[Community] CLIP Guided Images Mixing with Stable DIffusion Pipeline (#3587)

* added clip_guided_images_mixing_stable_diffusion file and readme description

* apply pre-commit

---------
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

examples/community/README.md

@@ -36,6 +36,7 @@ If a community doesn't work as expected, please open an issue and ping the autho
 | Stable Diffusion RePaint                                                                                                              | Stable Diffusion pipeline using [RePaint](https://arxiv.org/abs/2201.0986) for inpainting.                                                                                                                                                                                                                                                                                                                                                                                                               | [Stable Diffusion RePaint](#stable-diffusion-repaint )                                    | - |                  [Markus Pobitzer](https://github.com/Markus-Pobitzer) | 
 | TensorRT Stable Diffusion Image to Image Pipeline                                                                                                    | Accelerates the Stable Diffusion Image2Image Pipeline using TensorRT                                                                                                                                                                                                                                                                                                                                                                                                                                      | [TensorRT Stable Diffusion Image to Image Pipeline](#tensorrt-image2image-stable-diffusion-pipeline)      | - |              [Asfiya Baig](https://github.com/asfiyab-nvidia) |
 | Stable Diffusion IPEX Pipeline | Accelerate Stable Diffusion inference pipeline with BF16/FP32 precision on Intel Xeon CPUs with [IPEX](https://github.com/intel/intel-extension-for-pytorch) | [Stable Diffusion on IPEX](#stable-diffusion-on-ipex) | - | [Yingjie Han](https://github.com/yingjie-han/) | 
+| CLIP Guided Images Mixing Stable Diffusion Pipeline | Сombine images using usual diffusion models. | [CLIP Guided Images Mixing Using Stable Diffusion](#clip-guided-images-mixing-with-stable-diffusion) | - | [Karachev Denis](https://github.com/TheDenk) |  
 
 To load a custom pipeline you just need to pass the `custom_pipeline` argument to `DiffusionPipeline`, as one of the files in `diffusers/examples/community`. Feel free to send a PR with your own pipelines, we will merge them quickly.
 ```py
@@ -1516,6 +1517,89 @@ print("Latency of StableDiffusionPipeline--fp32",latency)
 
 ```
   
+### CLIP Guided Images Mixing With Stable Diffusion
+
+![clip_guided_images_mixing_examples](https://huggingface.co/datasets/TheDenk/images_mixing/resolve/main/main.png)
+
+CLIP guided stable diffusion images mixing pipline allows to combine two images using standard diffusion models.  
+This approach is using (optional) CoCa model to avoid writing image description.  
+[More code examples](https://github.com/TheDenk/images_mixing)
+
+## Example Images Mixing (with CoCa)
+```python
+import requests
+from io import BytesIO
+
+import PIL
+import torch
+import open_clip
+from open_clip import SimpleTokenizer
+from diffusers import DiffusionPipeline
+from transformers import CLIPFeatureExtractor, CLIPModel
+
+
+def download_image(url):
+    response = requests.get(url)
+    return PIL.Image.open(BytesIO(response.content)).convert("RGB")
+
+# Loading additional models
+feature_extractor = CLIPFeatureExtractor.from_pretrained(
+    "laion/CLIP-ViT-B-32-laion2B-s34B-b79K"
+)
+clip_model = CLIPModel.from_pretrained(
+    "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", torch_dtype=torch.float16
+)
+coca_model = open_clip.create_model('coca_ViT-L-14', pretrained='laion2B-s13B-b90k').to('cuda')
+coca_model.dtype = torch.float16
+coca_transform = open_clip.image_transform(
+    coca_model.visual.image_size,
+    is_train = False,
+    mean = getattr(coca_model.visual, 'image_mean', None),
+    std = getattr(coca_model.visual, 'image_std', None),
+)
+coca_tokenizer = SimpleTokenizer()
+
+# Pipline creating
+mixing_pipeline = DiffusionPipeline.from_pretrained(
+    "CompVis/stable-diffusion-v1-4",
+    custom_pipeline="clip_guided_images_mixing_stable_diffusion",
+    clip_model=clip_model,
+    feature_extractor=feature_extractor,
+    coca_model=coca_model,
+    coca_tokenizer=coca_tokenizer,
+    coca_transform=coca_transform,
+    torch_dtype=torch.float16,
+)
+mixing_pipeline.enable_attention_slicing()
+mixing_pipeline = mixing_pipeline.to("cuda")
+
+# Pipline running
+generator = torch.Generator(device="cuda").manual_seed(17) 
+
+def download_image(url):
+    response = requests.get(url)
+    return PIL.Image.open(BytesIO(response.content)).convert("RGB")
+
+content_image = download_image("https://huggingface.co/datasets/TheDenk/images_mixing/resolve/main/boromir.jpg")
+style_image = download_image("https://huggingface.co/datasets/TheDenk/images_mixing/resolve/main/gigachad.jpg")
+
+pipe_images = mixing_pipeline(
+    num_inference_steps=50,
+    content_image=content_image,
+    style_image=style_image,
+    noise_strength=0.65,
+    slerp_latent_style_strength=0.9,
+    slerp_prompt_style_strength=0.1,
+    slerp_clip_image_style_strength=0.1,
+    guidance_scale=9.0,
+    batch_size=1,
+    clip_guidance_scale=100,
+    generator=generator,
+).images
+```
+
+![image_mixing_result](https://huggingface.co/datasets/TheDenk/images_mixing/resolve/main/boromir_gigachad.png)
+
 ### Stable Diffusion Mixture
 
