LEDITS++¶
LEDITS++ was proposed in LEDITS++: Limitless Image Editing using Text-to-Image Models by Manuel Brack, Felix Friedrich, Katharina Kornmeier, Linoy Tsaban, Patrick Schramowski, Kristian Kersting, Apolinário Passos.
The abstract from the paper is:
Text-to-image diffusion models have recently received increasing interest for their astonishing ability to produce high-fidelity images from solely text inputs. Subsequent research efforts aim to exploit and apply their capabilities to real image editing. However, existing image-to-image methods are often inefficient, imprecise, and of limited versatility. They either require time-consuming fine-tuning, deviate unnecessarily strongly from the input image, and/or lack support for multiple, simultaneous edits. To address these issues, we introduce LEDITS++, an efficient yet versatile and precise textual image manipulation technique. LEDITS++'s novel inversion approach requires no tuning nor optimization and produces high-fidelity results with a few diffusion steps. Second, our methodology supports multiple simultaneous edits and is architecture-agnostic. Third, we use a novel implicit masking technique that limits changes to relevant image regions. We propose the novel TEdBench++ benchmark as part of our exhaustive evaluation. Our results demonstrate the capabilities of LEDITS++ and its improvements over previous methods. The project page is available at https://leditsplusplus-project.static.hf.space .
Tip
You can find additional information about LEDITS++ on the project page and try it out in a demo.
Tip
Due to some backward compatability issues with the current diffusers implementation of [~schedulers.DPMSolverMultistepScheduler
] this implementation of LEdits++ can no longer guarantee perfect inversion.
This issue is unlikely to have any noticeable effects on applied use-cases. However, we provide an alternative implementation that guarantees perfect inversion in a dedicated GitHub repo.
We provide two distinct pipelines based on different pre-trained models.
mindone.diffusers.pipelines.ledits_pp.LEditsPPPipelineStableDiffusion
¶
Bases: DiffusionPipeline
, TextualInversionLoaderMixin
, StableDiffusionLoraLoaderMixin
, IPAdapterMixin
, FromSingleFileMixin
Pipeline for textual image editing using LEDits++ with Stable Diffusion.
This model inherits from [DiffusionPipeline
] and builds on the [StableDiffusionPipeline
]. Check the superclass
documentation for the generic methods implemented for all pipelines (downloading, saving, running on a particular
device, etc.).
PARAMETER | DESCRIPTION |
---|---|
vae |
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
TYPE:
|
text_encoder |
Frozen text-encoder. Stable Diffusion uses the text portion of CLIP, specifically the clip-vit-large-patch14 variant.
TYPE:
|
tokenizer |
Tokenizer of class CLIPTokenizer.
TYPE:
|
unet |
Conditional U-Net architecture to denoise the encoded image latents.
TYPE:
|
scheduler |
A scheduler to be used in combination with
TYPE:
|
safety_checker |
Classification module that estimates whether generated images could be considered offensive or harmful. Please, refer to the model card for details.
TYPE:
|
feature_extractor |
Model that extracts features from generated images to be used as inputs for the
TYPE:
|
Source code in mindone/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py
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|
mindone.diffusers.pipelines.ledits_pp.LEditsPPPipelineStableDiffusion.__call__(negative_prompt=None, generator=None, output_type='pil', return_dict=False, editing_prompt=None, editing_prompt_embeds=None, negative_prompt_embeds=None, reverse_editing_direction=False, edit_guidance_scale=5, edit_warmup_steps=0, edit_cooldown_steps=None, edit_threshold=0.9, user_mask=None, sem_guidance=None, use_cross_attn_mask=False, use_intersect_mask=True, attn_store_steps=[], store_averaged_over_steps=True, cross_attention_kwargs=None, guidance_rescale=0.0, clip_skip=None, callback_on_step_end=None, callback_on_step_end_tensor_inputs=['latents'], **kwargs)
¶
The call function to the pipeline for editing. The
[~pipelines.ledits_pp.LEditsPPPipelineStableDiffusion.invert
] method has to be called beforehand. Edits will
always be performed for the last inverted image(s).
