# What does this PR do?

This updates the top-level readme files say the project has been tested with Python 3.8+ (instead of 3.7+) and PyTorch 1.10+ (instead of 1.9+). Versions prior to those are no longer supported as of [v4.31.0](https://github.com/huggingface/transformers/releases/tag/v4.31.0). It also updates some other versions in those lists, that had become out of date earlier. The non-English readme files were less up to date than the English readme file `README.md`.

I allowed my editor to remove trailing whitespace, since it does not appear to have been intentional. Rendered Markdown does not appear changed. After editing all seven READMEs, running `make fix-copies` (required to pass CI) propagated this whitespace removal to (just) one of the `index.md` files, which is why that is also changed. However, I understand if whitespace removal may be viewed as best done separately; if requested, I'd be pleased to modify this PR to retain the trailing whitespace.

## Rationale

My reasoning is similar as the reasoning that was given for the previous update to these versions in #24307. As of [**v4.31.0**](https://github.com/huggingface/transformers/releases/tag/v4.31.0), 🤗 Transformers has dropped support for Python 3.7 (#24091) and for PyTorch 1.9 (#24080). Because of that:

- If the version ranges noted in the readme files are not changed, some users are likely to be mislead into expecting new releases of 🤗 Transformers to support those versions.
- A lesser issue is that the claim that this repository is tested on all those versions will gradually become inaccurate as further contributions are made to the repository, now that those versions are not supported.

## Before submitting

- [x] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case.

<!-- I'm sure who, if anyone, I should ping for this. I may edit in a ping later.

## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

Your PR will be replied to more quickly if you can figure out the right person to tag with @

 If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
 Please tag fewer than 3 people.

Models:

- text models: @ArthurZucker and @younesbelkada
- vision models: @amyeroberts
- speech models: @sanchit-gandhi
- graph models: @clefourrier

Library:

- flax: @sanchit-gandhi
- generate: @gante
- pipelines: @Narsil
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @sgugger

Integrations:

- deepspeed: HF Trainer/Accelerate: @pacman100
- ray/raytune: @richardliaw, @amogkam

Documentation: @sgugger, @stevhliu and @MKhalusova

HF projects:

- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Maintained examples (not research project or legacy):

- Flax: @sanchit-gandhi
- PyTorch: @sgugger
- TensorFlow: @Rocketknight1

 -->

Update tested versions in READMEs. # What does this PR do?

This updates the top-level readme files say the project has been tested with Python 3.8+ (instead of 3.7+) and PyTorch 1.10+ (instead of 1.9+). Versions prior to those are no longer supported as of [v4.31.0](https://github.com/huggingface/transformers/releases/tag/v4.31.0). It also updates some other versions in those lists, that had become out of date earlier. The non-English readme files were less up to date than the English readme file `README.md`.

I allowed my editor to remove trailing whitespace, since it does not appear to have been intentional. Rendered Markdown does not appear changed. After editing all seven READMEs, running `make fix-copies` (required to pass CI) propagated this whitespace removal to (just) one of the `index.md` files, which is why that is also changed. However, I understand if whitespace removal may be viewed as best done separately; if requested, I'd be pleased to modify this PR to retain the trailing whitespace.

## Rationale

My reasoning is similar as the reasoning that was given for the previous update to these versions in #24307. As of [**v4.31.0**](https://github.com/huggingface/transformers/releases/tag/v4.31.0), 🤗 Transformers has dropped support for Python 3.7 (#24091) and for PyTorch 1.9 (#24080). Because of that:

- If the version ranges noted in the readme files are not changed, some users are likely to be mislead into expecting new releases of 🤗 Transformers to support those versions.
- A lesser issue is that the claim that this repository is tested on all those versions will gradually become inaccurate as further contributions are made to the repository, now that those versions are not supported.

## Before submitting

- [x] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case.

<!-- I'm sure who, if anyone, I should ping for this. I may edit in a ping later.

## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

Your PR will be replied to more quickly if you can figure out the right person to tag with @

 If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
 Please tag fewer than 3 people.