 This pipeline uses the Mixture. Refer to the [Mixture](https://arxiv.org/abs/2302.02412) paper for more details.

examples/community/clip_guided_images_mixing_stable_diffusion.py

+# -*- coding: utf-8 -*-
+import inspect
+from typing import Optional, Union
+
+import numpy as np
+import PIL
+import torch
+from torch.nn import functional as F
+from torchvision import transforms
+from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
+
+from diffusers import (
+    AutoencoderKL,
+    DDIMScheduler,
+    DiffusionPipeline,
+    DPMSolverMultistepScheduler,
+    LMSDiscreteScheduler,
+    PNDMScheduler,
+    UNet2DConditionModel,
+)
+from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
+from diffusers.utils import (
+    PIL_INTERPOLATION,
+    randn_tensor,
+)
+
+
+def preprocess(image, w, h):
+    if isinstance(image, torch.Tensor):
+        return image
+    elif isinstance(image, PIL.Image.Image):
+        image = [image]
+
+    if isinstance(image[0], PIL.Image.Image):
+        image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION['lanczos']))[
+            None, :] for i in image]
+        image = np.concatenate(image, axis=0)
+        image = np.array(image).astype(np.float32) / 255.0
+        image = image.transpose(0, 3, 1, 2)
+        image = 2.0 * image - 1.0
+        image = torch.from_numpy(image)
+    elif isinstance(image[0], torch.Tensor):
+        image = torch.cat(image, dim=0)
+    return image
+
+
+def slerp(t, v0, v1, DOT_THRESHOLD=0.9995):
+
+    if not isinstance(v0, np.ndarray):
+        inputs_are_torch = True
+        input_device = v0.device
+        v0 = v0.cpu().numpy()
+        v1 = v1.cpu().numpy()
+
+    dot = np.sum(v0 * v1 / (np.linalg.norm(v0) * np.linalg.norm(v1)))
+    if np.abs(dot) > DOT_THRESHOLD:
+        v2 = (1 - t) * v0 + t * v1
+    else:
+        theta_0 = np.arccos(dot)
+        sin_theta_0 = np.sin(theta_0)
+        theta_t = theta_0 * t
+        sin_theta_t = np.sin(theta_t)
+        s0 = np.sin(theta_0 - theta_t) / sin_theta_0
+        s1 = sin_theta_t / sin_theta_0
+        v2 = s0 * v0 + s1 * v1
+
+    if inputs_are_torch:
+        v2 = torch.from_numpy(v2).to(input_device)
+
+    return v2
+
+
+def spherical_dist_loss(x, y):
+    x = F.normalize(x, dim=-1)
+    y = F.normalize(y, dim=-1)
+    return (x - y).norm(dim=-1).div(2).arcsin().pow(2).mul(2)
+
+
+def set_requires_grad(model, value):
+    for param in model.parameters():
+        param.requires_grad = value
+
+
+class CLIPGuidedImagesMixingStableDiffusion(DiffusionPipeline):
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        clip_model: CLIPModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        scheduler: Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler],
+        feature_extractor: CLIPFeatureExtractor,
+        coca_model=None,
+        coca_tokenizer=None,
+        coca_transform=None,
+    ):
+        super().__init__()
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            clip_model=clip_model,
+            tokenizer=tokenizer,
+            unet=unet,
+            scheduler=scheduler,
+            feature_extractor=feature_extractor,
+            coca_model=coca_model,
+            coca_tokenizer=coca_tokenizer,
+            coca_transform=coca_transform,
+        )
+        self.feature_extractor_size = (
+            feature_extractor.size
+            if isinstance(feature_extractor.size, int)
+            else feature_extractor.size['shortest_edge']
+        )
+        self.normalize = transforms.Normalize(
+            mean=feature_extractor.image_mean, std=feature_extractor.image_std)
+        set_requires_grad(self.text_encoder, False)
+        set_requires_grad(self.clip_model, False)
+
+    def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = 'auto'):
+        if slice_size == 'auto':
+            # half the attention head size is usually a good trade-off between
+            # speed and memory
+            slice_size = self.unet.config.attention_head_dim // 2