PARAMETER | DESCRIPTION |
---|---|
negative_prompt |
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if
TYPE:
|
generator |
One or a list of np.random.Generator(s) to make generation deterministic.
TYPE:
|
output_type |
The output format of the generate image. Choose between
PIL:
TYPE:
|
return_dict |
Whether or not to return a [
TYPE:
|
editing_prompt |
The prompt or prompts to guide the image generation. The image is reconstructed by setting
TYPE:
|
editing_prompt_embeds |
Pre-computed embeddings to use for guiding the image generation. Guidance direction of embedding should
be specified via
TYPE:
|
negative_prompt_embeds |
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
not provided,
TYPE:
|
reverse_editing_direction |
Whether the corresponding prompt in
TYPE:
|
edit_guidance_scale |
Guidance scale for guiding the image generation. If provided as list values should correspond to
TYPE:
|
edit_warmup_steps |
Number of diffusion steps (for each prompt) for which guidance will not be applied.
TYPE:
|
edit_cooldown_steps |
Number of diffusion steps (for each prompt) after which guidance will no longer be applied.
TYPE:
|
edit_threshold |
Masking threshold of guidance. Threshold should be proportional to the image region that is modified. 'edit_threshold' is defined as 'λ' of equation 12 of LEDITS++ Paper.
TYPE:
|
user_mask |
User-provided mask for even better control over the editing process. This is helpful when LEDITS++'s implicit masks do not meet user preferences.
TYPE:
|
sem_guidance |
List of pre-generated guidance vectors to be applied at generation. Length of the list has to
correspond to
TYPE:
|
use_cross_attn_mask |
Whether cross-attention masks are used. Cross-attention masks are always used when use_intersect_mask is set to true. Cross-attention masks are defined as 'M^1' of equation 12 of LEDITS++ paper.
TYPE:
|
use_intersect_mask |
Whether the masking term is calculated as intersection of cross-attention masks and masks derived from the noise estimate. Cross-attention mask are defined as 'M^1' and masks derived from the noise estimate are defined as 'M^2' of equation 12 of LEDITS++ paper.
TYPE:
|
attn_store_steps |
Steps for which the attention maps are stored in the AttentionStore. Just for visualization purposes.
TYPE:
|
store_averaged_over_steps |
Whether the attention maps for the 'attn_store_steps' are stored averaged over the diffusion steps. If False, attention maps for each step are stores separately. Just for visualization purposes.
TYPE:
|
cross_attention_kwargs |
A kwargs dictionary that if specified is passed along to the [
TYPE:
|
guidance_rescale |
Guidance rescale factor from Common Diffusion Noise Schedules and Sample Steps are Flawed. Guidance rescale factor should fix overexposure when using zero terminal SNR.
TYPE:
|
clip_skip |
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that the output of the pre-final layer will be used for computing the prompt embeddings.
TYPE:
|
callback_on_step_end |
A function that calls at the end of each denoising steps during the inference. The function is called
with the following arguments:
TYPE:
|
callback_on_step_end_tensor_inputs |
The list of tensor inputs for the
TYPE:
|
RETURNS | DESCRIPTION |
---|---|
[ |
|
[ |
|
returning a tuple, the first element is a list with the generated images, and the second element is a list |
|
of |
|
content, according to the |
Source code in mindone/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py
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|
mindone.diffusers.pipelines.ledits_pp.LEditsPPPipelineStableDiffusion.disable_vae_slicing()
¶
Disable sliced VAE decoding. If enable_vae_slicing
was previously enabled, this method will go back to
computing decoding in one step.
Source code in mindone/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py
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|
mindone.diffusers.pipelines.ledits_pp.LEditsPPPipelineStableDiffusion.disable_vae_tiling()
¶
Disable tiled VAE decoding. If enable_vae_tiling
was previously enabled, this method will go back to
computing decoding in one step.
Source code in mindone/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py
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|
mindone.diffusers.pipelines.ledits_pp.LEditsPPPipelineStableDiffusion.enable_vae_slicing()
¶
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.
Source code in mindone/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py
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|
mindone.diffusers.pipelines.ledits_pp.LEditsPPPipelineStableDiffusion.enable_vae_tiling()
¶
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.
