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@@ -533,3 +533,4 @@ moondream/lib/python3.10/site-packages/torch/testing/_internal/distributed/__pyc
 moondream/lib/python3.10/site-packages/torch/_decomp/__pycache__/decompositions.cpython-310.pyc filter=lfs diff=lfs merge=lfs -text
 moondream/lib/python3.10/site-packages/sympy/printing/pretty/tests/__pycache__/test_pretty.cpython-310.pyc filter=lfs diff=lfs merge=lfs -text
 moondream/lib/python3.10/site-packages/torch/testing/_internal/__pycache__/common_nn.cpython-310.pyc filter=lfs diff=lfs merge=lfs -text
+mantis_evalkit/lib/python3.10/site-packages/pip/_vendor/__pycache__/typing_extensions.cpython-310.pyc filter=lfs diff=lfs merge=lfs -text

mantis_evalkit/lib/python3.10/site-packages/diffusers/pipelines/amused/__init__.py

@@ -0,0 +1,62 @@
+from typing import TYPE_CHECKING
+
+from ...utils import (
+    DIFFUSERS_SLOW_IMPORT,
+    OptionalDependencyNotAvailable,
+    _LazyModule,
+    is_torch_available,
+    is_transformers_available,
+)
+
+
+_dummy_objects = {}
+_import_structure = {}
+
+try:
+    if not (is_transformers_available() and is_torch_available()):
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    from ...utils.dummy_torch_and_transformers_objects import (
+        AmusedImg2ImgPipeline,
+        AmusedInpaintPipeline,
+        AmusedPipeline,
+    )
+
+    _dummy_objects.update(
+        {
+            "AmusedPipeline": AmusedPipeline,
+            "AmusedImg2ImgPipeline": AmusedImg2ImgPipeline,
+            "AmusedInpaintPipeline": AmusedInpaintPipeline,
+        }
+    )
+else:
+    _import_structure["pipeline_amused"] = ["AmusedPipeline"]
+    _import_structure["pipeline_amused_img2img"] = ["AmusedImg2ImgPipeline"]
+    _import_structure["pipeline_amused_inpaint"] = ["AmusedInpaintPipeline"]
+
+
+if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
+    try:
+        if not (is_transformers_available() and is_torch_available()):
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        from ...utils.dummy_torch_and_transformers_objects import (
+            AmusedPipeline,
+        )
+    else:
+        from .pipeline_amused import AmusedPipeline
+        from .pipeline_amused_img2img import AmusedImg2ImgPipeline
+        from .pipeline_amused_inpaint import AmusedInpaintPipeline
+
+else:
+    import sys
+
+    sys.modules[__name__] = _LazyModule(
+        __name__,
+        globals()["__file__"],
+        _import_structure,
+        module_spec=__spec__,
+    )
+
+    for name, value in _dummy_objects.items():
+        setattr(sys.modules[__name__], name, value)

mantis_evalkit/lib/python3.10/site-packages/diffusers/pipelines/amused/pipeline_amused.py

@@ -0,0 +1,328 @@
+# Copyright 2024 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.
+
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+import torch
+from transformers import CLIPTextModelWithProjection, CLIPTokenizer
+
+from ...image_processor import VaeImageProcessor
+from ...models import UVit2DModel, VQModel
+from ...schedulers import AmusedScheduler
+from ...utils import replace_example_docstring
+from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
+
+
+EXAMPLE_DOC_STRING = """
+    Examples:
+        ```py
+        >>> import torch
+        >>> from diffusers import AmusedPipeline
+
+        >>> pipe = AmusedPipeline.from_pretrained("amused/amused-512", variant="fp16", torch_dtype=torch.float16)
+        >>> pipe = pipe.to("cuda")
+
+        >>> prompt = "a photo of an astronaut riding a horse on mars"
+        >>> image = pipe(prompt).images[0]
+        ```
+"""
+
+
+class AmusedPipeline(DiffusionPipeline):
+    image_processor: VaeImageProcessor
+    vqvae: VQModel
+    tokenizer: CLIPTokenizer
+    text_encoder: CLIPTextModelWithProjection
+    transformer: UVit2DModel
+    scheduler: AmusedScheduler
+
+    model_cpu_offload_seq = "text_encoder->transformer->vqvae"
+
+    def __init__(
+        self,
+        vqvae: VQModel,
+        tokenizer: CLIPTokenizer,
+        text_encoder: CLIPTextModelWithProjection,
+        transformer: UVit2DModel,
+        scheduler: AmusedScheduler,
+    ):
+        super().__init__()
+
+        self.register_modules(
+            vqvae=vqvae,
+            tokenizer=tokenizer,
+            text_encoder=text_encoder,
+            transformer=transformer,
+            scheduler=scheduler,
+        )
+        self.vae_scale_factor = 2 ** (len(self.vqvae.config.block_out_channels) - 1)
+        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor, do_normalize=False)
+
+    @torch.no_grad()
+    @replace_example_docstring(EXAMPLE_DOC_STRING)
+    def __call__(
+        self,
+        prompt: Optional[Union[List[str], str]] = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: int = 12,
+        guidance_scale: float = 10.0,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        generator: Optional[torch.Generator] = None,
+        latents: Optional[torch.IntTensor] = None,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.Tensor] = None,
+        negative_encoder_hidden_states: Optional[torch.Tensor] = None,
+        output_type="pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,
+        callback_steps: int = 1,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        micro_conditioning_aesthetic_score: int = 6,
+        micro_conditioning_crop_coord: Tuple[int, int] = (0, 0),
+        temperature: Union[int, Tuple[int, int], List[int]] = (2, 0),
+    ):
+        """
+        The call function to the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
+            height (`int`, *optional*, defaults to `self.transformer.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 16):
+                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 10.0):
+                A higher guidance scale value encourages the model to generate images closely linked to the text
+                `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide what to not include in image generation. If not defined, you need to
+                pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            generator (`torch.Generator`, *optional*):
+                A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
+                generation deterministic.
+            latents (`torch.IntTensor`, *optional*):
+                Pre-generated tokens representing latent vectors in `self.vqvae`, to be used as inputs for image
+                gneration. If not provided, the starting latents will be completely masked.
+            prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
+                provided, text embeddings are generated from the `prompt` input argument. A single vector from the
+                pooled and projected final hidden states.
+            encoder_hidden_states (`torch.Tensor`, *optional*):
+                Pre-generated penultimate hidden states from the text encoder providing additional text conditioning.
+            negative_prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
+                not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
+            negative_encoder_hidden_states (`torch.Tensor`, *optional*):
+                Analogous to `encoder_hidden_states` for the positive prompt.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generated image. Choose between `PIL.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+            callback (`Callable`, *optional*):
+                A function that calls every `callback_steps` steps during inference. The function is called with the
+                following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.
+            callback_steps (`int`, *optional*, defaults to 1):
+                The frequency at which the `callback` function is called. If not specified, the callback is called at
+                every step.
+            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).
+            micro_conditioning_aesthetic_score (`int`, *optional*, defaults to 6):
+                The targeted aesthetic score according to the laion aesthetic classifier. See
+                https://laion.ai/blog/laion-aesthetics/ and the micro-conditioning section of
+                https://arxiv.org/abs/2307.01952.
+            micro_conditioning_crop_coord (`Tuple[int]`, *optional*, defaults to (0, 0)):
+                The targeted height, width crop coordinates. See the micro-conditioning section of
+                https://arxiv.org/abs/2307.01952.
+            temperature (`Union[int, Tuple[int, int], List[int]]`, *optional*, defaults to (2, 0)):
+                Configures the temperature scheduler on `self.scheduler` see `AmusedScheduler#set_timesteps`.
+
+        Examples:
+
+        Returns:
+            [`~pipelines.pipeline_utils.ImagePipelineOutput`] or `tuple`:
+                If `return_dict` is `True`, [`~pipelines.pipeline_utils.ImagePipelineOutput`] is returned, otherwise a
+                `tuple` is returned where the first element is a list with the generated images.
+        """
+        if (prompt_embeds is not None and encoder_hidden_states is None) or (
+            prompt_embeds is None and encoder_hidden_states is not None
+        ):
+            raise ValueError("pass either both `prompt_embeds` and `encoder_hidden_states` or neither")
+
+        if (negative_prompt_embeds is not None and negative_encoder_hidden_states is None) or (
+            negative_prompt_embeds is None and negative_encoder_hidden_states is not None
+        ):
+            raise ValueError(
+                "pass either both `negatve_prompt_embeds` and `negative_encoder_hidden_states` or neither"
+            )
+
+        if (prompt is None and prompt_embeds is None) or (prompt is not None and prompt_embeds is not None):
+            raise ValueError("pass only one of `prompt` or `prompt_embeds`")
+
+        if isinstance(prompt, str):
+            prompt = [prompt]
+
+        if prompt is not None:
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        batch_size = batch_size * num_images_per_prompt
+
+        if height is None:
+            height = self.transformer.config.sample_size * self.vae_scale_factor
+
+        if width is None:
+            width = self.transformer.config.sample_size * self.vae_scale_factor
+
+        if prompt_embeds is None:
+            input_ids = self.tokenizer(
+                prompt,
+                return_tensors="pt",
+                padding="max_length",
+                truncation=True,
+                max_length=self.tokenizer.model_max_length,
+            ).input_ids.to(self._execution_device)
+
+            outputs = self.text_encoder(input_ids, return_dict=True, output_hidden_states=True)
+            prompt_embeds = outputs.text_embeds
+            encoder_hidden_states = outputs.hidden_states[-2]
+
+        prompt_embeds = prompt_embeds.repeat(num_images_per_prompt, 1)
+        encoder_hidden_states = encoder_hidden_states.repeat(num_images_per_prompt, 1, 1)
+
+        if guidance_scale > 1.0:
+            if negative_prompt_embeds is None:
+                if negative_prompt is None:
+                    negative_prompt = [""] * len(prompt)
+
+                if isinstance(negative_prompt, str):
+                    negative_prompt = [negative_prompt]
+
+                input_ids = self.tokenizer(
+                    negative_prompt,
+                    return_tensors="pt",
+                    padding="max_length",
+                    truncation=True,
+                    max_length=self.tokenizer.model_max_length,
+                ).input_ids.to(self._execution_device)
+
+                outputs = self.text_encoder(input_ids, return_dict=True, output_hidden_states=True)
+                negative_prompt_embeds = outputs.text_embeds
+                negative_encoder_hidden_states = outputs.hidden_states[-2]
+
+            negative_prompt_embeds = negative_prompt_embeds.repeat(num_images_per_prompt, 1)
+            negative_encoder_hidden_states = negative_encoder_hidden_states.repeat(num_images_per_prompt, 1, 1)
+
+            prompt_embeds = torch.concat([negative_prompt_embeds, prompt_embeds])
+            encoder_hidden_states = torch.concat([negative_encoder_hidden_states, encoder_hidden_states])
+
+        # Note that the micro conditionings _do_ flip the order of width, height for the original size
+        # and the crop coordinates. This is how it was done in the original code base
+        micro_conds = torch.tensor(
+            [
+                width,
+                height,
+                micro_conditioning_crop_coord[0],
+                micro_conditioning_crop_coord[1],
+                micro_conditioning_aesthetic_score,
+            ],
+            device=self._execution_device,
+            dtype=encoder_hidden_states.dtype,
+        )
+        micro_conds = micro_conds.unsqueeze(0)
+        micro_conds = micro_conds.expand(2 * batch_size if guidance_scale > 1.0 else batch_size, -1)
+
+        shape = (batch_size, height // self.vae_scale_factor, width // self.vae_scale_factor)
+
+        if latents is None:
+            latents = torch.full(
+                shape, self.scheduler.config.mask_token_id, dtype=torch.long, device=self._execution_device
+            )
+
+        self.scheduler.set_timesteps(num_inference_steps, temperature, self._execution_device)
+
+        num_warmup_steps = len(self.scheduler.timesteps) - num_inference_steps * self.scheduler.order
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, timestep in enumerate(self.scheduler.timesteps):
+                if guidance_scale > 1.0:
+                    model_input = torch.cat([latents] * 2)
+                else:
+                    model_input = latents
+
+                model_output = self.transformer(
+                    model_input,
+                    micro_conds=micro_conds,
+                    pooled_text_emb=prompt_embeds,
+                    encoder_hidden_states=encoder_hidden_states,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                )
+
+                if guidance_scale > 1.0:
+                    uncond_logits, cond_logits = model_output.chunk(2)
+                    model_output = uncond_logits + guidance_scale * (cond_logits - uncond_logits)
+
+                latents = self.scheduler.step(
+                    model_output=model_output,
+                    timestep=timestep,
+                    sample=latents,
+                    generator=generator,
+                ).prev_sample
+
+                if i == len(self.scheduler.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, timestep, latents)
+
+        if output_type == "latent":
+            output = latents
+        else:
+            needs_upcasting = self.vqvae.dtype == torch.float16 and self.vqvae.config.force_upcast
+
+            if needs_upcasting:
+                self.vqvae.float()
+
+            output = self.vqvae.decode(
+                latents,
+                force_not_quantize=True,
+                shape=(
+                    batch_size,
+                    height // self.vae_scale_factor,
+                    width // self.vae_scale_factor,
+                    self.vqvae.config.latent_channels,
+                ),
+            ).sample.clip(0, 1)
+            output = self.image_processor.postprocess(output, output_type)
+
+            if needs_upcasting:
+                self.vqvae.half()
+
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (output,)
+
+        return ImagePipelineOutput(output)

mantis_evalkit/lib/python3.10/site-packages/diffusers/pipelines/amused/pipeline_amused_img2img.py

@@ -0,0 +1,349 @@
+# Copyright 2024 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.
+
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+import torch
+from transformers import CLIPTextModelWithProjection, CLIPTokenizer
+
+from ...image_processor import PipelineImageInput, VaeImageProcessor
+from ...models import UVit2DModel, VQModel
+from ...schedulers import AmusedScheduler
+from ...utils import replace_example_docstring
+from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
+
+
+EXAMPLE_DOC_STRING = """
+    Examples:
+        ```py
+        >>> import torch
+        >>> from diffusers import AmusedImg2ImgPipeline
+        >>> from diffusers.utils import load_image
+
+        >>> pipe = AmusedImg2ImgPipeline.from_pretrained(
+        ...     "amused/amused-512", variant="fp16", torch_dtype=torch.float16
+        ... )
+        >>> pipe = pipe.to("cuda")
+
+        >>> prompt = "winter mountains"
+        >>> input_image = (
+        ...     load_image(
+        ...         "https://huggingface.co/datasets/diffusers/docs-images/resolve/main/open_muse/mountains.jpg"
+        ...     )
+        ...     .resize((512, 512))
+        ...     .convert("RGB")
+        ... )
+        >>> image = pipe(prompt, input_image).images[0]
+        ```
+"""
+
+
+class AmusedImg2ImgPipeline(DiffusionPipeline):
+    image_processor: VaeImageProcessor
+    vqvae: VQModel
+    tokenizer: CLIPTokenizer
+    text_encoder: CLIPTextModelWithProjection
+    transformer: UVit2DModel
+    scheduler: AmusedScheduler
+
+    model_cpu_offload_seq = "text_encoder->transformer->vqvae"
+
+    # TODO - when calling self.vqvae.quantize, it uses self.vqvae.quantize.embedding.weight before
+    # the forward method of self.vqvae.quantize, so the hook doesn't get called to move the parameter
+    # off the meta device. There should be a way to fix this instead of just not offloading it
+    _exclude_from_cpu_offload = ["vqvae"]
+
+    def __init__(
+        self,
+        vqvae: VQModel,
+        tokenizer: CLIPTokenizer,
+        text_encoder: CLIPTextModelWithProjection,
+        transformer: UVit2DModel,
+        scheduler: AmusedScheduler,
+    ):
+        super().__init__()
+
+        self.register_modules(
+            vqvae=vqvae,
+            tokenizer=tokenizer,
+            text_encoder=text_encoder,
+            transformer=transformer,
+            scheduler=scheduler,
+        )
+        self.vae_scale_factor = 2 ** (len(self.vqvae.config.block_out_channels) - 1)
+        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor, do_normalize=False)
+
+    @torch.no_grad()
+    @replace_example_docstring(EXAMPLE_DOC_STRING)
+    def __call__(
+        self,
+        prompt: Optional[Union[List[str], str]] = None,
+        image: PipelineImageInput = None,
+        strength: float = 0.5,
+        num_inference_steps: int = 12,
+        guidance_scale: float = 10.0,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        generator: Optional[torch.Generator] = None,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.Tensor] = None,
+        negative_encoder_hidden_states: Optional[torch.Tensor] = None,
+        output_type="pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,
+        callback_steps: int = 1,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        micro_conditioning_aesthetic_score: int = 6,
+        micro_conditioning_crop_coord: Tuple[int, int] = (0, 0),
+        temperature: Union[int, Tuple[int, int], List[int]] = (2, 0),
+    ):
+        """
+        The call function to the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
+            image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
+                `Image`, numpy array or tensor representing an image batch to be used as the starting point. For both
+                numpy array and pytorch tensor, the expected value range is between `[0, 1]` If it's a tensor or a list
+                or tensors, the expected shape should be `(B, C, H, W)` or `(C, H, W)`. If it is a numpy array or a
+                list of arrays, the expected shape should be `(B, H, W, C)` or `(H, W, C)` It can also accept image
+                latents as `image`, but if passing latents directly it is not encoded again.
+            strength (`float`, *optional*, defaults to 0.5):
+                Indicates extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a
+                starting point and more noise is added the higher the `strength`. The number of denoising steps depends
+                on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising
+                process runs for the full number of iterations specified in `num_inference_steps`. A value of 1
+                essentially ignores `image`.
+            num_inference_steps (`int`, *optional*, defaults to 12):
+                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 10.0):
+                A higher guidance scale value encourages the model to generate images closely linked to the text
+                `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide what to not include in image generation. If not defined, you need to
+                pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            generator (`torch.Generator`, *optional*):
+                A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
+                generation deterministic.
+            prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
+                provided, text embeddings are generated from the `prompt` input argument. A single vector from the
+                pooled and projected final hidden states.
+            encoder_hidden_states (`torch.Tensor`, *optional*):
+                Pre-generated penultimate hidden states from the text encoder providing additional text conditioning.
+            negative_prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
+                not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
+            negative_encoder_hidden_states (`torch.Tensor`, *optional*):
+                Analogous to `encoder_hidden_states` for the positive prompt.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generated image. Choose between `PIL.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+            callback (`Callable`, *optional*):
+                A function that calls every `callback_steps` steps during inference. The function is called with the
+                following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.
+            callback_steps (`int`, *optional*, defaults to 1):
+                The frequency at which the `callback` function is called. If not specified, the callback is called at
+                every step.
+            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).
+            micro_conditioning_aesthetic_score (`int`, *optional*, defaults to 6):
+                The targeted aesthetic score according to the laion aesthetic classifier. See
+                https://laion.ai/blog/laion-aesthetics/ and the micro-conditioning section of
+                https://arxiv.org/abs/2307.01952.
+            micro_conditioning_crop_coord (`Tuple[int]`, *optional*, defaults to (0, 0)):
+                The targeted height, width crop coordinates. See the micro-conditioning section of
+                https://arxiv.org/abs/2307.01952.
+            temperature (`Union[int, Tuple[int, int], List[int]]`, *optional*, defaults to (2, 0)):
+                Configures the temperature scheduler on `self.scheduler` see `AmusedScheduler#set_timesteps`.
+
+        Examples:
+
+        Returns:
+            [`~pipelines.pipeline_utils.ImagePipelineOutput`] or `tuple`:
+                If `return_dict` is `True`, [`~pipelines.pipeline_utils.ImagePipelineOutput`] is returned, otherwise a
+                `tuple` is returned where the first element is a list with the generated images.
+        """
+
+        if (prompt_embeds is not None and encoder_hidden_states is None) or (
+            prompt_embeds is None and encoder_hidden_states is not None
+        ):
+            raise ValueError("pass either both `prompt_embeds` and `encoder_hidden_states` or neither")
+
+        if (negative_prompt_embeds is not None and negative_encoder_hidden_states is None) or (
+            negative_prompt_embeds is None and negative_encoder_hidden_states is not None
+        ):
+            raise ValueError(
+                "pass either both `negative_prompt_embeds` and `negative_encoder_hidden_states` or neither"
+            )
+
+        if (prompt is None and prompt_embeds is None) or (prompt is not None and prompt_embeds is not None):
+            raise ValueError("pass only one of `prompt` or `prompt_embeds`")
+
+        if isinstance(prompt, str):
+            prompt = [prompt]
+
+        if prompt is not None:
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        batch_size = batch_size * num_images_per_prompt
+
+        if prompt_embeds is None:
+            input_ids = self.tokenizer(
+                prompt,
+                return_tensors="pt",
+                padding="max_length",
+                truncation=True,
+                max_length=self.tokenizer.model_max_length,
+            ).input_ids.to(self._execution_device)
+
+            outputs = self.text_encoder(input_ids, return_dict=True, output_hidden_states=True)
+            prompt_embeds = outputs.text_embeds
+            encoder_hidden_states = outputs.hidden_states[-2]
+
+        prompt_embeds = prompt_embeds.repeat(num_images_per_prompt, 1)
+        encoder_hidden_states = encoder_hidden_states.repeat(num_images_per_prompt, 1, 1)
+
+        if guidance_scale > 1.0:
+            if negative_prompt_embeds is None:
+                if negative_prompt is None:
+                    negative_prompt = [""] * len(prompt)
+
+                if isinstance(negative_prompt, str):
+                    negative_prompt = [negative_prompt]
+
+                input_ids = self.tokenizer(
+                    negative_prompt,
+                    return_tensors="pt",
+                    padding="max_length",
+                    truncation=True,
+                    max_length=self.tokenizer.model_max_length,
+                ).input_ids.to(self._execution_device)
+
+                outputs = self.text_encoder(input_ids, return_dict=True, output_hidden_states=True)
+                negative_prompt_embeds = outputs.text_embeds
+                negative_encoder_hidden_states = outputs.hidden_states[-2]
+
+            negative_prompt_embeds = negative_prompt_embeds.repeat(num_images_per_prompt, 1)
+            negative_encoder_hidden_states = negative_encoder_hidden_states.repeat(num_images_per_prompt, 1, 1)
+
+            prompt_embeds = torch.concat([negative_prompt_embeds, prompt_embeds])
+            encoder_hidden_states = torch.concat([negative_encoder_hidden_states, encoder_hidden_states])
+
+        image = self.image_processor.preprocess(image)
+
+        height, width = image.shape[-2:]
+
+        # Note that the micro conditionings _do_ flip the order of width, height for the original size
+        # and the crop coordinates. This is how it was done in the original code base
+        micro_conds = torch.tensor(
+            [
+                width,
+                height,
+                micro_conditioning_crop_coord[0],
+                micro_conditioning_crop_coord[1],
+                micro_conditioning_aesthetic_score,
+            ],
+            device=self._execution_device,
+            dtype=encoder_hidden_states.dtype,
+        )
+
+        micro_conds = micro_conds.unsqueeze(0)
+        micro_conds = micro_conds.expand(2 * batch_size if guidance_scale > 1.0 else batch_size, -1)
+
+        self.scheduler.set_timesteps(num_inference_steps, temperature, self._execution_device)
+        num_inference_steps = int(len(self.scheduler.timesteps) * strength)
+        start_timestep_idx = len(self.scheduler.timesteps) - num_inference_steps
+
+        needs_upcasting = self.vqvae.dtype == torch.float16 and self.vqvae.config.force_upcast
+
+        if needs_upcasting:
+            self.vqvae.float()
+
+        latents = self.vqvae.encode(image.to(dtype=self.vqvae.dtype, device=self._execution_device)).latents
+        latents_bsz, channels, latents_height, latents_width = latents.shape
+        latents = self.vqvae.quantize(latents)[2][2].reshape(latents_bsz, latents_height, latents_width)
+        latents = self.scheduler.add_noise(
+            latents, self.scheduler.timesteps[start_timestep_idx - 1], generator=generator
+        )
+        latents = latents.repeat(num_images_per_prompt, 1, 1)
+
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i in range(start_timestep_idx, len(self.scheduler.timesteps)):
+                timestep = self.scheduler.timesteps[i]
+
+                if guidance_scale > 1.0:
+                    model_input = torch.cat([latents] * 2)
+                else:
+                    model_input = latents
+
+                model_output = self.transformer(
+                    model_input,
+                    micro_conds=micro_conds,
+                    pooled_text_emb=prompt_embeds,
+                    encoder_hidden_states=encoder_hidden_states,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                )
+
+                if guidance_scale > 1.0:
+                    uncond_logits, cond_logits = model_output.chunk(2)
+                    model_output = uncond_logits + guidance_scale * (cond_logits - uncond_logits)
+
+                latents = self.scheduler.step(
+                    model_output=model_output,
+                    timestep=timestep,
+                    sample=latents,
+                    generator=generator,
+                ).prev_sample
+
+                if i == len(self.scheduler.timesteps) - 1 or ((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, timestep, latents)
+
+        if output_type == "latent":
+            output = latents
+        else:
+            output = self.vqvae.decode(
+                latents,
+                force_not_quantize=True,
+                shape=(
+                    batch_size,
+                    height // self.vae_scale_factor,
+                    width // self.vae_scale_factor,
+                    self.vqvae.config.latent_channels,
+                ),
+            ).sample.clip(0, 1)
+            output = self.image_processor.postprocess(output, output_type)
+
+            if needs_upcasting:
+                self.vqvae.half()
+
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (output,)
+
+        return ImagePipelineOutput(output)

mantis_evalkit/lib/python3.10/site-packages/diffusers/pipelines/amused/pipeline_amused_inpaint.py

@@ -0,0 +1,380 @@
+# Copyright 2024 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.
+
+
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+import torch
+from transformers import CLIPTextModelWithProjection, CLIPTokenizer
+
+from ...image_processor import PipelineImageInput, VaeImageProcessor
+from ...models import UVit2DModel, VQModel
+from ...schedulers import AmusedScheduler
+from ...utils import replace_example_docstring
+from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
+
+
+EXAMPLE_DOC_STRING = """
+    Examples:
+        ```py
+        >>> import torch
+        >>> from diffusers import AmusedInpaintPipeline
+        >>> from diffusers.utils import load_image
+
+        >>> pipe = AmusedInpaintPipeline.from_pretrained(
+        ...     "amused/amused-512", variant="fp16", torch_dtype=torch.float16
+        ... )
+        >>> pipe = pipe.to("cuda")
+
+        >>> prompt = "fall mountains"
+        >>> input_image = (
+        ...     load_image(
+        ...         "https://huggingface.co/datasets/diffusers/docs-images/resolve/main/open_muse/mountains_1.jpg"
+        ...     )
+        ...     .resize((512, 512))
+        ...     .convert("RGB")
+        ... )
+        >>> mask = (
+        ...     load_image(
+        ...         "https://huggingface.co/datasets/diffusers/docs-images/resolve/main/open_muse/mountains_1_mask.png"
+        ...     )
+        ...     .resize((512, 512))
+        ...     .convert("L")
+        ... )
+        >>> pipe(prompt, input_image, mask).images[0].save("out.png")
+        ```
+"""
+
+
+class AmusedInpaintPipeline(DiffusionPipeline):
+    image_processor: VaeImageProcessor
+    vqvae: VQModel
+    tokenizer: CLIPTokenizer
+    text_encoder: CLIPTextModelWithProjection
+    transformer: UVit2DModel
+    scheduler: AmusedScheduler
+
+    model_cpu_offload_seq = "text_encoder->transformer->vqvae"
+
+    # TODO - when calling self.vqvae.quantize, it uses self.vqvae.quantize.embedding.weight before
+    # the forward method of self.vqvae.quantize, so the hook doesn't get called to move the parameter
+    # off the meta device. There should be a way to fix this instead of just not offloading it
+    _exclude_from_cpu_offload = ["vqvae"]
+
+    def __init__(
+        self,
+        vqvae: VQModel,
+        tokenizer: CLIPTokenizer,
+        text_encoder: CLIPTextModelWithProjection,
+        transformer: UVit2DModel,
+        scheduler: AmusedScheduler,
+    ):
+        super().__init__()
+
+        self.register_modules(
+            vqvae=vqvae,
+            tokenizer=tokenizer,
+            text_encoder=text_encoder,
+            transformer=transformer,
+            scheduler=scheduler,
+        )
+        self.vae_scale_factor = 2 ** (len(self.vqvae.config.block_out_channels) - 1)
+        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor, do_normalize=False)
+        self.mask_processor = VaeImageProcessor(
+            vae_scale_factor=self.vae_scale_factor,
+            do_normalize=False,
+            do_binarize=True,
+            do_convert_grayscale=True,
+            do_resize=True,
+        )
+        self.scheduler.register_to_config(masking_schedule="linear")
+
+    @torch.no_grad()
+    @replace_example_docstring(EXAMPLE_DOC_STRING)
+    def __call__(
+        self,
+        prompt: Optional[Union[List[str], str]] = None,
+        image: PipelineImageInput = None,
+        mask_image: PipelineImageInput = None,
+        strength: float = 1.0,
+        num_inference_steps: int = 12,
+        guidance_scale: float = 10.0,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        generator: Optional[torch.Generator] = None,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.Tensor] = None,
+        negative_encoder_hidden_states: Optional[torch.Tensor] = None,
+        output_type="pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,
+        callback_steps: int = 1,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        micro_conditioning_aesthetic_score: int = 6,
+        micro_conditioning_crop_coord: Tuple[int, int] = (0, 0),
+        temperature: Union[int, Tuple[int, int], List[int]] = (2, 0),
+    ):
+        """
+        The call function to the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
+            image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
+                `Image`, numpy array or tensor representing an image batch to be used as the starting point. For both
+                numpy array and pytorch tensor, the expected value range is between `[0, 1]` If it's a tensor or a list
+                or tensors, the expected shape should be `(B, C, H, W)` or `(C, H, W)`. If it is a numpy array or a
+                list of arrays, the expected shape should be `(B, H, W, C)` or `(H, W, C)` It can also accept image
+                latents as `image`, but if passing latents directly it is not encoded again.
+            mask_image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
+                `Image`, numpy array or tensor representing an image batch to mask `image`. White pixels in the mask
+                are repainted while black pixels are preserved. If `mask_image` is a PIL image, it is converted to a
+                single channel (luminance) before use. If it's a numpy array or pytorch tensor, it should contain one
+                color channel (L) instead of 3, so the expected shape for pytorch tensor would be `(B, 1, H, W)`, `(B,
+                H, W)`, `(1, H, W)`, `(H, W)`. And for numpy array would be for `(B, H, W, 1)`, `(B, H, W)`, `(H, W,
+                1)`, or `(H, W)`.
+            strength (`float`, *optional*, defaults to 1.0):
+                Indicates extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a
+                starting point and more noise is added the higher the `strength`. The number of denoising steps depends
+                on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising
+                process runs for the full number of iterations specified in `num_inference_steps`. A value of 1
+                essentially ignores `image`.
+            num_inference_steps (`int`, *optional*, defaults to 16):
+                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 10.0):
+                A higher guidance scale value encourages the model to generate images closely linked to the text
+                `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide what to not include in image generation. If not defined, you need to
+                pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            generator (`torch.Generator`, *optional*):
+                A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
+                generation deterministic.
+            prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
+                provided, text embeddings are generated from the `prompt` input argument. A single vector from the
+                pooled and projected final hidden states.
+            encoder_hidden_states (`torch.Tensor`, *optional*):
+                Pre-generated penultimate hidden states from the text encoder providing additional text conditioning.
+            negative_prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
+                not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
+            negative_encoder_hidden_states (`torch.Tensor`, *optional*):
+                Analogous to `encoder_hidden_states` for the positive prompt.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generated image. Choose between `PIL.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+            callback (`Callable`, *optional*):
+                A function that calls every `callback_steps` steps during inference. The function is called with the
+                following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.
+            callback_steps (`int`, *optional*, defaults to 1):
+                The frequency at which the `callback` function is called. If not specified, the callback is called at
+                every step.
+            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).
+            micro_conditioning_aesthetic_score (`int`, *optional*, defaults to 6):
+                The targeted aesthetic score according to the laion aesthetic classifier. See
+                https://laion.ai/blog/laion-aesthetics/ and the micro-conditioning section of
+                https://arxiv.org/abs/2307.01952.
+            micro_conditioning_crop_coord (`Tuple[int]`, *optional*, defaults to (0, 0)):
+                The targeted height, width crop coordinates. See the micro-conditioning section of
+                https://arxiv.org/abs/2307.01952.
+            temperature (`Union[int, Tuple[int, int], List[int]]`, *optional*, defaults to (2, 0)):
+                Configures the temperature scheduler on `self.scheduler` see `AmusedScheduler#set_timesteps`.
+
+        Examples:
+
+        Returns:
+            [`~pipelines.pipeline_utils.ImagePipelineOutput`] or `tuple`:
+                If `return_dict` is `True`, [`~pipelines.pipeline_utils.ImagePipelineOutput`] is returned, otherwise a
+                `tuple` is returned where the first element is a list with the generated images.
+        """
+
+        if (prompt_embeds is not None and encoder_hidden_states is None) or (
+            prompt_embeds is None and encoder_hidden_states is not None
+        ):
+            raise ValueError("pass either both `prompt_embeds` and `encoder_hidden_states` or neither")
+
+        if (negative_prompt_embeds is not None and negative_encoder_hidden_states is None) or (
+            negative_prompt_embeds is None and negative_encoder_hidden_states is not None
+        ):
+            raise ValueError(
+                "pass either both `negatve_prompt_embeds` and `negative_encoder_hidden_states` or neither"
+            )
+
+        if (prompt is None and prompt_embeds is None) or (prompt is not None and prompt_embeds is not None):
+            raise ValueError("pass only one of `prompt` or `prompt_embeds`")
+
+        if isinstance(prompt, str):
+            prompt = [prompt]
+
+        if prompt is not None:
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        batch_size = batch_size * num_images_per_prompt
+
+        if prompt_embeds is None:
+            input_ids = self.tokenizer(
+                prompt,
+                return_tensors="pt",
+                padding="max_length",
+                truncation=True,
+                max_length=self.tokenizer.model_max_length,
+            ).input_ids.to(self._execution_device)
+
+            outputs = self.text_encoder(input_ids, return_dict=True, output_hidden_states=True)
+            prompt_embeds = outputs.text_embeds
+            encoder_hidden_states = outputs.hidden_states[-2]
+
+        prompt_embeds = prompt_embeds.repeat(num_images_per_prompt, 1)
+        encoder_hidden_states = encoder_hidden_states.repeat(num_images_per_prompt, 1, 1)
+
+        if guidance_scale > 1.0:
+            if negative_prompt_embeds is None:
+                if negative_prompt is None:
+                    negative_prompt = [""] * len(prompt)
+
+                if isinstance(negative_prompt, str):
+                    negative_prompt = [negative_prompt]
+
+                input_ids = self.tokenizer(
+                    negative_prompt,
+                    return_tensors="pt",
+                    padding="max_length",
+                    truncation=True,
+                    max_length=self.tokenizer.model_max_length,
+                ).input_ids.to(self._execution_device)
+
+                outputs = self.text_encoder(input_ids, return_dict=True, output_hidden_states=True)
+                negative_prompt_embeds = outputs.text_embeds
+                negative_encoder_hidden_states = outputs.hidden_states[-2]
+
+            negative_prompt_embeds = negative_prompt_embeds.repeat(num_images_per_prompt, 1)
+            negative_encoder_hidden_states = negative_encoder_hidden_states.repeat(num_images_per_prompt, 1, 1)
+
+            prompt_embeds = torch.concat([negative_prompt_embeds, prompt_embeds])
+            encoder_hidden_states = torch.concat([negative_encoder_hidden_states, encoder_hidden_states])
+
+        image = self.image_processor.preprocess(image)
+
+        height, width = image.shape[-2:]
+
+        # Note that the micro conditionings _do_ flip the order of width, height for the original size
+        # and the crop coordinates. This is how it was done in the original code base
+        micro_conds = torch.tensor(
+            [
+                width,
+                height,
+                micro_conditioning_crop_coord[0],
+                micro_conditioning_crop_coord[1],
+                micro_conditioning_aesthetic_score,
+            ],
+            device=self._execution_device,
+            dtype=encoder_hidden_states.dtype,
+        )
+
+        micro_conds = micro_conds.unsqueeze(0)
+        micro_conds = micro_conds.expand(2 * batch_size if guidance_scale > 1.0 else batch_size, -1)
+
+        self.scheduler.set_timesteps(num_inference_steps, temperature, self._execution_device)
+        num_inference_steps = int(len(self.scheduler.timesteps) * strength)
+        start_timestep_idx = len(self.scheduler.timesteps) - num_inference_steps
+
+        needs_upcasting = self.vqvae.dtype == torch.float16 and self.vqvae.config.force_upcast
+
+        if needs_upcasting:
+            self.vqvae.float()
+
+        latents = self.vqvae.encode(image.to(dtype=self.vqvae.dtype, device=self._execution_device)).latents
+        latents_bsz, channels, latents_height, latents_width = latents.shape
+        latents = self.vqvae.quantize(latents)[2][2].reshape(latents_bsz, latents_height, latents_width)
+
+        mask = self.mask_processor.preprocess(
+            mask_image, height // self.vae_scale_factor, width // self.vae_scale_factor
+        )
+        mask = mask.reshape(mask.shape[0], latents_height, latents_width).bool().to(latents.device)
+        latents[mask] = self.scheduler.config.mask_token_id
+
+        starting_mask_ratio = mask.sum() / latents.numel()
+
+        latents = latents.repeat(num_images_per_prompt, 1, 1)
+
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i in range(start_timestep_idx, len(self.scheduler.timesteps)):
+                timestep = self.scheduler.timesteps[i]
+
+                if guidance_scale > 1.0:
+                    model_input = torch.cat([latents] * 2)
+                else:
+                    model_input = latents
+
+                model_output = self.transformer(
+                    model_input,
+                    micro_conds=micro_conds,
+                    pooled_text_emb=prompt_embeds,
+                    encoder_hidden_states=encoder_hidden_states,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                )
+
+                if guidance_scale > 1.0:
+                    uncond_logits, cond_logits = model_output.chunk(2)
+                    model_output = uncond_logits + guidance_scale * (cond_logits - uncond_logits)
+
+                latents = self.scheduler.step(
+                    model_output=model_output,
+                    timestep=timestep,
+                    sample=latents,
+                    generator=generator,
+                    starting_mask_ratio=starting_mask_ratio,
+                ).prev_sample
+
+                if i == len(self.scheduler.timesteps) - 1 or ((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, timestep, latents)
+
+        if output_type == "latent":
+            output = latents
+        else:
+            output = self.vqvae.decode(
+                latents,
+                force_not_quantize=True,
+                shape=(
+                    batch_size,
+                    height // self.vae_scale_factor,
+                    width // self.vae_scale_factor,
+                    self.vqvae.config.latent_channels,
+                ),
+            ).sample.clip(0, 1)
+            output = self.image_processor.postprocess(output, output_type)
+
+            if needs_upcasting:
+                self.vqvae.half()
+
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (output,)
+
+        return ImagePipelineOutput(output)

mantis_evalkit/lib/python3.10/site-packages/diffusers/pipelines/audioldm2/__init__.py

