| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
|
|
| from __future__ import annotations |
|
|
| import abc |
| from typing import Any, Callable, Dict, List, Optional, Tuple, Union |
|
|
| import numpy as np |
| import torch |
| import torch.nn.functional as F |
|
|
| from ...src.diffusers.models.attention import Attention |
| from ...src.diffusers.pipelines.stable_diffusion import StableDiffusionPipeline, StableDiffusionPipelineOutput |
|
|
|
|
| |
| def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0): |
| """ |
| Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and |
| Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4 |
| """ |
| std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True) |
| std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True) |
| |
| noise_pred_rescaled = noise_cfg * (std_text / std_cfg) |
| |
| noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg |
| return noise_cfg |
|
|
|
|
| class Prompt2PromptPipeline(StableDiffusionPipeline): |
| r""" |
| Args: |
| Prompt-to-Prompt-Pipeline for text-to-image generation using Stable Diffusion. This model inherits from |
| [`StableDiffusionPipeline`]. 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.) |
| vae ([`AutoencoderKL`]): |
| Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. |
| text_encoder ([`CLIPTextModel`]): |
| Frozen text-encoder. Stable Diffusion uses the text portion of |
| [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically |
| the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. |
| tokenizer (`CLIPTokenizer`): |
| Tokenizer of class |
| [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). |
| unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. scheduler |
| ([`SchedulerMixin`]): |
| A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of |
| [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. |
| safety_checker ([`StableDiffusionSafetyChecker`]): |
| Classification module that estimates whether generated images could be considered offensive or harmful. |
| Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details. |
| feature_extractor ([`CLIPFeatureExtractor`]): |
| Model that extracts features from generated images to be used as inputs for the `safety_checker`. |
| """ |
|
|
| _optional_components = ["safety_checker", "feature_extractor"] |
|
|
| @torch.no_grad() |
| def __call__( |
| self, |
| prompt: Union[str, List[str]], |
| height: Optional[int] = None, |
| width: Optional[int] = None, |
| num_inference_steps: int = 50, |
| guidance_scale: float = 7.5, |
| negative_prompt: Optional[Union[str, List[str]]] = None, |
| 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, |
| prompt_embeds: Optional[torch.FloatTensor] = None, |
| negative_prompt_embeds: Optional[torch.FloatTensor] = None, |
| output_type: Optional[str] = "pil", |
| return_dict: bool = True, |
| callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, |
| callback_steps: Optional[int] = 1, |
| cross_attention_kwargs: Optional[Dict[str, Any]] = None, |
| guidance_rescale: float = 0.0, |
| ): |
| r""" |
| Function invoked when calling the pipeline for generation. |
| |
| Args: |
| prompt (`str` or `List[str]`): |
| The prompt or prompts to guide the image generation. |
| 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. |
| guidance_scale (`float`, *optional*, defaults to 7.5): |
| 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. |
| negative_prompt (`str` or `List[str]`, *optional*): |
| The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored |
