src/diffusers/utils/dummy_torch_and_transformers_objects.py +++ b/src/diffusers/utils/dummy_torch_and_transformers_objects.py @@ -2297,6 +2297,21 @@ class QwenImageInpaintPipeline(metaclass=DummyObject): requires_backends(cls, ["torch", "transformers"]) +class QwenImageLayeredPipeline(metaclass=DummyObject): + _backends = ["torch", "transformers"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch", "transformers"]) + + @classmethod + def from_config(cls, *args, **kwargs): + requires_backends(cls, ["torch", "transformers"]) + + @classmethod + def from_pretrained(cls, *args, **kwargs): + requires_backends(cls, ["torch", "transformers"]) + + class QwenImagePipeline(metaclass=DummyObject): _backends = ["torch", "transformers"]