VisualCloze: A Universal Image Generation Framework via Visual In-Context Learning is an innovative in-context learning based universal image generation framework that offers key capabilities:
The abstract from the paper is:
Recent progress in diffusion models significantly advances various image generation tasks. However, the current mainstream approach remains focused on building task-specific models, which have limited efficiency when supporting a wide range of different needs. While universal models attempt to address this limitation, they face critical challenges, including generalizable task instruction, appropriate task distributions, and unified architectural design. To tackle these challenges, we propose VisualCloze, a universal image generation framework, which supports a wide range of in-domain tasks, generalization to unseen ones, unseen unification of multiple tasks, and reverse generation. Unlike existing methods that rely on language-based task instruction, leading to task ambiguity and weak generalization, we integrate visual in-context learning, allowing models to identify tasks from visual demonstrations. Meanwhile, the inherent sparsity of visual task distributions hampers the learning of transferable knowledge across tasks. To this end, we introduce Graph200K, a graph-structured dataset that establishes various interrelated tasks, enhancing task density and transferable knowledge. Furthermore, we uncover that our unified image generation formulation shared a consistent objective with image infilling, enabling us to leverage the strong generative priors of pre-trained infilling models without modifying the architectures. The codes, dataset, and models are available at https://visualcloze.github.io.
VisualCloze is a two-stage cascade pipeline, containing VisualClozeGenerationPipeline and VisualClozeUpsamplingPipeline.
VisualClozeGenerationPipeline, each image is downsampled before concatenating images into a grid layout, avoiding excessively high resolutions. VisualCloze releases two models suitable for diffusers, i.e., VisualClozePipeline-384 and VisualClozePipeline-512, which downsample images to resolutions of 384 and 512, respectively.VisualClozeUpsamplingPipeline uses SDEdit to enable high-resolution image synthesis.The VisualClozePipeline integrates both stages to support convenient end-to-end sampling, while also allowing users to utilize each pipeline independently as needed.
NoneList[str|None]List[List[Image|None]]None)List[List[List[Image|None]]]upsampling_height and upsampling_width parametersFor comprehensive examples covering a wide range of tasks, please refer to the Online Demo and GitHub Repository. Below are simple examples for three cases: mask-to-image conversion, edge detection, and subject-driven generation.
import torch
from diffusers import VisualClozePipeline
from diffusers.utils import load_image
pipe = VisualClozePipeline.from_pretrained("VisualCloze/VisualClozePipeline-384", resolution=384, torch_dtype=torch.bfloat16)
pipe.to("cuda")
# Load in-context images (make sure the paths are correct and accessible)
image_paths = [
# in-context examples
[
load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_mask2image_incontext-example-1_mask.jpg'),
load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_mask2image_incontext-example-1_image.jpg'),
],
# query with the target image
[
load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_mask2image_query_mask.jpg'),
None, # No image needed for the target image
],
]
# Task and content prompt
task_prompt = "In each row, a logical task is demonstrated to achieve [IMAGE2] an aesthetically pleasing photograph based on [IMAGE1] sam 2-generated masks with rich color coding."
content_prompt = """Majestic photo of a golden eagle perched on a rocky outcrop in a mountainous landscape.
The eagle is positioned in the right foreground, facing left, with its sharp beak and keen eyes prominently visible.
Its plumage is a mix of dark brown and golden hues, with intricate feather details.
The background features a soft-focus view of snow-capped mountains under a cloudy sky, creating a serene and grandiose atmosphere.
The foreground includes rugged rocks and patches of green moss. Photorealistic, medium depth of field,
soft natural lighting, cool color palette, high contrast, sharp focus on the eagle, blurred background,
tranquil, majestic, wildlife photography."""
