[Community] Implementation of the IADB community pipeline (#3996)

* community pipeline: implementation of iadb

* iadb.py: reformat using black

* iadb.py: linting update

examples/community/README.md

@@ -38,6 +38,7 @@ If a community doesn't work as expected, please open an issue and ping the autho
 | Stable Diffusion IPEX Pipeline | Accelerate Stable Diffusion inference pipeline with BF16/FP32 precision on Intel Xeon CPUs with [IPEX](https://github.com/intel/intel-extension-for-pytorch) | [Stable Diffusion on IPEX](#stable-diffusion-on-ipex) | - | [Yingjie Han](https://github.com/yingjie-han/) | 
 | CLIP Guided Images Mixing Stable Diffusion Pipeline | Сombine images using usual diffusion models. | [CLIP Guided Images Mixing Using Stable Diffusion](#clip-guided-images-mixing-with-stable-diffusion) | - | [Karachev Denis](https://github.com/TheDenk) |  
 | TensorRT Stable Diffusion Inpainting Pipeline                                                                                                    | Accelerates the Stable Diffusion Inpainting Pipeline using TensorRT                                                                                                                                                                                                                                                                                                                                                                                                                                      | [TensorRT Stable Diffusion Inpainting Pipeline](#tensorrt-inpainting-stable-diffusion-pipeline)      | - |              [Asfiya Baig](https://github.com/asfiyab-nvidia) |
+|   IADB Pipeline                                                                                                    | Implementation of [Iterative α-(de)Blending: a Minimalist Deterministic Diffusion Model](https://arxiv.org/abs/2305.03486)                                                                                                                                                                                                                                                                                                                                                                                                                                      | [IADB Pipeline](#iadb-pipeline)      | - |              [Thomas Chambon](https://github.com/tchambon) 
 
 To load a custom pipeline you just need to pass the `custom_pipeline` argument to `DiffusionPipeline`, as one of the files in `diffusers/examples/community`. Feel free to send a PR with your own pipelines, we will merge them quickly.
 ```py
@@ -1707,3 +1708,62 @@ output = pipeline(
 ```
 ![Input_Image](https://huggingface.co/datasets/kadirnar/diffusers_readme_images/resolve/main/input_image.png)
 ![mixture_canvas_results](https://huggingface.co/datasets/kadirnar/diffusers_readme_images/resolve/main/canvas.png)
+
+
+### IADB pipeline
+
+This pipeline is the implementation of the [α-(de)Blending: a Minimalist Deterministic Diffusion Model](https://arxiv.org/abs/2305.03486) paper.
+It is a simple and minimalist diffusion model.
+
+The following code shows how to use the IADB pipeline to generate images using a pretrained celebahq-256 model.
+
+```python
+
+pipeline_iadb = DiffusionPipeline.from_pretrained("thomasc4/iadb-celebahq-256", custom_pipeline='iadb')
+
+pipeline_iadb = pipeline_iadb.to('cuda')
+
+output = pipeline_iadb(batch_size=4,num_inference_steps=128)
+for i in range(len(output[0])):
+    plt.imshow(output[0][i])
+    plt.show()
+
+```
+
+Sampling with the IADB formulation is easy, and can be done in a few lines (the pipeline already implements it):
+
+```python
+
+def sample_iadb(model, x0, nb_step):
+    x_alpha = x0
+    for t in range(nb_step):
+        alpha = (t/nb_step)
+        alpha_next =((t+1)/nb_step)
+
+        d = model(x_alpha, torch.tensor(alpha, device=x_alpha.device))['sample']
+        x_alpha = x_alpha + (alpha_next-alpha)*d
+
+    return x_alpha
+
+```
+
+The training loop is also straightforward:
+
+```python
+
+# Training loop
+while True:
+    x0 = sample_noise()
+    x1 = sample_dataset()
+
+    alpha = torch.rand(batch_size)
+
+    # Blend
+    x_alpha = (1-alpha) * x0 + alpha * x1
+
+    # Loss
+    loss = torch.sum((D(x_alpha, alpha)- (x1-x0))**2)
+    optimizer.zero_grad()
+    loss.backward()
+    optimizer.step()
+```

