add app.py

app.py

@@ -0,0 +1,86 @@
+import gradio as gr
+import torch
+import numpy as np
+from PIL import Image
+import os
+
+from vslerp import UnCLIPImageInterpolationPipeline  # your pipeline + vSLERP
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# Load pipeline once
+pipe = UnCLIPImageInterpolationPipeline.from_pretrained(
+    "kakaobrain/karlo-v1-alpha-image-variations",
+    torch_dtype=torch.float16
+).to(device)
+
+# Put your own images in a local "bank" folder
+IMAGE_BANK = {
+    "Example 1": "lj.png",
+    "Example 2": "kd.png",
+    "Example 3": "vase.png",
+    "Example 4": "lamp.jpeg"
+}
+
+def run_vslerp(img0, img1, bank0, bank1, slerp_num_steps, vslerp_start_idx, vslerp_end_idx, vslerp_num_steps):
+    # Decide input images: uploaded takes precedence, else from bank
+    if img0 is None and bank0 != "None":
+        img0 = Image.open(IMAGE_BANK[bank0])
+    if img1 is None and bank1 != "None":
+        img1 = Image.open(IMAGE_BANK[bank1])
+
+    if img0 is None or img1 is None:
+        raise ValueError("Please provide two images (either upload or select from bank).")
+
+    images = [img0, img1]
+    generator = torch.Generator(device=device).manual_seed(42)
+
+    # Prepare a 2D list for the gallery
+    gallery_matrix = []
+
+    vslerp_values = np.linspace(vslerp_start_idx, vslerp_end_idx, vslerp_num_steps)
+    for m_val in vslerp_values:
+        row = []
+        for step in range(slerp_num_steps):
+            out = pipe(
+                image=images,
+                generator=generator,
+                steps=slerp_num_steps,
+                decoder_guidance_scale=1,
+                mean_val=m_val
+            )
+            row.append(out.images[0])  # assuming pipe returns a list with one image per call
+        gallery_matrix.append(row)
+
+    return gallery_matrix
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("## vSLERP Demo")
+    gr.Markdown("Note: The run may take a while, please be patient 🙏")
+
+    with gr.Row():
+        with gr.Column():
+            img0 = gr.Image(label="Upload Image 0", type="pil")
+            bank0 = gr.Dropdown(choices=["None"] + list(IMAGE_BANK.keys()), value="None", label="Or choose from bank")
+        with gr.Column():
+            img1 = gr.Image(label="Upload Image 1", type="pil")
+            bank1 = gr.Dropdown(choices=["None"] + list(IMAGE_BANK.keys()), value="None", label="Or choose from bank")
+
+    with gr.Row():
+        slerp_num_steps = gr.Slider(3, 6, value=6, step=1, label="slerp_num_steps")
+        vslerp_start_idx = gr.Slider(-2, 0, value=-1, step=1, label="vslerp_start_idx")
+        vslerp_end_idx = gr.Slider(1, 3, value=3, step=1, label="vslerp_end_idx")
+        vslerp_num_steps = gr.Slider(3, 6, value=6, step=1, label="vslerp_num_steps")
+
+    run_btn = gr.Button("Run vSLERP")
+    gallery = gr.Gallery(label="Generated Interpolations").style(grid=[4], height="auto")
+
+    run_btn.click(
+        run_vslerp,
+        inputs=[img0, img1, bank0, bank1, slerp_num_steps, vslerp_start_idx, vslerp_end_idx, vslerp_num_steps],
+        outputs=[gallery]
+    )
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

@@ -0,0 +1,8 @@
+# requirements.txt for CLIPLatent Space
+
+torch
+transformers
+gradio
+Pillow
+numpy
+matplotlib

vslerp.py

@@ -0,0 +1,557 @@
+import inspect
+from typing import List, Optional, Union
+
+import argparse
+import PIL
+from PIL import Image
+import torch
+from torch.nn import functional as F
+from transformers import (
+    CLIPFeatureExtractor,
+    CLIPTextModelWithProjection,
+    CLIPTokenizer,
+    CLIPVisionModelWithProjection,
+)
+
+from diffusers import (
+    DiffusionPipeline,
+    ImagePipelineOutput,
+    UnCLIPScheduler,
+    UNet2DConditionModel,
+    UNet2DModel,
+)
+from diffusers.pipelines.unclip import UnCLIPTextProjModel
+from diffusers.utils import is_accelerate_available, logging
+from diffusers.utils.torch_utils import randn_tensor
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+import os
+import scipy.io as sio
+import numpy as np
+from tqdm import tqdm
+os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
+
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+def vSLERP(val, low, high, mean_val = 1):
+    """
+    Find the interpolation point between the 'low' and 'high' values for the given 'val'. See https://en.wikipedia.org/wiki/Slerp for more details on the topic.