| if `guidance_scale` is less than `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) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to |
| [`schedulers.DDIMScheduler`], will be ignored for others. |
| generator (`torch.Generator`, *optional*): |
| One or a list of [torch generator(s)](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 will ge generated by sampling using the supplied random `generator`. |
| output_type (`str`, *optional*, defaults to `"pil"`): |
| The output format of the generate 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.stable_diffusion.StableDiffusionPipelineOutput`] 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. |
| cross_attention_kwargs (`dict`, *optional*): |
| A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in |
| [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). |
| |
| The keyword arguments to configure the edit are: |
| - edit_type (`str`). The edit type to apply. Can be either of `replace`, `refine`, `reweight`. |
| - n_cross_replace (`int`): Number of diffusion steps in which cross attention should be replaced |
| - n_self_replace (`int`): Number of diffusion steps in which self attention should be replaced |
| - local_blend_words(`List[str]`, *optional*, default to `None`): Determines which area should be |
| changed. If None, then the whole image can be changed. |
| - equalizer_words(`List[str]`, *optional*, default to `None`): Required for edit type `reweight`. |
| Determines which words should be enhanced. |
| - equalizer_strengths (`List[float]`, *optional*, default to `None`) Required for edit type `reweight`. |
| Determines which how much the words in `equalizer_words` should be enhanced. |
| |
| guidance_rescale (`float`, *optional*, defaults to 0.0): |
| Guidance rescale factor from [Common Diffusion Noise Schedules and Sample Steps are |
| Flawed](https://arxiv.org/pdf/2305.08891.pdf). Guidance rescale factor should fix overexposure when |
| using zero terminal SNR. |
| |
| Returns: |
| [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: |
| [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. |
| When returning a tuple, the first element is a list with the generated images, and the second element is a |
| list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" |
| (nsfw) content, according to the `safety_checker`. |
| """ |
|
|
| self.controller = create_controller( |
| prompt, cross_attention_kwargs, num_inference_steps, tokenizer=self.tokenizer, device=self.device |
| ) |
| self.register_attention_control(self.controller) |
|
|
| |
| height = height or self.unet.config.sample_size * self.vae_scale_factor |
| width = width or self.unet.config.sample_size * self.vae_scale_factor |
|
|
| |
| self.check_inputs(prompt, height, width, callback_steps) |
|
|
| |
| if prompt is not None and isinstance(prompt, str): |
| batch_size = 1 |
| elif prompt is not None and isinstance(prompt, list): |
| batch_size = len(prompt) |
| else: |
| batch_size = prompt_embeds.shape[0] |
|
|
| device = self._execution_device |
| |
| |
| |
| do_classifier_free_guidance = guidance_scale > 1.0 |
|
|
| |
| text_encoder_lora_scale = ( |
| cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None |
| ) |
| prompt_embeds = self._encode_prompt( |
| prompt, |
| device, |
| num_images_per_prompt, |
| do_classifier_free_guidance, |
| negative_prompt, |
| prompt_embeds=prompt_embeds, |
| negative_prompt_embeds=negative_prompt_embeds, |
| lora_scale=text_encoder_lora_scale, |
| ) |
|
|
| |
| self.scheduler.set_timesteps(num_inference_steps, device=device) |
