Upload webui_stable_diffusion_controlnet.py

webui_stable_diffusion_controlnet.py

@@ -0,0 +1,1837 @@
+# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
+# Copyright 2023 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# modified from https://github.com/AUTOMATIC1111/stable-diffusion-webui
+# Here is the AGPL-3.0 license https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob/master/LICENSE.txt
+from ppdiffusers.utils import check_min_version
+check_min_version("0.14.1")
+
+import inspect
+from typing import Any, Callable, Dict, List, Optional, Union
+
+import paddle
+import paddle.nn as nn
+import PIL
+import PIL.Image
+
+from paddlenlp.transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
+from ppdiffusers.models import AutoencoderKL, ControlNetModel, UNet2DConditionModel
+from ppdiffusers.pipelines.pipeline_utils import DiffusionPipeline
+from ppdiffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
+from ppdiffusers.pipelines.stable_diffusion.safety_checker import (
+    StableDiffusionSafetyChecker,
+)
+from ppdiffusers.schedulers import KarrasDiffusionSchedulers
+from ppdiffusers.utils import (
+    PIL_INTERPOLATION,
+    logging,
+    randn_tensor,
+    safetensors_load,
+    torch_load,
+)
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+class WebUIStableDiffusionControlNetPipeline(DiffusionPipeline):
+    r"""
+    Pipeline for text-to-image generation using Stable Diffusion with ControlNet guidance.
+    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:
+        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.
+        controlnet ([`ControlNetModel`]):
+            Provides additional conditioning to the unet during the denoising process.
+        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"]
+    enable_emphasis = True
+    comma_padding_backtrack = 20
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        controlnet: ControlNetModel,
+        scheduler: KarrasDiffusionSchedulers,
+        safety_checker: StableDiffusionSafetyChecker,
+        feature_extractor: CLIPFeatureExtractor,
+        requires_safety_checker: bool = True,
+    ):
+        super().__init__()
+
+        if safety_checker is None and requires_safety_checker:
+            logger.warning(
+                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
+                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
+                " results in services or applications open to the public. PaddleNLP team, diffusers team and Hugging Face"
+                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
+                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
+                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
+            )
+
+        if safety_checker is not None and feature_extractor is None:
+            raise ValueError(
+                f"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
+                " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
+            )
+
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            unet=unet,
+            controlnet=controlnet,
+            scheduler=scheduler,
+            safety_checker=safety_checker,
+            feature_extractor=feature_extractor,
+        )
+        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+        self.register_to_config(requires_safety_checker=requires_safety_checker)
+
+        # custom data
+        clip_model = FrozenCLIPEmbedder(text_encoder, tokenizer)
+        self.sj = StableDiffusionModelHijack(clip_model)
+        self.orginal_scheduler_config = self.scheduler.config
+        self.supported_scheduler = [
+            "pndm",
+            "lms",
+            "euler",
+            "euler-ancestral",
+            "dpm-multi",
+            "dpm-single",
+            "unipc-multi",
+            "ddim",
+            "ddpm",
+            "deis-multi",
+            "heun",
+            "kdpm2-ancestral",
+            "kdpm2",
+        ]
+
+    def add_ti_embedding_dir(self, embeddings_dir):
+        self.sj.embedding_db.add_embedding_dir(embeddings_dir)
+        self.sj.embedding_db.load_textual_inversion_embeddings()
+
+    def clear_ti_embedding(self):
+        self.sj.embedding_db.clear_embedding_dirs()
+        self.sj.embedding_db.load_textual_inversion_embeddings(True)
+
+    def switch_scheduler(self, scheduler_type="ddim"):
+        scheduler_type = scheduler_type.lower()
+        from ppdiffusers import (
+            DDIMScheduler,
+            DDPMScheduler,
+            DEISMultistepScheduler,
+            DPMSolverMultistepScheduler,
+            DPMSolverSinglestepScheduler,
+            EulerAncestralDiscreteScheduler,
+            EulerDiscreteScheduler,
+            HeunDiscreteScheduler,
+            KDPM2AncestralDiscreteScheduler,
+            KDPM2DiscreteScheduler,
+            LMSDiscreteScheduler,
+            PNDMScheduler,
+            UniPCMultistepScheduler,
+        )
+
+        if scheduler_type == "pndm":
+            scheduler = PNDMScheduler.from_config(self.orginal_scheduler_config, skip_prk_steps=True)
+        elif scheduler_type == "lms":
+            scheduler = LMSDiscreteScheduler.from_config(self.orginal_scheduler_config)
+        elif scheduler_type == "heun":
+            scheduler = HeunDiscreteScheduler.from_config(self.orginal_scheduler_config)
+        elif scheduler_type == "euler":
+            scheduler = EulerDiscreteScheduler.from_config(self.orginal_scheduler_config)
+        elif scheduler_type == "euler-ancestral":
+            scheduler = EulerAncestralDiscreteScheduler.from_config(self.orginal_scheduler_config)
+        elif scheduler_type == "dpm-multi":
+            scheduler = DPMSolverMultistepScheduler.from_config(self.orginal_scheduler_config)
+        elif scheduler_type == "dpm-single":
+            scheduler = DPMSolverSinglestepScheduler.from_config(self.orginal_scheduler_config)
+        elif scheduler_type == "kdpm2-ancestral":
+            scheduler = KDPM2AncestralDiscreteScheduler.from_config(self.orginal_scheduler_config)
+        elif scheduler_type == "kdpm2":
+            scheduler = KDPM2DiscreteScheduler.from_config(self.orginal_scheduler_config)
+        elif scheduler_type == "unipc-multi":
+            scheduler = UniPCMultistepScheduler.from_config(self.orginal_scheduler_config)
+        elif scheduler_type == "ddim":
+            scheduler = DDIMScheduler.from_config(
+                self.orginal_scheduler_config,
+                steps_offset=1,
+                clip_sample=False,
+                set_alpha_to_one=False,
+            )
+        elif scheduler_type == "ddpm":
+            scheduler = DDPMScheduler.from_config(
+                self.orginal_scheduler_config,
+            )
+        elif scheduler_type == "deis-multi":
+            scheduler = DEISMultistepScheduler.from_config(
+                self.orginal_scheduler_config,
+            )
+        else:
+            raise ValueError(
+                f"Scheduler of type {scheduler_type} doesn't exist! Please choose in {self.supported_scheduler}!"
+            )
+        self.scheduler = scheduler
+
+    @paddle.no_grad()
+    def _encode_prompt(
+        self,
+        prompt: str,
+        do_classifier_free_guidance: float = 7.5,
+        negative_prompt: str = None,
+        num_inference_steps: int = 50,
+    ):
+        if do_classifier_free_guidance:
+            assert isinstance(negative_prompt, str)
+            negative_prompt = [negative_prompt]
+            uc = get_learned_conditioning(self.sj.clip, negative_prompt, num_inference_steps)
+        else:
+            uc = None
+
+        c = get_multicond_learned_conditioning(self.sj.clip, prompt, num_inference_steps)
+        return c, uc
+
+    def run_safety_checker(self, image, dtype):
+        if self.safety_checker is not None:
+            safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pd")
+            image, has_nsfw_concept = self.safety_checker(
+                images=image, clip_input=safety_checker_input.pixel_values.cast(dtype)
+            )
+        else:
+            has_nsfw_concept = None
+        return image, has_nsfw_concept
+
+    def decode_latents(self, latents):
+        latents = 1 / self.vae.config.scaling_factor * latents
+        image = self.vae.decode(latents).sample
+        image = (image / 2 + 0.5).clip(0, 1)
+        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
+        image = image.transpose([0, 2, 3, 1]).cast("float32").numpy()
+        return image
+
+    def prepare_extra_step_kwargs(self, generator, eta):
+        # 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
+        return extra_step_kwargs
+
+    def check_inputs(
+        self,
+        prompt,
+        image,
+        height,
+        width,
+        callback_steps,
+        negative_prompt=None,
+        controlnet_conditioning_scale=1.0,
+    ):
+        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 (callback_steps is None) or (
+            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
+        ):
+            raise ValueError(
+                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
+                f" {type(callback_steps)}."
