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Zero-shot Image-to-Image Translation

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Zero-shot Image-to-Image Translation

Overview

Zero-shot Image-to-Image Translation.

The abstract of the paper is the following:

Large-scale text-to-image generative models have shown their remarkable ability to synthesize diverse and high-quality images. However, it is still challenging to directly apply these models for editing real images for two reasons. First, it is hard for users to come up with a perfect text prompt that accurately describes every visual detail in the input image. Second, while existing models can introduce desirable changes in certain regions, they often dramatically alter the input content and introduce unexpected changes in unwanted regions. In this work, we propose pix2pix-zero, an image-to-image translation method that can preserve the content of the original image without manual prompting. We first automatically discover editing directions that reflect desired edits in the text embedding space. To preserve the general content structure after editing, we further propose cross-attention guidance, which aims to retain the cross-attention maps of the input image throughout the diffusion process. In addition, our method does not need additional training for these edits and can directly use the existing pre-trained text-to-image diffusion model. We conduct extensive experiments and show that our method outperforms existing and concurrent works for both real and synthetic image editing.

Resources:

Tips

  • The pipeline can be conditioned on real input images. Check out the code examples below to know more.
  • The pipeline exposes two arguments namely source_embeds and target_embeds that let you control the direction of the semantic edits in the final image to be generated. Let’s say, you wanted to translate from “cat” to “dog”. In this case, the edit direction will be “cat -> dog”. To reflect this in the pipeline, you simply have to set the embeddings related to the phrases including “cat” to source_embeds and “dog” to target_embeds. Refer to the code example below for more details.
  • When you’re using this pipeline from a prompt, specify the source concept in the prompt. Taking the above example, a valid input prompt would be: “a high resolution painting of a cat in the style of van gough”.
  • If you wanted to reverse the direction in the example above, i.e., “dog -> cat”, then it’s recommended to:
    • Swap the source_embeds and target_embeds.
    • Change the input prompt to include “dog”.
  • To learn more about how the source and target embeddings are generated, refer to the original paper. Below, we also provide some directions on how to generate the embeddings.
  • Note that the quality of the outputs generated with this pipeline is dependent on how good the source_embeds and target_embeds are. Please, refer to this discussion for some suggestions on the topic.

Available Pipelines:

Pipeline Tasks Demo
StableDiffusionPix2PixZeroPipeline Text-Based Image Editing 🤗 Space

Usage example

Based on an image generated with the input prompt 

import requests
import torch

from diffusers import DDIMScheduler, StableDiffusionPix2PixZeroPipeline


def download(embedding_url, local_filepath):
    r = requests.get(embedding_url)
    with open(local_filepath, "wb") as f:
        f.write(r.content)


model_ckpt = "CompVis/stable-diffusion-v1-4"
pipeline = StableDiffusionPix2PixZeroPipeline.from_pretrained(
    model_ckpt, conditions_input_image=False, torch_dtype=torch.float16
)
pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
pipeline.to("cuda")

prompt = "a high resolution painting of a cat in the style of van gogh"
src_embs_url = "https://github.com/pix2pixzero/pix2pix-zero/raw/main/assets/embeddings_sd_1.4/cat.pt"
target_embs_url = "https://github.com/pix2pixzero/pix2pix-zero/raw/main/assets/embeddings_sd_1.4/dog.pt"

for url in [src_embs_url, target_embs_url]:
    download(url, url.split("/")[-1])

src_embeds = torch.load(src_embs_url.split("/")[-1])
target_embeds = torch.load(target_embs_url.split("/")[-1])

images = pipeline(
    prompt,
    source_embeds=src_embeds,
    target_embeds=target_embeds,
    num_inference_steps=50,
    cross_attention_guidance_amount=0.15,
).images
images[0].save("edited_image_dog.png")

Based on an input image

When the pipeline is conditioned on an input image, we first obtain an inverted noise from it using a DDIMInverseScheduler with the help of a generated caption. Then the inverted noise is used to start the generation process.