Models:

- text models: @ArthurZucker and @younesbelkada
- vision models: @amyeroberts
- speech models: @sanchit-gandhi
- graph models: @clefourrier

Library:

- flax: @sanchit-gandhi
- generate: @gante
- pipelines: @Narsil
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @sgugger

Integrations:

- deepspeed: HF Trainer/Accelerate: @pacman100
- ray/raytune: @richardliaw, @amogkam

Documentation: @sgugger, @stevhliu and @MKhalusova

HF projects:

- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Maintained examples (not research project or legacy):

- Flax: @sanchit-gandhi
- PyTorch: @sgugger
- TensorFlow: @Rocketknight1

 -->

import os
from glob import glob

import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn

from transformers import CLIPModel, CLIPTokenizerFast
from utils import get_device, get_timestamp, show_pil


class ProcessorGradientFlow:
    """
    This wraps the huggingface CLIP processor to allow backprop through the image processing step.
    The original processor forces conversion to PIL images, which is faster for image processing but breaks gradient flow.
    We call the original processor to get the text embeddings, but use our own image processing to keep images as torch tensors.
    """

    def __init__(self, device: str = "cpu", clip_model: str = "openai/clip-vit-large-patch14") -> None:
        self.device = device
        self.tokenizer = CLIPTokenizerFast.from_pretrained(clip_model)
        self.image_mean = [0.48145466, 0.4578275, 0.40821073]
        self.image_std = [0.26862954, 0.26130258, 0.27577711]
        self.normalize = torchvision.transforms.Normalize(self.image_mean, self.image_std)
        self.resize = torchvision.transforms.Resize(224)
        self.center_crop = torchvision.transforms.CenterCrop(224)

    def preprocess_img(self, images):
        images = self.resize(images)
        images = self.center_crop(images)
        images = self.normalize(images)
        return images

    def __call__(self, text=None, images=None, **kwargs):
        encoding = self.tokenizer(text=text, **kwargs)
        encoding["pixel_values"] = self.preprocess_img(images)
        encoding = {key: value.to(self.device) for (key, value) in encoding.items()}
        return encoding


class VQGAN_CLIP(nn.Module):
    def __init__(
        self,
        iterations=10,
        lr=0.01,
        vqgan=None,
        vqgan_config=None,
        vqgan_checkpoint=None,
        clip=None,
        clip_preprocessor=None,
        device=None,
        log=False,
        save_vector=True,
        return_val="image",
        quantize=True,
        save_intermediate=False,
        show_intermediate=False,
        make_grid=False,
    ) -> None:
        """
        Instantiate a VQGAN_CLIP model. If you want to use a custom VQGAN model, pass it as vqgan.
        """
        super().__init__()
        self.latent = None
        self.device = device if device else get_device()
        if vqgan:
            self.vqgan = vqgan
        else:
            self.vqgan = load_vqgan(self.device, conf_path=vqgan_config, ckpt_path=vqgan_checkpoint)
        self.vqgan.eval()
        if clip:
            self.clip = clip
        else:
            self.clip = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
        self.clip.to(self.device)
        self.clip_preprocessor = ProcessorGradientFlow(device=self.device)

        self.iterations = iterations
        self.lr = lr
        self.log = log
        self.make_grid = make_grid
        self.return_val = return_val
        self.quantize = quantize
        self.latent_dim = self.vqgan.decoder.z_shape

    def make_animation(self, input_path=None, output_path=None, total_duration=5, extend_frames=True):
        """
        Make an animation from the intermediate images saved during generation.
        By default, uses the images from the most recent generation created by the generate function.
        If you want to use images from a different generation, pass the path to the folder containing the images as input_path.
        """
        images = []
        if output_path is None:
            output_path = "./animation.gif"
        if input_path is None:
            input_path = self.save_path
        paths = sorted(glob(input_path + "/*"))
        if not len(paths):
            raise ValueError(
                "No images found in save path, aborting (did you pass save_intermediate=True to the generate"
                " function?)"
            )
        if len(paths) == 1:
            print("Only one image found in save path, (did you pass save_intermediate=True to the generate function?)")
        frame_duration = total_duration / len(paths)
        durations = [frame_duration] * len(paths)
        if extend_frames:
            durations[0] = 1.5
            durations[-1] = 3
        for file_name in paths:
            if file_name.endswith(".png"):
                images.append(imageio.imread(file_name))
        imageio.mimsave(output_path, images, duration=durations)
        print(f"gif saved to {output_path}")

