diffusers/pr_13813/en/api/pipelines/qwenimage.md

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QwenImage

Qwen-Image from the Qwen team is an image generation foundation model in the Qwen series that achieves significant advances in complex text rendering and precise image editing. Experiments show strong general capabilities in both image generation and editing, with exceptional performance in text rendering, especially for Chinese.

Qwen-Image comes in the following variants:

model type	model id
Qwen-Image	Qwen/Qwen-Image
Qwen-Image-Edit	Qwen/Qwen-Image-Edit
Qwen-Image-Edit Plus	Qwen/Qwen-Image-Edit-2509

See the Caching guide to speed up inference by storing and reusing intermediate outputs.

LoRA for faster inference

Use a LoRA from lightx2v/Qwen-Image-Lightning to speed up inference by reducing the number of steps. Refer to the code snippet below:

Code

from diffusers import DiffusionPipeline, FlowMatchEulerDiscreteScheduler
import torch 
import math

ckpt_id = "Qwen/Qwen-Image"

# From
# https://github.com/ModelTC/Qwen-Image-Lightning/blob/342260e8f5468d2f24d084ce04f55e101007118b/generate_with_diffusers.py#L82C9-L97C10
scheduler_config = {
    "base_image_seq_len": 256,
    "base_shift": math.log(3),  # We use shift=3 in distillation
    "invert_sigmas": False,
    "max_image_seq_len": 8192,
    "max_shift": math.log(3),  # We use shift=3 in distillation
    "num_train_timesteps": 1000,
    "shift": 1.0,
    "shift_terminal": None,  # set shift_terminal to None
    "stochastic_sampling": False,
    "time_shift_type": "exponential",
    "use_beta_sigmas": False,
    "use_dynamic_shifting": True,
    "use_exponential_sigmas": False,
    "use_karras_sigmas": False,
}
scheduler = FlowMatchEulerDiscreteScheduler.from_config(scheduler_config)
pipe = DiffusionPipeline.from_pretrained(
    ckpt_id, scheduler=scheduler, torch_dtype=torch.bfloat16
).to("cuda")
pipe.load_lora_weights(
    "lightx2v/Qwen-Image-Lightning", weight_name="Qwen-Image-Lightning-8steps-V1.0.safetensors"
)

prompt = "a tiny astronaut hatching from an egg on the moon, Ultra HD, 4K, cinematic composition."
negative_prompt = " "
image = pipe(
    prompt=prompt,
    negative_prompt=negative_prompt,
    width=1024,
    height=1024,
    num_inference_steps=8,
    true_cfg_scale=1.0,
    generator=torch.manual_seed(0),
).images[0]
image.save("qwen_fewsteps.png")

The guidance_scale 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.

Multi-image reference with QwenImageEditPlusPipeline

With QwenImageEditPlusPipeline, one can provide multiple images as input reference.

import torch
from PIL import Image
from diffusers import QwenImageEditPlusPipeline
from diffusers.utils import load_image

pipe = QwenImageEditPlusPipeline.from_pretrained(
    "Qwen/Qwen-Image-Edit-2509", torch_dtype=torch.bfloat16
).to("cuda")

image_1 = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/grumpy.jpg")
image_2 = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/peng.png")
image = pipe(
    image=[image_1, image_2],
    prompt='''put the penguin and the cat at a game show called "Qwen Edit Plus Games"''',
    num_inference_steps=50
).images[0]

Performance

torch.compile

Using torch.compile on the transformer provides ~2.4x speedup (A100 80GB: 4.70s → 1.93s):

import torch
from diffusers import QwenImagePipeline

pipe = QwenImagePipeline.from_pretrained("Qwen/Qwen-Image", torch_dtype=torch.bfloat16).to("cuda")
pipe.transformer = torch.compile(pipe.transformer)

# First call triggers compilation (~7s overhead)
# Subsequent calls run at ~2.4x faster
image = pipe("a cat", num_inference_steps=50).images[0]

Batched Inference with Variable-Length Prompts

When using classifier-free guidance (CFG) with prompts of different lengths, the pipeline properly handles padding through attention masking. This ensures padding tokens do not influence the generated output.

# CFG with different prompt lengths works correctly
image = pipe(
    prompt="A cat",
    negative_prompt="blurry, low quality, distorted",
    true_cfg_scale=3.5,
    num_inference_steps=50,
).images[0]

For detailed benchmark scripts and results, see this gist.

QwenImagePipeline[[diffusers.QwenImagePipeline]]

diffusers.QwenImagePipeline[[diffusers.QwenImagePipeline]]

Source

The QwenImage pipeline for text-to-image generation.