+        self.unet.set_attention_slice(slice_size)
+
+    def disable_attention_slicing(self):
+        self.enable_attention_slicing(None)
+
+    def freeze_vae(self):
+        set_requires_grad(self.vae, False)
+
+    def unfreeze_vae(self):
+        set_requires_grad(self.vae, True)
+
+    def freeze_unet(self):
+        set_requires_grad(self.unet, False)
+
+    def unfreeze_unet(self):
+        set_requires_grad(self.unet, True)
+
+    def get_timesteps(self, num_inference_steps, strength, device):
+        # get the original timestep using init_timestep
+        init_timestep = min(
+            int(num_inference_steps * strength), num_inference_steps)
+
+        t_start = max(num_inference_steps - init_timestep, 0)
+        timesteps = self.scheduler.timesteps[t_start:]
+
+        return timesteps, num_inference_steps - t_start
+
+    def prepare_latents(self, image, timestep, batch_size, dtype, device, generator=None):
+        if not isinstance(image, torch.Tensor):
+            raise ValueError(
+                f'`image` has to be of type `torch.Tensor` but is {type(image)}'
+            )
+
+        image = image.to(device=device, dtype=dtype)
+
+        if isinstance(generator, list):
+            init_latents = [
+                self.vae.encode(image[i: i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size)
+            ]
+            init_latents = torch.cat(init_latents, dim=0)
+        else:
+            init_latents = self.vae.encode(image).latent_dist.sample(generator)
+
+        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
+        init_latents = 0.18215 * init_latents
+        init_latents = init_latents.repeat_interleave(batch_size, dim=0)
+
+        noise = randn_tensor(init_latents.shape,
+                             generator=generator, device=device, dtype=dtype)
+
+        # get latents
+        init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
+        latents = init_latents
+
+        return latents
+
+    def get_image_description(self, image):
+        transformed_image = self.coca_transform(image).unsqueeze(0)
+        with torch.no_grad(), torch.cuda.amp.autocast():
+            generated = self.coca_model.generate(transformed_image.to(
+                device=self.device, dtype=self.coca_model.dtype))
+        generated = self.coca_tokenizer.decode(generated[0].cpu().numpy())
+        return generated.split('<end_of_text>')[0].replace('<start_of_text>', '').rstrip(' .,')
+
+    def get_clip_image_embeddings(self, image, batch_size):
+        clip_image_input = self.feature_extractor.preprocess(image)
+        clip_image_features = torch.from_numpy(
+            clip_image_input['pixel_values'][0]).unsqueeze(0).to(self.device).half()
+        image_embeddings_clip = self.clip_model.get_image_features(
+            clip_image_features)
+        image_embeddings_clip = image_embeddings_clip / \
+            image_embeddings_clip.norm(p=2, dim=-1, keepdim=True)
+        image_embeddings_clip = image_embeddings_clip.repeat_interleave(
+            batch_size, dim=0)
+        return image_embeddings_clip
+
+    @torch.enable_grad()
+    def cond_fn(
+        self,
+        latents,
+        timestep,
+        index,
+        text_embeddings,
+        noise_pred_original,
+        original_image_embeddings_clip,
+        clip_guidance_scale,
+    ):
+        latents = latents.detach().requires_grad_()
+
+        latent_model_input = self.scheduler.scale_model_input(
+            latents, timestep)
+
+        # predict the noise residual
+        noise_pred = self.unet(latent_model_input, timestep,
+                               encoder_hidden_states=text_embeddings).sample
+
+        if isinstance(self.scheduler, (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)):
+            alpha_prod_t = self.scheduler.alphas_cumprod[timestep]
+            beta_prod_t = 1 - alpha_prod_t
+            # compute predicted original sample from predicted noise also called