@@ -0,0 +1,50 @@
+from typing import TYPE_CHECKING
+
+from ...utils import (
+    DIFFUSERS_SLOW_IMPORT,
+    OptionalDependencyNotAvailable,
+    _LazyModule,
+    get_objects_from_module,
+    is_torch_available,
+    is_transformers_available,
+    is_transformers_version,
+)
+
+
+_dummy_objects = {}
+_import_structure = {}
+
+try:
+    if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.27.0")):
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    from ...utils import dummy_torch_and_transformers_objects
+
+    _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))
+else:
+    _import_structure["modeling_audioldm2"] = ["AudioLDM2ProjectionModel", "AudioLDM2UNet2DConditionModel"]
+    _import_structure["pipeline_audioldm2"] = ["AudioLDM2Pipeline"]
+
+
+if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
+    try:
+        if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.27.0")):
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        from ...utils.dummy_torch_and_transformers_objects import *
+
+    else:
+        from .modeling_audioldm2 import AudioLDM2ProjectionModel, AudioLDM2UNet2DConditionModel
+        from .pipeline_audioldm2 import AudioLDM2Pipeline
+
+else:
+    import sys
+
+    sys.modules[__name__] = _LazyModule(
+        __name__,
+        globals()["__file__"],
+        _import_structure,
+        module_spec=__spec__,
+    )
+    for name, value in _dummy_objects.items():
+        setattr(sys.modules[__name__], name, value)

mantis_evalkit/lib/python3.10/site-packages/diffusers/pipelines/audioldm2/modeling_audioldm2.py

@@ -0,0 +1,1530 @@
+# Copyright 2024 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.
+
+from dataclasses import dataclass
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+import torch.utils.checkpoint
+
+from ...configuration_utils import ConfigMixin, register_to_config
+from ...loaders import UNet2DConditionLoadersMixin
+from ...models.activations import get_activation
+from ...models.attention_processor import (
+    ADDED_KV_ATTENTION_PROCESSORS,
+    CROSS_ATTENTION_PROCESSORS,
+    AttentionProcessor,
+    AttnAddedKVProcessor,
+    AttnProcessor,
+)
+from ...models.embeddings import (
+    TimestepEmbedding,
+    Timesteps,
+)
+from ...models.modeling_utils import ModelMixin
+from ...models.resnet import Downsample2D, ResnetBlock2D, Upsample2D
+from ...models.transformers.transformer_2d import Transformer2DModel
+from ...models.unets.unet_2d_blocks import DownBlock2D, UpBlock2D
+from ...models.unets.unet_2d_condition import UNet2DConditionOutput
+from ...utils import BaseOutput, is_torch_version, logging
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+def add_special_tokens(hidden_states, attention_mask, sos_token, eos_token):
+    batch_size = hidden_states.shape[0]
+
+    if attention_mask is not None:
+        # Add two more steps to attn mask
+        new_attn_mask_step = attention_mask.new_ones((batch_size, 1))
+        attention_mask = torch.concat([new_attn_mask_step, attention_mask, new_attn_mask_step], dim=-1)
+
+    # Add the SOS / EOS tokens at the start / end of the sequence respectively
+    sos_token = sos_token.expand(batch_size, 1, -1)
+    eos_token = eos_token.expand(batch_size, 1, -1)
+    hidden_states = torch.concat([sos_token, hidden_states, eos_token], dim=1)
+    return hidden_states, attention_mask
+
+
+@dataclass
+class AudioLDM2ProjectionModelOutput(BaseOutput):
+    """
+    Args:
+    Class for AudioLDM2 projection layer's outputs.
+        hidden_states (`torch.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
+            Sequence of hidden-states obtained by linearly projecting the hidden-states for each of the text
+             encoders and subsequently concatenating them together.
+        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding token indices, formed by concatenating the attention masks
+             for the two text encoders together. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+    """
+
+    hidden_states: torch.Tensor
+    attention_mask: Optional[torch.LongTensor] = None
+
+
+class AudioLDM2ProjectionModel(ModelMixin, ConfigMixin):
+    """
+    A simple linear projection model to map two text embeddings to a shared latent space. It also inserts learned
+    embedding vectors at the start and end of each text embedding sequence respectively. Each variable appended with
+    `_1` refers to that corresponding to the second text encoder. Otherwise, it is from the first.
+
+    Args:
+        text_encoder_dim (`int`):
+            Dimensionality of the text embeddings from the first text encoder (CLAP).
+        text_encoder_1_dim (`int`):
+            Dimensionality of the text embeddings from the second text encoder (T5 or VITS).
+        langauge_model_dim (`int`):
+            Dimensionality of the text embeddings from the language model (GPT2).
+    """
+
+    @register_to_config
+    def __init__(
+        self,
+        text_encoder_dim,
+        text_encoder_1_dim,
+        langauge_model_dim,
+        use_learned_position_embedding=None,
+        max_seq_length=None,
+    ):
+        super().__init__()
+        # additional projection layers for each text encoder
+        self.projection = nn.Linear(text_encoder_dim, langauge_model_dim)
+        self.projection_1 = nn.Linear(text_encoder_1_dim, langauge_model_dim)
+
+        # learnable SOS / EOS token embeddings for each text encoder
+        self.sos_embed = nn.Parameter(torch.ones(langauge_model_dim))
+        self.eos_embed = nn.Parameter(torch.ones(langauge_model_dim))
+
+        self.sos_embed_1 = nn.Parameter(torch.ones(langauge_model_dim))
+        self.eos_embed_1 = nn.Parameter(torch.ones(langauge_model_dim))
+
+        self.use_learned_position_embedding = use_learned_position_embedding
+
+        # learable positional embedding for vits encoder
+        if self.use_learned_position_embedding is not None:
+            self.learnable_positional_embedding = torch.nn.Parameter(
+                torch.zeros((1, text_encoder_1_dim, max_seq_length))
+            )
+
+    def forward(
+        self,
+        hidden_states: Optional[torch.Tensor] = None,
+        hidden_states_1: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.LongTensor] = None,
+        attention_mask_1: Optional[torch.LongTensor] = None,
+    ):
+        hidden_states = self.projection(hidden_states)
+        hidden_states, attention_mask = add_special_tokens(
+            hidden_states, attention_mask, sos_token=self.sos_embed, eos_token=self.eos_embed
+        )
+
+        # Add positional embedding for Vits hidden state
+        if self.use_learned_position_embedding is not None:
+            hidden_states_1 = (hidden_states_1.permute(0, 2, 1) + self.learnable_positional_embedding).permute(0, 2, 1)
+
+        hidden_states_1 = self.projection_1(hidden_states_1)
+        hidden_states_1, attention_mask_1 = add_special_tokens(
+            hidden_states_1, attention_mask_1, sos_token=self.sos_embed_1, eos_token=self.eos_embed_1
+        )
+
+        # concatenate clap and t5 text encoding
+        hidden_states = torch.cat([hidden_states, hidden_states_1], dim=1)
+
+        # concatenate attention masks
+        if attention_mask is None and attention_mask_1 is not None:
+            attention_mask = attention_mask_1.new_ones((hidden_states[:2]))
+        elif attention_mask is not None and attention_mask_1 is None:
+            attention_mask_1 = attention_mask.new_ones((hidden_states_1[:2]))
+
+        if attention_mask is not None and attention_mask_1 is not None:
+            attention_mask = torch.cat([attention_mask, attention_mask_1], dim=-1)
+        else:
+            attention_mask = None
+
+        return AudioLDM2ProjectionModelOutput(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+        )
+
+
+class AudioLDM2UNet2DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
+    r"""
+    A conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a sample
+    shaped output. Compared to the vanilla [`UNet2DConditionModel`], this variant optionally includes an additional
+    self-attention layer in each Transformer block, as well as multiple cross-attention layers. It also allows for up
+    to two cross-attention embeddings, `encoder_hidden_states` and `encoder_hidden_states_1`.
+
+    This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
+    for all models (such as downloading or saving).
+
+    Parameters:
+        sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`):
+            Height and width of input/output sample.
+        in_channels (`int`, *optional*, defaults to 4): Number of channels in the input sample.
+        out_channels (`int`, *optional*, defaults to 4): Number of channels in the output.
+        flip_sin_to_cos (`bool`, *optional*, defaults to `False`):
+            Whether to flip the sin to cos in the time embedding.
+        freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding.
+        down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`):
+            The tuple of downsample blocks to use.
+        mid_block_type (`str`, *optional*, defaults to `"UNetMidBlock2DCrossAttn"`):
+            Block type for middle of UNet, it can only be `UNetMidBlock2DCrossAttn` for AudioLDM2.
+        up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")`):
+            The tuple of upsample blocks to use.
+        only_cross_attention (`bool` or `Tuple[bool]`, *optional*, default to `False`):
+            Whether to include self-attention in the basic transformer blocks, see
+            [`~models.attention.BasicTransformerBlock`].
+        block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):
+            The tuple of output channels for each block.
+        layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block.
+        downsample_padding (`int`, *optional*, defaults to 1): The padding to use for the downsampling convolution.
+        mid_block_scale_factor (`float`, *optional*, defaults to 1.0): The scale factor to use for the mid block.
+        act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
+        norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization.
+            If `None`, normalization and activation layers is skipped in post-processing.
+        norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon to use for the normalization.
+        cross_attention_dim (`int` or `Tuple[int]`, *optional*, defaults to 1280):
+            The dimension of the cross attention features.
+        transformer_layers_per_block (`int` or `Tuple[int]`, *optional*, defaults to 1):
+            The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for
+            [`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`],
+            [`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`].
+        attention_head_dim (`int`, *optional*, defaults to 8): The dimension of the attention heads.
+        num_attention_heads (`int`, *optional*):
+            The number of attention heads. If not defined, defaults to `attention_head_dim`
+        resnet_time_scale_shift (`str`, *optional*, defaults to `"default"`): Time scale shift config
+            for ResNet blocks (see [`~models.resnet.ResnetBlock2D`]). Choose from `default` or `scale_shift`.
+        class_embed_type (`str`, *optional*, defaults to `None`):
+            The type of class embedding to use which is ultimately summed with the time embeddings. Choose from `None`,
+            `"timestep"`, `"identity"`, `"projection"`, or `"simple_projection"`.
+        num_class_embeds (`int`, *optional*, defaults to `None`):
+            Input dimension of the learnable embedding matrix to be projected to `time_embed_dim`, when performing
+            class conditioning with `class_embed_type` equal to `None`.
+        time_embedding_type (`str`, *optional*, defaults to `positional`):
+            The type of position embedding to use for timesteps. Choose from `positional` or `fourier`.
+        time_embedding_dim (`int`, *optional*, defaults to `None`):
+            An optional override for the dimension of the projected time embedding.
+        time_embedding_act_fn (`str`, *optional*, defaults to `None`):
+            Optional activation function to use only once on the time embeddings before they are passed to the rest of
+            the UNet. Choose from `silu`, `mish`, `gelu`, and `swish`.
+        timestep_post_act (`str`, *optional*, defaults to `None`):
+            The second activation function to use in timestep embedding. Choose from `silu`, `mish` and `gelu`.
+        time_cond_proj_dim (`int`, *optional*, defaults to `None`):
+            The dimension of `cond_proj` layer in the timestep embedding.
+        conv_in_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_in` layer.
+        conv_out_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_out` layer.
+        projection_class_embeddings_input_dim (`int`, *optional*): The dimension of the `class_labels` input when
+            `class_embed_type="projection"`. Required when `class_embed_type="projection"`.
+        class_embeddings_concat (`bool`, *optional*, defaults to `False`): Whether to concatenate the time
+            embeddings with the class embeddings.
+    """
+
+    _supports_gradient_checkpointing = True
+
+    @register_to_config
+    def __init__(
+        self,
+        sample_size: Optional[int] = None,
+        in_channels: int = 4,
+        out_channels: int = 4,
+        flip_sin_to_cos: bool = True,
+        freq_shift: int = 0,
+        down_block_types: Tuple[str] = (
+            "CrossAttnDownBlock2D",
+            "CrossAttnDownBlock2D",
+            "CrossAttnDownBlock2D",
+            "DownBlock2D",
+        ),
+        mid_block_type: Optional[str] = "UNetMidBlock2DCrossAttn",
+        up_block_types: Tuple[str] = ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"),
+        only_cross_attention: Union[bool, Tuple[bool]] = False,
+        block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
+        layers_per_block: Union[int, Tuple[int]] = 2,
+        downsample_padding: int = 1,
+        mid_block_scale_factor: float = 1,
+        act_fn: str = "silu",
+        norm_num_groups: Optional[int] = 32,
+        norm_eps: float = 1e-5,
+        cross_attention_dim: Union[int, Tuple[int]] = 1280,
+        transformer_layers_per_block: Union[int, Tuple[int]] = 1,
+        attention_head_dim: Union[int, Tuple[int]] = 8,
+        num_attention_heads: Optional[Union[int, Tuple[int]]] = None,
+        use_linear_projection: bool = False,
+        class_embed_type: Optional[str] = None,
+        num_class_embeds: Optional[int] = None,
+        upcast_attention: bool = False,
+        resnet_time_scale_shift: str = "default",
+        time_embedding_type: str = "positional",
+        time_embedding_dim: Optional[int] = None,
+        time_embedding_act_fn: Optional[str] = None,
+        timestep_post_act: Optional[str] = None,
+        time_cond_proj_dim: Optional[int] = None,
+        conv_in_kernel: int = 3,
+        conv_out_kernel: int = 3,
+        projection_class_embeddings_input_dim: Optional[int] = None,
+        class_embeddings_concat: bool = False,
+    ):
+        super().__init__()
+
+        self.sample_size = sample_size
+
+        if num_attention_heads is not None:
+            raise ValueError(
+                "At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19."
+            )
+
+        # If `num_attention_heads` is not defined (which is the case for most models)
+        # it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
+        # The reason for this behavior is to correct for incorrectly named variables that were introduced
+        # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
+        # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
+        # which is why we correct for the naming here.
+        num_attention_heads = num_attention_heads or attention_head_dim
+
+        # Check inputs
+        if len(down_block_types) != len(up_block_types):
+            raise ValueError(
+                f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}."
+            )
+
+        if len(block_out_channels) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."
+            )
+
+        if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}."
+            )
+
+        if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}."
+            )
+
+        if not isinstance(attention_head_dim, int) and len(attention_head_dim) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `attention_head_dim` as `down_block_types`. `attention_head_dim`: {attention_head_dim}. `down_block_types`: {down_block_types}."
+            )
+
+        if isinstance(cross_attention_dim, list) and len(cross_attention_dim) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `cross_attention_dim` as `down_block_types`. `cross_attention_dim`: {cross_attention_dim}. `down_block_types`: {down_block_types}."
+            )
+
+        if not isinstance(layers_per_block, int) and len(layers_per_block) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `layers_per_block` as `down_block_types`. `layers_per_block`: {layers_per_block}. `down_block_types`: {down_block_types}."
+            )
+
+        # input
+        conv_in_padding = (conv_in_kernel - 1) // 2
+        self.conv_in = nn.Conv2d(
+            in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding
+        )
+
+        # time
+        if time_embedding_type == "positional":
+            time_embed_dim = time_embedding_dim or block_out_channels[0] * 4
+
+            self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
+            timestep_input_dim = block_out_channels[0]
+        else:
+            raise ValueError(f"{time_embedding_type} does not exist. Please make sure to use `positional`.")
+
+        self.time_embedding = TimestepEmbedding(
+            timestep_input_dim,
+            time_embed_dim,
+            act_fn=act_fn,
+            post_act_fn=timestep_post_act,
+            cond_proj_dim=time_cond_proj_dim,
+        )
+
+        # class embedding
+        if class_embed_type is None and num_class_embeds is not None:
+            self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
+        elif class_embed_type == "timestep":
+            self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim, act_fn=act_fn)
+        elif class_embed_type == "identity":
+            self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)
+        elif class_embed_type == "projection":
+            if projection_class_embeddings_input_dim is None:
+                raise ValueError(
+                    "`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set"
+                )
+            # The projection `class_embed_type` is the same as the timestep `class_embed_type` except
+            # 1. the `class_labels` inputs are not first converted to sinusoidal embeddings
+            # 2. it projects from an arbitrary input dimension.
+            #
+            # Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations.
+            # When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings.
+            # As a result, `TimestepEmbedding` can be passed arbitrary vectors.
+            self.class_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
+        elif class_embed_type == "simple_projection":
+            if projection_class_embeddings_input_dim is None:
+                raise ValueError(
+                    "`class_embed_type`: 'simple_projection' requires `projection_class_embeddings_input_dim` be set"
+                )
+            self.class_embedding = nn.Linear(projection_class_embeddings_input_dim, time_embed_dim)
+        else:
+            self.class_embedding = None
+
+        if time_embedding_act_fn is None:
+            self.time_embed_act = None
+        else:
+            self.time_embed_act = get_activation(time_embedding_act_fn)
+
+        self.down_blocks = nn.ModuleList([])
+        self.up_blocks = nn.ModuleList([])
+
+        if isinstance(only_cross_attention, bool):
+            only_cross_attention = [only_cross_attention] * len(down_block_types)
+
+        if isinstance(num_attention_heads, int):
+            num_attention_heads = (num_attention_heads,) * len(down_block_types)
+
+        if isinstance(cross_attention_dim, int):
+            cross_attention_dim = (cross_attention_dim,) * len(down_block_types)
+
+        if isinstance(layers_per_block, int):
+            layers_per_block = [layers_per_block] * len(down_block_types)
+
+        if isinstance(transformer_layers_per_block, int):
+            transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types)
+
+        if class_embeddings_concat:
+            # The time embeddings are concatenated with the class embeddings. The dimension of the
+            # time embeddings passed to the down, middle, and up blocks is twice the dimension of the
+            # regular time embeddings
+            blocks_time_embed_dim = time_embed_dim * 2
+        else:
+            blocks_time_embed_dim = time_embed_dim
+
+        # down
+        output_channel = block_out_channels[0]
+        for i, down_block_type in enumerate(down_block_types):
+            input_channel = output_channel
+            output_channel = block_out_channels[i]
+            is_final_block = i == len(block_out_channels) - 1
+
+            down_block = get_down_block(
+                down_block_type,
+                num_layers=layers_per_block[i],
+                transformer_layers_per_block=transformer_layers_per_block[i],
+                in_channels=input_channel,
+                out_channels=output_channel,
+                temb_channels=blocks_time_embed_dim,
+                add_downsample=not is_final_block,
+                resnet_eps=norm_eps,
+                resnet_act_fn=act_fn,
+                resnet_groups=norm_num_groups,
+                cross_attention_dim=cross_attention_dim[i],
+                num_attention_heads=num_attention_heads[i],
+                downsample_padding=downsample_padding,
+                use_linear_projection=use_linear_projection,
+                only_cross_attention=only_cross_attention[i],
+                upcast_attention=upcast_attention,
+                resnet_time_scale_shift=resnet_time_scale_shift,
+            )
+            self.down_blocks.append(down_block)
+
+        # mid
+        if mid_block_type == "UNetMidBlock2DCrossAttn":
+            self.mid_block = UNetMidBlock2DCrossAttn(
+                transformer_layers_per_block=transformer_layers_per_block[-1],
+                in_channels=block_out_channels[-1],
+                temb_channels=blocks_time_embed_dim,
+                resnet_eps=norm_eps,
+                resnet_act_fn=act_fn,
+                output_scale_factor=mid_block_scale_factor,
+                resnet_time_scale_shift=resnet_time_scale_shift,
+                cross_attention_dim=cross_attention_dim[-1],
+                num_attention_heads=num_attention_heads[-1],
+                resnet_groups=norm_num_groups,
+                use_linear_projection=use_linear_projection,
+                upcast_attention=upcast_attention,
+            )
+        else:
+            raise ValueError(
+                f"unknown mid_block_type : {mid_block_type}. Should be `UNetMidBlock2DCrossAttn` for AudioLDM2."
+            )
+
+        # count how many layers upsample the images
+        self.num_upsamplers = 0
+
+        # up
+        reversed_block_out_channels = list(reversed(block_out_channels))
+        reversed_num_attention_heads = list(reversed(num_attention_heads))
+        reversed_layers_per_block = list(reversed(layers_per_block))
+        reversed_cross_attention_dim = list(reversed(cross_attention_dim))
+        reversed_transformer_layers_per_block = list(reversed(transformer_layers_per_block))
+        only_cross_attention = list(reversed(only_cross_attention))
+
+        output_channel = reversed_block_out_channels[0]
+        for i, up_block_type in enumerate(up_block_types):
+            is_final_block = i == len(block_out_channels) - 1
+
+            prev_output_channel = output_channel
+            output_channel = reversed_block_out_channels[i]
+            input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]
+
+            # add upsample block for all BUT final layer
+            if not is_final_block:
+                add_upsample = True
+                self.num_upsamplers += 1
+            else:
+                add_upsample = False
+
+            up_block = get_up_block(
+                up_block_type,
+                num_layers=reversed_layers_per_block[i] + 1,
+                transformer_layers_per_block=reversed_transformer_layers_per_block[i],
+                in_channels=input_channel,
+                out_channels=output_channel,
+                prev_output_channel=prev_output_channel,
+                temb_channels=blocks_time_embed_dim,
+                add_upsample=add_upsample,
+                resnet_eps=norm_eps,
+                resnet_act_fn=act_fn,
+                resnet_groups=norm_num_groups,
+                cross_attention_dim=reversed_cross_attention_dim[i],
+                num_attention_heads=reversed_num_attention_heads[i],
+                use_linear_projection=use_linear_projection,
+                only_cross_attention=only_cross_attention[i],
+                upcast_attention=upcast_attention,
+                resnet_time_scale_shift=resnet_time_scale_shift,
+            )
+            self.up_blocks.append(up_block)
+            prev_output_channel = output_channel
+
+        # out
+        if norm_num_groups is not None:
+            self.conv_norm_out = nn.GroupNorm(
+                num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps
+            )
+
+            self.conv_act = get_activation(act_fn)
+
+        else:
+            self.conv_norm_out = None
+            self.conv_act = None
+
+        conv_out_padding = (conv_out_kernel - 1) // 2
+        self.conv_out = nn.Conv2d(
+            block_out_channels[0], out_channels, kernel_size=conv_out_kernel, padding=conv_out_padding
+        )
+
+    @property
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
+    def attn_processors(self) -> Dict[str, AttentionProcessor]:
+        r"""
+        Returns:
+            `dict` of attention processors: A dictionary containing all attention processors used in the model with
+            indexed by its weight name.
+        """
+        # set recursively
+        processors = {}
+
+        def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
+            if hasattr(module, "get_processor"):
+                processors[f"{name}.processor"] = module.get_processor()
+
+            for sub_name, child in module.named_children():
+                fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
+
+            return processors
+
+        for name, module in self.named_children():
+            fn_recursive_add_processors(name, module, processors)
+
+        return processors
+
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
+    def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
+        r"""
+        Sets the attention processor to use to compute attention.
+
+        Parameters:
+            processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
+                The instantiated processor class or a dictionary of processor classes that will be set as the processor
+                for **all** `Attention` layers.
+
+                If `processor` is a dict, the key needs to define the path to the corresponding cross attention
+                processor. This is strongly recommended when setting trainable attention processors.
+
+        """
+        count = len(self.attn_processors.keys())
+
+        if isinstance(processor, dict) and len(processor) != count:
+            raise ValueError(
+                f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
+                f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
+            )
+
+        def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
+            if hasattr(module, "set_processor"):
+                if not isinstance(processor, dict):
+                    module.set_processor(processor)
+                else:
+                    module.set_processor(processor.pop(f"{name}.processor"))
+
+            for sub_name, child in module.named_children():
+                fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
+
+        for name, module in self.named_children():
+            fn_recursive_attn_processor(name, module, processor)
+
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor
+    def set_default_attn_processor(self):
+        """
+        Disables custom attention processors and sets the default attention implementation.
+        """
+        if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
+            processor = AttnAddedKVProcessor()
+        elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
+            processor = AttnProcessor()
+        else:
+            raise ValueError(
+                f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}"
+            )
+
+        self.set_attn_processor(processor)
+
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attention_slice
+    def set_attention_slice(self, slice_size):
+        r"""
+        Enable sliced attention computation.
+
+        When this option is enabled, the attention module splits the input tensor in slices to compute attention in
+        several steps. This is useful for saving some memory in exchange for a small decrease in speed.
+
+        Args:
+            slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`):
+                When `"auto"`, input to the attention heads is halved, so attention is computed in two steps. If
+                `"max"`, maximum amount of memory is saved by running only one slice at a time. If a number is
+                provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
+                must be a multiple of `slice_size`.
+        """
+        sliceable_head_dims = []
+
+        def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module):
+            if hasattr(module, "set_attention_slice"):
+                sliceable_head_dims.append(module.sliceable_head_dim)
+
+            for child in module.children():
+                fn_recursive_retrieve_sliceable_dims(child)
+
+        # retrieve number of attention layers
+        for module in self.children():
+            fn_recursive_retrieve_sliceable_dims(module)
+
+        num_sliceable_layers = len(sliceable_head_dims)
+
+        if slice_size == "auto":
+            # half the attention head size is usually a good trade-off between
+            # speed and memory
+            slice_size = [dim // 2 for dim in sliceable_head_dims]
+        elif slice_size == "max":
+            # make smallest slice possible
+            slice_size = num_sliceable_layers * [1]
+
+        slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size
+
+        if len(slice_size) != len(sliceable_head_dims):
+            raise ValueError(
+                f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different"
+                f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}."
+            )
+
+        for i in range(len(slice_size)):
+            size = slice_size[i]
+            dim = sliceable_head_dims[i]
+            if size is not None and size > dim:
+                raise ValueError(f"size {size} has to be smaller or equal to {dim}.")
+
+        # Recursively walk through all the children.
+        # Any children which exposes the set_attention_slice method
+        # gets the message
+        def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):
+            if hasattr(module, "set_attention_slice"):
+                module.set_attention_slice(slice_size.pop())
+
+            for child in module.children():
+                fn_recursive_set_attention_slice(child, slice_size)
+
+        reversed_slice_size = list(reversed(slice_size))
+        for module in self.children():
+            fn_recursive_set_attention_slice(module, reversed_slice_size)
+
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel._set_gradient_checkpointing
+    def _set_gradient_checkpointing(self, module, value=False):
+        if hasattr(module, "gradient_checkpointing"):
+            module.gradient_checkpointing = value
+
+    def forward(
+        self,
+        sample: torch.Tensor,
+        timestep: Union[torch.Tensor, float, int],
+        encoder_hidden_states: torch.Tensor,
+        class_labels: Optional[torch.Tensor] = None,
+        timestep_cond: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        encoder_attention_mask: Optional[torch.Tensor] = None,
+        return_dict: bool = True,
+        encoder_hidden_states_1: Optional[torch.Tensor] = None,
+        encoder_attention_mask_1: Optional[torch.Tensor] = None,
+    ) -> Union[UNet2DConditionOutput, Tuple]:
+        r"""
+        The [`AudioLDM2UNet2DConditionModel`] forward method.
+
+        Args:
+            sample (`torch.Tensor`):
+                The noisy input tensor with the following shape `(batch, channel, height, width)`.
+            timestep (`torch.Tensor` or `float` or `int`): The number of timesteps to denoise an input.
+            encoder_hidden_states (`torch.Tensor`):
+                The encoder hidden states with shape `(batch, sequence_length, feature_dim)`.
+            encoder_attention_mask (`torch.Tensor`):
+                A cross-attention mask of shape `(batch, sequence_length)` is applied to `encoder_hidden_states`. If
+                `True` the mask is kept, otherwise if `False` it is discarded. Mask will be converted into a bias,
+                which adds large negative values to the attention scores corresponding to "discard" tokens.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] instead of a plain
+                tuple.
+            cross_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the [`AttnProcessor`].
+            encoder_hidden_states_1 (`torch.Tensor`, *optional*):
+                A second set of encoder hidden states with shape `(batch, sequence_length_2, feature_dim_2)`. Can be
+                used to condition the model on a different set of embeddings to `encoder_hidden_states`.
+            encoder_attention_mask_1 (`torch.Tensor`, *optional*):
+                A cross-attention mask of shape `(batch, sequence_length_2)` is applied to `encoder_hidden_states_1`.
+                If `True` the mask is kept, otherwise if `False` it is discarded. Mask will be converted into a bias,
+                which adds large negative values to the attention scores corresponding to "discard" tokens.
+
+        Returns:
+            [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:
+                If `return_dict` is True, an [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] is returned,
+                otherwise a `tuple` is returned where the first element is the sample tensor.
+        """
+        # By default samples have to be AT least a multiple of the overall upsampling factor.
+        # The overall upsampling factor is equal to 2 ** (# num of upsampling layers).
+        # However, the upsampling interpolation output size can be forced to fit any upsampling size
+        # on the fly if necessary.
+        default_overall_up_factor = 2**self.num_upsamplers
+
+        # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`
+        forward_upsample_size = False
+        upsample_size = None
+
+        if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):
+            logger.info("Forward upsample size to force interpolation output size.")
+            forward_upsample_size = True
+
+        # ensure attention_mask is a bias, and give it a singleton query_tokens dimension
+        # expects mask of shape:
+        #   [batch, key_tokens]
+        # adds singleton query_tokens dimension:
+        #   [batch,                    1, key_tokens]
+        # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
+        #   [batch,  heads, query_tokens, key_tokens] (e.g. torch sdp attn)
+        #   [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
+        if attention_mask is not None:
+            # assume that mask is expressed as:
+            #   (1 = keep,      0 = discard)
+            # convert mask into a bias that can be added to attention scores:
+            #       (keep = +0,     discard = -10000.0)
+            attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
+            attention_mask = attention_mask.unsqueeze(1)
+
+        # convert encoder_attention_mask to a bias the same way we do for attention_mask
+        if encoder_attention_mask is not None:
+            encoder_attention_mask = (1 - encoder_attention_mask.to(sample.dtype)) * -10000.0
+            encoder_attention_mask = encoder_attention_mask.unsqueeze(1)
+
+        if encoder_attention_mask_1 is not None:
+            encoder_attention_mask_1 = (1 - encoder_attention_mask_1.to(sample.dtype)) * -10000.0
+            encoder_attention_mask_1 = encoder_attention_mask_1.unsqueeze(1)
+
+        # 1. time
+        timesteps = timestep
+        if not torch.is_tensor(timesteps):
+            # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
+            # This would be a good case for the `match` statement (Python 3.10+)
+            is_mps = sample.device.type == "mps"
+            if isinstance(timestep, float):
+                dtype = torch.float32 if is_mps else torch.float64
+            else:
+                dtype = torch.int32 if is_mps else torch.int64
+            timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
+        elif len(timesteps.shape) == 0:
+            timesteps = timesteps[None].to(sample.device)
+
+        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+        timesteps = timesteps.expand(sample.shape[0])
+
+        t_emb = self.time_proj(timesteps)
+
+        # `Timesteps` does not contain any weights and will always return f32 tensors
+        # but time_embedding might actually be running in fp16. so we need to cast here.
+        # there might be better ways to encapsulate this.
+        t_emb = t_emb.to(dtype=sample.dtype)
+
+        emb = self.time_embedding(t_emb, timestep_cond)
+        aug_emb = None
+
+        if self.class_embedding is not None:
+            if class_labels is None:
+                raise ValueError("class_labels should be provided when num_class_embeds > 0")
+
+            if self.config.class_embed_type == "timestep":
+                class_labels = self.time_proj(class_labels)
+
+                # `Timesteps` does not contain any weights and will always return f32 tensors
+                # there might be better ways to encapsulate this.
+                class_labels = class_labels.to(dtype=sample.dtype)
+
+            class_emb = self.class_embedding(class_labels).to(dtype=sample.dtype)
+
+            if self.config.class_embeddings_concat:
+                emb = torch.cat([emb, class_emb], dim=-1)
+            else:
+                emb = emb + class_emb
+
+        emb = emb + aug_emb if aug_emb is not None else emb
+
+        if self.time_embed_act is not None:
+            emb = self.time_embed_act(emb)
+
+        # 2. pre-process
+        sample = self.conv_in(sample)
+
+        # 3. down
+        down_block_res_samples = (sample,)
+        for downsample_block in self.down_blocks:
+            if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
+                sample, res_samples = downsample_block(
+                    hidden_states=sample,
+                    temb=emb,
+                    encoder_hidden_states=encoder_hidden_states,
+                    attention_mask=attention_mask,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                    encoder_attention_mask=encoder_attention_mask,
+                    encoder_hidden_states_1=encoder_hidden_states_1,
+                    encoder_attention_mask_1=encoder_attention_mask_1,
+                )
+            else:
+                sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
+
+            down_block_res_samples += res_samples
+
+        # 4. mid
+        if self.mid_block is not None:
+            sample = self.mid_block(
+                sample,
+                emb,
+                encoder_hidden_states=encoder_hidden_states,
+                attention_mask=attention_mask,
+                cross_attention_kwargs=cross_attention_kwargs,
+                encoder_attention_mask=encoder_attention_mask,
+                encoder_hidden_states_1=encoder_hidden_states_1,
+                encoder_attention_mask_1=encoder_attention_mask_1,
+            )
+
+        # 5. up
+        for i, upsample_block in enumerate(self.up_blocks):
+            is_final_block = i == len(self.up_blocks) - 1
+
+            res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
+            down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
+
+            # if we have not reached the final block and need to forward the
+            # upsample size, we do it here
+            if not is_final_block and forward_upsample_size:
+                upsample_size = down_block_res_samples[-1].shape[2:]
+
+            if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
+                sample = upsample_block(
+                    hidden_states=sample,
+                    temb=emb,
+                    res_hidden_states_tuple=res_samples,
+                    encoder_hidden_states=encoder_hidden_states,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                    upsample_size=upsample_size,
+                    attention_mask=attention_mask,
+                    encoder_attention_mask=encoder_attention_mask,
+                    encoder_hidden_states_1=encoder_hidden_states_1,
+                    encoder_attention_mask_1=encoder_attention_mask_1,
+                )
+            else:
+                sample = upsample_block(
+                    hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size
+                )
+
+        # 6. post-process
+        if self.conv_norm_out:
+            sample = self.conv_norm_out(sample)
+            sample = self.conv_act(sample)
+        sample = self.conv_out(sample)
+
+        if not return_dict:
+            return (sample,)
+
+        return UNet2DConditionOutput(sample=sample)
+
+
+def get_down_block(
+    down_block_type,
+    num_layers,
+    in_channels,
+    out_channels,
+    temb_channels,
+    add_downsample,
+    resnet_eps,
+    resnet_act_fn,
+    transformer_layers_per_block=1,
+    num_attention_heads=None,
+    resnet_groups=None,
+    cross_attention_dim=None,
+    downsample_padding=None,
+    use_linear_projection=False,
+    only_cross_attention=False,
+    upcast_attention=False,
+    resnet_time_scale_shift="default",
+):
+    down_block_type = down_block_type[7:] if down_block_type.startswith("UNetRes") else down_block_type
+    if down_block_type == "DownBlock2D":
+        return DownBlock2D(
+            num_layers=num_layers,
+            in_channels=in_channels,
+            out_channels=out_channels,
+            temb_channels=temb_channels,
+            add_downsample=add_downsample,
+            resnet_eps=resnet_eps,
+            resnet_act_fn=resnet_act_fn,
+            resnet_groups=resnet_groups,
+            downsample_padding=downsample_padding,
+            resnet_time_scale_shift=resnet_time_scale_shift,
+        )
+    elif down_block_type == "CrossAttnDownBlock2D":
+        if cross_attention_dim is None:
+            raise ValueError("cross_attention_dim must be specified for CrossAttnDownBlock2D")
+        return CrossAttnDownBlock2D(
+            num_layers=num_layers,
+            transformer_layers_per_block=transformer_layers_per_block,
+            in_channels=in_channels,
+            out_channels=out_channels,
+            temb_channels=temb_channels,
+            add_downsample=add_downsample,
+            resnet_eps=resnet_eps,
+            resnet_act_fn=resnet_act_fn,
+            resnet_groups=resnet_groups,
+            downsample_padding=downsample_padding,
+            cross_attention_dim=cross_attention_dim,
+            num_attention_heads=num_attention_heads,
+            use_linear_projection=use_linear_projection,
+            only_cross_attention=only_cross_attention,
+            upcast_attention=upcast_attention,
+            resnet_time_scale_shift=resnet_time_scale_shift,
+        )
+    raise ValueError(f"{down_block_type} does not exist.")
+
+
+def get_up_block(
+    up_block_type,
+    num_layers,
+    in_channels,
+    out_channels,
+    prev_output_channel,
+    temb_channels,
+    add_upsample,
+    resnet_eps,
+    resnet_act_fn,
+    transformer_layers_per_block=1,
+    num_attention_heads=None,
+    resnet_groups=None,
+    cross_attention_dim=None,
+    use_linear_projection=False,
+    only_cross_attention=False,
+    upcast_attention=False,
+    resnet_time_scale_shift="default",
+):
+    up_block_type = up_block_type[7:] if up_block_type.startswith("UNetRes") else up_block_type
+    if up_block_type == "UpBlock2D":
+        return UpBlock2D(
+            num_layers=num_layers,
+            in_channels=in_channels,
+            out_channels=out_channels,
+            prev_output_channel=prev_output_channel,
+            temb_channels=temb_channels,
+            add_upsample=add_upsample,
+            resnet_eps=resnet_eps,
+            resnet_act_fn=resnet_act_fn,
+            resnet_groups=resnet_groups,
+            resnet_time_scale_shift=resnet_time_scale_shift,
+        )
+    elif up_block_type == "CrossAttnUpBlock2D":
+        if cross_attention_dim is None:
+            raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlock2D")
+        return CrossAttnUpBlock2D(
+            num_layers=num_layers,
+            transformer_layers_per_block=transformer_layers_per_block,
+            in_channels=in_channels,
+            out_channels=out_channels,
+            prev_output_channel=prev_output_channel,
+            temb_channels=temb_channels,
+            add_upsample=add_upsample,
+            resnet_eps=resnet_eps,
+            resnet_act_fn=resnet_act_fn,
+            resnet_groups=resnet_groups,
+            cross_attention_dim=cross_attention_dim,
+            num_attention_heads=num_attention_heads,
+            use_linear_projection=use_linear_projection,
+            only_cross_attention=only_cross_attention,
+            upcast_attention=upcast_attention,
+            resnet_time_scale_shift=resnet_time_scale_shift,
+        )
+    raise ValueError(f"{up_block_type} does not exist.")
+
+
+class CrossAttnDownBlock2D(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        temb_channels: int,
+        dropout: float = 0.0,
+        num_layers: int = 1,
+        transformer_layers_per_block: int = 1,
+        resnet_eps: float = 1e-6,
+        resnet_time_scale_shift: str = "default",
+        resnet_act_fn: str = "swish",
+        resnet_groups: int = 32,
+        resnet_pre_norm: bool = True,
+        num_attention_heads=1,
+        cross_attention_dim=1280,
+        output_scale_factor=1.0,
+        downsample_padding=1,
+        add_downsample=True,
+        use_linear_projection=False,
+        only_cross_attention=False,
+        upcast_attention=False,
+    ):
+        super().__init__()
+        resnets = []
+        attentions = []
+
+        self.has_cross_attention = True
+        self.num_attention_heads = num_attention_heads
+
+        if isinstance(cross_attention_dim, int):
+            cross_attention_dim = (cross_attention_dim,)
+        if isinstance(cross_attention_dim, (list, tuple)) and len(cross_attention_dim) > 4:
+            raise ValueError(
+                "Only up to 4 cross-attention layers are supported. Ensure that the length of cross-attention "
+                f"dims is less than or equal to 4. Got cross-attention dims {cross_attention_dim} of length {len(cross_attention_dim)}"
+            )
+        self.cross_attention_dim = cross_attention_dim
+
+        for i in range(num_layers):
+            in_channels = in_channels if i == 0 else out_channels
+            resnets.append(
+                ResnetBlock2D(
+                    in_channels=in_channels,
+                    out_channels=out_channels,
+                    temb_channels=temb_channels,
+                    eps=resnet_eps,
+                    groups=resnet_groups,
+                    dropout=dropout,
+                    time_embedding_norm=resnet_time_scale_shift,
+                    non_linearity=resnet_act_fn,
+                    output_scale_factor=output_scale_factor,
+                    pre_norm=resnet_pre_norm,
+                )
+            )
+            for j in range(len(cross_attention_dim)):
+                attentions.append(
+                    Transformer2DModel(
+                        num_attention_heads,
+                        out_channels // num_attention_heads,
+                        in_channels=out_channels,
+                        num_layers=transformer_layers_per_block,
+                        cross_attention_dim=cross_attention_dim[j],
+                        norm_num_groups=resnet_groups,
+                        use_linear_projection=use_linear_projection,
+                        only_cross_attention=only_cross_attention,
+                        upcast_attention=upcast_attention,
+                        double_self_attention=True if cross_attention_dim[j] is None else False,
+                    )
+                )
+        self.attentions = nn.ModuleList(attentions)
+        self.resnets = nn.ModuleList(resnets)
+
+        if add_downsample:
+            self.downsamplers = nn.ModuleList(
+                [
+                    Downsample2D(
+                        out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op"
+                    )
+                ]
+            )
+        else:
+            self.downsamplers = None
+
+        self.gradient_checkpointing = False
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        temb: Optional[torch.Tensor] = None,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        encoder_attention_mask: Optional[torch.Tensor] = None,
+        encoder_hidden_states_1: Optional[torch.Tensor] = None,
+        encoder_attention_mask_1: Optional[torch.Tensor] = None,
+    ):
+        output_states = ()
+        num_layers = len(self.resnets)
+        num_attention_per_layer = len(self.attentions) // num_layers
+
+        encoder_hidden_states_1 = (
+            encoder_hidden_states_1 if encoder_hidden_states_1 is not None else encoder_hidden_states
+        )
+        encoder_attention_mask_1 = (
+            encoder_attention_mask_1 if encoder_hidden_states_1 is not None else encoder_attention_mask
+        )
+
+        for i in range(num_layers):
+            if torch.is_grad_enabled() and self.gradient_checkpointing:
+
+                def create_custom_forward(module, return_dict=None):
+                    def custom_forward(*inputs):
+                        if return_dict is not None:
+                            return module(*inputs, return_dict=return_dict)
+                        else:
+                            return module(*inputs)
+
+                    return custom_forward
+
+                ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
+                hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(self.resnets[i]),
+                    hidden_states,
+                    temb,
+                    **ckpt_kwargs,
+                )
+                for idx, cross_attention_dim in enumerate(self.cross_attention_dim):
+                    if cross_attention_dim is not None and idx <= 1:
+                        forward_encoder_hidden_states = encoder_hidden_states
+                        forward_encoder_attention_mask = encoder_attention_mask
+                    elif cross_attention_dim is not None and idx > 1:
+                        forward_encoder_hidden_states = encoder_hidden_states_1
+                        forward_encoder_attention_mask = encoder_attention_mask_1
+                    else:
+                        forward_encoder_hidden_states = None
+                        forward_encoder_attention_mask = None
+                    hidden_states = torch.utils.checkpoint.checkpoint(
+                        create_custom_forward(self.attentions[i * num_attention_per_layer + idx], return_dict=False),
+                        hidden_states,
+                        forward_encoder_hidden_states,
+                        None,  # timestep
+                        None,  # class_labels
+                        cross_attention_kwargs,
+                        attention_mask,
+                        forward_encoder_attention_mask,
+                        **ckpt_kwargs,
+                    )[0]
+            else:
+                hidden_states = self.resnets[i](hidden_states, temb)
+                for idx, cross_attention_dim in enumerate(self.cross_attention_dim):
+                    if cross_attention_dim is not None and idx <= 1:
+                        forward_encoder_hidden_states = encoder_hidden_states
+                        forward_encoder_attention_mask = encoder_attention_mask
+                    elif cross_attention_dim is not None and idx > 1:
+                        forward_encoder_hidden_states = encoder_hidden_states_1
+                        forward_encoder_attention_mask = encoder_attention_mask_1
+                    else:
+                        forward_encoder_hidden_states = None
+                        forward_encoder_attention_mask = None
+                    hidden_states = self.attentions[i * num_attention_per_layer + idx](
+                        hidden_states,
+                        attention_mask=attention_mask,
+                        encoder_hidden_states=forward_encoder_hidden_states,
+                        encoder_attention_mask=forward_encoder_attention_mask,
+                        return_dict=False,
+                    )[0]
+
+            output_states = output_states + (hidden_states,)
+
+        if self.downsamplers is not None:
+            for downsampler in self.downsamplers:
+                hidden_states = downsampler(hidden_states)
+
+            output_states = output_states + (hidden_states,)
+
+        return hidden_states, output_states
+
+
+class UNetMidBlock2DCrossAttn(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        temb_channels: int,
+        dropout: float = 0.0,
+        num_layers: int = 1,
+        transformer_layers_per_block: int = 1,
+        resnet_eps: float = 1e-6,
+        resnet_time_scale_shift: str = "default",
+        resnet_act_fn: str = "swish",
+        resnet_groups: int = 32,
+        resnet_pre_norm: bool = True,
+        num_attention_heads=1,
+        output_scale_factor=1.0,
+        cross_attention_dim=1280,
+        use_linear_projection=False,
+        upcast_attention=False,
+    ):
+        super().__init__()
+
+        self.has_cross_attention = True
+        self.num_attention_heads = num_attention_heads
+        resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)
+
+        if isinstance(cross_attention_dim, int):
+            cross_attention_dim = (cross_attention_dim,)
+        if isinstance(cross_attention_dim, (list, tuple)) and len(cross_attention_dim) > 4:
+            raise ValueError(
+                "Only up to 4 cross-attention layers are supported. Ensure that the length of cross-attention "
+                f"dims is less than or equal to 4. Got cross-attention dims {cross_attention_dim} of length {len(cross_attention_dim)}"
+            )
+        self.cross_attention_dim = cross_attention_dim
+
+        # there is always at least one resnet
+        resnets = [
+            ResnetBlock2D(
+                in_channels=in_channels,
+                out_channels=in_channels,
+                temb_channels=temb_channels,
+                eps=resnet_eps,
+                groups=resnet_groups,
+                dropout=dropout,
+                time_embedding_norm=resnet_time_scale_shift,
+                non_linearity=resnet_act_fn,
+                output_scale_factor=output_scale_factor,
+                pre_norm=resnet_pre_norm,
+            )
+        ]
+        attentions = []
+
+        for i in range(num_layers):
+            for j in range(len(cross_attention_dim)):
+                attentions.append(
+                    Transformer2DModel(
+                        num_attention_heads,
+                        in_channels // num_attention_heads,
+                        in_channels=in_channels,
+                        num_layers=transformer_layers_per_block,
+                        cross_attention_dim=cross_attention_dim[j],
+                        norm_num_groups=resnet_groups,
+                        use_linear_projection=use_linear_projection,
+                        upcast_attention=upcast_attention,
+                        double_self_attention=True if cross_attention_dim[j] is None else False,
+                    )
+                )
+            resnets.append(
+                ResnetBlock2D(
+                    in_channels=in_channels,
+                    out_channels=in_channels,
+                    temb_channels=temb_channels,
+                    eps=resnet_eps,
+                    groups=resnet_groups,
+                    dropout=dropout,
+                    time_embedding_norm=resnet_time_scale_shift,
+                    non_linearity=resnet_act_fn,
+                    output_scale_factor=output_scale_factor,
+                    pre_norm=resnet_pre_norm,
+                )
+            )
+
+        self.attentions = nn.ModuleList(attentions)
+        self.resnets = nn.ModuleList(resnets)
+
+        self.gradient_checkpointing = False
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        temb: Optional[torch.Tensor] = None,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        encoder_attention_mask: Optional[torch.Tensor] = None,
+        encoder_hidden_states_1: Optional[torch.Tensor] = None,
+        encoder_attention_mask_1: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        hidden_states = self.resnets[0](hidden_states, temb)
+        num_attention_per_layer = len(self.attentions) // (len(self.resnets) - 1)
+
+        encoder_hidden_states_1 = (
+            encoder_hidden_states_1 if encoder_hidden_states_1 is not None else encoder_hidden_states
+        )
+        encoder_attention_mask_1 = (
+            encoder_attention_mask_1 if encoder_hidden_states_1 is not None else encoder_attention_mask
+        )
+
+        for i in range(len(self.resnets[1:])):
+            if torch.is_grad_enabled() and self.gradient_checkpointing:
+
+                def create_custom_forward(module, return_dict=None):
+                    def custom_forward(*inputs):
+                        if return_dict is not None:
+                            return module(*inputs, return_dict=return_dict)
+                        else:
+                            return module(*inputs)
+
+                    return custom_forward
+
+                ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
+                for idx, cross_attention_dim in enumerate(self.cross_attention_dim):
+                    if cross_attention_dim is not None and idx <= 1:
+                        forward_encoder_hidden_states = encoder_hidden_states
+                        forward_encoder_attention_mask = encoder_attention_mask
+                    elif cross_attention_dim is not None and idx > 1:
+                        forward_encoder_hidden_states = encoder_hidden_states_1
+                        forward_encoder_attention_mask = encoder_attention_mask_1
+                    else:
+                        forward_encoder_hidden_states = None
+                        forward_encoder_attention_mask = None
+                    hidden_states = torch.utils.checkpoint.checkpoint(
+                        create_custom_forward(self.attentions[i * num_attention_per_layer + idx], return_dict=False),
+                        hidden_states,
+                        forward_encoder_hidden_states,
+                        None,  # timestep
+                        None,  # class_labels
+                        cross_attention_kwargs,
+                        attention_mask,
+                        forward_encoder_attention_mask,
+                        **ckpt_kwargs,
+                    )[0]
+                hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(self.resnets[i + 1]),
+                    hidden_states,
+                    temb,
+                    **ckpt_kwargs,
+                )
+            else:
+                for idx, cross_attention_dim in enumerate(self.cross_attention_dim):
+                    if cross_attention_dim is not None and idx <= 1:
+                        forward_encoder_hidden_states = encoder_hidden_states
+                        forward_encoder_attention_mask = encoder_attention_mask
+                    elif cross_attention_dim is not None and idx > 1:
+                        forward_encoder_hidden_states = encoder_hidden_states_1
+                        forward_encoder_attention_mask = encoder_attention_mask_1
+                    else:
+                        forward_encoder_hidden_states = None
+                        forward_encoder_attention_mask = None
+                    hidden_states = self.attentions[i * num_attention_per_layer + idx](
+                        hidden_states,
+                        attention_mask=attention_mask,
+                        encoder_hidden_states=forward_encoder_hidden_states,
+                        encoder_attention_mask=forward_encoder_attention_mask,
+                        return_dict=False,
+                    )[0]
+
+                hidden_states = self.resnets[i + 1](hidden_states, temb)
+
+        return hidden_states
+
+
+class CrossAttnUpBlock2D(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        prev_output_channel: int,
+        temb_channels: int,
+        dropout: float = 0.0,
+        num_layers: int = 1,
+        transformer_layers_per_block: int = 1,
+        resnet_eps: float = 1e-6,
+        resnet_time_scale_shift: str = "default",
+        resnet_act_fn: str = "swish",
+        resnet_groups: int = 32,
+        resnet_pre_norm: bool = True,
+        num_attention_heads=1,
+        cross_attention_dim=1280,
+        output_scale_factor=1.0,
+        add_upsample=True,
+        use_linear_projection=False,
+        only_cross_attention=False,
+        upcast_attention=False,
+    ):
+        super().__init__()
+        resnets = []
+        attentions = []
+
+        self.has_cross_attention = True
+        self.num_attention_heads = num_attention_heads
+
+        if isinstance(cross_attention_dim, int):
+            cross_attention_dim = (cross_attention_dim,)
+        if isinstance(cross_attention_dim, (list, tuple)) and len(cross_attention_dim) > 4:
+            raise ValueError(
+                "Only up to 4 cross-attention layers are supported. Ensure that the length of cross-attention "
+                f"dims is less than or equal to 4. Got cross-attention dims {cross_attention_dim} of length {len(cross_attention_dim)}"
+            )
+        self.cross_attention_dim = cross_attention_dim
+
+        for i in range(num_layers):
+            res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
+            resnet_in_channels = prev_output_channel if i == 0 else out_channels
+
+            resnets.append(
+                ResnetBlock2D(
+                    in_channels=resnet_in_channels + res_skip_channels,
+                    out_channels=out_channels,
+                    temb_channels=temb_channels,
+                    eps=resnet_eps,
+                    groups=resnet_groups,
+                    dropout=dropout,
+                    time_embedding_norm=resnet_time_scale_shift,
+                    non_linearity=resnet_act_fn,
+                    output_scale_factor=output_scale_factor,
+                    pre_norm=resnet_pre_norm,
+                )
+            )
+            for j in range(len(cross_attention_dim)):
+                attentions.append(
+                    Transformer2DModel(
+                        num_attention_heads,
+                        out_channels // num_attention_heads,
+                        in_channels=out_channels,
+                        num_layers=transformer_layers_per_block,
+                        cross_attention_dim=cross_attention_dim[j],
+                        norm_num_groups=resnet_groups,
+                        use_linear_projection=use_linear_projection,
+                        only_cross_attention=only_cross_attention,
+                        upcast_attention=upcast_attention,
+                        double_self_attention=True if cross_attention_dim[j] is None else False,
+                    )
+                )
+        self.attentions = nn.ModuleList(attentions)
+        self.resnets = nn.ModuleList(resnets)
+
+        if add_upsample:
+            self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])
+        else:
+            self.upsamplers = None
+
+        self.gradient_checkpointing = False
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        res_hidden_states_tuple: Tuple[torch.Tensor, ...],
+        temb: Optional[torch.Tensor] = None,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        upsample_size: Optional[int] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        encoder_attention_mask: Optional[torch.Tensor] = None,
+        encoder_hidden_states_1: Optional[torch.Tensor] = None,
+        encoder_attention_mask_1: Optional[torch.Tensor] = None,
+    ):
+        num_layers = len(self.resnets)
+        num_attention_per_layer = len(self.attentions) // num_layers
+
+        encoder_hidden_states_1 = (
+            encoder_hidden_states_1 if encoder_hidden_states_1 is not None else encoder_hidden_states
+        )
+        encoder_attention_mask_1 = (
+            encoder_attention_mask_1 if encoder_hidden_states_1 is not None else encoder_attention_mask
+        )
+
+        for i in range(num_layers):
+            # pop res hidden states
+            res_hidden_states = res_hidden_states_tuple[-1]
+            res_hidden_states_tuple = res_hidden_states_tuple[:-1]
+            hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
+
+            if torch.is_grad_enabled() and self.gradient_checkpointing:
+
+                def create_custom_forward(module, return_dict=None):
+                    def custom_forward(*inputs):
+                        if return_dict is not None:
+                            return module(*inputs, return_dict=return_dict)
+                        else:
+                            return module(*inputs)
+
+                    return custom_forward
+
+                ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
+                hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(self.resnets[i]),
+                    hidden_states,
+                    temb,
+                    **ckpt_kwargs,
+                )
+                for idx, cross_attention_dim in enumerate(self.cross_attention_dim):
+                    if cross_attention_dim is not None and idx <= 1:
+                        forward_encoder_hidden_states = encoder_hidden_states
+                        forward_encoder_attention_mask = encoder_attention_mask
+                    elif cross_attention_dim is not None and idx > 1:
+                        forward_encoder_hidden_states = encoder_hidden_states_1
+                        forward_encoder_attention_mask = encoder_attention_mask_1
+                    else:
+                        forward_encoder_hidden_states = None
+                        forward_encoder_attention_mask = None
+                    hidden_states = torch.utils.checkpoint.checkpoint(
+                        create_custom_forward(self.attentions[i * num_attention_per_layer + idx], return_dict=False),
+                        hidden_states,
+                        forward_encoder_hidden_states,
+                        None,  # timestep
+                        None,  # class_labels
+                        cross_attention_kwargs,
+                        attention_mask,
+                        forward_encoder_attention_mask,
+                        **ckpt_kwargs,
+                    )[0]
+            else:
+                hidden_states = self.resnets[i](hidden_states, temb)
+                for idx, cross_attention_dim in enumerate(self.cross_attention_dim):
+                    if cross_attention_dim is not None and idx <= 1:
+                        forward_encoder_hidden_states = encoder_hidden_states
+                        forward_encoder_attention_mask = encoder_attention_mask
+                    elif cross_attention_dim is not None and idx > 1:
+                        forward_encoder_hidden_states = encoder_hidden_states_1
+                        forward_encoder_attention_mask = encoder_attention_mask_1
+                    else:
+                        forward_encoder_hidden_states = None
+                        forward_encoder_attention_mask = None
+                    hidden_states = self.attentions[i * num_attention_per_layer + idx](
+                        hidden_states,
+                        attention_mask=attention_mask,
+                        encoder_hidden_states=forward_encoder_hidden_states,
+                        encoder_attention_mask=forward_encoder_attention_mask,
+                        return_dict=False,
+                    )[0]
+
+        if self.upsamplers is not None:
+            for upsampler in self.upsamplers:
+                hidden_states = upsampler(hidden_states, upsample_size)
+
+        return hidden_states