| timesteps = self.scheduler.timesteps |
|
|
| |
| num_channels_latents = self.unet.config.in_channels |
| latents = self.prepare_latents( |
| batch_size * num_images_per_prompt, |
| num_channels_latents, |
| height, |
| width, |
| prompt_embeds.dtype, |
| device, |
| generator, |
| latents, |
| ) |
|
|
| |
| extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) |
|
|
| |
| num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order |
| with self.progress_bar(total=num_inference_steps) as progress_bar: |
| for i, t in enumerate(timesteps): |
| |
| 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) |
|
|
| |
| noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=prompt_embeds).sample |
|
|
| |
| 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) |
|
|
| if do_classifier_free_guidance and guidance_rescale > 0.0: |
| |
| noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale) |
|
|
| |
| latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample |
|
|
| |
| latents = self.controller.step_callback(latents) |
|
|
| |
| 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) |
|
|
| |
| 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, prompt_embeds.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) |
|
|
| |
| if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None: |
| self.final_offload_hook.offload() |
|
|
| if not return_dict: |
| return (image, has_nsfw_concept) |
|
|
| return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept) |
|
|
| def register_attention_control(self, controller): |
| attn_procs = {} |
| cross_att_count = 0 |
| for name in self.unet.attn_processors.keys(): |
| None if name.endswith("attn1.processor") else self.unet.config.cross_attention_dim |
| if name.startswith("mid_block"): |
| self.unet.config.block_out_channels[-1] |
| place_in_unet = "mid" |
| elif name.startswith("up_blocks"): |
| block_id = int(name[len("up_blocks.")]) |
| list(reversed(self.unet.config.block_out_channels))[block_id] |
| place_in_unet = "up" |
| elif name.startswith("down_blocks"): |
| block_id = int(name[len("down_blocks.")]) |
| self.unet.config.block_out_channels[block_id] |
| place_in_unet = "down" |
| else: |
| continue |
| cross_att_count += 1 |
| attn_procs[name] = P2PCrossAttnProcessor(controller=controller, place_in_unet=place_in_unet) |
|
|
| self.unet.set_attn_processor(attn_procs) |
| controller.num_att_layers = cross_att_count |
|
|
|
|
| class P2PCrossAttnProcessor: |
| def __init__(self, controller, place_in_unet): |
| super().__init__() |
| self.controller = controller |
| self.place_in_unet = place_in_unet |
|
|
| def __call__(self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None): |
| batch_size, sequence_length, _ = hidden_states.shape |
| attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) |
|
|
| query = attn.to_q(hidden_states) |
|
|
| is_cross = encoder_hidden_states is not None |
| encoder_hidden_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states |
| key = attn.to_k(encoder_hidden_states) |
| value = attn.to_v(encoder_hidden_states) |
|
|
| query = attn.head_to_batch_dim(query) |
| key = attn.head_to_batch_dim(key) |
| value = attn.head_to_batch_dim(value) |
|
|
| attention_probs = attn.get_attention_scores(query, key, attention_mask) |
|
|
| |
| self.controller(attention_probs, is_cross, self.place_in_unet) |
|
|
| hidden_states = torch.bmm(attention_probs, value) |
| hidden_states = attn.batch_to_head_dim(hidden_states) |
|
|
| |
| hidden_states = attn.to_out[0](hidden_states) |
