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	# Community Examples

	> For more information about community pipelines, please have a look at [this issue](https://github.com/huggingface/diffusers/issues/841).

	Community examples consist of both inference and training examples that have been added by the community.
	Please have a look at the following table to get an overview of all community examples. Click on the Code Example to get a copy-and-paste ready code example that you can try out.
	If a community doesn't work as expected, please open an issue and ping the author on it.

	\| Example \| Description \| Code Example \| Colab \| Author \|
	\|:--------------------------------------------------------------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|--------------------------------------------------------------:\|
	\| CLIP Guided Stable Diffusion \| Doing CLIP guidance for text to image generation with Stable Diffusion \| [CLIP Guided Stable Diffusion](#clip-guided-stable-diffusion) \| [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/CLIP_Guided_Stable_diffusion_with_diffusers.ipynb) \| [Suraj Patil](https://github.com/patil-suraj/) \|
	\| One Step U-Net (Dummy) \| Example showcasing of how to use Community Pipelines (see https://github.com/huggingface/diffusers/issues/841) \| [One Step U-Net](#one-step-unet) \| - \| [Patrick von Platen](https://github.com/patrickvonplaten/) \|
	\| Stable Diffusion Interpolation \| Interpolate the latent space of Stable Diffusion between different prompts/seeds \| [Stable Diffusion Interpolation](#stable-diffusion-interpolation) \| - \| [Nate Raw](https://github.com/nateraw/) \|
	\| Stable Diffusion Mega \| One Stable Diffusion Pipeline with all functionalities of [Text2Image](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py), [Image2Image](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py) and [Inpainting](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py) \| [Stable Diffusion Mega](#stable-diffusion-mega) \| - \| [Patrick von Platen](https://github.com/patrickvonplaten/) \|
	\| Long Prompt Weighting Stable Diffusion \| One Stable Diffusion Pipeline without tokens length limit, and support parsing weighting in prompt. \| [Long Prompt Weighting Stable Diffusion](#long-prompt-weighting-stable-diffusion) \| - \| [SkyTNT](https://github.com/SkyTNT) \|
	\| Speech to Image \| Using automatic-speech-recognition to transcribe text and Stable Diffusion to generate images \| [Speech to Image](#speech-to-image) \| - \| [Mikail Duzenli](https://github.com/MikailINTech)
	\| Wild Card Stable Diffusion \| Stable Diffusion Pipeline that supports prompts that contain wildcard terms (indicated by surrounding double underscores), with values instantiated randomly from a corresponding txt file or a dictionary of possible values \| [Wildcard Stable Diffusion](#wildcard-stable-diffusion) \| - \| [Shyam Sudhakaran](https://github.com/shyamsn97) \|
	\| [Composable Stable Diffusion](https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/) \| Stable Diffusion Pipeline that supports prompts that contain "\|" in prompts (as an AND condition) and weights (separated by "\|" as well) to positively / negatively weight prompts. \| [Composable Stable Diffusion](#composable-stable-diffusion) \| - \| [Mark Rich](https://github.com/MarkRich) \|
	\| Seed Resizing Stable Diffusion \| Stable Diffusion Pipeline that supports resizing an image and retaining the concepts of the 512 by 512 generation. \| [Seed Resizing](#seed-resizing) \| - \| [Mark Rich](https://github.com/MarkRich) \|
	\| Imagic Stable Diffusion \| Stable Diffusion Pipeline that enables writing a text prompt to edit an existing image \| [Imagic Stable Diffusion](#imagic-stable-diffusion) \| - \| [Mark Rich](https://github.com/MarkRich) \|
	\| Multilingual Stable Diffusion \| Stable Diffusion Pipeline that supports prompts in 50 different languages. \| [Multilingual Stable Diffusion](#multilingual-stable-diffusion-pipeline) \| - \| [Juan Carlos Piñeros](https://github.com/juancopi81) \|
	\| Image to Image Inpainting Stable Diffusion \| Stable Diffusion Pipeline that enables the overlaying of two images and subsequent inpainting \| [Image to Image Inpainting Stable Diffusion](#image-to-image-inpainting-stable-diffusion) \| - \| [Alex McKinney](https://github.com/vvvm23) \|
	\| Text Based Inpainting Stable Diffusion \| Stable Diffusion Inpainting Pipeline that enables passing a text prompt to generate the mask for inpainting \| [Text Based Inpainting Stable Diffusion](#image-to-image-inpainting-stable-diffusion) \| - \| [Dhruv Karan](https://github.com/unography) \|
	\| Bit Diffusion \| Diffusion on discrete data \| [Bit Diffusion](#bit-diffusion) \| - \| [Stuti R.](https://github.com/kingstut) \|