    def _get_latent(self, path=None, img=None):
        if not (path or img):
            raise ValueError("Input either path or tensor")
        if img is not None:
            raise NotImplementedError
        x = preprocess(Image.open(path), target_image_size=256).to(self.device)
        x_processed = preprocess_vqgan(x)
        z, *_ = self.vqgan.encode(x_processed)
        return z

    def _add_vector(self, transform_vector):
        """Add a vector transform to the base latent and returns the resulting image."""
        base_latent = self.latent.detach().requires_grad_()
        trans_latent = base_latent + transform_vector
        if self.quantize:
            z_q, *_ = self.vqgan.quantize(trans_latent)
        else:
            z_q = trans_latent
        return self.vqgan.decode(z_q)

    def _get_clip_similarity(self, prompts, image, weights=None):
        clip_inputs = self.clip_preprocessor(text=prompts, images=image, return_tensors="pt", padding=True)
        clip_outputs = self.clip(**clip_inputs)
        similarity_logits = clip_outputs.logits_per_image
        if weights is not None:
            similarity_logits = similarity_logits * weights
        return similarity_logits.sum()

    def _get_clip_loss(self, pos_prompts, neg_prompts, image):
        pos_logits = self._get_clip_similarity(pos_prompts["prompts"], image, weights=(1 / pos_prompts["weights"]))
        if neg_prompts:
            neg_logits = self._get_clip_similarity(neg_prompts["prompts"], image, weights=neg_prompts["weights"])
        else:
            neg_logits = torch.tensor([1], device=self.device)
        loss = -torch.log(pos_logits) + torch.log(neg_logits)
        return loss

    def _optimize_CLIP(self, original_img, pos_prompts, neg_prompts):
        vector = torch.randn_like(self.latent, requires_grad=True, device=self.device)
        optim = torch.optim.Adam([vector], lr=self.lr)

        for i in range(self.iterations):
            optim.zero_grad()
            transformed_img = self._add_vector(vector)
            processed_img = loop_post_process(transformed_img)
            clip_loss = self._get_CLIP_loss(pos_prompts, neg_prompts, processed_img)
            print("CLIP loss", clip_loss)
            if self.log:
                wandb.log({"CLIP Loss": clip_loss})
            clip_loss.backward(retain_graph=True)
            optim.step()
            if self.return_val == "image":
                yield custom_to_pil(transformed_img[0])
            else:
                yield vector

    def _init_logging(self, positive_prompts, negative_prompts, image_path):
        wandb.init(reinit=True, project="face-editor")
        wandb.config.update({"Positive Prompts": positive_prompts})
        wandb.config.update({"Negative Prompts": negative_prompts})
        wandb.config.update({"lr": self.lr, "iterations": self.iterations})
        if image_path:
            image = Image.open(image_path)
            image = image.resize((256, 256))
            wandb.log("Original Image", wandb.Image(image))

    def process_prompts(self, prompts):
        if not prompts:
            return []
        processed_prompts = []
        weights = []
        if isinstance(prompts, str):
            prompts = [prompt.strip() for prompt in prompts.split("|")]
        for prompt in prompts:
            if isinstance(prompt, (tuple, list)):
                processed_prompt = prompt[0]
                weight = float(prompt[1])
            elif ":" in prompt:
                processed_prompt, weight = prompt.split(":")
                weight = float(weight)
            else:
                processed_prompt = prompt
                weight = 1.0
            processed_prompts.append(processed_prompt)
            weights.append(weight)
        return {
            "prompts": processed_prompts,
            "weights": torch.tensor(weights, device=self.device),
        }