+            )
+
+        if prompt is not None and not isinstance(prompt, str):
+            raise ValueError(f"`prompt` has to be of type `str` but is {type(prompt)}")
+
+        if negative_prompt is not None and not isinstance(negative_prompt, str):
+            raise ValueError(f"`negative_prompt` has to be of type `str` but is {type(negative_prompt)}")
+
+        # Check `image`
+
+        if isinstance(self.controlnet, ControlNetModel):
+            self.check_image(image, prompt)
+        else:
+            assert False
+
+        # Check `controlnet_conditioning_scale`
+        if isinstance(self.controlnet, ControlNetModel):
+            if not isinstance(controlnet_conditioning_scale, (float, list, tuple)):
+                raise TypeError(
+                    "For single controlnet: `controlnet_conditioning_scale` must be type `float, list(float) or tuple(float)`."
+                )
+
+    def check_image(self, image, prompt):
+        image_is_pil = isinstance(image, PIL.Image.Image)
+        image_is_tensor = isinstance(image, paddle.Tensor)
+        image_is_pil_list = isinstance(image, list) and isinstance(image[0], PIL.Image.Image)
+        image_is_tensor_list = isinstance(image, list) and isinstance(image[0], paddle.Tensor)
+
+        if not image_is_pil and not image_is_tensor and not image_is_pil_list and not image_is_tensor_list:
+            raise TypeError(
+                "image must be one of PIL image, paddle tensor, list of PIL images, or list of paddle tensors"
+            )
+
+        if image_is_pil:
+            image_batch_size = 1
+        elif image_is_tensor:
+            image_batch_size = image.shape[0]
+        elif image_is_pil_list:
+            image_batch_size = len(image)
+        elif image_is_tensor_list:
+            image_batch_size = len(image)
+
+        if prompt is not None and isinstance(prompt, str):
+            prompt_batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            prompt_batch_size = len(prompt)
+
+        if image_batch_size != 1 and image_batch_size != prompt_batch_size:
+            raise ValueError(
+                f"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {image_batch_size}, prompt batch size: {prompt_batch_size}"
+            )
+
+    def prepare_image(self, image, width, height, dtype):
+        if not isinstance(image, paddle.Tensor):
+            if isinstance(image, PIL.Image.Image):
+                image = [image]
+
+            if isinstance(image[0], PIL.Image.Image):
+                images = []
+                for image_ in image:
+                    image_ = image_.convert("RGB")
+                    image_ = image_.resize((width, height), resample=PIL_INTERPOLATION["lanczos"])
+                    image_ = np.array(image_)
+                    image_ = image_[None, :]
+                    images.append(image_)
+
+                image = np.concatenate(images, axis=0)
+                image = np.array(image).astype(np.float32) / 255.0
+                image = image.transpose(0, 3, 1, 2)
+                image = paddle.to_tensor(image)
+            elif isinstance(image[0], paddle.Tensor):
+                image = paddle.concat(image, axis=0)
+
+        image = image.cast(dtype)
+        return image
+
+    def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, generator, latents=None):
+        shape = [batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor]
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+
+        if latents is None:
+            latents = randn_tensor(shape, generator=generator, dtype=dtype)
+
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = latents * self.scheduler.init_noise_sigma
+        return latents
+
+    def _default_height_width(self, height, width, image):
+        while isinstance(image, list):
+            image = image[0]
+
+        if height is None:
+            if isinstance(image, PIL.Image.Image):
+                height = image.height
+            elif isinstance(image, paddle.Tensor):
+                height = image.shape[3]
+
+            height = (height // 8) * 8  # round down to nearest multiple of 8
+
+        if width is None:
+            if isinstance(image, PIL.Image.Image):
+                width = image.width
+            elif isinstance(image, paddle.Tensor):
+                width = image.shape[2]
+
+            width = (width // 8) * 8  # round down to nearest multiple of 8
+
+        return height, width
+
+    @paddle.no_grad()
+    def __call__(
+        self,
+        prompt: str = None,
+        image: PIL.Image.Image = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 7.5,
+        negative_prompt: str = None,
+        eta: float = 0.0,
+        generator: Optional[Union[paddle.Generator, List[paddle.Generator]]] = None,
+        latents: Optional[paddle.Tensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, paddle.Tensor], None]] = None,
+        callback_steps: Optional[int] = 1,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        clip_skip: int = 0,
+        controlnet_conditioning_scale: Union[float, List[float]] = 1.0,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str`, *optional*):
+                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
+                instead.
+            image (`paddle.Tensor`, `PIL.Image.Image`):
+                The ControlNet input condition. ControlNet uses this input condition to generate guidance to Unet. If
+                the type is specified as `paddle.Tensor`, it is passed to ControlNet as is. `PIL.Image.Image` can
+                also be accepted as an image. The dimensions of the output image defaults to `image`'s dimensions. If
+                height and/or width are passed, `image` is resized according to them. If multiple ControlNets are
+                specified in init, images must be passed as a list such that each element of the list can be correctly
+                batched for input to a single controlnet.
+            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`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead.
+                Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
+            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 (`paddle.Generator` or `List[paddle.Generator]`, *optional*):
+                One or a list of paddle generator(s) to make generation deterministic.
+            latents (`paddle.Tensor`, *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: paddle.Tensor)`.
+            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 `AttnProcessor` as defined under
+                `self.processor` in
+                [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).
+            clip_skip (`int`, *optional*, defaults to 0):
+                CLIP_stop_at_last_layers, if clip_skip < 1, we will use the last_hidden_state from text_encoder.
+            controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0):
+                The outputs of the controlnet are multiplied by `controlnet_conditioning_scale` before they are added
+                to the residual in the original unet. If multiple ControlNets are specified in init, you can set the
+                corresponding scale as a list.
+        Examples:
+
+        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`.
