[Pipelines] Add community pipeline for Zero123 (#4295)

* add zero123 pipeline to community

* add community doc

* reformat

* update zero123 pipeline, including cc_projection within diffusers; add convert ckpt scripts; support diffusers weights

Author: kxhit

examples/community/README.md

@@ -39,6 +39,8 @@ If a community doesn't work as expected, please open an issue and ping the autho
 | 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) 
+|   Zero1to3 Pipeline                                                                                                    | Implementation of [Zero-1-to-3: Zero-shot One Image to 3D Object](https://arxiv.org/abs/2303.11328)                                                                                                                                                                                                                                                                                                                                                                                                                                      | [Zero1to3 Pipeline](#Zero1to3-pipeline)      | - |              [Xin Kong](https://github.com/kxhit) 
+
 
 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
@@ -1767,3 +1769,84 @@ while True:
     loss.backward()
     optimizer.step()
 ```
+
+### Zero1to3 pipeline
+
+This pipeline is the implementation of the [Zero-1-to-3: Zero-shot One Image to 3D Object](https://arxiv.org/abs/2303.11328) paper.
+The original pytorch-lightning [repo](https://github.com/cvlab-columbia/zero123) and a diffusers [repo](https://github.com/kxhit/zero123-hf).
+
+The following code shows how to use the Zero1to3 pipeline to generate novel view synthesis images using a pretrained stable diffusion model.
+
+```python
+import os
+import torch
+from pipeline_zero1to3 import Zero1to3StableDiffusionPipeline
+from diffusers.utils import load_image
+
+model_id = "kxic/zero123-165000" # zero123-105000, zero123-165000, zero123-xl
+
+pipe = Zero1to3StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
+
+pipe.enable_xformers_memory_efficient_attention()
+pipe.enable_vae_tiling()
+pipe.enable_attention_slicing()
+pipe = pipe.to("cuda")
+
+num_images_per_prompt = 4
+
+# test inference pipeline
+# x y z, Polar angle (vertical rotation in degrees) 	Azimuth angle (horizontal rotation in degrees) 	Zoom (relative distance from center)
+query_pose1 = [-75.0, 100.0, 0.0]
+query_pose2 = [-20.0, 125.0, 0.0]
+query_pose3 = [-55.0, 90.0, 0.0]
+
+# load image
+# H, W = (256, 256) # H, W = (512, 512)   # zero123 training is 256,256
+
+# for batch input
+input_image1 = load_image("./demo/4_blackarm.png") #load_image("https://cvlab-zero123-live.hf.space/file=/home/user/app/configs/4_blackarm.png")
+input_image2 = load_image("./demo/8_motor.png") #load_image("https://cvlab-zero123-live.hf.space/file=/home/user/app/configs/8_motor.png")
+input_image3 = load_image("./demo/7_london.png") #load_image("https://cvlab-zero123-live.hf.space/file=/home/user/app/configs/7_london.png")
+input_images = [input_image1, input_image2, input_image3]
+query_poses = [query_pose1, query_pose2, query_pose3]
+
+# # for single input
+# H, W = (256, 256)
+# input_images = [input_image2.resize((H, W), PIL.Image.NEAREST)]
+# query_poses = [query_pose2]
+
+
+# better do preprocessing
+from gradio_new import preprocess_image, create_carvekit_interface
+import numpy as np
+import PIL.Image as Image
+
+pre_images = []
+models = dict()
+print('Instantiating Carvekit HiInterface...')
+models['carvekit'] = create_carvekit_interface()
+if not isinstance(input_images, list):
+    input_images = [input_images]
+for raw_im in input_images:
+    input_im = preprocess_image(models, raw_im, True)
+    H, W = input_im.shape[:2]
+    pre_images.append(Image.fromarray((input_im * 255.0).astype(np.uint8)))
+input_images = pre_images
+
+# infer pipeline, in original zero123 num_inference_steps=76
+images = pipe(input_imgs=input_images, prompt_imgs=input_images, poses=query_poses, height=H, width=W,
+              guidance_scale=3.0, num_images_per_prompt=num_images_per_prompt, num_inference_steps=50).images
+
+
+# save imgs
+log_dir = "logs"
+os.makedirs(log_dir, exist_ok=True)
+bs = len(input_images)
+i = 0
+for obj in range(bs):
+    for idx in range(num_images_per_prompt):
+        images[i].save(os.path.join(log_dir,f"obj{obj}_{idx}.jpg"))
+        i += 1
+
+```
+