    def generate(
        self,
        pos_prompts,
        neg_prompts=None,
        image_path=None,
        show_intermediate=True,
        save_intermediate=False,
        show_final=True,
        save_final=True,
        save_path=None,
    ):
        """Generate an image from the given prompts.
        If image_path is provided, the image is used as a starting point for the optimization.
        If image_path is not provided, a random latent vector is used as a starting point.
        You must provide at least one positive prompt, and optionally provide negative prompts.
        Prompts must be formatted in one of the following ways:
        - A single prompt as a string, e.g "A smiling woman"
        - A set of prompts separated by pipes: "A smiling woman | a woman with brown hair"
        - A set of prompts and their weights separated by colons: "A smiling woman:1 | a woman with brown hair: 3" (default weight is 1)
        - A list of prompts, e.g ["A smiling woman", "a woman with brown hair"]
        - A list of prompts and weights, e.g [("A smiling woman", 1), ("a woman with brown hair", 3)]
        """
        if image_path:
            self.latent = self._get_latent(image_path)
        else:
            self.latent = torch.randn(self.latent_dim, device=self.device)
        if self.log:
            self._init_logging(pos_prompts, neg_prompts, image_path)

        assert pos_prompts, "You must provide at least one positive prompt."
        pos_prompts = self.process_prompts(pos_prompts)
        neg_prompts = self.process_prompts(neg_prompts)
        if save_final and save_path is None:
            save_path = os.path.join("./outputs/", "_".join(pos_prompts["prompts"]))
        if not os.path.exists(save_path):
            os.makedirs(save_path)
        else:
            save_path = save_path + "_" + get_timestamp()
            os.makedirs(save_path)
        self.save_path = save_path

        original_img = self.vqgan.decode(self.latent)[0]
        if show_intermediate:
            print("Original Image")
            show_pil(custom_to_pil(original_img))

        original_img = loop_post_process(original_img)
        for iter, transformed_img in enumerate(self._optimize_CLIP(original_img, pos_prompts, neg_prompts)):
            if show_intermediate:
                show_pil(transformed_img)
            if save_intermediate:
                transformed_img.save(os.path.join(self.save_path, f"iter_{iter:03d}.png"))
            if self.log:
                wandb.log({"Image": wandb.Image(transformed_img)})
        if show_final:
            show_pil(transformed_img)
        if save_final:
            transformed_img.save(os.path.join(self.save_path, f"iter_{iter:03d}_final.png"))

import os
from glob import glob

import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn

from transformers import CLIPModel, CLIPTokenizerFast
from utils import get_device, get_timestamp, show_pil


class ProcessorGradientFlow:
    """
    This wraps the huggingface CLIP processor to allow backprop through the image processing step.
    The original processor forces conversion to PIL images, which is faster for image processing but breaks gradient flow.
    We call the original processor to get the text embeddings, but use our own image processing to keep images as torch tensors.
    """

    def __init__(self, device: str = "cpu", clip_model: str = "openai/clip-vit-large-patch14") -> None:
        self.device = device
        self.tokenizer = CLIPTokenizerFast.from_pretrained(clip_model)
        self.image_mean = [0.48145466, 0.4578275, 0.40821073]
        self.image_std = [0.26862954, 0.26130258, 0.27577711]
        self.normalize = torchvision.transforms.Normalize(self.image_mean, self.image_std)
        self.resize = torchvision.transforms.Resize(224)
        self.center_crop = torchvision.transforms.CenterCrop(224)

    def preprocess_img(self, images):
        images = self.resize(images)
        images = self.center_crop(images)
        images = self.normalize(images)
        return images

    def __call__(self, text=None, images=None, **kwargs):
        encoding = self.tokenizer(text=text, **kwargs)
        encoding["pixel_values"] = self.preprocess_img(images)
        encoding = {key: value.to(self.device) for (key, value) in encoding.items()}
        return encoding


