feature

app.py

@@ -5,11 +5,7 @@ import torch
 import spaces
 
 from PIL import Image
-
-from optimization import optimize_pipeline_
-from qwenimage.pipeline_qwen_image_edit import QwenImageEditPipeline
-from qwenimage.transformer_qwenimage import QwenImageTransformer2DModel
-from qwenimage.qwen_fa3_processor import QwenDoubleStreamAttnProcessorFA3
+from diffusers import QwenImageEditPipeline
 
 import os
 import base64
@@ -144,11 +140,6 @@ device = "cuda" if torch.cuda.is_available() else "cpu"
 
 # Load the model pipeline
 pipe = QwenImageEditPipeline.from_pretrained("Qwen/Qwen-Image-Edit", torch_dtype=dtype).to(device)
-pipe.transformer.__class__ = QwenImageTransformer2DModel
-pipe.transformer.set_attn_processor(QwenDoubleStreamAttnProcessorFA3())
-
-# --- Ahead-of-time compilation ---
-optimize_pipeline_(pipe, image=Image.new("RGB", (1024, 1024)), prompt="prompt")
 
 # --- UI Constants and Helpers ---
 MAX_SEED = np.iinfo(np.int32).max
@@ -160,7 +151,7 @@ def infer(
     prompt,
     seed=120,
     randomize_seed=False,
-    true_guidance_scale=4.0,
+    true_guidance_scale=1.0,
     num_inference_steps=50,
     rewrite_prompt=True,
     progress=gr.Progress(track_tqdm=True),

optimization.py

@@ -1,70 +0,0 @@
-"""
-"""
-
-from typing import Any
-from typing import Callable
-from typing import ParamSpec
-from torchao.quantization import quantize_
-from torchao.quantization import Float8DynamicActivationFloat8WeightConfig
-import spaces
-import torch
-from torch.utils._pytree import tree_map
-
-
-P = ParamSpec('P')
-
-
-TRANSFORMER_IMAGE_SEQ_LENGTH_DIM = torch.export.Dim('image_seq_length')
-TRANSFORMER_TEXT_SEQ_LENGTH_DIM = torch.export.Dim('text_seq_length')
-
-TRANSFORMER_DYNAMIC_SHAPES = {
-    'hidden_states': {
-        1: TRANSFORMER_IMAGE_SEQ_LENGTH_DIM,
-    },
-    'encoder_hidden_states': {
-        1: TRANSFORMER_TEXT_SEQ_LENGTH_DIM,
-    },
-    'encoder_hidden_states_mask': {
-        1: TRANSFORMER_TEXT_SEQ_LENGTH_DIM,
-    },
-    'image_rotary_emb': ({
-        0: TRANSFORMER_IMAGE_SEQ_LENGTH_DIM,
-    }, {
-        0: TRANSFORMER_TEXT_SEQ_LENGTH_DIM,
-    }),
-}
-
-
-INDUCTOR_CONFIGS = {
-    'conv_1x1_as_mm': True,
-    'epilogue_fusion': False,
-    'coordinate_descent_tuning': True,
-    'coordinate_descent_check_all_directions': True,
-    'max_autotune': True,
-    'triton.cudagraphs': True,
-}
-
-
-def optimize_pipeline_(pipeline: Callable[P, Any], *args: P.args, **kwargs: P.kwargs):
-
-    @spaces.GPU(duration=1500)
-    def compile_transformer():
-
-        with spaces.aoti_capture(pipeline.transformer) as call:
-            pipeline(*args, **kwargs)
-
-        dynamic_shapes = tree_map(lambda t: None, call.kwargs)
-        dynamic_shapes |= TRANSFORMER_DYNAMIC_SHAPES
-
-        # quantize_(pipeline.transformer, Float8DynamicActivationFloat8WeightConfig())
-        
-        exported = torch.export.export(
-            mod=pipeline.transformer,
-            args=call.args,
-            kwargs=call.kwargs,
-            dynamic_shapes=dynamic_shapes,
-        )
-
-        return spaces.aoti_compile(exported, INDUCTOR_CONFIGS)
-
-    spaces.aoti_apply(compile_transformer(), pipeline.transformer)