mantis_evalkit/lib/python3.10/site-packages/diffusers/pipelines/controlnet_xs/__init__.py

@@ -0,0 +1,68 @@
+from typing import TYPE_CHECKING
+
+from ...utils import (
+    DIFFUSERS_SLOW_IMPORT,
+    OptionalDependencyNotAvailable,
+    _LazyModule,
+    get_objects_from_module,
+    is_flax_available,
+    is_torch_available,
+    is_transformers_available,
+)
+
+
+_dummy_objects = {}
+_import_structure = {}
+
+try:
+    if not (is_transformers_available() and is_torch_available()):
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    from ...utils import dummy_torch_and_transformers_objects  # noqa F403
+
+    _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))
+else:
+    _import_structure["pipeline_controlnet_xs"] = ["StableDiffusionControlNetXSPipeline"]
+    _import_structure["pipeline_controlnet_xs_sd_xl"] = ["StableDiffusionXLControlNetXSPipeline"]
+try:
+    if not (is_transformers_available() and is_flax_available()):
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    from ...utils import dummy_flax_and_transformers_objects  # noqa F403
+
+    _dummy_objects.update(get_objects_from_module(dummy_flax_and_transformers_objects))
+else:
+    pass  # _import_structure["pipeline_flax_controlnet"] = ["FlaxStableDiffusionControlNetPipeline"]
+
+
+if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
+    try:
+        if not (is_transformers_available() and is_torch_available()):
+            raise OptionalDependencyNotAvailable()
+
+    except OptionalDependencyNotAvailable:
+        from ...utils.dummy_torch_and_transformers_objects import *
+    else:
+        from .pipeline_controlnet_xs import StableDiffusionControlNetXSPipeline
+        from .pipeline_controlnet_xs_sd_xl import StableDiffusionXLControlNetXSPipeline
+
+    try:
+        if not (is_transformers_available() and is_flax_available()):
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        from ...utils.dummy_flax_and_transformers_objects import *  # noqa F403
+    else:
+        pass  # from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline
+
+
+else:
+    import sys
+
+    sys.modules[__name__] = _LazyModule(
+        __name__,
+        globals()["__file__"],
+        _import_structure,
+        module_spec=__spec__,
+    )
+    for name, value in _dummy_objects.items():
+        setattr(sys.modules[__name__], name, value)

mantis_evalkit/lib/python3.10/site-packages/diffusers/pipelines/controlnet_xs/pipeline_controlnet_xs.py

@@ -0,0 +1,916 @@
+# Copyright 2024 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.
+
+import inspect
+from typing import Any, Callable, Dict, List, Optional, Union
+
+import numpy as np
+import PIL.Image
+import torch
+import torch.nn.functional as F
+from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
+
+from ...callbacks import MultiPipelineCallbacks, PipelineCallback
+from ...image_processor import PipelineImageInput, VaeImageProcessor
+from ...loaders import FromSingleFileMixin, StableDiffusionLoraLoaderMixin, TextualInversionLoaderMixin
+from ...models import AutoencoderKL, ControlNetXSAdapter, UNet2DConditionModel, UNetControlNetXSModel
+from ...models.lora import adjust_lora_scale_text_encoder
+from ...schedulers import KarrasDiffusionSchedulers
+from ...utils import (
+    USE_PEFT_BACKEND,
+    deprecate,
+    logging,
+    replace_example_docstring,
+    scale_lora_layers,
+    unscale_lora_layers,
+)
+from ...utils.torch_utils import is_compiled_module, is_torch_version, randn_tensor
+from ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin
+from ..stable_diffusion.pipeline_output import StableDiffusionPipelineOutput
+from ..stable_diffusion.safety_checker import StableDiffusionSafetyChecker
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+EXAMPLE_DOC_STRING = """
+    Examples:
+        ```py
+        >>> # !pip install opencv-python transformers accelerate
+        >>> from diffusers import StableDiffusionControlNetXSPipeline, ControlNetXSAdapter
+        >>> from diffusers.utils import load_image
+        >>> import numpy as np
+        >>> import torch
+
+        >>> import cv2
+        >>> from PIL import Image
+
+        >>> prompt = "aerial view, a futuristic research complex in a bright foggy jungle, hard lighting"
+        >>> negative_prompt = "low quality, bad quality, sketches"
+
+        >>> # download an image
+        >>> image = load_image(
+        ...     "https://hf.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/hf-logo.png"
+        ... )
+
+        >>> # initialize the models and pipeline
+        >>> controlnet_conditioning_scale = 0.5
+
+        >>> controlnet = ControlNetXSAdapter.from_pretrained(
+        ...     "UmerHA/Testing-ConrolNetXS-SD2.1-canny", torch_dtype=torch.float16
+        ... )
+        >>> pipe = StableDiffusionControlNetXSPipeline.from_pretrained(
+        ...     "stabilityai/stable-diffusion-2-1-base", controlnet=controlnet, torch_dtype=torch.float16
+        ... )
+        >>> pipe.enable_model_cpu_offload()
+
+        >>> # get canny image
+        >>> image = np.array(image)
+        >>> image = cv2.Canny(image, 100, 200)
+        >>> image = image[:, :, None]
+        >>> image = np.concatenate([image, image, image], axis=2)
+        >>> canny_image = Image.fromarray(image)
+        >>> # generate image
+        >>> image = pipe(
+        ...     prompt, controlnet_conditioning_scale=controlnet_conditioning_scale, image=canny_image
+        ... ).images[0]
+        ```
+"""
+
+
+class StableDiffusionControlNetXSPipeline(
+    DiffusionPipeline,
+    StableDiffusionMixin,
+    TextualInversionLoaderMixin,
+    StableDiffusionLoraLoaderMixin,
+    FromSingleFileMixin,
+):
+    r"""
+    Pipeline for text-to-image generation using Stable Diffusion with ControlNet-XS guidance.
+
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
+    implemented for all pipelines (downloading, saving, running on a particular device, etc.).
+
+    The pipeline also inherits the following loading methods:
+        - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
+        - [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] for loading LoRA weights
+        - [`~loaders.StableDiffusionLoraLoaderMixin.save_lora_weights`] for saving LoRA weights
+        - [`loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files
+
+    Args:
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
+        text_encoder ([`~transformers.CLIPTextModel`]):
+            Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
+        tokenizer ([`~transformers.CLIPTokenizer`]):
+            A `CLIPTokenizer` to tokenize text.
+        unet ([`UNet2DConditionModel`]):
+            A [`UNet2DConditionModel`] used to create a UNetControlNetXSModel to denoise the encoded image latents.
+        controlnet ([`ControlNetXSAdapter`]):
+            A [`ControlNetXSAdapter`] to be used in combination with `unet` 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 more details
+            about a model's potential harms.
+        feature_extractor ([`~transformers.CLIPImageProcessor`]):
+            A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
+    """
+
+    model_cpu_offload_seq = "text_encoder->unet->vae"
+    _optional_components = ["safety_checker", "feature_extractor"]
+    _exclude_from_cpu_offload = ["safety_checker"]
+    _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        unet: Union[UNet2DConditionModel, UNetControlNetXSModel],
+        controlnet: ControlNetXSAdapter,
+        scheduler: KarrasDiffusionSchedulers,
+        safety_checker: StableDiffusionSafetyChecker,
+        feature_extractor: CLIPImageProcessor,
+        requires_safety_checker: bool = True,
+    ):
+        super().__init__()
+
+        if isinstance(unet, UNet2DConditionModel):
+            unet = UNetControlNetXSModel.from_unet(unet, controlnet)
+
+        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. Both the 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(
+                "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.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True)
+        self.control_image_processor = VaeImageProcessor(
+            vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True, do_normalize=False
+        )
+        self.register_to_config(requires_safety_checker=requires_safety_checker)
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
+    def _encode_prompt(
+        self,
+        prompt,
+        device,
+        num_images_per_prompt,
+        do_classifier_free_guidance,
+        negative_prompt=None,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.Tensor] = None,
+        lora_scale: Optional[float] = None,
+        **kwargs,
+    ):
+        deprecation_message = "`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple."
+        deprecate("_encode_prompt()", "1.0.0", deprecation_message, standard_warn=False)
+
+        prompt_embeds_tuple = self.encode_prompt(
+            prompt=prompt,
+            device=device,
+            num_images_per_prompt=num_images_per_prompt,
+            do_classifier_free_guidance=do_classifier_free_guidance,
+            negative_prompt=negative_prompt,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            lora_scale=lora_scale,
+            **kwargs,
+        )
+
+        # concatenate for backwards comp
+        prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])
+
+        return prompt_embeds
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt
+    def encode_prompt(
+        self,
+        prompt,
+        device,
+        num_images_per_prompt,
+        do_classifier_free_guidance,
+        negative_prompt=None,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.Tensor] = None,
+        lora_scale: Optional[float] = None,
+        clip_skip: Optional[int] = None,
+    ):
+        r"""
+        Encodes the prompt into text encoder hidden states.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                prompt to be encoded
+            device: (`torch.device`):
+                torch device
+            num_images_per_prompt (`int`):
+                number of images that should be generated per prompt
+            do_classifier_free_guidance (`bool`):
+                whether to use classifier free guidance or not
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. 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`).
+            prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+            lora_scale (`float`, *optional*):
+                A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
+            clip_skip (`int`, *optional*):
+                Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
+                the output of the pre-final layer will be used for computing the prompt embeddings.
+        """
+        # set lora scale so that monkey patched LoRA
+        # function of text encoder can correctly access it
+        if lora_scale is not None and isinstance(self, StableDiffusionLoraLoaderMixin):
+            self._lora_scale = lora_scale
+
+            # dynamically adjust the LoRA scale
+            if not USE_PEFT_BACKEND:
+                adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
+            else:
+                scale_lora_layers(self.text_encoder, lora_scale)
+
+        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]
+
+        if prompt_embeds is None:
+            # textual inversion: process multi-vector tokens if necessary
+            if isinstance(self, TextualInversionLoaderMixin):
+                prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
+
+            text_inputs = self.tokenizer(
+                prompt,
+                padding="max_length",
+                max_length=self.tokenizer.model_max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+            text_input_ids = text_inputs.input_ids
+            untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
+
+            if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
+                text_input_ids, untruncated_ids
+            ):
+                removed_text = self.tokenizer.batch_decode(
+                    untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
+                )
+                logger.warning(
+                    "The following part of your input was truncated because CLIP can only handle sequences up to"
+                    f" {self.tokenizer.model_max_length} tokens: {removed_text}"
+                )
+
+            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
+                attention_mask = text_inputs.attention_mask.to(device)
+            else:
+                attention_mask = None
+
+            if clip_skip is None:
+                prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)
+                prompt_embeds = prompt_embeds[0]
+            else:
+                prompt_embeds = self.text_encoder(
+                    text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True
+                )
+                # Access the `hidden_states` first, that contains a tuple of
+                # all the hidden states from the encoder layers. Then index into
+                # the tuple to access the hidden states from the desired layer.
+                prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]
+                # We also need to apply the final LayerNorm here to not mess with the
+                # representations. The `last_hidden_states` that we typically use for
+                # obtaining the final prompt representations passes through the LayerNorm
+                # layer.
+                prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)
+
+        if self.text_encoder is not None:
+            prompt_embeds_dtype = self.text_encoder.dtype
+        elif self.unet is not None:
+            prompt_embeds_dtype = self.unet.dtype
+        else:
+            prompt_embeds_dtype = prompt_embeds.dtype
+
+        prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
+
+        bs_embed, seq_len, _ = prompt_embeds.shape
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
+        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
+
+        # get unconditional embeddings for classifier free guidance
+        if do_classifier_free_guidance and negative_prompt_embeds is None:
+            uncond_tokens: List[str]
+            if negative_prompt is None:
+                uncond_tokens = [""] * batch_size
+            elif prompt is not None and type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+            elif isinstance(negative_prompt, str):
+                uncond_tokens = [negative_prompt]
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+            else:
+                uncond_tokens = negative_prompt
+
+            # textual inversion: process multi-vector tokens if necessary
+            if isinstance(self, TextualInversionLoaderMixin):
+                uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
+
+            max_length = prompt_embeds.shape[1]
+            uncond_input = self.tokenizer(
+                uncond_tokens,
+                padding="max_length",
+                max_length=max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+
+            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
+                attention_mask = uncond_input.attention_mask.to(device)
+            else:
+                attention_mask = None
+
+            negative_prompt_embeds = self.text_encoder(
+                uncond_input.input_ids.to(device),
+                attention_mask=attention_mask,
+            )
+            negative_prompt_embeds = negative_prompt_embeds[0]
+
+        if do_classifier_free_guidance:
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+            seq_len = negative_prompt_embeds.shape[1]
+
+            negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
+
+            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
+            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
+
+        if self.text_encoder is not None:
+            if isinstance(self, StableDiffusionLoraLoaderMixin) and USE_PEFT_BACKEND:
+                # Retrieve the original scale by scaling back the LoRA layers
+                unscale_lora_layers(self.text_encoder, lora_scale)
+
+        return prompt_embeds, negative_prompt_embeds
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
+    def run_safety_checker(self, image, device, dtype):
+        if self.safety_checker is None:
+            has_nsfw_concept = None
+        else:
+            if torch.is_tensor(image):
+                feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
+            else:
+                feature_extractor_input = self.image_processor.numpy_to_pil(image)
+            safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device)
+            image, has_nsfw_concept = self.safety_checker(
+                images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
+            )
+        return image, has_nsfw_concept
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
+    def decode_latents(self, latents):
+        deprecation_message = "The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead"
+        deprecate("decode_latents", "1.0.0", deprecation_message, standard_warn=False)
+
+        latents = 1 / self.vae.config.scaling_factor * latents
+        image = self.vae.decode(latents, return_dict=False)[0]
+        image = (image / 2 + 0.5).clamp(0, 1)
+        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
+        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
+        return image
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
+    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,
+        negative_prompt=None,
+        prompt_embeds=None,
+        negative_prompt_embeds=None,
+        controlnet_conditioning_scale=1.0,
+        control_guidance_start=0.0,
+        control_guidance_end=1.0,
+        callback_on_step_end_tensor_inputs=None,
+    ):
+        if callback_on_step_end_tensor_inputs is not None and not all(
+            k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
+        ):
+            raise ValueError(
+                f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
+            )
+
+        if prompt is not None and prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
+                " only forward one of the two."
+            )
+        elif prompt is None and prompt_embeds is None:
+            raise ValueError(
+                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
+            )
+        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+
+        if negative_prompt is not None and negative_prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
+                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
+            )
+
+        if prompt_embeds is not None and negative_prompt_embeds is not None:
+            if prompt_embeds.shape != negative_prompt_embeds.shape:
+                raise ValueError(
+                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
+                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
+                    f" {negative_prompt_embeds.shape}."
+                )
+
+        # Check `image` and `controlnet_conditioning_scale`
+        is_compiled = hasattr(F, "scaled_dot_product_attention") and isinstance(
+            self.unet, torch._dynamo.eval_frame.OptimizedModule
+        )
+        if (
+            isinstance(self.unet, UNetControlNetXSModel)
+            or is_compiled
+            and isinstance(self.unet._orig_mod, UNetControlNetXSModel)
+        ):
+            self.check_image(image, prompt, prompt_embeds)
+            if not isinstance(controlnet_conditioning_scale, float):
+                raise TypeError("For single controlnet: `controlnet_conditioning_scale` must be type `float`.")
+        else:
+            assert False
+
+        start, end = control_guidance_start, control_guidance_end
+        if start >= end:
+            raise ValueError(
+                f"control guidance start: {start} cannot be larger or equal to control guidance end: {end}."
+            )
+        if start < 0.0:
+            raise ValueError(f"control guidance start: {start} can't be smaller than 0.")
+        if end > 1.0:
+            raise ValueError(f"control guidance end: {end} can't be larger than 1.0.")
+
+    def check_image(self, image, prompt, prompt_embeds):
+        image_is_pil = isinstance(image, PIL.Image.Image)
+        image_is_tensor = isinstance(image, torch.Tensor)
+        image_is_np = isinstance(image, np.ndarray)
+        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], torch.Tensor)
+        image_is_np_list = isinstance(image, list) and isinstance(image[0], np.ndarray)
+
+        if (
+            not image_is_pil
+            and not image_is_tensor
+            and not image_is_np
+            and not image_is_pil_list
+            and not image_is_tensor_list
+            and not image_is_np_list
+        ):
+            raise TypeError(
+                f"image must be passed and be one of PIL image, numpy array, torch tensor, list of PIL images, list of numpy arrays or list of torch tensors, but is {type(image)}"
+            )
+
+        if image_is_pil:
+            image_batch_size = 1
+        else:
+            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)
+        elif prompt_embeds is not None:
+            prompt_batch_size = prompt_embeds.shape[0]
+
+        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,
+        batch_size,
+        num_images_per_prompt,
+        device,
+        dtype,
+        do_classifier_free_guidance=False,
+    ):
+        image = self.control_image_processor.preprocess(image, height=height, width=width).to(dtype=torch.float32)
+        image_batch_size = image.shape[0]
+
+        if image_batch_size == 1:
+            repeat_by = batch_size
+        else:
+            # image batch size is the same as prompt batch size
+            repeat_by = num_images_per_prompt
+
+        image = image.repeat_interleave(repeat_by, dim=0)
+
+        image = image.to(device=device, dtype=dtype)
+
+        if do_classifier_free_guidance:
+            image = torch.cat([image] * 2)
+
+        return image
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
+    def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
+        shape = (
+            batch_size,
+            num_channels_latents,
+            int(height) // self.vae_scale_factor,
+            int(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, device=device, dtype=dtype)
+        else:
+            latents = latents.to(device)
+
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = latents * self.scheduler.init_noise_sigma
+        return latents
+
+    @property
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.guidance_scale
+    def guidance_scale(self):
+        return self._guidance_scale
+
+    @property
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.clip_skip
+    def clip_skip(self):
+        return self._clip_skip
+
+    @property
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.do_classifier_free_guidance
+    def do_classifier_free_guidance(self):
+        return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None
+
+    @property
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.cross_attention_kwargs
+    def cross_attention_kwargs(self):
+        return self._cross_attention_kwargs
+
+    @property
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.num_timesteps
+    def num_timesteps(self):
+        return self._num_timesteps
+
+    @torch.no_grad()
+    @replace_example_docstring(EXAMPLE_DOC_STRING)
+    def __call__(
+        self,
+        prompt: Union[str, List[str]] = None,
+        image: PipelineImageInput = None,
+        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.Tensor] = None,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.Tensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        controlnet_conditioning_scale: Union[float, List[float]] = 1.0,
+        control_guidance_start: float = 0.0,
+        control_guidance_end: float = 1.0,
+        clip_skip: Optional[int] = None,
+        callback_on_step_end: Optional[
+            Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]
+        ] = None,
+        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+    ):
+        r"""
+        The call function to the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
+            image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,:
+                    `List[List[torch.Tensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`):
+                The ControlNet input condition to provide guidance to the `unet` for generation. If the type is
+                specified as `torch.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 accordingly. 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):
+                A higher guidance scale value encourages the model to generate images closely linked to the text
+                `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide what to not include in image generation. If not defined, you need to
+                pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
+                to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
+                generation deterministic.
+            latents (`torch.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 is generated by sampling using the supplied random `generator`.
+            prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
+                provided, text embeddings are generated from the `prompt` input argument.
+            negative_prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
+                not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generated image. Choose between `PIL.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+            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).
+            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.
+            control_guidance_start (`float` or `List[float]`, *optional*, defaults to 0.0):
+                The percentage of total steps at which the ControlNet starts applying.
+            control_guidance_end (`float` or `List[float]`, *optional*, defaults to 1.0):
+                The percentage of total steps at which the ControlNet stops applying.
+            clip_skip (`int`, *optional*):
+                Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
+                the output of the pre-final layer will be used for computing the prompt embeddings.
+            callback_on_step_end (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*):
+                A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of
+                each denoising step during the inference. with the following arguments: `callback_on_step_end(self:
+                DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a
+                list of all tensors as specified by `callback_on_step_end_tensor_inputs`.
+            callback_on_step_end_tensor_inputs (`List`, *optional*):
+                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
+                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
+                `._callback_tensor_inputs` attribute of your pipeine class.
+        Examples:
+
+        Returns:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
+                If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
+                otherwise a `tuple` is returned where the first element is a list with the generated images and the
+                second element is a list of `bool`s indicating whether the corresponding generated image contains
+                "not-safe-for-work" (nsfw) content.
+        """
+
+        if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
+            callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
+
+        unet = self.unet._orig_mod if is_compiled_module(self.unet) else self.unet
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt,
+            image,
+            negative_prompt,
+            prompt_embeds,
+            negative_prompt_embeds,
+            controlnet_conditioning_scale,
+            control_guidance_start,
+            control_guidance_end,
+            callback_on_step_end_tensor_inputs,
+        )
+
+        self._guidance_scale = guidance_scale
+        self._clip_skip = clip_skip
+        self._cross_attention_kwargs = cross_attention_kwargs
+        self._interrupt = False
+
+        # 2. Define call parameters
+        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
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = guidance_scale > 1.0
+
+        # 3. Encode input prompt
+        text_encoder_lora_scale = (
+            cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
+        )
+        prompt_embeds, negative_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,
+            clip_skip=clip_skip,
+        )
+
+        # For classifier free guidance, we need to do two forward passes.
+        # Here we concatenate the unconditional and text embeddings into a single batch
+        # to avoid doing two forward passes
+        if do_classifier_free_guidance:
+            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
+
+        # 4. Prepare image
+        image = self.prepare_image(
+            image=image,
+            width=width,
+            height=height,
+            batch_size=batch_size * num_images_per_prompt,
+            num_images_per_prompt=num_images_per_prompt,
+            device=device,
+            dtype=unet.dtype,
+            do_classifier_free_guidance=do_classifier_free_guidance,
+        )
+        height, width = image.shape[-2:]
+
+        # 5. Prepare timesteps
+        self.scheduler.set_timesteps(num_inference_steps, device=device)
+        timesteps = self.scheduler.timesteps
+
+        # 6. Prepare latent variables
+        num_channels_latents = self.unet.in_channels
+        latents = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            latents,
+        )
+
+        # 7. 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)
+
+        # 8. Denoising loop
+        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
+        self._num_timesteps = len(timesteps)
+        is_controlnet_compiled = is_compiled_module(self.unet)
+        is_torch_higher_equal_2_1 = is_torch_version(">=", "2.1")
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                # Relevant thread:
+                # https://dev-discuss.pytorch.org/t/cudagraphs-in-pytorch-2-0/1428
+                if is_controlnet_compiled and is_torch_higher_equal_2_1:
+                    torch._inductor.cudagraph_mark_step_begin()
+                # expand the latents if we are doing classifier free guidance
+                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+                # predict the noise residual
+                apply_control = (
+                    i / len(timesteps) >= control_guidance_start and (i + 1) / len(timesteps) <= control_guidance_end
+                )
+                noise_pred = self.unet(
+                    sample=latent_model_input,
+                    timestep=t,
+                    encoder_hidden_states=prompt_embeds,
+                    controlnet_cond=image,
+                    conditioning_scale=controlnet_conditioning_scale,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                    return_dict=True,
+                    apply_control=apply_control,
+                ).sample
+
+                # perform guidance
+                if do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
+
+                if callback_on_step_end is not None:
+                    callback_kwargs = {}
+                    for k in callback_on_step_end_tensor_inputs:
+                        callback_kwargs[k] = locals()[k]
+                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+
+                    latents = callback_outputs.pop("latents", latents)
+                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
+                    negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
+
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+
+        # If we do sequential model offloading, let's offload unet and controlnet
+        # manually for max memory savings
+        if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
+            self.unet.to("cpu")
+            self.controlnet.to("cpu")
+            torch.cuda.empty_cache()
+
+        if not output_type == "latent":
+            image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False, generator=generator)[
+                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)
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (image, has_nsfw_concept)
+
+        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)

mantis_evalkit/lib/python3.10/site-packages/diffusers/pipelines/controlnet_xs/pipeline_controlnet_xs_sd_xl.py