+        """
+        # 0. Default height and width to unet
+        height, width = self._default_height_width(height, width, image)
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt,
+            image,
+            height,
+            width,
+            callback_steps,
+            negative_prompt,
+            controlnet_conditioning_scale,
+        )
+
+        batch_size = 1
+
+        image = self.prepare_image(
+            image=image,
+            width=width,
+            height=height,
+            dtype=self.controlnet.dtype,
+        )
+
+        # 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
+
+        prompts, extra_network_data = parse_prompts([prompt])
+
+        self.sj.clip.CLIP_stop_at_last_layers = clip_skip
+        # 3. Encode input prompt
+        prompt_embeds, negative_prompt_embeds = self._encode_prompt(
+            prompts,
+            do_classifier_free_guidance,
+            negative_prompt,
+            num_inference_steps=num_inference_steps,
+        )
+
+        # 4. Prepare timesteps
+        self.scheduler.set_timesteps(num_inference_steps)
+        timesteps = self.scheduler.timesteps
+
+        # 5. Prepare latent variables
+        num_channels_latents = self.unet.in_channels
+        latents = self.prepare_latents(
+            batch_size,
+            num_channels_latents,
+            height,
+            width,
+            self.unet.dtype,
+            generator,
+            latents,
+        )
+
+        # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
+        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:
+            for i, t in enumerate(timesteps):
+                step = i // self.scheduler.order
+                do_batch = False
+                conds_list, cond_tensor = reconstruct_multicond_batch(prompt_embeds, step)
+                try:
+                    weight = conds_list[0][0][1]
+                except Exception:
+                    weight = 1.0
+                if do_classifier_free_guidance:
+                    uncond_tensor = reconstruct_cond_batch(negative_prompt_embeds, step)
+                    do_batch = cond_tensor.shape[1] == uncond_tensor.shape[1]
+
+                # expand the latents if we are doing classifier free guidance
+                latent_model_input = paddle.concat([latents] * 2) if do_batch else latents
+                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+                if do_batch:
+                    encoder_hidden_states = paddle.concat([uncond_tensor, cond_tensor])
+                    down_block_res_samples, mid_block_res_sample = self.controlnet(
+                        latent_model_input,
+                        t,
+                        encoder_hidden_states=encoder_hidden_states,
+                        controlnet_cond=paddle.concat([image, image]),
+                        conditioning_scale=controlnet_conditioning_scale,
+                        return_dict=False,
+                    )
+                    noise_pred = self.unet(
+                        latent_model_input,
+                        t,
+                        encoder_hidden_states=encoder_hidden_states,
+                        cross_attention_kwargs=cross_attention_kwargs,
+                        down_block_additional_residuals=down_block_res_samples,
+                        mid_block_additional_residual=mid_block_res_sample,
+                    ).sample
+                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                    noise_pred = noise_pred_uncond + weight * guidance_scale * (noise_pred_text - noise_pred_uncond)
+                else:
+                    down_block_res_samples, mid_block_res_sample = self.controlnet(
+                        latent_model_input,
+                        t,
+                        encoder_hidden_states=cond_tensor,
+                        controlnet_cond=image,
+                        conditioning_scale=controlnet_conditioning_scale,
+                        return_dict=False,
+                    )
+                    noise_pred = self.unet(
+                        latent_model_input,
+                        t,
+                        encoder_hidden_states=cond_tensor,
+                        cross_attention_kwargs=cross_attention_kwargs,
+                        down_block_additional_residuals=down_block_res_samples,
+                        mid_block_additional_residual=mid_block_res_sample,
+                    ).sample
+
+                    if do_classifier_free_guidance:
+                        down_block_res_samples, mid_block_res_sample = self.controlnet(
+                            latent_model_input,
+                            t,
+                            encoder_hidden_states=uncond_tensor,
+                            controlnet_cond=image,
+                            conditioning_scale=controlnet_conditioning_scale,
+                            return_dict=False,
+                        )
+                        noise_pred_uncond = self.unet(
+                            latent_model_input,
+                            t,
+                            encoder_hidden_states=uncond_tensor,
+                            cross_attention_kwargs=cross_attention_kwargs,
+                            down_block_additional_residuals=down_block_res_samples,
+                            mid_block_additional_residual=mid_block_res_sample,
+                        ).sample
+                        noise_pred = noise_pred_uncond + weight * guidance_scale * (noise_pred - 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:
+                        callback(i, t, latents)
+
+        if output_type == "latent":
+            image = latents
+            has_nsfw_concept = None
+        elif output_type == "pil":
+            # 8. Post-processing
+            image = self.decode_latents(latents)
+
+            # 9. Run safety checker
+            image, has_nsfw_concept = self.run_safety_checker(image, self.unet.dtype)
+
+            # 10. Convert to PIL
+            image = self.numpy_to_pil(image)
+        else:
+            # 8. Post-processing
+            image = self.decode_latents(latents)
+
+            # 9. Run safety checker
+            image, has_nsfw_concept = self.run_safety_checker(image, self.unet.dtype)
+
+        if not return_dict:
+            return (image, has_nsfw_concept)
+
+        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
+
+
+# clip.py
+import math
+from collections import namedtuple
+
+
+class PromptChunk:
+    """
+    This object contains token ids, weight (multipliers:1.4) and textual inversion embedding info for a chunk of prompt.
+    If a prompt is short, it is represented by one PromptChunk, otherwise, multiple are necessary.
+    Each PromptChunk contains an exact amount of tokens - 77, which includes one for start and end token,
+    so just 75 tokens from prompt.
+    """
+
+    def __init__(self):
+        self.tokens = []
+        self.multipliers = []
+        self.fixes = []
+
+
+PromptChunkFix = namedtuple("PromptChunkFix", ["offset", "embedding"])
+"""An object of this type is a marker showing that textual inversion embedding's vectors have to placed at offset in the prompt
+chunk. Thos objects are found in PromptChunk.fixes and, are placed into FrozenCLIPEmbedderWithCustomWordsBase.hijack.fixes, and finally
+are applied by sd_hijack.EmbeddingsWithFixes's forward function."""
+
+
+class FrozenCLIPEmbedder(nn.Layer):
+    """Uses the CLIP transformer encoder for text (from huggingface)"""
+
+    LAYERS = ["last", "pooled", "hidden"]
+
+    def __init__(self, text_encoder, tokenizer, freeze=True, layer="last", layer_idx=None):
+        super().__init__()
+        assert layer in self.LAYERS
+        self.tokenizer = tokenizer
+        self.text_encoder = text_encoder
+        if freeze:
+            self.freeze()
+        self.layer = layer
+        self.layer_idx = layer_idx
+        if layer == "hidden":
+            assert layer_idx is not None
+            assert 0 <= abs(layer_idx) <= 12
+
+    def freeze(self):
+        self.text_encoder.eval()
+        for param in self.parameters():
+            param.stop_gradient = False
+
+    def forward(self, text):
+        batch_encoding = self.tokenizer(
+            text,
+            truncation=True,
+            max_length=self.tokenizer.model_max_length,
+            padding="max_length",
+            return_tensors="pd",
+        )
+        tokens = batch_encoding["input_ids"]
+        outputs = self.text_encoder(input_ids=tokens, output_hidden_states=self.layer == "hidden", return_dict=True)
+        if self.layer == "last":
+            z = outputs.last_hidden_state
+        elif self.layer == "pooled":
+            z = outputs.pooler_output[:, None, :]
+        else:
+            z = outputs.hidden_states[self.layer_idx]
+        return z
+
+    def encode(self, text):
+        return self(text)
+
+
+class FrozenCLIPEmbedderWithCustomWordsBase(nn.Layer):
+    """A pytorch module that is a wrapper for FrozenCLIPEmbedder module. it enhances FrozenCLIPEmbedder, making it possible to
+    have unlimited prompt length and assign weights to tokens in prompt.
+    """
+
+    def __init__(self, wrapped, hijack):
+        super().__init__()
+
+        self.wrapped = wrapped
+        """Original FrozenCLIPEmbedder module; can also be FrozenOpenCLIPEmbedder or xlmr.BertSeriesModelWithTransformation,
+        depending on model."""
+
+        self.hijack = hijack
+        self.chunk_length = 75
+
+    def empty_chunk(self):
+        """creates an empty PromptChunk and returns it"""
+
+        chunk = PromptChunk()
+        chunk.tokens = [self.id_start] + [self.id_end] * (self.chunk_length + 1)
+        chunk.multipliers = [1.0] * (self.chunk_length + 2)
+        return chunk
+
+    def get_target_prompt_token_count(self, token_count):
+        """returns the maximum number of tokens a prompt of a known length can have before it requires one more PromptChunk to be represented"""
+
+        return math.ceil(max(token_count, 1) / self.chunk_length) * self.chunk_length
+
+    def tokenize(self, texts):
+        """Converts a batch of texts into a batch of token ids"""
+
+        raise NotImplementedError
+
+    def encode_with_text_encoder(self, tokens):
+        """
+        converts a batch of token ids (in python lists) into a single tensor with numeric respresentation of those tokens;
+        All python lists with tokens are assumed to have same length, usually 77.