# Run the pipeline
image_result = pipe(
task_prompt=task_prompt,
content_prompt=content_prompt,
image=image_paths,
upsampling_width=1344,
upsampling_height=768,
upsampling_strength=0.4,
guidance_scale=30,
num_inference_steps=30,
max_sequence_length=512,
generator=torch.Generator("cpu").manual_seed(0)
).images[0][0]
# Save the resulting image
image_result.save("visualcloze.png")import torch
from diffusers import VisualClozePipeline
from diffusers.utils import load_image
pipe = VisualClozePipeline.from_pretrained("VisualCloze/VisualClozePipeline-384", resolution=384, torch_dtype=torch.bfloat16)
pipe.to("cuda")
# Load in-context images (make sure the paths are correct and accessible)
image_paths = [
# in-context examples
[
load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_edgedetection_incontext-example-1_image.jpg'),
load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_edgedetection_incontext-example-1_edge.jpg'),
],
[
load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_edgedetection_incontext-example-2_image.jpg'),
load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_edgedetection_incontext-example-2_edge.jpg'),
],
# query with the target image
[
load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_edgedetection_query_image.jpg'),
None, # No image needed for the target image
],
]
# Task and content prompt
task_prompt = "Each row illustrates a pathway from [IMAGE1] a sharp and beautifully composed photograph to [IMAGE2] edge map with natural well-connected outlines using a clear logical task."
content_prompt = ""
# Run the pipeline
image_result = pipe(
task_prompt=task_prompt,
content_prompt=content_prompt,
image=image_paths,
upsampling_width=864,
upsampling_height=1152,
upsampling_strength=0.4,
guidance_scale=30,
num_inference_steps=30,
max_sequence_length=512,
generator=torch.Generator("cpu").manual_seed(0)
).images[0][0]
# Save the resulting image
image_result.save("visualcloze.png")import torch
from diffusers import VisualClozePipeline
from diffusers.utils import load_image
pipe = VisualClozePipeline.from_pretrained("VisualCloze/VisualClozePipeline-384", resolution=384, torch_dtype=torch.bfloat16)
pipe.to("cuda")
# Load in-context images (make sure the paths are correct and accessible)
image_paths = [
# in-context examples
[
load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_subjectdriven_incontext-example-1_reference.jpg'),
load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_subjectdriven_incontext-example-1_depth.jpg'),
load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_subjectdriven_incontext-example-1_image.jpg'),
],
[
load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_subjectdriven_incontext-example-2_reference.jpg'),
load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_subjectdriven_incontext-example-2_depth.jpg'),
load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_subjectdriven_incontext-example-2_image.jpg'),
],
# query with the target image
[
load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_subjectdriven_query_reference.jpg'),
load_image('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_subjectdriven_query_depth.jpg'),
None, # No image needed for the target image
],
]
# Task and content prompt
task_prompt = """Each row describes a process that begins with [IMAGE1] an image containing the key object,
[IMAGE2] depth map revealing gray-toned spatial layers and results in
[IMAGE3] an image with artistic qualitya high-quality image with exceptional detail."""
content_prompt = """A vintage porcelain collector's item. Beneath a blossoming cherry tree in early spring,
this treasure is photographed up close, with soft pink petals drifting through the air and vibrant blossoms framing the scene."""
# Run the pipeline
image_result = pipe(
task_prompt=task_prompt,
content_prompt=content_prompt,
image=image_paths,
upsampling_width=1024,
upsampling_height=1024,
upsampling_strength=0.2,
guidance_scale=30,
num_inference_steps=30,
max_sequence_length=512,
generator=torch.Generator("cpu").manual_seed(0)
).images[0][0]
# Save the resulting image
image_result.save("visualcloze.png")import torch
from diffusers import VisualClozeGenerationPipeline, FluxFillPipeline as VisualClozeUpsamplingPipeline
from diffusers.utils import load_image
from PIL import Image
pipe = VisualClozeGenerationPipeline.from_pretrained(
"VisualCloze/VisualClozePipeline-384", resolution=384, torch_dtype=torch.bfloat16
)
pipe.to("cuda")
image_paths = [
# in-context examples
[
load_image(
"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_mask2image_incontext-example-1_mask.jpg"
),
load_image(
"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_mask2image_incontext-example-1_image.jpg"
),
],
# query with the target image
[
load_image(
"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_mask2image_query_mask.jpg"
),
None, # No image needed for the target image
],
]
task_prompt = "In each row, a logical task is demonstrated to achieve [IMAGE2] an aesthetically pleasing photograph based on [IMAGE1] sam 2-generated masks with rich color coding."