Source code in mindone/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py
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|
mindone.diffusers.pipelines.ledits_pp.LEditsPPPipelineStableDiffusion.encode_prompt(num_images_per_prompt, enable_edit_guidance, negative_prompt=None, editing_prompt=None, negative_prompt_embeds=None, editing_prompt_embeds=None, lora_scale=None, clip_skip=None)
¶
Encodes the prompt into text encoder hidden states.
PARAMETER | DESCRIPTION |
---|---|
num_images_per_prompt |
number of images that should be generated per prompt
TYPE:
|
enable_edit_guidance |
whether to perform any editing or reconstruct the input image instead
TYPE:
|
negative_prompt |
The prompt or prompts not to guide the image generation. If not defined, one has to pass
TYPE:
|
editing_prompt |
Editing prompt(s) to be encoded. If not defined, one has to pass
TYPE:
|
editing_prompt_embeds |
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
TYPE:
|
negative_prompt_embeds |
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
TYPE:
|
lora_scale |
A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
TYPE:
|
clip_skip |
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that the output of the pre-final layer will be used for computing the prompt embeddings.
TYPE:
|
Source code in mindone/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py
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|
mindone.diffusers.pipelines.ledits_pp.LEditsPPPipelineStableDiffusion.invert(image, source_prompt='', source_guidance_scale=3.5, num_inversion_steps=30, skip=0.15, generator=None, cross_attention_kwargs=None, clip_skip=None, height=None, width=None, resize_mode='default', crops_coords=None)
¶
The function to the pipeline for image inversion as described by the LEDITS++
Paper. If the scheduler is set to [~schedulers.DDIMScheduler
] the
inversion proposed by edit-friendly DPDM will be performed instead.
PARAMETER | DESCRIPTION |
---|---|
image |
Input for the image(s) that are to be edited. Multiple input images have to default to the same aspect ratio.
TYPE:
|
source_prompt |
Prompt describing the input image that will be used for guidance during inversion. Guidance is disabled
if the
TYPE:
|
source_guidance_scale |
Strength of guidance during inversion.
TYPE:
|
num_inversion_steps |
Number of total performed inversion steps after discarding the initial
TYPE:
|
skip |
Portion of initial steps that will be ignored for inversion and subsequent generation. Lower values
will lead to stronger changes to the input image.
TYPE:
|
generator |
A
TYPE:
|
cross_attention_kwargs |
A kwargs dictionary that if specified is passed along to the [
TYPE:
|
clip_skip |
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that the output of the pre-final layer will be used for computing the prompt embeddings.
TYPE:
|
height |
The height in preprocessed image. If
TYPE:
|
width |
The width in preprocessed. If
TYPE:
|
resize_mode |
The resize mode, can be one of
TYPE:
|
crops_coords |
The crop coordinates for each image in the batch. If
TYPE:
|
RETURNS | DESCRIPTION |
---|---|
[ |
|
and respective VAE reconstruction(s). |
Source code in mindone/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py
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|
mindone.diffusers.pipelines.ledits_pp.LEditsPPPipelineStableDiffusionXL
¶
Bases: DiffusionPipeline
, FromSingleFileMixin
, StableDiffusionXLLoraLoaderMixin
, TextualInversionLoaderMixin
, IPAdapterMixin
Pipeline for textual image editing using LEDits++ with Stable Diffusion XL.
This model inherits from [DiffusionPipeline
] and builds on the [StableDiffusionXLPipeline
]. Check the
superclass documentation for the generic methods implemented for all pipelines (downloading, saving, running on a
particular device, etc.).
In addition the pipeline inherits the following loading methods
- LoRA: [
LEditsPPPipelineStableDiffusionXL.load_lora_weights
] - Ckpt: [
loaders.FromSingleFileMixin.from_single_file
]
as well as the following saving methods
- LoRA: [
loaders.StableDiffusionXLPipeline.save_lora_weights
]
PARAMETER | DESCRIPTION |
---|---|
vae |
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
TYPE:
|
text_encoder |
Frozen text-encoder. Stable Diffusion XL uses the text portion of CLIP, specifically the clip-vit-large-patch14 variant.
