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clip_camera_projection/config.json

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+{
+  "_class_name": "CLIPCameraProjection",
+  "_diffusers_version": "0.24.0",
+  "additional_embeddings": 4,
+  "embedding_dim": 768
+}

clip_camera_projection/diffusion_pytorch_model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0cb292c6da02a0d8866c4378cd6053e097c7a6bdf0886cc4ac1fba2bda3af6f0
+size 1187512

clip_camera_projection/zero123.py

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+# Copyright 2023 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import inspect
+import math
+import warnings
+from typing import Any, Callable, Dict, List, Optional, Union
+
+import PIL
+import torch
+import torchvision.transforms.functional as TF
+from diffusers.configuration_utils import ConfigMixin, FrozenDict, register_to_config
+from diffusers.image_processor import VaeImageProcessor
+from diffusers.models import AutoencoderKL, UNet2DConditionModel
+from diffusers.models.modeling_utils import ModelMixin
+from diffusers.pipelines.pipeline_utils import DiffusionPipeline
+from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
+from diffusers.pipelines.stable_diffusion.safety_checker import (
+    StableDiffusionSafetyChecker,
+)
+from diffusers.schedulers import KarrasDiffusionSchedulers
+from diffusers.utils import deprecate, is_accelerate_available, logging
+from diffusers.utils.torch_utils import randn_tensor
+from packaging import version
+from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+class CLIPCameraProjection(ModelMixin, ConfigMixin):
+    """
+    A Projection layer for CLIP embedding and camera embedding.
+
+    Parameters:
+        embedding_dim (`int`, *optional*, defaults to 768): The dimension of the model input `clip_embed`
+        additional_embeddings (`int`, *optional*, defaults to 4): The number of additional tokens appended to the
+            projected `hidden_states`. The actual length of the used `hidden_states` is `num_embeddings +
+            additional_embeddings`.
+    """
+
+    @register_to_config
+    def __init__(self, embedding_dim: int = 768, additional_embeddings: int = 4):
+        super().__init__()
+        self.embedding_dim = embedding_dim
+        self.additional_embeddings = additional_embeddings
+
+        self.input_dim = self.embedding_dim + self.additional_embeddings
+        self.output_dim = self.embedding_dim
+
+        self.proj = torch.nn.Linear(self.input_dim, self.output_dim)
+
+    def forward(
+        self,
+        embedding: torch.FloatTensor,
+    ):
+        """
+        The [`PriorTransformer`] forward method.
+
+        Args:
+            hidden_states (`torch.FloatTensor` of shape `(batch_size, input_dim)`):
+                The currently input embeddings.
+
+        Returns:
+            The output embedding projection (`torch.FloatTensor` of shape `(batch_size, output_dim)`).
+        """
+        proj_embedding = self.proj(embedding)
+        return proj_embedding
+
+
+class Zero123Pipeline(DiffusionPipeline):
+    r"""
+    Pipeline to generate variations from an input image using Stable Diffusion.
+
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
+    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
+
+    Args:
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
+        image_encoder ([`CLIPVisionModelWithProjection`]):
+            Frozen CLIP image-encoder. Stable Diffusion Image Variation uses the vision portion of
+            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPVisionModelWithProjection),
+            specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
+        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
+        scheduler ([`SchedulerMixin`]):
+            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
+            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
+        safety_checker ([`StableDiffusionSafetyChecker`]):
+            Classification module that estimates whether generated images could be considered offensive or harmful.
+            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
+        feature_extractor ([`CLIPImageProcessor`]):
+            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
+    """
+    # TODO: feature_extractor is required to encode images (if they are in PIL format),
+    # we should give a descriptive message if the pipeline doesn't have one.
+    _optional_components = ["safety_checker"]
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        image_encoder: CLIPVisionModelWithProjection,
+        unet: UNet2DConditionModel,
+        scheduler: KarrasDiffusionSchedulers,
+        safety_checker: StableDiffusionSafetyChecker,
+        feature_extractor: CLIPImageProcessor,
+        clip_camera_projection: CLIPCameraProjection,
+        requires_safety_checker: bool = True,
+    ):
+        super().__init__()
+
+        if safety_checker is None and requires_safety_checker:
+            logger.warn(
+                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."
+            )
+
+        is_unet_version_less_0_9_0 = hasattr(
+            unet.config, "_diffusers_version"
+        ) and version.parse(
+            version.parse(unet.config._diffusers_version).base_version
+        ) < version.parse(
+            "0.9.0.dev0"
+        )
+        is_unet_sample_size_less_64 = (
+            hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
+        )
+        if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