__call__diffusers.QwenImagePipeline.__call__https://github.com/huggingface/diffusers/blob/vr_13813/src/diffusers/pipelines/qwenimage/pipeline_qwenimage.py#L462[{"name": "prompt", "val": ": str | list[str] = None"}, {"name": "negative_prompt", "val": ": str | list[str] = None"}, {"name": "true_cfg_scale", "val": ": float = 4.0"}, {"name": "height", "val": ": int | None = None"}, {"name": "width", "val": ": int | None = None"}, {"name": "num_inference_steps", "val": ": int = 50"}, {"name": "sigmas", "val": ": list[float] | None = None"}, {"name": "guidance_scale", "val": ": float | None = None"}, {"name": "num_images_per_prompt", "val": ": int = 1"}, {"name": "generator", "val": ": torch._C.Generator | list[torch._C.Generator] | None = None"}, {"name": "latents", "val": ": torch.Tensor | None = None"}, {"name": "prompt_embeds", "val": ": torch.Tensor | None = None"}, {"name": "prompt_embeds_mask", "val": ": torch.Tensor | None = None"}, {"name": "negative_prompt_embeds", "val": ": torch.Tensor | None = None"}, {"name": "negative_prompt_embeds_mask", "val": ": torch.Tensor | None = None"}, {"name": "output_type", "val": ": str | None = 'pil'"}, {"name": "return_dict", "val": ": bool = True"}, {"name": "attention_kwargs", "val": ": dict[str, typing.Any] | None = None"}, {"name": "callback_on_step_end", "val": ": typing.Optional[typing.Callable[[int, int], NoneType]] = None"}, {"name": "callback_on_step_end_tensor_inputs", "val": ": list = ['latents']"}, {"name": "max_sequence_length", "val": ": int = 512"}]- 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) -- Guidance scale as defined in Classifier-Free Diffusion Guidance. true_cfg_scale is defined as w of equation 2. of Imagen Paper. 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.
• 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 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) 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.
• prompt_embeds_mask (torch.Tensor, optional) -- Attention mask for prompt_embeds.
• 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.
• negative_prompt_embeds_mask (torch.Tensor, optional) -- Attention mask for negative_prompt_embeds.
• output_type (str, optional, defaults to "pil") -- The output format of the generate image. Choose between PIL: 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.
• 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.0~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.

Function invoked when calling the pipeline for generation.

Examples:

>>> import torch
>>> from diffusers import QwenImagePipeline

>>> pipe = QwenImagePipeline.from_pretrained("Qwen/Qwen-Image", torch_dtype=torch.bfloat16)
>>> pipe.to("cuda")
>>> prompt = "A cat holding a sign that says hello world"
>>> # Depending on the variant being used, the pipeline call will slightly vary.
>>> # Refer to the pipeline documentation for more details.
>>> image = pipe(prompt, num_inference_steps=50).images[0]
>>> image.save("qwenimage.png")

Parameters:

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, specifically the Qwen2.5-VL-7B-Instruct variant.

tokenizer (QwenTokenizer) : Tokenizer of class CLIPTokenizer.

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.

disable_vae_slicing[[diffusers.QwenImagePipeline.disable_vae_slicing]]

Source

Disable sliced VAE decoding. If enable_vae_slicing was previously enabled, this method will go back to computing decoding in one step.

disable_vae_tiling[[diffusers.QwenImagePipeline.disable_vae_tiling]]

Source

Disable tiled VAE decoding. If enable_vae_tiling was previously enabled, this method will go back to computing decoding in one step.

enable_vae_slicing[[diffusers.QwenImagePipeline.enable_vae_slicing]]

Source

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.

enable_vae_tiling[[diffusers.QwenImagePipeline.enable_vae_tiling]]

Source

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.

encode_prompt[[diffusers.QwenImagePipeline.encode_prompt]]

Source

Parameters:

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.

QwenImageImg2ImgPipeline[[diffusers.QwenImageImg2ImgPipeline]]

diffusers.QwenImageImg2ImgPipeline[[diffusers.QwenImageImg2ImgPipeline]]

Source

The QwenImage pipeline for text-to-image generation.

__call__diffusers.QwenImageImg2ImgPipeline.__call__https://github.com/huggingface/diffusers/blob/vr_13813/src/diffusers/pipelines/qwenimage/pipeline_qwenimage_img2img.py#L536[{"name": "prompt", "val": ": str | list[str] = None"}, {"name": "negative_prompt", "val": ": str | list[str] = None"}, {"name": "true_cfg_scale", "val": ": float = 4.0"}, {"name": "image", "val": ": PIL.Image.Image | numpy.ndarray | torch.Tensor | list[PIL.Image.Image] | list[numpy.ndarray] | list[torch.Tensor] = None"}, {"name": "height", "val": ": int | None = None"}, {"name": "width", "val": ": int | None = None"}, {"name": "strength", "val": ": float = 0.6"}, {"name": "num_inference_steps", "val": ": int = 50"}, {"name": "sigmas", "val": ": list[float] | None = None"}, {"name": "guidance_scale", "val": ": float | None = None"}, {"name": "num_images_per_prompt", "val": ": int = 1"}, {"name": "generator", "val": ": torch._C.Generator | list[torch._C.Generator] | None = None"}, {"name": "latents", "val": ": torch.Tensor | None = None"}, {"name": "prompt_embeds", "val": ": torch.Tensor | None = None"}, {"name": "prompt_embeds_mask", "val": ": torch.Tensor | None = None"}, {"name": "negative_prompt_embeds", "val": ": torch.Tensor | None = None"}, {"name": "negative_prompt_embeds_mask", "val": ": torch.Tensor | None = None"}, {"name": "output_type", "val": ": str | None = 'pil'"}, {"name": "return_dict", "val": ": bool = True"}, {"name": "attention_kwargs", "val": ": dict[str, typing.Any] | None = None"}, {"name": "callback_on_step_end", "val": ": typing.Optional[typing.Callable[[int, int], NoneType]] = None"}, {"name": "callback_on_step_end_tensor_inputs", "val": ": list = ['latents']"}, {"name": "max_sequence_length", "val": ": int = 512"}]- 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).
• 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.
• true_cfg_scale (float, optional, defaults to 1.0) -- Guidance scale as defined in Classifier-Free Diffusion Guidance. true_cfg_scale is defined as w of equation 2. of Imagen Paper. 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.
• 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.
• strength (float, optional, defaults to 1.0) -- Indicates extent to transform the reference image. Must be between 0 and 1. image is used as a starting point and more noise is added the higher the strength. The number of denoising steps depends on the amount of noise initially added. When strength is 1, added noise is maximum and the denoising process runs for the full number of iterations specified in num_inference_steps. A value of 1 essentially ignores image.
• 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) 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.
• prompt_embeds_mask (torch.Tensor, optional) -- Attention mask for prompt_embeds.
• 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.
• negative_prompt_embeds_mask (torch.Tensor, optional) -- Attention mask for negative_prompt_embeds.
• output_type (str, optional, defaults to "pil") -- The output format of the generate image. Choose between PIL: 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.
• 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.0~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.

