add pipeline

pipeline_img2mvimg.py

@@ -0,0 +1,290 @@
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+import numpy as np
+import torch
+
+from diffusers import AutoencoderKL, StableDiffusionImageVariationPipeline, UNet2DConditionModel
+from diffusers.schedulers import KarrasDiffusionSchedulers, DDPMScheduler
+from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker, StableDiffusionPipelineOutput
+from PIL import Image
+from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
+from omegaconf import DictConfig, OmegaConf
+
+
+
+class StableDiffusionImage2MVCustomPipeline(
+    StableDiffusionImageVariationPipeline
+):       
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        image_encoder: CLIPVisionModelWithProjection,
+        unet: UNet2DConditionModel, 
+        scheduler: KarrasDiffusionSchedulers,
+        safety_checker: StableDiffusionSafetyChecker,
+        feature_extractor: CLIPImageProcessor,
+        requires_safety_checker: bool = True,
+        latents_offset=None,
+        noisy_cond_latents=False,
+        condition_offset=True,
+    ):
+        super().__init__(
+            vae=vae,
+            image_encoder=image_encoder,
+            unet=unet,
+            scheduler=scheduler,
+            safety_checker=safety_checker,
+            feature_extractor=feature_extractor,
+            requires_safety_checker=requires_safety_checker
+        )
+        latents_offset = tuple(latents_offset) if latents_offset is not None else None
+        self.latents_offset = latents_offset
+        if latents_offset is not None:
+            self.register_to_config(latents_offset=latents_offset)
+        if noisy_cond_latents:
+            raise NotImplementedError("Noisy condition latents not supported Now.")
+        self.condition_offset = condition_offset
+        self.register_to_config(condition_offset=condition_offset)
+    
+    def encode_latents(self, image: Image.Image, device, dtype, height, width):
+        images = self.image_processor.preprocess(image.convert("RGB"), height=height, width=width).to(device, dtype=dtype)
+        # NOTE: .mode() for condition
+        latents = self.vae.encode(images).latent_dist.mode() * self.vae.config.scaling_factor
+        if self.latents_offset is not None and self.condition_offset:
+            return latents - torch.tensor(self.latents_offset).to(latents.device)[None, :, None, None]
+        else:
+            return latents
+    
+    def _encode_image(self, image, device, num_images_per_prompt, do_classifier_free_guidance):
+        dtype = next(self.image_encoder.parameters()).dtype
+
+        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.unsqueeze(1)
+
+        # duplicate image embeddings for each generation per prompt, using mps friendly method
+        bs_embed, seq_len, _ = image_embeddings.shape
+        image_embeddings = image_embeddings.repeat(1, num_images_per_prompt, 1)
+        image_embeddings = image_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
+
+        if do_classifier_free_guidance:
+            # NOTE: the same as original code
+            negative_prompt_embeds = torch.zeros_like(image_embeddings)
+            # 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
+            image_embeddings = torch.cat([negative_prompt_embeds, image_embeddings])
+
+        return image_embeddings
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        image: Union[Image.Image, List[Image.Image], torch.FloatTensor],
+        height: Optional[int] = 1024,
+        width: Optional[int] = 1024,
+        height_cond: Optional[int] = 512,
+        width_cond: Optional[int] = 512,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 7.5,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        callback_steps: int = 1,
+        strength: float = 0.0,
+    ):
+        r"""
+        The call function to the pipeline for generation.
+
+        Args:
+            image (`Image.Image` or `List[Image.Image]` or `torch.FloatTensor`):
+                Image or images to guide image generation. If you provide a tensor, it needs to be compatible with
+                [`CLIPImageProcessor`](https://huggingface.co/lambdalabs/sd-image-variations-diffusers/blob/main/feature_extractor/preprocessor_config.json).
+            height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
+                The height in pixels of the generated image.
+            width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
+                The width in pixels of the generated image.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference. This parameter is modulated by `strength`.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                A higher guidance scale value encourages the model to generate images closely linked to the text
+                `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
+                to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
+                generation deterministic.
+            latents (`torch.FloatTensor`, *optional*):
+                Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor is generated by sampling using the supplied random `generator`.config.j
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generated image. Choose between `PIL.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+            callback (`Callable`, *optional*):
+                A function that calls every `callback_steps` steps during inference. The function is 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 is called. If not specified, the callback is called at
+                every step.