First, let’s load our pipeline: 

import torch
from transformers import BlipForConditionalGeneration, BlipProcessor
from diffusers import DDIMScheduler, DDIMInverseScheduler, StableDiffusionPix2PixZeroPipeline

captioner_id = "Salesforce/blip-image-captioning-base"
processor = BlipProcessor.from_pretrained(captioner_id)
model = BlipForConditionalGeneration.from_pretrained(captioner_id, torch_dtype=torch.float16, low_cpu_mem_usage=True)

sd_model_ckpt = "CompVis/stable-diffusion-v1-4"
pipeline = StableDiffusionPix2PixZeroPipeline.from_pretrained(
    sd_model_ckpt,
    caption_generator=model,
    caption_processor=processor,
    torch_dtype=torch.float16,
    safety_checker=None,
)
pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
pipeline.inverse_scheduler = DDIMInverseScheduler.from_config(pipeline.scheduler.config)
pipeline.enable_model_cpu_offload()

Then, we load an input image for conditioning and obtain a suitable caption for it: 

import requests
from PIL import Image

img_url = "https://github.com/pix2pixzero/pix2pix-zero/raw/main/assets/test_images/cats/cat_6.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB").resize((512, 512))
caption = pipeline.generate_caption(raw_image)

Then we employ the generated caption and the input image to get the inverted noise: 

generator = torch.manual_seed(0)
inv_latents = pipeline.invert(caption, image=raw_image, generator=generator).latents

Now, generate the image with edit directions: 

# See the "Generating source and target embeddings" section below to
# automate the generation of these captions with a pre-trained model like Flan-T5 as explained below.
source_prompts = ["a cat sitting on the street", "a cat playing in the field", "a face of a cat"]
target_prompts = ["a dog sitting on the street", "a dog playing in the field", "a face of a dog"]

source_embeds = pipeline.get_embeds(source_prompts, batch_size=2)
target_embeds = pipeline.get_embeds(target_prompts, batch_size=2)


image = pipeline(
    caption,
    source_embeds=source_embeds,
    target_embeds=target_embeds,
    num_inference_steps=50,
    cross_attention_guidance_amount=0.15,
    generator=generator,
    latents=inv_latents,
    negative_prompt=caption,
).images[0]
image.save("edited_image.png")

Generating source and target embeddings

The authors originally used the GPT-3 API to generate the source and target captions for discovering edit directions. However, we can also leverage open source and public models for the same purpose. Below, we provide an end-to-end example with the Flan-T5 model for generating captions and CLIP for computing embeddings on the generated captions.

1. Load the generation model:

import torch
from transformers import AutoTokenizer, T5ForConditionalGeneration

tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-xl")
model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-xl", device_map="auto", torch_dtype=torch.float16)

2. Construct a starting prompt: 

source_concept = "cat"
target_concept = "dog"

source_text = f"Provide a caption for images containing a {source_concept}. "
"The captions should be in English and should be no longer than 150 characters."

target_text = f"Provide a caption for images containing a {target_concept}. "
"The captions should be in English and should be no longer than 150 characters."

Here, we’re interested in the “cat -> dog” direction.

3. Generate captions:

We can use a utility like so for this purpose. 

def generate_captions(input_prompt):
    input_ids = tokenizer(input_prompt, return_tensors="pt").input_ids.to("cuda")

    outputs = model.generate(
        input_ids, temperature=0.8, num_return_sequences=16, do_sample=True, max_new_tokens=128, top_k=10
    )
    return tokenizer.batch_decode(outputs, skip_special_tokens=True)

And then we just call it to generate our captions:

source_captions = generate_captions(source_text)
target_captions = generate_captions(target_concept)

We encourage you to play around with the different parameters supported by the generate() method (documentation) for the generation quality you are looking for.

4. Load the embedding model:

Here, we need to use the same text encoder model used by the subsequent Stable Diffusion model.

from diffusers import StableDiffusionPix2PixZeroPipeline 

pipeline = StableDiffusionPix2PixZeroPipeline.from_pretrained(
    "CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16
)
pipeline = pipeline.to("cuda")
tokenizer = pipeline.tokenizer
text_encoder = pipeline.text_encoder

5. Compute embeddings:

import torch 

def embed_captions(sentences, tokenizer, text_encoder, device="cuda"):
    with torch.no_grad():
        embeddings = []
        for sent in sentences:
            text_inputs = tokenizer(
                sent,
                padding="max_length",
                max_length=tokenizer.model_max_length,
                truncation=True,
                return_tensors="pt",
            )
            text_input_ids = text_inputs.input_ids
            prompt_embeds = text_encoder(text_input_ids.to(device), attention_mask=None)[0]
            embeddings.append(prompt_embeds)
    return torch.concatenate(embeddings, dim=0).mean(dim=0).unsqueeze(0)

source_embeddings = embed_captions(source_captions, tokenizer, text_encoder)
target_embeddings = embed_captions(target_captions, tokenizer, text_encoder)

And you’re done! Here is a Colab Notebook that you can use to interact with the entire process.