Function invoked when calling the pipeline for generation.

Examples:

>>> import torch
>>> from diffusers import QwenImageImg2ImgPipeline
>>> from diffusers.utils import load_image

>>> pipe = QwenImageImg2ImgPipeline.from_pretrained("Qwen/Qwen-Image", torch_dtype=torch.bfloat16)
>>> pipe = pipe.to("cuda")
>>> url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
>>> init_image = load_image(url).resize((1024, 1024))
>>> prompt = "cat wizard, gandalf, lord of the rings, detailed, fantasy, cute, adorable, Pixar, Disney"
>>> images = pipe(prompt=prompt, negative_prompt=" ", image=init_image, strength=0.95).images[0]
>>> images.save("qwenimage_img2img.png")

Parameters:

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, specifically the Qwen2.5-VL-7B-Instruct variant.

tokenizer (QwenTokenizer) : Tokenizer of class CLIPTokenizer.

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.

disable_vae_slicing[[diffusers.QwenImageImg2ImgPipeline.disable_vae_slicing]]

Source

Disable sliced VAE decoding. If enable_vae_slicing was previously enabled, this method will go back to computing decoding in one step.

disable_vae_tiling[[diffusers.QwenImageImg2ImgPipeline.disable_vae_tiling]]

Source

Disable tiled VAE decoding. If enable_vae_tiling was previously enabled, this method will go back to computing decoding in one step.

enable_vae_slicing[[diffusers.QwenImageImg2ImgPipeline.enable_vae_slicing]]

Source

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.

enable_vae_tiling[[diffusers.QwenImageImg2ImgPipeline.enable_vae_tiling]]

Source

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.

encode_prompt[[diffusers.QwenImageImg2ImgPipeline.encode_prompt]]

Source

Parameters:

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.

QwenImageInpaintPipeline[[diffusers.QwenImageInpaintPipeline]]

diffusers.QwenImageInpaintPipeline[[diffusers.QwenImageInpaintPipeline]]

Source

The QwenImage pipeline for text-to-image generation.

__call__diffusers.QwenImageInpaintPipeline.__call__https://github.com/huggingface/diffusers/blob/vr_13813/src/diffusers/pipelines/qwenimage/pipeline_qwenimage_inpaint.py#L646[{"name": "prompt", "val": ": str | list[str] = None"}, {"name": "negative_prompt", "val": ": str | list[str] = None"}, {"name": "true_cfg_scale", "val": ": float = 4.0"}, {"name": "image", "val": ": PIL.Image.Image | numpy.ndarray | torch.Tensor | list[PIL.Image.Image] | list[numpy.ndarray] | list[torch.Tensor] = None"}, {"name": "mask_image", "val": ": PIL.Image.Image | numpy.ndarray | torch.Tensor | list[PIL.Image.Image] | list[numpy.ndarray] | list[torch.Tensor] = None"}, {"name": "masked_image_latents", "val": ": PIL.Image.Image | numpy.ndarray | torch.Tensor | list[PIL.Image.Image] | list[numpy.ndarray] | list[torch.Tensor] = None"}, {"name": "height", "val": ": int | None = None"}, {"name": "width", "val": ": int | None = None"}, {"name": "padding_mask_crop", "val": ": int | None = None"}, {"name": "strength", "val": ": float = 0.6"}, {"name": "num_inference_steps", "val": ": int = 50"}, {"name": "sigmas", "val": ": list[float] | None = None"}, {"name": "guidance_scale", "val": ": float | None = None"}, {"name": "num_images_per_prompt", "val": ": int = 1"}, {"name": "generator", "val": ": torch._C.Generator | list[torch._C.Generator] | None = None"}, {"name": "latents", "val": ": torch.Tensor | None = None"}, {"name": "prompt_embeds", "val": ": torch.Tensor | None = None"}, {"name": "prompt_embeds_mask", "val": ": torch.Tensor | None = None"}, {"name": "negative_prompt_embeds", "val": ": torch.Tensor | None = None"}, {"name": "negative_prompt_embeds_mask", "val": ": torch.Tensor | None = None"}, {"name": "output_type", "val": ": str | None = 'pil'"}, {"name": "return_dict", "val": ": bool = True"}, {"name": "attention_kwargs", "val": ": dict[str, typing.Any] | None = None"}, {"name": "callback_on_step_end", "val": ": typing.Optional[typing.Callable[[int, int], NoneType]] = None"}, {"name": "callback_on_step_end_tensor_inputs", "val": ": list = ['latents']"}, {"name": "max_sequence_length", "val": ": int = 512"}]- 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).
• 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.
• true_cfg_scale (float, optional, defaults to 1.0) -- Guidance scale as defined in Classifier-Free Diffusion Guidance. true_cfg_scale is defined as w of equation 2. of Imagen Paper. 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.
• mask_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 mask image. White pixels in the mask are repainted while black pixels are preserved. If mask_image is a PIL image, it is converted to a single channel (luminance) before use. If it's a numpy array or pytorch tensor, it should contain one color channel (L) instead of 3, so the expected shape for pytorch tensor would be (B, 1, H, W), (B, H, W), (1, H, W), (H, W). And for numpy array would be for (B, H, W, 1), (B, H, W), (H, W, 1), or (H, W).
• masked_image_latents (torch.Tensor, list[torch.Tensor]) -- Tensor representing an image batch to mask image generated by VAE. If not provided, the mask latents tensor will be generated by mask_image.
• 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.
• padding_mask_crop (int, optional, defaults to None) -- The size of margin in the crop to be applied to the image and masking. If None, no crop is applied to image and mask_image. If padding_mask_crop is not None, it will first find a rectangular region with the same aspect ration of the image and contains all masked area, and then expand that area based on padding_mask_crop. The image and mask_image will then be cropped based on the expanded area before resizing to the original image size for inpainting. This is useful when the masked area is small while the image is large and contain information irrelevant for inpainting, such as background.
• strength (float, optional, defaults to 1.0) -- Indicates extent to transform the reference image. Must be between 0 and 1. image is used as a starting point and more noise is added the higher the strength. The number of denoising steps depends on the amount of noise initially added. When strength is 1, added noise is maximum and the denoising process runs for the full number of iterations specified in num_inference_steps. A value of 1 essentially ignores image.
• 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) 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.
• prompt_embeds_mask (torch.Tensor, optional) -- Attention mask for prompt_embeds.
• 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.
• negative_prompt_embeds_mask (torch.Tensor, optional) -- Attention mask for negative_prompt_embeds.
• output_type (str, optional, defaults to "pil") -- The output format of the generate image. Choose between PIL: 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.
• 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.0~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.