content_prompt = "Majestic photo of a golden eagle perched on a rocky outcrop in a mountainous landscape. The eagle is positioned in the right foreground, facing left, with its sharp beak and keen eyes prominently visible. Its plumage is a mix of dark brown and golden hues, with intricate feather details. The background features a soft-focus view of snow-capped mountains under a cloudy sky, creating a serene and grandiose atmosphere. The foreground includes rugged rocks and patches of green moss. Photorealistic, medium depth of field, soft natural lighting, cool color palette, high contrast, sharp focus on the eagle, blurred background, tranquil, majestic, wildlife photography."
# Stage 1: Generate initial image
image = pipe(
task_prompt=task_prompt,
content_prompt=content_prompt,
image=image_paths,
guidance_scale=30,
num_inference_steps=30,
max_sequence_length=512,
generator=torch.Generator("cpu").manual_seed(0),
).images[0][0]
# Stage 2 (optional): Upsample the generated image
pipe_upsample = VisualClozeUpsamplingPipeline.from_pipe(pipe)
pipe_upsample.to("cuda")
mask_image = Image.new("RGB", image.size, (255, 255, 255))
image = pipe_upsample(
image=image,
mask_image=mask_image,
prompt=content_prompt,
width=1344,
height=768,
strength=0.4,
guidance_scale=30,
num_inference_steps=30,
max_sequence_length=512,
generator=torch.Generator("cpu").manual_seed(0),
).images[0]
image.save("visualcloze.png")( scheduler: FlowMatchEulerDiscreteScheduler vae: AutoencoderKL text_encoder: CLIPTextModel tokenizer: CLIPTokenizer text_encoder_2: T5EncoderModel tokenizer_2: T5TokenizerFast transformer: FluxTransformer2DModel resolution: int = 384 )
Parameters
transformer to denoise the encoded image latents. CLIPTextModel) —
CLIP, specifically
the clip-vit-large-patch14 variant. T5EncoderModel) —
T5, specifically
the google/t5-v1_1-xxl variant. CLIPTokenizer) —
Tokenizer of class
CLIPTokenizer. T5TokenizerFast) —
Second Tokenizer of class
T5TokenizerFast. int, optional, defaults to 384) —
The resolution of each image when concatenating images from the query and in-context examples. The VisualCloze pipeline for image generation with visual context. Reference: https://github.com/lzyhha/VisualCloze/tree/main. This pipeline is designed to generate images based on visual in-context examples.