| |
| hidden_states = attn.to_out[1](hidden_states) |
|
|
| return hidden_states |
|
|
|
|
| def create_controller( |
| prompts: List[str], cross_attention_kwargs: Dict, num_inference_steps: int, tokenizer, device |
| ) -> AttentionControl: |
| edit_type = cross_attention_kwargs.get("edit_type", None) |
| local_blend_words = cross_attention_kwargs.get("local_blend_words", None) |
| equalizer_words = cross_attention_kwargs.get("equalizer_words", None) |
| equalizer_strengths = cross_attention_kwargs.get("equalizer_strengths", None) |
| n_cross_replace = cross_attention_kwargs.get("n_cross_replace", 0.4) |
| n_self_replace = cross_attention_kwargs.get("n_self_replace", 0.4) |
|
|
| |
| if edit_type == "replace" and local_blend_words is None: |
| return AttentionReplace( |
| prompts, num_inference_steps, n_cross_replace, n_self_replace, tokenizer=tokenizer, device=device |
| ) |
|
|
| |
| if edit_type == "replace" and local_blend_words is not None: |
| lb = LocalBlend(prompts, local_blend_words, tokenizer=tokenizer, device=device) |
| return AttentionReplace( |
| prompts, num_inference_steps, n_cross_replace, n_self_replace, lb, tokenizer=tokenizer, device=device |
| ) |
|
|
| |
| if edit_type == "refine" and local_blend_words is None: |
| return AttentionRefine( |
| prompts, num_inference_steps, n_cross_replace, n_self_replace, tokenizer=tokenizer, device=device |
| ) |
|
|
| |
| if edit_type == "refine" and local_blend_words is not None: |
| lb = LocalBlend(prompts, local_blend_words, tokenizer=tokenizer, device=device) |
| return AttentionRefine( |
| prompts, num_inference_steps, n_cross_replace, n_self_replace, lb, tokenizer=tokenizer, device=device |
| ) |
|
|
| |
| if edit_type == "reweight": |
| assert ( |
| equalizer_words is not None and equalizer_strengths is not None |
| ), "To use reweight edit, please specify equalizer_words and equalizer_strengths." |
| assert len(equalizer_words) == len( |
| equalizer_strengths |
| ), "equalizer_words and equalizer_strengths must be of same length." |
| equalizer = get_equalizer(prompts[1], equalizer_words, equalizer_strengths, tokenizer=tokenizer) |
| return AttentionReweight( |
| prompts, |
| num_inference_steps, |
| n_cross_replace, |
| n_self_replace, |
| tokenizer=tokenizer, |
| device=device, |
| equalizer=equalizer, |
| ) |
|
|
| raise ValueError(f"Edit type {edit_type} not recognized. Use one of: replace, refine, reweight.") |
|
|
|
|
| class AttentionControl(abc.ABC): |
| def step_callback(self, x_t): |
| return x_t |
|
|
| def between_steps(self): |
| return |
|
|
| @property |
| def num_uncond_att_layers(self): |
| return 0 |
|
|
| @abc.abstractmethod |
| def forward(self, attn, is_cross: bool, place_in_unet: str): |
| raise NotImplementedError |
|
|
| def __call__(self, attn, is_cross: bool, place_in_unet: str): |
| if self.cur_att_layer >= self.num_uncond_att_layers: |
| h = attn.shape[0] |
| attn[h // 2 :] = self.forward(attn[h // 2 :], is_cross, place_in_unet) |
| self.cur_att_layer += 1 |
| if self.cur_att_layer == self.num_att_layers + self.num_uncond_att_layers: |
| self.cur_att_layer = 0 |
| self.cur_step += 1 |
| self.between_steps() |
| return attn |
|
|
| def reset(self): |
| self.cur_step = 0 |
| self.cur_att_layer = 0 |
|
|
| def __init__(self): |
| self.cur_step = 0 |
| self.num_att_layers = -1 |
| self.cur_att_layer = 0 |
|
|
|
|
| class EmptyControl(AttentionControl): |
| def forward(self, attn, is_cross: bool, place_in_unet: str): |
| return attn |
|
|
|
|
| class AttentionStore(AttentionControl): |