+            deprecation_message = (
+                "The configuration file of the unet has set the default `sample_size` to smaller than"
+                " 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the"
+                " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
+                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
+                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
+                " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
+                " in the config might lead to incorrect results in future versions. If you have downloaded this"
+                " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
+                " the `unet/config.json` file"
+            )
+            deprecate(
+                "sample_size<64", "1.0.0", deprecation_message, standard_warn=False
+            )
+            new_config = dict(unet.config)
+            new_config["sample_size"] = 64
+            unet._internal_dict = FrozenDict(new_config)
+
+        self.register_modules(
+            vae=vae,
+            image_encoder=image_encoder,
+            unet=unet,
+            scheduler=scheduler,
+            safety_checker=safety_checker,
+            feature_extractor=feature_extractor,
+            clip_camera_projection=clip_camera_projection,
+        )
+        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
+        self.register_to_config(requires_safety_checker=requires_safety_checker)
+
+    def enable_sequential_cpu_offload(self, gpu_id=0):
+        r"""
+        Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
+        text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a
+        `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.
+        """
+        if is_accelerate_available():
+            from accelerate import cpu_offload
+        else:
+            raise ImportError("Please install accelerate via `pip install accelerate`")
+
+        device = torch.device(f"cuda:{gpu_id}")
+
+        for cpu_offloaded_model in [
+            self.unet,
+            self.image_encoder,
+            self.vae,
+            self.safety_checker,
+        ]:
+            if cpu_offloaded_model is not None:
+                cpu_offload(cpu_offloaded_model, device)
+
+    @property
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
+    def _execution_device(self):
+        r"""
+        Returns the device on which the pipeline's models will be executed. After calling
+        `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
+        hooks.
+        """
+        if not hasattr(self.unet, "_hf_hook"):
+            return self.device
+        for module in self.unet.modules():
+            if (
+                hasattr(module, "_hf_hook")
+                and hasattr(module._hf_hook, "execution_device")
+                and module._hf_hook.execution_device is not None
+            ):
+                return torch.device(module._hf_hook.execution_device)
+        return self.device
+
+    def _encode_image(
+        self,
+        image,
+        elevation,
+        azimuth,
+        distance,
+        device,
+        num_images_per_prompt,
+        do_classifier_free_guidance,
+        clip_image_embeddings=None,
+        image_camera_embeddings=None,
+    ):
+        dtype = next(self.image_encoder.parameters()).dtype
+
+        if image_camera_embeddings is None:
+            if image is None:
+                assert clip_image_embeddings is not None
+                image_embeddings = clip_image_embeddings.to(device=device, dtype=dtype)
+            else:
+                if not isinstance(image, torch.Tensor):
+                    image = self.feature_extractor(
+                        images=image, return_tensors="pt"
+                    ).pixel_values
+
+                image = image.to(device=device, dtype=dtype)
+                image_embeddings = self.image_encoder(image).image_embeds
+                image_embeddings = image_embeddings.unsqueeze(1)
+
+            bs_embed, seq_len, _ = image_embeddings.shape
+
+            if isinstance(elevation, float):
+                elevation = torch.as_tensor(
+                    [elevation] * bs_embed, dtype=dtype, device=device
+                )
+            if isinstance(azimuth, float):
+                azimuth = torch.as_tensor(
+                    [azimuth] * bs_embed, dtype=dtype, device=device
+                )
+            if isinstance(distance, float):
+                distance = torch.as_tensor(
+                    [distance] * bs_embed, dtype=dtype, device=device
+                )
+
+            camera_embeddings = torch.stack(
+                [
+                    torch.deg2rad(elevation),
+                    torch.sin(torch.deg2rad(azimuth)),
+                    torch.cos(torch.deg2rad(azimuth)),
+                    distance,
+                ],
+                dim=-1,
+            )[:, None, :]
+
+            image_embeddings = torch.cat([image_embeddings, camera_embeddings], dim=-1)
+
+            # project (image, camera) embeddings to the same dimension as clip embeddings
+            image_embeddings = self.clip_camera_projection(image_embeddings)
+        else:
+            image_embeddings = image_camera_embeddings.to(device=device, dtype=dtype)
+            bs_embed, seq_len, _ = image_embeddings.shape
+
+        # duplicate image embeddings for each generation per prompt, using mps friendly method
+        image_embeddings = image_embeddings.repeat(1, num_images_per_prompt, 1)