Now, you can use these embeddings directly while calling the pipeline: 

from diffusers import DDIMScheduler

pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)

images = pipeline(
    prompt,
    source_embeds=source_embeddings,
    target_embeds=target_embeddings,
    num_inference_steps=50,
    cross_attention_guidance_amount=0.15,
).images
images[0].save("edited_image_dog.png")

StableDiffusionPix2PixZeroPipeline

class diffusers.StableDiffusionPix2PixZeroPipeline

< >

( vae: AutoencoderKL text_encoder: CLIPTextModel tokenizer: CLIPTokenizer unet: UNet2DConditionModel scheduler: typing.Union[diffusers.schedulers.scheduling_ddpm.DDPMScheduler, diffusers.schedulers.scheduling_ddim.DDIMScheduler, diffusers.schedulers.scheduling_euler_ancestral_discrete.EulerAncestralDiscreteScheduler, diffusers.schedulers.scheduling_lms_discrete.LMSDiscreteScheduler] feature_extractor: CLIPImageProcessor safety_checker: StableDiffusionSafetyChecker inverse_scheduler: DDIMInverseScheduler caption_generator: BlipForConditionalGeneration caption_processor: BlipProcessor requires_safety_checker: bool = True )

Parameters

  • vae (AutoencoderKL) — Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
  • text_encoder (CLIPTextModel) — Frozen text-encoder. Stable Diffusion uses the text portion of CLIP, specifically the clip-vit-large-patch14 variant.
  • tokenizer (CLIPTokenizer) — Tokenizer of class CLIPTokenizer.
  • unet (UNet2DConditionModel) — Conditional U-Net architecture to denoise the encoded image latents.
  • scheduler (SchedulerMixin) — A scheduler to be used in combination with unet to denoise the encoded image latents. Can be one of DDIMScheduler, LMSDiscreteScheduler, EulerAncestralDiscreteScheduler, or DDPMScheduler.
  • safety_checker (StableDiffusionSafetyChecker) — Classification module that estimates whether generated images could be considered offensive or harmful. Please, refer to the model card for details.
  • feature_extractor (CLIPImageProcessor) — Model that extracts features from generated images to be used as inputs for the safety_checker.
  • requires_safety_checker (bool) — Whether the pipeline requires a safety checker. We recommend setting it to True if you’re using the pipeline publicly.

Pipeline for pixel-levl image editing using Pix2Pix Zero. Based on Stable Diffusion.

This model inherits from DiffusionPipeline. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)

__call__

< >

( prompt: typing.Union[str, typing.List[str], NoneType] = None source_embeds: Tensor = None target_embeds: Tensor = None height: typing.Optional[int] = None width: typing.Optional[int] = None num_inference_steps: int = 50 guidance_scale: float = 7.5 negative_prompt: typing.Union[str, typing.List[str], NoneType] = None num_images_per_prompt: typing.Optional[int] = 1 eta: float = 0.0 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 negative_prompt_embeds: typing.Optional[torch.FloatTensor] = None cross_attention_guidance_amount: float = 0.1 output_type: typing.Optional[str] = 'pil' return_dict: bool = True callback: typing.Union[typing.Callable[[int, int, torch.FloatTensor], NoneType], NoneType] = None callback_steps: typing.Optional[int] = 1 cross_attention_kwargs: typing.Union[typing.Dict[str, typing.Any], NoneType] = None ) → StableDiffusionPipelineOutput or tuple