Function invoked when calling the pipeline for generation.

Examples:

>>> import torch
>>> from diffusers import QwenImageInpaintPipeline
>>> from diffusers.utils import load_image

>>> pipe = QwenImageInpaintPipeline.from_pretrained("Qwen/Qwen-Image", torch_dtype=torch.bfloat16)
>>> pipe.to("cuda")
>>> prompt = "Face of a yellow cat, high resolution, sitting on a park bench"
>>> img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
>>> mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
>>> source = load_image(img_url)
>>> mask = load_image(mask_url)
>>> image = pipe(prompt=prompt, negative_prompt=" ", image=source, mask_image=mask, strength=0.85).images[0]
>>> image.save("qwenimage_inpainting.png")

Parameters:

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, specifically the Qwen2.5-VL-7B-Instruct variant.

tokenizer (QwenTokenizer) : Tokenizer of class CLIPTokenizer.

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.

disable_vae_slicing[[diffusers.QwenImageInpaintPipeline.disable_vae_slicing]]

Source

Disable sliced VAE decoding. If enable_vae_slicing was previously enabled, this method will go back to computing decoding in one step.

disable_vae_tiling[[diffusers.QwenImageInpaintPipeline.disable_vae_tiling]]

Source

Disable tiled VAE decoding. If enable_vae_tiling was previously enabled, this method will go back to computing decoding in one step.

enable_vae_slicing[[diffusers.QwenImageInpaintPipeline.enable_vae_slicing]]

Source

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.

enable_vae_tiling[[diffusers.QwenImageInpaintPipeline.enable_vae_tiling]]

Source

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.

encode_prompt[[diffusers.QwenImageInpaintPipeline.encode_prompt]]

Source

Parameters:

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.

QwenImageEditPipeline[[diffusers.QwenImageEditPipeline]]

diffusers.QwenImageEditPipeline[[diffusers.QwenImageEditPipeline]]

Source

The Qwen-Image-Edit pipeline for image editing.

__call__diffusers.QwenImageEditPipeline.__call__https://github.com/huggingface/diffusers/blob/vr_13813/src/diffusers/pipelines/qwenimage/pipeline_qwenimage_edit.py#L558[{"name": "image", "val": ": PIL.Image.Image | numpy.ndarray | torch.Tensor | list[PIL.Image.Image] | list[numpy.ndarray] | list[torch.Tensor] | None = None"}, {"name": "prompt", "val": ": str | list[str] = None"}, {"name": "negative_prompt", "val": ": str | list[str] = None"}, {"name": "true_cfg_scale", "val": ": float = 4.0"}, {"name": "height", "val": ": int | None = None"}, {"name": "width", "val": ": int | None = None"}, {"name": "num_inference_steps", "val": ": int = 50"}, {"name": "sigmas", "val": ": list[float] | None = None"}, {"name": "guidance_scale", "val": ": float | None = None"}, {"name": "num_images_per_prompt", "val": ": int = 1"}, {"name": "generator", "val": ": torch._C.Generator | list[torch._C.Generator] | None = None"}, {"name": "latents", "val": ": torch.Tensor | None = None"}, {"name": "prompt_embeds", "val": ": torch.Tensor | None = None"}, {"name": "prompt_embeds_mask", "val": ": torch.Tensor | None = None"}, {"name": "negative_prompt_embeds", "val": ": torch.Tensor | None = None"}, {"name": "negative_prompt_embeds_mask", "val": ": torch.Tensor | None = None"}, {"name": "output_type", "val": ": str | None = 'pil'"}, {"name": "return_dict", "val": ": bool = True"}, {"name": "attention_kwargs", "val": ": dict[str, typing.Any] | None = None"}, {"name": "callback_on_step_end", "val": ": typing.Optional[typing.Callable[[int, int], NoneType]] = None"}, {"name": "callback_on_step_end_tensor_inputs", "val": ": list = ['latents']"}, {"name": "max_sequence_length", "val": ": int = 512"}]- 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. true_cfg_scale is defined as w of equation 2. of Imagen Paper. 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.
• 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 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) 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.
• prompt_embeds_mask (torch.Tensor, optional) -- Attention mask for prompt_embeds.
• 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.
• negative_prompt_embeds_mask (torch.Tensor, optional) -- Attention mask for negative_prompt_embeds.
• output_type (str, optional, defaults to "pil") -- The output format of the generate image. Choose between PIL: 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.
• 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.0~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.