class VQGAN_CLIP(nn.Module):
    def __init__(
        self,
        iterations=10,
        lr=0.01,
        vqgan=None,
        vqgan_config=None,
        vqgan_checkpoint=None,
        clip=None,
        clip_preprocessor=None,
        device=None,
        log=False,
        save_vector=True,
        return_val="image",
        quantize=True,
        save_intermediate=False,
        show_intermediate=False,
        make_grid=False,
    ) -> None:
        """
        Instantiate a VQGAN_CLIP model. If you want to use a custom VQGAN model, pass it as vqgan.
        """
        super().__init__()
        self.latent = None
        self.device = device if device else get_device()
        if vqgan:
            self.vqgan = vqgan
        else:
            self.vqgan = load_vqgan(self.device, conf_path=vqgan_config, ckpt_path=vqgan_checkpoint)
        self.vqgan.eval()
        if clip:
            self.clip = clip
        else:
            self.clip = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
        self.clip.to(self.device)
        self.clip_preprocessor = ProcessorGradientFlow(device=self.device)

        self.iterations = iterations
        self.lr = lr
        self.log = log
        self.make_grid = make_grid
        self.return_val = return_val
        self.quantize = quantize
        self.latent_dim = self.vqgan.decoder.z_shape

    def make_animation(self, input_path=None, output_path=None, total_duration=5, extend_frames=True):
        """
        Make an animation from the intermediate images saved during generation.
        By default, uses the images from the most recent generation created by the generate function.
        If you want to use images from a different generation, pass the path to the folder containing the images as input_path.
        """
        images = []
        if output_path is None:
            output_path = "./animation.gif"
        if input_path is None:
            input_path = self.save_path
        paths = sorted(glob(input_path + "/*"))
        if not len(paths):
            raise ValueError(
                "No images found in save path, aborting (did you pass save_intermediate=True to the generate"
                " function?)"
            )
        if len(paths) == 1:
            print("Only one image found in save path, (did you pass save_intermediate=True to the generate function?)")
        frame_duration = total_duration / len(paths)
        durations = [frame_duration] * len(paths)
        if extend_frames:
            durations[0] = 1.5
            durations[-1] = 3
        for file_name in paths:
            if file_name.endswith(".png"):
                images.append(imageio.imread(file_name))
        imageio.mimsave(output_path, images, duration=durations)
        print(f"gif saved to {output_path}")

    def _get_latent(self, path=None, img=None):
        if not (path or img):
            raise ValueError("Input either path or tensor")
        if img is not None:
            raise NotImplementedError
        x = preprocess(Image.open(path), target_image_size=256).to(self.device)
        x_processed = preprocess_vqgan(x)
        z, *_ = self.vqgan.encode(x_processed)
        return z

    def _add_vector(self, transform_vector):
        """Add a vector transform to the base latent and returns the resulting image."""
        base_latent = self.latent.detach().requires_grad_()
        trans_latent = base_latent + transform_vector
        if self.quantize:
            z_q, *_ = self.vqgan.quantize(trans_latent)
        else:
            z_q = trans_latent
        return self.vqgan.decode(z_q)

    def _get_clip_similarity(self, prompts, image, weights=None):
        clip_inputs = self.clip_preprocessor(text=prompts, images=image, return_tensors="pt", padding=True)
        clip_outputs = self.clip(**clip_inputs)
        similarity_logits = clip_outputs.logits_per_image
        if weights is not None:
            similarity_logits = similarity_logits * weights
        return similarity_logits.sum()

    def _get_clip_loss(self, pos_prompts, neg_prompts, image):
        pos_logits = self._get_clip_similarity(pos_prompts["prompts"], image, weights=(1 / pos_prompts["weights"]))
        if neg_prompts:
            neg_logits = self._get_clip_similarity(neg_prompts["prompts"], image, weights=neg_prompts["weights"])
        else:
            neg_logits = torch.tensor([1], device=self.device)
        loss = -torch.log(pos_logits) + torch.log(neg_logits)
        return loss

    def _optimize_CLIP(self, original_img, pos_prompts, neg_prompts):
        vector = torch.randn_like(self.latent, requires_grad=True, device=self.device)
        optim = torch.optim.Adam([vector], lr=self.lr)

