Added Callback steps for my progressbar in StableDiffusionDeluxe

pipeline.py

@@ -0,0 +1,595 @@
+import inspect
+from typing import List, Optional, Tuple, Union, Callable
+
+import torch
+from torch.nn import functional as F
+from transformers import CLIPTextModelWithProjection, CLIPTokenizer
+from transformers.models.clip.modeling_clip import CLIPTextModelOutput
+
+from diffusers import (
+    DiffusionPipeline,
+    ImagePipelineOutput,
+    PriorTransformer,
+    UnCLIPScheduler,
+    UNet2DConditionModel,
+    UNet2DModel,
+)
+from diffusers.pipelines.unclip import UnCLIPTextProjModel
+from diffusers.utils import is_accelerate_available, logging, randn_tensor
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+def slerp(val, low, high):
+    """
+    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.
+    """
+    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
+    return res
+
+
+class UnCLIPTextInterpolationPipeline(DiffusionPipeline):
+
+    """
+    Pipeline for prompt-to-prompt interpolation on CLIP text embeddings and using the UnCLIP / Dall-E to decode them to images.
+
+    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).
+        prior ([`PriorTransformer`]):
+            The canonincal unCLIP prior to approximate the image embedding from the text embedding.
+        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.
+        prior_scheduler ([`UnCLIPScheduler`]):
+            Scheduler used in the prior denoising process. Just a modified DDPMScheduler.
+        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.
+
+    """
+
+    prior: PriorTransformer
+    decoder: UNet2DConditionModel
+    text_proj: UnCLIPTextProjModel
+    text_encoder: CLIPTextModelWithProjection
+    tokenizer: CLIPTokenizer
+    super_res_first: UNet2DModel
+    super_res_last: UNet2DModel
+
+    prior_scheduler: UnCLIPScheduler
+    decoder_scheduler: UnCLIPScheduler
+    super_res_scheduler: UnCLIPScheduler
+
+    # Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline.__init__
+    def __init__(
+        self,
+        prior: PriorTransformer,
+        decoder: UNet2DConditionModel,
+        text_encoder: CLIPTextModelWithProjection,
+        tokenizer: CLIPTokenizer,
+        text_proj: UnCLIPTextProjModel,
+        super_res_first: UNet2DModel,
+        super_res_last: UNet2DModel,
+        prior_scheduler: UnCLIPScheduler,
+        decoder_scheduler: UnCLIPScheduler,
+        super_res_scheduler: UnCLIPScheduler,
+    ):
+        super().__init__()
+
+        self.register_modules(
+            prior=prior,
+            decoder=decoder,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            text_proj=text_proj,
+            super_res_first=super_res_first,
+            super_res_last=super_res_last,
+            prior_scheduler=prior_scheduler,
+            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.UnCLIPPipeline._encode_prompt
+    def _encode_prompt(
+        self,
+        prompt,
+        device,
+        num_images_per_prompt,
+        do_classifier_free_guidance,
+        text_model_output: Optional[Union[CLIPTextModelOutput, Tuple]] = None,
+        text_attention_mask: Optional[torch.Tensor] = None,
+    ):
+        if text_model_output is None:
+            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,
+                truncation=True,
+                return_tensors="pt",
+            )
+            text_input_ids = text_inputs.input_ids
+            text_mask = text_inputs.attention_mask.bool().to(device)
+
+            untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
+
+            if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
+                text_input_ids, untruncated_ids
+            ):
+                removed_text = self.tokenizer.batch_decode(
+                    untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
+                )
+                logger.warning(
+                    "The following part of your input was truncated because CLIP can only handle sequences up to"
+                    f" {self.tokenizer.model_max_length} tokens: {removed_text}"
+                )
+                text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
+
+            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
+
+        else:
+            batch_size = text_model_output[0].shape[0]
+            prompt_embeds, text_encoder_hidden_states = text_model_output[0], text_model_output[1]
+            text_mask = text_attention_mask
+
+        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
+
+            uncond_input = self.tokenizer(
+                uncond_tokens,
+                padding="max_length",
+                max_length=self.tokenizer.model_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.UnCLIPPipeline.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}")
+
+        # TODO: self.prior.post_process_latents is not covered by the offload hooks, so it fails if added to the list
+        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,
+        start_prompt: str,
+        end_prompt: str,
+        steps: int = 5,
+        prior_num_inference_steps: int = 25,
+        decoder_num_inference_steps: int = 25,
+        super_res_num_inference_steps: int = 7,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        prior_guidance_scale: float = 4.0,
+        decoder_guidance_scale: float = 8.0,
+        enable_sequential_cpu_offload=True,
+        gpu_id=0,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        callback_steps: int = 1,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+    ):
+        """
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            start_prompt (`str`):
+                The prompt to start the image generation interpolation from.
+            end_prompt (`str`):
+                The prompt to end the image generation interpolation at.
+            steps (`int`, *optional*, defaults to 5):
+                The number of steps over which to interpolate from start_prompt to end_prompt. The pipeline returns
+                the same number of images as this value.
+            prior_num_inference_steps (`int`, *optional*, defaults to 25):
+                The number of denoising steps for the prior. More denoising steps usually lead to a higher quality
+                image at the expense of slower inference.
+            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.
+            prior_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.
+            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`.