+            # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
+            pred_original_sample = (
+                latents - beta_prod_t ** (0.5) * noise_pred) / alpha_prod_t ** (0.5)
+
+            fac = torch.sqrt(beta_prod_t)
+            sample = pred_original_sample * (fac) + latents * (1 - fac)
+        elif isinstance(self.scheduler, LMSDiscreteScheduler):
+            sigma = self.scheduler.sigmas[index]
+            sample = latents - sigma * noise_pred
+        else:
+            raise ValueError(
+                f'scheduler type {type(self.scheduler)} not supported')
+
+        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
+        sample = 1 / 0.18215 * sample
+        image = self.vae.decode(sample).sample
+        image = (image / 2 + 0.5).clamp(0, 1)
+
+        image = transforms.Resize(self.feature_extractor_size)(image)
+        image = self.normalize(image).to(latents.dtype)
+
+        image_embeddings_clip = self.clip_model.get_image_features(image)
+        image_embeddings_clip = image_embeddings_clip / \
+            image_embeddings_clip.norm(p=2, dim=-1, keepdim=True)
+
+        loss = spherical_dist_loss(
+            image_embeddings_clip, original_image_embeddings_clip).mean() * clip_guidance_scale
+
+        grads = -torch.autograd.grad(loss, latents)[0]
+
+        if isinstance(self.scheduler, LMSDiscreteScheduler):
+            latents = latents.detach() + grads * (sigma**2)
+            noise_pred = noise_pred_original
+        else:
+            noise_pred = noise_pred_original - torch.sqrt(beta_prod_t) * grads
+        return noise_pred, latents
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        style_image: Union[torch.FloatTensor, PIL.Image.Image],
+        content_image: Union[torch.FloatTensor, PIL.Image.Image],
+        style_prompt: Optional[str] = None,
+        content_prompt: Optional[str] = None,
+        height: Optional[int] = 512,
+        width: Optional[int] = 512,
+        noise_strength: float = 0.6,
+        num_inference_steps: Optional[int] = 50,
+        guidance_scale: Optional[float] = 7.5,
+        batch_size: Optional[int] = 1,
+        eta: float = 0.0,
+        clip_guidance_scale: Optional[float] = 100,
+        generator: Optional[torch.Generator] = None,
+        output_type: Optional[str] = 'pil',
+        return_dict: bool = True,
+        slerp_latent_style_strength: float = 0.8,
+        slerp_prompt_style_strength: float = 0.1,
+        slerp_clip_image_style_strength: float = 0.1,
+    ):
+
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f'You have passed {batch_size} batch_size, but only {len(generator)} generators.')
+
+        if height % 8 != 0 or width % 8 != 0:
+            raise ValueError(
+                f'`height` and `width` have to be divisible by 8 but are {height} and {width}.')
+
+        if isinstance(generator, torch.Generator) and batch_size > 1:
+            generator = [generator] + [None] * (batch_size - 1)
+
+        coca_is_none = [
+            ('model', self.coca_model is None),
+            ('tokenizer', self.coca_tokenizer is None),
+            ('transform', self.coca_transform is None)
+        ]
+        coca_is_none = [x[0] for x in coca_is_none if x[1]]
+        coca_is_none_str = ', '.join(coca_is_none)
+        # generate prompts with coca model if prompt is None
+        if content_prompt is None:
+            if len(coca_is_none):
+                raise ValueError(
+                    f'Content prompt is None and CoCa [{coca_is_none_str}] is None.'
+                    f'Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.'
+                )
+            content_prompt = self.get_image_description(content_image)
+        if style_prompt is None:
+            if len(coca_is_none):
+                raise ValueError(
+                    f'Style prompt is None and CoCa [{coca_is_none_str}] is None.'
+                    f' Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.'
+                )