qwenimage/pipeline_qwen_image_edit.py

@@ -1,857 +0,0 @@
-# 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 diffusers.image_processor import PipelineImageInput, VaeImageProcessor
-from diffusers.loaders import QwenImageLoraLoaderMixin
-from diffusers.models import AutoencoderKLQwenImage, QwenImageTransformer2DModel
-from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
-from diffusers.utils import is_torch_xla_available, logging, replace_example_docstring
-from diffusers.utils.torch_utils import randn_tensor
-from diffusers.pipelines.pipeline_utils import DiffusionPipeline
-from diffusers.pipelines.qwenimage.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 QwenImageEditPipeline
-        >>> from diffusers.utils import load_image
-
-        >>> pipe = QwenImageEditPipeline.from_pretrained("Qwen/Qwen-Image-Edit", 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("RGB")
-        >>> prompt = (
-        ...     "Make Pikachu hold a sign that says 'Qwen Edit is awesome', yarn art style, detailed, vibrant colors"
-        ... )
-        >>> # Depending on the variant being used, the pipeline call will slightly vary.
-        >>> # Refer to the pipeline documentation for more details.
-        >>> image = pipe(image, prompt, num_inference_steps=50).images[0]
-        >>> image.save("qwenimage_edit.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")
-
-
-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, None
-
-
-class QwenImageEditPipeline(DiffusionPipeline, QwenImageLoraLoaderMixin):
-    r"""
-    The Qwen-Image-Edit pipeline for image editing.
-
-    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 key features of the input image (color, shape, size, texture, objects, background), then explain how the user's text instruction should alter or modify the image. Generate a new image that meets the user's requirements while maintaining consistency with the original input where appropriate.<|im_end|>\n<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>{}<|im_end|>\n<|im_start|>assistant\n"
-        self.prompt_template_encode_start_idx = 64
-        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,
-        image: Optional[torch.Tensor] = 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]
-
-        model_inputs = self.processor(
-            text=txt,
-            images=image,
-            padding=True,
-            return_tensors="pt",
-        ).to(device)
-
-        outputs = self.text_encoder(
-            input_ids=model_inputs.input_ids,
-            attention_mask=model_inputs.attention_mask,
-            pixel_values=model_inputs.pixel_values,
-            image_grid_thw=model_inputs.image_grid_thw,
-            output_hidden_states=True,
-        )
-
-        hidden_states = outputs.hidden_states[-1]
-        split_hidden_states = self._extract_masked_hidden(hidden_states, model_inputs.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
-
-    def encode_prompt(
-        self,
-        prompt: Union[str, List[str]],
-        image: Optional[torch.Tensor] = None,
-        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
-            image (`torch.Tensor`, *optional*):
-                image 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, image, device)
-
-        _, 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 check_inputs(
-        self,
-        prompt,
-        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 prompt is not None and prompt_embeds is not None:
-            raise ValueError(
-                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
-                " only forward one of the two."
-            )
-        elif prompt is None and prompt_embeds is None:
-            raise ValueError(
-                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
-            )
-        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
-            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
-
-        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
-    # Copied from diffusers.pipelines.qwenimage.pipeline_qwenimage.QwenImagePipeline._pack_latents
-    def _pack_latents(latents, batch_size, num_channels_latents, height, width):
-        latents = latents.view(batch_size, num_channels_latents, height // 2, 2, width // 2, 2)
-        latents = latents.permute(0, 2, 4, 1, 3, 5)
-        latents = latents.reshape(batch_size, (height // 2) * (width // 2), num_channels_latents * 4)
-
-        return latents
-
-    @staticmethod
-    # Copied from diffusers.pipelines.qwenimage.pipeline_qwenimage.QwenImagePipeline._unpack_latents
-    def _unpack_latents(latents, height, width, 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, height // 2, width // 2, channels // 4, 2, 2)
-        latents = latents.permute(0, 3, 1, 4, 2, 5)
-
-        latents = latents.reshape(batch_size, channels // (2 * 2), 1, height, width)
-
-        return latents
-
-    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 enable_vae_slicing(self):
-        r"""
-        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
-        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
-        """
-        self.vae.enable_slicing()
-
-    def disable_vae_slicing(self):
-        r"""
-        Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
-        computing decoding in one step.
-        """
-        self.vae.disable_slicing()
-
-    def enable_vae_tiling(self):
-        r"""
-        Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
-        compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
-        processing larger images.
-        """
-        self.vae.enable_tiling()
-
-    def disable_vae_tiling(self):
-        r"""
-        Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
-        computing decoding in one step.
-        """
-        self.vae.disable_tiling()