+    """
+    # fetch and fit the mean magnitude
+    data = torch.load('mean_feat.pt').to(device).half()
+    mean_feats = data[0]
+    
+    mean_feats = mean_feats*mean_val
+
+    # shift both features
+    low = low-mean_feats
+    high = high-mean_feats
+    
+    # apply slerp
+    low_norm = low / torch.norm(low)
+    high_norm = high / torch.norm(high)
+    omega = torch.acos((low_norm * high_norm))
+    so = torch.sin(omega)
+    res = (torch.sin((1.0 - val) * omega) / so) * low + (torch.sin(val * omega) / so) * high
+    
+    # reshift both features back 
+    res = res+mean_feats
+    return res
+
+
+class UnCLIPImageInterpolationPipeline(DiffusionPipeline):
+    """
+    Pipeline to generate variations from an input image using unCLIP
+
+    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.)
+
+    Args:
+        text_encoder ([`CLIPTextModelWithProjection`]):
+            Frozen text-encoder.
+        tokenizer (`CLIPTokenizer`):
+            Tokenizer of class
+            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
+        feature_extractor ([`CLIPFeatureExtractor`]):
+            Model that extracts features from generated images to be used as inputs for the `image_encoder`.
+        image_encoder ([`CLIPVisionModelWithProjection`]):
+            Frozen CLIP image-encoder. unCLIP Image Variation uses the vision portion of
+            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPVisionModelWithProjection),
+            specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
+        text_proj ([`UnCLIPTextProjModel`]):
+            Utility class to prepare and combine the embeddings before they are passed to the decoder.
+        decoder ([`UNet2DConditionModel`]):
+            The decoder to invert the image embedding into an image.
+        super_res_first ([`UNet2DModel`]):
+            Super resolution unet. Used in all but the last step of the super resolution diffusion process.
+        super_res_last ([`UNet2DModel`]):
+            Super resolution unet. Used in the last step of the super resolution diffusion process.
+        decoder_scheduler ([`UnCLIPScheduler`]):
+            Scheduler used in the decoder denoising process. Just a modified DDPMScheduler.
+        super_res_scheduler ([`UnCLIPScheduler`]):
+            Scheduler used in the super resolution denoising process. Just a modified DDPMScheduler.
+
+    """
+
+    decoder: UNet2DConditionModel
+    text_proj: UnCLIPTextProjModel
+    text_encoder: CLIPTextModelWithProjection
+    tokenizer: CLIPTokenizer
+    feature_extractor: CLIPFeatureExtractor
+    image_encoder: CLIPVisionModelWithProjection
+    super_res_first: UNet2DModel
+    super_res_last: UNet2DModel
+
+    decoder_scheduler: UnCLIPScheduler
+    super_res_scheduler: UnCLIPScheduler
+
+    # Copied from diffusers.pipelines.unclip.pipeline_unclip_image_variation.UnCLIPImageVariationPipeline.__init__
+    def __init__(
+        self,
+        decoder: UNet2DConditionModel,
+        text_encoder: CLIPTextModelWithProjection,
+        tokenizer: CLIPTokenizer,
+        text_proj: UnCLIPTextProjModel,
+        feature_extractor: CLIPFeatureExtractor,
+        image_encoder: CLIPVisionModelWithProjection,
+        super_res_first: UNet2DModel,
+        super_res_last: UNet2DModel,
+        decoder_scheduler: UnCLIPScheduler,
+        super_res_scheduler: UnCLIPScheduler,
+    ):
+        super().__init__()
+
+        self.register_modules(
+            decoder=decoder,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            text_proj=text_proj,
+            feature_extractor=feature_extractor,
+            image_encoder=image_encoder,
+            super_res_first=super_res_first,
+            super_res_last=super_res_last,
+            decoder_scheduler=decoder_scheduler,
+            super_res_scheduler=super_res_scheduler,
+        )
+
+    # Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline.prepare_latents
+    def prepare_latents(self, shape, dtype, device, generator, latents, scheduler):
+        if latents is None:
+            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+        else:
+            if latents.shape != shape:
