Delete clip_guided_images_mixing_stable_diffusion.py

clip_guided_images_mixing_stable_diffusion.py

@@ -1,445 +0,0 @@
-# -*- coding: utf-8 -*-
-import inspect
-from typing import Optional, Union
-
-import numpy as np
-import PIL.Image
-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,
-    DPMSolverMultistepScheduler,
-    LMSDiscreteScheduler,
-    PNDMScheduler,
-    UNet2DConditionModel,
-)
-from diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin
-from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
-from diffusers.utils import PIL_INTERPOLATION
-from diffusers.utils.torch_utils import 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, StableDiffusionMixin):
-    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 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.Tensor, PIL.Image.Image],
-        content_image: Union[torch.Tensor, 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) as progress_bar:
-            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
-
-                progress_bar.update()
-        # 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)