Function invoked when calling the pipeline for generation.

Examples:

>>> 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")

Parameters:

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, specifically the Qwen2.5-VL-7B-Instruct variant.

tokenizer (QwenTokenizer) : Tokenizer of class CLIPTokenizer.

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.

disable_vae_slicing[[diffusers.QwenImageEditPipeline.disable_vae_slicing]]

Source

Disable sliced VAE decoding. If enable_vae_slicing was previously enabled, this method will go back to computing decoding in one step.

disable_vae_tiling[[diffusers.QwenImageEditPipeline.disable_vae_tiling]]

Source

Disable tiled VAE decoding. If enable_vae_tiling was previously enabled, this method will go back to computing decoding in one step.

enable_vae_slicing[[diffusers.QwenImageEditPipeline.enable_vae_slicing]]

Source

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.

enable_vae_tiling[[diffusers.QwenImageEditPipeline.enable_vae_tiling]]

Source

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.

encode_prompt[[diffusers.QwenImageEditPipeline.encode_prompt]]

Source

Parameters:

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.

QwenImageEditInpaintPipeline[[diffusers.QwenImageEditInpaintPipeline]]

diffusers.QwenImageEditInpaintPipeline[[diffusers.QwenImageEditInpaintPipeline]]

Source

The Qwen-Image-Edit pipeline for image editing.

__call__diffusers.QwenImageEditInpaintPipeline.__call__https://github.com/huggingface/diffusers/blob/vr_13813/src/diffusers/pipelines/qwenimage/pipeline_qwenimage_edit_inpaint.py#L691[{"name": "image", "val": ": PIL.Image.Image | numpy.ndarray | torch.Tensor | list[PIL.Image.Image] | list[numpy.ndarray] | list[torch.Tensor] | None = None"}, {"name": "prompt", "val": ": str | list[str] = None"}, {"name": "negative_prompt", "val": ": str | list[str] = None"}, {"name": "mask_image", "val": ": PIL.Image.Image | numpy.ndarray | torch.Tensor | list[PIL.Image.Image] | list[numpy.ndarray] | list[torch.Tensor] = None"}, {"name": "masked_image_latents", "val": ": PIL.Image.Image | numpy.ndarray | torch.Tensor | list[PIL.Image.Image] | list[numpy.ndarray] | list[torch.Tensor] = None"}, {"name": "true_cfg_scale", "val": ": float = 4.0"}, {"name": "height", "val": ": int | None = None"}, {"name": "width", "val": ": int | None = None"}, {"name": "padding_mask_crop", "val": ": int | None = None"}, {"name": "strength", "val": ": float = 0.6"}, {"name": "num_inference_steps", "val": ": int = 50"}, {"name": "sigmas", "val": ": list[float] | None = None"}, {"name": "guidance_scale", "val": ": float | None = None"}, {"name": "num_images_per_prompt", "val": ": int = 1"}, {"name": "generator", "val": ": torch._C.Generator | list[torch._C.Generator] | None = None"}, {"name": "latents", "val": ": torch.Tensor | None = None"}, {"name": "prompt_embeds", "val": ": torch.Tensor | None = None"}, {"name": "prompt_embeds_mask", "val": ": torch.Tensor | None = None"}, {"name": "negative_prompt_embeds", "val": ": torch.Tensor | None = None"}, {"name": "negative_prompt_embeds_mask", "val": ": torch.Tensor | None = None"}, {"name": "output_type", "val": ": str | None = 'pil'"}, {"name": "return_dict", "val": ": bool = True"}, {"name": "attention_kwargs", "val": ": dict[str, typing.Any] | None = None"}, {"name": "callback_on_step_end", "val": ": typing.Optional[typing.Callable[[int, int], NoneType]] = None"}, {"name": "callback_on_step_end_tensor_inputs", "val": ": list = ['latents']"}, {"name": "max_sequence_length", "val": ": int = 512"}]- 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. true_cfg_scale is defined as w of equation 2. of Imagen Paper. 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.
• mask_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 mask image. White pixels in the mask are repainted while black pixels are preserved. If mask_image is a PIL image, it is converted to a single channel (luminance) before use. If it's a numpy array or pytorch tensor, it should contain one color channel (L) instead of 3, so the expected shape for pytorch tensor would be (B, 1, H, W), (B, H, W), (1, H, W), (H, W). And for numpy array would be for (B, H, W, 1), (B, H, W), (H, W, 1), or (H, W).
• masked_image_latents (torch.Tensor, list[torch.Tensor]) -- Tensor representing an image batch to mask image generated by VAE. If not provided, the mask latents tensor will ge generated by mask_image.
• 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.
• padding_mask_crop (int, optional, defaults to None) -- The size of margin in the crop to be applied to the image and masking. If None, no crop is applied to image and mask_image. If padding_mask_crop is not None, it will first find a rectangular region with the same aspect ration of the image and contains all masked area, and then expand that area based on padding_mask_crop. The image and mask_image will then be cropped based on the expanded area before resizing to the original image size for inpainting. This is useful when the masked area is small while the image is large and contain information irrelevant for inpainting, such as background.
• strength (float, optional, defaults to 1.0) -- Indicates extent to transform the reference image. Must be between 0 and 1. image is used as a starting point and more noise is added the higher the strength. The number of denoising steps depends on the amount of noise initially added. When strength is 1, added noise is maximum and the denoising process runs for the full number of iterations specified in num_inference_steps. A value of 1 essentially ignores image.
• 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) 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.
• prompt_embeds_mask (torch.Tensor, optional) -- Attention mask for prompt_embeds.
• 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.
• negative_prompt_embeds_mask (torch.Tensor, optional) -- Attention mask for negative_prompt_embeds.
• output_type (str, optional, defaults to "pil") -- The output format of the generate image. Choose between PIL: 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.
• 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.0~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.