+            style_prompt = self.get_image_description(style_image)
+
+        # get prompt text embeddings for content and style
+        content_text_input = self.tokenizer(
+            content_prompt,
+            padding='max_length',
+            max_length=self.tokenizer.model_max_length,
+            truncation=True,
+            return_tensors='pt',
+        )
+        content_text_embeddings = self.text_encoder(
+            content_text_input.input_ids.to(self.device))[0]
+
+        style_text_input = self.tokenizer(
+            style_prompt,
+            padding='max_length',
+            max_length=self.tokenizer.model_max_length,
+            truncation=True,
+            return_tensors='pt',
+        )
+        style_text_embeddings = self.text_encoder(
+            style_text_input.input_ids.to(self.device))[0]
+
+        text_embeddings = slerp(
+            slerp_prompt_style_strength, content_text_embeddings, style_text_embeddings)
+
+        # duplicate text embeddings for each generation per prompt
+        text_embeddings = text_embeddings.repeat_interleave(batch_size, dim=0)
+
+        # set timesteps
+        accepts_offset = 'offset' in set(inspect.signature(
+            self.scheduler.set_timesteps).parameters.keys())
+        extra_set_kwargs = {}
+        if accepts_offset:
+            extra_set_kwargs['offset'] = 1
+
+        self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs)
+        # Some schedulers like PNDM have timesteps as arrays
+        # It's more optimized to move all timesteps to correct device beforehand
+        self.scheduler.timesteps.to(self.device)
+
+        timesteps, num_inference_steps = self.get_timesteps(
+            num_inference_steps, noise_strength, self.device)
+        latent_timestep = timesteps[:1].repeat(batch_size)
+
+        # Preprocess image
+        preprocessed_content_image = preprocess(content_image, width, height)
+        content_latents = self.prepare_latents(
+            preprocessed_content_image,
+            latent_timestep,
+            batch_size,
+            text_embeddings.dtype,
+            self.device,
+            generator
+        )
+
+        preprocessed_style_image = preprocess(style_image, width, height)
+        style_latents = self.prepare_latents(
+            preprocessed_style_image,
+            latent_timestep,
+            batch_size,
+            text_embeddings.dtype,
+            self.device,
+            generator
+        )
+
+        latents = slerp(slerp_latent_style_strength,
+                        content_latents, style_latents)
+
+        if clip_guidance_scale > 0:
+            content_clip_image_embedding = self.get_clip_image_embeddings(
+                content_image, batch_size)
+            style_clip_image_embedding = self.get_clip_image_embeddings(
+                style_image, batch_size)
+            clip_image_embeddings = slerp(
+                slerp_clip_image_style_strength, content_clip_image_embedding, style_clip_image_embedding)
+
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = guidance_scale > 1.0
+        # get unconditional embeddings for classifier free guidance
+        if do_classifier_free_guidance:
+            max_length = content_text_input.input_ids.shape[-1]
+            uncond_input = self.tokenizer(
+                [''], padding='max_length', max_length=max_length, return_tensors='pt')
+            uncond_embeddings = self.text_encoder(
+                uncond_input.input_ids.to(self.device))[0]
+            # duplicate unconditional embeddings for each generation per prompt
+            uncond_embeddings = uncond_embeddings.repeat_interleave(
+                batch_size, dim=0)
+
+            # For classifier free guidance, we need to do two forward passes.
+            # Here we concatenate the unconditional and text embeddings into a single batch
+            # to avoid doing two forward passes
+            text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
+
+        # get the initial random noise unless the user supplied it