+                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
+            latents = latents.to(device)
+
+        latents = latents * scheduler.init_noise_sigma
+        return latents
+
+    # Copied from diffusers.pipelines.unclip.pipeline_unclip_image_variation.UnCLIPImageVariationPipeline._encode_prompt
+    def _encode_prompt(self, prompt, device, num_images_per_prompt, do_classifier_free_guidance):
+        batch_size = len(prompt) if isinstance(prompt, list) else 1
+
+        # get prompt text embeddings
+        text_inputs = self.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=self.tokenizer.model_max_length,
+            return_tensors="pt",
+        )
+        text_input_ids = text_inputs.input_ids
+        text_mask = text_inputs.attention_mask.bool().to(device)
+        text_encoder_output = self.text_encoder(text_input_ids.to(device))
+
+        prompt_embeds = text_encoder_output.text_embeds
+        text_encoder_hidden_states = text_encoder_output.last_hidden_state
+
+        prompt_embeds = prompt_embeds.repeat_interleave(num_images_per_prompt, dim=0)
+        text_encoder_hidden_states = text_encoder_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
+        text_mask = text_mask.repeat_interleave(num_images_per_prompt, dim=0)
+
+        if do_classifier_free_guidance:
+            uncond_tokens = [""] * batch_size
+
+            max_length = text_input_ids.shape[-1]
+            uncond_input = self.tokenizer(
+                uncond_tokens,
+                padding="max_length",
+                max_length=max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+            uncond_text_mask = uncond_input.attention_mask.bool().to(device)
+            negative_prompt_embeds_text_encoder_output = self.text_encoder(uncond_input.input_ids.to(device))
+
+            negative_prompt_embeds = negative_prompt_embeds_text_encoder_output.text_embeds
+            uncond_text_encoder_hidden_states = negative_prompt_embeds_text_encoder_output.last_hidden_state
+
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+
+            seq_len = negative_prompt_embeds.shape[1]
+            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt)
+            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len)
+
+            seq_len = uncond_text_encoder_hidden_states.shape[1]
+            uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.repeat(1, num_images_per_prompt, 1)
+            uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.view(
+                batch_size * num_images_per_prompt, seq_len, -1
+            )
+            uncond_text_mask = uncond_text_mask.repeat_interleave(num_images_per_prompt, dim=0)
+
+            # done duplicates
+
+            # 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
+            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
+            text_encoder_hidden_states = torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states])
+
+            text_mask = torch.cat([uncond_text_mask, text_mask])
+
+        return prompt_embeds, text_encoder_hidden_states, text_mask
+
+    # Copied from diffusers.pipelines.unclip.pipeline_unclip_image_variation.UnCLIPImageVariationPipeline._encode_image
+    def _encode_image(self, image, device, num_images_per_prompt, image_embeddings: Optional[torch.Tensor] = None):
+        dtype = next(self.image_encoder.parameters()).dtype
+
+        if image_embeddings is None:
+            if not isinstance(image, torch.Tensor):
+                image = self.feature_extractor(images=image, return_tensors="pt").pixel_values
+
+            image = image.to(device=device, dtype=dtype)
+            image_embeddings = self.image_encoder(image).image_embeds
+
+        image_embeddings = image_embeddings.repeat_interleave(num_images_per_prompt, dim=0)
+
+        return image_embeddings
+
+    # Copied from diffusers.pipelines.unclip.pipeline_unclip_image_variation.UnCLIPImageVariationPipeline.enable_sequential_cpu_offload
+    def enable_sequential_cpu_offload(self, gpu_id=0):
+        r"""
+        Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, the pipeline's
+        models 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 `forward` method called.