+        if input is a list with B elements and each element has T tokens, expected output shape is (B, T, C), where C depends on
+        model - can be 768 and 1024.
+        Among other things, this call will read self.hijack.fixes, apply it to its inputs, and clear it (setting it to None).
+        """
+
+        raise NotImplementedError
+
+    def encode_embedding_init_text(self, init_text, nvpt):
+        """Converts text into a tensor with this text's tokens' embeddings. Note that those are embeddings before they are passed through
+        transformers. nvpt is used as a maximum length in tokens. If text produces less teokens than nvpt, only this many is returned."""
+
+        raise NotImplementedError
+
+    def tokenize_line(self, line):
+        """
+        this transforms a single prompt into a list of PromptChunk objects - as many as needed to
+        represent the prompt.
+        Returns the list and the total number of tokens in the prompt.
+        """
+
+        if WebUIStableDiffusionControlNetPipeline.enable_emphasis:
+            parsed = parse_prompt_attention(line)
+        else:
+            parsed = [[line, 1.0]]
+
+        tokenized = self.tokenize([text for text, _ in parsed])
+
+        chunks = []
+        chunk = PromptChunk()
+        token_count = 0
+        last_comma = -1
+
+        def next_chunk(is_last=False):
+            """puts current chunk into the list of results and produces the next one - empty;
+            if is_last is true, tokens <end-of-text> tokens at the end won't add to token_count"""
+            nonlocal token_count
+            nonlocal last_comma
+            nonlocal chunk
+
+            if is_last:
+                token_count += len(chunk.tokens)
+            else:
+                token_count += self.chunk_length
+
+            to_add = self.chunk_length - len(chunk.tokens)
+            if to_add > 0:
+                chunk.tokens += [self.id_end] * to_add
+                chunk.multipliers += [1.0] * to_add
+
+            chunk.tokens = [self.id_start] + chunk.tokens + [self.id_end]
+            chunk.multipliers = [1.0] + chunk.multipliers + [1.0]
+
+            last_comma = -1
+            chunks.append(chunk)
+            chunk = PromptChunk()
+
+        for tokens, (text, weight) in zip(tokenized, parsed):
+            if text == "BREAK" and weight == -1:
+                next_chunk()
+                continue
+
+            position = 0
+            while position < len(tokens):
+                token = tokens[position]
+
+                if token == self.comma_token:
+                    last_comma = len(chunk.tokens)
+
+                # this is when we are at the end of alloted 75 tokens for the current chunk, and the current token is not a comma. opts.comma_padding_backtrack
+                # is a setting that specifies that if there is a comma nearby, the text after the comma should be moved out of this chunk and into the next.
+                elif (
+                    WebUIStableDiffusionControlNetPipeline.comma_padding_backtrack != 0
+                    and len(chunk.tokens) == self.chunk_length
+                    and last_comma != -1
+                    and len(chunk.tokens) - last_comma
+                    <= WebUIStableDiffusionControlNetPipeline.comma_padding_backtrack
+                ):
+                    break_location = last_comma + 1
+
+                    reloc_tokens = chunk.tokens[break_location:]
+                    reloc_mults = chunk.multipliers[break_location:]
+
+                    chunk.tokens = chunk.tokens[:break_location]
+                    chunk.multipliers = chunk.multipliers[:break_location]
+
+                    next_chunk()
+                    chunk.tokens = reloc_tokens
+                    chunk.multipliers = reloc_mults
+
+                if len(chunk.tokens) == self.chunk_length:
+                    next_chunk()
+
+                embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(
+                    tokens, position
+                )
+                if embedding is None:
+                    chunk.tokens.append(token)
+                    chunk.multipliers.append(weight)
+                    position += 1
+                    continue
+
+                emb_len = int(embedding.vec.shape[0])
+                if len(chunk.tokens) + emb_len > self.chunk_length:
+                    next_chunk()
+
+                chunk.fixes.append(PromptChunkFix(len(chunk.tokens), embedding))
+
+                chunk.tokens += [0] * emb_len
+                chunk.multipliers += [weight] * emb_len
+                position += embedding_length_in_tokens
+
+        if len(chunk.tokens) > 0 or len(chunks) == 0:
+            next_chunk(is_last=True)
+
+        return chunks, token_count
+
+    def process_texts(self, texts):
+        """
+        Accepts a list of texts and calls tokenize_line() on each, with cache. Returns the list of results and maximum
+        length, in tokens, of all texts.
+        """
+
+        token_count = 0
+
+        cache = {}
+        batch_chunks = []
+        for line in texts:
+            if line in cache:
+                chunks = cache[line]
+            else:
+                chunks, current_token_count = self.tokenize_line(line)
+                token_count = max(current_token_count, token_count)
+
+                cache[line] = chunks
+
+            batch_chunks.append(chunks)
+
+        return batch_chunks, token_count
+
+    def forward(self, texts):
+        """
+        Accepts an array of texts; Passes texts through transformers network to create a tensor with numerical representation of those texts.
+        Returns a tensor with shape of (B, T, C), where B is length of the array; T is length, in tokens, of texts (including padding) - T will
+        be a multiple of 77; and C is dimensionality of each token - for SD1 it's 768, and for SD2 it's 1024.
+        An example shape returned by this function can be: (2, 77, 768).
+        Webui usually sends just one text at a time through this function - the only time when texts is an array with more than one elemenet
+        is when you do prompt editing: "a picture of a [cat:dog:0.4] eating ice cream"
+        """
+
+        batch_chunks, token_count = self.process_texts(texts)
+
+        used_embeddings = {}
+        chunk_count = max([len(x) for x in batch_chunks])
+
+        zs = []
+        for i in range(chunk_count):
+            batch_chunk = [chunks[i] if i < len(chunks) else self.empty_chunk() for chunks in batch_chunks]
+
+            tokens = [x.tokens for x in batch_chunk]
+            multipliers = [x.multipliers for x in batch_chunk]
+            self.hijack.fixes = [x.fixes for x in batch_chunk]
+
+            for fixes in self.hijack.fixes:
+                for position, embedding in fixes:
+                    used_embeddings[embedding.name] = embedding
+
+            z = self.process_tokens(tokens, multipliers)
+            zs.append(z)
+
+        if len(used_embeddings) > 0:
+            embeddings_list = ", ".join(
+                [f"{name} [{embedding.checksum()}]" for name, embedding in used_embeddings.items()]
+            )
+            self.hijack.comments.append(f"Used embeddings: {embeddings_list}")
+
+        return paddle.concat(zs, axis=1)
+
+    def process_tokens(self, remade_batch_tokens, batch_multipliers):
+        """
+        sends one single prompt chunk to be encoded by transformers neural network.
+        remade_batch_tokens is a batch of tokens - a list, where every element is a list of tokens; usually
+        there are exactly 77 tokens in the list. batch_multipliers is the same but for multipliers instead of tokens.
+        Multipliers are used to give more or less weight to the outputs of transformers network. Each multiplier
+        corresponds to one token.
+        """
+        tokens = paddle.to_tensor(remade_batch_tokens)
+
+        # this is for SD2: SD1 uses the same token for padding and end of text, while SD2 uses different ones.