Parameters

  • 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.
  • source_embeds (torch.Tensor) — Source concept embeddings. Generation of the embeddings as per the original paper. Used in discovering the edit direction.
  • target_embeds (torch.Tensor) — Target concept embeddings. Generation of the embeddings as per the original paper. Used in discovering the edit direction.
  • height (int, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) — The height in pixels of the generated image.
  • width (int, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) — The width in pixels of the generated 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.
  • guidance_scale (float, optional, defaults to 7.5) — 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.
  • 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 guidance_scale is less than 1).
  • num_images_per_prompt (int, optional, defaults to 1) — The number of images to generate per prompt.
  • eta (float, optional, defaults to 0.0) — Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to schedulers.DDIMScheduler, will be ignored for others.
  • generator (torch.Generator or List[torch.Generator], optional) — One or a list of torch generator(s) to make generation deterministic.
  • latents (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.
  • prompt_embeds (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.
  • negative_prompt_embeds (torch.FloatTensor, 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.
  • cross_attention_guidance_amount (float, defaults to 0.1) — Amount of guidance needed from the reference cross-attention maps.
  • 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 StableDiffusionPipelineOutput instead of a plain tuple.
  • callback (Callable, optional) — A function that will be called every callback_steps steps during inference. The function will be called with the following arguments: callback(step: int, timestep: int, latents: torch.FloatTensor).
  • callback_steps (int, optional, defaults to 1) — The frequency at which the callback function will be called. If not specified, the callback will be called at every step.

Returns

StableDiffusionPipelineOutput or tuple

StableDiffusionPipelineOutput if return_dict is True, otherwise a tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of bools denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the safety_checker`.

Function invoked when calling the pipeline for generation.

Examples:

>>> import requests
>>> import torch

>>> from diffusers import DDIMScheduler, StableDiffusionPix2PixZeroPipeline


>>> def download(embedding_url, local_filepath):
...     r = requests.get(embedding_url)
...     with open(local_filepath, "wb") as f:
...         f.write(r.content)


>>> model_ckpt = "CompVis/stable-diffusion-v1-4"
>>> pipeline = StableDiffusionPix2PixZeroPipeline.from_pretrained(model_ckpt, torch_dtype=torch.float16)
>>> pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
>>> pipeline.to("cuda")

>>> prompt = "a high resolution painting of a cat in the style of van gough"
>>> source_emb_url = "https://hf.co/datasets/sayakpaul/sample-datasets/resolve/main/cat.pt"
>>> target_emb_url = "https://hf.co/datasets/sayakpaul/sample-datasets/resolve/main/dog.pt"

>>> for url in [source_emb_url, target_emb_url]:
...     download(url, url.split("/")[-1])

>>> src_embeds = torch.load(source_emb_url.split("/")[-1])
>>> target_embeds = torch.load(target_emb_url.split("/")[-1])
>>> images = pipeline(
...     prompt,
...     source_embeds=src_embeds,
...     target_embeds=target_embeds,
...     num_inference_steps=50,
...     cross_attention_guidance_amount=0.15,
... ).images

>>> images[0].save("edited_image_dog.png")

construct_direction

< >

( embs_source: Tensor embs_target: Tensor )

Constructs the edit direction to steer the image generation process semantically.

enable_model_cpu_offload

< >

( gpu_id = 0 )

Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared to enable_sequential_cpu_offload, this method moves one whole model at a time to the GPU when its forward method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with enable_sequential_cpu_offload, but performance is much better due to the iterative execution of the unet.

enable_sequential_cpu_offload

< >

( gpu_id = 0 )

Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet, text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a torch.device('meta') and loaded to GPU only when their specific submodule has its forwardmethod called. Note that offloading happens on a submodule basis. Memory savings are higher than withenable_model_cpu_offload`, but performance is lower.

generate_caption

< >

( images )

Generates caption for a given image.

invert

< >

( prompt: typing.Optional[str] = None image: typing.Union[torch.FloatTensor, PIL.Image.Image, numpy.ndarray, typing.List[torch.FloatTensor], typing.List[PIL.Image.Image], typing.List[numpy.ndarray]] = None num_inference_steps: int = 50 guidance_scale: float = 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 cross_attention_guidance_amount: float = 0.1 output_type: typing.Optional[str] = 'pil' return_dict: bool = True callback: typing.Union[typing.Callable[[int, int, torch.FloatTensor], NoneType], NoneType] = None callback_steps: typing.Optional[int] = 1 cross_attention_kwargs: typing.Union[typing.Dict[str, typing.Any], NoneType] = None lambda_auto_corr: float = 20.0 lambda_kl: float = 20.0 num_reg_steps: int = 5 num_auto_corr_rolls: int = 5 )