+
+        Returns:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
+                If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
+                otherwise a `tuple` is returned where the first element is a list with the generated images and the
+                second element is a list of `bool`s indicating whether the corresponding generated image contains
+                "not-safe-for-work" (nsfw) content.
+
+        Examples:
+
+        ```py
+        from diffusers import StableDiffusionImageVariationPipeline
+        from PIL import Image
+        from io import BytesIO
+        import requests
+
+        pipe = StableDiffusionImageVariationPipeline.from_pretrained(
+            "lambdalabs/sd-image-variations-diffusers", revision="v2.0"
+        )
+        pipe = pipe.to("cuda")
+
+        url = "https://lh3.googleusercontent.com/y-iFOHfLTwkuQSUegpwDdgKmOjRSTvPxat63dQLB25xkTs4lhIbRUFeNBWZzYf370g=s1200"
+
+        response = requests.get(url)
+        image = Image.open(BytesIO(response.content)).convert("RGB")
+
+        out = pipe(image, num_images_per_prompt=3, guidance_scale=15)
+        out["images"][0].save("result.jpg")
+        ```
+        """
+        # 0. Default height and width to unet
+        height = height or self.unet.config.sample_size * self.vae_scale_factor
+        width = width or self.unet.config.sample_size * self.vae_scale_factor
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(image, height, width, callback_steps)
+
+        # 2. Define call parameters
+        if isinstance(image, Image.Image):
+            batch_size = 1
+        elif len(image) == 1:
+            image = image[0]
+            batch_size = 1
+        else:
+            raise NotImplementedError()
+        # elif isinstance(image, list):
+        #     batch_size = len(image)
+        # else:
+        #     batch_size = image.shape[0]
+        device = self._execution_device
+        # 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
+
+        # 3. Encode input image
+        emb_image = image
+        
+        image_embeddings = self._encode_image(emb_image, device, num_images_per_prompt, do_classifier_free_guidance)
+        cond_latents = self.encode_latents(image, image_embeddings.device, image_embeddings.dtype, height_cond, width_cond)
+        cond_latents = torch.cat([torch.zeros_like(cond_latents), cond_latents]) if do_classifier_free_guidance else cond_latents
+        image_pixels = self.feature_extractor(images=emb_image, return_tensors="pt").pixel_values
+        if do_classifier_free_guidance:
+            image_pixels = torch.cat([torch.zeros_like(image_pixels), image_pixels], dim=0)
+
+        # 4. Prepare timesteps
+        self.scheduler.set_timesteps(num_inference_steps, device=device)
+        timesteps = self.scheduler.timesteps
+
+        # 5. Prepare latent variables
+        num_channels_latents = self.unet.config.out_channels
+        latents = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            image_embeddings.dtype,
+            device,
+            generator,
+            latents,
+        )
+
+
+        # 6. Prepare extra step kwargs.
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+        # 7. Denoising loop
+        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            
+            class_labels = torch.tensor([0, 1, 2, 3, 0, 1, 2, 3]).to(latents.device)
+
+            for i, t in enumerate(timesteps[int(len(timesteps)*strength):]):
+                # 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)
+                
+                num_repeat = len(latent_model_input) // len(cond_latents) # [2, 4, 32, 32]
+                cat_latents = torch.stack([cond_latents] * num_repeat, 1).reshape(*latent_model_input.shape)
+
+                sample = torch.cat([latent_model_input, cat_latents], dim=1)
+                noise_pred = self.unet( 
+                    sample, 
+                    t, 
+                    image_embeddings,  
+                    cross_attention_kwargs=None, 
+                    class_labels=class_labels,   
+                ).sample 
+                # perform 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)
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
+
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+                    if callback is not None and i % callback_steps == 0:
+                        step_idx = i // getattr(self.scheduler, "order", 1)
+                        callback(step_idx, t, latents)
+
+        self.maybe_free_model_hooks()
+
+        if self.latents_offset is not None:
+            latents = latents + torch.tensor(self.latents_offset).to(latents.device)[None, :, None, None]
+
+        if not output_type == "latent":
+            image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
+            image, has_nsfw_concept = self.run_safety_checker(image, device, image_embeddings.dtype)
+        else:
+            image = latents
+            has_nsfw_concept = None
+
+        if has_nsfw_concept is None:
+            do_denormalize = [True] * image.shape[0]
+        else:
+            do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
+
+        image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
+
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (image, has_nsfw_concept)
+
+        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
+