Function invoked when calling the pipeline for generation.

Examples:

>>> import torch
>>> from PIL import Image
>>> from diffusers import QwenImageEditInpaintPipeline
>>> from diffusers.utils import load_image

>>> pipe = QwenImageEditInpaintPipeline.from_pretrained("Qwen/Qwen-Image-Edit", torch_dtype=torch.bfloat16)
>>> pipe.to("cuda")
>>> prompt = "Face of a yellow cat, high resolution, sitting on a park bench"

>>> img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
>>> mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
>>> source = load_image(img_url)
>>> mask = load_image(mask_url)
>>> image = pipe(
...     prompt=prompt, negative_prompt=" ", image=source, mask_image=mask, strength=1.0, num_inference_steps=50
... ).images[0]
>>> image.save("qwenimage_inpainting.png")

Parameters:

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, specifically the Qwen2.5-VL-7B-Instruct variant.

tokenizer (QwenTokenizer) : Tokenizer of class CLIPTokenizer.

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.

disable_vae_slicing[[diffusers.QwenImageEditInpaintPipeline.disable_vae_slicing]]

Source

Disable sliced VAE decoding. If enable_vae_slicing was previously enabled, this method will go back to computing decoding in one step.

disable_vae_tiling[[diffusers.QwenImageEditInpaintPipeline.disable_vae_tiling]]

Source

Disable tiled VAE decoding. If enable_vae_tiling was previously enabled, this method will go back to computing decoding in one step.

enable_vae_slicing[[diffusers.QwenImageEditInpaintPipeline.enable_vae_slicing]]

Source

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.

enable_vae_tiling[[diffusers.QwenImageEditInpaintPipeline.enable_vae_tiling]]

Source

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.

encode_prompt[[diffusers.QwenImageEditInpaintPipeline.encode_prompt]]

Source

Parameters:

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.

QwenImageControlNetPipeline[[diffusers.QwenImageControlNetPipeline]]

diffusers.QwenImageControlNetPipeline[[diffusers.QwenImageControlNetPipeline]]

Source

The QwenImage pipeline for text-to-image generation.

__call__diffusers.QwenImageControlNetPipeline.__call__https://github.com/huggingface/diffusers/blob/vr_13813/src/diffusers/pipelines/qwenimage/pipeline_qwenimage_controlnet.py#L565[{"name": "prompt", "val": ": str | list[str] = None"}, {"name": "negative_prompt", "val": ": str | list[str] = None"}, {"name": "true_cfg_scale", "val": ": float = 4.0"}, {"name": "height", "val": ": int | None = None"}, {"name": "width", "val": ": int | None = None"}, {"name": "num_inference_steps", "val": ": int = 50"}, {"name": "sigmas", "val": ": list[float] | None = None"}, {"name": "guidance_scale", "val": ": float | None = None"}, {"name": "control_guidance_start", "val": ": float | list[float] = 0.0"}, {"name": "control_guidance_end", "val": ": float | list[float] = 1.0"}, {"name": "control_image", "val": ": PIL.Image.Image | numpy.ndarray | torch.Tensor | list[PIL.Image.Image] | list[numpy.ndarray] | list[torch.Tensor] = None"}, {"name": "controlnet_conditioning_scale", "val": ": float | list[float] = 1.0"}, {"name": "num_images_per_prompt", "val": ": int = 1"}, {"name": "generator", "val": ": torch._C.Generator | list[torch._C.Generator] | None = None"}, {"name": "latents", "val": ": torch.Tensor | None = None"}, {"name": "prompt_embeds", "val": ": torch.Tensor | None = None"}, {"name": "prompt_embeds_mask", "val": ": torch.Tensor | None = None"}, {"name": "negative_prompt_embeds", "val": ": torch.Tensor | None = None"}, {"name": "negative_prompt_embeds_mask", "val": ": torch.Tensor | None = None"}, {"name": "output_type", "val": ": str | None = 'pil'"}, {"name": "return_dict", "val": ": bool = True"}, {"name": "attention_kwargs", "val": ": dict[str, typing.Any] | None = None"}, {"name": "callback_on_step_end", "val": ": typing.Optional[typing.Callable[[int, int], NoneType]] = None"}, {"name": "callback_on_step_end_tensor_inputs", "val": ": list = ['latents']"}, {"name": "max_sequence_length", "val": ": int = 512"}]- 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) -- Guidance scale as defined in Classifier-Free Diffusion Guidance. true_cfg_scale is defined as w of equation 2. of Imagen Paper. 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.
• 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 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).
• control_guidance_start (float or list[float], optional, defaults to 0.0) -- The percentage of total steps at which the ControlNet starts applying.
• control_guidance_end (float or list[float], optional, defaults to 1.0) -- The percentage of total steps at which the ControlNet stops applying.
• control_image (PipelineImageInput, optional) -- The ControlNet input condition to provide guidance for the generation.
• controlnet_conditioning_scale (float or list[float], optional, defaults to 1.0) -- The outputs of the ControlNet are multiplied by controlnet_conditioning_scale before they are added to the residual in the original transformer.
• 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) 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.
• prompt_embeds_mask (torch.Tensor, optional) -- Attention mask for prompt_embeds.
• 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.
• negative_prompt_embeds_mask (torch.Tensor, optional) -- Attention mask for negative_prompt_embeds.
• output_type (str, optional, defaults to "pil") -- The output format of the generate image. Choose between PIL: 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.
• 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.0~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.