        for i in range(self.iterations):
            optim.zero_grad()
            transformed_img = self._add_vector(vector)
            processed_img = loop_post_process(transformed_img)
            clip_loss = self._get_CLIP_loss(pos_prompts, neg_prompts, processed_img)
            print("CLIP loss", clip_loss)
            if self.log:
                wandb.log({"CLIP Loss": clip_loss})
            clip_loss.backward(retain_graph=True)
            optim.step()
            if self.return_val == "image":
                yield custom_to_pil(transformed_img[0])
            else:
                yield vector

    def _init_logging(self, positive_prompts, negative_prompts, image_path):
        wandb.init(reinit=True, project="face-editor")
        wandb.config.update({"Positive Prompts": positive_prompts})
        wandb.config.update({"Negative Prompts": negative_prompts})
        wandb.config.update({"lr": self.lr, "iterations": self.iterations})
        if image_path:
            image = Image.open(image_path)
            image = image.resize((256, 256))
            wandb.log("Original Image", wandb.Image(image))

    def process_prompts(self, prompts):
        if not prompts:
            return []
        processed_prompts = []
        weights = []
        if isinstance(prompts, str):
            prompts = [prompt.strip() for prompt in prompts.split("|")]
        for prompt in prompts:
            if isinstance(prompt, (tuple, list)):
                processed_prompt = prompt[0]
                weight = float(prompt[1])
            elif ":" in prompt:
                processed_prompt, weight = prompt.split(":")
                weight = float(weight)
            else:
                processed_prompt = prompt
                weight = 1.0
            processed_prompts.append(processed_prompt)
            weights.append(weight)
        return {
            "prompts": processed_prompts,
            "weights": torch.tensor(weights, device=self.device),
        }

    def generate(
        self,
        pos_prompts,
        neg_prompts=None,
        image_path=None,
        show_intermediate=True,
        save_intermediate=False,
        show_final=True,
        save_final=True,
        save_path=None,
    ):
        """Generate an image from the given prompts.
        If image_path is provided, the image is used as a starting point for the optimization.
        If image_path is not provided, a random latent vector is used as a starting point.
        You must provide at least one positive prompt, and optionally provide negative prompts.
        Prompts must be formatted in one of the following ways:
        - A single prompt as a string, e.g "A smiling woman"
        - A set of prompts separated by pipes: "A smiling woman | a woman with brown hair"
        - A set of prompts and their weights separated by colons: "A smiling woman:1 | a woman with brown hair: 3" (default weight is 1)
        - A list of prompts, e.g ["A smiling woman", "a woman with brown hair"]
        - A list of prompts and weights, e.g [("A smiling woman", 1), ("a woman with brown hair", 3)]
        """
        if image_path:
            self.latent = self._get_latent(image_path)
        else:
            self.latent = torch.randn(self.latent_dim, device=self.device)
        if self.log:
            self._init_logging(pos_prompts, neg_prompts, image_path)

        assert pos_prompts, "You must provide at least one positive prompt."
        pos_prompts = self.process_prompts(pos_prompts)
        neg_prompts = self.process_prompts(neg_prompts)
        if save_final and save_path is None:
            save_path = os.path.join("./outputs/", "_".join(pos_prompts["prompts"]))
        if not os.path.exists(save_path):
            os.makedirs(save_path)
        else:
            save_path = save_path + "_" + get_timestamp()
            os.makedirs(save_path)
        self.save_path = save_path

        original_img = self.vqgan.decode(self.latent)[0]
        if show_intermediate:
            print("Original Image")
            show_pil(custom_to_pil(original_img))

        original_img = loop_post_process(original_img)
        for iter, transformed_img in enumerate(self._optimize_CLIP(original_img, pos_prompts, neg_prompts)):
            if show_intermediate:
                show_pil(transformed_img)
            if save_intermediate:
                transformed_img.save(os.path.join(self.save_path, f"iter_{iter:03d}.png"))
            if self.log:
                wandb.log({"Image": wandb.Image(transformed_img)})
        if show_final:
            show_pil(transformed_img)
        if save_final:
            transformed_img.save(os.path.join(self.save_path, f"iter_{iter:03d}_final.png"))