-
-    def prepare_latents(
-        self,
-        image,
-        batch_size,
-        num_channels_latents,
-        height,
-        width,
-        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, 1, num_channels_latents, height, width)
-
-        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 = self._pack_latents(
-                image_latents, batch_size, num_channels_latents, image_latent_height, image_latent_width
-            )
-
-        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)
-        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,
-        height: Optional[int] = None,
-        width: Optional[int] = None,
-        num_inference_steps: int = 50,
-        sigmas: Optional[List[float]] = None,
-        guidance_scale: float = 1.0,
-        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,
-    ):
-        r"""
-        Function invoked when calling the pipeline for generation.
-
-        Args:
-            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):
-                When > 1.0 and a provided `negative_prompt`, enables true classifier-free guidance.
-            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
-                The height in pixels of the generated image. This is set to 1024 by default for the best results.
-            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
-                The width in pixels of the generated image. This is set to 1024 by default for the best results.
-            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 3.5):
-                Guidance scale as defined in [Classifier-Free Diffusion
-                Guidance](https://huggingface.co/papers/2207.12598). `guidance_scale` is defined as `w` of equation 2.
-                of [Imagen Paper](https://huggingface.co/papers/2205.11487). 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.
-                Note that passing `guidance_scale` to the pipeline is ineffective. To enable classifier-free guidance,
-                please pass `true_cfg_scale` 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`.
-
-        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
-        calculated_width, calculated_height, _ = calculate_dimensions(1024 * 1024, image_size[0] / image_size[1])
-        height = height or calculated_height
-        width = width or 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(
-            prompt,
-            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
-
-        # 2. 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]
-
-        device = self._execution_device
-        # 3. 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)
-
-        has_neg_prompt = negative_prompt is not None or (
-            negative_prompt_embeds is not None and negative_prompt_embeds_mask is not None
-        )
-        do_true_cfg = true_cfg_scale > 1 and has_neg_prompt
-        prompt_embeds, prompt_embeds_mask = self.encode_prompt(
-            image=prompt_image,
-            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(
-                image=prompt_image,
-                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,
-            prompt_embeds.dtype,
-            device,
-            generator,
-            latents,
-        )
-        img_shapes = [
-            [
-                (1, height // self.vae_scale_factor // 2, width // self.vae_scale_factor // 2),
-                (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, 1 / num_inference_steps, num_inference_steps) if sigmas is None else sigmas
-        image_seq_len = latents.shape[1]
-        mu = calculate_shift(
-            image_seq_len,
-            self.scheduler.config.get("base_image_seq_len", 256),
-            self.scheduler.config.get("max_image_seq_len", 4096),
-            self.scheduler.config.get("base_shift", 0.5),
-            self.scheduler.config.get("max_shift", 1.15),
-        )
-        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:
-            guidance = torch.full([1], guidance_scale, device=device, dtype=torch.float32)
-            guidance = guidance.expand(latents.shape[0])
-        else:
-            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
-
-        image_rotary_emb = self.transformer.pos_embed(img_shapes, txt_seq_lens, device=latents.device)
-        if do_true_cfg:
-            negative_txt_seq_lens = (
-                negative_prompt_embeds_mask.sum(dim=1).tolist()
-                if negative_prompt_embeds_mask is not None
-                else None
-            )
-            uncond_image_rotary_emb = self.transformer.pos_embed(
-                img_shapes, negative_txt_seq_lens, device=latents.device
-            )
-        else:
-            uncond_image_rotary_emb = None
-
-        # 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,
-                        image_rotary_emb=image_rotary_emb,
-                        attention_kwargs=self.attention_kwargs,
-                        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,
-                            image_rotary_emb=uncond_image_rotary_emb,
-                            attention_kwargs=self.attention_kwargs,
-                            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)
-
-                    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)
-
-                # 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, 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
-            image = self.vae.decode(latents, return_dict=False)[0][:, :, 0]
-            image = self.image_processor.postprocess(image, output_type=output_type)
-
-        # Offload all models
-        self.maybe_free_model_hooks()
-
-        if not return_dict:
-            return (image,)
-
-        return QwenImagePipelineOutput(images=image)