+        if self.id_end != self.id_pad:
+            for batch_pos in range(len(remade_batch_tokens)):
+                index = remade_batch_tokens[batch_pos].index(self.id_end)
+                tokens[batch_pos, index + 1 : tokens.shape[1]] = self.id_pad
+
+        z = self.encode_with_text_encoder(tokens)
+
+        # restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
+        batch_multipliers = paddle.to_tensor(batch_multipliers)
+        original_mean = z.mean()
+        z = z * batch_multipliers.reshape(
+            batch_multipliers.shape
+            + [
+                1,
+            ]
+        ).expand(z.shape)
+        new_mean = z.mean()
+        z = z * (original_mean / new_mean)
+
+        return z
+
+
+class FrozenCLIPEmbedderWithCustomWords(FrozenCLIPEmbedderWithCustomWordsBase):
+    def __init__(self, wrapped, hijack, CLIP_stop_at_last_layers=-1):
+        super().__init__(wrapped, hijack)
+        self.CLIP_stop_at_last_layers = CLIP_stop_at_last_layers
+        self.tokenizer = wrapped.tokenizer
+
+        vocab = self.tokenizer.get_vocab()
+
+        self.comma_token = vocab.get(",</w>", None)
+
+        self.token_mults = {}
+        tokens_with_parens = [(k, v) for k, v in vocab.items() if "(" in k or ")" in k or "[" in k or "]" in k]
+        for text, ident in tokens_with_parens:
+            mult = 1.0
+            for c in text:
+                if c == "[":
+                    mult /= 1.1
+                if c == "]":
+                    mult *= 1.1
+                if c == "(":
+                    mult *= 1.1
+                if c == ")":
+                    mult /= 1.1
+
+            if mult != 1.0:
+                self.token_mults[ident] = mult
+
+        self.id_start = self.wrapped.tokenizer.bos_token_id
+        self.id_end = self.wrapped.tokenizer.eos_token_id
+        self.id_pad = self.id_end
+
+    def tokenize(self, texts):
+        tokenized = self.wrapped.tokenizer(texts, truncation=False, add_special_tokens=False)["input_ids"]
+
+        return tokenized
+
+    def encode_with_text_encoder(self, tokens):
+        output_hidden_states = self.CLIP_stop_at_last_layers > 1
+        outputs = self.wrapped.text_encoder(
+            input_ids=tokens, output_hidden_states=output_hidden_states, return_dict=True
+        )
+
+        if output_hidden_states:
+            z = outputs.hidden_states[-self.CLIP_stop_at_last_layers]
+            z = self.wrapped.text_encoder.text_model.ln_final(z)
+        else:
+            z = outputs.last_hidden_state
+
+        return z
+
+    def encode_embedding_init_text(self, init_text, nvpt):
+        embedding_layer = self.wrapped.text_encoder.text_model
+        ids = self.wrapped.tokenizer(init_text, max_length=nvpt, return_tensors="pd", add_special_tokens=False)[
+            "input_ids"
+        ]
+        embedded = embedding_layer.token_embedding.wrapped(ids).squeeze(0)
+
+        return embedded
+
+
+# extra_networks.py
+import re
+from collections import defaultdict
+
+
+class ExtraNetworkParams:
+    def __init__(self, items=None):
+        self.items = items or []
+
+
+re_extra_net = re.compile(r"<(\w+):([^>]+)>")
+
+
+def parse_prompt(prompt):
+    res = defaultdict(list)
+
+    def found(m):
+        name = m.group(1)
+        args = m.group(2)
+
+        res[name].append(ExtraNetworkParams(items=args.split(":")))
+
+        return ""
+
+    prompt = re.sub(re_extra_net, found, prompt)
+
+    return prompt, res
+
+
+def parse_prompts(prompts):
+    res = []
+    extra_data = None
+
+    for prompt in prompts:
+        updated_prompt, parsed_extra_data = parse_prompt(prompt)
+
+        if extra_data is None:
+            extra_data = parsed_extra_data
+
+        res.append(updated_prompt)
+
+    return res, extra_data
+
+
+# image_embeddings.py
+
+import base64
+import json
+import zlib
+
+import numpy as np
+from PIL import Image
+
+
+class EmbeddingDecoder(json.JSONDecoder):
+    def __init__(self, *args, **kwargs):
+        json.JSONDecoder.__init__(self, object_hook=self.object_hook, *args, **kwargs)
+
+    def object_hook(self, d):
+        if "TORCHTENSOR" in d:
+            return paddle.to_tensor(np.array(d["TORCHTENSOR"]))
+        return d
+
+
+def embedding_from_b64(data):
+    d = base64.b64decode(data)
+    return json.loads(d, cls=EmbeddingDecoder)
+
+
+def lcg(m=2**32, a=1664525, c=1013904223, seed=0):
+    while True:
+        seed = (a * seed + c) % m
+        yield seed % 255
+
+
+def xor_block(block):
+    g = lcg()
+    randblock = np.array([next(g) for _ in range(np.product(block.shape))]).astype(np.uint8).reshape(block.shape)
+    return np.bitwise_xor(block.astype(np.uint8), randblock & 0x0F)
+
+
+def crop_black(img, tol=0):
+    mask = (img > tol).all(2)
+    mask0, mask1 = mask.any(0), mask.any(1)
+    col_start, col_end = mask0.argmax(), mask.shape[1] - mask0[::-1].argmax()
+    row_start, row_end = mask1.argmax(), mask.shape[0] - mask1[::-1].argmax()
+    return img[row_start:row_end, col_start:col_end]
+
+
+def extract_image_data_embed(image):
+    d = 3
+    outarr = (
+        crop_black(np.array(image.convert("RGB").getdata()).reshape(image.size[1], image.size[0], d).astype(np.uint8))
+        & 0x0F
+    )
+    black_cols = np.where(np.sum(outarr, axis=(0, 2)) == 0)
+    if black_cols[0].shape[0] < 2:
+        print("No Image data blocks found.")
+        return None
+
+    data_block_lower = outarr[:, : black_cols[0].min(), :].astype(np.uint8)
+    data_block_upper = outarr[:, black_cols[0].max() + 1 :, :].astype(np.uint8)
+
+    data_block_lower = xor_block(data_block_lower)
+    data_block_upper = xor_block(data_block_upper)
+
+    data_block = (data_block_upper << 4) | (data_block_lower)
+    data_block = data_block.flatten().tobytes()
+
+    data = zlib.decompress(data_block)
+    return json.loads(data, cls=EmbeddingDecoder)
+
+
+# prompt_parser.py
+import re
+from collections import namedtuple
+from typing import List
+
+import lark
+
+# a prompt like this: "fantasy landscape with a [mountain:lake:0.25] and [an oak:a christmas tree:0.75][ in foreground::0.6][ in background:0.25] [shoddy:masterful:0.5]"
+# will be represented with prompt_schedule like this (assuming steps=100):
+# [25, 'fantasy landscape with a mountain and an oak in foreground shoddy']
+# [50, 'fantasy landscape with a lake and an oak in foreground in background shoddy']
+# [60, 'fantasy landscape with a lake and an oak in foreground in background masterful']
+# [75, 'fantasy landscape with a lake and an oak in background masterful']
+# [100, 'fantasy landscape with a lake and a christmas tree in background masterful']
+
+schedule_parser = lark.Lark(
+    r"""
+!start: (prompt | /[][():]/+)*
+prompt: (emphasized | scheduled | alternate | plain | WHITESPACE)*
+!emphasized: "(" prompt ")"
+        | "(" prompt ":" prompt ")"