@@ -0,0 +1,1111 @@
+# Copyright 2024 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.
+
+import inspect
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+import numpy as np
+import PIL.Image
+import torch
+import torch.nn.functional as F
+from transformers import (
+    CLIPImageProcessor,
+    CLIPTextModel,
+    CLIPTextModelWithProjection,
+    CLIPTokenizer,
+)
+
+from diffusers.utils.import_utils import is_invisible_watermark_available
+
+from ...callbacks import MultiPipelineCallbacks, PipelineCallback
+from ...image_processor import PipelineImageInput, VaeImageProcessor
+from ...loaders import FromSingleFileMixin, StableDiffusionXLLoraLoaderMixin, TextualInversionLoaderMixin
+from ...models import AutoencoderKL, ControlNetXSAdapter, UNet2DConditionModel, UNetControlNetXSModel
+from ...models.attention_processor import (
+    AttnProcessor2_0,
+    XFormersAttnProcessor,
+)
+from ...models.lora import adjust_lora_scale_text_encoder
+from ...schedulers import KarrasDiffusionSchedulers
+from ...utils import (
+    USE_PEFT_BACKEND,
+    logging,
+    replace_example_docstring,
+    scale_lora_layers,
+    unscale_lora_layers,
+)
+from ...utils.torch_utils import is_compiled_module, is_torch_version, randn_tensor
+from ..pipeline_utils import DiffusionPipeline
+from ..stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput
+
+
+if is_invisible_watermark_available():
+    from ..stable_diffusion_xl.watermark import StableDiffusionXLWatermarker
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+EXAMPLE_DOC_STRING = """
+    Examples:
+        ```py
+        >>> # !pip install opencv-python transformers accelerate
+        >>> from diffusers import StableDiffusionXLControlNetXSPipeline, ControlNetXSAdapter, AutoencoderKL
+        >>> from diffusers.utils import load_image
+        >>> import numpy as np
+        >>> import torch
+
+        >>> import cv2
+        >>> from PIL import Image
+
+        >>> prompt = "aerial view, a futuristic research complex in a bright foggy jungle, hard lighting"
+        >>> negative_prompt = "low quality, bad quality, sketches"
+
+        >>> # download an image
+        >>> image = load_image(
+        ...     "https://hf.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/hf-logo.png"
+        ... )
+
+        >>> # initialize the models and pipeline
+        >>> controlnet_conditioning_scale = 0.5
+        >>> vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
+        >>> controlnet = ControlNetXSAdapter.from_pretrained(
+        ...     "UmerHA/Testing-ConrolNetXS-SDXL-canny", torch_dtype=torch.float16
+        ... )
+        >>> pipe = StableDiffusionXLControlNetXSPipeline.from_pretrained(
+        ...     "stabilityai/stable-diffusion-xl-base-1.0", controlnet=controlnet, torch_dtype=torch.float16
+        ... )
+        >>> pipe.enable_model_cpu_offload()
+
+        >>> # get canny image
+        >>> image = np.array(image)
+        >>> image = cv2.Canny(image, 100, 200)
+        >>> image = image[:, :, None]
+        >>> image = np.concatenate([image, image, image], axis=2)
+        >>> canny_image = Image.fromarray(image)
+
+        >>> # generate image
+        >>> image = pipe(
+        ...     prompt, controlnet_conditioning_scale=controlnet_conditioning_scale, image=canny_image
+        ... ).images[0]
+        ```
+"""
+
+
+class StableDiffusionXLControlNetXSPipeline(
+    DiffusionPipeline,
+    TextualInversionLoaderMixin,
+    StableDiffusionXLLoraLoaderMixin,
+    FromSingleFileMixin,
+):
+    r"""
+    Pipeline for text-to-image generation using Stable Diffusion XL with ControlNet-XS guidance.
+
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
+    implemented for all pipelines (downloading, saving, running on a particular device, etc.).
+
+    The pipeline also inherits the following loading methods:
+        - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
+        - [`loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights`] for loading LoRA weights
+        - [`loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files
+
+    Args:
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
+        text_encoder ([`~transformers.CLIPTextModel`]):
+            Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
+        text_encoder_2 ([`~transformers.CLIPTextModelWithProjection`]):
+            Second frozen text-encoder
+            ([laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)).
+        tokenizer ([`~transformers.CLIPTokenizer`]):
+            A `CLIPTokenizer` to tokenize text.
+        tokenizer_2 ([`~transformers.CLIPTokenizer`]):
+            A `CLIPTokenizer` to tokenize text.
+        unet ([`UNet2DConditionModel`]):
+            A [`UNet2DConditionModel`] used to create a UNetControlNetXSModel to denoise the encoded image latents.
+        controlnet ([`ControlNetXSAdapter`]):
+            A [`ControlNetXSAdapter`] to be used in combination with `unet` 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`].
+        force_zeros_for_empty_prompt (`bool`, *optional*, defaults to `"True"`):
+            Whether the negative prompt embeddings should always be set to 0. Also see the config of
+            `stabilityai/stable-diffusion-xl-base-1-0`.
+        add_watermarker (`bool`, *optional*):
+            Whether to use the [invisible_watermark](https://github.com/ShieldMnt/invisible-watermark/) library to
+            watermark output images. If not defined, it defaults to `True` if the package is installed; otherwise no
+            watermarker is used.
+    """
+
+    model_cpu_offload_seq = "text_encoder->text_encoder_2->unet->vae"
+    _optional_components = [
+        "tokenizer",
+        "tokenizer_2",
+        "text_encoder",
+        "text_encoder_2",
+        "feature_extractor",
+    ]
+    _callback_tensor_inputs = [
+        "latents",
+        "prompt_embeds",
+        "negative_prompt_embeds",
+        "add_text_embeds",
+        "add_time_ids",
+        "negative_pooled_prompt_embeds",
+        "negative_add_time_ids",
+    ]
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        text_encoder_2: CLIPTextModelWithProjection,
+        tokenizer: CLIPTokenizer,
+        tokenizer_2: CLIPTokenizer,
+        unet: Union[UNet2DConditionModel, UNetControlNetXSModel],
+        controlnet: ControlNetXSAdapter,
+        scheduler: KarrasDiffusionSchedulers,
+        force_zeros_for_empty_prompt: bool = True,
+        add_watermarker: Optional[bool] = None,
+        feature_extractor: CLIPImageProcessor = None,
+    ):
+        super().__init__()
+
+        if isinstance(unet, UNet2DConditionModel):
+            unet = UNetControlNetXSModel.from_unet(unet, controlnet)
+
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            text_encoder_2=text_encoder_2,
+            tokenizer=tokenizer,
+            tokenizer_2=tokenizer_2,
+            unet=unet,
+            controlnet=controlnet,
+            scheduler=scheduler,
+            feature_extractor=feature_extractor,
+        )
+        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True)
+        self.control_image_processor = VaeImageProcessor(
+            vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True, do_normalize=False
+        )
+        add_watermarker = add_watermarker if add_watermarker is not None else is_invisible_watermark_available()
+
+        if add_watermarker:
+            self.watermark = StableDiffusionXLWatermarker()
+        else:
+            self.watermark = None
+
+        self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)
+
+    # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.encode_prompt
+    def encode_prompt(
+        self,
+        prompt: str,
+        prompt_2: Optional[str] = None,
+        device: Optional[torch.device] = None,
+        num_images_per_prompt: int = 1,
+        do_classifier_free_guidance: bool = True,
+        negative_prompt: Optional[str] = None,
+        negative_prompt_2: Optional[str] = None,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.Tensor] = None,
+        pooled_prompt_embeds: Optional[torch.Tensor] = None,
+        negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,
+        lora_scale: Optional[float] = None,
+        clip_skip: Optional[int] = None,
+    ):
+        r"""
+        Encodes the prompt into text encoder hidden states.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                prompt to be encoded
+            prompt_2 (`str` or `List[str]`, *optional*):
+                The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
+                used in both text-encoders
+            device: (`torch.device`):
+                torch device
+            num_images_per_prompt (`int`):
+                number of images that should be generated per prompt
+            do_classifier_free_guidance (`bool`):
+                whether to use classifier free guidance or not
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. 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`).
+            negative_prompt_2 (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
+                `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
+            prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+            pooled_prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
+                If not provided, pooled text embeddings will be generated from `prompt` input argument.
+            negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
+                input argument.
+            lora_scale (`float`, *optional*):
+                A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
+            clip_skip (`int`, *optional*):
+                Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
+                the output of the pre-final layer will be used for computing the prompt embeddings.
+        """
+        device = device or self._execution_device
+
+        # set lora scale so that monkey patched LoRA
+        # function of text encoder can correctly access it
+        if lora_scale is not None and isinstance(self, StableDiffusionXLLoraLoaderMixin):
+            self._lora_scale = lora_scale
+
+            # dynamically adjust the LoRA scale
+            if self.text_encoder is not None:
+                if not USE_PEFT_BACKEND:
+                    adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
+                else:
+                    scale_lora_layers(self.text_encoder, lora_scale)
+
+            if self.text_encoder_2 is not None:
+                if not USE_PEFT_BACKEND:
+                    adjust_lora_scale_text_encoder(self.text_encoder_2, lora_scale)
+                else:
+                    scale_lora_layers(self.text_encoder_2, lora_scale)
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+
+        if prompt is not None:
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        # Define tokenizers and text encoders
+        tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2]
+        text_encoders = (
+            [self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]
+        )
+
+        if prompt_embeds is None:
+            prompt_2 = prompt_2 or prompt
+            prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2
+
+            # textual inversion: process multi-vector tokens if necessary
+            prompt_embeds_list = []
+            prompts = [prompt, prompt_2]
+            for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders):
+                if isinstance(self, TextualInversionLoaderMixin):
+                    prompt = self.maybe_convert_prompt(prompt, tokenizer)
+
+                text_inputs = tokenizer(
+                    prompt,
+                    padding="max_length",
+                    max_length=tokenizer.model_max_length,
+                    truncation=True,
+                    return_tensors="pt",
+                )
+
+                text_input_ids = text_inputs.input_ids
+                untruncated_ids = tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
+
+                if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
+                    text_input_ids, untruncated_ids
+                ):
+                    removed_text = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1])
+                    logger.warning(
+                        "The following part of your input was truncated because CLIP can only handle sequences up to"
+                        f" {tokenizer.model_max_length} tokens: {removed_text}"
+                    )
+
+                prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)
+
+                # We are only ALWAYS interested in the pooled output of the final text encoder
+                pooled_prompt_embeds = prompt_embeds[0]
+                if clip_skip is None:
+                    prompt_embeds = prompt_embeds.hidden_states[-2]
+                else:
+                    # "2" because SDXL always indexes from the penultimate layer.
+                    prompt_embeds = prompt_embeds.hidden_states[-(clip_skip + 2)]
+
+                prompt_embeds_list.append(prompt_embeds)
+
+            prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)
+
+        # get unconditional embeddings for classifier free guidance
+        zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt
+        if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt:
+            negative_prompt_embeds = torch.zeros_like(prompt_embeds)
+            negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds)
+        elif do_classifier_free_guidance and negative_prompt_embeds is None:
+            negative_prompt = negative_prompt or ""
+            negative_prompt_2 = negative_prompt_2 or negative_prompt
+
+            # normalize str to list
+            negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
+            negative_prompt_2 = (
+                batch_size * [negative_prompt_2] if isinstance(negative_prompt_2, str) else negative_prompt_2
+            )
+
+            uncond_tokens: List[str]
+            if prompt is not None and type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+            else:
+                uncond_tokens = [negative_prompt, negative_prompt_2]
+
+            negative_prompt_embeds_list = []
+            for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders):
+                if isinstance(self, TextualInversionLoaderMixin):
+                    negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer)
+
+                max_length = prompt_embeds.shape[1]
+                uncond_input = tokenizer(
+                    negative_prompt,
+                    padding="max_length",
+                    max_length=max_length,
+                    truncation=True,
+                    return_tensors="pt",
+                )
+
+                negative_prompt_embeds = text_encoder(
+                    uncond_input.input_ids.to(device),
+                    output_hidden_states=True,
+                )
+                # We are only ALWAYS interested in the pooled output of the final text encoder
+                negative_pooled_prompt_embeds = negative_prompt_embeds[0]
+                negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]
+
+                negative_prompt_embeds_list.append(negative_prompt_embeds)
+
+            negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)
+
+        if self.text_encoder_2 is not None:
+            prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
+        else:
+            prompt_embeds = prompt_embeds.to(dtype=self.unet.dtype, device=device)
+
+        bs_embed, seq_len, _ = prompt_embeds.shape
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
+        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
+
+        if do_classifier_free_guidance:
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+            seq_len = negative_prompt_embeds.shape[1]
+
+            if self.text_encoder_2 is not None:
+                negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
+            else:
+                negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.unet.dtype, device=device)
+
+            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
+            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
+
+        pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
+            bs_embed * num_images_per_prompt, -1
+        )
+        if do_classifier_free_guidance:
+            negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
+                bs_embed * num_images_per_prompt, -1
+            )
+
+        if self.text_encoder is not None:
+            if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:
+                # Retrieve the original scale by scaling back the LoRA layers
+                unscale_lora_layers(self.text_encoder, lora_scale)
+
+        if self.text_encoder_2 is not None:
+            if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:
+                # Retrieve the original scale by scaling back the LoRA layers
+                unscale_lora_layers(self.text_encoder_2, lora_scale)
+
+        return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
+    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,
+        prompt_2,
+        image,
+        negative_prompt=None,
+        negative_prompt_2=None,
+        prompt_embeds=None,
+        negative_prompt_embeds=None,
+        pooled_prompt_embeds=None,
+        negative_pooled_prompt_embeds=None,
+        controlnet_conditioning_scale=1.0,
+        control_guidance_start=0.0,
+        control_guidance_end=1.0,
+        callback_on_step_end_tensor_inputs=None,
+    ):
+        if callback_on_step_end_tensor_inputs is not None and not all(
+            k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
+        ):
+            raise ValueError(
+                f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
+            )
+
+        if prompt is not None and prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
+                " only forward one of the two."
+            )
+        elif prompt_2 is not None and prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
+                " only forward one of the two."
+            )
+        elif prompt is None and prompt_embeds is None:
+            raise ValueError(
+                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
+            )
+        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+        elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):
+            raise ValueError(f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}")
+
+        if negative_prompt is not None and negative_prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
+                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
+            )
+        elif negative_prompt_2 is not None and negative_prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:"
+                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
+            )
+
+        if prompt_embeds is not None and negative_prompt_embeds is not None:
+            if prompt_embeds.shape != negative_prompt_embeds.shape:
+                raise ValueError(
+                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
+                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
+                    f" {negative_prompt_embeds.shape}."
+                )
+
+        if prompt_embeds is not None and pooled_prompt_embeds is None:
+            raise ValueError(
+                "If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`."
+            )
+
+        if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None:
+            raise ValueError(
+                "If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used to generate `negative_prompt_embeds`."
+            )
+
+        # Check `image` and ``controlnet_conditioning_scale``
+        is_compiled = hasattr(F, "scaled_dot_product_attention") and isinstance(
+            self.unet, torch._dynamo.eval_frame.OptimizedModule
+        )
+        if (
+            isinstance(self.unet, UNetControlNetXSModel)
+            or is_compiled
+            and isinstance(self.unet._orig_mod, UNetControlNetXSModel)
+        ):
+            self.check_image(image, prompt, prompt_embeds)
+            if not isinstance(controlnet_conditioning_scale, float):
+                raise TypeError("For single controlnet: `controlnet_conditioning_scale` must be type `float`.")
+        else:
+            assert False
+
+        start, end = control_guidance_start, control_guidance_end
+        if start >= end:
+            raise ValueError(
+                f"control guidance start: {start} cannot be larger or equal to control guidance end: {end}."
+            )
+        if start < 0.0:
+            raise ValueError(f"control guidance start: {start} can't be smaller than 0.")
+        if end > 1.0:
+            raise ValueError(f"control guidance end: {end} can't be larger than 1.0.")
+
+    # Copied from diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline.check_image
+    def check_image(self, image, prompt, prompt_embeds):
+        image_is_pil = isinstance(image, PIL.Image.Image)
+        image_is_tensor = isinstance(image, torch.Tensor)
+        image_is_np = isinstance(image, np.ndarray)
+        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], torch.Tensor)
+        image_is_np_list = isinstance(image, list) and isinstance(image[0], np.ndarray)
+
+        if (
+            not image_is_pil
+            and not image_is_tensor
+            and not image_is_np
+            and not image_is_pil_list
+            and not image_is_tensor_list
+            and not image_is_np_list
+        ):
+            raise TypeError(
+                f"image must be passed and be one of PIL image, numpy array, torch tensor, list of PIL images, list of numpy arrays or list of torch tensors, but is {type(image)}"
+            )
+
+        if image_is_pil:
+            image_batch_size = 1
+        else:
+            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)
+        elif prompt_embeds is not None:
+            prompt_batch_size = prompt_embeds.shape[0]
+
+        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,
+        batch_size,
+        num_images_per_prompt,
+        device,
+        dtype,
+        do_classifier_free_guidance=False,
+    ):
+        image = self.control_image_processor.preprocess(image, height=height, width=width).to(dtype=torch.float32)
+        image_batch_size = image.shape[0]
+
+        if image_batch_size == 1:
+            repeat_by = batch_size
+        else:
+            # image batch size is the same as prompt batch size
+            repeat_by = num_images_per_prompt
+
+        image = image.repeat_interleave(repeat_by, dim=0)
+
+        image = image.to(device=device, dtype=dtype)
+
+        if do_classifier_free_guidance:
+            image = torch.cat([image] * 2)
+
+        return image
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
+    def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
+        shape = (
+            batch_size,
+            num_channels_latents,
+            int(height) // self.vae_scale_factor,
+            int(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, device=device, dtype=dtype)
+        else:
+            latents = latents.to(device)
+
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = latents * self.scheduler.init_noise_sigma
+        return latents
+
+    def _get_add_time_ids(
+        self, original_size, crops_coords_top_left, target_size, dtype, text_encoder_projection_dim=None
+    ):
+        add_time_ids = list(original_size + crops_coords_top_left + target_size)
+
+        passed_add_embed_dim = (
+            self.unet.config.addition_time_embed_dim * len(add_time_ids) + text_encoder_projection_dim
+        )
+        expected_add_embed_dim = self.unet.base_add_embedding.linear_1.in_features
+
+        if expected_add_embed_dim != passed_add_embed_dim:
+            raise ValueError(
+                f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`."
+            )
+
+        add_time_ids = torch.tensor([add_time_ids], dtype=dtype)
+        return add_time_ids
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae
+    def upcast_vae(self):
+        dtype = self.vae.dtype
+        self.vae.to(dtype=torch.float32)
+        use_torch_2_0_or_xformers = isinstance(
+            self.vae.decoder.mid_block.attentions[0].processor,
+            (
+                AttnProcessor2_0,
+                XFormersAttnProcessor,
+            ),
+        )
+        # if xformers or torch_2_0 is used attention block does not need
+        # to be in float32 which can save lots of memory
+        if use_torch_2_0_or_xformers:
+            self.vae.post_quant_conv.to(dtype)
+            self.vae.decoder.conv_in.to(dtype)
+            self.vae.decoder.mid_block.to(dtype)
+
+    @property
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.guidance_scale
+    def guidance_scale(self):
+        return self._guidance_scale
+
+    @property
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.clip_skip
+    def clip_skip(self):
+        return self._clip_skip
+
+    @property
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.do_classifier_free_guidance
+    def do_classifier_free_guidance(self):
+        return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None
+
+    @property
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.cross_attention_kwargs
+    def cross_attention_kwargs(self):
+        return self._cross_attention_kwargs
+
+    @property
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.num_timesteps
+    def num_timesteps(self):
+        return self._num_timesteps
+
+    @torch.no_grad()
+    @replace_example_docstring(EXAMPLE_DOC_STRING)
+    def __call__(
+        self,
+        prompt: Union[str, List[str]] = None,
+        prompt_2: Optional[Union[str, List[str]]] = None,
+        image: PipelineImageInput = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 5.0,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        negative_prompt_2: 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.Tensor] = None,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.Tensor] = None,
+        pooled_prompt_embeds: Optional[torch.Tensor] = None,
+        negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        controlnet_conditioning_scale: Union[float, List[float]] = 1.0,
+        control_guidance_start: float = 0.0,
+        control_guidance_end: float = 1.0,
+        original_size: Tuple[int, int] = None,
+        crops_coords_top_left: Tuple[int, int] = (0, 0),
+        target_size: Tuple[int, int] = None,
+        negative_original_size: Optional[Tuple[int, int]] = None,
+        negative_crops_coords_top_left: Tuple[int, int] = (0, 0),
+        negative_target_size: Optional[Tuple[int, int]] = None,
+        clip_skip: Optional[int] = None,
+        callback_on_step_end: Optional[
+            Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]
+        ] = None,
+        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+    ):
+        r"""
+        The call function to the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
+            prompt_2 (`str` or `List[str]`, *optional*):
+                The prompt or prompts to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
+                used in both text-encoders.
+            image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,:
+                    `List[List[torch.Tensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`):
+                The ControlNet input condition to provide guidance to the `unet` for generation. If the type is
+                specified as `torch.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 accordingly. 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. Anything below 512 pixels won't work well for
+                [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
+                and checkpoints that are not specifically fine-tuned on low resolutions.
+            width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
+                The width in pixels of the generated image. Anything below 512 pixels won't work well for
+                [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
+                and checkpoints that are not specifically fine-tuned on low resolutions.
+            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 5.0):
+                A higher guidance scale value encourages the model to generate images closely linked to the text
+                `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide what to not include in image generation. If not defined, you need to
+                pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
+            negative_prompt_2 (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide what to not include in image generation. This is sent to `tokenizer_2`
+                and `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders.
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
+                to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
+                generation deterministic.
+            latents (`torch.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 is generated by sampling using the supplied random `generator`.
+            prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
+                provided, text embeddings are generated from the `prompt` input argument.
+            negative_prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
+                not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
+            pooled_prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated pooled text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
+                not provided, pooled text embeddings are generated from `prompt` input argument.
+            negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs (prompt
+                weighting). If not provided, pooled `negative_prompt_embeds` are generated from `negative_prompt` input
+                argument.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generated image. Choose between `PIL.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+            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).
+            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`.
+            control_guidance_start (`float`, *optional*, defaults to 0.0):
+                The percentage of total steps at which the ControlNet starts applying.
+            control_guidance_end (`float`, *optional*, defaults to 1.0):
+                The percentage of total steps at which the ControlNet stops applying.
+            original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+                If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.
+                `original_size` defaults to `(width, height)` if not specified. Part of SDXL's micro-conditioning as
+                explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
+            crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
+                `crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position
+                `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting
+                `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
+            target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+                For most cases, `target_size` should be set to the desired height and width of the generated image. If
+                not specified it will default to `(width, height)`. Part of SDXL's micro-conditioning as explained in
+                section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
+            negative_original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+                To negatively condition the generation process based on a specific image resolution. Part of SDXL's
+                micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
+                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
+            negative_crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
+                To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's
+                micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
+                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
+            negative_target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+                To negatively condition the generation process based on a target image resolution. It should be as same
+                as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
+                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
+            clip_skip (`int`, *optional*):
+                Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
+                the output of the pre-final layer will be used for computing the prompt embeddings.
+            callback_on_step_end (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*):
+                A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of
+                each denoising step during the inference. with the following arguments: `callback_on_step_end(self:
+                DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a
+                list of all tensors as specified by `callback_on_step_end_tensor_inputs`.
+            callback_on_step_end_tensor_inputs (`List`, *optional*):
+                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
+                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
+                `._callback_tensor_inputs` attribute of your pipeine class.
+
+        Examples:
+
+        Returns:
+            [`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput`] or `tuple`:
+                If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput`] is
+                returned, otherwise a `tuple` is returned containing the output images.
+        """
+
+        if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
+            callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
+
+        unet = self.unet._orig_mod if is_compiled_module(self.unet) else self.unet
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt,
+            prompt_2,
+            image,
+            negative_prompt,
+            negative_prompt_2,
+            prompt_embeds,
+            negative_prompt_embeds,
+            pooled_prompt_embeds,
+            negative_pooled_prompt_embeds,
+            controlnet_conditioning_scale,
+            control_guidance_start,
+            control_guidance_end,
+            callback_on_step_end_tensor_inputs,
+        )
+
+        self._guidance_scale = guidance_scale
+        self._clip_skip = clip_skip
+        self._cross_attention_kwargs = cross_attention_kwargs
+        self._interrupt = False
+
+        # 2. Define call parameters
+        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
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = guidance_scale > 1.0
+
+        # 3. Encode input prompt
+        text_encoder_lora_scale = (
+            cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
+        )
+        (
+            prompt_embeds,
+            negative_prompt_embeds,
+            pooled_prompt_embeds,
+            negative_pooled_prompt_embeds,
+        ) = self.encode_prompt(
+            prompt,
+            prompt_2,
+            device,
+            num_images_per_prompt,
+            do_classifier_free_guidance,
+            negative_prompt,
+            negative_prompt_2,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            pooled_prompt_embeds=pooled_prompt_embeds,
+            negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
+            lora_scale=text_encoder_lora_scale,
+            clip_skip=clip_skip,
+        )
+
+        # 4. Prepare image
+        if isinstance(unet, UNetControlNetXSModel):
+            image = self.prepare_image(
+                image=image,
+                width=width,
+                height=height,
+                batch_size=batch_size * num_images_per_prompt,
+                num_images_per_prompt=num_images_per_prompt,
+                device=device,
+                dtype=unet.dtype,
+                do_classifier_free_guidance=do_classifier_free_guidance,
+            )
+            height, width = image.shape[-2:]
+        else:
+            assert False
+
+        # 5. Prepare timesteps
+        self.scheduler.set_timesteps(num_inference_steps, device=device)
+        timesteps = self.scheduler.timesteps
+
+        # 6. Prepare latent variables
+        num_channels_latents = self.unet.in_channels
+        latents = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            latents,
+        )
+
+        # 7. 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)
+
+        # 7.1 Prepare added time ids & embeddings
+        if isinstance(image, list):
+            original_size = original_size or image[0].shape[-2:]
+        else:
+            original_size = original_size or image.shape[-2:]
+        target_size = target_size or (height, width)
+
+        add_text_embeds = pooled_prompt_embeds
+        if self.text_encoder_2 is None:
+            text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])
+        else:
+            text_encoder_projection_dim = self.text_encoder_2.config.projection_dim
+
+        add_time_ids = self._get_add_time_ids(
+            original_size,
+            crops_coords_top_left,
+            target_size,
+            dtype=prompt_embeds.dtype,
+            text_encoder_projection_dim=text_encoder_projection_dim,
+        )
+
+        if negative_original_size is not None and negative_target_size is not None:
+            negative_add_time_ids = self._get_add_time_ids(
+                negative_original_size,
+                negative_crops_coords_top_left,
+                negative_target_size,
+                dtype=prompt_embeds.dtype,
+                text_encoder_projection_dim=text_encoder_projection_dim,
+            )
+        else:
+            negative_add_time_ids = add_time_ids
+
+        if do_classifier_free_guidance:
+            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
+            add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)
+            add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0)
+
+        prompt_embeds = prompt_embeds.to(device)
+        add_text_embeds = add_text_embeds.to(device)
+        add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)
+
+        # 8. Denoising loop
+        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
+        self._num_timesteps = len(timesteps)
+        is_controlnet_compiled = is_compiled_module(self.unet)
+        is_torch_higher_equal_2_1 = is_torch_version(">=", "2.1")
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                # Relevant thread:
+                # https://dev-discuss.pytorch.org/t/cudagraphs-in-pytorch-2-0/1428
+                if is_controlnet_compiled and is_torch_higher_equal_2_1:
+                    torch._inductor.cudagraph_mark_step_begin()
+                # expand the latents if we are doing classifier free guidance
+                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+                added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
+
+                # predict the noise residual
+                apply_control = (
+                    i / len(timesteps) >= control_guidance_start and (i + 1) / len(timesteps) <= control_guidance_end
+                )
+                noise_pred = self.unet(
+                    sample=latent_model_input,
+                    timestep=t,
+                    encoder_hidden_states=prompt_embeds,
+                    controlnet_cond=image,
+                    conditioning_scale=controlnet_conditioning_scale,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                    added_cond_kwargs=added_cond_kwargs,
+                    return_dict=True,
+                    apply_control=apply_control,
+                ).sample
+
+                # perform guidance
+                if do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
+
+                if callback_on_step_end is not None:
+                    callback_kwargs = {}
+                    for k in callback_on_step_end_tensor_inputs:
+                        callback_kwargs[k] = locals()[k]
+                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+
+                    latents = callback_outputs.pop("latents", latents)
+                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
+                    negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
+
+                # 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()
+
+        # manually for max memory savings
+        if self.vae.dtype == torch.float16 and self.vae.config.force_upcast:
+            self.upcast_vae()
+            latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
+
+        if not output_type == "latent":
+            # make sure the VAE is in float32 mode, as it overflows in float16
+            needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
+
+            if needs_upcasting:
+                self.upcast_vae()
+                latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
+
+            image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
+
+            # cast back to fp16 if needed
+            if needs_upcasting:
+                self.vae.to(dtype=torch.float16)
+        else:
+            image = latents
+
+        if not output_type == "latent":
+            # apply watermark if available
+            if self.watermark is not None:
+                image = self.watermark.apply_watermark(image)
+
+            image = self.image_processor.postprocess(image, output_type=output_type)
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (image,)
+
+        return StableDiffusionXLPipelineOutput(images=image)

mantis_evalkit/lib/python3.10/site-packages/diffusers/pipelines/deepfloyd_if/__init__.py

@@ -0,0 +1,85 @@
+from typing import TYPE_CHECKING
+
+from ...utils import (
+    DIFFUSERS_SLOW_IMPORT,
+    OptionalDependencyNotAvailable,
+    _LazyModule,
+    get_objects_from_module,
+    is_torch_available,
+    is_transformers_available,
+)
+
+
+_dummy_objects = {}
+_import_structure = {
+    "timesteps": [
+        "fast27_timesteps",
+        "smart100_timesteps",
+        "smart185_timesteps",
+        "smart27_timesteps",
+        "smart50_timesteps",
+        "super100_timesteps",
+        "super27_timesteps",
+        "super40_timesteps",
+    ]
+}
+
+try:
+    if not (is_transformers_available() and is_torch_available()):
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    from ...utils import dummy_torch_and_transformers_objects  # noqa F403
+
+    _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))
+else:
+    _import_structure["pipeline_if"] = ["IFPipeline"]
+    _import_structure["pipeline_if_img2img"] = ["IFImg2ImgPipeline"]
+    _import_structure["pipeline_if_img2img_superresolution"] = ["IFImg2ImgSuperResolutionPipeline"]
+    _import_structure["pipeline_if_inpainting"] = ["IFInpaintingPipeline"]
+    _import_structure["pipeline_if_inpainting_superresolution"] = ["IFInpaintingSuperResolutionPipeline"]
+    _import_structure["pipeline_if_superresolution"] = ["IFSuperResolutionPipeline"]
+    _import_structure["pipeline_output"] = ["IFPipelineOutput"]
+    _import_structure["safety_checker"] = ["IFSafetyChecker"]
+    _import_structure["watermark"] = ["IFWatermarker"]
+
+
+if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
+    try:
+        if not (is_transformers_available() and is_torch_available()):
+            raise OptionalDependencyNotAvailable()
+
+    except OptionalDependencyNotAvailable:
+        from ...utils.dummy_torch_and_transformers_objects import *
+    else:
+        from .pipeline_if import IFPipeline
+        from .pipeline_if_img2img import IFImg2ImgPipeline
+        from .pipeline_if_img2img_superresolution import IFImg2ImgSuperResolutionPipeline
+        from .pipeline_if_inpainting import IFInpaintingPipeline
+        from .pipeline_if_inpainting_superresolution import IFInpaintingSuperResolutionPipeline
+        from .pipeline_if_superresolution import IFSuperResolutionPipeline
+        from .pipeline_output import IFPipelineOutput
+        from .safety_checker import IFSafetyChecker
+        from .timesteps import (
+            fast27_timesteps,
+            smart27_timesteps,
+            smart50_timesteps,
+            smart100_timesteps,
+            smart185_timesteps,
+            super27_timesteps,
+            super40_timesteps,
+            super100_timesteps,
+        )
+        from .watermark import IFWatermarker
+
+else:
+    import sys
+
+    sys.modules[__name__] = _LazyModule(
+        __name__,
+        globals()["__file__"],
+        _import_structure,
+        module_spec=__spec__,
+    )
+
+    for name, value in _dummy_objects.items():
+        setattr(sys.modules[__name__], name, value)

mantis_evalkit/lib/python3.10/site-packages/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting_superresolution.py