+        """
+        if is_accelerate_available():
+            from accelerate import cpu_offload
+        else:
+            raise ImportError("Please install accelerate via `pip install accelerate`")
+
+        device = torch.device(f"cuda:{gpu_id}")
+
+        models = [
+            self.decoder,
+            self.text_proj,
+            self.text_encoder,
+            self.super_res_first,
+            self.super_res_last,
+        ]
+        for cpu_offloaded_model in models:
+            if cpu_offloaded_model is not None:
+                cpu_offload(cpu_offloaded_model, device)
+
+    @property
+    # Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline._execution_device
+    def _execution_device(self):
+        r"""
+        Returns the device on which the pipeline's models will be executed. After calling
+        `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
+        hooks.
+        """
+        if self.device != torch.device("meta") or not hasattr(self.decoder, "_hf_hook"):
+            return self.device
+        for module in self.decoder.modules():
+            if (
+                hasattr(module, "_hf_hook")
+                and hasattr(module._hf_hook, "execution_device")
+                and module._hf_hook.execution_device is not None
+            ):
+                return torch.device(module._hf_hook.execution_device)
+        return self.device
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        image: Optional[Union[List[PIL.Image.Image], torch.FloatTensor]] = None,
+        steps: int = 5,
+        decoder_num_inference_steps: int = 25,
+        super_res_num_inference_steps: int = 7,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        image_embeddings: Optional[torch.Tensor] = None,
+        decoder_latents: Optional[torch.FloatTensor] = None,
+        super_res_latents: Optional[torch.FloatTensor] = None,
+        decoder_guidance_scale: float = 8.0,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        mean_val: float = 1.0
+    ):
+        """
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            image (`List[PIL.Image.Image]` or `torch.FloatTensor`):
+                The images to use for the image interpolation. Only accepts a list of two PIL Images or If you provide a tensor, it needs to comply with the
+                configuration of
+                [this](https://huggingface.co/fusing/karlo-image-variations-diffusers/blob/main/feature_extractor/preprocessor_config.json)
+                `CLIPFeatureExtractor` while still having a shape of two in the 0th dimension. Can be left to `None` only when `image_embeddings` are passed.
+            steps (`int`, *optional*, defaults to 5):
+                The number of interpolation images to generate.
+            decoder_num_inference_steps (`int`, *optional*, defaults to 25):
+                The number of denoising steps for the decoder. More denoising steps usually lead to a higher quality
+                image at the expense of slower inference.
+            super_res_num_inference_steps (`int`, *optional*, defaults to 7):
+                The number of denoising steps for super resolution. More denoising steps usually lead to a higher
+                quality image at the expense of slower inference.
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
+                to make generation deterministic.
+            image_embeddings (`torch.Tensor`, *optional*):
+                Pre-defined image embeddings that can be derived from the image encoder. Pre-defined image embeddings
+                can be passed for tasks like image interpolations. `image` can the be left to `None`.
+            decoder_latents (`torch.FloatTensor` of shape (batch size, channels, height, width), *optional*):
+                Pre-generated noisy latents to be used as inputs for the decoder.
+            super_res_latents (`torch.FloatTensor` of shape (batch size, channels, super res height, super res width), *optional*):
+                Pre-generated noisy latents to be used as inputs for the decoder.