+        | "[" prompt "]"
+scheduled: "[" [prompt ":"] prompt ":" [WHITESPACE] NUMBER "]"
+alternate: "[" prompt ("|" prompt)+ "]"
+WHITESPACE: /\s+/
+plain: /([^\\\[\]():|]|\\.)+/
+%import common.SIGNED_NUMBER -> NUMBER
+"""
+)
+
+
+def get_learned_conditioning_prompt_schedules(prompts, steps):
+    """
+    >>> g = lambda p: get_learned_conditioning_prompt_schedules([p], 10)[0]
+    >>> g("test")
+    [[10, 'test']]
+    >>> g("a [b:3]")
+    [[3, 'a '], [10, 'a b']]
+    >>> g("a [b: 3]")
+    [[3, 'a '], [10, 'a b']]
+    >>> g("a [[[b]]:2]")
+    [[2, 'a '], [10, 'a [[b]]']]
+    >>> g("[(a:2):3]")
+    [[3, ''], [10, '(a:2)']]
+    >>> g("a [b : c : 1] d")
+    [[1, 'a b  d'], [10, 'a  c  d']]
+    >>> g("a[b:[c:d:2]:1]e")
+    [[1, 'abe'], [2, 'ace'], [10, 'ade']]
+    >>> g("a [unbalanced")
+    [[10, 'a [unbalanced']]
+    >>> g("a [b:.5] c")
+    [[5, 'a  c'], [10, 'a b c']]
+    >>> g("a [{b|d{:.5] c")  # not handling this right now
+    [[5, 'a  c'], [10, 'a {b|d{ c']]
+    >>> g("((a][:b:c [d:3]")
+    [[3, '((a][:b:c '], [10, '((a][:b:c d']]
+    >>> g("[a|(b:1.1)]")
+    [[1, 'a'], [2, '(b:1.1)'], [3, 'a'], [4, '(b:1.1)'], [5, 'a'], [6, '(b:1.1)'], [7, 'a'], [8, '(b:1.1)'], [9, 'a'], [10, '(b:1.1)']]
+    """
+
+    def collect_steps(steps, tree):
+        l = [steps]
+
+        class CollectSteps(lark.Visitor):
+            def scheduled(self, tree):
+                tree.children[-1] = float(tree.children[-1])
+                if tree.children[-1] < 1:
+                    tree.children[-1] *= steps
+                tree.children[-1] = min(steps, int(tree.children[-1]))
+                l.append(tree.children[-1])
+
+            def alternate(self, tree):
+                l.extend(range(1, steps + 1))
+
+        CollectSteps().visit(tree)
+        return sorted(set(l))
+
+    def at_step(step, tree):
+        class AtStep(lark.Transformer):
+            def scheduled(self, args):
+                before, after, _, when = args
+                yield before or () if step <= when else after
+
+            def alternate(self, args):
+                yield next(args[(step - 1) % len(args)])
+
+            def start(self, args):
+                def flatten(x):
+                    if type(x) == str:
+                        yield x
+                    else:
+                        for gen in x:
+                            yield from flatten(gen)
+
+                return "".join(flatten(args))
+
+            def plain(self, args):
+                yield args[0].value
+
+            def __default__(self, data, children, meta):
+                for child in children:
+                    yield child
+
+        return AtStep().transform(tree)
+
+    def get_schedule(prompt):
+        try:
+            tree = schedule_parser.parse(prompt)
+        except lark.exceptions.LarkError:
+            if 0:
+                import traceback
+
+                traceback.print_exc()
+            return [[steps, prompt]]
+        return [[t, at_step(t, tree)] for t in collect_steps(steps, tree)]
+
+    promptdict = {prompt: get_schedule(prompt) for prompt in set(prompts)}
+    return [promptdict[prompt] for prompt in prompts]
+
+
+ScheduledPromptConditioning = namedtuple("ScheduledPromptConditioning", ["end_at_step", "cond"])
+
+
+def get_learned_conditioning(model, prompts, steps):
+    """converts a list of prompts into a list of prompt schedules - each schedule is a list of ScheduledPromptConditioning, specifying the comdition (cond),
+    and the sampling step at which this condition is to be replaced by the next one.
+
+    Input:
+    (model, ['a red crown', 'a [blue:green:5] jeweled crown'], 20)
+
+    Output:
+    [
+        [
+            ScheduledPromptConditioning(end_at_step=20, cond=tensor([[-0.3886,  0.0229, -0.0523,  ..., -0.4901, -0.3066,  0.0674], ..., [ 0.3317, -0.5102, -0.4066,  ...,  0.4119, -0.7647, -1.0160]], device='cuda:0'))
+        ],
+        [
+            ScheduledPromptConditioning(end_at_step=5, cond=tensor([[-0.3886,  0.0229, -0.0522,  ..., -0.4901, -0.3067,  0.0673], ..., [-0.0192,  0.3867, -0.4644,  ...,  0.1135, -0.3696, -0.4625]], device='cuda:0')),
+            ScheduledPromptConditioning(end_at_step=20, cond=tensor([[-0.3886,  0.0229, -0.0522,  ..., -0.4901, -0.3067,  0.0673], ..., [-0.7352, -0.4356, -0.7888,  ...,  0.6994, -0.4312, -1.2593]], device='cuda:0'))
+        ]
+    ]
+    """
+    res = []
+
+    prompt_schedules = get_learned_conditioning_prompt_schedules(prompts, steps)
+    cache = {}
+
+    for prompt, prompt_schedule in zip(prompts, prompt_schedules):
+
+        cached = cache.get(prompt, None)
+        if cached is not None:
+            res.append(cached)
+            continue
+
+        texts = [x[1] for x in prompt_schedule]
+        conds = model(texts)
+
+        cond_schedule = []
+        for i, (end_at_step, text) in enumerate(prompt_schedule):
+            cond_schedule.append(ScheduledPromptConditioning(end_at_step, conds[i]))
+
+        cache[prompt] = cond_schedule
+        res.append(cond_schedule)
+
+    return res
+
+
+re_AND = re.compile(r"\bAND\b")
+re_weight = re.compile(r"^(.*?)(?:\s*:\s*([-+]?(?:\d+\.?|\d*\.\d+)))?\s*$")
+
+
+def get_multicond_prompt_list(prompts):
+    res_indexes = []
+
+    prompt_flat_list = []
+    prompt_indexes = {}
+
+    for prompt in prompts:
+        subprompts = re_AND.split(prompt)
+
+        indexes = []
+        for subprompt in subprompts:
+            match = re_weight.search(subprompt)
+
+            text, weight = match.groups() if match is not None else (subprompt, 1.0)
+
+            weight = float(weight) if weight is not None else 1.0
+
+            index = prompt_indexes.get(text, None)
+            if index is None:
+                index = len(prompt_flat_list)
+                prompt_flat_list.append(text)
+                prompt_indexes[text] = index
+
+            indexes.append((index, weight))
+
+        res_indexes.append(indexes)
+
+    return res_indexes, prompt_flat_list, prompt_indexes
+
+
+class ComposableScheduledPromptConditioning:
+    def __init__(self, schedules, weight=1.0):
+        self.schedules: List[ScheduledPromptConditioning] = schedules
+        self.weight: float = weight
+
+
+class MulticondLearnedConditioning:
+    def __init__(self, shape, batch):
+        self.shape: tuple = shape  # the shape field is needed to send this object to DDIM/PLMS
+        self.batch: List[List[ComposableScheduledPromptConditioning]] = batch
+
+
+def get_multicond_learned_conditioning(model, prompts, steps) -> MulticondLearnedConditioning:
+    """same as get_learned_conditioning, but returns a list of ScheduledPromptConditioning along with the weight objects for each prompt.
+    For each prompt, the list is obtained by splitting the prompt using the AND separator.