@@ -0,0 +1,1121 @@
+import html
+import inspect
+import re
+import urllib.parse as ul
+from typing import Any, Callable, Dict, List, Optional, Union
+
+import numpy as np
+import PIL.Image
+import torch
+import torch.nn.functional as F
+from transformers import CLIPImageProcessor, T5EncoderModel, T5Tokenizer
+
+from ...loaders import StableDiffusionLoraLoaderMixin
+from ...models import UNet2DConditionModel
+from ...schedulers import DDPMScheduler
+from ...utils import (
+    BACKENDS_MAPPING,
+    PIL_INTERPOLATION,
+    is_bs4_available,
+    is_ftfy_available,
+    logging,
+    replace_example_docstring,
+)
+from ...utils.torch_utils import randn_tensor
+from ..pipeline_utils import DiffusionPipeline
+from .pipeline_output import IFPipelineOutput
+from .safety_checker import IFSafetyChecker
+from .watermark import IFWatermarker
+
+
+if is_bs4_available():
+    from bs4 import BeautifulSoup
+
+if is_ftfy_available():
+    import ftfy
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if_img2img.resize
+def resize(images: PIL.Image.Image, img_size: int) -> PIL.Image.Image:
+    w, h = images.size
+
+    coef = w / h
+
+    w, h = img_size, img_size
+
+    if coef >= 1:
+        w = int(round(img_size / 8 * coef) * 8)
+    else:
+        h = int(round(img_size / 8 / coef) * 8)
+
+    images = images.resize((w, h), resample=PIL_INTERPOLATION["bicubic"], reducing_gap=None)
+
+    return images
+
+
+EXAMPLE_DOC_STRING = """
+    Examples:
+        ```py
+        >>> from diffusers import IFInpaintingPipeline, IFInpaintingSuperResolutionPipeline, DiffusionPipeline
+        >>> from diffusers.utils import pt_to_pil
+        >>> import torch
+        >>> from PIL import Image
+        >>> import requests
+        >>> from io import BytesIO
+
+        >>> url = "https://huggingface.co/datasets/diffusers/docs-images/resolve/main/if/person.png"
+        >>> response = requests.get(url)
+        >>> original_image = Image.open(BytesIO(response.content)).convert("RGB")
+        >>> original_image = original_image
+
+        >>> url = "https://huggingface.co/datasets/diffusers/docs-images/resolve/main/if/glasses_mask.png"
+        >>> response = requests.get(url)
+        >>> mask_image = Image.open(BytesIO(response.content))
+        >>> mask_image = mask_image
+
+        >>> pipe = IFInpaintingPipeline.from_pretrained(
+        ...     "DeepFloyd/IF-I-XL-v1.0", variant="fp16", torch_dtype=torch.float16
+        ... )
+        >>> pipe.enable_model_cpu_offload()
+
+        >>> prompt = "blue sunglasses"
+
+        >>> prompt_embeds, negative_embeds = pipe.encode_prompt(prompt)
+        >>> image = pipe(
+        ...     image=original_image,
+        ...     mask_image=mask_image,
+        ...     prompt_embeds=prompt_embeds,
+        ...     negative_prompt_embeds=negative_embeds,
+        ...     output_type="pt",
+        ... ).images
+
+        >>> # save intermediate image
+        >>> pil_image = pt_to_pil(image)
+        >>> pil_image[0].save("./if_stage_I.png")
+
+        >>> super_res_1_pipe = IFInpaintingSuperResolutionPipeline.from_pretrained(
+        ...     "DeepFloyd/IF-II-L-v1.0", text_encoder=None, variant="fp16", torch_dtype=torch.float16
+        ... )
+        >>> super_res_1_pipe.enable_model_cpu_offload()
+
+        >>> image = super_res_1_pipe(
+        ...     image=image,
+        ...     mask_image=mask_image,
+        ...     original_image=original_image,
+        ...     prompt_embeds=prompt_embeds,
+        ...     negative_prompt_embeds=negative_embeds,
+        ... ).images
+        >>> image[0].save("./if_stage_II.png")
+        ```
+    """
+
+
+class IFInpaintingSuperResolutionPipeline(DiffusionPipeline, StableDiffusionLoraLoaderMixin):
+    tokenizer: T5Tokenizer
+    text_encoder: T5EncoderModel
+
+    unet: UNet2DConditionModel
+    scheduler: DDPMScheduler
+    image_noising_scheduler: DDPMScheduler
+
+    feature_extractor: Optional[CLIPImageProcessor]
+    safety_checker: Optional[IFSafetyChecker]
+
+    watermarker: Optional[IFWatermarker]
+
+    bad_punct_regex = re.compile(
+        r"["
+        + "#®•©™&@·º½¾¿¡§~"
+        + r"\)"
+        + r"\("
+        + r"\]"
+        + r"\["
+        + r"\}"
+        + r"\{"
+        + r"\|"
+        + "\\"
+        + r"\/"
+        + r"\*"
+        + r"]{1,}"
+    )  # noqa
+
+    model_cpu_offload_seq = "text_encoder->unet"
+    _optional_components = ["tokenizer", "text_encoder", "safety_checker", "feature_extractor", "watermarker"]
+    _exclude_from_cpu_offload = ["watermarker"]
+
+    def __init__(
+        self,
+        tokenizer: T5Tokenizer,
+        text_encoder: T5EncoderModel,
+        unet: UNet2DConditionModel,
+        scheduler: DDPMScheduler,
+        image_noising_scheduler: DDPMScheduler,
+        safety_checker: Optional[IFSafetyChecker],
+        feature_extractor: Optional[CLIPImageProcessor],
+        watermarker: Optional[IFWatermarker],
+        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 IF license and do not expose unfiltered"
+                " results in services or applications open to the public. Both the 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(
+                "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."
+            )
+
+        if unet.config.in_channels != 6:
+            logger.warning(
+                "It seems like you have loaded a checkpoint that shall not be used for super resolution from {unet.config._name_or_path} as it accepts {unet.config.in_channels} input channels instead of 6. Please make sure to pass a super resolution checkpoint as the `'unet'`: IFSuperResolutionPipeline.from_pretrained(unet=super_resolution_unet, ...)`."
+            )
+
+        self.register_modules(
+            tokenizer=tokenizer,
+            text_encoder=text_encoder,
+            unet=unet,
+            scheduler=scheduler,
+            image_noising_scheduler=image_noising_scheduler,
+            safety_checker=safety_checker,
+            feature_extractor=feature_extractor,
+            watermarker=watermarker,
+        )
+        self.register_to_config(requires_safety_checker=requires_safety_checker)
+
+    # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._text_preprocessing
+    def _text_preprocessing(self, text, clean_caption=False):
+        if clean_caption and not is_bs4_available():
+            logger.warning(BACKENDS_MAPPING["bs4"][-1].format("Setting `clean_caption=True`"))
+            logger.warning("Setting `clean_caption` to False...")
+            clean_caption = False
+
+        if clean_caption and not is_ftfy_available():
+            logger.warning(BACKENDS_MAPPING["ftfy"][-1].format("Setting `clean_caption=True`"))
+            logger.warning("Setting `clean_caption` to False...")
+            clean_caption = False
+
+        if not isinstance(text, (tuple, list)):
+            text = [text]
+
+        def process(text: str):
+            if clean_caption:
+                text = self._clean_caption(text)
+                text = self._clean_caption(text)
+            else:
+                text = text.lower().strip()
+            return text
+
+        return [process(t) for t in text]
+
+    # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._clean_caption
+    def _clean_caption(self, caption):
+        caption = str(caption)
+        caption = ul.unquote_plus(caption)
+        caption = caption.strip().lower()
+        caption = re.sub("<person>", "person", caption)
+        # urls:
+        caption = re.sub(
+            r"\b((?:https?:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))",  # noqa
+            "",
+            caption,
+        )  # regex for urls
+        caption = re.sub(
+            r"\b((?:www:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))",  # noqa
+            "",
+            caption,
+        )  # regex for urls
+        # html:
+        caption = BeautifulSoup(caption, features="html.parser").text
+
+        # @<nickname>
+        caption = re.sub(r"@[\w\d]+\b", "", caption)
+
+        # 31C0—31EF CJK Strokes
+        # 31F0—31FF Katakana Phonetic Extensions
+        # 3200—32FF Enclosed CJK Letters and Months
+        # 3300—33FF CJK Compatibility
+        # 3400—4DBF CJK Unified Ideographs Extension A
+        # 4DC0—4DFF Yijing Hexagram Symbols
+        # 4E00—9FFF CJK Unified Ideographs
+        caption = re.sub(r"[\u31c0-\u31ef]+", "", caption)
+        caption = re.sub(r"[\u31f0-\u31ff]+", "", caption)
+        caption = re.sub(r"[\u3200-\u32ff]+", "", caption)
+        caption = re.sub(r"[\u3300-\u33ff]+", "", caption)
+        caption = re.sub(r"[\u3400-\u4dbf]+", "", caption)
+        caption = re.sub(r"[\u4dc0-\u4dff]+", "", caption)
+        caption = re.sub(r"[\u4e00-\u9fff]+", "", caption)
+        #######################################################
+
+        # все виды тире / all types of dash --> "-"
+        caption = re.sub(
+            r"[\u002D\u058A\u05BE\u1400\u1806\u2010-\u2015\u2E17\u2E1A\u2E3A\u2E3B\u2E40\u301C\u3030\u30A0\uFE31\uFE32\uFE58\uFE63\uFF0D]+",  # noqa
+            "-",
+            caption,
+        )
+
+        # кавычки к одному стандарту
+        caption = re.sub(r"[`´«»“”¨]", '"', caption)
+        caption = re.sub(r"[‘’]", "'", caption)
+
+        # &quot;
+        caption = re.sub(r"&quot;?", "", caption)
+        # &amp
+        caption = re.sub(r"&amp", "", caption)
+
+        # ip adresses:
+        caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption)
+
+        # article ids:
+        caption = re.sub(r"\d:\d\d\s+$", "", caption)
+
+        # \n
+        caption = re.sub(r"\\n", " ", caption)
+
+        # "#123"
+        caption = re.sub(r"#\d{1,3}\b", "", caption)
+        # "#12345.."
+        caption = re.sub(r"#\d{5,}\b", "", caption)
+        # "123456.."
+        caption = re.sub(r"\b\d{6,}\b", "", caption)
+        # filenames:
+        caption = re.sub(r"[\S]+\.(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)", "", caption)
+
+        #
+        caption = re.sub(r"[\"\']{2,}", r'"', caption)  # """AUSVERKAUFT"""
+        caption = re.sub(r"[\.]{2,}", r" ", caption)  # """AUSVERKAUFT"""
+
+        caption = re.sub(self.bad_punct_regex, r" ", caption)  # ***AUSVERKAUFT***, #AUSVERKAUFT
+        caption = re.sub(r"\s+\.\s+", r" ", caption)  # " . "
+
+        # this-is-my-cute-cat / this_is_my_cute_cat
+        regex2 = re.compile(r"(?:\-|\_)")
+        if len(re.findall(regex2, caption)) > 3:
+            caption = re.sub(regex2, " ", caption)
+
+        caption = ftfy.fix_text(caption)
+        caption = html.unescape(html.unescape(caption))
+
+        caption = re.sub(r"\b[a-zA-Z]{1,3}\d{3,15}\b", "", caption)  # jc6640
+        caption = re.sub(r"\b[a-zA-Z]+\d+[a-zA-Z]+\b", "", caption)  # jc6640vc
+        caption = re.sub(r"\b\d+[a-zA-Z]+\d+\b", "", caption)  # 6640vc231
+
+        caption = re.sub(r"(worldwide\s+)?(free\s+)?shipping", "", caption)
+        caption = re.sub(r"(free\s)?download(\sfree)?", "", caption)
+        caption = re.sub(r"\bclick\b\s(?:for|on)\s\w+", "", caption)
+        caption = re.sub(r"\b(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)(\simage[s]?)?", "", caption)
+        caption = re.sub(r"\bpage\s+\d+\b", "", caption)
+
+        caption = re.sub(r"\b\d*[a-zA-Z]+\d+[a-zA-Z]+\d+[a-zA-Z\d]*\b", r" ", caption)  # j2d1a2a...
+
+        caption = re.sub(r"\b\d+\.?\d*[xх×]\d+\.?\d*\b", "", caption)
+
+        caption = re.sub(r"\b\s+\:\s+", r": ", caption)
+        caption = re.sub(r"(\D[,\./])\b", r"\1 ", caption)
+        caption = re.sub(r"\s+", " ", caption)
+
+        caption.strip()
+
+        caption = re.sub(r"^[\"\']([\w\W]+)[\"\']$", r"\1", caption)
+        caption = re.sub(r"^[\'\_,\-\:;]", r"", caption)
+        caption = re.sub(r"[\'\_,\-\:\-\+]$", r"", caption)
+        caption = re.sub(r"^\.\S+$", "", caption)
+
+        return caption.strip()
+
+    @torch.no_grad()
+    # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.encode_prompt
+    def encode_prompt(
+        self,
+        prompt: Union[str, List[str]],
+        do_classifier_free_guidance: bool = True,
+        num_images_per_prompt: int = 1,
+        device: Optional[torch.device] = None,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.Tensor] = None,
+        clean_caption: bool = False,
+    ):
+        r"""
+        Encodes the prompt into text encoder hidden states.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                prompt to be encoded
+            do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):
+                whether to use classifier free guidance or not
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                number of images that should be generated per prompt
+            device: (`torch.device`, *optional*):
+                torch device to place the resulting embeddings on
+            negative_prompt (`str` or `List[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`).
+            prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+            clean_caption (bool, defaults to `False`):
+                If `True`, the function will preprocess and clean the provided caption before encoding.
+        """
+        if prompt is not None and negative_prompt is not None:
+            if type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+
+        if device is None:
+            device = self._execution_device
+
+        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]
+
+        # while T5 can handle much longer input sequences than 77, the text encoder was trained with a max length of 77 for IF
+        max_length = 77
+
+        if prompt_embeds is None:
+            prompt = self._text_preprocessing(prompt, clean_caption=clean_caption)
+            text_inputs = self.tokenizer(
+                prompt,
+                padding="max_length",
+                max_length=max_length,
+                truncation=True,
+                add_special_tokens=True,
+                return_tensors="pt",
+            )
+            text_input_ids = text_inputs.input_ids
+            untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
+
+            if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
+                text_input_ids, untruncated_ids
+            ):
+                removed_text = self.tokenizer.batch_decode(untruncated_ids[:, max_length - 1 : -1])
+                logger.warning(
+                    "The following part of your input was truncated because CLIP can only handle sequences up to"
+                    f" {max_length} tokens: {removed_text}"
+                )
+
+            attention_mask = text_inputs.attention_mask.to(device)
+
+            prompt_embeds = self.text_encoder(
+                text_input_ids.to(device),
+                attention_mask=attention_mask,
+            )
+            prompt_embeds = prompt_embeds[0]
+
+        if self.text_encoder is not None:
+            dtype = self.text_encoder.dtype
+        elif self.unet is not None:
+            dtype = self.unet.dtype
+        else:
+            dtype = None
+
+        prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
+
+        bs_embed, seq_len, _ = prompt_embeds.shape
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
+        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
+
+        # get unconditional embeddings for classifier free guidance
+        if do_classifier_free_guidance and negative_prompt_embeds is None:
+            uncond_tokens: List[str]
+            if negative_prompt is None:
+                uncond_tokens = [""] * batch_size
+            elif isinstance(negative_prompt, str):
+                uncond_tokens = [negative_prompt]
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+            else:
+                uncond_tokens = negative_prompt
+
+            uncond_tokens = self._text_preprocessing(uncond_tokens, clean_caption=clean_caption)
+            max_length = prompt_embeds.shape[1]
+            uncond_input = self.tokenizer(
+                uncond_tokens,
+                padding="max_length",
+                max_length=max_length,
+                truncation=True,
+                return_attention_mask=True,
+                add_special_tokens=True,
+                return_tensors="pt",
+            )
+            attention_mask = uncond_input.attention_mask.to(device)
+
+            negative_prompt_embeds = self.text_encoder(
+                uncond_input.input_ids.to(device),
+                attention_mask=attention_mask,
+            )
+            negative_prompt_embeds = negative_prompt_embeds[0]
+
+        if do_classifier_free_guidance:
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+            seq_len = negative_prompt_embeds.shape[1]
+
+            negative_prompt_embeds = negative_prompt_embeds.to(dtype=dtype, device=device)
+
+            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
+            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
+
+            # For classifier free guidance, we need to do two forward passes.
+            # Here we concatenate the unconditional and text embeddings into a single batch
+            # to avoid doing two forward passes
+        else:
+            negative_prompt_embeds = None
+
+        return prompt_embeds, negative_prompt_embeds
+
+    # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.run_safety_checker
+    def run_safety_checker(self, image, device, dtype):
+        if self.safety_checker is not None:
+            safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device)
+            image, nsfw_detected, watermark_detected = self.safety_checker(
+                images=image,
+                clip_input=safety_checker_input.pixel_values.to(dtype=dtype),
+            )
+        else:
+            nsfw_detected = None
+            watermark_detected = None
+
+        return image, nsfw_detected, watermark_detected
+
+    # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.prepare_extra_step_kwargs
+    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,
+        original_image,
+        mask_image,
+        batch_size,
+        callback_steps,
+        negative_prompt=None,
+        prompt_embeds=None,
+        negative_prompt_embeds=None,
+    ):
+        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 prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
+                " only forward one of the two."
+            )
+        elif prompt is None and prompt_embeds is None:
+            raise ValueError(
+                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
+            )
+        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+
+        if negative_prompt is not None and negative_prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
+                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
+            )
+
+        if prompt_embeds is not None and negative_prompt_embeds is not None:
+            if prompt_embeds.shape != negative_prompt_embeds.shape:
+                raise ValueError(
+                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
+                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
+                    f" {negative_prompt_embeds.shape}."
+                )
+
+        # image
+
+        if isinstance(image, list):
+            check_image_type = image[0]
+        else:
+            check_image_type = image
+
+        if (
+            not isinstance(check_image_type, torch.Tensor)
+            and not isinstance(check_image_type, PIL.Image.Image)
+            and not isinstance(check_image_type, np.ndarray)
+        ):
+            raise ValueError(
+                "`image` has to be of type `torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, or List[...] but is"
+                f" {type(check_image_type)}"
+            )
+
+        if isinstance(image, list):
+            image_batch_size = len(image)
+        elif isinstance(image, torch.Tensor):
+            image_batch_size = image.shape[0]
+        elif isinstance(image, PIL.Image.Image):
+            image_batch_size = 1
+        elif isinstance(image, np.ndarray):
+            image_batch_size = image.shape[0]
+        else:
+            assert False
+
+        if batch_size != image_batch_size:
+            raise ValueError(f"image batch size: {image_batch_size} must be same as prompt batch size {batch_size}")
+
+        # original_image
+
+        if isinstance(original_image, list):
+            check_image_type = original_image[0]
+        else:
+            check_image_type = original_image
+
+        if (
+            not isinstance(check_image_type, torch.Tensor)
+            and not isinstance(check_image_type, PIL.Image.Image)
+            and not isinstance(check_image_type, np.ndarray)
+        ):
+            raise ValueError(
+                "`original_image` has to be of type `torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, or List[...] but is"
+                f" {type(check_image_type)}"
+            )
+
+        if isinstance(original_image, list):
+            image_batch_size = len(original_image)
+        elif isinstance(original_image, torch.Tensor):
+            image_batch_size = original_image.shape[0]
+        elif isinstance(original_image, PIL.Image.Image):
+            image_batch_size = 1
+        elif isinstance(original_image, np.ndarray):
+            image_batch_size = original_image.shape[0]
+        else:
+            assert False
+
+        if batch_size != image_batch_size:
+            raise ValueError(
+                f"original_image batch size: {image_batch_size} must be same as prompt batch size {batch_size}"
+            )
+
+        # mask_image
+
+        if isinstance(mask_image, list):
+            check_image_type = mask_image[0]
+        else:
+            check_image_type = mask_image
+
+        if (
+            not isinstance(check_image_type, torch.Tensor)
+            and not isinstance(check_image_type, PIL.Image.Image)
+            and not isinstance(check_image_type, np.ndarray)
+        ):
+            raise ValueError(
+                "`mask_image` has to be of type `torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, or List[...] but is"
+                f" {type(check_image_type)}"
+            )
+
+        if isinstance(mask_image, list):
+            image_batch_size = len(mask_image)
+        elif isinstance(mask_image, torch.Tensor):
+            image_batch_size = mask_image.shape[0]
+        elif isinstance(mask_image, PIL.Image.Image):
+            image_batch_size = 1
+        elif isinstance(mask_image, np.ndarray):
+            image_batch_size = mask_image.shape[0]
+        else:
+            assert False
+
+        if image_batch_size != 1 and batch_size != image_batch_size:
+            raise ValueError(
+                f"mask_image batch size: {image_batch_size} must be `1` or the same as prompt batch size {batch_size}"
+            )
+
+    # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if_img2img.IFImg2ImgPipeline.preprocess_image with preprocess_image -> preprocess_original_image
+    def preprocess_original_image(self, image: PIL.Image.Image) -> torch.Tensor:
+        if not isinstance(image, list):
+            image = [image]
+
+        def numpy_to_pt(images):
+            if images.ndim == 3:
+                images = images[..., None]
+
+            images = torch.from_numpy(images.transpose(0, 3, 1, 2))
+            return images
+
+        if isinstance(image[0], PIL.Image.Image):
+            new_image = []
+
+            for image_ in image:
+                image_ = image_.convert("RGB")
+                image_ = resize(image_, self.unet.config.sample_size)
+                image_ = np.array(image_)
+                image_ = image_.astype(np.float32)
+                image_ = image_ / 127.5 - 1
+                new_image.append(image_)
+
+            image = new_image
+
+            image = np.stack(image, axis=0)  # to np
+            image = numpy_to_pt(image)  # to pt
+
+        elif isinstance(image[0], np.ndarray):
+            image = np.concatenate(image, axis=0) if image[0].ndim == 4 else np.stack(image, axis=0)
+            image = numpy_to_pt(image)
+
+        elif isinstance(image[0], torch.Tensor):
+            image = torch.cat(image, axis=0) if image[0].ndim == 4 else torch.stack(image, axis=0)
+
+        return image
+
+    # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if_superresolution.IFSuperResolutionPipeline.preprocess_image
+    def preprocess_image(self, image: PIL.Image.Image, num_images_per_prompt, device) -> torch.Tensor:
+        if not isinstance(image, torch.Tensor) and not isinstance(image, list):
+            image = [image]
+
+        if isinstance(image[0], PIL.Image.Image):
+            image = [np.array(i).astype(np.float32) / 127.5 - 1.0 for i in image]
+
+            image = np.stack(image, axis=0)  # to np
+            image = torch.from_numpy(image.transpose(0, 3, 1, 2))
+        elif isinstance(image[0], np.ndarray):
+            image = np.stack(image, axis=0)  # to np
+            if image.ndim == 5:
+                image = image[0]
+
+            image = torch.from_numpy(image.transpose(0, 3, 1, 2))
+        elif isinstance(image, list) and isinstance(image[0], torch.Tensor):
+            dims = image[0].ndim
+
+            if dims == 3:
+                image = torch.stack(image, dim=0)
+            elif dims == 4:
+                image = torch.concat(image, dim=0)
+            else:
+                raise ValueError(f"Image must have 3 or 4 dimensions, instead got {dims}")
+
+        image = image.to(device=device, dtype=self.unet.dtype)
+
+        image = image.repeat_interleave(num_images_per_prompt, dim=0)
+
+        return image
+
+    # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if_inpainting.IFInpaintingPipeline.preprocess_mask_image
+    def preprocess_mask_image(self, mask_image) -> torch.Tensor:
+        if not isinstance(mask_image, list):
+            mask_image = [mask_image]
+
+        if isinstance(mask_image[0], torch.Tensor):
+            mask_image = torch.cat(mask_image, axis=0) if mask_image[0].ndim == 4 else torch.stack(mask_image, axis=0)
+
+            if mask_image.ndim == 2:
+                # Batch and add channel dim for single mask
+                mask_image = mask_image.unsqueeze(0).unsqueeze(0)
+            elif mask_image.ndim == 3 and mask_image.shape[0] == 1:
+                # Single mask, the 0'th dimension is considered to be
+                # the existing batch size of 1
+                mask_image = mask_image.unsqueeze(0)
+            elif mask_image.ndim == 3 and mask_image.shape[0] != 1:
+                # Batch of mask, the 0'th dimension is considered to be
+                # the batching dimension
+                mask_image = mask_image.unsqueeze(1)
+
+            mask_image[mask_image < 0.5] = 0
+            mask_image[mask_image >= 0.5] = 1
+
+        elif isinstance(mask_image[0], PIL.Image.Image):
+            new_mask_image = []
+
+            for mask_image_ in mask_image:
+                mask_image_ = mask_image_.convert("L")
+                mask_image_ = resize(mask_image_, self.unet.config.sample_size)
+                mask_image_ = np.array(mask_image_)
+                mask_image_ = mask_image_[None, None, :]
+                new_mask_image.append(mask_image_)
+
+            mask_image = new_mask_image
+
+            mask_image = np.concatenate(mask_image, axis=0)
+            mask_image = mask_image.astype(np.float32) / 255.0
+            mask_image[mask_image < 0.5] = 0
+            mask_image[mask_image >= 0.5] = 1
+            mask_image = torch.from_numpy(mask_image)
+
+        elif isinstance(mask_image[0], np.ndarray):
+            mask_image = np.concatenate([m[None, None, :] for m in mask_image], axis=0)
+
+            mask_image[mask_image < 0.5] = 0
+            mask_image[mask_image >= 0.5] = 1
+            mask_image = torch.from_numpy(mask_image)
+
+        return mask_image
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps
+    def get_timesteps(self, num_inference_steps, strength):
+        # get the original timestep using init_timestep
+        init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
+
+        t_start = max(num_inference_steps - init_timestep, 0)
+        timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
+        if hasattr(self.scheduler, "set_begin_index"):
+            self.scheduler.set_begin_index(t_start * self.scheduler.order)
+
+        return timesteps, num_inference_steps - t_start
+
+    # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if_inpainting.IFInpaintingPipeline.prepare_intermediate_images
+    def prepare_intermediate_images(
+        self, image, timestep, batch_size, num_images_per_prompt, dtype, device, mask_image, generator=None
+    ):
+        image_batch_size, channels, height, width = image.shape
+
+        batch_size = batch_size * num_images_per_prompt
+
+        shape = (batch_size, channels, height, width)
+
+        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."
+            )
+
+        noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+
+        image = image.repeat_interleave(num_images_per_prompt, dim=0)
+        noised_image = self.scheduler.add_noise(image, noise, timestep)
+
+        image = (1 - mask_image) * image + mask_image * noised_image
+
+        return image
+
+    @torch.no_grad()
+    @replace_example_docstring(EXAMPLE_DOC_STRING)
+    def __call__(
+        self,
+        image: Union[PIL.Image.Image, np.ndarray, torch.Tensor],
+        original_image: Union[
+            PIL.Image.Image, torch.Tensor, np.ndarray, List[PIL.Image.Image], List[torch.Tensor], List[np.ndarray]
+        ] = None,
+        mask_image: Union[
+            PIL.Image.Image, torch.Tensor, np.ndarray, List[PIL.Image.Image], List[torch.Tensor], List[np.ndarray]
+        ] = None,
+        strength: float = 0.8,
+        prompt: Union[str, List[str]] = None,
+        num_inference_steps: int = 100,
+        timesteps: List[int] = None,
+        guidance_scale: float = 4.0,
+        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,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.Tensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,
+        callback_steps: int = 1,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        noise_level: int = 0,
+        clean_caption: bool = True,
+    ):
+        """
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            image (`torch.Tensor` or `PIL.Image.Image`):
+                `Image`, or tensor representing an image batch, that will be used as the starting point for the
+                process.
+            original_image (`torch.Tensor` or `PIL.Image.Image`):
+                The original image that `image` was varied from.
+            mask_image (`PIL.Image.Image`):
+                `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be
+                repainted, while black pixels will be preserved. If `mask_image` is a PIL image, it will be converted
+                to a single channel (luminance) before use. If it's a tensor, it should contain one color channel (L)
+                instead of 3, so the expected shape would be `(B, H, W, 1)`.
+            strength (`float`, *optional*, defaults to 0.8):
+                Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`
+                will be used as a starting point, adding more noise to it the larger the `strength`. The number of
+                denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will
+                be maximum and the denoising process will run for the full number of iterations specified in
+                `num_inference_steps`. A value of 1, therefore, essentially ignores `image`.
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
+                instead.
+            num_inference_steps (`int`, *optional*, defaults to 100):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            timesteps (`List[int]`, *optional*):
+                Custom timesteps to use for the denoising process. If not defined, equal spaced `num_inference_steps`
+                timesteps are used. Must be in descending order.
+            guidance_scale (`float`, *optional*, defaults to 4.0):
+                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. 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`).
+            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` or `List[torch.Generator]`, *optional*):
+                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
+                to make generation deterministic.
+            prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+            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.IFPipelineOutput`] 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.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 `AttentionProcessor` as defined under
+                `self.processor` in
+                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+            noise_level (`int`, *optional*, defaults to 0):
+                The amount of noise to add to the upscaled image. Must be in the range `[0, 1000)`
+            clean_caption (`bool`, *optional*, defaults to `True`):
+                Whether or not to clean the caption before creating embeddings. Requires `beautifulsoup4` and `ftfy` to
+                be installed. If the dependencies are not installed, the embeddings will be created from the raw
+                prompt.
+
+        Examples:
+
+        Returns:
+            [`~pipelines.stable_diffusion.IFPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion.IFPipelineOutput`] 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)
+            or watermarked content, according to the `safety_checker`.
+        """
+        # 1. Check inputs. Raise error if not correct
+        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]
+
+        self.check_inputs(
+            prompt,
+            image,
+            original_image,
+            mask_image,
+            batch_size,
+            callback_steps,
+            negative_prompt,
+            prompt_embeds,
+            negative_prompt_embeds,
+        )
+
+        # 2. Define call parameters
+
+        # 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
+
+        device = self._execution_device
+
+        # 3. Encode input prompt
+        prompt_embeds, negative_prompt_embeds = self.encode_prompt(
+            prompt,
+            do_classifier_free_guidance,
+            num_images_per_prompt=num_images_per_prompt,
+            device=device,
+            negative_prompt=negative_prompt,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            clean_caption=clean_caption,
+        )
+
+        if do_classifier_free_guidance:
+            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
+
+        dtype = prompt_embeds.dtype
+
+        # 4. Prepare timesteps
+        if timesteps is not None:
+            self.scheduler.set_timesteps(timesteps=timesteps, device=device)
+            timesteps = self.scheduler.timesteps
+            num_inference_steps = len(timesteps)
+        else:
+            self.scheduler.set_timesteps(num_inference_steps, device=device)
+            timesteps = self.scheduler.timesteps
+
+        timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength)
+
+        # 5. prepare original image
+        original_image = self.preprocess_original_image(original_image)
+        original_image = original_image.to(device=device, dtype=dtype)
+
+        # 6. prepare mask image
+        mask_image = self.preprocess_mask_image(mask_image)
+        mask_image = mask_image.to(device=device, dtype=dtype)
+
+        if mask_image.shape[0] == 1:
+            mask_image = mask_image.repeat_interleave(batch_size * num_images_per_prompt, dim=0)
+        else:
+            mask_image = mask_image.repeat_interleave(num_images_per_prompt, dim=0)
+
+        # 6. Prepare intermediate images
+        noise_timestep = timesteps[0:1]
+        noise_timestep = noise_timestep.repeat(batch_size * num_images_per_prompt)
+
+        intermediate_images = self.prepare_intermediate_images(
+            original_image,
+            noise_timestep,
+            batch_size,
+            num_images_per_prompt,
+            dtype,
+            device,
+            mask_image,
+            generator,
+        )
+
+        # 7. Prepare upscaled image and noise level
+        _, _, height, width = original_image.shape
+
+        image = self.preprocess_image(image, num_images_per_prompt, device)
+
+        upscaled = F.interpolate(image, (height, width), mode="bilinear", align_corners=True)
+
+        noise_level = torch.tensor([noise_level] * upscaled.shape[0], device=upscaled.device)
+        noise = randn_tensor(upscaled.shape, generator=generator, device=upscaled.device, dtype=upscaled.dtype)
+        upscaled = self.image_noising_scheduler.add_noise(upscaled, noise, timesteps=noise_level)
+
+        if do_classifier_free_guidance:
+            noise_level = torch.cat([noise_level] * 2)
+
+        # 8. 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)
+
+        # HACK: see comment in `enable_model_cpu_offload`
+        if hasattr(self, "text_encoder_offload_hook") and self.text_encoder_offload_hook is not None:
+            self.text_encoder_offload_hook.offload()
+
+        # 9. 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):
+                model_input = torch.cat([intermediate_images, upscaled], dim=1)
+
+                model_input = torch.cat([model_input] * 2) if do_classifier_free_guidance else model_input
+                model_input = self.scheduler.scale_model_input(model_input, t)
+
+                # predict the noise residual
+                noise_pred = self.unet(
+                    model_input,
+                    t,
+                    encoder_hidden_states=prompt_embeds,
+                    class_labels=noise_level,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                    return_dict=False,
+                )[0]
+
+                # perform guidance
+                if do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                    noise_pred_uncond, _ = noise_pred_uncond.split(model_input.shape[1] // 2, dim=1)
+                    noise_pred_text, predicted_variance = noise_pred_text.split(model_input.shape[1] // 2, dim=1)
+                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+                    noise_pred = torch.cat([noise_pred, predicted_variance], dim=1)
+
+                if self.scheduler.config.variance_type not in ["learned", "learned_range"]:
+                    noise_pred, _ = noise_pred.split(intermediate_images.shape[1], dim=1)
+
+                # compute the previous noisy sample x_t -> x_t-1
+                prev_intermediate_images = intermediate_images
+
+                intermediate_images = self.scheduler.step(
+                    noise_pred, t, intermediate_images, **extra_step_kwargs, return_dict=False
+                )[0]
+
+                intermediate_images = (1 - mask_image) * prev_intermediate_images + mask_image * intermediate_images
+
+                # 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, intermediate_images)
+
+        image = intermediate_images
+
+        if output_type == "pil":
+            # 10. Post-processing
+            image = (image / 2 + 0.5).clamp(0, 1)
+            image = image.cpu().permute(0, 2, 3, 1).float().numpy()
+
+            # 11. Run safety checker
+            image, nsfw_detected, watermark_detected = self.run_safety_checker(image, device, prompt_embeds.dtype)
+
+            # 12. Convert to PIL
+            image = self.numpy_to_pil(image)
+
+            # 13. Apply watermark
+            if self.watermarker is not None:
+                self.watermarker.apply_watermark(image, self.unet.config.sample_size)
+        elif output_type == "pt":
+            nsfw_detected = None
+            watermark_detected = None
+
+        else:
+            # 10. Post-processing
+            image = (image / 2 + 0.5).clamp(0, 1)
+            image = image.cpu().permute(0, 2, 3, 1).float().numpy()
+
+            # 11. Run safety checker
+            image, nsfw_detected, watermark_detected = self.run_safety_checker(image, device, prompt_embeds.dtype)
+
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (image, nsfw_detected, watermark_detected)
+
+        return IFPipelineOutput(images=image, nsfw_detected=nsfw_detected, watermark_detected=watermark_detected)

mantis_evalkit/lib/python3.10/site-packages/diffusers/pipelines/deepfloyd_if/pipeline_if_superresolution.py