+            decoder_guidance_scale (`float`, *optional*, defaults to 4.0):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). 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.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generated image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
+        """
+
+        batch_size = steps
+
+        device = self._execution_device
+
+        if isinstance(image, List):
+            if len(image) != 2:
+                raise AssertionError(
+                    f"Expected 'image' List to be of size 2, but passed 'image' length is {len(image)}"
+                )
+            elif not (isinstance(image[0], PIL.Image.Image) and isinstance(image[0], PIL.Image.Image)):
+                raise AssertionError(
+                    f"Expected 'image' List to contain PIL.Image.Image, but passed 'image' contents are {type(image[0])} and {type(image[1])}"
+                )
+        elif isinstance(image, torch.FloatTensor):
+            if image.shape[0] != 2:
+                raise AssertionError(
+                    f"Expected 'image' to be torch.FloatTensor of shape 2 in 0th dimension, but passed 'image' size is {image.shape[0]}"
+                )
+        elif isinstance(image_embeddings, torch.Tensor):
+            if image_embeddings.shape[0] != 2:
+                raise AssertionError(
+                    f"Expected 'image_embeddings' to be torch.FloatTensor of shape 2 in 0th dimension, but passed 'image_embeddings' shape is {image_embeddings.shape[0]}"
+                )
+        else:
+            raise AssertionError(
+                f"Expected 'image' or 'image_embeddings' to be not None with types List[PIL.Image] or Torch.FloatTensor respectively. Received {type(image)} and {type(image_embeddings)} repsectively"
+            )
+
+        original_image_embeddings = self._encode_image(
+            image=image, device=device, num_images_per_prompt=1, image_embeddings=image_embeddings
+        )
+
+        image_embeddings = []
+
+        for interp_step in torch.linspace(0, 1, steps):
+            temp_image_embeddings = vSLERP(
+                interp_step, original_image_embeddings[0], original_image_embeddings[1], mean_val = mean_val
+            ).unsqueeze(0)
+            image_embeddings.append(temp_image_embeddings)
+
+        image_embeddings = torch.cat(image_embeddings).to(device)
+        
+        
+        do_classifier_free_guidance = decoder_guidance_scale > 1.0
+
+        prompt_embeds, text_encoder_hidden_states, text_mask = self._encode_prompt(
+            prompt=["" for i in range(steps)],
+            device=device,
+            num_images_per_prompt=1,
+            do_classifier_free_guidance=do_classifier_free_guidance,
+        )
+        
+        text_encoder_hidden_states, additive_clip_time_embeddings = self.text_proj(
+            image_embeddings=image_embeddings,
+            prompt_embeds=prompt_embeds,
+            text_encoder_hidden_states=text_encoder_hidden_states,
+            do_classifier_free_guidance=do_classifier_free_guidance,
+        )
+
+        if device.type == "mps":
+            # HACK: MPS: There is a panic when padding bool tensors,
+            # so cast to int tensor for the pad and back to bool afterwards
+            text_mask = text_mask.type(torch.int)
+            decoder_text_mask = F.pad(text_mask, (self.text_proj.clip_extra_context_tokens, 0), value=1)
+            decoder_text_mask = decoder_text_mask.type(torch.bool)
+        else:
+            decoder_text_mask = F.pad(text_mask, (self.text_proj.clip_extra_context_tokens, 0), value=True)
+
+        self.decoder_scheduler.set_timesteps(decoder_num_inference_steps, device=device)
+        decoder_timesteps_tensor = self.decoder_scheduler.timesteps
+
+        num_channels_latents = self.decoder.in_channels
+        height = self.decoder.sample_size
+        width = self.decoder.sample_size
+
+        #decoder_latents = self.prepare_latents(
+        #    (batch_size, num_channels_latents, height, width),
+        #    text_encoder_hidden_states.dtype,
+        #    device,
+        #    generator,
+        #    decoder_latents,
+        #    self.decoder_scheduler,
+        #)
+        
+        decoder_latents = self.prepare_latents(
+            (1, num_channels_latents, height, height),
+            text_encoder_hidden_states.dtype,
+            device,
+            generator,
+            None,
+            self.decoder_scheduler,
+        )
+        
+        decoder_latents = decoder_latents.repeat(steps,1,1,1)
+        
+        for i, t in enumerate(self.progress_bar(decoder_timesteps_tensor)):
+            # expand the latents if we are doing classifier free guidance
+            latent_model_input = torch.cat([decoder_latents] * 2) if do_classifier_free_guidance else decoder_latents
+
+            noise_pred = self.decoder(
+                sample=latent_model_input,
+                timestep=t,
+                encoder_hidden_states=text_encoder_hidden_states,
+                class_labels=additive_clip_time_embeddings,