( task_prompt: typing.Union[str, typing.List[str]] = None content_prompt: typing.Union[str, typing.List[str]] = None image: typing.Optional[torch.FloatTensor] = None upsampling_height: typing.Optional[int] = None upsampling_width: typing.Optional[int] = None num_inference_steps: int = 50 sigmas: typing.Optional[typing.List[float]] = None guidance_scale: float = 30.0 num_images_per_prompt: typing.Optional[int] = 1 generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None latents: typing.Optional[torch.FloatTensor] = None prompt_embeds: typing.Optional[torch.FloatTensor] = None pooled_prompt_embeds: typing.Optional[torch.FloatTensor] = None output_type: typing.Optional[str] = 'pil' return_dict: bool = True joint_attention_kwargs: typing.Optional[typing.Dict[str, typing.Any]] = None callback_on_step_end: typing.Optional[typing.Callable[[int, int, typing.Dict], NoneType]] = None callback_on_step_end_tensor_inputs: typing.List[str] = ['latents'] max_sequence_length: int = 512 upsampling_strength: float = 1.0 ) → ~pipelines.flux.FluxPipelineOutput or tuple
Parameters
str or List[str], optional) —
The prompt or prompts to define the task intention. str or List[str], optional) —
The prompt or prompts to define the content or caption of the target image to be generated. 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). int, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) —
The height in pixels of the generated image (i.e., output image) after upsampling via SDEdit. By
default, the image is upsampled by a factor of three, and the base resolution is determined by the
resolution parameter of the pipeline. When only one of upsampling_height or upsampling_width is
specified, the other will be automatically set based on the aspect ratio. int, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) —
The width in pixels of the generated image (i.e., output image) after upsampling via SDEdit. By
default, the image is upsampled by a factor of three, and the base resolution is determined by the
resolution parameter of the pipeline. When only one of upsampling_height or upsampling_width is
specified, the other will be automatically set based on the aspect ratio. 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. 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. float, optional, defaults to 30.0) —
Guidance scale as defined in Classifier-Free Diffusion
Guidance. guidance_scale is defined as w of equation 2.
of Imagen Paper. 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. int, optional, defaults to 1) —
The number of images to generate per prompt. torch.Generator or List[torch.Generator], optional) —
One or a list of torch generator(s)
to make generation deterministic. torch.FloatTensor, 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 ge generated by sampling using the supplied random generator. torch.FloatTensor, 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. torch.FloatTensor, optional) —
Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting.
If not provided, pooled text embeddings will be generated from prompt input argument. str, optional, defaults to "pil") —
The output format of the generate image. Choose between
PIL: PIL.Image.Image or np.array. bool, optional, defaults to True) —
Whether or not to return a ~pipelines.flux.FluxPipelineOutput instead of a plain tuple. dict, optional) —
A kwargs dictionary that if specified is passed along to the AttentionProcessor as defined under
self.processor in
diffusers.models.attention_processor. 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. 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. int defaults to 512) — Maximum sequence length to use with the prompt. float, optional, defaults to 1.0) —
Indicates extent to transform the reference image when upsampling the results. Must be between 0 andupsampling_strength. The number of denoising steps depends on the amount of noise initially added.
When upsampling_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 0 skips the upsampling step and
output the results at the resolution of self.resolution.Returns
~pipelines.flux.FluxPipelineOutput or tuple
~pipelines.flux.FluxPipelineOutput 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 VisualCloze pipeline for generation.
Examples:
>>> import torch
>>> from diffusers import VisualClozePipeline
>>> from diffusers.utils import load_image
>>> image_paths = [
... # in-context examples
... [
... load_image(
... "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_mask2image_incontext-example-1_mask.jpg"
... ),
... load_image(
... "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_mask2image_incontext-example-1_image.jpg"
... ),
... ],
... # query with the target image
... [
... load_image(
... "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_mask2image_query_mask.jpg"
... ),
... None, # No image needed for the target image
... ],
... ]
>>> task_prompt = "In each row, a logical task is demonstrated to achieve [IMAGE2] an aesthetically pleasing photograph based on [IMAGE1] sam 2-generated masks with rich color coding."
>>> content_prompt = "Majestic photo of a golden eagle perched on a rocky outcrop in a mountainous landscape. The eagle is positioned in the right foreground, facing left, with its sharp beak and keen eyes prominently visible. Its plumage is a mix of dark brown and golden hues, with intricate feather details. The background features a soft-focus view of snow-capped mountains under a cloudy sky, creating a serene and grandiose atmosphere. The foreground includes rugged rocks and patches of green moss. Photorealistic, medium depth of field, soft natural lighting, cool color palette, high contrast, sharp focus on the eagle, blurred background, tranquil, majestic, wildlife photography."