Function invoked when calling the pipeline for generation.

Examples:

>>> import torch
>>> from diffusers.utils import load_image
>>> from diffusers import QwenImageControlNetModel, QwenImageMultiControlNetModel, QwenImageControlNetPipeline

>>> # QwenImageControlNetModel
>>> controlnet = QwenImageControlNetModel.from_pretrained(
...     "InstantX/Qwen-Image-ControlNet-Union", torch_dtype=torch.bfloat16
... )
>>> pipe = QwenImageControlNetPipeline.from_pretrained(
...     "Qwen/Qwen-Image", controlnet=controlnet, torch_dtype=torch.bfloat16
... )
>>> pipe.to("cuda")
>>> prompt = "Aesthetics art, traditional asian pagoda, elaborate golden accents, sky blue and white color palette, swirling cloud pattern, digital illustration, east asian architecture, ornamental rooftop, intricate detailing on building, cultural representation."
>>> negative_prompt = " "
>>> control_image = load_image(
...     "https://huggingface.co/InstantX/Qwen-Image-ControlNet-Union/resolve/main/conds/canny.png"
... )
>>> # Depending on the variant being used, the pipeline call will slightly vary.
>>> # Refer to the pipeline documentation for more details.
>>> image = pipe(
...     prompt,
...     negative_prompt=negative_prompt,
...     control_image=control_image,
...     controlnet_conditioning_scale=1.0,
...     num_inference_steps=30,
...     true_cfg_scale=4.0,
... ).images[0]
>>> image.save("qwenimage_cn_union.png")

>>> # QwenImageMultiControlNetModel
>>> controlnet = QwenImageControlNetModel.from_pretrained(
...     "InstantX/Qwen-Image-ControlNet-Union", torch_dtype=torch.bfloat16
... )
>>> controlnet = QwenImageMultiControlNetModel([controlnet])
>>> pipe = QwenImageControlNetPipeline.from_pretrained(
...     "Qwen/Qwen-Image", controlnet=controlnet, torch_dtype=torch.bfloat16
... )
>>> pipe.to("cuda")
>>> prompt = "Aesthetics art, traditional asian pagoda, elaborate golden accents, sky blue and white color palette, swirling cloud pattern, digital illustration, east asian architecture, ornamental rooftop, intricate detailing on building, cultural representation."
>>> negative_prompt = " "
>>> control_image = load_image(
...     "https://huggingface.co/InstantX/Qwen-Image-ControlNet-Union/resolve/main/conds/canny.png"
... )
>>> # Depending on the variant being used, the pipeline call will slightly vary.
>>> # Refer to the pipeline documentation for more details.
>>> image = pipe(
...     prompt,
...     negative_prompt=negative_prompt,
...     control_image=[control_image, control_image],
...     controlnet_conditioning_scale=[0.5, 0.5],
...     num_inference_steps=30,
...     true_cfg_scale=4.0,
... ).images[0]
>>> image.save("qwenimage_cn_union_multi.png")

Parameters:

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, specifically the Qwen2.5-VL-7B-Instruct variant.

tokenizer (QwenTokenizer) : Tokenizer of class CLIPTokenizer.

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.

disable_vae_slicing[[diffusers.QwenImageControlNetPipeline.disable_vae_slicing]]

Source

Disable sliced VAE decoding. If enable_vae_slicing was previously enabled, this method will go back to computing decoding in one step.

disable_vae_tiling[[diffusers.QwenImageControlNetPipeline.disable_vae_tiling]]

Source

Disable tiled VAE decoding. If enable_vae_tiling was previously enabled, this method will go back to computing decoding in one step.

enable_vae_slicing[[diffusers.QwenImageControlNetPipeline.enable_vae_slicing]]

Source

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.

enable_vae_tiling[[diffusers.QwenImageControlNetPipeline.enable_vae_tiling]]

Source

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.

encode_prompt[[diffusers.QwenImageControlNetPipeline.encode_prompt]]

Source

Parameters:

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.

QwenImageEditPlusPipeline[[diffusers.QwenImageEditPlusPipeline]]

diffusers.QwenImageEditPlusPipeline[[diffusers.QwenImageEditPlusPipeline]]

Source

The Qwen-Image-Edit pipeline for image editing.

__call__diffusers.QwenImageEditPlusPipeline.__call__https://github.com/huggingface/diffusers/blob/vr_13813/src/diffusers/pipelines/qwenimage/pipeline_qwenimage_edit_plus.py#L527[{"name": "image", "val": ": PIL.Image.Image | numpy.ndarray | torch.Tensor | list[PIL.Image.Image] | list[numpy.ndarray] | list[torch.Tensor] | None = None"}, {"name": "prompt", "val": ": str | list[str] = None"}, {"name": "negative_prompt", "val": ": str | list[str] = None"}, {"name": "true_cfg_scale", "val": ": float = 4.0"}, {"name": "height", "val": ": int | None = None"}, {"name": "width", "val": ": int | None = None"}, {"name": "num_inference_steps", "val": ": int = 50"}, {"name": "sigmas", "val": ": list[float] | None = None"}, {"name": "guidance_scale", "val": ": float | None = None"}, {"name": "num_images_per_prompt", "val": ": int = 1"}, {"name": "generator", "val": ": torch._C.Generator | list[torch._C.Generator] | None = None"}, {"name": "latents", "val": ": torch.Tensor | None = None"}, {"name": "prompt_embeds", "val": ": torch.Tensor | None = None"}, {"name": "prompt_embeds_mask", "val": ": torch.Tensor | None = None"}, {"name": "negative_prompt_embeds", "val": ": torch.Tensor | None = None"}, {"name": "negative_prompt_embeds_mask", "val": ": torch.Tensor | None = None"}, {"name": "output_type", "val": ": str | None = 'pil'"}, {"name": "return_dict", "val": ": bool = True"}, {"name": "attention_kwargs", "val": ": dict[str, typing.Any] | None = None"}, {"name": "callback_on_step_end", "val": ": typing.Optional[typing.Callable[[int, int], NoneType]] = None"}, {"name": "callback_on_step_end_tensor_inputs", "val": ": list = ['latents']"}, {"name": "max_sequence_length", "val": ": int = 512"}]- 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. true_cfg_scale is defined as w of equation 2. of Imagen Paper. 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.
• 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 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) 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.
• prompt_embeds_mask (torch.Tensor, optional) -- Attention mask for prompt_embeds.
• 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.
• negative_prompt_embeds_mask (torch.Tensor, optional) -- Attention mask for negative_prompt_embeds.
• output_type (str, optional, defaults to "pil") -- The output format of the generate image. Choose between PIL: 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.
• 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.0~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.