+        image_embeddings = image_embeddings.view(
+            bs_embed * num_images_per_prompt, seq_len, -1
+        )
+
+        if do_classifier_free_guidance:
+            negative_prompt_embeds = torch.zeros_like(image_embeddings)
+
+            # For classifier free guidance, we need to do two forward passes.
+            # Here we concatenate the unconditional and text embeddings into a single batch
+            # to avoid doing two forward passes
+            image_embeddings = torch.cat([negative_prompt_embeds, image_embeddings])
+
+        return image_embeddings
+
+    # 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):
+        warnings.warn(
+            "The decode_latents method is deprecated and will be removed in a future version. Please"
+            " use VaeImageProcessor instead",
+            FutureWarning,
+        )
+        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, image, height, width, callback_steps):
+        # TODO: check image size or adjust image size to (height, width)
+
+        if height % 8 != 0 or width % 8 != 0:
+            raise ValueError(
+                f"`height` and `width` have to be divisible by 8 but are {height} and {width}."
+            )
+
+        if (callback_steps is None) or (
+            callback_steps is not None
+            and (not isinstance(callback_steps, int) or callback_steps <= 0)
+        ):
+            raise ValueError(
+                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
+                f" {type(callback_steps)}."
+            )
+
+    # 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,
+            height // self.vae_scale_factor,
+            width // self.vae_scale_factor,
+        )
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+
+        if latents is None:
+            latents = randn_tensor(
+                shape, generator=generator, 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_latent_model_input(
+        self,
+        latents: torch.FloatTensor,
+        image: Optional[
+            Union[PIL.Image.Image, List[PIL.Image.Image], torch.FloatTensor]
+        ],
+        num_images_per_prompt: int,
+        do_classifier_free_guidance: bool,
+        image_latents: Optional[torch.FloatTensor] = None,
+    ):
+        if isinstance(image, PIL.Image.Image):
+            image_pt = TF.to_tensor(image).unsqueeze(0).to(latents)
+        elif isinstance(image, list):
+            image_pt = torch.stack([TF.to_tensor(img) for img in image], dim=0).to(
+                latents
+            )
+        elif isinstance(image, torch.Tensor):
+            image_pt = image
+        else:
+            image_pt = None
+
+        if image_pt is None:
+            assert image_latents is not None
+            image_pt = image_latents.repeat_interleave(num_images_per_prompt, dim=0)
+        else:
+            image_pt = image_pt * 2.0 - 1.0  # scale to [-1, 1]
+            # FIXME: encoded latents should be multiplied with self.vae.config.scaling_factor
+            # but zero123 was not trained this way
+            image_pt = self.vae.encode(image_pt).latent_dist.mode()
+            image_pt = image_pt.repeat_interleave(num_images_per_prompt, dim=0)
+        if do_classifier_free_guidance:
+            latent_model_input = torch.cat(
+                [
+                    torch.cat([latents, latents], dim=0),
+                    torch.cat([torch.zeros_like(image_pt), image_pt], dim=0),
+                ],
+                dim=1,
+            )
+        else:
+            latent_model_input = torch.cat([latents, image_pt], dim=1)
+
+        return latent_model_input
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        image: Optional[
+            Union[PIL.Image.Image, List[PIL.Image.Image], torch.FloatTensor]
+        ] = None,
+        elevation: Optional[Union[float, torch.FloatTensor]] = None,
+        azimuth: Optional[Union[float, torch.FloatTensor]] = None,
+        distance: Optional[Union[float, torch.FloatTensor]] = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 3.0,
+        num_images_per_prompt: int = 1,
+        eta: float = 0.0,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        clip_image_embeddings: Optional[torch.FloatTensor] = None,
+        image_camera_embeddings: Optional[torch.FloatTensor] = None,
+        image_latents: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        callback_steps: int = 1,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            image (`PIL.Image.Image` or `List[PIL.Image.Image]` or `torch.FloatTensor`):
+                The image or images to guide the image generation. If you provide a tensor, it needs to comply with the
+                configuration of
+                [this](https://huggingface.co/lambdalabs/sd-image-variations-diffusers/blob/main/feature_extractor/preprocessor_config.json)
+                `CLIPImageProcessor`
+            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The height in pixels of the generated image.
+            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The width in pixels of the generated image.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+            generator (`torch.Generator`, *optional*):
+                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
+                to make generation deterministic.
+            latents (`torch.FloatTensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+            callback (`Callable`, *optional*):