qwenimage/qwen_fa3_processor.py

@@ -1,142 +0,0 @@
-"""
-Paired with a good language model. Thanks!
-"""
-
-import torch
-from typing import Optional, Tuple
-from diffusers.models.transformers.transformer_qwenimage import apply_rotary_emb_qwen
-
-try:
-    from kernels import get_kernel
-    _k = get_kernel("kernels-community/vllm-flash-attn3")
-    _flash_attn_func = _k.flash_attn_func
-except Exception as e:
-    _flash_attn_func = None
-    _kernels_err = e
-
-
-def _ensure_fa3_available():
-    if _flash_attn_func is None:
-        raise ImportError(
-            "FlashAttention-3 via Hugging Face `kernels` is required. "
-            "Tried `get_kernel('kernels-community/vllm-flash-attn3')` and failed with:\n"
-            f"{_kernels_err}"
-        )
-
-@torch.library.custom_op("flash::flash_attn_func", mutates_args=())
-def flash_attn_func(
-    q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, causal: bool = False
-) -> torch.Tensor:
-    outputs, lse = _flash_attn_func(q, k, v, causal=causal)
-    return outputs
-
-@flash_attn_func.register_fake
-def _(q, k, v, **kwargs):
-    # two outputs:
-    # 1. output: (batch, seq_len, num_heads, head_dim)
-    # 2. softmax_lse: (batch, num_heads, seq_len) with dtype=torch.float32
-    meta_q = torch.empty_like(q).contiguous()
-    return meta_q #, q.new_empty((q.size(0), q.size(2), q.size(1)), dtype=torch.float32)
-
-
-class QwenDoubleStreamAttnProcessorFA3:
-    """
-    FA3-based attention processor for Qwen double-stream architecture.
-    Computes joint attention over concatenated [text, image] streams using vLLM FlashAttention-3
-    accessed via Hugging Face `kernels`.
-
-    Notes / limitations:
-    - General attention masks are not supported here (FA3 path). `is_causal=False` and no arbitrary mask.
-    - Optional windowed attention / sink tokens / softcap can be plumbed through if you use those features.
-    - Expects an available `apply_rotary_emb_qwen` in scope (same as your non-FA3 processor).
-    """
-
-    _attention_backend = "fa3"  # for parity with your other processors, not used internally
-
-    def __init__(self):
-        _ensure_fa3_available()
-
-    @torch.no_grad()
-    def __call__(
-        self,
-        attn,  # Attention module with to_q/to_k/to_v/add_*_proj, norms, to_out, to_add_out, and .heads
-        hidden_states: torch.FloatTensor,                 # (B, S_img, D_model)  image stream
-        encoder_hidden_states: torch.FloatTensor = None,  # (B, S_txt, D_model)  text stream
-        encoder_hidden_states_mask: torch.FloatTensor = None,  # unused in FA3 path
-        attention_mask: Optional[torch.FloatTensor] = None,    # unused in FA3 path
-        image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,  # (img_freqs, txt_freqs)
-    ) -> Tuple[torch.FloatTensor, torch.FloatTensor]:
-        if encoder_hidden_states is None:
-            raise ValueError("QwenDoubleStreamAttnProcessorFA3 requires encoder_hidden_states (text stream).")
-        if attention_mask is not None:
-            # FA3 kernel path here does not consume arbitrary masks; fail fast to avoid silent correctness issues.
-            raise NotImplementedError("attention_mask is not supported in this FA3 implementation.")
-
-        _ensure_fa3_available()
-
-        B, S_img, _ = hidden_states.shape
-        S_txt = encoder_hidden_states.shape[1]
-
-        # ---- QKV projections (image/sample stream) ----
-        img_q = attn.to_q(hidden_states)   # (B, S_img, D)
-        img_k = attn.to_k(hidden_states)
-        img_v = attn.to_v(hidden_states)
-
-        # ---- QKV projections (text/context stream) ----
-        txt_q = attn.add_q_proj(encoder_hidden_states)  # (B, S_txt, D)
-        txt_k = attn.add_k_proj(encoder_hidden_states)
-        txt_v = attn.add_v_proj(encoder_hidden_states)
-
-        # ---- Reshape to (B, S, H, D_h) ----
-        H = attn.heads
-        img_q = img_q.unflatten(-1, (H, -1))
-        img_k = img_k.unflatten(-1, (H, -1))
-        img_v = img_v.unflatten(-1, (H, -1))
-
-        txt_q = txt_q.unflatten(-1, (H, -1))
-        txt_k = txt_k.unflatten(-1, (H, -1))
-        txt_v = txt_v.unflatten(-1, (H, -1))
-
-        # ---- Q/K normalization (per your module contract) ----
-        if getattr(attn, "norm_q", None) is not None:
-            img_q = attn.norm_q(img_q)
-        if getattr(attn, "norm_k", None) is not None:
-            img_k = attn.norm_k(img_k)
-        if getattr(attn, "norm_added_q", None) is not None:
-            txt_q = attn.norm_added_q(txt_q)
-        if getattr(attn, "norm_added_k", None) is not None:
-            txt_k = attn.norm_added_k(txt_k)
-
-        # ---- RoPE (Qwen variant) ----
-        if image_rotary_emb is not None:
-            img_freqs, txt_freqs = image_rotary_emb
-            # expects tensors shaped (B, S, H, D_h)
-            img_q = apply_rotary_emb_qwen(img_q, img_freqs, use_real=False)
-            img_k = apply_rotary_emb_qwen(img_k, img_freqs, use_real=False)
-            txt_q = apply_rotary_emb_qwen(txt_q, txt_freqs, use_real=False)
-            txt_k = apply_rotary_emb_qwen(txt_k, txt_freqs, use_real=False)
-
-        # ---- Joint attention over [text, image] along sequence axis ----
-        # Shapes: (B, S_total, H, D_h)
-        q = torch.cat([txt_q, img_q], dim=1)
-        k = torch.cat([txt_k, img_k], dim=1)
-        v = torch.cat([txt_v, img_v], dim=1)
-
-        # FlashAttention-3 path expects (B, S, H, D_h) and returns (out, softmax_lse)
-        out = flash_attn_func(q, k, v, causal=False)  # out: (B, S_total, H, D_h)
-
-        # ---- Back to (B, S, D_model) ----
-        out = out.flatten(2, 3).to(q.dtype)
-
-        # Split back to text / image segments
-        txt_attn_out = out[:, :S_txt, :]
-        img_attn_out = out[:, S_txt:, :]
-
-        # ---- Output projections ----
-        img_attn_out = attn.to_out[0](img_attn_out)
-        if len(attn.to_out) > 1:
-            img_attn_out = attn.to_out[1](img_attn_out)  # dropout if present
-
-        txt_attn_out = attn.to_add_out(txt_attn_out)
-
-        return img_attn_out, txt_attn_out