@@ -0,0 +1,870 @@
+import html
+import inspect
+import re
+import urllib.parse as ul
+from typing import Any, Callable, Dict, List, Optional, Union
+
+import numpy as np
+import PIL.Image
+import torch
+import torch.nn.functional as F
+from transformers import CLIPImageProcessor, T5EncoderModel, T5Tokenizer
+
+from ...loaders import StableDiffusionLoraLoaderMixin
+from ...models import UNet2DConditionModel
+from ...schedulers import DDPMScheduler
+from ...utils import (
+    BACKENDS_MAPPING,
+    is_bs4_available,
+    is_ftfy_available,
+    logging,
+    replace_example_docstring,
+)
+from ...utils.torch_utils import randn_tensor
+from ..pipeline_utils import DiffusionPipeline
+from .pipeline_output import IFPipelineOutput
+from .safety_checker import IFSafetyChecker
+from .watermark import IFWatermarker
+
+
+if is_bs4_available():
+    from bs4 import BeautifulSoup
+
+if is_ftfy_available():
+    import ftfy
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+EXAMPLE_DOC_STRING = """
+    Examples:
+        ```py
+        >>> from diffusers import IFPipeline, IFSuperResolutionPipeline, DiffusionPipeline
+        >>> from diffusers.utils import pt_to_pil
+        >>> import torch
+
+        >>> pipe = IFPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0", variant="fp16", torch_dtype=torch.float16)
+        >>> pipe.enable_model_cpu_offload()
+
+        >>> prompt = 'a photo of a kangaroo wearing an orange hoodie and blue sunglasses standing in front of the eiffel tower holding a sign that says "very deep learning"'
+        >>> prompt_embeds, negative_embeds = pipe.encode_prompt(prompt)
+
+        >>> image = pipe(prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_embeds, output_type="pt").images
+
+        >>> # save intermediate image
+        >>> pil_image = pt_to_pil(image)
+        >>> pil_image[0].save("./if_stage_I.png")
+
+        >>> super_res_1_pipe = IFSuperResolutionPipeline.from_pretrained(
+        ...     "DeepFloyd/IF-II-L-v1.0", text_encoder=None, variant="fp16", torch_dtype=torch.float16
+        ... )
+        >>> super_res_1_pipe.enable_model_cpu_offload()
+
+        >>> image = super_res_1_pipe(
+        ...     image=image, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_embeds
+        ... ).images
+        >>> image[0].save("./if_stage_II.png")
+        ```
+"""
+
+
+class IFSuperResolutionPipeline(DiffusionPipeline, StableDiffusionLoraLoaderMixin):
+    tokenizer: T5Tokenizer
+    text_encoder: T5EncoderModel
+
+    unet: UNet2DConditionModel
+    scheduler: DDPMScheduler
+    image_noising_scheduler: DDPMScheduler
+
+    feature_extractor: Optional[CLIPImageProcessor]
+    safety_checker: Optional[IFSafetyChecker]
+
+    watermarker: Optional[IFWatermarker]
+
+    bad_punct_regex = re.compile(
+        r"["
+        + "#®•©™&@·º½¾¿¡§~"
+        + r"\)"
+        + r"\("
+        + r"\]"
+        + r"\["
+        + r"\}"
+        + r"\{"
+        + r"\|"
+        + "\\"
+        + r"\/"
+        + r"\*"
+        + r"]{1,}"
+    )  # noqa
+
+    _optional_components = ["tokenizer", "text_encoder", "safety_checker", "feature_extractor", "watermarker"]
+    model_cpu_offload_seq = "text_encoder->unet"
+    _exclude_from_cpu_offload = ["watermarker"]
+
+    def __init__(
+        self,
+        tokenizer: T5Tokenizer,
+        text_encoder: T5EncoderModel,
+        unet: UNet2DConditionModel,
+        scheduler: DDPMScheduler,
+        image_noising_scheduler: DDPMScheduler,
+        safety_checker: Optional[IFSafetyChecker],
+        feature_extractor: Optional[CLIPImageProcessor],
+        watermarker: Optional[IFWatermarker],
+        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 IF license and do not expose unfiltered"
+                " results in services or applications open to the public. Both the 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(
+                "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."
+            )
+
+        if unet.config.in_channels != 6:
+            logger.warning(
+                "It seems like you have loaded a checkpoint that shall not be used for super resolution from {unet.config._name_or_path} as it accepts {unet.config.in_channels} input channels instead of 6. Please make sure to pass a super resolution checkpoint as the `'unet'`: IFSuperResolutionPipeline.from_pretrained(unet=super_resolution_unet, ...)`."
+            )
+
+        self.register_modules(
+            tokenizer=tokenizer,
+            text_encoder=text_encoder,
+            unet=unet,
+            scheduler=scheduler,
+            image_noising_scheduler=image_noising_scheduler,
+            safety_checker=safety_checker,
+            feature_extractor=feature_extractor,
+            watermarker=watermarker,
+        )
+        self.register_to_config(requires_safety_checker=requires_safety_checker)
+
+    # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._text_preprocessing
+    def _text_preprocessing(self, text, clean_caption=False):
+        if clean_caption and not is_bs4_available():
+            logger.warning(BACKENDS_MAPPING["bs4"][-1].format("Setting `clean_caption=True`"))
+            logger.warning("Setting `clean_caption` to False...")
+            clean_caption = False
+
+        if clean_caption and not is_ftfy_available():
+            logger.warning(BACKENDS_MAPPING["ftfy"][-1].format("Setting `clean_caption=True`"))
+            logger.warning("Setting `clean_caption` to False...")
+            clean_caption = False
+
+        if not isinstance(text, (tuple, list)):
+            text = [text]
+
+        def process(text: str):
+            if clean_caption:
+                text = self._clean_caption(text)
+                text = self._clean_caption(text)
+            else:
+                text = text.lower().strip()
+            return text
+
+        return [process(t) for t in text]
+
+    # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._clean_caption
+    def _clean_caption(self, caption):
+        caption = str(caption)
+        caption = ul.unquote_plus(caption)
+        caption = caption.strip().lower()
+        caption = re.sub("<person>", "person", caption)
+        # urls:
+        caption = re.sub(
+            r"\b((?:https?:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))",  # noqa
+            "",
+            caption,
+        )  # regex for urls
+        caption = re.sub(
+            r"\b((?:www:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))",  # noqa
+            "",
+            caption,
+        )  # regex for urls
+        # html:
+        caption = BeautifulSoup(caption, features="html.parser").text
+
+        # @<nickname>
+        caption = re.sub(r"@[\w\d]+\b", "", caption)
+
+        # 31C0—31EF CJK Strokes
+        # 31F0—31FF Katakana Phonetic Extensions
+        # 3200—32FF Enclosed CJK Letters and Months
+        # 3300—33FF CJK Compatibility
+        # 3400—4DBF CJK Unified Ideographs Extension A
+        # 4DC0—4DFF Yijing Hexagram Symbols
+        # 4E00—9FFF CJK Unified Ideographs
+        caption = re.sub(r"[\u31c0-\u31ef]+", "", caption)
+        caption = re.sub(r"[\u31f0-\u31ff]+", "", caption)
+        caption = re.sub(r"[\u3200-\u32ff]+", "", caption)
+        caption = re.sub(r"[\u3300-\u33ff]+", "", caption)
+        caption = re.sub(r"[\u3400-\u4dbf]+", "", caption)
+        caption = re.sub(r"[\u4dc0-\u4dff]+", "", caption)
+        caption = re.sub(r"[\u4e00-\u9fff]+", "", caption)
+        #######################################################
+
+        # все виды тире / all types of dash --> "-"
+        caption = re.sub(
+            r"[\u002D\u058A\u05BE\u1400\u1806\u2010-\u2015\u2E17\u2E1A\u2E3A\u2E3B\u2E40\u301C\u3030\u30A0\uFE31\uFE32\uFE58\uFE63\uFF0D]+",  # noqa
+            "-",
+            caption,
+        )
+
+        # кавычки к одному стандарту
+        caption = re.sub(r"[`´«»“”¨]", '"', caption)
+        caption = re.sub(r"[‘’]", "'", caption)
+
+        # &quot;
+        caption = re.sub(r"&quot;?", "", caption)
+        # &amp
+        caption = re.sub(r"&amp", "", caption)
+
+        # ip adresses:
+        caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption)
+
+        # article ids:
+        caption = re.sub(r"\d:\d\d\s+$", "", caption)
+
+        # \n
+        caption = re.sub(r"\\n", " ", caption)
+
+        # "#123"
+        caption = re.sub(r"#\d{1,3}\b", "", caption)
+        # "#12345.."
+        caption = re.sub(r"#\d{5,}\b", "", caption)
+        # "123456.."
+        caption = re.sub(r"\b\d{6,}\b", "", caption)
+        # filenames:
+        caption = re.sub(r"[\S]+\.(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)", "", caption)
+
+        #
+        caption = re.sub(r"[\"\']{2,}", r'"', caption)  # """AUSVERKAUFT"""
+        caption = re.sub(r"[\.]{2,}", r" ", caption)  # """AUSVERKAUFT"""
+
+        caption = re.sub(self.bad_punct_regex, r" ", caption)  # ***AUSVERKAUFT***, #AUSVERKAUFT
+        caption = re.sub(r"\s+\.\s+", r" ", caption)  # " . "
+
+        # this-is-my-cute-cat / this_is_my_cute_cat
+        regex2 = re.compile(r"(?:\-|\_)")
+        if len(re.findall(regex2, caption)) > 3:
+            caption = re.sub(regex2, " ", caption)
+
+        caption = ftfy.fix_text(caption)
+        caption = html.unescape(html.unescape(caption))
+
+        caption = re.sub(r"\b[a-zA-Z]{1,3}\d{3,15}\b", "", caption)  # jc6640
+        caption = re.sub(r"\b[a-zA-Z]+\d+[a-zA-Z]+\b", "", caption)  # jc6640vc
+        caption = re.sub(r"\b\d+[a-zA-Z]+\d+\b", "", caption)  # 6640vc231
+
+        caption = re.sub(r"(worldwide\s+)?(free\s+)?shipping", "", caption)
+        caption = re.sub(r"(free\s)?download(\sfree)?", "", caption)
+        caption = re.sub(r"\bclick\b\s(?:for|on)\s\w+", "", caption)
+        caption = re.sub(r"\b(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)(\simage[s]?)?", "", caption)
+        caption = re.sub(r"\bpage\s+\d+\b", "", caption)
+
+        caption = re.sub(r"\b\d*[a-zA-Z]+\d+[a-zA-Z]+\d+[a-zA-Z\d]*\b", r" ", caption)  # j2d1a2a...
+
+        caption = re.sub(r"\b\d+\.?\d*[xх×]\d+\.?\d*\b", "", caption)
+
+        caption = re.sub(r"\b\s+\:\s+", r": ", caption)
+        caption = re.sub(r"(\D[,\./])\b", r"\1 ", caption)
+        caption = re.sub(r"\s+", " ", caption)
+
+        caption.strip()
+
+        caption = re.sub(r"^[\"\']([\w\W]+)[\"\']$", r"\1", caption)
+        caption = re.sub(r"^[\'\_,\-\:;]", r"", caption)
+        caption = re.sub(r"[\'\_,\-\:\-\+]$", r"", caption)
+        caption = re.sub(r"^\.\S+$", "", caption)
+
+        return caption.strip()
+
+    @torch.no_grad()
+    # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.encode_prompt
+    def encode_prompt(
+        self,
+        prompt: Union[str, List[str]],
+        do_classifier_free_guidance: bool = True,
+        num_images_per_prompt: int = 1,
+        device: Optional[torch.device] = None,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.Tensor] = None,
+        clean_caption: bool = False,
+    ):
+        r"""
+        Encodes the prompt into text encoder hidden states.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                prompt to be encoded
+            do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):
+                whether to use classifier free guidance or not
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                number of images that should be generated per prompt
+            device: (`torch.device`, *optional*):
+                torch device to place the resulting embeddings on
+            negative_prompt (`str` or `List[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`).
+            prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+            clean_caption (bool, defaults to `False`):
+                If `True`, the function will preprocess and clean the provided caption before encoding.
+        """
+        if prompt is not None and negative_prompt is not None:
+            if type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+
+        if device is None:
+            device = self._execution_device
+
+        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]
+
+        # while T5 can handle much longer input sequences than 77, the text encoder was trained with a max length of 77 for IF
+        max_length = 77
+
+        if prompt_embeds is None:
+            prompt = self._text_preprocessing(prompt, clean_caption=clean_caption)
+            text_inputs = self.tokenizer(
+                prompt,
+                padding="max_length",
+                max_length=max_length,
+                truncation=True,
+                add_special_tokens=True,
+                return_tensors="pt",
+            )
+            text_input_ids = text_inputs.input_ids
+            untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
+
+            if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
+                text_input_ids, untruncated_ids
+            ):
+                removed_text = self.tokenizer.batch_decode(untruncated_ids[:, max_length - 1 : -1])
+                logger.warning(
+                    "The following part of your input was truncated because CLIP can only handle sequences up to"
+                    f" {max_length} tokens: {removed_text}"
+                )
+
+            attention_mask = text_inputs.attention_mask.to(device)
+
+            prompt_embeds = self.text_encoder(
+                text_input_ids.to(device),
+                attention_mask=attention_mask,
+            )
+            prompt_embeds = prompt_embeds[0]
+
+        if self.text_encoder is not None:
+            dtype = self.text_encoder.dtype
+        elif self.unet is not None:
+            dtype = self.unet.dtype
+        else:
+            dtype = None
+
+        prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
+
+        bs_embed, seq_len, _ = prompt_embeds.shape
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
+        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
+
+        # get unconditional embeddings for classifier free guidance
+        if do_classifier_free_guidance and negative_prompt_embeds is None:
+            uncond_tokens: List[str]
+            if negative_prompt is None:
+                uncond_tokens = [""] * batch_size
+            elif isinstance(negative_prompt, str):
+                uncond_tokens = [negative_prompt]
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+            else:
+                uncond_tokens = negative_prompt
+
+            uncond_tokens = self._text_preprocessing(uncond_tokens, clean_caption=clean_caption)
+            max_length = prompt_embeds.shape[1]
+            uncond_input = self.tokenizer(
+                uncond_tokens,
+                padding="max_length",
+                max_length=max_length,
+                truncation=True,
+                return_attention_mask=True,
+                add_special_tokens=True,
+                return_tensors="pt",
+            )
+            attention_mask = uncond_input.attention_mask.to(device)
+
+            negative_prompt_embeds = self.text_encoder(
+                uncond_input.input_ids.to(device),
+                attention_mask=attention_mask,
+            )
+            negative_prompt_embeds = negative_prompt_embeds[0]
+
+        if do_classifier_free_guidance:
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+            seq_len = negative_prompt_embeds.shape[1]
+
+            negative_prompt_embeds = negative_prompt_embeds.to(dtype=dtype, device=device)
+
+            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
+            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
+
+            # For classifier free guidance, we need to do two forward passes.
+            # Here we concatenate the unconditional and text embeddings into a single batch
+            # to avoid doing two forward passes
+        else:
+            negative_prompt_embeds = None
+
+        return prompt_embeds, negative_prompt_embeds
+
+    # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.run_safety_checker
+    def run_safety_checker(self, image, device, dtype):
+        if self.safety_checker is not None:
+            safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device)
+            image, nsfw_detected, watermark_detected = self.safety_checker(
+                images=image,
+                clip_input=safety_checker_input.pixel_values.to(dtype=dtype),
+            )
+        else:
+            nsfw_detected = None
+            watermark_detected = None
+
+        return image, nsfw_detected, watermark_detected
+
+    # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.prepare_extra_step_kwargs
+    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,
+        batch_size,
+        noise_level,
+        callback_steps,
+        negative_prompt=None,
+        prompt_embeds=None,
+        negative_prompt_embeds=None,
+    ):
+        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 prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
+                " only forward one of the two."
+            )
+        elif prompt is None and prompt_embeds is None:
+            raise ValueError(
+                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
+            )
+        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+
+        if negative_prompt is not None and negative_prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
+                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
+            )
+
+        if prompt_embeds is not None and negative_prompt_embeds is not None:
+            if prompt_embeds.shape != negative_prompt_embeds.shape:
+                raise ValueError(
+                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
+                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
+                    f" {negative_prompt_embeds.shape}."
+                )
+
+        if noise_level < 0 or noise_level >= self.image_noising_scheduler.config.num_train_timesteps:
+            raise ValueError(
+                f"`noise_level`: {noise_level} must be a valid timestep in `self.noising_scheduler`, [0, {self.image_noising_scheduler.config.num_train_timesteps})"
+            )
+
+        if isinstance(image, list):
+            check_image_type = image[0]
+        else:
+            check_image_type = image
+
+        if (
+            not isinstance(check_image_type, torch.Tensor)
+            and not isinstance(check_image_type, PIL.Image.Image)
+            and not isinstance(check_image_type, np.ndarray)
+        ):
+            raise ValueError(
+                "`image` has to be of type `torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, or List[...] but is"
+                f" {type(check_image_type)}"
+            )
+
+        if isinstance(image, list):
+            image_batch_size = len(image)
+        elif isinstance(image, torch.Tensor):
+            image_batch_size = image.shape[0]
+        elif isinstance(image, PIL.Image.Image):
+            image_batch_size = 1
+        elif isinstance(image, np.ndarray):
+            image_batch_size = image.shape[0]
+        else:
+            assert False
+
+        if batch_size != image_batch_size:
+            raise ValueError(f"image batch size: {image_batch_size} must be same as prompt batch size {batch_size}")
+
+    # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.prepare_intermediate_images
+    def prepare_intermediate_images(self, batch_size, num_channels, height, width, dtype, device, generator):
+        shape = (batch_size, num_channels, height, width)
+        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."
+            )
+
+        intermediate_images = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+
+        # scale the initial noise by the standard deviation required by the scheduler
+        intermediate_images = intermediate_images * self.scheduler.init_noise_sigma
+        return intermediate_images
+
+    def preprocess_image(self, image, num_images_per_prompt, device):
+        if not isinstance(image, torch.Tensor) and not isinstance(image, list):
+            image = [image]
+
+        if isinstance(image[0], PIL.Image.Image):
+            image = [np.array(i).astype(np.float32) / 127.5 - 1.0 for i in image]
+
+            image = np.stack(image, axis=0)  # to np
+            image = torch.from_numpy(image.transpose(0, 3, 1, 2))
+        elif isinstance(image[0], np.ndarray):
+            image = np.stack(image, axis=0)  # to np
+            if image.ndim == 5:
+                image = image[0]
+
+            image = torch.from_numpy(image.transpose(0, 3, 1, 2))
+        elif isinstance(image, list) and isinstance(image[0], torch.Tensor):
+            dims = image[0].ndim
+
+            if dims == 3:
+                image = torch.stack(image, dim=0)
+            elif dims == 4:
+                image = torch.concat(image, dim=0)
+            else:
+                raise ValueError(f"Image must have 3 or 4 dimensions, instead got {dims}")
+
+        image = image.to(device=device, dtype=self.unet.dtype)
+
+        image = image.repeat_interleave(num_images_per_prompt, dim=0)
+
+        return image
+
+    @torch.no_grad()
+    @replace_example_docstring(EXAMPLE_DOC_STRING)
+    def __call__(
+        self,
+        prompt: Union[str, List[str]] = None,
+        height: int = None,
+        width: int = None,
+        image: Union[PIL.Image.Image, np.ndarray, torch.Tensor] = None,
+        num_inference_steps: int = 50,
+        timesteps: List[int] = None,
+        guidance_scale: float = 4.0,
+        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,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.Tensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,
+        callback_steps: int = 1,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        noise_level: int = 250,
+        clean_caption: bool = True,
+    ):
+        """
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
+                instead.
+            height (`int`, *optional*, defaults to None):
+                The height in pixels of the generated image.
+            width (`int`, *optional*, defaults to None):
+                The width in pixels of the generated image.
+            image (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`):
+                The image to be upscaled.
+            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.
+            timesteps (`List[int]`, *optional*, defaults to None):
+                Custom timesteps to use for the denoising process. If not defined, equal spaced `num_inference_steps`
+                timesteps are used. Must be in descending order.
+            guidance_scale (`float`, *optional*, defaults to 4.0):
+                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. 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`).
+            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` or `List[torch.Generator]`, *optional*):
+                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
+                to make generation deterministic.
+            prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+            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.IFPipelineOutput`] 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.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 `AttentionProcessor` as defined under
+                `self.processor` in
+                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+            noise_level (`int`, *optional*, defaults to 250):
+                The amount of noise to add to the upscaled image. Must be in the range `[0, 1000)`
+            clean_caption (`bool`, *optional*, defaults to `True`):
+                Whether or not to clean the caption before creating embeddings. Requires `beautifulsoup4` and `ftfy` to
+                be installed. If the dependencies are not installed, the embeddings will be created from the raw
+                prompt.
+
+        Examples:
+
+        Returns:
+            [`~pipelines.stable_diffusion.IFPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion.IFPipelineOutput`] 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)
+            or watermarked content, according to the `safety_checker`.
+        """
+        # 1. Check inputs. Raise error if not correct
+
+        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]
+
+        self.check_inputs(
+            prompt,
+            image,
+            batch_size,
+            noise_level,
+            callback_steps,
+            negative_prompt,
+            prompt_embeds,
+            negative_prompt_embeds,
+        )
+
+        # 2. Define call parameters
+
+        height = height or self.unet.config.sample_size
+        width = width or self.unet.config.sample_size
+
+        device = self._execution_device
+
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = guidance_scale > 1.0
+
+        # 3. Encode input prompt
+        prompt_embeds, negative_prompt_embeds = self.encode_prompt(
+            prompt,
+            do_classifier_free_guidance,
+            num_images_per_prompt=num_images_per_prompt,
+            device=device,
+            negative_prompt=negative_prompt,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            clean_caption=clean_caption,
+        )
+
+        if do_classifier_free_guidance:
+            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
+
+        # 4. Prepare timesteps
+        if timesteps is not None:
+            self.scheduler.set_timesteps(timesteps=timesteps, device=device)
+            timesteps = self.scheduler.timesteps
+            num_inference_steps = len(timesteps)
+        else:
+            self.scheduler.set_timesteps(num_inference_steps, device=device)
+            timesteps = self.scheduler.timesteps
+
+        if hasattr(self.scheduler, "set_begin_index"):
+            self.scheduler.set_begin_index(0)
+
+        # 5. Prepare intermediate images
+        num_channels = self.unet.config.in_channels // 2
+        intermediate_images = self.prepare_intermediate_images(
+            batch_size * num_images_per_prompt,
+            num_channels,
+            height,
+            width,
+            prompt_embeds.dtype,
+            device,
+            generator,
+        )
+
+        # 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. Prepare upscaled image and noise level
+        image = self.preprocess_image(image, num_images_per_prompt, device)
+        upscaled = F.interpolate(image, (height, width), mode="bilinear", align_corners=True)
+
+        noise_level = torch.tensor([noise_level] * upscaled.shape[0], device=upscaled.device)
+        noise = randn_tensor(upscaled.shape, generator=generator, device=upscaled.device, dtype=upscaled.dtype)
+        upscaled = self.image_noising_scheduler.add_noise(upscaled, noise, timesteps=noise_level)
+
+        if do_classifier_free_guidance:
+            noise_level = torch.cat([noise_level] * 2)
+
+        # HACK: see comment in `enable_model_cpu_offload`
+        if hasattr(self, "text_encoder_offload_hook") and self.text_encoder_offload_hook is not None:
+            self.text_encoder_offload_hook.offload()
+
+        # 8. 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):
+                model_input = torch.cat([intermediate_images, upscaled], dim=1)
+
+                model_input = torch.cat([model_input] * 2) if do_classifier_free_guidance else model_input
+                model_input = self.scheduler.scale_model_input(model_input, t)
+
+                # predict the noise residual
+                noise_pred = self.unet(
+                    model_input,
+                    t,
+                    encoder_hidden_states=prompt_embeds,
+                    class_labels=noise_level,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                    return_dict=False,
+                )[0]
+
+                # perform guidance
+                if do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                    noise_pred_uncond, _ = noise_pred_uncond.split(model_input.shape[1] // 2, dim=1)
+                    noise_pred_text, predicted_variance = noise_pred_text.split(model_input.shape[1] // 2, dim=1)
+                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+                    noise_pred = torch.cat([noise_pred, predicted_variance], dim=1)
+
+                if self.scheduler.config.variance_type not in ["learned", "learned_range"]:
+                    noise_pred, _ = noise_pred.split(intermediate_images.shape[1], dim=1)
+
+                # compute the previous noisy sample x_t -> x_t-1
+                intermediate_images = self.scheduler.step(
+                    noise_pred, t, intermediate_images, **extra_step_kwargs, return_dict=False
+                )[0]
+
+                # 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, intermediate_images)
+
+        image = intermediate_images
+
+        if output_type == "pil":
+            # 9. Post-processing
+            image = (image / 2 + 0.5).clamp(0, 1)
+            image = image.cpu().permute(0, 2, 3, 1).float().numpy()
+
+            # 10. Run safety checker
+            image, nsfw_detected, watermark_detected = self.run_safety_checker(image, device, prompt_embeds.dtype)
+
+            # 11. Convert to PIL
+            image = self.numpy_to_pil(image)
+
+            # 12. Apply watermark
+            if self.watermarker is not None:
+                self.watermarker.apply_watermark(image, self.unet.config.sample_size)
+        elif output_type == "pt":
+            nsfw_detected = None
+            watermark_detected = None
+
+            if hasattr(self, "unet_offload_hook") and self.unet_offload_hook is not None:
+                self.unet_offload_hook.offload()
+        else:
+            # 9. Post-processing
+            image = (image / 2 + 0.5).clamp(0, 1)
+            image = image.cpu().permute(0, 2, 3, 1).float().numpy()
+
+            # 10. Run safety checker
+            image, nsfw_detected, watermark_detected = self.run_safety_checker(image, device, prompt_embeds.dtype)
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (image, nsfw_detected, watermark_detected)
+
+        return IFPipelineOutput(images=image, nsfw_detected=nsfw_detected, watermark_detected=watermark_detected)

mantis_evalkit/lib/python3.10/site-packages/diffusers/pipelines/deepfloyd_if/pipeline_output.py

@@ -0,0 +1,29 @@
+from dataclasses import dataclass
+from typing import List, Optional, Union
+
+import numpy as np
+import PIL.Image
+
+from ...utils import BaseOutput
+
+
+@dataclass
+class IFPipelineOutput(BaseOutput):
+    r"""
+    Output class for Stable Diffusion pipelines.
+
+    Args:
+        images (`List[PIL.Image.Image]` or `np.ndarray`):
+            List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width,
+            num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline.
+        nsfw_detected (`List[bool]`):
+            List of flags denoting whether the corresponding generated image likely represents "not-safe-for-work"
+            (nsfw) content or a watermark. `None` if safety checking could not be performed.
+        watermark_detected (`List[bool]`):
+            List of flags denoting whether the corresponding generated image likely has a watermark. `None` if safety
+            checking could not be performed.
+    """
+
+    images: Union[List[PIL.Image.Image], np.ndarray]
+    nsfw_detected: Optional[List[bool]]
+    watermark_detected: Optional[List[bool]]

mantis_evalkit/lib/python3.10/site-packages/diffusers/pipelines/deepfloyd_if/timesteps.py

@@ -0,0 +1,579 @@
+fast27_timesteps = [
+    999,
+    800,
+    799,
+    600,
+    599,
+    500,
+    400,
+    399,
+    377,
+    355,
+    333,
+    311,
+    288,
+    266,
+    244,
+    222,
+    200,
+    199,
+    177,
+    155,
+    133,
+    111,
+    88,
+    66,
+    44,
+    22,
+    0,
+]
+
+smart27_timesteps = [
+    999,
+    976,
+    952,
+    928,
+    905,
+    882,
+    858,
+    857,
+    810,
+    762,
+    715,
+    714,
+    572,
+    429,
+    428,
+    286,
+    285,
+    238,
+    190,
+    143,
+    142,
+    118,
+    95,
+    71,
+    47,
+    24,
+    0,
+]
+
+smart50_timesteps = [
+    999,
+    988,
+    977,
+    966,
+    955,
+    944,
+    933,
+    922,
+    911,
+    900,
+    899,
+    879,
+    859,
+    840,
+    820,
+    800,
+    799,
+    766,
+    733,
+    700,
+    699,
+    650,
+    600,
+    599,
+    500,
+    499,
+    400,
+    399,
+    350,
+    300,
+    299,
+    266,
+    233,
+    200,
+    199,
+    179,
+    159,
+    140,
+    120,
+    100,
+    99,
+    88,
+    77,
+    66,
+    55,
+    44,
+    33,
+    22,
+    11,
+    0,
+]
+
+smart100_timesteps = [
+    999,
+    995,
+    992,
+    989,
+    985,
+    981,
+    978,
+    975,
+    971,
+    967,
+    964,
+    961,
+    957,
+    956,
+    951,
+    947,
+    942,
+    937,
+    933,
+    928,
+    923,
+    919,
+    914,
+    913,
+    908,
+    903,
+    897,
+    892,
+    887,
+    881,
+    876,
+    871,
+    870,
+    864,
+    858,
+    852,
+    846,
+    840,
+    834,
+    828,
+    827,
+    820,
+    813,
+    806,
+    799,
+    792,
+    785,
+    784,
+    777,
+    770,
+    763,
+    756,
+    749,
+    742,
+    741,
+    733,
+    724,
+    716,
+    707,
+    699,
+    698,
+    688,
+    677,
+    666,
+    656,
+    655,
+    645,
+    634,
+    623,
+    613,
+    612,
+    598,
+    584,
+    570,
+    569,
+    555,
+    541,
+    527,
+    526,
+    505,
+    484,
+    483,
+    462,
+    440,
+    439,
+    396,
+    395,
+    352,
+    351,
+    308,
+    307,
+    264,
+    263,
+    220,
+    219,
+    176,
+    132,
+    88,
+    44,
+    0,
+]
+
+smart185_timesteps = [
+    999,
+    997,
+    995,
+    992,
+    990,
+    988,
+    986,
+    984,
+    981,
+    979,
+    977,
+    975,
+    972,
+    970,
+    968,
+    966,
+    964,
+    961,
+    959,
+    957,
+    956,
+    954,
+    951,
+    949,
+    946,
+    944,
+    941,
+    939,
+    936,
+    934,
+    931,
+    929,
+    926,
+    924,
+    921,
+    919,
+    916,
+    914,
+    913,
+    910,
+    907,
+    905,
+    902,
+    899,
+    896,
+    893,
+    891,
+    888,
+    885,
+    882,
+    879,
+    877,
+    874,
+    871,
+    870,
+    867,
+    864,
+    861,
+    858,
+    855,
+    852,
+    849,
+    846,
+    843,
+    840,
+    837,
+    834,
+    831,
+    828,
+    827,
+    824,
+    821,
+    817,
+    814,
+    811,
+    808,
+    804,
+    801,
+    798,
+    795,
+    791,
+    788,
+    785,
+    784,
+    780,
+    777,
+    774,
+    770,
+    766,
+    763,
+    760,
+    756,
+    752,
+    749,
+    746,
+    742,
+    741,
+    737,
+    733,
+    730,
+    726,
+    722,
+    718,
+    714,
+    710,
+    707,
+    703,
+    699,
+    698,
+    694,
+    690,
+    685,
+    681,
+    677,
+    673,
+    669,
+    664,
+    660,
+    656,
+    655,
+    650,
+    646,
+    641,
+    636,
+    632,
+    627,
+    622,
+    618,
+    613,
+    612,
+    607,
+    602,
+    596,
+    591,
+    586,
+    580,
+    575,
+    570,
+    569,
+    563,
+    557,
+    551,
+    545,
+    539,
+    533,
+    527,
+    526,
+    519,
+    512,
+    505,
+    498,
+    491,
+    484,
+    483,
+    474,
+    466,
+    457,
+    449,
+    440,
+    439,
+    428,
+    418,
+    407,
+    396,
+    395,
+    381,
+    366,
+    352,
+    351,
+    330,
+    308,
+    307,
+    286,
+    264,
+    263,
+    242,
+    220,
+    219,
+    176,
+    175,
+    132,
+    131,
+    88,
+    44,
+    0,
+]
+
+super27_timesteps = [
+    999,
+    991,
+    982,
+    974,
+    966,
+    958,
+    950,
+    941,
+    933,
+    925,
+    916,
+    908,
+    900,
+    899,
+    874,
+    850,
+    825,
+    800,
+    799,
+    700,
+    600,
+    500,
+    400,
+    300,
+    200,
+    100,
+    0,
+]
+
+super40_timesteps = [
+    999,
+    992,
+    985,
+    978,
+    971,
+    964,
+    957,
+    949,
+    942,
+    935,
+    928,
+    921,
+    914,
+    907,
+    900,
+    899,
+    879,
+    859,
+    840,
+    820,
+    800,
+    799,
+    766,
+    733,
+    700,
+    699,
+    650,
+    600,
+    599,
+    500,
+    499,
+    400,
+    399,
+    300,
+    299,
+    200,
+    199,
+    100,
+    99,
+    0,
+]
+
+super100_timesteps = [
+    999,
+    996,
+    992,
+    989,
+    985,
+    982,
+    979,
+    975,
+    972,
+    968,
+    965,
+    961,
+    958,
+    955,
+    951,
+    948,
+    944,
+    941,
+    938,
+    934,
+    931,
+    927,
+    924,
+    920,
+    917,
+    914,
+    910,
+    907,
+    903,
+    900,
+    899,
+    891,
+    884,
+    876,
+    869,
+    861,
+    853,
+    846,
+    838,
+    830,
+    823,
+    815,
+    808,
+    800,
+    799,
+    788,
+    777,
+    766,
+    755,
+    744,
+    733,
+    722,
+    711,
+    700,
+    699,
+    688,
+    677,
+    666,
+    655,
+    644,
+    633,
+    622,
+    611,
+    600,
+    599,
+    585,
+    571,
+    557,
+    542,
+    528,
+    514,
+    500,
+    499,
+    485,
+    471,
+    457,
+    442,
+    428,
+    414,
+    400,
+    399,
+    379,
+    359,
+    340,
+    320,
+    300,
+    299,
+    279,
+    259,
+    240,
+    220,
+    200,
+    199,
+    166,
+    133,
+    100,
+    99,
+    66,
+    33,
+    0,
+]

mantis_evalkit/lib/python3.10/site-packages/diffusers/pipelines/deepfloyd_if/watermark.py

@@ -0,0 +1,46 @@
+from typing import List
+
+import PIL.Image
+import torch
+from PIL import Image
+
+from ...configuration_utils import ConfigMixin
+from ...models.modeling_utils import ModelMixin
+from ...utils import PIL_INTERPOLATION
+
+
+class IFWatermarker(ModelMixin, ConfigMixin):
+    def __init__(self):
+        super().__init__()
+
+        self.register_buffer("watermark_image", torch.zeros((62, 62, 4)))
+        self.watermark_image_as_pil = None
+
+    def apply_watermark(self, images: List[PIL.Image.Image], sample_size=None):
+        # Copied from https://github.com/deep-floyd/IF/blob/b77482e36ca2031cb94dbca1001fc1e6400bf4ab/deepfloyd_if/modules/base.py#L287
+
+        h = images[0].height
+        w = images[0].width
+
+        sample_size = sample_size or h
+
+        coef = min(h / sample_size, w / sample_size)
+        img_h, img_w = (int(h / coef), int(w / coef)) if coef < 1 else (h, w)
+
+        S1, S2 = 1024**2, img_w * img_h
+        K = (S2 / S1) ** 0.5
+        wm_size, wm_x, wm_y = int(K * 62), img_w - int(14 * K), img_h - int(14 * K)
+
+        if self.watermark_image_as_pil is None:
+            watermark_image = self.watermark_image.to(torch.uint8).cpu().numpy()
+            watermark_image = Image.fromarray(watermark_image, mode="RGBA")
+            self.watermark_image_as_pil = watermark_image
+
+        wm_img = self.watermark_image_as_pil.resize(
+            (wm_size, wm_size), PIL_INTERPOLATION["bicubic"], reducing_gap=None
+        )
+
+        for pil_img in images:
+            pil_img.paste(wm_img, box=(wm_x - wm_size, wm_y - wm_size, wm_x, wm_y), mask=wm_img.split()[-1])
+
+        return images

mantis_evalkit/lib/python3.10/site-packages/diffusers/pipelines/stable_diffusion/__init__.py

@@ -0,0 +1,202 @@
+from typing import TYPE_CHECKING
+
+from ...utils import (
+    DIFFUSERS_SLOW_IMPORT,
+    OptionalDependencyNotAvailable,
+    _LazyModule,
+    get_objects_from_module,
+    is_flax_available,
+    is_k_diffusion_available,
+    is_k_diffusion_version,
+    is_onnx_available,
+    is_torch_available,
+    is_transformers_available,
+    is_transformers_version,
+)
+
+
+_dummy_objects = {}
+_additional_imports = {}
+_import_structure = {"pipeline_output": ["StableDiffusionPipelineOutput"]}
+
+if is_transformers_available() and is_flax_available():
+    _import_structure["pipeline_output"].extend(["FlaxStableDiffusionPipelineOutput"])
+try:
+    if not (is_transformers_available() and is_torch_available()):
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    from ...utils import dummy_torch_and_transformers_objects  # noqa F403
+
+    _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))
+else:
+    _import_structure["clip_image_project_model"] = ["CLIPImageProjection"]
+    _import_structure["pipeline_cycle_diffusion"] = ["CycleDiffusionPipeline"]
+    _import_structure["pipeline_stable_diffusion"] = ["StableDiffusionPipeline"]
+    _import_structure["pipeline_stable_diffusion_attend_and_excite"] = ["StableDiffusionAttendAndExcitePipeline"]
+    _import_structure["pipeline_stable_diffusion_gligen"] = ["StableDiffusionGLIGENPipeline"]
+    _import_structure["pipeline_stable_diffusion_gligen_text_image"] = ["StableDiffusionGLIGENTextImagePipeline"]
+    _import_structure["pipeline_stable_diffusion_img2img"] = ["StableDiffusionImg2ImgPipeline"]
+    _import_structure["pipeline_stable_diffusion_inpaint"] = ["StableDiffusionInpaintPipeline"]
+    _import_structure["pipeline_stable_diffusion_inpaint_legacy"] = ["StableDiffusionInpaintPipelineLegacy"]
+    _import_structure["pipeline_stable_diffusion_instruct_pix2pix"] = ["StableDiffusionInstructPix2PixPipeline"]
+    _import_structure["pipeline_stable_diffusion_latent_upscale"] = ["StableDiffusionLatentUpscalePipeline"]
+    _import_structure["pipeline_stable_diffusion_model_editing"] = ["StableDiffusionModelEditingPipeline"]
+    _import_structure["pipeline_stable_diffusion_paradigms"] = ["StableDiffusionParadigmsPipeline"]
+    _import_structure["pipeline_stable_diffusion_upscale"] = ["StableDiffusionUpscalePipeline"]
+    _import_structure["pipeline_stable_unclip"] = ["StableUnCLIPPipeline"]
+    _import_structure["pipeline_stable_unclip_img2img"] = ["StableUnCLIPImg2ImgPipeline"]
+    _import_structure["safety_checker"] = ["StableDiffusionSafetyChecker"]
+    _import_structure["stable_unclip_image_normalizer"] = ["StableUnCLIPImageNormalizer"]
+try:
+    if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    from ...utils.dummy_torch_and_transformers_objects import (
+        StableDiffusionImageVariationPipeline,
+    )
+
+    _dummy_objects.update({"StableDiffusionImageVariationPipeline": StableDiffusionImageVariationPipeline})
+else:
+    _import_structure["pipeline_stable_diffusion_image_variation"] = ["StableDiffusionImageVariationPipeline"]
+try:
+    if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.26.0")):
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    from ...utils.dummy_torch_and_transformers_objects import (
+        StableDiffusionDepth2ImgPipeline,
+    )
+
+    _dummy_objects.update(
+        {
+            "StableDiffusionDepth2ImgPipeline": StableDiffusionDepth2ImgPipeline,
+        }
+    )
+else:
+    _import_structure["pipeline_stable_diffusion_depth2img"] = ["StableDiffusionDepth2ImgPipeline"]
+
+try:
+    if not (is_transformers_available() and is_onnx_available()):
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    from ...utils import dummy_onnx_objects  # noqa F403
+
+    _dummy_objects.update(get_objects_from_module(dummy_onnx_objects))
+else:
+    _import_structure["pipeline_onnx_stable_diffusion"] = [
+        "OnnxStableDiffusionPipeline",
+        "StableDiffusionOnnxPipeline",
+    ]
+    _import_structure["pipeline_onnx_stable_diffusion_img2img"] = ["OnnxStableDiffusionImg2ImgPipeline"]
+    _import_structure["pipeline_onnx_stable_diffusion_inpaint"] = ["OnnxStableDiffusionInpaintPipeline"]
+    _import_structure["pipeline_onnx_stable_diffusion_inpaint_legacy"] = ["OnnxStableDiffusionInpaintPipelineLegacy"]
+    _import_structure["pipeline_onnx_stable_diffusion_upscale"] = ["OnnxStableDiffusionUpscalePipeline"]
+
+if is_transformers_available() and is_flax_available():
+    from ...schedulers.scheduling_pndm_flax import PNDMSchedulerState
+
+    _additional_imports.update({"PNDMSchedulerState": PNDMSchedulerState})
+    _import_structure["pipeline_flax_stable_diffusion"] = ["FlaxStableDiffusionPipeline"]
+    _import_structure["pipeline_flax_stable_diffusion_img2img"] = ["FlaxStableDiffusionImg2ImgPipeline"]
+    _import_structure["pipeline_flax_stable_diffusion_inpaint"] = ["FlaxStableDiffusionInpaintPipeline"]
+    _import_structure["safety_checker_flax"] = ["FlaxStableDiffusionSafetyChecker"]
+
+if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
+    try:
+        if not (is_transformers_available() and is_torch_available()):
+            raise OptionalDependencyNotAvailable()
+
+    except OptionalDependencyNotAvailable:
+        from ...utils.dummy_torch_and_transformers_objects import *
+
+    else:
+        from .clip_image_project_model import CLIPImageProjection
+        from .pipeline_stable_diffusion import (
+            StableDiffusionPipeline,
+            StableDiffusionPipelineOutput,
+        )
+        from .pipeline_stable_diffusion_img2img import StableDiffusionImg2ImgPipeline
+        from .pipeline_stable_diffusion_inpaint import StableDiffusionInpaintPipeline
+        from .pipeline_stable_diffusion_instruct_pix2pix import (
+            StableDiffusionInstructPix2PixPipeline,
+        )
+        from .pipeline_stable_diffusion_latent_upscale import (
+            StableDiffusionLatentUpscalePipeline,
+        )
+        from .pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
+        from .pipeline_stable_unclip import StableUnCLIPPipeline
+        from .pipeline_stable_unclip_img2img import StableUnCLIPImg2ImgPipeline
+        from .safety_checker import StableDiffusionSafetyChecker
+        from .stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
+
+    try:
+        if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        from ...utils.dummy_torch_and_transformers_objects import (
+            StableDiffusionImageVariationPipeline,
+        )
+    else:
+        from .pipeline_stable_diffusion_image_variation import (
+            StableDiffusionImageVariationPipeline,
+        )
+
+    try:
+        if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.26.0")):
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        from ...utils.dummy_torch_and_transformers_objects import StableDiffusionDepth2ImgPipeline
+    else:
+        from .pipeline_stable_diffusion_depth2img import (
+            StableDiffusionDepth2ImgPipeline,
+        )
+
+    try:
+        if not (is_transformers_available() and is_onnx_available()):
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        from ...utils.dummy_onnx_objects import *
+    else:
+        from .pipeline_onnx_stable_diffusion import (
+            OnnxStableDiffusionPipeline,
+            StableDiffusionOnnxPipeline,
+        )
+        from .pipeline_onnx_stable_diffusion_img2img import (
+            OnnxStableDiffusionImg2ImgPipeline,
+        )
+        from .pipeline_onnx_stable_diffusion_inpaint import (
+            OnnxStableDiffusionInpaintPipeline,
+        )
+        from .pipeline_onnx_stable_diffusion_upscale import (
+            OnnxStableDiffusionUpscalePipeline,
+        )
+
+    try:
+        if not (is_transformers_available() and is_flax_available()):
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        from ...utils.dummy_flax_objects import *
+    else:
+        from .pipeline_flax_stable_diffusion import FlaxStableDiffusionPipeline
+        from .pipeline_flax_stable_diffusion_img2img import (
+            FlaxStableDiffusionImg2ImgPipeline,
+        )
+        from .pipeline_flax_stable_diffusion_inpaint import (
+            FlaxStableDiffusionInpaintPipeline,
+        )
+        from .pipeline_output import FlaxStableDiffusionPipelineOutput
+        from .safety_checker_flax import FlaxStableDiffusionSafetyChecker
+
+else:
+    import sys
+
+    sys.modules[__name__] = _LazyModule(
+        __name__,
+        globals()["__file__"],
+        _import_structure,
+        module_spec=__spec__,
+    )
+
+    for name, value in _dummy_objects.items():
+        setattr(sys.modules[__name__], name, value)
+    for name, value in _additional_imports.items():
+        setattr(sys.modules[__name__], name, value)

mantis_evalkit/lib/python3.10/site-packages/diffusers/pipelines/stable_diffusion/convert_from_ckpt.py