+                A function that will be called every `callback_steps` steps during inference. The function will be
+                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
+            callback_steps (`int`, *optional*, defaults to 1):
+                The frequency at which the `callback` function will be called. If not specified, the callback will be
+                called at every step.
+
+        Returns:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
+            When returning a tuple, the first element is a list with the generated images, and the second element is a
+            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
+            (nsfw) content, according to the `safety_checker`.
+        """
+        # 0. Default height and width to unet
+        height = height or self.unet.config.sample_size * self.vae_scale_factor
+        width = width or self.unet.config.sample_size * self.vae_scale_factor
+
+        # 1. Check inputs. Raise error if not correct
+        # TODO: check input elevation, azimuth, and distance
+        # TODO: check image, clip_image_embeddings, image_latents
+        self.check_inputs(image, height, width, callback_steps)
+
+        # 2. Define call parameters
+        if isinstance(image, PIL.Image.Image):
+            batch_size = 1
+        elif isinstance(image, list):
+            batch_size = len(image)
+        elif isinstance(image, torch.Tensor):
+            batch_size = image.shape[0]
+        else:
+            assert image_latents is not None
+            assert (
+                clip_image_embeddings is not None or image_camera_embeddings is not None
+            )
+            batch_size = image_latents.shape[0]
+
+        device = self._execution_device
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = guidance_scale > 1.0
+
+        # 3. Encode input image
+        if isinstance(image, PIL.Image.Image) or isinstance(image, list):
+            pil_image = image
+        elif isinstance(image, torch.Tensor):
+            pil_image = [TF.to_pil_image(image[i]) for i in range(image.shape[0])]
+        else:
+            pil_image = None
+        image_embeddings = self._encode_image(
+            pil_image,
+            elevation,
+            azimuth,
+            distance,
+            device,
+            num_images_per_prompt,
+            do_classifier_free_guidance,
+            clip_image_embeddings,
+            image_camera_embeddings,
+        )
+
+        # 4. Prepare timesteps
+        self.scheduler.set_timesteps(num_inference_steps, device=device)
+        timesteps = self.scheduler.timesteps
+
+        # 5. Prepare latent variables
+        # num_channels_latents = self.unet.config.in_channels
+        num_channels_latents = 4  # FIXME: hard-coded
+        latents = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            image_embeddings.dtype,
+            device,
+            generator,
+            latents,
+        )
+
+        # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+        # 7. Denoising loop
+        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                # expand the latents if we are doing classifier free guidance
+                latent_model_input = self._get_latent_model_input(
+                    latents,
+                    image,
+                    num_images_per_prompt,
+                    do_classifier_free_guidance,
+                    image_latents,
+                )
+                latent_model_input = self.scheduler.scale_model_input(
+                    latent_model_input, t
+                )
+
+                # predict the noise residual
+                noise_pred = self.unet(
+                    latent_model_input,
+                    t,
+                    encoder_hidden_states=image_embeddings,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                ).sample
+
+                # perform guidance
+                if do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                    noise_pred = noise_pred_uncond + guidance_scale * (
+                        noise_pred_text - noise_pred_uncond
+                    )
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents = self.scheduler.step(
+                    noise_pred, t, latents, **extra_step_kwargs
+                ).prev_sample
+
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or (
+                    (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0
+                ):
+                    progress_bar.update()
+                    if callback is not None and i % callback_steps == 0:
+                        callback(i, t, latents)
+
+        if not output_type == "latent":
+            image = self.vae.decode(
+                latents / self.vae.config.scaling_factor, return_dict=False
+            )[0]
+            image, has_nsfw_concept = self.run_safety_checker(
+                image, device, image_embeddings.dtype
+            )
+        else:
+            image = latents
+            has_nsfw_concept = None
+
+        if has_nsfw_concept is None:
+            do_denormalize = [True] * image.shape[0]
+        else:
+            do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
+
+        image = self.image_processor.postprocess(
+            image, output_type=output_type, do_denormalize=do_denormalize
+        )
+
+        if not return_dict:
+            return (image, has_nsfw_concept)
+
+        return StableDiffusionPipelineOutput(
+            images=image, nsfw_content_detected=has_nsfw_concept
+        )