>>> pipe = VisualClozePipeline.from_pretrained(
... "VisualCloze/VisualClozePipeline-384", resolution=384, torch_dtype=torch.bfloat16
... )
>>> pipe.to("cuda")
>>> image = pipe(
... task_prompt=task_prompt,
... content_prompt=content_prompt,
... image=image_paths,
... upsampling_width=1344,
... upsampling_height=768,
... upsampling_strength=0.4,
... guidance_scale=30,
... num_inference_steps=30,
... max_sequence_length=512,
... generator=torch.Generator("cpu").manual_seed(0),
... ).images[0][0]
>>> image.save("visualcloze.png")( scheduler: FlowMatchEulerDiscreteScheduler vae: AutoencoderKL text_encoder: CLIPTextModel tokenizer: CLIPTokenizer text_encoder_2: T5EncoderModel tokenizer_2: T5TokenizerFast transformer: FluxTransformer2DModel resolution: int = 384 )
Parameters
transformer to denoise the encoded image latents. CLIPTextModel) —
CLIP, specifically
the clip-vit-large-patch14 variant. T5EncoderModel) —
T5, specifically
the google/t5-v1_1-xxl variant. CLIPTokenizer) —
Tokenizer of class
CLIPTokenizer. T5TokenizerFast) —
Second Tokenizer of class
T5TokenizerFast. int, optional, defaults to 384) —
The resolution of each image when concatenating images from the query and in-context examples. The VisualCloze pipeline for image generation with visual context. Reference: https://github.com/lzyhha/VisualCloze/tree/main This pipeline is designed to generate images based on visual in-context examples.
( task_prompt: typing.Union[str, typing.List[str]] = None content_prompt: typing.Union[str, typing.List[str]] = None image: typing.Optional[torch.FloatTensor] = None num_inference_steps: int = 50 sigmas: typing.Optional[typing.List[float]] = None guidance_scale: float = 30.0 num_images_per_prompt: typing.Optional[int] = 1 generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None latents: typing.Optional[torch.FloatTensor] = None prompt_embeds: typing.Optional[torch.FloatTensor] = None pooled_prompt_embeds: typing.Optional[torch.FloatTensor] = None output_type: typing.Optional[str] = 'pil' return_dict: bool = True joint_attention_kwargs: typing.Optional[typing.Dict[str, typing.Any]] = None callback_on_step_end: typing.Optional[typing.Callable[[int, int, typing.Dict], NoneType]] = None callback_on_step_end_tensor_inputs: typing.List[str] = ['latents'] max_sequence_length: int = 512 ) → ~pipelines.flux.FluxPipelineOutput or tuple
Parameters
str or List[str], optional) —
The prompt or prompts to define the task intention. str or List[str], optional) —
The prompt or prompts to define the content or caption of the target image to be generated. 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). 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. 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. float, optional, defaults to 30.0) —
Guidance scale as defined in Classifier-Free Diffusion
Guidance. guidance_scale is defined as w of equation 2.
of Imagen Paper. 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. int, optional, defaults to 1) —
The number of images to generate per prompt. torch.Generator or List[torch.Generator], optional) —
One or a list of torch generator(s)
to make generation deterministic. torch.FloatTensor, 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 ge generated by sampling using the supplied random generator. torch.FloatTensor, 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. torch.FloatTensor, optional) —
Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting.
If not provided, pooled text embeddings will be generated from prompt input argument. str, optional, defaults to "pil") —
The output format of the generate image. Choose between
PIL: PIL.Image.Image or np.array. bool, optional, defaults to True) —
Whether or not to return a ~pipelines.flux.FluxPipelineOutput instead of a plain tuple. dict, optional) —
A kwargs dictionary that if specified is passed along to the AttentionProcessor as defined under
self.processor in
diffusers.models.attention_processor. 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. 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. int defaults to 512) — Maximum sequence length to use with the prompt. Returns
~pipelines.flux.FluxPipelineOutput or tuple
~pipelines.flux.FluxPipelineOutput 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 VisualCloze pipeline for generation.