+
+        # Unlike in other pipelines, latents need to be generated in the target device
+        # for 1-to-1 results reproducibility with the CompVis implementation.
+        # However this currently doesn't work in `mps`.
+        latents_shape = (
+            batch_size, self.unet.config.in_channels, height // 8, width // 8)
+        latents_dtype = text_embeddings.dtype
+        if latents is None:
+            if self.device.type == 'mps':
+                # randn does not work reproducibly on mps
+                latents = torch.randn(
+                    latents_shape,
+                    generator=generator,
+                    device='cpu',
+                    dtype=latents_dtype
+                ).to(self.device)
+            else:
+                latents = torch.randn(
+                    latents_shape, generator=generator, device=self.device, dtype=latents_dtype)
+        else:
+            if latents.shape != latents_shape:
+                raise ValueError(
+                    f'Unexpected latents shape, got {latents.shape}, expected {latents_shape}')
+            latents = latents.to(self.device)
+
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = latents * self.scheduler.init_noise_sigma
+
+        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+        # and should be between [0, 1]
+        accepts_eta = 'eta' in set(inspect.signature(
+            self.scheduler.step).parameters.keys())
+        extra_step_kwargs = {}
+        if accepts_eta:
+            extra_step_kwargs['eta'] = eta
+
+        # check if the scheduler accepts generator
+        accepts_generator = 'generator' in set(
+            inspect.signature(self.scheduler.step).parameters.keys())
+        if accepts_generator:
+            extra_step_kwargs['generator'] = generator
+
+        with self.progress_bar(total=num_inference_steps):
+            for i, t in enumerate(timesteps):
+                # expand the latents if we are doing classifier free guidance
+                latent_model_input = torch.cat(
+                    [latents] * 2) if do_classifier_free_guidance else latents
+                latent_model_input = self.scheduler.scale_model_input(
+                    latent_model_input, t)
+
+                # predict the noise residual
+                noise_pred = self.unet(
+                    latent_model_input, t, encoder_hidden_states=text_embeddings).sample
+
+                # perform classifier free guidance
+                if do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                    noise_pred = noise_pred_uncond + guidance_scale * \
+                        (noise_pred_text - noise_pred_uncond)
+
+                # perform clip guidance
+                if clip_guidance_scale > 0:
+                    text_embeddings_for_guidance = (
+                        text_embeddings.chunk(
+                            2)[1] if do_classifier_free_guidance else text_embeddings
+                    )
+                    noise_pred, latents = self.cond_fn(
+                        latents,
+                        t,
+                        i,
+                        text_embeddings_for_guidance,
+                        noise_pred,
+                        clip_image_embeddings,
+                        clip_guidance_scale,
+                    )
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents = self.scheduler.step(
+                    noise_pred, t, latents, **extra_step_kwargs).prev_sample
+
+        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
+        latents = 1 / 0.18215 * latents
+        image = self.vae.decode(latents).sample
+
+        image = (image / 2 + 0.5).clamp(0, 1)
+        image = image.cpu().permute(0, 2, 3, 1).numpy()
+
+        if output_type == 'pil':
+            image = self.numpy_to_pil(image)
+
+        if not return_dict:
+            return (image, None)
+
+        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=None)