feature_extractor/preprocessor_config.json

@@ -0,0 +1,29 @@
+{
+  "crop_size": {
+    "height": 224,
+    "width": 224
+  },
+  "do_center_crop": true,
+  "do_convert_rgb": true,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "feature_extractor_type": "CLIPFeatureExtractor",
+  "image_mean": [
+    0.48145466,
+    0.4578275,
+    0.40821073
+  ],
+  "image_processor_type": "CLIPImageProcessor",
+  "image_std": [
+    0.26862954,
+    0.26130258,
+    0.27577711
+  ],
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "shortest_edge": 224
+  },
+  "use_square_size": false
+}

image_encoder/config.json

@@ -0,0 +1,23 @@
+{
+  "_name_or_path": "lambdalabs/sd-image-variations-diffusers",
+  "architectures": [
+    "CLIPVisionModelWithProjection"
+  ],
+  "attention_dropout": 0.0,
+  "dropout": 0.0,
+  "hidden_act": "quick_gelu",
+  "hidden_size": 1024,
+  "image_size": 224,
+  "initializer_factor": 1.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-05,
+  "model_type": "clip_vision_model",
+  "num_attention_heads": 16,
+  "num_channels": 3,
+  "num_hidden_layers": 24,
+  "patch_size": 14,
+  "projection_dim": 768,
+  "torch_dtype": "float16",
+  "transformers_version": "4.35.2"
+}

image_encoder/model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0dd0c1777bb75e2e9b7aa29b799b3ee8ebaaaa731e2c471a9ec589f12542cce7
+size 607980096