Parameters

  • 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.
  • image (torch.FloatTensor np.ndarray, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image], or List[np.ndarray]) — Image, or tensor representing an image batch which will be used for conditioning. Can also accpet image latents as image, if passing latents directly, it will not be encoded again.
  • 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.
  • guidance_scale (float, optional, defaults to 1) — 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.
  • generator (torch.Generator or List[torch.Generator], optional) — One or a list of torch generator(s) to make generation deterministic.
  • latents (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.
  • prompt_embeds (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.
  • cross_attention_guidance_amount (float, defaults to 0.1) — Amount of guidance needed from the reference cross-attention maps.
  • 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 StableDiffusionPipelineOutput instead of a plain tuple.
  • callback (Callable, optional) — A function that will be called every callback_steps steps during inference. The function will be called with the following arguments: callback(step: int, timestep: int, latents: torch.FloatTensor).
  • callback_steps (int, optional, defaults to 1) — The frequency at which the callback function will be called. If not specified, the callback will be called at every step.
  • lambda_auto_corr (float, optional, defaults to 20.0) — Lambda parameter to control auto correction
  • lambda_kl (float, optional, defaults to 20.0) — Lambda parameter to control Kullback–Leibler divergence output
  • num_reg_steps (int, optional, defaults to 5) — Number of regularization loss steps
  • num_auto_corr_rolls (int, optional, defaults to 5) — Number of auto correction roll steps

Function used to generate inverted latents given a prompt and image.

Examples:

>>> import torch
>>> from transformers import BlipForConditionalGeneration, BlipProcessor
>>> from diffusers import DDIMScheduler, DDIMInverseScheduler, StableDiffusionPix2PixZeroPipeline

>>> import requests
>>> from PIL import Image

>>> captioner_id = "Salesforce/blip-image-captioning-base"
>>> processor = BlipProcessor.from_pretrained(captioner_id)
>>> model = BlipForConditionalGeneration.from_pretrained(
...     captioner_id, torch_dtype=torch.float16, low_cpu_mem_usage=True
... )

>>> sd_model_ckpt = "CompVis/stable-diffusion-v1-4"
>>> pipeline = StableDiffusionPix2PixZeroPipeline.from_pretrained(
...     sd_model_ckpt,
...     caption_generator=model,
...     caption_processor=processor,
...     torch_dtype=torch.float16,
...     safety_checker=None,
... )

>>> pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
>>> pipeline.inverse_scheduler = DDIMInverseScheduler.from_config(pipeline.scheduler.config)
>>> pipeline.enable_model_cpu_offload()

>>> img_url = "https://github.com/pix2pixzero/pix2pix-zero/raw/main/assets/test_images/cats/cat_6.png"

>>> raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB").resize((512, 512))
>>> # generate caption
>>> caption = pipeline.generate_caption(raw_image)

>>> # "a photography of a cat with flowers and dai dai daie - daie - daie kasaii"
>>> inv_latents = pipeline.invert(caption, image=raw_image).latents
>>> # we need to generate source and target embeds

>>> source_prompts = ["a cat sitting on the street", "a cat playing in the field", "a face of a cat"]

>>> target_prompts = ["a dog sitting on the street", "a dog playing in the field", "a face of a dog"]

>>> source_embeds = pipeline.get_embeds(source_prompts)
>>> target_embeds = pipeline.get_embeds(target_prompts)
>>> # the latents can then be used to edit a real image
>>> # when using Stable Diffusion 2 or other models that use v-prediction
>>> # set `cross_attention_guidance_amount` to 0.01 or less to avoid input latent gradient explosion

>>> image = pipeline(
...     caption,
...     source_embeds=source_embeds,
...     target_embeds=target_embeds,
...     num_inference_steps=50,
...     cross_attention_guidance_amount=0.15,
...     generator=generator,
...     latents=inv_latents,
...     negative_prompt=caption,
... ).images[0]
>>> image.save("edited_image.png")
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