TYPE:
|
text_encoder_2 |
Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of CLIP, specifically the laion/CLIP-ViT-bigG-14-laion2B-39B-b160k variant.
TYPE:
|
tokenizer |
Tokenizer of class CLIPTokenizer.
TYPE:
|
tokenizer_2 |
Second Tokenizer of class CLIPTokenizer.
TYPE:
|
unet |
Conditional U-Net architecture to denoise the encoded image latents.
TYPE:
|
scheduler |
A scheduler to be used in combination with
TYPE:
|
force_zeros_for_empty_prompt |
Whether the negative prompt embeddings shall be forced to always be set to 0. Also see the config of
TYPE:
|
add_watermarker |
Whether to use the invisible_watermark library to watermark output images. If not defined, it will default to True if the package is installed, otherwise no watermarker will be used.
TYPE:
|
Source code in mindone/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py
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mindone.diffusers.pipelines.ledits_pp.LEditsPPPipelineStableDiffusionXL.__call__(denoising_end=None, negative_prompt=None, negative_prompt_2=None, negative_prompt_embeds=None, negative_pooled_prompt_embeds=None, ip_adapter_image=None, output_type='pil', return_dict=False, cross_attention_kwargs=None, guidance_rescale=0.0, crops_coords_top_left=(0, 0), target_size=None, editing_prompt=None, editing_prompt_embeddings=None, editing_pooled_prompt_embeds=None, reverse_editing_direction=False, edit_guidance_scale=5, edit_warmup_steps=0, edit_cooldown_steps=None, edit_threshold=0.9, sem_guidance=None, use_cross_attn_mask=False, use_intersect_mask=False, user_mask=None, attn_store_steps=[], store_averaged_over_steps=True, clip_skip=None, callback_on_step_end=None, callback_on_step_end_tensor_inputs=['latents'], **kwargs)
¶
The call function to the pipeline for editing. The
[~pipelines.ledits_pp.LEditsPPPipelineStableDiffusionXL.invert
] method has to be called beforehand. Edits
will always be performed for the last inverted image(s).
PARAMETER | DESCRIPTION |
---|---|
denoising_end |
When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be completed before it is intentionally prematurely terminated. As a result, the returned sample will still retain a substantial amount of noise as determined by the discrete timesteps selected by the scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a "Mixture of Denoisers" multi-pipeline setup, as elaborated in [**Refining the Image
TYPE:
|
negative_prompt |
The prompt or prompts not to guide the image generation. If not defined, one has to pass
TYPE:
|
negative_prompt_2 |
The prompt or prompts not to guide the image generation to be sent to
TYPE:
|
negative_prompt_embeds |
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
TYPE:
|
negative_pooled_prompt_embeds |
Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, e.g. prompt
weighting. If not provided, pooled negative_prompt_embeds will be generated from
TYPE:
|
ip_adapter_image |
(
TYPE:
|
output_type |
The output format of the generate image. Choose between
PIL:
TYPE:
|
return_dict |
Whether or not to return a [
TYPE:
|
callback |
A function that will be called every
TYPE:
|
callback_steps |
The frequency at which the
TYPE:
|
cross_attention_kwargs |
A kwargs dictionary that if specified is passed along to the
TYPE:
|
guidance_rescale |
Guidance rescale factor proposed by Common Diffusion Noise Schedules and Sample Steps are
Flawed
TYPE:
|
crops_coords_top_left |
TYPE:
|
target_size |
For most cases,
TYPE:
|
editing_prompt |
The prompt or prompts to guide the image generation. The image is reconstructed by setting
TYPE:
|
editing_prompt_embeddings |
Pre-generated edit text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting.
If not provided, editing_prompt_embeddings will be generated from
TYPE:
|
editing_pooled_prompt_embeddings |
Pre-generated pooled edit text embeddings. Can be used to easily tweak text inputs, e.g. prompt
weighting. If not provided, editing_prompt_embeddings will be generated from
TYPE:
|
reverse_editing_direction |
Whether the corresponding prompt in
TYPE:
|
edit_guidance_scale |
Guidance scale for guiding the image generation. If provided as list values should correspond to
TYPE:
|
edit_warmup_steps |
Number of diffusion steps (for each prompt) for which guidance is not applied.