@@ -0,0 +1,1869 @@
+# coding=utf-8
+# Copyright 2024 The HuggingFace Inc. team.
+#
+# 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.
+"""Conversion script for the Stable Diffusion checkpoints."""
+
+import re
+from contextlib import nullcontext
+from io import BytesIO
+from typing import Dict, Optional, Union
+
+import requests
+import torch
+import yaml
+from transformers import (
+    AutoFeatureExtractor,
+    BertTokenizerFast,
+    CLIPImageProcessor,
+    CLIPTextConfig,
+    CLIPTextModel,
+    CLIPTextModelWithProjection,
+    CLIPTokenizer,
+    CLIPVisionConfig,
+    CLIPVisionModelWithProjection,
+)
+
+from ...models import (
+    AutoencoderKL,
+    ControlNetModel,
+    PriorTransformer,
+    UNet2DConditionModel,
+)
+from ...schedulers import (
+    DDIMScheduler,
+    DDPMScheduler,
+    DPMSolverMultistepScheduler,
+    EulerAncestralDiscreteScheduler,
+    EulerDiscreteScheduler,
+    HeunDiscreteScheduler,
+    LMSDiscreteScheduler,
+    PNDMScheduler,
+    UnCLIPScheduler,
+)
+from ...utils import is_accelerate_available, logging
+from ..latent_diffusion.pipeline_latent_diffusion import LDMBertConfig, LDMBertModel
+from ..paint_by_example import PaintByExampleImageEncoder
+from ..pipeline_utils import DiffusionPipeline
+from .safety_checker import StableDiffusionSafetyChecker
+from .stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
+
+
+if is_accelerate_available():
+    from accelerate import init_empty_weights
+    from accelerate.utils import set_module_tensor_to_device
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+def shave_segments(path, n_shave_prefix_segments=1):
+    """
+    Removes segments. Positive values shave the first segments, negative shave the last segments.
+    """
+    if n_shave_prefix_segments >= 0:
+        return ".".join(path.split(".")[n_shave_prefix_segments:])
+    else:
+        return ".".join(path.split(".")[:n_shave_prefix_segments])
+
+
+def renew_resnet_paths(old_list, n_shave_prefix_segments=0):
+    """
+    Updates paths inside resnets to the new naming scheme (local renaming)
+    """
+    mapping = []
+    for old_item in old_list:
+        new_item = old_item.replace("in_layers.0", "norm1")
+        new_item = new_item.replace("in_layers.2", "conv1")
+
+        new_item = new_item.replace("out_layers.0", "norm2")
+        new_item = new_item.replace("out_layers.3", "conv2")
+
+        new_item = new_item.replace("emb_layers.1", "time_emb_proj")
+        new_item = new_item.replace("skip_connection", "conv_shortcut")
+
+        new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
+
+        mapping.append({"old": old_item, "new": new_item})
+
+    return mapping
+
+
+def renew_vae_resnet_paths(old_list, n_shave_prefix_segments=0):
+    """
+    Updates paths inside resnets to the new naming scheme (local renaming)
+    """
+    mapping = []
+    for old_item in old_list:
+        new_item = old_item
+
+        new_item = new_item.replace("nin_shortcut", "conv_shortcut")
+        new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
+
+        mapping.append({"old": old_item, "new": new_item})
+
+    return mapping
+
+
+def renew_attention_paths(old_list, n_shave_prefix_segments=0):
+    """
+    Updates paths inside attentions to the new naming scheme (local renaming)
+    """
+    mapping = []
+    for old_item in old_list:
+        new_item = old_item
+
+        #         new_item = new_item.replace('norm.weight', 'group_norm.weight')
+        #         new_item = new_item.replace('norm.bias', 'group_norm.bias')
+
+        #         new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')
+        #         new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')
+
+        #         new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
+
+        mapping.append({"old": old_item, "new": new_item})
+
+    return mapping
+
+
+def renew_vae_attention_paths(old_list, n_shave_prefix_segments=0):
+    """
+    Updates paths inside attentions to the new naming scheme (local renaming)
+    """
+    mapping = []
+    for old_item in old_list:
+        new_item = old_item
+
+        new_item = new_item.replace("norm.weight", "group_norm.weight")
+        new_item = new_item.replace("norm.bias", "group_norm.bias")
+
+        new_item = new_item.replace("q.weight", "to_q.weight")
+        new_item = new_item.replace("q.bias", "to_q.bias")
+
+        new_item = new_item.replace("k.weight", "to_k.weight")
+        new_item = new_item.replace("k.bias", "to_k.bias")
+
+        new_item = new_item.replace("v.weight", "to_v.weight")
+        new_item = new_item.replace("v.bias", "to_v.bias")
+
+        new_item = new_item.replace("proj_out.weight", "to_out.0.weight")
+        new_item = new_item.replace("proj_out.bias", "to_out.0.bias")
+
+        new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
+
+        mapping.append({"old": old_item, "new": new_item})
+
+    return mapping
+
+
+def assign_to_checkpoint(
+    paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None
+):
+    """
+    This does the final conversion step: take locally converted weights and apply a global renaming to them. It splits
+    attention layers, and takes into account additional replacements that may arise.
+
+    Assigns the weights to the new checkpoint.
+    """
+    assert isinstance(paths, list), "Paths should be a list of dicts containing 'old' and 'new' keys."
+
+    # Splits the attention layers into three variables.
+    if attention_paths_to_split is not None:
+        for path, path_map in attention_paths_to_split.items():
+            old_tensor = old_checkpoint[path]
+            channels = old_tensor.shape[0] // 3
+
+            target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1)
+
+            num_heads = old_tensor.shape[0] // config["num_head_channels"] // 3
+
+            old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:])
+            query, key, value = old_tensor.split(channels // num_heads, dim=1)
+
+            checkpoint[path_map["query"]] = query.reshape(target_shape)
+            checkpoint[path_map["key"]] = key.reshape(target_shape)
+            checkpoint[path_map["value"]] = value.reshape(target_shape)
+
+    for path in paths:
+        new_path = path["new"]
+
+        # These have already been assigned
+        if attention_paths_to_split is not None and new_path in attention_paths_to_split:
+            continue
+
+        # Global renaming happens here
+        new_path = new_path.replace("middle_block.0", "mid_block.resnets.0")
+        new_path = new_path.replace("middle_block.1", "mid_block.attentions.0")
+        new_path = new_path.replace("middle_block.2", "mid_block.resnets.1")
+
+        if additional_replacements is not None:
+            for replacement in additional_replacements:
+                new_path = new_path.replace(replacement["old"], replacement["new"])
+
+        # proj_attn.weight has to be converted from conv 1D to linear
+        is_attn_weight = "proj_attn.weight" in new_path or ("attentions" in new_path and "to_" in new_path)
+        shape = old_checkpoint[path["old"]].shape
+        if is_attn_weight and len(shape) == 3:
+            checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0]
+        elif is_attn_weight and len(shape) == 4:
+            checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0, 0]
+        else:
+            checkpoint[new_path] = old_checkpoint[path["old"]]
+
+
+def conv_attn_to_linear(checkpoint):
+    keys = list(checkpoint.keys())
+    attn_keys = ["query.weight", "key.weight", "value.weight"]
+    for key in keys:
+        if ".".join(key.split(".")[-2:]) in attn_keys:
+            if checkpoint[key].ndim > 2:
+                checkpoint[key] = checkpoint[key][:, :, 0, 0]
+        elif "proj_attn.weight" in key:
+            if checkpoint[key].ndim > 2:
+                checkpoint[key] = checkpoint[key][:, :, 0]
+
+
+def create_unet_diffusers_config(original_config, image_size: int, controlnet=False):
+    """
+    Creates a config for the diffusers based on the config of the LDM model.
+    """
+    if controlnet:
+        unet_params = original_config["model"]["params"]["control_stage_config"]["params"]
+    else:
+        if (
+            "unet_config" in original_config["model"]["params"]
+            and original_config["model"]["params"]["unet_config"] is not None
+        ):
+            unet_params = original_config["model"]["params"]["unet_config"]["params"]
+        else:
+            unet_params = original_config["model"]["params"]["network_config"]["params"]
+
+    vae_params = original_config["model"]["params"]["first_stage_config"]["params"]["ddconfig"]
+
+    block_out_channels = [unet_params["model_channels"] * mult for mult in unet_params["channel_mult"]]
+
+    down_block_types = []
+    resolution = 1
+    for i in range(len(block_out_channels)):
+        block_type = "CrossAttnDownBlock2D" if resolution in unet_params["attention_resolutions"] else "DownBlock2D"
+        down_block_types.append(block_type)
+        if i != len(block_out_channels) - 1:
+            resolution *= 2
+
+    up_block_types = []
+    for i in range(len(block_out_channels)):
+        block_type = "CrossAttnUpBlock2D" if resolution in unet_params["attention_resolutions"] else "UpBlock2D"
+        up_block_types.append(block_type)
+        resolution //= 2
+
+    if unet_params["transformer_depth"] is not None:
+        transformer_layers_per_block = (
+            unet_params["transformer_depth"]
+            if isinstance(unet_params["transformer_depth"], int)
+            else list(unet_params["transformer_depth"])
+        )
+    else:
+        transformer_layers_per_block = 1
+
+    vae_scale_factor = 2 ** (len(vae_params["ch_mult"]) - 1)
+
+    head_dim = unet_params["num_heads"] if "num_heads" in unet_params else None
+    use_linear_projection = (
+        unet_params["use_linear_in_transformer"] if "use_linear_in_transformer" in unet_params else False
+    )
+    if use_linear_projection:
+        # stable diffusion 2-base-512 and 2-768
+        if head_dim is None:
+            head_dim_mult = unet_params["model_channels"] // unet_params["num_head_channels"]
+            head_dim = [head_dim_mult * c for c in list(unet_params["channel_mult"])]
+
+    class_embed_type = None
+    addition_embed_type = None
+    addition_time_embed_dim = None
+    projection_class_embeddings_input_dim = None
+    context_dim = None
+
+    if unet_params["context_dim"] is not None:
+        context_dim = (
+            unet_params["context_dim"]
+            if isinstance(unet_params["context_dim"], int)
+            else unet_params["context_dim"][0]
+        )
+
+    if "num_classes" in unet_params:
+        if unet_params["num_classes"] == "sequential":
+            if context_dim in [2048, 1280]:
+                # SDXL
+                addition_embed_type = "text_time"
+                addition_time_embed_dim = 256
+            else:
+                class_embed_type = "projection"
+            assert "adm_in_channels" in unet_params
+            projection_class_embeddings_input_dim = unet_params["adm_in_channels"]
+
+    config = {
+        "sample_size": image_size // vae_scale_factor,
+        "in_channels": unet_params["in_channels"],
+        "down_block_types": tuple(down_block_types),
+        "block_out_channels": tuple(block_out_channels),
+        "layers_per_block": unet_params["num_res_blocks"],
+        "cross_attention_dim": context_dim,
+        "attention_head_dim": head_dim,
+        "use_linear_projection": use_linear_projection,
+        "class_embed_type": class_embed_type,
+        "addition_embed_type": addition_embed_type,
+        "addition_time_embed_dim": addition_time_embed_dim,
+        "projection_class_embeddings_input_dim": projection_class_embeddings_input_dim,
+        "transformer_layers_per_block": transformer_layers_per_block,
+    }
+
+    if "disable_self_attentions" in unet_params:
+        config["only_cross_attention"] = unet_params["disable_self_attentions"]
+
+    if "num_classes" in unet_params and isinstance(unet_params["num_classes"], int):
+        config["num_class_embeds"] = unet_params["num_classes"]
+
+    if controlnet:
+        config["conditioning_channels"] = unet_params["hint_channels"]
+    else:
+        config["out_channels"] = unet_params["out_channels"]
+        config["up_block_types"] = tuple(up_block_types)
+
+    return config
+
+
+def create_vae_diffusers_config(original_config, image_size: int):
+    """
+    Creates a config for the diffusers based on the config of the LDM model.
+    """
+    vae_params = original_config["model"]["params"]["first_stage_config"]["params"]["ddconfig"]
+    _ = original_config["model"]["params"]["first_stage_config"]["params"]["embed_dim"]
+
+    block_out_channels = [vae_params["ch"] * mult for mult in vae_params["ch_mult"]]
+    down_block_types = ["DownEncoderBlock2D"] * len(block_out_channels)
+    up_block_types = ["UpDecoderBlock2D"] * len(block_out_channels)
+
+    config = {
+        "sample_size": image_size,
+        "in_channels": vae_params["in_channels"],
+        "out_channels": vae_params["out_ch"],
+        "down_block_types": tuple(down_block_types),
+        "up_block_types": tuple(up_block_types),
+        "block_out_channels": tuple(block_out_channels),
+        "latent_channels": vae_params["z_channels"],
+        "layers_per_block": vae_params["num_res_blocks"],
+    }
+    return config
+
+
+def create_diffusers_schedular(original_config):
+    schedular = DDIMScheduler(
+        num_train_timesteps=original_config["model"]["params"]["timesteps"],
+        beta_start=original_config["model"]["params"]["linear_start"],
+        beta_end=original_config["model"]["params"]["linear_end"],
+        beta_schedule="scaled_linear",
+    )
+    return schedular
+
+
+def create_ldm_bert_config(original_config):
+    bert_params = original_config["model"]["params"]["cond_stage_config"]["params"]
+    config = LDMBertConfig(
+        d_model=bert_params.n_embed,
+        encoder_layers=bert_params.n_layer,
+        encoder_ffn_dim=bert_params.n_embed * 4,
+    )
+    return config
+
+
+def convert_ldm_unet_checkpoint(
+    checkpoint, config, path=None, extract_ema=False, controlnet=False, skip_extract_state_dict=False
+):
+    """
+    Takes a state dict and a config, and returns a converted checkpoint.
+    """
+
+    if skip_extract_state_dict:
+        unet_state_dict = checkpoint
+    else:
+        # extract state_dict for UNet
+        unet_state_dict = {}
+        keys = list(checkpoint.keys())
+
+        if controlnet:
+            unet_key = "control_model."
+        else:
+            unet_key = "model.diffusion_model."
+
+        # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
+        if sum(k.startswith("model_ema") for k in keys) > 100 and extract_ema:
+            logger.warning(f"Checkpoint {path} has both EMA and non-EMA weights.")
+            logger.warning(
+                "In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA"
+                " weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag."
+            )
+            for key in keys:
+                if key.startswith("model.diffusion_model"):
+                    flat_ema_key = "model_ema." + "".join(key.split(".")[1:])
+                    unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(flat_ema_key)
+        else:
+            if sum(k.startswith("model_ema") for k in keys) > 100:
+                logger.warning(
+                    "In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA"
+                    " weights (usually better for inference), please make sure to add the `--extract_ema` flag."
+                )
+
+            for key in keys:
+                if key.startswith(unet_key):
+                    unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(key)
+
+    new_checkpoint = {}
+
+    new_checkpoint["time_embedding.linear_1.weight"] = unet_state_dict["time_embed.0.weight"]
+    new_checkpoint["time_embedding.linear_1.bias"] = unet_state_dict["time_embed.0.bias"]
+    new_checkpoint["time_embedding.linear_2.weight"] = unet_state_dict["time_embed.2.weight"]
+    new_checkpoint["time_embedding.linear_2.bias"] = unet_state_dict["time_embed.2.bias"]
+
+    if config["class_embed_type"] is None:
+        # No parameters to port
+        ...
+    elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection":
+        new_checkpoint["class_embedding.linear_1.weight"] = unet_state_dict["label_emb.0.0.weight"]
+        new_checkpoint["class_embedding.linear_1.bias"] = unet_state_dict["label_emb.0.0.bias"]
+        new_checkpoint["class_embedding.linear_2.weight"] = unet_state_dict["label_emb.0.2.weight"]
+        new_checkpoint["class_embedding.linear_2.bias"] = unet_state_dict["label_emb.0.2.bias"]
+    else:
+        raise NotImplementedError(f"Not implemented `class_embed_type`: {config['class_embed_type']}")
+
+    if config["addition_embed_type"] == "text_time":
+        new_checkpoint["add_embedding.linear_1.weight"] = unet_state_dict["label_emb.0.0.weight"]
+        new_checkpoint["add_embedding.linear_1.bias"] = unet_state_dict["label_emb.0.0.bias"]
+        new_checkpoint["add_embedding.linear_2.weight"] = unet_state_dict["label_emb.0.2.weight"]
+        new_checkpoint["add_embedding.linear_2.bias"] = unet_state_dict["label_emb.0.2.bias"]
+
+    # Relevant to StableDiffusionUpscalePipeline
+    if "num_class_embeds" in config:
+        if (config["num_class_embeds"] is not None) and ("label_emb.weight" in unet_state_dict):
+            new_checkpoint["class_embedding.weight"] = unet_state_dict["label_emb.weight"]
+
+    new_checkpoint["conv_in.weight"] = unet_state_dict["input_blocks.0.0.weight"]
+    new_checkpoint["conv_in.bias"] = unet_state_dict["input_blocks.0.0.bias"]
+
+    if not controlnet:
+        new_checkpoint["conv_norm_out.weight"] = unet_state_dict["out.0.weight"]
+        new_checkpoint["conv_norm_out.bias"] = unet_state_dict["out.0.bias"]
+        new_checkpoint["conv_out.weight"] = unet_state_dict["out.2.weight"]
+        new_checkpoint["conv_out.bias"] = unet_state_dict["out.2.bias"]
+
+    # Retrieves the keys for the input blocks only
+    num_input_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "input_blocks" in layer})
+    input_blocks = {
+        layer_id: [key for key in unet_state_dict if f"input_blocks.{layer_id}" in key]
+        for layer_id in range(num_input_blocks)
+    }
+
+    # Retrieves the keys for the middle blocks only
+    num_middle_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "middle_block" in layer})
+    middle_blocks = {
+        layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}" in key]
+        for layer_id in range(num_middle_blocks)
+    }
+
+    # Retrieves the keys for the output blocks only
+    num_output_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "output_blocks" in layer})
+    output_blocks = {
+        layer_id: [key for key in unet_state_dict if f"output_blocks.{layer_id}" in key]
+        for layer_id in range(num_output_blocks)
+    }
+
+    for i in range(1, num_input_blocks):
+        block_id = (i - 1) // (config["layers_per_block"] + 1)
+        layer_in_block_id = (i - 1) % (config["layers_per_block"] + 1)
+
+        resnets = [
+            key for key in input_blocks[i] if f"input_blocks.{i}.0" in key and f"input_blocks.{i}.0.op" not in key
+        ]
+        attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key]
+
+        if f"input_blocks.{i}.0.op.weight" in unet_state_dict:
+            new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = unet_state_dict.pop(
+                f"input_blocks.{i}.0.op.weight"
+            )
+            new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = unet_state_dict.pop(
+                f"input_blocks.{i}.0.op.bias"
+            )
+
+        paths = renew_resnet_paths(resnets)
+        meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
+        assign_to_checkpoint(
+            paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
+        )
+
+        if len(attentions):
+            paths = renew_attention_paths(attentions)
+
+            meta_path = {"old": f"input_blocks.{i}.1", "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}"}
+            assign_to_checkpoint(
+                paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
+            )
+
+    resnet_0 = middle_blocks[0]
+    attentions = middle_blocks[1]
+    resnet_1 = middle_blocks[2]
+
+    resnet_0_paths = renew_resnet_paths(resnet_0)
+    assign_to_checkpoint(resnet_0_paths, new_checkpoint, unet_state_dict, config=config)
+
+    resnet_1_paths = renew_resnet_paths(resnet_1)
+    assign_to_checkpoint(resnet_1_paths, new_checkpoint, unet_state_dict, config=config)
+
+    attentions_paths = renew_attention_paths(attentions)
+    meta_path = {"old": "middle_block.1", "new": "mid_block.attentions.0"}
+    assign_to_checkpoint(
+        attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
+    )
+
+    for i in range(num_output_blocks):
+        block_id = i // (config["layers_per_block"] + 1)
+        layer_in_block_id = i % (config["layers_per_block"] + 1)
+        output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]]
+        output_block_list = {}
+
+        for layer in output_block_layers:
+            layer_id, layer_name = layer.split(".")[0], shave_segments(layer, 1)
+            if layer_id in output_block_list:
+                output_block_list[layer_id].append(layer_name)
+            else:
+                output_block_list[layer_id] = [layer_name]
+
+        if len(output_block_list) > 1:
+            resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key]
+            attentions = [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key]
+
+            resnet_0_paths = renew_resnet_paths(resnets)
+            paths = renew_resnet_paths(resnets)
+
+            meta_path = {"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"}
+            assign_to_checkpoint(
+                paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
+            )
+
+            output_block_list = {k: sorted(v) for k, v in sorted(output_block_list.items())}
+            if ["conv.bias", "conv.weight"] in output_block_list.values():
+                index = list(output_block_list.values()).index(["conv.bias", "conv.weight"])
+                new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[
+                    f"output_blocks.{i}.{index}.conv.weight"
+                ]
+                new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = unet_state_dict[
+                    f"output_blocks.{i}.{index}.conv.bias"
+                ]
+
+                # Clear attentions as they have been attributed above.
+                if len(attentions) == 2:
+                    attentions = []
+
+            if len(attentions):
+                paths = renew_attention_paths(attentions)
+                meta_path = {
+                    "old": f"output_blocks.{i}.1",
+                    "new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}",
+                }
+                assign_to_checkpoint(
+                    paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
+                )
+        else:
+            resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1)
+            for path in resnet_0_paths:
+                old_path = ".".join(["output_blocks", str(i), path["old"]])
+                new_path = ".".join(["up_blocks", str(block_id), "resnets", str(layer_in_block_id), path["new"]])
+
+                new_checkpoint[new_path] = unet_state_dict[old_path]
+
+    if controlnet:
+        # conditioning embedding
+
+        orig_index = 0
+
+        new_checkpoint["controlnet_cond_embedding.conv_in.weight"] = unet_state_dict.pop(
+            f"input_hint_block.{orig_index}.weight"
+        )
+        new_checkpoint["controlnet_cond_embedding.conv_in.bias"] = unet_state_dict.pop(
+            f"input_hint_block.{orig_index}.bias"
+        )
+
+        orig_index += 2
+
+        diffusers_index = 0
+
+        while diffusers_index < 6:
+            new_checkpoint[f"controlnet_cond_embedding.blocks.{diffusers_index}.weight"] = unet_state_dict.pop(
+                f"input_hint_block.{orig_index}.weight"
+            )
+            new_checkpoint[f"controlnet_cond_embedding.blocks.{diffusers_index}.bias"] = unet_state_dict.pop(
+                f"input_hint_block.{orig_index}.bias"
+            )
+            diffusers_index += 1
+            orig_index += 2
+
+        new_checkpoint["controlnet_cond_embedding.conv_out.weight"] = unet_state_dict.pop(
+            f"input_hint_block.{orig_index}.weight"
+        )
+        new_checkpoint["controlnet_cond_embedding.conv_out.bias"] = unet_state_dict.pop(
+            f"input_hint_block.{orig_index}.bias"
+        )
+
+        # down blocks
+        for i in range(num_input_blocks):
+            new_checkpoint[f"controlnet_down_blocks.{i}.weight"] = unet_state_dict.pop(f"zero_convs.{i}.0.weight")
+            new_checkpoint[f"controlnet_down_blocks.{i}.bias"] = unet_state_dict.pop(f"zero_convs.{i}.0.bias")
+
+        # mid block
+        new_checkpoint["controlnet_mid_block.weight"] = unet_state_dict.pop("middle_block_out.0.weight")
+        new_checkpoint["controlnet_mid_block.bias"] = unet_state_dict.pop("middle_block_out.0.bias")
+
+    return new_checkpoint
+
+
+def convert_ldm_vae_checkpoint(checkpoint, config):
+    # extract state dict for VAE
+    vae_state_dict = {}
+    keys = list(checkpoint.keys())
+    vae_key = "first_stage_model." if any(k.startswith("first_stage_model.") for k in keys) else ""
+    for key in keys:
+        if key.startswith(vae_key):
+            vae_state_dict[key.replace(vae_key, "")] = checkpoint.get(key)
+
+    new_checkpoint = {}
+
+    new_checkpoint["encoder.conv_in.weight"] = vae_state_dict["encoder.conv_in.weight"]
+    new_checkpoint["encoder.conv_in.bias"] = vae_state_dict["encoder.conv_in.bias"]
+    new_checkpoint["encoder.conv_out.weight"] = vae_state_dict["encoder.conv_out.weight"]
+    new_checkpoint["encoder.conv_out.bias"] = vae_state_dict["encoder.conv_out.bias"]
+    new_checkpoint["encoder.conv_norm_out.weight"] = vae_state_dict["encoder.norm_out.weight"]
+    new_checkpoint["encoder.conv_norm_out.bias"] = vae_state_dict["encoder.norm_out.bias"]
+
+    new_checkpoint["decoder.conv_in.weight"] = vae_state_dict["decoder.conv_in.weight"]
+    new_checkpoint["decoder.conv_in.bias"] = vae_state_dict["decoder.conv_in.bias"]
+    new_checkpoint["decoder.conv_out.weight"] = vae_state_dict["decoder.conv_out.weight"]
+    new_checkpoint["decoder.conv_out.bias"] = vae_state_dict["decoder.conv_out.bias"]
+    new_checkpoint["decoder.conv_norm_out.weight"] = vae_state_dict["decoder.norm_out.weight"]
+    new_checkpoint["decoder.conv_norm_out.bias"] = vae_state_dict["decoder.norm_out.bias"]
+
+    new_checkpoint["quant_conv.weight"] = vae_state_dict["quant_conv.weight"]
+    new_checkpoint["quant_conv.bias"] = vae_state_dict["quant_conv.bias"]
+    new_checkpoint["post_quant_conv.weight"] = vae_state_dict["post_quant_conv.weight"]
+    new_checkpoint["post_quant_conv.bias"] = vae_state_dict["post_quant_conv.bias"]
+
+    # Retrieves the keys for the encoder down blocks only
+    num_down_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "encoder.down" in layer})
+    down_blocks = {
+        layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key] for layer_id in range(num_down_blocks)
+    }
+
+    # Retrieves the keys for the decoder up blocks only
+    num_up_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "decoder.up" in layer})
+    up_blocks = {
+        layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key] for layer_id in range(num_up_blocks)
+    }
+
+    for i in range(num_down_blocks):
+        resnets = [key for key in down_blocks[i] if f"down.{i}" in key and f"down.{i}.downsample" not in key]
+
+        if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict:
+            new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"] = vae_state_dict.pop(
+                f"encoder.down.{i}.downsample.conv.weight"
+            )
+            new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"] = vae_state_dict.pop(
+                f"encoder.down.{i}.downsample.conv.bias"
+            )
+
+        paths = renew_vae_resnet_paths(resnets)
+        meta_path = {"old": f"down.{i}.block", "new": f"down_blocks.{i}.resnets"}
+        assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
+
+    mid_resnets = [key for key in vae_state_dict if "encoder.mid.block" in key]
+    num_mid_res_blocks = 2
+    for i in range(1, num_mid_res_blocks + 1):
+        resnets = [key for key in mid_resnets if f"encoder.mid.block_{i}" in key]
+
+        paths = renew_vae_resnet_paths(resnets)
+        meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"}
+        assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
+
+    mid_attentions = [key for key in vae_state_dict if "encoder.mid.attn" in key]
+    paths = renew_vae_attention_paths(mid_attentions)
+    meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
+    assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
+    conv_attn_to_linear(new_checkpoint)
+
+    for i in range(num_up_blocks):
+        block_id = num_up_blocks - 1 - i
+        resnets = [
+            key for key in up_blocks[block_id] if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key
+        ]
+
+        if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict:
+            new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.weight"] = vae_state_dict[
+                f"decoder.up.{block_id}.upsample.conv.weight"
+            ]
+            new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.bias"] = vae_state_dict[
+                f"decoder.up.{block_id}.upsample.conv.bias"
+            ]
+
+        paths = renew_vae_resnet_paths(resnets)
+        meta_path = {"old": f"up.{block_id}.block", "new": f"up_blocks.{i}.resnets"}
+        assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
+
+    mid_resnets = [key for key in vae_state_dict if "decoder.mid.block" in key]
+    num_mid_res_blocks = 2
+    for i in range(1, num_mid_res_blocks + 1):
+        resnets = [key for key in mid_resnets if f"decoder.mid.block_{i}" in key]
+
+        paths = renew_vae_resnet_paths(resnets)
+        meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"}
+        assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
+
+    mid_attentions = [key for key in vae_state_dict if "decoder.mid.attn" in key]
+    paths = renew_vae_attention_paths(mid_attentions)
+    meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
+    assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
+    conv_attn_to_linear(new_checkpoint)
+    return new_checkpoint
+
+
+def convert_ldm_bert_checkpoint(checkpoint, config):
+    def _copy_attn_layer(hf_attn_layer, pt_attn_layer):
+        hf_attn_layer.q_proj.weight.data = pt_attn_layer.to_q.weight
+        hf_attn_layer.k_proj.weight.data = pt_attn_layer.to_k.weight
+        hf_attn_layer.v_proj.weight.data = pt_attn_layer.to_v.weight
+
+        hf_attn_layer.out_proj.weight = pt_attn_layer.to_out.weight
+        hf_attn_layer.out_proj.bias = pt_attn_layer.to_out.bias
+
+    def _copy_linear(hf_linear, pt_linear):
+        hf_linear.weight = pt_linear.weight
+        hf_linear.bias = pt_linear.bias
+
+    def _copy_layer(hf_layer, pt_layer):
+        # copy layer norms
+        _copy_linear(hf_layer.self_attn_layer_norm, pt_layer[0][0])
+        _copy_linear(hf_layer.final_layer_norm, pt_layer[1][0])
+
+        # copy attn
+        _copy_attn_layer(hf_layer.self_attn, pt_layer[0][1])
+
+        # copy MLP
+        pt_mlp = pt_layer[1][1]
+        _copy_linear(hf_layer.fc1, pt_mlp.net[0][0])
+        _copy_linear(hf_layer.fc2, pt_mlp.net[2])
+
+    def _copy_layers(hf_layers, pt_layers):
+        for i, hf_layer in enumerate(hf_layers):
+            if i != 0:
+                i += i
+            pt_layer = pt_layers[i : i + 2]
+            _copy_layer(hf_layer, pt_layer)
+
+    hf_model = LDMBertModel(config).eval()
+
+    # copy  embeds
+    hf_model.model.embed_tokens.weight = checkpoint.transformer.token_emb.weight
+    hf_model.model.embed_positions.weight.data = checkpoint.transformer.pos_emb.emb.weight
+
+    # copy layer norm
+    _copy_linear(hf_model.model.layer_norm, checkpoint.transformer.norm)
+
+    # copy hidden layers
+    _copy_layers(hf_model.model.layers, checkpoint.transformer.attn_layers.layers)
+
+    _copy_linear(hf_model.to_logits, checkpoint.transformer.to_logits)
+
+    return hf_model
+
+
+def convert_ldm_clip_checkpoint(checkpoint, local_files_only=False, text_encoder=None):
+    if text_encoder is None:
+        config_name = "openai/clip-vit-large-patch14"
+        try:
+            config = CLIPTextConfig.from_pretrained(config_name, local_files_only=local_files_only)
+        except Exception:
+            raise ValueError(
+                f"With local_files_only set to {local_files_only}, you must first locally save the configuration in the following path: 'openai/clip-vit-large-patch14'."
+            )
+
+        ctx = init_empty_weights if is_accelerate_available() else nullcontext
+        with ctx():
+            text_model = CLIPTextModel(config)
+    else:
+        text_model = text_encoder
+
+    keys = list(checkpoint.keys())
+
+    text_model_dict = {}
+
+    remove_prefixes = ["cond_stage_model.transformer", "conditioner.embedders.0.transformer"]
+
+    for key in keys:
+        for prefix in remove_prefixes:
+            if key.startswith(prefix):
+                text_model_dict[key[len(prefix + ".") :]] = checkpoint[key]
+
+    if is_accelerate_available():
+        for param_name, param in text_model_dict.items():
+            set_module_tensor_to_device(text_model, param_name, "cpu", value=param)
+    else:
+        if not (hasattr(text_model, "embeddings") and hasattr(text_model.embeddings.position_ids)):
+            text_model_dict.pop("text_model.embeddings.position_ids", None)
+
+        text_model.load_state_dict(text_model_dict)
+
+    return text_model
+
+
+textenc_conversion_lst = [
+    ("positional_embedding", "text_model.embeddings.position_embedding.weight"),
+    ("token_embedding.weight", "text_model.embeddings.token_embedding.weight"),
+    ("ln_final.weight", "text_model.final_layer_norm.weight"),
+    ("ln_final.bias", "text_model.final_layer_norm.bias"),
+    ("text_projection", "text_projection.weight"),
+]
+textenc_conversion_map = {x[0]: x[1] for x in textenc_conversion_lst}
+
+textenc_transformer_conversion_lst = [
+    # (stable-diffusion, HF Diffusers)
+    ("resblocks.", "text_model.encoder.layers."),
+    ("ln_1", "layer_norm1"),
+    ("ln_2", "layer_norm2"),
+    (".c_fc.", ".fc1."),
+    (".c_proj.", ".fc2."),
+    (".attn", ".self_attn"),
+    ("ln_final.", "transformer.text_model.final_layer_norm."),
+    ("token_embedding.weight", "transformer.text_model.embeddings.token_embedding.weight"),
+    ("positional_embedding", "transformer.text_model.embeddings.position_embedding.weight"),
+]
+protected = {re.escape(x[0]): x[1] for x in textenc_transformer_conversion_lst}
+textenc_pattern = re.compile("|".join(protected.keys()))
+
+
+def convert_paint_by_example_checkpoint(checkpoint, local_files_only=False):
+    config = CLIPVisionConfig.from_pretrained("openai/clip-vit-large-patch14", local_files_only=local_files_only)
+    model = PaintByExampleImageEncoder(config)
+
+    keys = list(checkpoint.keys())
+
+    text_model_dict = {}
+
+    for key in keys:
+        if key.startswith("cond_stage_model.transformer"):
+            text_model_dict[key[len("cond_stage_model.transformer.") :]] = checkpoint[key]
+
+    # load clip vision
+    model.model.load_state_dict(text_model_dict)
+
+    # load mapper
+    keys_mapper = {
+        k[len("cond_stage_model.mapper.res") :]: v
+        for k, v in checkpoint.items()
+        if k.startswith("cond_stage_model.mapper")
+    }
+
+    MAPPING = {
+        "attn.c_qkv": ["attn1.to_q", "attn1.to_k", "attn1.to_v"],
+        "attn.c_proj": ["attn1.to_out.0"],
+        "ln_1": ["norm1"],
+        "ln_2": ["norm3"],
+        "mlp.c_fc": ["ff.net.0.proj"],
+        "mlp.c_proj": ["ff.net.2"],
+    }
+
+    mapped_weights = {}
+    for key, value in keys_mapper.items():
+        prefix = key[: len("blocks.i")]
+        suffix = key.split(prefix)[-1].split(".")[-1]
+        name = key.split(prefix)[-1].split(suffix)[0][1:-1]
+        mapped_names = MAPPING[name]
+
+        num_splits = len(mapped_names)
+        for i, mapped_name in enumerate(mapped_names):
+            new_name = ".".join([prefix, mapped_name, suffix])
+            shape = value.shape[0] // num_splits
+            mapped_weights[new_name] = value[i * shape : (i + 1) * shape]
+
+    model.mapper.load_state_dict(mapped_weights)
+
+    # load final layer norm
+    model.final_layer_norm.load_state_dict(
+        {
+            "bias": checkpoint["cond_stage_model.final_ln.bias"],
+            "weight": checkpoint["cond_stage_model.final_ln.weight"],
+        }
+    )
+
+    # load final proj
+    model.proj_out.load_state_dict(
+        {
+            "bias": checkpoint["proj_out.bias"],
+            "weight": checkpoint["proj_out.weight"],
+        }
+    )
+
+    # load uncond vector
+    model.uncond_vector.data = torch.nn.Parameter(checkpoint["learnable_vector"])
+    return model
+
+
+def convert_open_clip_checkpoint(
+    checkpoint,
+    config_name,
+    prefix="cond_stage_model.model.",
+    has_projection=False,
+    local_files_only=False,
+    **config_kwargs,
+):
+    # text_model = CLIPTextModel.from_pretrained("stabilityai/stable-diffusion-2", subfolder="text_encoder")
+    # text_model = CLIPTextModelWithProjection.from_pretrained(
+    #    "laion/CLIP-ViT-bigG-14-laion2B-39B-b160k", projection_dim=1280
+    # )
+    try:
+        config = CLIPTextConfig.from_pretrained(config_name, **config_kwargs, local_files_only=local_files_only)
+    except Exception:
+        raise ValueError(
+            f"With local_files_only set to {local_files_only}, you must first locally save the configuration in the following path: '{config_name}'."
+        )
+
+    ctx = init_empty_weights if is_accelerate_available() else nullcontext
+    with ctx():
+        text_model = CLIPTextModelWithProjection(config) if has_projection else CLIPTextModel(config)
+
+    keys = list(checkpoint.keys())
+
+    keys_to_ignore = []
+    if config_name == "stabilityai/stable-diffusion-2" and config.num_hidden_layers == 23:
+        # make sure to remove all keys > 22
+        keys_to_ignore += [k for k in keys if k.startswith("cond_stage_model.model.transformer.resblocks.23")]
+        keys_to_ignore += ["cond_stage_model.model.text_projection"]
+
+    text_model_dict = {}
+
+    if prefix + "text_projection" in checkpoint:
+        d_model = int(checkpoint[prefix + "text_projection"].shape[0])
+    else:
+        d_model = 1024
+
+    text_model_dict["text_model.embeddings.position_ids"] = text_model.text_model.embeddings.get_buffer("position_ids")
+
+    for key in keys:
+        if key in keys_to_ignore:
+            continue
+        if key[len(prefix) :] in textenc_conversion_map:
+            if key.endswith("text_projection"):
+                value = checkpoint[key].T.contiguous()
+            else:
+                value = checkpoint[key]
+
+            text_model_dict[textenc_conversion_map[key[len(prefix) :]]] = value
+
+        if key.startswith(prefix + "transformer."):
+            new_key = key[len(prefix + "transformer.") :]
+            if new_key.endswith(".in_proj_weight"):
+                new_key = new_key[: -len(".in_proj_weight")]
+                new_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], new_key)
+                text_model_dict[new_key + ".q_proj.weight"] = checkpoint[key][:d_model, :]
+                text_model_dict[new_key + ".k_proj.weight"] = checkpoint[key][d_model : d_model * 2, :]
+                text_model_dict[new_key + ".v_proj.weight"] = checkpoint[key][d_model * 2 :, :]