+            enable_sequential_cpu_offload (`bool`, *optional*, defaults to `True`):
+                If True, 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.
+            gpu_id (`int`, *optional*, defaults to `0`):
+                The gpu_id to be passed to enable_sequential_cpu_offload. Only works when enable_sequential_cpu_offload is set to True.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.ImagePipelineOutput`] 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.
+        """
+
+        if not isinstance(start_prompt, str) or not isinstance(end_prompt, str):
+            raise ValueError(
+                f"`start_prompt` and `end_prompt` should be of type `str` but got {type(start_prompt)} and"
+                f" {type(end_prompt)} instead"
+            )
+
+        if enable_sequential_cpu_offload:
+            self.enable_sequential_cpu_offload(gpu_id=gpu_id)
+
+        device = self._execution_device
+
+        # Turn the prompts into embeddings.
+        inputs = self.tokenizer(
+            [start_prompt, end_prompt],
+            padding="max_length",
+            truncation=True,
+            max_length=self.tokenizer.model_max_length,
+            return_tensors="pt",
+        )
+        inputs.to(device)
+        text_model_output = self.text_encoder(**inputs)
+
+        text_attention_mask = torch.max(inputs.attention_mask[0], inputs.attention_mask[1])
+        text_attention_mask = torch.cat([text_attention_mask.unsqueeze(0)] * steps).to(device)
+
+        # Interpolate from the start to end prompt using slerp and add the generated images to an image output pipeline
+        batch_text_embeds = []
+        batch_last_hidden_state = []
+
+        for interp_val in torch.linspace(0, 1, steps):
+            text_embeds = slerp(interp_val, text_model_output.text_embeds[0], text_model_output.text_embeds[1])
+            last_hidden_state = slerp(
+                interp_val, text_model_output.last_hidden_state[0], text_model_output.last_hidden_state[1]
+            )
+            batch_text_embeds.append(text_embeds.unsqueeze(0))
+            batch_last_hidden_state.append(last_hidden_state.unsqueeze(0))
+
+        batch_text_embeds = torch.cat(batch_text_embeds)
+        batch_last_hidden_state = torch.cat(batch_last_hidden_state)
+
+        text_model_output = CLIPTextModelOutput(
+            text_embeds=batch_text_embeds, last_hidden_state=batch_last_hidden_state
+        )
+
+        batch_size = text_model_output[0].shape[0]
+
+        do_classifier_free_guidance = prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0
+
+        prompt_embeds, text_encoder_hidden_states, text_mask = self._encode_prompt(
+            prompt=None,
+            device=device,
+            num_images_per_prompt=1,
+            do_classifier_free_guidance=do_classifier_free_guidance,
+            text_model_output=text_model_output,
+            text_attention_mask=text_attention_mask,
+        )
+
+        # prior
+        current_step = 0
+        self.prior_scheduler.set_timesteps(prior_num_inference_steps, device=device)
+        prior_timesteps_tensor = self.prior_scheduler.timesteps
+
+        embedding_dim = self.prior.config.embedding_dim
+
+        prior_latents = self.prepare_latents(
+            (batch_size, embedding_dim),
+            prompt_embeds.dtype,
+            device,
+            generator,
+            None,
+            self.prior_scheduler,
+        )
+
+        for i, t in enumerate(self.progress_bar(prior_timesteps_tensor)):
+            # expand the latents if we are doing classifier free guidance
+            latent_model_input = torch.cat([prior_latents] * 2) if do_classifier_free_guidance else prior_latents
+
+            predicted_image_embedding = self.prior(
+                latent_model_input,
+                timestep=t,
+                proj_embedding=prompt_embeds,
+                encoder_hidden_states=text_encoder_hidden_states,
+                attention_mask=text_mask,
+            ).predicted_image_embedding
+
+            if do_classifier_free_guidance:
+                predicted_image_embedding_uncond, predicted_image_embedding_text = predicted_image_embedding.chunk(2)
+                predicted_image_embedding = predicted_image_embedding_uncond + prior_guidance_scale * (
+                    predicted_image_embedding_text - predicted_image_embedding_uncond
+                )
+
+            if i + 1 == prior_timesteps_tensor.shape[0]:
+                prev_timestep = None
+            else:
+                prev_timestep = prior_timesteps_tensor[i + 1]
+
+            prior_latents = self.prior_scheduler.step(
+                predicted_image_embedding,
+                timestep=t,
+                sample=prior_latents,
+                generator=generator,
+                prev_timestep=prev_timestep,
+            ).prev_sample
+            # call the callback, if provided
+            current_step += 1
+            if callback is not None and current_step % callback_steps == 0:
+                callback(current_step, t, prior_latents)
+                
+        prior_latents = self.prior.post_process_latents(prior_latents)
+
+        image_embeddings = prior_latents
+
+        # done prior
+
+        # decoder
+
+        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,
+            None,
+            self.decoder_scheduler,
+        )
+
+        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
+            
+            # call the callback, if provided
+            current_step += 1
+            if callback is not None and current_step % callback_steps == 0:
+                callback(current_step, t, decoder_latents)
+        
+        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,
+            None,
+            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
+
+            # call the callback, if provided
+            current_step += 1
+            if callback is not None and current_step % callback_steps == 0:
+                callback(current_step, t, super_res_latents)
+            
+        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)