| @staticmethod |
| def get_empty_store(): |
| return {"down_cross": [], "mid_cross": [], "up_cross": [], "down_self": [], "mid_self": [], "up_self": []} |
|
|
| def forward(self, attn, is_cross: bool, place_in_unet: str): |
| key = f"{place_in_unet}_{'cross' if is_cross else 'self'}" |
| if attn.shape[1] <= 32**2: |
| self.step_store[key].append(attn) |
| return attn |
|
|
| def between_steps(self): |
| if len(self.attention_store) == 0: |
| self.attention_store = self.step_store |
| else: |
| for key in self.attention_store: |
| for i in range(len(self.attention_store[key])): |
| self.attention_store[key][i] += self.step_store[key][i] |
| self.step_store = self.get_empty_store() |
|
|
| def get_average_attention(self): |
| average_attention = { |
| key: [item / self.cur_step for item in self.attention_store[key]] for key in self.attention_store |
| } |
| return average_attention |
|
|
| def reset(self): |
| super(AttentionStore, self).reset() |
| self.step_store = self.get_empty_store() |
| self.attention_store = {} |
|
|
| def __init__(self): |
| super(AttentionStore, self).__init__() |
| self.step_store = self.get_empty_store() |
| self.attention_store = {} |
|
|
|
|
| class LocalBlend: |
| def __call__(self, x_t, attention_store): |
| k = 1 |
| maps = attention_store["down_cross"][2:4] + attention_store["up_cross"][:3] |
| maps = [item.reshape(self.alpha_layers.shape[0], -1, 1, 16, 16, self.max_num_words) for item in maps] |
| maps = torch.cat(maps, dim=1) |
| maps = (maps * self.alpha_layers).sum(-1).mean(1) |
| mask = F.max_pool2d(maps, (k * 2 + 1, k * 2 + 1), (1, 1), padding=(k, k)) |
| mask = F.interpolate(mask, size=(x_t.shape[2:])) |
| mask = mask / mask.max(2, keepdims=True)[0].max(3, keepdims=True)[0] |
| mask = mask.gt(self.threshold) |
| mask = (mask[:1] + mask[1:]).float() |
| x_t = x_t[:1] + mask * (x_t - x_t[:1]) |
| return x_t |
|
|
| def __init__( |
| self, prompts: List[str], words: [List[List[str]]], tokenizer, device, threshold=0.3, max_num_words=77 |
| ): |
| self.max_num_words = 77 |
|
|
| alpha_layers = torch.zeros(len(prompts), 1, 1, 1, 1, self.max_num_words) |
| for i, (prompt, words_) in enumerate(zip(prompts, words)): |
| if isinstance(words_, str): |
| words_ = [words_] |
| for word in words_: |
| ind = get_word_inds(prompt, word, tokenizer) |
| alpha_layers[i, :, :, :, :, ind] = 1 |
| self.alpha_layers = alpha_layers.to(device) |
| self.threshold = threshold |
|
|
|
|
| class AttentionControlEdit(AttentionStore, abc.ABC): |
| def step_callback(self, x_t): |
| if self.local_blend is not None: |
| x_t = self.local_blend(x_t, self.attention_store) |
| return x_t |
|
|
| def replace_self_attention(self, attn_base, att_replace): |
| if att_replace.shape[2] <= 16**2: |
| return attn_base.unsqueeze(0).expand(att_replace.shape[0], *attn_base.shape) |
| else: |
| return att_replace |
|
|
| @abc.abstractmethod |
| def replace_cross_attention(self, attn_base, att_replace): |
| raise NotImplementedError |
|
|
| def forward(self, attn, is_cross: bool, place_in_unet: str): |
| super(AttentionControlEdit, self).forward(attn, is_cross, place_in_unet) |
| |
| if is_cross or (self.num_self_replace[0] <= self.cur_step < self.num_self_replace[1]): |
| h = attn.shape[0] // (self.batch_size) |
| attn = attn.reshape(self.batch_size, h, *attn.shape[1:]) |
| attn_base, attn_repalce = attn[0], attn[1:] |
| if is_cross: |
| alpha_words = self.cross_replace_alpha[self.cur_step] |
| attn_repalce_new = ( |
| self.replace_cross_attention(attn_base, attn_repalce) * alpha_words |