qwenimage/transformer_qwenimage.py

@@ -1,642 +0,0 @@
-# Copyright 2025 Qwen-Image Team, 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 functools
-import math
-from typing import Any, Dict, List, Optional, Tuple, Union
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from diffusers.configuration_utils import ConfigMixin, register_to_config
-from diffusers.loaders import FromOriginalModelMixin, PeftAdapterMixin
-from diffusers.utils import USE_PEFT_BACKEND, logging, scale_lora_layers, unscale_lora_layers
-from diffusers.utils.torch_utils import maybe_allow_in_graph
-from diffusers.models.attention import FeedForward, AttentionMixin
-from diffusers.models.attention_dispatch import dispatch_attention_fn
-from diffusers.models.attention_processor import Attention
-from diffusers.models.cache_utils import CacheMixin
-from diffusers.models.embeddings import TimestepEmbedding, Timesteps
-from diffusers.models.modeling_outputs import Transformer2DModelOutput
-from diffusers.models.modeling_utils import ModelMixin
-from diffusers.models.normalization import AdaLayerNormContinuous, RMSNorm
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-def get_timestep_embedding(
-    timesteps: torch.Tensor,
-    embedding_dim: int,
-    flip_sin_to_cos: bool = False,
-    downscale_freq_shift: float = 1,
-    scale: float = 1,
-    max_period: int = 10000,
-) -> torch.Tensor:
-    """
-    This matches the implementation in Denoising Diffusion Probabilistic Models: Create sinusoidal timestep embeddings.
-
-    Args
-        timesteps (torch.Tensor):
-            a 1-D Tensor of N indices, one per batch element. These may be fractional.
-        embedding_dim (int):
-            the dimension of the output.
-        flip_sin_to_cos (bool):
-            Whether the embedding order should be `cos, sin` (if True) or `sin, cos` (if False)
-        downscale_freq_shift (float):
-            Controls the delta between frequencies between dimensions
-        scale (float):
-            Scaling factor applied to the embeddings.
-        max_period (int):
-            Controls the maximum frequency of the embeddings
-    Returns
-        torch.Tensor: an [N x dim] Tensor of positional embeddings.
-    """
-    assert len(timesteps.shape) == 1, "Timesteps should be a 1d-array"
-
-    half_dim = embedding_dim // 2
-    exponent = -math.log(max_period) * torch.arange(
-        start=0, end=half_dim, dtype=torch.float32, device=timesteps.device
-    )
-    exponent = exponent / (half_dim - downscale_freq_shift)
-
-    emb = torch.exp(exponent).to(timesteps.dtype)
-    emb = timesteps[:, None].float() * emb[None, :]
-
-    # scale embeddings
-    emb = scale * emb
-
-    # concat sine and cosine embeddings
-    emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=-1)
-
-    # flip sine and cosine embeddings
-    if flip_sin_to_cos:
-        emb = torch.cat([emb[:, half_dim:], emb[:, :half_dim]], dim=-1)
-
-    # zero pad
-    if embedding_dim % 2 == 1:
-        emb = torch.nn.functional.pad(emb, (0, 1, 0, 0))
-    return emb
-
-
-def apply_rotary_emb_qwen(
-    x: torch.Tensor,
-    freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]],
-    use_real: bool = True,
-    use_real_unbind_dim: int = -1,
-) -> Tuple[torch.Tensor, torch.Tensor]:
-    """
-    Apply rotary embeddings to input tensors using the given frequency tensor. This function applies rotary embeddings
-    to the given query or key 'x' tensors using the provided frequency tensor 'freqs_cis'. The input tensors are
-    reshaped as complex numbers, and the frequency tensor is reshaped for broadcasting compatibility. The resulting
-    tensors contain rotary embeddings and are returned as real tensors.
-
-    Args:
-        x (`torch.Tensor`):
-            Query or key tensor to apply rotary embeddings. [B, S, H, D] xk (torch.Tensor): Key tensor to apply
-        freqs_cis (`Tuple[torch.Tensor]`): Precomputed frequency tensor for complex exponentials. ([S, D], [S, D],)
-
-    Returns:
-        Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor and key tensor with rotary embeddings.
-    """
-    if use_real:
-        cos, sin = freqs_cis  # [S, D]
-        cos = cos[None, None]
-        sin = sin[None, None]
-        cos, sin = cos.to(x.device), sin.to(x.device)
-
-        if use_real_unbind_dim == -1:
-            # Used for flux, cogvideox, hunyuan-dit
-            x_real, x_imag = x.reshape(*x.shape[:-1], -1, 2).unbind(-1)  # [B, S, H, D//2]
-            x_rotated = torch.stack([-x_imag, x_real], dim=-1).flatten(3)
-        elif use_real_unbind_dim == -2:
-            # Used for Stable Audio, OmniGen, CogView4 and Cosmos
-            x_real, x_imag = x.reshape(*x.shape[:-1], 2, -1).unbind(-2)  # [B, S, H, D//2]
-            x_rotated = torch.cat([-x_imag, x_real], dim=-1)
-        else:
-            raise ValueError(f"`use_real_unbind_dim={use_real_unbind_dim}` but should be -1 or -2.")
-
-        out = (x.float() * cos + x_rotated.float() * sin).to(x.dtype)
-
-        return out
-    else:
-        x_rotated = torch.view_as_complex(x.float().reshape(*x.shape[:-1], -1, 2))
-        freqs_cis = freqs_cis.unsqueeze(1)
-        x_out = torch.view_as_real(x_rotated * freqs_cis).flatten(3)
-
-        return x_out.type_as(x)
-
-
-class QwenTimestepProjEmbeddings(nn.Module):
-    def __init__(self, embedding_dim):
-        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)
-
-    def forward(self, timestep, hidden_states):
-        timesteps_proj = self.time_proj(timestep)
-        timesteps_emb = self.timestep_embedder(timesteps_proj.to(dtype=hidden_states.dtype))  # (N, D)
-
-        conditioning = timesteps_emb
-
-        return conditioning
-
-
-class QwenEmbedRope(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.rope_cache = {}
-
-        # DO NOT USING REGISTER BUFFER HERE, IT WILL CAUSE COMPLEX NUMBERS LOSE ITS IMAGINARY PART