model_index.json

@@ -0,0 +1,33 @@
+{
+  "_class_name": "Zero123Pipeline",
+  "_diffusers_version": "0.24.0",
+  "clip_camera_projection": [
+    "zero123",
+    "CLIPCameraProjection"
+  ],
+  "feature_extractor": [
+    "transformers",
+    "CLIPImageProcessor"
+  ],
+  "image_encoder": [
+    "transformers",
+    "CLIPVisionModelWithProjection"
+  ],
+  "requires_safety_checker": false,
+  "safety_checker": [
+    null,
+    null
+  ],
+  "scheduler": [
+    "diffusers",
+    "DDIMScheduler"
+  ],
+  "unet": [
+    "diffusers",
+    "UNet2DConditionModel"
+  ],
+  "vae": [
+    "diffusers",
+    "AutoencoderKL"
+  ]
+}

readme.md

@@ -0,0 +1,72 @@
+---
+license: mit
+---
+
+# Uses
+_Note: This section is originally taken from the [Stable Diffusion v2 model card](https://huggingface.co/stabilityai/stable-diffusion-2), but applies in the same way to Zero-1-to-3._
+
+## Direct Use 
+The model is intended for research purposes only. Possible research areas and tasks include:
+
+- Safe deployment of large-scale models.
+- Probing and understanding the limitations and biases of generative models.
+- Generation of artworks and use in design and other artistic processes.
+- Applications in educational or creative tools.
+- Research on generative models.
+
+Excluded uses are described below.
+
+### Misuse, Malicious Use, and Out-of-Scope Use
+The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
+
+#### Out-of-Scope Use
+The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.
+
+#### Misuse and Malicious Use
+Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:
+
+- Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc.
+- Intentionally promoting or propagating discriminatory content or harmful stereotypes.
+- Impersonating individuals without their consent.
+- Sexual content without consent of the people who might see it.
+- Mis- and disinformation
+- Representations of egregious violence and gore
+- Sharing of copyrighted or licensed material in violation of its terms of use.
+- Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use.
+
+## Limitations and Bias
+
+### Limitations
+
+- The model does not achieve perfect photorealism.
+- The model cannot render legible text.
+- Faces and people in general may not be parsed or generated properly.
+- The autoencoding part of the model is lossy.
+- Stable Diffusion was trained on a subset of the large-scale dataset [LAION-5B](https://laion.ai/blog/laion-5b/), which contains adult, violent and sexual content. To partially mitigate this, Stability AI has filtered the dataset using LAION's NSFW detector.
+- Zero-1-to-3 was subsequently finetuned on a subset of the large-scale dataset [Objaverse](https://objaverse.allenai.org/), which might also potentially contain inappropriate content. To partially mitigate this, our demo applies a safety check to every uploaded image.
+
+### Bias
+While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. 
+Stable Diffusion was primarily trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/), which consists of images that are limited to English descriptions. 
+Images and concepts from communities and cultures that use other languages are likely to be insufficiently accounted for. 
+This affects the overall output of the model, as Western cultures are often overrepresented. 
+Stable Diffusion mirrors and exacerbates biases to such a degree that viewer discretion must be advised irrespective of the input or its intent.
+
+
+### Safety Module
+The intended use of this model is with the [Safety Checker](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py) in Diffusers. 
+This checker works by checking model inputs against known hard-coded NSFW concepts.
+Specifically, the checker compares the class probability of harmful concepts in the embedding space of the uploaded input images. 
+The concepts are passed into the model with the image and compared to a hand-engineered weight for each NSFW concept.
+
+## Citation
+```
+@misc{liu2023zero1to3,
+      title={Zero-1-to-3: Zero-shot One Image to 3D Object}, 
+      author={Ruoshi Liu and Rundi Wu and Basile Van Hoorick and Pavel Tokmakov and Sergey Zakharov and Carl Vondrick},
+      year={2023},
+      eprint={2303.11328},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+```