TYPE:
|
edit_cooldown_steps |
Number of diffusion steps (for each prompt) after which guidance is no longer applied.
TYPE:
|
edit_threshold |
Masking threshold of guidance. Threshold should be proportional to the image region that is modified. 'edit_threshold' is defined as 'λ' of equation 12 of LEDITS++ Paper.
TYPE:
|
sem_guidance |
List of pre-generated guidance vectors to be applied at generation. Length of the list has to
correspond to
TYPE:
|
use_cross_attn_mask |
Whether cross-attention masks are used. Cross-attention masks are always used when use_intersect_mask is set to true. Cross-attention masks are defined as 'M^1' of equation 12 of LEDITS++ paper.
TYPE:
|
use_intersect_mask |
Whether the masking term is calculated as intersection of cross-attention masks and masks derived from the noise estimate. Cross-attention mask are defined as 'M^1' and masks derived from the noise estimate are defined as 'M^2' of equation 12 of LEDITS++ paper.
TYPE:
|
user_mask |
User-provided mask for even better control over the editing process. This is helpful when LEDITS++'s implicit masks do not meet user preferences.
TYPE:
|
attn_store_steps |
Steps for which the attention maps are stored in the AttentionStore. Just for visualization purposes.
TYPE:
|
store_averaged_over_steps |
Whether the attention maps for the 'attn_store_steps' are stored averaged over the diffusion steps. If False, attention maps for each step are stores separately. Just for visualization purposes.
TYPE:
|
clip_skip |
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that the output of the pre-final layer will be used for computing the prompt embeddings.
TYPE:
|
callback_on_step_end |
A function that calls at the end of each denoising steps during the inference. The function is called
with the following arguments:
TYPE:
|
callback_on_step_end_tensor_inputs |
The list of tensor inputs for the
TYPE:
|
RETURNS | DESCRIPTION |
---|---|
[ |
|
[ |
|
returning a tuple, the first element is a list with the generated images. |
Source code in mindone/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py
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|
mindone.diffusers.pipelines.ledits_pp.LEditsPPPipelineStableDiffusionXL.disable_vae_slicing()
¶
Disable sliced VAE decoding. If enable_vae_slicing
was previously enabled, this method will go back to
computing decoding in one step.
Source code in mindone/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py
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|
mindone.diffusers.pipelines.ledits_pp.LEditsPPPipelineStableDiffusionXL.disable_vae_tiling()
¶
Disable tiled VAE decoding. If enable_vae_tiling
was previously enabled, this method will go back to
computing decoding in one step.
Source code in mindone/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py
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|
mindone.diffusers.pipelines.ledits_pp.LEditsPPPipelineStableDiffusionXL.enable_vae_slicing()
¶
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.
Source code in mindone/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py
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|
mindone.diffusers.pipelines.ledits_pp.LEditsPPPipelineStableDiffusionXL.enable_vae_tiling()
¶
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.
Source code in mindone/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py
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|
mindone.diffusers.pipelines.ledits_pp.LEditsPPPipelineStableDiffusionXL.encode_prompt(num_images_per_prompt=1, negative_prompt=None, negative_prompt_2=None, negative_prompt_embeds=None, negative_pooled_prompt_embeds=None, lora_scale=None, clip_skip=None, enable_edit_guidance=True, editing_prompt=None, editing_prompt_embeds=None, editing_pooled_prompt_embeds=None)
¶
Encodes the prompt into text encoder hidden states.
PARAMETER | DESCRIPTION |
---|---|
num_images_per_prompt |
number of images that should be generated per prompt
TYPE:
|
negative_prompt |
The prompt or prompts not to guide the image generation. If not defined, one has to pass
TYPE:
|
negative_prompt_2 |
The prompt or prompts not to guide the image generation to be sent to
TYPE:
|
negative_prompt_embeds |
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
TYPE:
|
negative_pooled_prompt_embeds |
Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, e.g. prompt
weighting. If not provided, pooled negative_prompt_embeds will be generated from
TYPE:
|
lora_scale |
A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
TYPE:
|
clip_skip |
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that the output of the pre-final layer will be used for computing the prompt embeddings.