Examples:
>>> import torch
>>> from diffusers import VisualClozeGenerationPipeline, FluxFillPipeline as VisualClozeUpsamplingPipeline
>>> from diffusers.utils import load_image
>>> from PIL import Image
>>> image_paths = [
... # in-context examples
... [
... load_image(
... "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_mask2image_incontext-example-1_mask.jpg"
... ),
... load_image(
... "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_mask2image_incontext-example-1_image.jpg"
... ),
... ],
... # query with the target image
... [
... load_image(
... "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/visualcloze/visualcloze_mask2image_query_mask.jpg"
... ),
... None, # No image needed for the target image
... ],
... ]
>>> task_prompt = "In each row, a logical task is demonstrated to achieve [IMAGE2] an aesthetically pleasing photograph based on [IMAGE1] sam 2-generated masks with rich color coding."
>>> content_prompt = "Majestic photo of a golden eagle perched on a rocky outcrop in a mountainous landscape. The eagle is positioned in the right foreground, facing left, with its sharp beak and keen eyes prominently visible. Its plumage is a mix of dark brown and golden hues, with intricate feather details. The background features a soft-focus view of snow-capped mountains under a cloudy sky, creating a serene and grandiose atmosphere. The foreground includes rugged rocks and patches of green moss. Photorealistic, medium depth of field, soft natural lighting, cool color palette, high contrast, sharp focus on the eagle, blurred background, tranquil, majestic, wildlife photography."
>>> pipe = VisualClozeGenerationPipeline.from_pretrained(
... "VisualCloze/VisualClozePipeline-384", resolution=384, torch_dtype=torch.bfloat16
... )
>>> pipe.to("cuda")
>>> image = pipe(
... task_prompt=task_prompt,
... content_prompt=content_prompt,
... image=image_paths,
... guidance_scale=30,
... num_inference_steps=30,
... max_sequence_length=512,
... generator=torch.Generator("cpu").manual_seed(0),
... ).images[0][0]
>>> # optional, upsampling the generated image
>>> pipe_upsample = VisualClozeUpsamplingPipeline.from_pipe(pipe)
>>> pipe_upsample.to("cuda")
>>> mask_image = Image.new("RGB", image.size, (255, 255, 255))
>>> image = pipe_upsample(
... image=image,
... mask_image=mask_image,
... prompt=content_prompt,
... width=1344,
... height=768,
... strength=0.4,
... guidance_scale=30,
... num_inference_steps=30,
... max_sequence_length=512,
... generator=torch.Generator("cpu").manual_seed(0),
... ).images[0]
>>> image.save("visualcloze.png")Disable sliced VAE decoding. If enable_vae_slicing was previously enabled, this method will go back to
computing decoding in one step.
Disable tiled VAE decoding. If enable_vae_tiling was previously enabled, this method will go back to
computing decoding in one step.
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 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.
( layout_prompt: typing.Union[str, typing.List[str]] task_prompt: typing.Union[str, typing.List[str]] content_prompt: typing.Union[str, typing.List[str]] device: typing.Optional[torch.device] = None num_images_per_prompt: int = 1 prompt_embeds: typing.Optional[torch.FloatTensor] = None pooled_prompt_embeds: typing.Optional[torch.FloatTensor] = None max_sequence_length: int = 512 lora_scale: typing.Optional[float] = None )
Parameters
str or List[str], optional) —
The prompt or prompts to define the number of in-context examples and the number of images involved in
the task. str or List[str], optional) —
The prompt or prompts to define the task intention. str or List[str], optional) —
The prompt or prompts to define the content or caption of the target image to be generated. torch.device):
torch device int) —
number of images that should be generated per prompt torch.FloatTensor, 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. torch.FloatTensor, optional) —
Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting.
If not provided, pooled text embeddings will be generated from prompt input argument. float, optional) —
A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.