+
+    https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/
+    """
+
+    res_indexes, prompt_flat_list, prompt_indexes = get_multicond_prompt_list(prompts)
+
+    learned_conditioning = get_learned_conditioning(model, prompt_flat_list, steps)
+
+    res = []
+    for indexes in res_indexes:
+        res.append([ComposableScheduledPromptConditioning(learned_conditioning[i], weight) for i, weight in indexes])
+
+    return MulticondLearnedConditioning(shape=(len(prompts),), batch=res)
+
+
+def reconstruct_cond_batch(c: List[List[ScheduledPromptConditioning]], current_step):
+    param = c[0][0].cond
+    res = paddle.zeros(
+        [
+            len(c),
+        ]
+        + param.shape,
+        dtype=param.dtype,
+    )
+    for i, cond_schedule in enumerate(c):
+        target_index = 0
+        for current, (end_at, cond) in enumerate(cond_schedule):
+            if current_step <= end_at:
+                target_index = current
+                break
+        res[i] = cond_schedule[target_index].cond
+
+    return res
+
+
+def reconstruct_multicond_batch(c: MulticondLearnedConditioning, current_step):
+    param = c.batch[0][0].schedules[0].cond
+
+    tensors = []
+    conds_list = []
+
+    for batch_no, composable_prompts in enumerate(c.batch):
+        conds_for_batch = []
+
+        for cond_index, composable_prompt in enumerate(composable_prompts):
+            target_index = 0
+            for current, (end_at, cond) in enumerate(composable_prompt.schedules):
+                if current_step <= end_at:
+                    target_index = current
+                    break
+
+            conds_for_batch.append((len(tensors), composable_prompt.weight))
+            tensors.append(composable_prompt.schedules[target_index].cond)
+
+        conds_list.append(conds_for_batch)
+
+    # if prompts have wildly different lengths above the limit we'll get tensors fo different shapes
+    # and won't be able to torch.stack them. So this fixes that.
+    token_count = max([x.shape[0] for x in tensors])
+    for i in range(len(tensors)):
+        if tensors[i].shape[0] != token_count:
+            last_vector = tensors[i][-1:]
+            last_vector_repeated = last_vector.tile([token_count - tensors[i].shape[0], 1])
+            tensors[i] = paddle.concat([tensors[i], last_vector_repeated], axis=0)
+
+    return conds_list, paddle.stack(tensors).cast(dtype=param.dtype)
+
+
+re_attention = re.compile(
+    r"""
+\\\(|
+\\\)|
+\\\[|
+\\]|
+\\\\|
+\\|
+\(|
+\[|
+:([+-]?[.\d]+)\)|
+\)|
+]|
+[^\\()\[\]:]+|
+:
+""",
+    re.X,
+)
+
+re_break = re.compile(r"\s*\bBREAK\b\s*", re.S)
+
+
+def parse_prompt_attention(text):
+    """
+    Parses a string with attention tokens and returns a list of pairs: text and its associated weight.
+    Accepted tokens are:
+      (abc) - increases attention to abc by a multiplier of 1.1
+      (abc:3.12) - increases attention to abc by a multiplier of 3.12
+      [abc] - decreases attention to abc by a multiplier of 1.1
+      \( - literal character '('
+      \[ - literal character '['
+      \) - literal character ')'
+      \] - literal character ']'
+      \\ - literal character '\'
+      anything else - just text
+
+    >>> parse_prompt_attention('normal text')
+    [['normal text', 1.0]]
+    >>> parse_prompt_attention('an (important) word')
+    [['an ', 1.0], ['important', 1.1], [' word', 1.0]]
+    >>> parse_prompt_attention('(unbalanced')
+    [['unbalanced', 1.1]]
+    >>> parse_prompt_attention('\(literal\]')
+    [['(literal]', 1.0]]
+    >>> parse_prompt_attention('(unnecessary)(parens)')
+    [['unnecessaryparens', 1.1]]
+    >>> parse_prompt_attention('a (((house:1.3)) [on] a (hill:0.5), sun, (((sky))).')
+    [['a ', 1.0],
+     ['house', 1.5730000000000004],
+     [' ', 1.1],
+     ['on', 1.0],
+     [' a ', 1.1],
+     ['hill', 0.55],
+     [', sun, ', 1.1],
+     ['sky', 1.4641000000000006],
+     ['.', 1.1]]
+    """
+
+    res = []
+    round_brackets = []
+    square_brackets = []
+
+    round_bracket_multiplier = 1.1
+    square_bracket_multiplier = 1 / 1.1
+
+    def multiply_range(start_position, multiplier):
+        for p in range(start_position, len(res)):
+            res[p][1] *= multiplier
+
+    for m in re_attention.finditer(text):
+        text = m.group(0)
+        weight = m.group(1)
+
+        if text.startswith("\\"):
+            res.append([text[1:], 1.0])
+        elif text == "(":
+            round_brackets.append(len(res))
+        elif text == "[":
+            square_brackets.append(len(res))
+        elif weight is not None and len(round_brackets) > 0:
+            multiply_range(round_brackets.pop(), float(weight))
+        elif text == ")" and len(round_brackets) > 0:
+            multiply_range(round_brackets.pop(), round_bracket_multiplier)
+        elif text == "]" and len(square_brackets) > 0:
+            multiply_range(square_brackets.pop(), square_bracket_multiplier)
+        else:
+            parts = re.split(re_break, text)
+            for i, part in enumerate(parts):
+                if i > 0:
+                    res.append(["BREAK", -1])
+                res.append([part, 1.0])
+
+    for pos in round_brackets:
+        multiply_range(pos, round_bracket_multiplier)
+
+    for pos in square_brackets:
+        multiply_range(pos, square_bracket_multiplier)
+
+    if len(res) == 0:
+        res = [["", 1.0]]
+
+    # merge runs of identical weights
+    i = 0
+    while i + 1 < len(res):
+        if res[i][1] == res[i + 1][1]:
+            res[i][0] += res[i + 1][0]
+            res.pop(i + 1)
+        else:
+            i += 1
+
+    return res
+
+
+# sd_hijack.py
+
+
+class StableDiffusionModelHijack:
+    fixes = None
+    comments = []
+    layers = None
+    circular_enabled = False
+
+    def __init__(self, clip_model, embeddings_dir=None, CLIP_stop_at_last_layers=-1):
+        model_embeddings = clip_model.text_encoder.text_model
+        model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.token_embedding, self)
+        clip_model = FrozenCLIPEmbedderWithCustomWords(
+            clip_model, self, CLIP_stop_at_last_layers=CLIP_stop_at_last_layers
+        )
+
+        self.embedding_db = EmbeddingDatabase(clip_model)
+        self.embedding_db.add_embedding_dir(embeddings_dir)
+
+        # hack this!