+            elif new_key.endswith(".in_proj_bias"):
+                new_key = new_key[: -len(".in_proj_bias")]
+                new_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], new_key)
+                text_model_dict[new_key + ".q_proj.bias"] = checkpoint[key][:d_model]
+                text_model_dict[new_key + ".k_proj.bias"] = checkpoint[key][d_model : d_model * 2]
+                text_model_dict[new_key + ".v_proj.bias"] = checkpoint[key][d_model * 2 :]
+            else:
+                new_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], new_key)
+
+                text_model_dict[new_key] = checkpoint[key]
+
+    if is_accelerate_available():
+        for param_name, param in text_model_dict.items():
+            set_module_tensor_to_device(text_model, param_name, "cpu", value=param)
+    else:
+        if not (hasattr(text_model, "embeddings") and hasattr(text_model.embeddings.position_ids)):
+            text_model_dict.pop("text_model.embeddings.position_ids", None)
+
+        text_model.load_state_dict(text_model_dict)
+
+    return text_model
+
+
+def stable_unclip_image_encoder(original_config, local_files_only=False):
+    """
+    Returns the image processor and clip image encoder for the img2img unclip pipeline.
+
+    We currently know of two types of stable unclip models which separately use the clip and the openclip image
+    encoders.
+    """
+
+    image_embedder_config = original_config["model"]["params"]["embedder_config"]
+
+    sd_clip_image_embedder_class = image_embedder_config["target"]
+    sd_clip_image_embedder_class = sd_clip_image_embedder_class.split(".")[-1]
+
+    if sd_clip_image_embedder_class == "ClipImageEmbedder":
+        clip_model_name = image_embedder_config.params.model
+
+        if clip_model_name == "ViT-L/14":
+            feature_extractor = CLIPImageProcessor()
+            image_encoder = CLIPVisionModelWithProjection.from_pretrained(
+                "openai/clip-vit-large-patch14", local_files_only=local_files_only
+            )
+        else:
+            raise NotImplementedError(f"Unknown CLIP checkpoint name in stable diffusion checkpoint {clip_model_name}")
+
+    elif sd_clip_image_embedder_class == "FrozenOpenCLIPImageEmbedder":
+        feature_extractor = CLIPImageProcessor()
+        image_encoder = CLIPVisionModelWithProjection.from_pretrained(
+            "laion/CLIP-ViT-H-14-laion2B-s32B-b79K", local_files_only=local_files_only
+        )
+    else:
+        raise NotImplementedError(
+            f"Unknown CLIP image embedder class in stable diffusion checkpoint {sd_clip_image_embedder_class}"
+        )
+
+    return feature_extractor, image_encoder
+
+
+def stable_unclip_image_noising_components(
+    original_config, clip_stats_path: Optional[str] = None, device: Optional[str] = None
+):
+    """
+    Returns the noising components for the img2img and txt2img unclip pipelines.
+
+    Converts the stability noise augmentor into
+    1. a `StableUnCLIPImageNormalizer` for holding the CLIP stats
+    2. a `DDPMScheduler` for holding the noise schedule
+
+    If the noise augmentor config specifies a clip stats path, the `clip_stats_path` must be provided.
+    """
+    noise_aug_config = original_config["model"]["params"]["noise_aug_config"]
+    noise_aug_class = noise_aug_config["target"]
+    noise_aug_class = noise_aug_class.split(".")[-1]
+
+    if noise_aug_class == "CLIPEmbeddingNoiseAugmentation":
+        noise_aug_config = noise_aug_config.params
+        embedding_dim = noise_aug_config.timestep_dim
+        max_noise_level = noise_aug_config.noise_schedule_config.timesteps
+        beta_schedule = noise_aug_config.noise_schedule_config.beta_schedule
+
+        image_normalizer = StableUnCLIPImageNormalizer(embedding_dim=embedding_dim)
+        image_noising_scheduler = DDPMScheduler(num_train_timesteps=max_noise_level, beta_schedule=beta_schedule)
+
+        if "clip_stats_path" in noise_aug_config:
+            if clip_stats_path is None:
+                raise ValueError("This stable unclip config requires a `clip_stats_path`")
+
+            clip_mean, clip_std = torch.load(clip_stats_path, map_location=device)
+            clip_mean = clip_mean[None, :]
+            clip_std = clip_std[None, :]
+
+            clip_stats_state_dict = {
+                "mean": clip_mean,
+                "std": clip_std,
+            }
+
+            image_normalizer.load_state_dict(clip_stats_state_dict)
+    else:
+        raise NotImplementedError(f"Unknown noise augmentor class: {noise_aug_class}")
+
+    return image_normalizer, image_noising_scheduler
+
+
+def convert_controlnet_checkpoint(
+    checkpoint,
+    original_config,
+    checkpoint_path,
+    image_size,
+    upcast_attention,
+    extract_ema,
+    use_linear_projection=None,
+    cross_attention_dim=None,
+):
+    ctrlnet_config = create_unet_diffusers_config(original_config, image_size=image_size, controlnet=True)
+    ctrlnet_config["upcast_attention"] = upcast_attention
+
+    ctrlnet_config.pop("sample_size")
+
+    if use_linear_projection is not None:
+        ctrlnet_config["use_linear_projection"] = use_linear_projection
+
+    if cross_attention_dim is not None:
+        ctrlnet_config["cross_attention_dim"] = cross_attention_dim
+
+    ctx = init_empty_weights if is_accelerate_available() else nullcontext
+    with ctx():
+        controlnet = ControlNetModel(**ctrlnet_config)
+
+    # Some controlnet ckpt files are distributed independently from the rest of the
+    # model components i.e. https://huggingface.co/thibaud/controlnet-sd21/
+    if "time_embed.0.weight" in checkpoint:
+        skip_extract_state_dict = True
+    else:
+        skip_extract_state_dict = False
+
+    converted_ctrl_checkpoint = convert_ldm_unet_checkpoint(
+        checkpoint,
+        ctrlnet_config,
+        path=checkpoint_path,
+        extract_ema=extract_ema,
+        controlnet=True,
+        skip_extract_state_dict=skip_extract_state_dict,
+    )
+
+    if is_accelerate_available():
+        for param_name, param in converted_ctrl_checkpoint.items():
+            set_module_tensor_to_device(controlnet, param_name, "cpu", value=param)
+    else:
+        controlnet.load_state_dict(converted_ctrl_checkpoint)
+
+    return controlnet
+
+
+def download_from_original_stable_diffusion_ckpt(
+    checkpoint_path_or_dict: Union[str, Dict[str, torch.Tensor]],
+    original_config_file: str = None,
+    image_size: Optional[int] = None,
+    prediction_type: str = None,
+    model_type: str = None,
+    extract_ema: bool = False,
+    scheduler_type: str = "pndm",
+    num_in_channels: Optional[int] = None,
+    upcast_attention: Optional[bool] = None,
+    device: str = None,
+    from_safetensors: bool = False,
+    stable_unclip: Optional[str] = None,
+    stable_unclip_prior: Optional[str] = None,
+    clip_stats_path: Optional[str] = None,
+    controlnet: Optional[bool] = None,
+    adapter: Optional[bool] = None,
+    load_safety_checker: bool = True,
+    safety_checker: Optional[StableDiffusionSafetyChecker] = None,
+    feature_extractor: Optional[AutoFeatureExtractor] = None,
+    pipeline_class: DiffusionPipeline = None,
+    local_files_only=False,
+    vae_path=None,
+    vae=None,
+    text_encoder=None,
+    text_encoder_2=None,
+    tokenizer=None,
+    tokenizer_2=None,
+    config_files=None,
+) -> DiffusionPipeline:
+    """
+    Load a Stable Diffusion pipeline object from a CompVis-style `.ckpt`/`.safetensors` file and (ideally) a `.yaml`
+    config file.
+
+    Although many of the arguments can be automatically inferred, some of these rely on brittle checks against the
+    global step count, which will likely fail for models that have undergone further fine-tuning. Therefore, it is
+    recommended that you override the default values and/or supply an `original_config_file` wherever possible.
+
+    Args:
+        checkpoint_path_or_dict (`str` or `dict`): Path to `.ckpt` file, or the state dict.
+        original_config_file (`str`):
+            Path to `.yaml` config file corresponding to the original architecture. If `None`, will be automatically
+            inferred by looking for a key that only exists in SD2.0 models.
+        image_size (`int`, *optional*, defaults to 512):
+            The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Diffusion v2
+            Base. Use 768 for Stable Diffusion v2.
+        prediction_type (`str`, *optional*):
+            The prediction type that the model was trained on. Use `'epsilon'` for Stable Diffusion v1.X and Stable
+            Diffusion v2 Base. Use `'v_prediction'` for Stable Diffusion v2.
+        num_in_channels (`int`, *optional*, defaults to None):
+            The number of input channels. If `None`, it will be automatically inferred.
+        scheduler_type (`str`, *optional*, defaults to 'pndm'):
+            Type of scheduler to use. Should be one of `["pndm", "lms", "heun", "euler", "euler-ancestral", "dpm",
+            "ddim"]`.
+        model_type (`str`, *optional*, defaults to `None`):
+            The pipeline type. `None` to automatically infer, or one of `["FrozenOpenCLIPEmbedder",
+            "FrozenCLIPEmbedder", "PaintByExample"]`.
+        is_img2img (`bool`, *optional*, defaults to `False`):
+            Whether the model should be loaded as an img2img pipeline.
+        extract_ema (`bool`, *optional*, defaults to `False`): Only relevant for
+            checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights or not. Defaults to
+            `False`. Pass `True` to extract the EMA weights. EMA weights usually yield higher quality images for
+            inference. Non-EMA weights are usually better to continue fine-tuning.
+        upcast_attention (`bool`, *optional*, defaults to `None`):
+            Whether the attention computation should always be upcasted. This is necessary when running stable
+            diffusion 2.1.
+        device (`str`, *optional*, defaults to `None`):
+            The device to use. Pass `None` to determine automatically.
+        from_safetensors (`str`, *optional*, defaults to `False`):
+            If `checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.
+        load_safety_checker (`bool`, *optional*, defaults to `True`):
+            Whether to load the safety checker or not. Defaults to `True`.
+        safety_checker (`StableDiffusionSafetyChecker`, *optional*, defaults to `None`):
+            Safety checker to use. If this parameter is `None`, the function will load a new instance of
+            [StableDiffusionSafetyChecker] by itself, if needed.
+        feature_extractor (`AutoFeatureExtractor`, *optional*, defaults to `None`):
+            Feature extractor to use. If this parameter is `None`, the function will load a new instance of
+            [AutoFeatureExtractor] by itself, if needed.
+        pipeline_class (`str`, *optional*, defaults to `None`):
+            The pipeline class to use. Pass `None` to determine automatically.
+        local_files_only (`bool`, *optional*, defaults to `False`):
+            Whether or not to only look at local files (i.e., do not try to download the model).
+        vae (`AutoencoderKL`, *optional*, defaults to `None`):
+            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. If
+            this parameter is `None`, the function will load a new instance of [CLIP] by itself, if needed.
+        text_encoder (`CLIPTextModel`, *optional*, defaults to `None`):
+            An instance of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel)
+            to use, specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)
+            variant. If this parameter is `None`, the function will load a new instance of [CLIP] by itself, if needed.
+        tokenizer (`CLIPTokenizer`, *optional*, defaults to `None`):
+            An instance of
+            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer)
+            to use. If this parameter is `None`, the function will load a new instance of [CLIPTokenizer] by itself, if
+            needed.
+        config_files (`Dict[str, str]`, *optional*, defaults to `None`):
+            A dictionary mapping from config file names to their contents. If this parameter is `None`, the function
+            will load the config files by itself, if needed. Valid keys are:
+                - `v1`: Config file for Stable Diffusion v1
+                - `v2`: Config file for Stable Diffusion v2
+                - `xl`: Config file for Stable Diffusion XL
+                - `xl_refiner`: Config file for Stable Diffusion XL Refiner
+        return: A StableDiffusionPipeline object representing the passed-in `.ckpt`/`.safetensors` file.
+    """
+
+    # import pipelines here to avoid circular import error when using from_single_file method
+    from diffusers import (
+        LDMTextToImagePipeline,
+        PaintByExamplePipeline,
+        StableDiffusionControlNetPipeline,
+        StableDiffusionInpaintPipeline,
+        StableDiffusionPipeline,
+        StableDiffusionUpscalePipeline,
+        StableDiffusionXLControlNetInpaintPipeline,
+        StableDiffusionXLImg2ImgPipeline,
+        StableDiffusionXLInpaintPipeline,
+        StableDiffusionXLPipeline,
+        StableUnCLIPImg2ImgPipeline,
+        StableUnCLIPPipeline,
+    )
+
+    if prediction_type == "v-prediction":
+        prediction_type = "v_prediction"
+
+    if isinstance(checkpoint_path_or_dict, str):
+        if from_safetensors:
+            from safetensors.torch import load_file as safe_load
+
+            checkpoint = safe_load(checkpoint_path_or_dict, device="cpu")
+        else:
+            if device is None:
+                device = "cuda" if torch.cuda.is_available() else "cpu"
+                checkpoint = torch.load(checkpoint_path_or_dict, map_location=device)
+            else:
+                checkpoint = torch.load(checkpoint_path_or_dict, map_location=device)
+    elif isinstance(checkpoint_path_or_dict, dict):
+        checkpoint = checkpoint_path_or_dict
+
+    # Sometimes models don't have the global_step item
+    if "global_step" in checkpoint:
+        global_step = checkpoint["global_step"]
+    else:
+        logger.debug("global_step key not found in model")
+        global_step = None
+
+    # NOTE: this while loop isn't great but this controlnet checkpoint has one additional
+    # "state_dict" key https://huggingface.co/thibaud/controlnet-canny-sd21
+    while "state_dict" in checkpoint:
+        checkpoint = checkpoint["state_dict"]
+
+    if original_config_file is None:
+        key_name_v2_1 = "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight"
+        key_name_sd_xl_base = "conditioner.embedders.1.model.transformer.resblocks.9.mlp.c_proj.bias"
+        key_name_sd_xl_refiner = "conditioner.embedders.0.model.transformer.resblocks.9.mlp.c_proj.bias"
+        is_upscale = pipeline_class == StableDiffusionUpscalePipeline
+
+        config_url = None
+
+        # model_type = "v1"
+        if config_files is not None and "v1" in config_files:
+            original_config_file = config_files["v1"]
+        else:
+            config_url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"
+
+        if key_name_v2_1 in checkpoint and checkpoint[key_name_v2_1].shape[-1] == 1024:
+            # model_type = "v2"
+            if config_files is not None and "v2" in config_files:
+                original_config_file = config_files["v2"]
+            else:
+                config_url = "https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/v2-inference-v.yaml"
+            if global_step == 110000:
+                # v2.1 needs to upcast attention
+                upcast_attention = True
+        elif key_name_sd_xl_base in checkpoint:
+            # only base xl has two text embedders
+            if config_files is not None and "xl" in config_files:
+                original_config_file = config_files["xl"]
+            else:
+                config_url = "https://raw.githubusercontent.com/Stability-AI/generative-models/main/configs/inference/sd_xl_base.yaml"
+        elif key_name_sd_xl_refiner in checkpoint:
+            # only refiner xl has embedder and one text embedders
+            if config_files is not None and "xl_refiner" in config_files:
+                original_config_file = config_files["xl_refiner"]
+            else:
+                config_url = "https://raw.githubusercontent.com/Stability-AI/generative-models/main/configs/inference/sd_xl_refiner.yaml"
+
+        if is_upscale:
+            config_url = "https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/x4-upscaling.yaml"
+
+        if config_url is not None:
+            original_config_file = BytesIO(requests.get(config_url).content)
+        else:
+            with open(original_config_file, "r") as f:
+                original_config_file = f.read()
+    else:
+        with open(original_config_file, "r") as f:
+            original_config_file = f.read()
+
+    original_config = yaml.safe_load(original_config_file)
+
+    # Convert the text model.
+    if (
+        model_type is None
+        and "cond_stage_config" in original_config["model"]["params"]
+        and original_config["model"]["params"]["cond_stage_config"] is not None
+    ):
+        model_type = original_config["model"]["params"]["cond_stage_config"]["target"].split(".")[-1]
+        logger.debug(f"no `model_type` given, `model_type` inferred as: {model_type}")
+    elif model_type is None and original_config["model"]["params"]["network_config"] is not None:
+        if original_config["model"]["params"]["network_config"]["params"]["context_dim"] == 2048:
+            model_type = "SDXL"
+        else:
+            model_type = "SDXL-Refiner"
+        if image_size is None:
+            image_size = 1024
+
+    if pipeline_class is None:
+        # Check if we have a SDXL or SD model and initialize default pipeline
+        if model_type not in ["SDXL", "SDXL-Refiner"]:
+            pipeline_class = StableDiffusionPipeline if not controlnet else StableDiffusionControlNetPipeline
+        else:
+            pipeline_class = StableDiffusionXLPipeline if model_type == "SDXL" else StableDiffusionXLImg2ImgPipeline
+
+    if num_in_channels is None and pipeline_class in [
+        StableDiffusionInpaintPipeline,
+        StableDiffusionXLInpaintPipeline,
+        StableDiffusionXLControlNetInpaintPipeline,
+    ]:
+        num_in_channels = 9
+    if num_in_channels is None and pipeline_class == StableDiffusionUpscalePipeline:
+        num_in_channels = 7
+    elif num_in_channels is None:
+        num_in_channels = 4
+
+    if "unet_config" in original_config["model"]["params"]:
+        original_config["model"]["params"]["unet_config"]["params"]["in_channels"] = num_in_channels
+    elif "network_config" in original_config["model"]["params"]:
+        original_config["model"]["params"]["network_config"]["params"]["in_channels"] = num_in_channels
+
+    if (
+        "parameterization" in original_config["model"]["params"]
+        and original_config["model"]["params"]["parameterization"] == "v"
+    ):
+        if prediction_type is None:
+            # NOTE: For stable diffusion 2 base it is recommended to pass `prediction_type=="epsilon"`
+            # as it relies on a brittle global step parameter here
+            prediction_type = "epsilon" if global_step == 875000 else "v_prediction"
+        if image_size is None:
+            # NOTE: For stable diffusion 2 base one has to pass `image_size==512`
+            # as it relies on a brittle global step parameter here
+            image_size = 512 if global_step == 875000 else 768
+    else:
+        if prediction_type is None:
+            prediction_type = "epsilon"
+        if image_size is None:
+            image_size = 512
+
+    if controlnet is None and "control_stage_config" in original_config["model"]["params"]:
+        path = checkpoint_path_or_dict if isinstance(checkpoint_path_or_dict, str) else ""
+        controlnet = convert_controlnet_checkpoint(
+            checkpoint, original_config, path, image_size, upcast_attention, extract_ema
+        )
+
+    if "timesteps" in original_config["model"]["params"]:
+        num_train_timesteps = original_config["model"]["params"]["timesteps"]
+    else:
+        num_train_timesteps = 1000
+
+    if model_type in ["SDXL", "SDXL-Refiner"]:
+        scheduler_dict = {
+            "beta_schedule": "scaled_linear",
+            "beta_start": 0.00085,
+            "beta_end": 0.012,
+            "interpolation_type": "linear",
+            "num_train_timesteps": num_train_timesteps,
+            "prediction_type": "epsilon",
+            "sample_max_value": 1.0,
+            "set_alpha_to_one": False,
+            "skip_prk_steps": True,
+            "steps_offset": 1,
+            "timestep_spacing": "leading",
+        }
+        scheduler = EulerDiscreteScheduler.from_config(scheduler_dict)
+        scheduler_type = "euler"
+    else:
+        if "linear_start" in original_config["model"]["params"]:
+            beta_start = original_config["model"]["params"]["linear_start"]
+        else:
+            beta_start = 0.02
+
+        if "linear_end" in original_config["model"]["params"]:
+            beta_end = original_config["model"]["params"]["linear_end"]
+        else:
+            beta_end = 0.085
+        scheduler = DDIMScheduler(
+            beta_end=beta_end,
+            beta_schedule="scaled_linear",
+            beta_start=beta_start,
+            num_train_timesteps=num_train_timesteps,
+            steps_offset=1,
+            clip_sample=False,
+            set_alpha_to_one=False,
+            prediction_type=prediction_type,
+        )
+    # make sure scheduler works correctly with DDIM
+    scheduler.register_to_config(clip_sample=False)
+
+    if scheduler_type == "pndm":
+        config = dict(scheduler.config)
+        config["skip_prk_steps"] = True
+        scheduler = PNDMScheduler.from_config(config)
+    elif scheduler_type == "lms":
+        scheduler = LMSDiscreteScheduler.from_config(scheduler.config)
+    elif scheduler_type == "heun":
+        scheduler = HeunDiscreteScheduler.from_config(scheduler.config)
+    elif scheduler_type == "euler":
+        scheduler = EulerDiscreteScheduler.from_config(scheduler.config)
+    elif scheduler_type == "euler-ancestral":
+        scheduler = EulerAncestralDiscreteScheduler.from_config(scheduler.config)
+    elif scheduler_type == "dpm":
+        scheduler = DPMSolverMultistepScheduler.from_config(scheduler.config)
+    elif scheduler_type == "ddim":
+        scheduler = scheduler
+    else:
+        raise ValueError(f"Scheduler of type {scheduler_type} doesn't exist!")
+
+    if pipeline_class == StableDiffusionUpscalePipeline:
+        image_size = original_config["model"]["params"]["unet_config"]["params"]["image_size"]
+
+    # Convert the UNet2DConditionModel model.
+    unet_config = create_unet_diffusers_config(original_config, image_size=image_size)
+    unet_config["upcast_attention"] = upcast_attention
+
+    path = checkpoint_path_or_dict if isinstance(checkpoint_path_or_dict, str) else ""
+    converted_unet_checkpoint = convert_ldm_unet_checkpoint(
+        checkpoint, unet_config, path=path, extract_ema=extract_ema
+    )
+
+    ctx = init_empty_weights if is_accelerate_available() else nullcontext
+    with ctx():
+        unet = UNet2DConditionModel(**unet_config)
+
+    if is_accelerate_available():
+        if model_type not in ["SDXL", "SDXL-Refiner"]:  # SBM Delay this.
+            for param_name, param in converted_unet_checkpoint.items():
+                set_module_tensor_to_device(unet, param_name, "cpu", value=param)
+    else:
+        unet.load_state_dict(converted_unet_checkpoint)
+
+    # Convert the VAE model.
+    if vae_path is None and vae is None:
+        vae_config = create_vae_diffusers_config(original_config, image_size=image_size)
+        converted_vae_checkpoint = convert_ldm_vae_checkpoint(checkpoint, vae_config)
+
+        if (
+            "model" in original_config
+            and "params" in original_config["model"]
+            and "scale_factor" in original_config["model"]["params"]
+        ):
+            vae_scaling_factor = original_config["model"]["params"]["scale_factor"]
+        else:
+            vae_scaling_factor = 0.18215  # default SD scaling factor
+
+        vae_config["scaling_factor"] = vae_scaling_factor
+
+        ctx = init_empty_weights if is_accelerate_available() else nullcontext
+        with ctx():
+            vae = AutoencoderKL(**vae_config)
+
+        if is_accelerate_available():
+            for param_name, param in converted_vae_checkpoint.items():
+                set_module_tensor_to_device(vae, param_name, "cpu", value=param)
+        else:
+            vae.load_state_dict(converted_vae_checkpoint)
+    elif vae is None:
+        vae = AutoencoderKL.from_pretrained(vae_path, local_files_only=local_files_only)
+
+    if model_type == "FrozenOpenCLIPEmbedder":
+        config_name = "stabilityai/stable-diffusion-2"
+        config_kwargs = {"subfolder": "text_encoder"}
+
+        if text_encoder is None:
+            text_model = convert_open_clip_checkpoint(
+                checkpoint, config_name, local_files_only=local_files_only, **config_kwargs
+            )
+        else:
+            text_model = text_encoder
+
+        try:
+            tokenizer = CLIPTokenizer.from_pretrained(
+                "stabilityai/stable-diffusion-2", subfolder="tokenizer", local_files_only=local_files_only
+            )
+        except Exception:
+            raise ValueError(
+                f"With local_files_only set to {local_files_only}, you must first locally save the tokenizer in the following path: 'stabilityai/stable-diffusion-2'."
+            )
+
+        if stable_unclip is None:
+            if controlnet:
+                pipe = pipeline_class(
+                    vae=vae,
+                    text_encoder=text_model,
+                    tokenizer=tokenizer,
+                    unet=unet,
+                    scheduler=scheduler,
+                    controlnet=controlnet,
+                    safety_checker=safety_checker,
+                    feature_extractor=feature_extractor,
+                )
+                if hasattr(pipe, "requires_safety_checker"):
+                    pipe.requires_safety_checker = False
+
+            elif pipeline_class == StableDiffusionUpscalePipeline:
+                scheduler = DDIMScheduler.from_pretrained(
+                    "stabilityai/stable-diffusion-x4-upscaler", subfolder="scheduler"
+                )
+                low_res_scheduler = DDPMScheduler.from_pretrained(
+                    "stabilityai/stable-diffusion-x4-upscaler", subfolder="low_res_scheduler"
+                )
+
+                pipe = pipeline_class(
+                    vae=vae,
+                    text_encoder=text_model,
+                    tokenizer=tokenizer,
+                    unet=unet,
+                    scheduler=scheduler,
+                    low_res_scheduler=low_res_scheduler,
+                    safety_checker=safety_checker,
+                    feature_extractor=feature_extractor,
+                )
+
+            else:
+                pipe = pipeline_class(
+                    vae=vae,
+                    text_encoder=text_model,
+                    tokenizer=tokenizer,
+                    unet=unet,
+                    scheduler=scheduler,
+                    safety_checker=safety_checker,
+                    feature_extractor=feature_extractor,
+                )
+                if hasattr(pipe, "requires_safety_checker"):
+                    pipe.requires_safety_checker = False
+
+        else:
+            image_normalizer, image_noising_scheduler = stable_unclip_image_noising_components(
+                original_config, clip_stats_path=clip_stats_path, device=device
+            )
+
+            if stable_unclip == "img2img":
+                feature_extractor, image_encoder = stable_unclip_image_encoder(original_config)
+
+                pipe = StableUnCLIPImg2ImgPipeline(
+                    # image encoding components
+                    feature_extractor=feature_extractor,
+                    image_encoder=image_encoder,
+                    # image noising components
+                    image_normalizer=image_normalizer,
+                    image_noising_scheduler=image_noising_scheduler,
+                    # regular denoising components
+                    tokenizer=tokenizer,
+                    text_encoder=text_model,
+                    unet=unet,
+                    scheduler=scheduler,
+                    # vae
+                    vae=vae,
+                )
+            elif stable_unclip == "txt2img":
+                if stable_unclip_prior is None or stable_unclip_prior == "karlo":
+                    karlo_model = "kakaobrain/karlo-v1-alpha"
+                    prior = PriorTransformer.from_pretrained(
+                        karlo_model, subfolder="prior", local_files_only=local_files_only
+                    )
+
+                    try:
+                        prior_tokenizer = CLIPTokenizer.from_pretrained(
+                            "openai/clip-vit-large-patch14", local_files_only=local_files_only
+                        )
+                    except Exception:
+                        raise ValueError(
+                            f"With local_files_only set to {local_files_only}, you must first locally save the tokenizer in the following path: 'openai/clip-vit-large-patch14'."
+                        )
+                    prior_text_model = CLIPTextModelWithProjection.from_pretrained(
+                        "openai/clip-vit-large-patch14", local_files_only=local_files_only
+                    )
+
+                    prior_scheduler = UnCLIPScheduler.from_pretrained(
+                        karlo_model, subfolder="prior_scheduler", local_files_only=local_files_only
+                    )
+                    prior_scheduler = DDPMScheduler.from_config(prior_scheduler.config)
+                else:
+                    raise NotImplementedError(f"unknown prior for stable unclip model: {stable_unclip_prior}")
+
+                pipe = StableUnCLIPPipeline(
+                    # prior components
+                    prior_tokenizer=prior_tokenizer,
+                    prior_text_encoder=prior_text_model,
+                    prior=prior,
+                    prior_scheduler=prior_scheduler,
+                    # image noising components
+                    image_normalizer=image_normalizer,
+                    image_noising_scheduler=image_noising_scheduler,
+                    # regular denoising components
+                    tokenizer=tokenizer,
+                    text_encoder=text_model,
+                    unet=unet,
+                    scheduler=scheduler,
+                    # vae
+                    vae=vae,
+                )
+            else:
+                raise NotImplementedError(f"unknown `stable_unclip` type: {stable_unclip}")
+    elif model_type == "PaintByExample":
+        vision_model = convert_paint_by_example_checkpoint(checkpoint)
+        try:
+            tokenizer = CLIPTokenizer.from_pretrained(
+                "openai/clip-vit-large-patch14", local_files_only=local_files_only
+            )
+        except Exception:
+            raise ValueError(
+                f"With local_files_only set to {local_files_only}, you must first locally save the tokenizer in the following path: 'openai/clip-vit-large-patch14'."
+            )
+        try:
+            feature_extractor = AutoFeatureExtractor.from_pretrained(
+                "CompVis/stable-diffusion-safety-checker", local_files_only=local_files_only
+            )
+        except Exception:
+            raise ValueError(
+                f"With local_files_only set to {local_files_only}, you must first locally save the feature_extractor in the following path: 'CompVis/stable-diffusion-safety-checker'."
+            )
+        pipe = PaintByExamplePipeline(
+            vae=vae,
+            image_encoder=vision_model,
+            unet=unet,
+            scheduler=scheduler,
+            safety_checker=None,
+            feature_extractor=feature_extractor,
+        )
+    elif model_type == "FrozenCLIPEmbedder":
+        text_model = convert_ldm_clip_checkpoint(
+            checkpoint, local_files_only=local_files_only, text_encoder=text_encoder
+        )
+        try:
+            tokenizer = (
+                CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14", local_files_only=local_files_only)
+                if tokenizer is None
+                else tokenizer
+            )
+        except Exception:
+            raise ValueError(
+                f"With local_files_only set to {local_files_only}, you must first locally save the tokenizer in the following path: 'openai/clip-vit-large-patch14'."
+            )
+
+        if load_safety_checker:
+            safety_checker = StableDiffusionSafetyChecker.from_pretrained(
+                "CompVis/stable-diffusion-safety-checker", local_files_only=local_files_only
+            )
+            feature_extractor = AutoFeatureExtractor.from_pretrained(
+                "CompVis/stable-diffusion-safety-checker", local_files_only=local_files_only
+            )
+
+        if controlnet:
+            pipe = pipeline_class(
+                vae=vae,
+                text_encoder=text_model,
+                tokenizer=tokenizer,
+                unet=unet,
+                controlnet=controlnet,
+                scheduler=scheduler,
+                safety_checker=safety_checker,
+                feature_extractor=feature_extractor,
+            )
+        else:
+            pipe = pipeline_class(
+                vae=vae,
+                text_encoder=text_model,
+                tokenizer=tokenizer,
+                unet=unet,
+                scheduler=scheduler,
+                safety_checker=safety_checker,
+                feature_extractor=feature_extractor,
+            )
+    elif model_type in ["SDXL", "SDXL-Refiner"]:
+        is_refiner = model_type == "SDXL-Refiner"
+
+        if (is_refiner is False) and (tokenizer is None):
+            try:
+                tokenizer = CLIPTokenizer.from_pretrained(
+                    "openai/clip-vit-large-patch14", local_files_only=local_files_only
+                )
+            except Exception:
+                raise ValueError(
+                    f"With local_files_only set to {local_files_only}, you must first locally save the tokenizer in the following path: 'openai/clip-vit-large-patch14'."
+                )
+
+        if (is_refiner is False) and (text_encoder is None):
+            text_encoder = convert_ldm_clip_checkpoint(checkpoint, local_files_only=local_files_only)
+
+        if tokenizer_2 is None:
+            try:
+                tokenizer_2 = CLIPTokenizer.from_pretrained(
+                    "laion/CLIP-ViT-bigG-14-laion2B-39B-b160k", pad_token="!", local_files_only=local_files_only
+                )
+            except Exception:
+                raise ValueError(
+                    f"With local_files_only set to {local_files_only}, you must first locally save the tokenizer in the following path: 'laion/CLIP-ViT-bigG-14-laion2B-39B-b160k' with `pad_token` set to '!'."
+                )
+
+        if text_encoder_2 is None:
+            config_name = "laion/CLIP-ViT-bigG-14-laion2B-39B-b160k"
+            config_kwargs = {"projection_dim": 1280}
+            prefix = "conditioner.embedders.0.model." if is_refiner else "conditioner.embedders.1.model."
+
+            text_encoder_2 = convert_open_clip_checkpoint(
+                checkpoint,
+                config_name,
+                prefix=prefix,
+                has_projection=True,
+                local_files_only=local_files_only,
+                **config_kwargs,
+            )
+
+        if is_accelerate_available():  # SBM Now move model to cpu.
+            for param_name, param in converted_unet_checkpoint.items():
+                set_module_tensor_to_device(unet, param_name, "cpu", value=param)
+
+        if controlnet:
+            pipe = pipeline_class(
+                vae=vae,
+                text_encoder=text_encoder,
+                tokenizer=tokenizer,
+                text_encoder_2=text_encoder_2,
+                tokenizer_2=tokenizer_2,
+                unet=unet,
+                controlnet=controlnet,
+                scheduler=scheduler,
+                force_zeros_for_empty_prompt=True,
+            )
+        elif adapter:
+            pipe = pipeline_class(
+                vae=vae,
+                text_encoder=text_encoder,
+                tokenizer=tokenizer,
+                text_encoder_2=text_encoder_2,
+                tokenizer_2=tokenizer_2,
+                unet=unet,
+                adapter=adapter,
+                scheduler=scheduler,
+                force_zeros_for_empty_prompt=True,
+            )
+
+        else:
+            pipeline_kwargs = {
+                "vae": vae,
+                "text_encoder": text_encoder,
+                "tokenizer": tokenizer,
+                "text_encoder_2": text_encoder_2,
+                "tokenizer_2": tokenizer_2,
+                "unet": unet,
+                "scheduler": scheduler,
+            }
+
+            if (pipeline_class == StableDiffusionXLImg2ImgPipeline) or (
+                pipeline_class == StableDiffusionXLInpaintPipeline
+            ):
+                pipeline_kwargs.update({"requires_aesthetics_score": is_refiner})
+
+            if is_refiner:
+                pipeline_kwargs.update({"force_zeros_for_empty_prompt": False})
+
+            pipe = pipeline_class(**pipeline_kwargs)
+    else:
+        text_config = create_ldm_bert_config(original_config)
+        text_model = convert_ldm_bert_checkpoint(checkpoint, text_config)
+        tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased", local_files_only=local_files_only)
+        pipe = LDMTextToImagePipeline(vqvae=vae, bert=text_model, tokenizer=tokenizer, unet=unet, scheduler=scheduler)
+
+    return pipe
+
+
+def download_controlnet_from_original_ckpt(
+    checkpoint_path: str,
+    original_config_file: str,
+    image_size: int = 512,
+    extract_ema: bool = False,
+    num_in_channels: Optional[int] = None,
+    upcast_attention: Optional[bool] = None,
+    device: str = None,
+    from_safetensors: bool = False,
+    use_linear_projection: Optional[bool] = None,
+    cross_attention_dim: Optional[bool] = None,
+) -> DiffusionPipeline:
+    if from_safetensors:
+        from safetensors import safe_open
+
+        checkpoint = {}
+        with safe_open(checkpoint_path, framework="pt", device="cpu") as f:
+            for key in f.keys():
+                checkpoint[key] = f.get_tensor(key)
+    else:
+        if device is None:
+            device = "cuda" if torch.cuda.is_available() else "cpu"
+            checkpoint = torch.load(checkpoint_path, map_location=device)
+        else:
+            checkpoint = torch.load(checkpoint_path, map_location=device)
+
+    # NOTE: this while loop isn't great but this controlnet checkpoint has one additional
+    # "state_dict" key https://huggingface.co/thibaud/controlnet-canny-sd21
+    while "state_dict" in checkpoint:
+        checkpoint = checkpoint["state_dict"]
+
+    with open(original_config_file, "r") as f:
+        original_config_file = f.read()
+    original_config = yaml.safe_load(original_config_file)
+
+    if num_in_channels is not None:
+        original_config["model"]["params"]["unet_config"]["params"]["in_channels"] = num_in_channels
+
+    if "control_stage_config" not in original_config["model"]["params"]:
+        raise ValueError("`control_stage_config` not present in original config")
+
+    controlnet = convert_controlnet_checkpoint(
+        checkpoint,
+        original_config,
+        checkpoint_path,
+        image_size,
+        upcast_attention,
+        extract_ema,
+        use_linear_projection=use_linear_projection,
+        cross_attention_dim=cross_attention_dim,
+    )
+
+    return controlnet