-        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
-        for idx, fhw in enumerate(video_fhw):
-            frame, height, width = fhw
-            rope_key = f"{idx}_{height}_{width}"
-
-            if not torch.compiler.is_compiling():
-                if rope_key not in self.rope_cache:
-                    self.rope_cache[rope_key] = self._compute_video_freqs(frame, height, width, idx)
-                video_freq = self.rope_cache[rope_key]
-            else:
-                video_freq = self._compute_video_freqs(frame, height, width, idx)
-            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_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()
-
-
-class QwenDoubleStreamAttnProcessor2_0:
-    """
-    Attention processor for Qwen double-stream architecture, matching DoubleStreamLayerMegatron logic. This processor
-    implements joint attention computation where text and image streams are processed together.
-    """
-
-    _attention_backend = None
-
-    def __init__(self):
-        if not hasattr(F, "scaled_dot_product_attention"):
-            raise ImportError(
-                "QwenDoubleStreamAttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0."
-            )
-
-    def __call__(
-        self,
-        attn: Attention,
-        hidden_states: torch.FloatTensor,  # Image stream
-        encoder_hidden_states: torch.FloatTensor = None,  # Text stream
-        encoder_hidden_states_mask: torch.FloatTensor = None,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        image_rotary_emb: Optional[torch.Tensor] = None,
-    ) -> torch.FloatTensor:
-        if encoder_hidden_states is None:
-            raise ValueError("QwenDoubleStreamAttnProcessor2_0 requires encoder_hidden_states (text stream)")
-
-        seq_txt = encoder_hidden_states.shape[1]
-
-        # Compute QKV for image stream (sample projections)
-        img_query = attn.to_q(hidden_states)
-        img_key = attn.to_k(hidden_states)
-        img_value = attn.to_v(hidden_states)
-
-        # Compute QKV for text stream (context projections)
-        txt_query = attn.add_q_proj(encoder_hidden_states)
-        txt_key = attn.add_k_proj(encoder_hidden_states)
-        txt_value = attn.add_v_proj(encoder_hidden_states)
-
-        # Reshape for multi-head attention
-        img_query = img_query.unflatten(-1, (attn.heads, -1))
-        img_key = img_key.unflatten(-1, (attn.heads, -1))
-        img_value = img_value.unflatten(-1, (attn.heads, -1))
-
-        txt_query = txt_query.unflatten(-1, (attn.heads, -1))
-        txt_key = txt_key.unflatten(-1, (attn.heads, -1))
-        txt_value = txt_value.unflatten(-1, (attn.heads, -1))
-
-        # Apply QK normalization
-        if attn.norm_q is not None:
-            img_query = attn.norm_q(img_query)
-        if attn.norm_k is not None:
-            img_key = attn.norm_k(img_key)
-        if attn.norm_added_q is not None:
-            txt_query = attn.norm_added_q(txt_query)
-        if attn.norm_added_k is not None:
-            txt_key = attn.norm_added_k(txt_key)
-
-        # Apply RoPE
-        if image_rotary_emb is not None:
-            img_freqs, txt_freqs = image_rotary_emb
-            img_query = apply_rotary_emb_qwen(img_query, img_freqs, use_real=False)
-            img_key = apply_rotary_emb_qwen(img_key, img_freqs, use_real=False)
-            txt_query = apply_rotary_emb_qwen(txt_query, txt_freqs, use_real=False)
-            txt_key = apply_rotary_emb_qwen(txt_key, txt_freqs, use_real=False)
-
-        # Concatenate for joint attention
-        # Order: [text, image]
-        joint_query = torch.cat([txt_query, img_query], dim=1)
-        joint_key = torch.cat([txt_key, img_key], dim=1)
-        joint_value = torch.cat([txt_value, img_value], dim=1)
-
-        # Compute joint attention
-        joint_hidden_states = dispatch_attention_fn(
-            joint_query,
-            joint_key,
-            joint_value,
-            attn_mask=attention_mask,
-            dropout_p=0.0,
-            is_causal=False,
-            backend=self._attention_backend,
-        )
-
-        # Reshape back
-        joint_hidden_states = joint_hidden_states.flatten(2, 3)
-        joint_hidden_states = joint_hidden_states.to(joint_query.dtype)
-
-        # Split attention outputs back
-        txt_attn_output = joint_hidden_states[:, :seq_txt, :]  # Text part
-        img_attn_output = joint_hidden_states[:, seq_txt:, :]  # Image part
-
-        # Apply output projections
-        img_attn_output = attn.to_out[0](img_attn_output)
-        if len(attn.to_out) > 1:
-            img_attn_output = attn.to_out[1](img_attn_output)  # dropout
-
-        txt_attn_output = attn.to_add_out(txt_attn_output)
-
-        return img_attn_output, txt_attn_output
-
-
-@maybe_allow_in_graph
-class QwenImageTransformerBlock(nn.Module):
-    def __init__(
-        self, dim: int, num_attention_heads: int, attention_head_dim: int, qk_norm: str = "rms_norm", eps: float = 1e-6
-    ):
-        super().__init__()
-
-        self.dim = dim
-        self.num_attention_heads = num_attention_heads
-        self.attention_head_dim = attention_head_dim
-
-        # Image processing modules
-        self.img_mod = nn.Sequential(
-            nn.SiLU(),
-            nn.Linear(dim, 6 * dim, bias=True),  # For scale, shift, gate for norm1 and norm2
-        )
-        self.img_norm1 = nn.LayerNorm(dim, elementwise_affine=False, eps=eps)
-        self.attn = Attention(
-            query_dim=dim,
-            cross_attention_dim=None,  # Enable cross attention for joint computation
-            added_kv_proj_dim=dim,  # Enable added KV projections for text stream
-            dim_head=attention_head_dim,
-            heads=num_attention_heads,
-            out_dim=dim,
-            context_pre_only=False,
-            bias=True,
-            processor=QwenDoubleStreamAttnProcessor2_0(),