TYPE:
|
enable_edit_guidance |
Whether to guide towards an editing prompt or not.
TYPE:
|
editing_prompt |
Editing prompt(s) to be encoded. If not defined and 'enable_edit_guidance' is True, one has to pass
TYPE:
|
editing_prompt_embeds |
Pre-generated edit text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting.
If not provided and 'enable_edit_guidance' is True, editing_prompt_embeds will be generated from
TYPE:
|
editing_pooled_prompt_embeds |
Pre-generated edit pooled text embeddings. Can be used to easily tweak text inputs, e.g. prompt
weighting. If not provided, pooled editing_pooled_prompt_embeds will be generated from
TYPE:
|
Source code in mindone/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py
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|
mindone.diffusers.pipelines.ledits_pp.LEditsPPPipelineStableDiffusionXL.get_guidance_scale_embedding(w, embedding_dim=512, dtype=ms.float32)
¶
See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
PARAMETER | DESCRIPTION |
---|---|
w |
Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings.
TYPE:
|
embedding_dim |
Dimension of the embeddings to generate.
TYPE:
|
dtype |
Data type of the generated embeddings.
TYPE:
|
RETURNS | DESCRIPTION |
---|---|
Tensor
|
|
Source code in mindone/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py
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mindone.diffusers.pipelines.ledits_pp.LEditsPPPipelineStableDiffusionXL.invert(image, source_prompt='', source_guidance_scale=3.5, negative_prompt=None, negative_prompt_2=None, num_inversion_steps=50, skip=0.15, generator=None, crops_coords_top_left=(0, 0), num_zero_noise_steps=3, cross_attention_kwargs=None, height=None, width=None, resize_mode='default', crops_coords=None)
¶
The function to the pipeline for image inversion as described by the LEDITS++
Paper. If the scheduler is set to [~schedulers.DDIMScheduler
] the
inversion proposed by edit-friendly DPDM will be performed instead.
PARAMETER | DESCRIPTION |
---|---|
image |
Input for the image(s) that are to be edited. Multiple input images have to default to the same aspect ratio.
TYPE:
|
source_prompt |
Prompt describing the input image that will be used for guidance during inversion. Guidance is disabled
if the
TYPE:
|
source_guidance_scale |
Strength of guidance during inversion.
TYPE:
|
negative_prompt |
The prompt or prompts not to guide the image generation. If not defined, one has to pass
TYPE:
|
negative_prompt_2 |
The prompt or prompts not to guide the image generation to be sent to
TYPE:
|
num_inversion_steps |
Number of total performed inversion steps after discarding the initial
TYPE:
|
skip |
Portion of initial steps that will be ignored for inversion and subsequent generation. Lower values
will lead to stronger changes to the input image.
TYPE:
|
generator |
A
TYPE:
|
crops_coords_top_left |
TYPE:
|
num_zero_noise_steps |
Number of final diffusion steps that will not renoise the current image. If no steps are set to zero
SD-XL in combination with [
TYPE:
|
cross_attention_kwargs |
A kwargs dictionary that if specified is passed along to the
TYPE:
|
RETURNS | DESCRIPTION |
---|---|
[ |
|
and respective VAE reconstruction(s). |
Source code in mindone/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py
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mindone.diffusers.pipelines.ledits_pp.pipeline_output.LEditsPPDiffusionPipelineOutput
dataclass
¶
Bases: BaseOutput
Output class for LEdits++ Diffusion pipelines.
Source code in mindone/diffusers/pipelines/ledits_pp/pipeline_output.py
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mindone.diffusers.pipelines.ledits_pp.pipeline_output.LEditsPPInversionPipelineOutput
dataclass
¶
Bases: BaseOutput
Output class for LEdits++ Diffusion pipelines.
Source code in mindone/diffusers/pipelines/ledits_pp/pipeline_output.py
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