+                attention_mask=decoder_text_mask,
+            ).sample
+
+            if do_classifier_free_guidance:
+                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                noise_pred_uncond, _ = noise_pred_uncond.split(latent_model_input.shape[1], dim=1)
+                noise_pred_text, predicted_variance = noise_pred_text.split(latent_model_input.shape[1], dim=1)
+                noise_pred = noise_pred_uncond + decoder_guidance_scale * (noise_pred_text - noise_pred_uncond)
+                noise_pred = torch.cat([noise_pred, predicted_variance], dim=1)
+
+            if i + 1 == decoder_timesteps_tensor.shape[0]:
+                prev_timestep = None
+            else:
+                prev_timestep = decoder_timesteps_tensor[i + 1]
+
+            # compute the previous noisy sample x_t -> x_t-1
+            decoder_latents = self.decoder_scheduler.step(
+                noise_pred, t, decoder_latents, prev_timestep=prev_timestep, generator=generator
+            ).prev_sample
+
+        decoder_latents = decoder_latents.clamp(-1, 1)
+
+        image_small = decoder_latents
+        # done decoder
+
+        # super res
+
+        self.super_res_scheduler.set_timesteps(super_res_num_inference_steps, device=device)
+        super_res_timesteps_tensor = self.super_res_scheduler.timesteps
+
+        channels = self.super_res_first.in_channels // 2
+        height = self.super_res_first.sample_size
+        width = self.super_res_first.sample_size
+
+        super_res_latents = self.prepare_latents(
+            (batch_size, channels, height, width),
+            image_small.dtype,
+            device,
+            generator,
+            super_res_latents,
+            self.super_res_scheduler,
+        )
+
+        if device.type == "mps":
+            # MPS does not support many interpolations
+            image_upscaled = F.interpolate(image_small, size=[height, width])
+        else:
+            interpolate_antialias = {}
+            if "antialias" in inspect.signature(F.interpolate).parameters:
+                interpolate_antialias["antialias"] = True
+
+            image_upscaled = F.interpolate(
+                image_small, size=[height, width], mode="bicubic", align_corners=False, **interpolate_antialias
+            )
+
+        for i, t in enumerate(self.progress_bar(super_res_timesteps_tensor)):
+            # no classifier free guidance
+
+            if i == super_res_timesteps_tensor.shape[0] - 1:
+                unet = self.super_res_last
+            else:
+                unet = self.super_res_first
+
+            latent_model_input = torch.cat([super_res_latents, image_upscaled], dim=1)
+
+            noise_pred = unet(
+                sample=latent_model_input,
+                timestep=t,
+            ).sample
+
+            if i + 1 == super_res_timesteps_tensor.shape[0]:
+                prev_timestep = None
+            else:
+                prev_timestep = super_res_timesteps_tensor[i + 1]
+
+            # compute the previous noisy sample x_t -> x_t-1
+            super_res_latents = self.super_res_scheduler.step(
+                noise_pred, t, super_res_latents, prev_timestep=prev_timestep, generator=generator
+            ).prev_sample
+
+        image = super_res_latents
+        # done super res
+
+        # post processing
+
+        image = image * 0.5 + 0.5
+        image = image.clamp(0, 1)
+        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
+
+        if output_type == "pil":
+            image = self.numpy_to_pil(image)
+
+        if not return_dict:
+            return (image,)
+
+        return ImagePipelineOutput(images=image)
+        
+
+def main(args):
+    pipe = UnCLIPImageInterpolationPipeline.from_pretrained("kakaobrain/karlo-v1-alpha-image-variations", torch_dtype = torch.float16)
+    pipe.to(device)
+    
+    images = [Image.open(args.image_path0), Image.open(args.image_path1)]
+    for m_iter, m_val in enumerate(np.linspace(args.vslerp_start_idx,args.vslerp_end_idx, args.vslerp_num_steps)):
+        generator = torch.Generator(device=device)
+        generator.manual_seed(42)
+        out = pipe(image = images, generator = generator, steps=args.slerp_num_steps, decoder_guidance_scale=1, mean_val = m_val)
+        for ii, image in enumerate(out.images):
+            img = Image.fromarray(np.array(image))
+            if not os.path.exists(f'{ii}'):
+              os.makedirs(f'{ii}')
+            img.save(os.path.join(f'{ii}', f'{m_iter}.png'))
+        
+if __name__ == "__main__":
+    args = argparse.ArgumentParser(description="Example script")
+    args.add_argument("--vslerp_start_idx", type=float, default=-1)
+    args.add_argument("--vslerp_end_idx", type=float, default=3)
+    args.add_argument("--vslerp_num_steps", type=int, default=16)
+    args.add_argument("--slerp_num_steps", type=int, default=6)
+    args.add_argument("--image_path0", type=str, default='path.to.image0')
+    args.add_argument("--image_path1", type=str, default='path.to.image1')
+    args = args.parse_args()
+    main(args)
+