| + (1 - alpha_words) * attn_repalce |
| ) |
| attn[1:] = attn_repalce_new |
| else: |
| attn[1:] = self.replace_self_attention(attn_base, attn_repalce) |
| attn = attn.reshape(self.batch_size * h, *attn.shape[2:]) |
| return attn |
|
|
| def __init__( |
| self, |
| prompts, |
| num_steps: int, |
| cross_replace_steps: Union[float, Tuple[float, float], Dict[str, Tuple[float, float]]], |
| self_replace_steps: Union[float, Tuple[float, float]], |
| local_blend: Optional[LocalBlend], |
| tokenizer, |
| device, |
| ): |
| super(AttentionControlEdit, self).__init__() |
| |
|
|
| self.tokenizer = tokenizer |
| self.device = device |
|
|
| self.batch_size = len(prompts) |
| self.cross_replace_alpha = get_time_words_attention_alpha( |
| prompts, num_steps, cross_replace_steps, self.tokenizer |
| ).to(self.device) |
| if isinstance(self_replace_steps, float): |
| self_replace_steps = 0, self_replace_steps |
| self.num_self_replace = int(num_steps * self_replace_steps[0]), int(num_steps * self_replace_steps[1]) |
| self.local_blend = local_blend |
|
|
|
|
| class AttentionReplace(AttentionControlEdit): |
| def replace_cross_attention(self, attn_base, att_replace): |
| return torch.einsum("hpw,bwn->bhpn", attn_base, self.mapper) |
|
|
| def __init__( |
| self, |
| prompts, |
| num_steps: int, |
| cross_replace_steps: float, |
| self_replace_steps: float, |
| local_blend: Optional[LocalBlend] = None, |
| tokenizer=None, |
| device=None, |
| ): |
| super(AttentionReplace, self).__init__( |
| prompts, num_steps, cross_replace_steps, self_replace_steps, local_blend, tokenizer, device |
| ) |
| self.mapper = get_replacement_mapper(prompts, self.tokenizer).to(self.device) |
|
|
|
|
| class AttentionRefine(AttentionControlEdit): |
| def replace_cross_attention(self, attn_base, att_replace): |
| attn_base_replace = attn_base[:, :, self.mapper].permute(2, 0, 1, 3) |
| attn_replace = attn_base_replace * self.alphas + att_replace * (1 - self.alphas) |
| return attn_replace |
|
|
| def __init__( |
| self, |
| prompts, |
| num_steps: int, |
| cross_replace_steps: float, |
| self_replace_steps: float, |
| local_blend: Optional[LocalBlend] = None, |
| tokenizer=None, |
| device=None, |
| ): |
| super(AttentionRefine, self).__init__( |
| prompts, num_steps, cross_replace_steps, self_replace_steps, local_blend, tokenizer, device |
| ) |
| self.mapper, alphas = get_refinement_mapper(prompts, self.tokenizer) |
| self.mapper, alphas = self.mapper.to(self.device), alphas.to(self.device) |
| self.alphas = alphas.reshape(alphas.shape[0], 1, 1, alphas.shape[1]) |
|
|
|
|
| class AttentionReweight(AttentionControlEdit): |
| def replace_cross_attention(self, attn_base, att_replace): |
| if self.prev_controller is not None: |
| attn_base = self.prev_controller.replace_cross_attention(attn_base, att_replace) |
| attn_replace = attn_base[None, :, :, :] * self.equalizer[:, None, None, :] |
| return attn_replace |
|
|
| def __init__( |
| self, |
| prompts, |
| num_steps: int, |
| cross_replace_steps: float, |
| self_replace_steps: float, |
| equalizer, |
| local_blend: Optional[LocalBlend] = None, |
| controller: Optional[AttentionControlEdit] = None, |
| tokenizer=None, |
| device=None, |
| ): |
| super(AttentionReweight, self).__init__( |
| prompts, num_steps, cross_replace_steps, self_replace_steps, local_blend, tokenizer, device |
| ) |
| self.equalizer = equalizer.to(self.device) |
| self.prev_controller = controller |
|
|
|
|
| |
| def update_alpha_time_word( |
| alpha, bounds: Union[float, Tuple[float, float]], prompt_ind: int, word_inds: Optional[torch.Tensor] = None |