scheduler/scheduler_config.json

@@ -0,0 +1,19 @@
+{
+  "_class_name": "DDIMScheduler",
+  "_diffusers_version": "0.24.0",
+  "beta_end": 0.012,
+  "beta_schedule": "scaled_linear",
+  "beta_start": 0.00085,
+  "clip_sample": false,
+  "clip_sample_range": 1.0,
+  "dynamic_thresholding_ratio": 0.995,
+  "num_train_timesteps": 1000,
+  "prediction_type": "epsilon",
+  "rescale_betas_zero_snr": false,
+  "sample_max_value": 1.0,
+  "set_alpha_to_one": false,
+  "steps_offset": 1,
+  "thresholding": false,
+  "timestep_spacing": "leading",
+  "trained_betas": null
+}

unet/config.json

@@ -0,0 +1,67 @@
+{
+  "_class_name": "UNet2DConditionModel",
+  "_diffusers_version": "0.24.0",
+  "act_fn": "silu",
+  "addition_embed_type": null,
+  "addition_embed_type_num_heads": 64,
+  "addition_time_embed_dim": null,
+  "attention_head_dim": 8,
+  "attention_type": "default",
+  "block_out_channels": [
+    320,
+    640,
+    1280,
+    1280
+  ],
+  "center_input_sample": false,
+  "class_embed_type": null,
+  "class_embeddings_concat": false,
+  "conv_in_kernel": 3,
+  "conv_out_kernel": 3,
+  "cross_attention_dim": 768,
+  "cross_attention_norm": null,
+  "down_block_types": [
+    "CrossAttnDownBlock2D",
+    "CrossAttnDownBlock2D",
+    "CrossAttnDownBlock2D",
+    "DownBlock2D"
+  ],
+  "downsample_padding": 1,
+  "dropout": 0.0,
+  "dual_cross_attention": false,
+  "encoder_hid_dim": null,
+  "encoder_hid_dim_type": null,
+  "flip_sin_to_cos": true,
+  "freq_shift": 0,
+  "in_channels": 8,
+  "layers_per_block": 2,
+  "mid_block_only_cross_attention": null,
+  "mid_block_scale_factor": 1,
+  "mid_block_type": "UNetMidBlock2DCrossAttn",
+  "norm_eps": 1e-05,
+  "norm_num_groups": 32,
+  "num_attention_heads": null,
+  "num_class_embeds": null,
+  "only_cross_attention": false,
+  "out_channels": 4,
+  "projection_class_embeddings_input_dim": null,
+  "resnet_out_scale_factor": 1.0,
+  "resnet_skip_time_act": false,
+  "resnet_time_scale_shift": "default",
+  "reverse_transformer_layers_per_block": null,
+  "sample_size": 32,
+  "time_cond_proj_dim": null,
+  "time_embedding_act_fn": null,
+  "time_embedding_dim": null,
+  "time_embedding_type": "positional",
+  "timestep_post_act": null,
+  "transformer_layers_per_block": 1,
+  "up_block_types": [
+    "UpBlock2D",
+    "CrossAttnUpBlock2D",
+    "CrossAttnUpBlock2D",
+    "CrossAttnUpBlock2D"
+  ],
+  "upcast_attention": null,
+  "use_linear_projection": false
+}

unet/diffusion_pytorch_model.safetensors

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vae/config.json

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+{
+  "_class_name": "AutoencoderKL",
+  "_diffusers_version": "0.24.0",
+  "act_fn": "silu",
+  "block_out_channels": [
+    128,
+    256,
+    512,
+    512
+  ],
+  "down_block_types": [
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D"
+  ],
+  "force_upcast": true,
+  "in_channels": 3,
+  "latent_channels": 4,
+  "layers_per_block": 2,
+  "norm_num_groups": 32,
+  "out_channels": 3,
+  "sample_size": 256,
+  "scaling_factor": 0.18215,
+  "up_block_types": [
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D"
+  ]
+}

vae/diffusion_pytorch_model.safetensors

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