Function invoked when calling the pipeline for generation.

Examples:

>>> import torch
>>> from PIL import Image
>>> from diffusers import QwenImageEditPlusPipeline
>>> from diffusers.utils import load_image

>>> pipe = QwenImageEditPlusPipeline.from_pretrained("Qwen/Qwen-Image-Edit-2509", 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_plus.png")

Parameters:

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, specifically the Qwen2.5-VL-7B-Instruct variant.

tokenizer (QwenTokenizer) : Tokenizer of class CLIPTokenizer.

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.

encode_prompt[[diffusers.QwenImageEditPlusPipeline.encode_prompt]]

Source

Parameters:

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.

QwenImageLayeredPipeline[[diffusers.QwenImageLayeredPipeline]]

diffusers.QwenImageLayeredPipeline[[diffusers.QwenImageLayeredPipeline]]

Source

The Qwen-Image-Layered pipeline for image decomposing.

__call__diffusers.QwenImageLayeredPipeline.__call__https://github.com/huggingface/diffusers/blob/vr_13813/src/diffusers/pipelines/qwenimage/pipeline_qwenimage_layered.py#L538[{"name": "image", "val": ": PIL.Image.Image | numpy.ndarray | torch.Tensor | list[PIL.Image.Image] | list[numpy.ndarray] | list[torch.Tensor] | None = None"}, {"name": "prompt", "val": ": str | list[str] = None"}, {"name": "negative_prompt", "val": ": str | list[str] = None"}, {"name": "true_cfg_scale", "val": ": float = 4.0"}, {"name": "layers", "val": ": int | None = 4"}, {"name": "num_inference_steps", "val": ": int = 50"}, {"name": "sigmas", "val": ": list[float] | None = None"}, {"name": "guidance_scale", "val": ": float | None = None"}, {"name": "num_images_per_prompt", "val": ": int = 1"}, {"name": "generator", "val": ": torch._C.Generator | list[torch._C.Generator] | None = None"}, {"name": "latents", "val": ": torch.Tensor | None = None"}, {"name": "prompt_embeds", "val": ": torch.Tensor | None = None"}, {"name": "prompt_embeds_mask", "val": ": torch.Tensor | None = None"}, {"name": "negative_prompt_embeds", "val": ": torch.Tensor | None = None"}, {"name": "negative_prompt_embeds_mask", "val": ": torch.Tensor | None = None"}, {"name": "output_type", "val": ": str | None = 'pil'"}, {"name": "return_dict", "val": ": bool = True"}, {"name": "attention_kwargs", "val": ": dict[str, typing.Any] | None = None"}, {"name": "callback_on_step_end", "val": ": typing.Optional[typing.Callable[[int, int], NoneType]] = None"}, {"name": "callback_on_step_end_tensor_inputs", "val": ": list = ['latents']"}, {"name": "max_sequence_length", "val": ": int = 512"}, {"name": "resolution", "val": ": int = 640"}, {"name": "cfg_normalize", "val": ": bool = False"}, {"name": "use_en_prompt", "val": ": bool = False"}]- 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. true_cfg_scale is defined as w of equation 2. of Imagen Paper. 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.
• layers (int, optional, defaults to 4) -- Number of latent layers to generate for the layered output.
• 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) 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.
• prompt_embeds_mask (torch.Tensor, optional) -- Attention mask for prompt_embeds.
• 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.
• negative_prompt_embeds_mask (torch.Tensor, optional) -- Attention mask for negative_prompt_embeds.
• output_type (str, optional, defaults to "pil") -- The output format of the generate image. Choose between PIL: 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.
• 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 caption0~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.

Function invoked when calling the pipeline for generation.

Examples:

>>> 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")

Parameters:

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, specifically the Qwen2.5-VL-7B-Instruct variant.

tokenizer (QwenTokenizer) : Tokenizer of class CLIPTokenizer.

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.

encode_prompt[[diffusers.QwenImageLayeredPipeline.encode_prompt]]

Source

Parameters:

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.

QwenImagePipelineOutput[[diffusers.pipelines.qwenimage.pipeline_output.QwenImagePipelineOutput]]

diffusers.pipelines.qwenimage.pipeline_output.QwenImagePipelineOutput[[diffusers.pipelines.qwenimage.pipeline_output.QwenImagePipelineOutput]]

Source

Output class for Stable Diffusion pipelines.

Parameters:

images (list[PIL.Image.Image] or np.ndarray) : list of denoised PIL images of length batch_size or numpy array of shape (batch_size, height, width, num_channels). PIL images or numpy array present the denoised images of the diffusion pipeline.