-            qk_norm=qk_norm,
-            eps=eps,
-        )
-        self.img_norm2 = nn.LayerNorm(dim, elementwise_affine=False, eps=eps)
-        self.img_mlp = FeedForward(dim=dim, dim_out=dim, activation_fn="gelu-approximate")
-
-        # Text processing modules
-        self.txt_mod = nn.Sequential(
-            nn.SiLU(),
-            nn.Linear(dim, 6 * dim, bias=True),  # For scale, shift, gate for norm1 and norm2
-        )
-        self.txt_norm1 = nn.LayerNorm(dim, elementwise_affine=False, eps=eps)
-        # Text doesn't need separate attention - it's handled by img_attn joint computation
-        self.txt_norm2 = nn.LayerNorm(dim, elementwise_affine=False, eps=eps)
-        self.txt_mlp = FeedForward(dim=dim, dim_out=dim, activation_fn="gelu-approximate")
-
-    def _modulate(self, x, mod_params):
-        """Apply modulation to input tensor"""
-        shift, scale, gate = mod_params.chunk(3, dim=-1)
-        return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1), gate.unsqueeze(1)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        encoder_hidden_states: torch.Tensor,
-        encoder_hidden_states_mask: torch.Tensor,
-        temb: torch.Tensor,
-        image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
-        joint_attention_kwargs: Optional[Dict[str, Any]] = None,
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        # Get modulation parameters for both streams
-        img_mod_params = self.img_mod(temb)  # [B, 6*dim]
-        txt_mod_params = self.txt_mod(temb)  # [B, 6*dim]
-
-        # Split modulation parameters for norm1 and norm2
-        img_mod1, img_mod2 = img_mod_params.chunk(2, dim=-1)  # Each [B, 3*dim]
-        txt_mod1, txt_mod2 = txt_mod_params.chunk(2, dim=-1)  # Each [B, 3*dim]
-
-        # Process image stream - norm1 + modulation
-        img_normed = self.img_norm1(hidden_states)
-        img_modulated, img_gate1 = self._modulate(img_normed, img_mod1)
-
-        # Process text stream - norm1 + modulation
-        txt_normed = self.txt_norm1(encoder_hidden_states)
-        txt_modulated, txt_gate1 = self._modulate(txt_normed, txt_mod1)
-
-        # Use QwenAttnProcessor2_0 for joint attention computation
-        # This directly implements the DoubleStreamLayerMegatron logic:
-        # 1. Computes QKV for both streams
-        # 2. Applies QK normalization and RoPE
-        # 3. Concatenates and runs joint attention
-        # 4. Splits results back to separate streams
-        joint_attention_kwargs = joint_attention_kwargs or {}
-        attn_output = self.attn(
-            hidden_states=img_modulated,  # Image stream (will be processed as "sample")
-            encoder_hidden_states=txt_modulated,  # Text stream (will be processed as "context")
-            encoder_hidden_states_mask=encoder_hidden_states_mask,
-            image_rotary_emb=image_rotary_emb,
-            **joint_attention_kwargs,
-        )
-
-        # QwenAttnProcessor2_0 returns (img_output, txt_output) when encoder_hidden_states is provided
-        img_attn_output, txt_attn_output = attn_output
-
-        # Apply attention gates and add residual (like in Megatron)
-        hidden_states = hidden_states + img_gate1 * img_attn_output
-        encoder_hidden_states = encoder_hidden_states + txt_gate1 * txt_attn_output
-
-        # Process image stream - norm2 + MLP
-        img_normed2 = self.img_norm2(hidden_states)
-        img_modulated2, img_gate2 = self._modulate(img_normed2, img_mod2)
-        img_mlp_output = self.img_mlp(img_modulated2)
-        hidden_states = hidden_states + img_gate2 * img_mlp_output
-
-        # Process text stream - norm2 + MLP
-        txt_normed2 = self.txt_norm2(encoder_hidden_states)
-        txt_modulated2, txt_gate2 = self._modulate(txt_normed2, txt_mod2)
-        txt_mlp_output = self.txt_mlp(txt_modulated2)
-        encoder_hidden_states = encoder_hidden_states + txt_gate2 * txt_mlp_output
-
-        # Clip to prevent overflow for fp16
-        if encoder_hidden_states.dtype == torch.float16:
-            encoder_hidden_states = encoder_hidden_states.clip(-65504, 65504)
-        if hidden_states.dtype == torch.float16:
-            hidden_states = hidden_states.clip(-65504, 65504)
-
-        return encoder_hidden_states, hidden_states
-
-
-class QwenImageTransformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin, CacheMixin, AttentionMixin):
-    """
-    The Transformer model introduced in Qwen.
-
-    Args:
-        patch_size (`int`, defaults to `2`):
-            Patch size to turn the input data into small patches.
-        in_channels (`int`, defaults to `64`):
-            The number of channels in the input.
-        out_channels (`int`, *optional*, defaults to `None`):
-            The number of channels in the output. If not specified, it defaults to `in_channels`.
-        num_layers (`int`, defaults to `60`):
-            The number of layers of dual stream DiT blocks to use.
-        attention_head_dim (`int`, defaults to `128`):
-            The number of dimensions to use for each attention head.
-        num_attention_heads (`int`, defaults to `24`):
-            The number of attention heads to use.
-        joint_attention_dim (`int`, defaults to `3584`):
-            The number of dimensions to use for the joint attention (embedding/channel dimension of
-            `encoder_hidden_states`).
-        guidance_embeds (`bool`, defaults to `False`):
-            Whether to use guidance embeddings for guidance-distilled variant of the model.
-        axes_dims_rope (`Tuple[int]`, defaults to `(16, 56, 56)`):
-            The dimensions to use for the rotary positional embeddings.
-    """
-
-    _supports_gradient_checkpointing = True
-    _no_split_modules = ["QwenImageTransformerBlock"]
-    _skip_layerwise_casting_patterns = ["pos_embed", "norm"]