| ): |
| if isinstance(bounds, float): |
| bounds = 0, bounds |
| start, end = int(bounds[0] * alpha.shape[0]), int(bounds[1] * alpha.shape[0]) |
| if word_inds is None: |
| word_inds = torch.arange(alpha.shape[2]) |
| alpha[:start, prompt_ind, word_inds] = 0 |
| alpha[start:end, prompt_ind, word_inds] = 1 |
| alpha[end:, prompt_ind, word_inds] = 0 |
| return alpha |
|
|
|
|
| def get_time_words_attention_alpha( |
| prompts, num_steps, cross_replace_steps: Union[float, Dict[str, Tuple[float, float]]], tokenizer, max_num_words=77 |
| ): |
| if not isinstance(cross_replace_steps, dict): |
| cross_replace_steps = {"default_": cross_replace_steps} |
| if "default_" not in cross_replace_steps: |
| cross_replace_steps["default_"] = (0.0, 1.0) |
| alpha_time_words = torch.zeros(num_steps + 1, len(prompts) - 1, max_num_words) |
| for i in range(len(prompts) - 1): |
| alpha_time_words = update_alpha_time_word(alpha_time_words, cross_replace_steps["default_"], i) |
| for key, item in cross_replace_steps.items(): |
| if key != "default_": |
| inds = [get_word_inds(prompts[i], key, tokenizer) for i in range(1, len(prompts))] |
| for i, ind in enumerate(inds): |
| if len(ind) > 0: |
| alpha_time_words = update_alpha_time_word(alpha_time_words, item, i, ind) |
| alpha_time_words = alpha_time_words.reshape(num_steps + 1, len(prompts) - 1, 1, 1, max_num_words) |
| return alpha_time_words |
|
|
|
|
| |
| def get_word_inds(text: str, word_place: int, tokenizer): |
| split_text = text.split(" ") |
| if isinstance(word_place, str): |
| word_place = [i for i, word in enumerate(split_text) if word_place == word] |
| elif isinstance(word_place, int): |
| word_place = [word_place] |
| out = [] |
| if len(word_place) > 0: |
| words_encode = [tokenizer.decode([item]).strip("#") for item in tokenizer.encode(text)][1:-1] |
| cur_len, ptr = 0, 0 |
|
|
| for i in range(len(words_encode)): |
| cur_len += len(words_encode[i]) |
| if ptr in word_place: |
| out.append(i + 1) |
| if cur_len >= len(split_text[ptr]): |
| ptr += 1 |
| cur_len = 0 |
| return np.array(out) |
|
|
|
|
| |
| def get_replacement_mapper_(x: str, y: str, tokenizer, max_len=77): |
| words_x = x.split(" ") |
| words_y = y.split(" ") |
| if len(words_x) != len(words_y): |
| raise ValueError( |
| f"attention replacement edit can only be applied on prompts with the same length" |
| f" but prompt A has {len(words_x)} words and prompt B has {len(words_y)} words." |
| ) |
| inds_replace = [i for i in range(len(words_y)) if words_y[i] != words_x[i]] |
| inds_source = [get_word_inds(x, i, tokenizer) for i in inds_replace] |
| inds_target = [get_word_inds(y, i, tokenizer) for i in inds_replace] |
| mapper = np.zeros((max_len, max_len)) |
| i = j = 0 |
| cur_inds = 0 |
| while i < max_len and j < max_len: |
| if cur_inds < len(inds_source) and inds_source[cur_inds][0] == i: |
| inds_source_, inds_target_ = inds_source[cur_inds], inds_target[cur_inds] |
| if len(inds_source_) == len(inds_target_): |
| mapper[inds_source_, inds_target_] = 1 |
| else: |
| ratio = 1 / len(inds_target_) |
| for i_t in inds_target_: |
| mapper[inds_source_, i_t] = ratio |
| cur_inds += 1 |
| i += len(inds_source_) |
| j += len(inds_target_) |
| elif cur_inds < len(inds_source): |
| mapper[i, j] = 1 |
| i += 1 |
| j += 1 |
| else: |
| mapper[j, j] = 1 |
| i += 1 |
| j += 1 |
|
|
| return torch.from_numpy(mapper).float() |
|
|
|
|
| def get_replacement_mapper(prompts, tokenizer, max_len=77): |
| x_seq = prompts[0] |
| mappers = [] |