examples/community/iadb.py

+from typing import List, Optional, Tuple, Union
+
+import torch
+
+from diffusers import DiffusionPipeline
+from diffusers.configuration_utils import ConfigMixin
+from diffusers.pipeline_utils import ImagePipelineOutput
+from diffusers.schedulers.scheduling_utils import SchedulerMixin
+
+
+class IADBScheduler(SchedulerMixin, ConfigMixin):
+    """
+    IADBScheduler is a scheduler for the Iterative α-(de)Blending denoising method. It is simple and minimalist.
+
+    For more details, see the original paper: https://arxiv.org/abs/2305.03486 and the blog post: https://ggx-research.github.io/publication/2023/05/10/publication-iadb.html
+    """
+
+    def step(
+        self,
+        model_output: torch.FloatTensor,
+        timestep: int,
+        x_alpha: torch.FloatTensor,
+    ) -> torch.FloatTensor:
+        """
+        Predict the sample at the previous timestep by reversing the ODE. Core function to propagate the diffusion
+        process from the learned model outputs (most often the predicted noise).
+
+        Args:
+            model_output (`torch.FloatTensor`): direct output from learned diffusion model. It is the direction from x0 to x1.
+            timestep (`float`): current timestep in the diffusion chain.
+            x_alpha (`torch.FloatTensor`): x_alpha sample for the current timestep
+
+        Returns:
+            `torch.FloatTensor`: the sample at the previous timestep
+
+        """
+        if self.num_inference_steps is None:
+            raise ValueError(
+                "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"
+            )
+
+        alpha = timestep / self.num_inference_steps
+        alpha_next = (timestep + 1) / self.num_inference_steps
+
+        d = model_output
+
+        x_alpha = x_alpha + (alpha_next - alpha) * d
+
+        return x_alpha
+
+    def set_timesteps(self, num_inference_steps: int):
+        self.num_inference_steps = num_inference_steps
+
+    def add_noise(
+        self,
+        original_samples: torch.FloatTensor,
+        noise: torch.FloatTensor,
+        alpha: torch.FloatTensor,
+    ) -> torch.FloatTensor:
+        return original_samples * alpha + noise * (1 - alpha)
+
+    def __len__(self):
+        return self.config.num_train_timesteps
+
+
+class IADBPipeline(DiffusionPipeline):
+    r"""
+    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.)
+
+    Parameters:
+        unet ([`UNet2DModel`]): U-Net architecture to denoise the encoded image.
+        scheduler ([`SchedulerMixin`]):
+            A scheduler to be used in combination with `unet` to denoise the encoded image. Can be one of
+            [`DDPMScheduler`], or [`DDIMScheduler`].
+    """
+
+    def __init__(self, unet, scheduler):
+        super().__init__()
+
+        self.register_modules(unet=unet, scheduler=scheduler)
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        batch_size: int = 1,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        num_inference_steps: int = 50,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+    ) -> Union[ImagePipelineOutput, Tuple]:
+        r"""
+        Args:
+            batch_size (`int`, *optional*, defaults to 1):
+                The number of images to generate.
+            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.
+            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.ImagePipelineOutput`] instead of a plain tuple.
+
+        Returns:
+            [`~pipelines.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if `return_dict` is
+            True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images.
+        """
+
+        # Sample gaussian noise to begin loop
+        if isinstance(self.unet.config.sample_size, int):
+            image_shape = (
+                batch_size,
+                self.unet.config.in_channels,
+                self.unet.config.sample_size,
+                self.unet.config.sample_size,
+            )
+        else:
+            image_shape = (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)
+
+        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."
+            )
+
+        image = torch.randn(image_shape, generator=generator, device=self.device, dtype=self.unet.dtype)
+
+        # set step values
+        self.scheduler.set_timesteps(num_inference_steps)
+        x_alpha = image.clone()
+        for t in self.progress_bar(range(num_inference_steps)):
+            alpha = t / num_inference_steps
+
+            # 1. predict noise model_output
+            model_output = self.unet(x_alpha, torch.tensor(alpha, device=x_alpha.device)).sample
+
+            # 2. step
+            x_alpha = self.scheduler.step(model_output, t, x_alpha)
+
+        image = (x_alpha * 0.5 + 0.5).clamp(0, 1)
+        image = image.cpu().permute(0, 2, 3, 1).numpy()
+        if output_type == "pil":
+            image = self.numpy_to_pil(image)
+
+        if not return_dict:
+            return (image,)
+
+        return ImagePipelineOutput(images=image)