-    _repeated_blocks = ["QwenImageTransformerBlock"]
-
-    @register_to_config
-    def __init__(
-        self,
-        patch_size: int = 2,
-        in_channels: int = 64,
-        out_channels: Optional[int] = 16,
-        num_layers: int = 60,
-        attention_head_dim: int = 128,
-        num_attention_heads: int = 24,
-        joint_attention_dim: int = 3584,
-        guidance_embeds: bool = False,  # TODO: this should probably be removed
-        axes_dims_rope: Tuple[int, int, int] = (16, 56, 56),
-    ):
-        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)
-
-        self.time_text_embed = QwenTimestepProjEmbeddings(embedding_dim=self.inner_dim)
-
-        self.txt_norm = RMSNorm(joint_attention_dim, eps=1e-6)
-
-        self.img_in = nn.Linear(in_channels, self.inner_dim)
-        self.txt_in = nn.Linear(joint_attention_dim, self.inner_dim)
-
-        self.transformer_blocks = nn.ModuleList(
-            [
-                QwenImageTransformerBlock(
-                    dim=self.inner_dim,
-                    num_attention_heads=num_attention_heads,
-                    attention_head_dim=attention_head_dim,
-                )
-                for _ in range(num_layers)
-            ]
-        )
-
-        self.norm_out = AdaLayerNormContinuous(self.inner_dim, self.inner_dim, elementwise_affine=False, eps=1e-6)
-        self.proj_out = nn.Linear(self.inner_dim, patch_size * patch_size * self.out_channels, bias=True)
-
-        self.gradient_checkpointing = False
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        encoder_hidden_states: torch.Tensor = None,
-        encoder_hidden_states_mask: torch.Tensor = None,
-        timestep: torch.LongTensor = None,
-        image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
-        guidance: torch.Tensor = None,  # TODO: this should probably be removed
-        attention_kwargs: Optional[Dict[str, Any]] = None,
-        return_dict: bool = True,
-    ) -> Union[torch.Tensor, Transformer2DModelOutput]:
-        """
-        The [`QwenTransformer2DModel`] forward method.
-
-        Args:
-            hidden_states (`torch.Tensor` of shape `(batch_size, image_sequence_length, in_channels)`):
-                Input `hidden_states`.
-            encoder_hidden_states (`torch.Tensor` of shape `(batch_size, text_sequence_length, joint_attention_dim)`):
-                Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.
-            encoder_hidden_states_mask (`torch.Tensor` of shape `(batch_size, text_sequence_length)`):
-                Mask of the input conditions.
-            timestep ( `torch.LongTensor`):
-                Used to indicate denoising step.
-            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).
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~models.transformer_2d.Transformer2DModelOutput`] instead of a plain
-                tuple.
-
-        Returns:
-            If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
-            `tuple` where the first element is the sample tensor.
-        """
-        if attention_kwargs is not None:
-            attention_kwargs = attention_kwargs.copy()
-            lora_scale = attention_kwargs.pop("scale", 1.0)
-        else:
-            lora_scale = 1.0
-
-        if USE_PEFT_BACKEND:
-            # weight the lora layers by setting `lora_scale` for each PEFT layer
-            scale_lora_layers(self, lora_scale)
-        else:
-            if attention_kwargs is not None and attention_kwargs.get("scale", None) is not None:
-                logger.warning(
-                    "Passing `scale` via `joint_attention_kwargs` when not using the PEFT backend is ineffective."
-                )
-
-        hidden_states = self.img_in(hidden_states)
-
-        timestep = timestep.to(hidden_states.dtype)
-        encoder_hidden_states = self.txt_norm(encoder_hidden_states)
-        encoder_hidden_states = self.txt_in(encoder_hidden_states)
-
-        if guidance is not None:
-            guidance = guidance.to(hidden_states.dtype) * 1000
-
-        temb = (
-            self.time_text_embed(timestep, hidden_states)
-            if guidance is None
-            else self.time_text_embed(timestep, guidance, hidden_states)
-        )
-
-        for index_block, block in enumerate(self.transformer_blocks):
-            if torch.is_grad_enabled() and self.gradient_checkpointing:
-                encoder_hidden_states, hidden_states = self._gradient_checkpointing_func(
-                    block,
-                    hidden_states,
-                    encoder_hidden_states,
-                    encoder_hidden_states_mask,
-                    temb,
-                    image_rotary_emb,
-                )
-
-            else:
-                encoder_hidden_states, hidden_states = block(
-                    hidden_states=hidden_states,
-                    encoder_hidden_states=encoder_hidden_states,
-                    encoder_hidden_states_mask=encoder_hidden_states_mask,
-                    temb=temb,
-                    image_rotary_emb=image_rotary_emb,
-                    joint_attention_kwargs=attention_kwargs,
-                )
-
-        # Use only the image part (hidden_states) from the dual-stream blocks
-        hidden_states = self.norm_out(hidden_states, temb)
-        output = self.proj_out(hidden_states)
-
-        if USE_PEFT_BACKEND:
-            # remove `lora_scale` from each PEFT layer
-            unscale_lora_layers(self, lora_scale)
-
-        if not return_dict:
-            return (output,)
-
-        return Transformer2DModelOutput(sample=output)

requirements.txt

@@ -1,10 +1,8 @@
-git+https://github.com/huggingface/diffusers.git@qwenimage-lru-cache-bypass
-kernels
-torchao==0.11.0
+git+https://github.com/huggingface/diffusers.git
+
 transformers
 accelerate
 safetensors
 sentencepiece
 dashscope
-torchvision
-peft
+torchvision