+        self.clip = clip_model
+
+        def flatten(el):
+            flattened = [flatten(children) for children in el.children()]
+            res = [el]
+            for c in flattened:
+                res += c
+            return res
+
+        self.layers = flatten(clip_model)
+
+    def clear_comments(self):
+        self.comments = []
+
+    def get_prompt_lengths(self, text):
+        _, token_count = self.clip.process_texts([text])
+
+        return token_count, self.clip.get_target_prompt_token_count(token_count)
+
+
+class EmbeddingsWithFixes(nn.Layer):
+    def __init__(self, wrapped, embeddings):
+        super().__init__()
+        self.wrapped = wrapped
+        self.embeddings = embeddings
+
+    def forward(self, input_ids):
+        batch_fixes = self.embeddings.fixes
+        self.embeddings.fixes = None
+
+        inputs_embeds = self.wrapped(input_ids)
+
+        if batch_fixes is None or len(batch_fixes) == 0 or max([len(x) for x in batch_fixes]) == 0:
+            return inputs_embeds
+
+        vecs = []
+        for fixes, tensor in zip(batch_fixes, inputs_embeds):
+            for offset, embedding in fixes:
+                emb = embedding.vec.cast(self.wrapped.dtype)
+                emb_len = min(tensor.shape[0] - offset - 1, emb.shape[0])
+                tensor = paddle.concat([tensor[0 : offset + 1], emb[0:emb_len], tensor[offset + 1 + emb_len :]])
+
+            vecs.append(tensor)
+
+        return paddle.stack(vecs)
+
+
+# textual_inversion.py
+
+import os
+import sys
+import traceback
+
+
+class Embedding:
+    def __init__(self, vec, name, step=None):
+        self.vec = vec
+        self.name = name
+        self.step = step
+        self.shape = None
+        self.vectors = 0
+        self.cached_checksum = None
+        self.sd_checkpoint = None
+        self.sd_checkpoint_name = None
+        self.optimizer_state_dict = None
+        self.filename = None
+
+    def save(self, filename):
+        embedding_data = {
+            "string_to_token": {"*": 265},
+            "string_to_param": {"*": self.vec},
+            "name": self.name,
+            "step": self.step,
+            "sd_checkpoint": self.sd_checkpoint,
+            "sd_checkpoint_name": self.sd_checkpoint_name,
+        }
+
+        paddle.save(embedding_data, filename)
+
+    def checksum(self):
+        if self.cached_checksum is not None:
+            return self.cached_checksum
+
+        def const_hash(a):
+            r = 0
+            for v in a:
+                r = (r * 281 ^ int(v) * 997) & 0xFFFFFFFF
+            return r
+
+        self.cached_checksum = f"{const_hash(self.vec.flatten() * 100) & 0xffff:04x}"
+        return self.cached_checksum
+
+
+class DirWithTextualInversionEmbeddings:
+    def __init__(self, path):
+        self.path = path
+        self.mtime = None
+
+    def has_changed(self):
+        if not os.path.isdir(self.path):
+            return False
+
+        mt = os.path.getmtime(self.path)
+        if self.mtime is None or mt > self.mtime:
+            return True
+
+    def update(self):
+        if not os.path.isdir(self.path):
+            return
+
+        self.mtime = os.path.getmtime(self.path)
+
+
+class EmbeddingDatabase:
+    def __init__(self, clip):
+        self.clip = clip
+        self.ids_lookup = {}
+        self.word_embeddings = {}
+        self.skipped_embeddings = {}
+        self.expected_shape = -1
+        self.embedding_dirs = {}
+        self.previously_displayed_embeddings = ()
+
+    def add_embedding_dir(self, path):
+        if path is not None:
+            self.embedding_dirs[path] = DirWithTextualInversionEmbeddings(path)
+
+    def clear_embedding_dirs(self):
+        self.embedding_dirs.clear()
+
+    def register_embedding(self, embedding, model):
+        self.word_embeddings[embedding.name] = embedding
+
+        ids = model.tokenize([embedding.name])[0]
+
+        first_id = ids[0]
+        if first_id not in self.ids_lookup:
+            self.ids_lookup[first_id] = []
+
+        self.ids_lookup[first_id] = sorted(
+            self.ids_lookup[first_id] + [(ids, embedding)], key=lambda x: len(x[0]), reverse=True
+        )
+
+        return embedding
+
+    def get_expected_shape(self):
+        vec = self.clip.encode_embedding_init_text(",", 1)
+        return vec.shape[1]
+
+    def load_from_file(self, path, filename):
+        name, ext = os.path.splitext(filename)
+        ext = ext.upper()
+
+        if ext in [".PNG", ".WEBP", ".JXL", ".AVIF"]:
+            _, second_ext = os.path.splitext(name)
+            if second_ext.upper() == ".PREVIEW":
+                return
+
+            embed_image = Image.open(path)
+            if hasattr(embed_image, "text") and "sd-ti-embedding" in embed_image.text:
+                data = embedding_from_b64(embed_image.text["sd-ti-embedding"])
+                name = data.get("name", name)
+            else:
+                data = extract_image_data_embed(embed_image)
+                if data:
+                    name = data.get("name", name)
+                else:
+                    # if data is None, means this is not an embeding, just a preview image
+                    return
+        elif ext in [".BIN", ".PT"]:
+            data = torch_load(path)
+        elif ext in [".SAFETENSORS"]:
+            data = safetensors_load(path)
+        else:
+            return
+
+        # textual inversion embeddings
+        if "string_to_param" in data:
+            param_dict = data["string_to_param"]
+            if hasattr(param_dict, "_parameters"):
+                param_dict = getattr(param_dict, "_parameters")
+            assert len(param_dict) == 1, "embedding file has multiple terms in it"
+            emb = next(iter(param_dict.items()))[1]
+        # diffuser concepts
+        elif type(data) == dict and type(next(iter(data.values()))) == paddle.Tensor:
+            assert len(data.keys()) == 1, "embedding file has multiple terms in it"
+
+            emb = next(iter(data.values()))
+            if len(emb.shape) == 1:
+                emb = emb.unsqueeze(0)
+        else:
+            raise Exception(
+                f"Couldn't identify {filename} as neither textual inversion embedding nor diffuser concept."
+            )
+
+        with paddle.no_grad():
+            if hasattr(emb, "detach"):
+                emb = emb.detach()
+            if hasattr(emb, "cpu"):
+                emb = emb.cpu()
+            if hasattr(emb, "numpy"):
+                emb = emb.numpy()
+            emb = paddle.to_tensor(emb)
+            vec = emb.detach().cast(paddle.float32)
+        embedding = Embedding(vec, name)
+        embedding.step = data.get("step", None)
+        embedding.sd_checkpoint = data.get("sd_checkpoint", None)
+        embedding.sd_checkpoint_name = data.get("sd_checkpoint_name", None)
+        embedding.vectors = vec.shape[0]
+        embedding.shape = vec.shape[-1]
+        embedding.filename = path
+
+        if self.expected_shape == -1 or self.expected_shape == embedding.shape:
+            self.register_embedding(embedding, self.clip)
+        else:
+            self.skipped_embeddings[name] = embedding
+
+    def load_from_dir(self, embdir):
+        if not os.path.isdir(embdir.path):
+            return
+
+        for root, dirs, fns in os.walk(embdir.path, followlinks=True):
+            for fn in fns:
+                try:
+                    fullfn = os.path.join(root, fn)
+
+                    if os.stat(fullfn).st_size == 0:
+                        continue
+
+                    self.load_from_file(fullfn, fn)
+                except Exception:
+                    print(f"Error loading embedding {fn}:", file=sys.stderr)
+                    print(traceback.format_exc(), file=sys.stderr)
+                    continue
+
+    def load_textual_inversion_embeddings(self, force_reload=False):
+        if not force_reload:
+            need_reload = False
+            for path, embdir in self.embedding_dirs.items():
+                if embdir.has_changed():
+                    need_reload = True
+                    break
+
+            if not need_reload:
+                return
+
+        self.ids_lookup.clear()
+        self.word_embeddings.clear()
+        self.skipped_embeddings.clear()
+        self.expected_shape = self.get_expected_shape()
+
+        for path, embdir in self.embedding_dirs.items():
+            self.load_from_dir(embdir)
+            embdir.update()
+
+        displayed_embeddings = (tuple(self.word_embeddings.keys()), tuple(self.skipped_embeddings.keys()))
+        if self.previously_displayed_embeddings != displayed_embeddings:
+            self.previously_displayed_embeddings = displayed_embeddings
+            print(
+                f"Textual inversion embeddings loaded({len(self.word_embeddings)}): {', '.join(self.word_embeddings.keys())}"
+            )
+            if len(self.skipped_embeddings) > 0:
+                print(
+                    f"Textual inversion embeddings skipped({len(self.skipped_embeddings)}): {', '.join(self.skipped_embeddings.keys())}"
+                )
+
+    def find_embedding_at_position(self, tokens, offset):
+        token = tokens[offset]
+        possible_matches = self.ids_lookup.get(token, None)
+
+        if possible_matches is None:
+            return None, None
+
+        for ids, embedding in possible_matches:
+            if tokens[offset : offset + len(ids)] == ids:
+                return embedding, len(ids)
+
+        return None, None