rm old pipline

pipeline_img2mvimg.py

@@ -1,290 +0,0 @@
-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)
-