| for i in range(1, len(prompts)): |
| mapper = get_replacement_mapper_(x_seq, prompts[i], tokenizer, max_len) |
| mappers.append(mapper) |
| return torch.stack(mappers) |
|
|
|
|
| |
| def get_equalizer( |
| text: str, word_select: Union[int, Tuple[int, ...]], values: Union[List[float], Tuple[float, ...]], tokenizer |
| ): |
| if isinstance(word_select, (int, str)): |
| word_select = (word_select,) |
| equalizer = torch.ones(len(values), 77) |
| values = torch.tensor(values, dtype=torch.float32) |
| for word in word_select: |
| inds = get_word_inds(text, word, tokenizer) |
| equalizer[:, inds] = values |
| return equalizer |
|
|
|
|
| |
| class ScoreParams: |
| def __init__(self, gap, match, mismatch): |
| self.gap = gap |
| self.match = match |
| self.mismatch = mismatch |
|
|
| def mis_match_char(self, x, y): |
| if x != y: |
| return self.mismatch |
| else: |
| return self.match |
|
|
|
|
| def get_matrix(size_x, size_y, gap): |
| matrix = np.zeros((size_x + 1, size_y + 1), dtype=np.int32) |
| matrix[0, 1:] = (np.arange(size_y) + 1) * gap |
| matrix[1:, 0] = (np.arange(size_x) + 1) * gap |
| return matrix |
|
|
|
|
| def get_traceback_matrix(size_x, size_y): |
| matrix = np.zeros((size_x + 1, size_y + 1), dtype=np.int32) |
| matrix[0, 1:] = 1 |
| matrix[1:, 0] = 2 |
| matrix[0, 0] = 4 |
| return matrix |
|
|
|
|
| def global_align(x, y, score): |
| matrix = get_matrix(len(x), len(y), score.gap) |
| trace_back = get_traceback_matrix(len(x), len(y)) |
| for i in range(1, len(x) + 1): |
| for j in range(1, len(y) + 1): |
| left = matrix[i, j - 1] + score.gap |
| up = matrix[i - 1, j] + score.gap |
| diag = matrix[i - 1, j - 1] + score.mis_match_char(x[i - 1], y[j - 1]) |
| matrix[i, j] = max(left, up, diag) |
| if matrix[i, j] == left: |
| trace_back[i, j] = 1 |
| elif matrix[i, j] == up: |
| trace_back[i, j] = 2 |
| else: |
| trace_back[i, j] = 3 |
| return matrix, trace_back |
|
|
|
|
| def get_aligned_sequences(x, y, trace_back): |
| x_seq = [] |
| y_seq = [] |
| i = len(x) |
| j = len(y) |
| mapper_y_to_x = [] |
| while i > 0 or j > 0: |
| if trace_back[i, j] == 3: |
| x_seq.append(x[i - 1]) |
| y_seq.append(y[j - 1]) |
| i = i - 1 |
| j = j - 1 |
| mapper_y_to_x.append((j, i)) |
| elif trace_back[i][j] == 1: |
| x_seq.append("-") |
| y_seq.append(y[j - 1]) |
| j = j - 1 |
| mapper_y_to_x.append((j, -1)) |
| elif trace_back[i][j] == 2: |
| x_seq.append(x[i - 1]) |
| y_seq.append("-") |
| i = i - 1 |
| elif trace_back[i][j] == 4: |
| break |
| mapper_y_to_x.reverse() |
| return x_seq, y_seq, torch.tensor(mapper_y_to_x, dtype=torch.int64) |
|
|
|
|
| def get_mapper(x: str, y: str, tokenizer, max_len=77): |
| x_seq = tokenizer.encode(x) |
| y_seq = tokenizer.encode(y) |
| score = ScoreParams(0, 1, -1) |
| matrix, trace_back = global_align(x_seq, y_seq, score) |
| mapper_base = get_aligned_sequences(x_seq, y_seq, trace_back)[-1] |
| alphas = torch.ones(max_len) |
| alphas[: mapper_base.shape[0]] = mapper_base[:, 1].ne(-1).float() |
| mapper = torch.zeros(max_len, dtype=torch.int64) |
| mapper[: mapper_base.shape[0]] = mapper_base[:, 1] |
| mapper[mapper_base.shape[0] :] = len(y_seq) + torch.arange(max_len - len(y_seq)) |
| return mapper, alphas |
|
|
|
|
| def get_refinement_mapper(prompts, tokenizer, max_len=77): |
| x_seq = prompts[0] |
| mappers, alphas = [], [] |
| for i in range(1, len(prompts)): |
| mapper, alpha = get_mapper(x_seq, prompts[i], tokenizer, max_len) |
| mappers.append(mapper) |
| alphas.append(alpha) |
| return torch.stack(mappers), torch.stack(alphas) |
|
|