	\| K-Diffusion Stable Diffusion \| Run Stable Diffusion with any of [K-Diffusion's samplers](https://github.com/crowsonkb/k-diffusion/blob/master/k_diffusion/sampling.py) \| [Stable Diffusion with K Diffusion](#stable-diffusion-with-k-diffusion) \| - \| [Patrick von Platen](https://github.com/patrickvonplaten/) \|
	\| Checkpoint Merger Pipeline \| Diffusion Pipeline that enables merging of saved model checkpoints \| [Checkpoint Merger Pipeline](#checkpoint-merger-pipeline) \| - \| [Naga Sai Abhinay Devarinti](https://github.com/Abhinay1997/) \|
	Stable Diffusion v1.1-1.4 Comparison \| Run all 4 model checkpoints for Stable Diffusion and compare their results together \| [Stable Diffusion Comparison](#stable-diffusion-comparisons) \| - \| [Suvaditya Mukherjee](https://github.com/suvadityamuk) \|
	MagicMix \| Diffusion Pipeline for semantic mixing of an image and a text prompt \| [MagicMix](#magic-mix) \| - \| [Partho Das](https://github.com/daspartho) \|
	\| Stable UnCLIP \| Diffusion Pipeline for combining prior model (generate clip image embedding from text, UnCLIPPipeline `"kakaobrain/karlo-v1-alpha"`) and decoder pipeline (decode clip image embedding to image, StableDiffusionImageVariationPipeline `"lambdalabs/sd-image-variations-diffusers"` ). \| [Stable UnCLIP](#stable-unclip) \| - \| [Ray Wang](https://wrong.wang) \|
	\| UnCLIP Text Interpolation Pipeline \| Diffusion Pipeline that allows passing two prompts and produces images while interpolating between the text-embeddings of the two prompts \| [UnCLIP Text Interpolation Pipeline](#unclip-text-interpolation-pipeline) \| - \| [Naga Sai Abhinay Devarinti](https://github.com/Abhinay1997/) \|
	\| UnCLIP Image Interpolation Pipeline \| Diffusion Pipeline that allows passing two images/image_embeddings and produces images while interpolating between their image-embeddings \| [UnCLIP Image Interpolation Pipeline](#unclip-image-interpolation-pipeline) \| - \| [Naga Sai Abhinay Devarinti](https://github.com/Abhinay1997/) \|
	\| DDIM Noise Comparative Analysis Pipeline \| Investigating how the diffusion models learn visual concepts from each noise level (which is a contribution of [P2 weighting (CVPR 2022)](https://arxiv.org/abs/2204.00227)) \| [DDIM Noise Comparative Analysis Pipeline](#ddim-noise-comparative-analysis-pipeline) \| - \| [Aengus (Duc-Anh)](https://github.com/aengusng8) \|
	\| CLIP Guided Img2Img Stable Diffusion Pipeline \| Doing CLIP guidance for image to image generation with Stable Diffusion \| [CLIP Guided Img2Img Stable Diffusion](#clip-guided-img2img-stable-diffusion) \| - \| [Nipun Jindal](https://github.com/nipunjindal/) \|
	\| TensorRT Stable Diffusion Text to Image Pipeline \| Accelerates the Stable Diffusion Text2Image Pipeline using TensorRT \| [TensorRT Stable Diffusion Text to Image Pipeline](#tensorrt-text2image-stable-diffusion-pipeline) \| - \| [Asfiya Baig](https://github.com/asfiyab-nvidia) \|
	\| EDICT Image Editing Pipeline \| Diffusion pipeline for text-guided image editing \| [EDICT Image Editing Pipeline](#edict-image-editing-pipeline) \| - \| [Joqsan Azocar](https://github.com/Joqsan) \|
	\| Stable Diffusion RePaint \| Stable Diffusion pipeline using [RePaint](https://arxiv.org/abs/2201.0986) for inpainting. \| [Stable Diffusion RePaint](#stable-diffusion-repaint ) \| - \| [Markus Pobitzer](https://github.com/Markus-Pobitzer) \|
	\| TensorRT Stable Diffusion Image to Image Pipeline \| Accelerates the Stable Diffusion Image2Image Pipeline using TensorRT \| [TensorRT Stable Diffusion Image to Image Pipeline](#tensorrt-image2image-stable-diffusion-pipeline) \| - \| [Asfiya Baig](https://github.com/asfiyab-nvidia) \|
	\| 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)
	\| 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
	pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", custom_pipeline="filename_in_the_community_folder")
	```

	## Example usages

	### CLIP Guided Stable Diffusion

	CLIP guided stable diffusion can help to generate more realistic images
	by guiding stable diffusion at every denoising step with an additional CLIP model.

	The following code requires roughly 12GB of GPU RAM.

	```python
	from diffusers import DiffusionPipeline
	from transformers import CLIPImageProcessor, CLIPModel
	import torch


	feature_extractor = CLIPImageProcessor.from_pretrained("laion/CLIP-ViT-B-32-laion2B-s34B-b79K")
	clip_model = CLIPModel.from_pretrained("laion/CLIP-ViT-B-32-laion2B-s34B-b79K", torch_dtype=torch.float16)


	guided_pipeline = DiffusionPipeline.from_pretrained(
	"runwayml/stable-diffusion-v1-5",
	custom_pipeline="clip_guided_stable_diffusion",
	clip_model=clip_model,
	feature_extractor=feature_extractor,

	torch_dtype=torch.float16,
	)
	guided_pipeline.enable_attention_slicing()
	guided_pipeline = guided_pipeline.to("cuda")

	prompt = "fantasy book cover, full moon, fantasy forest landscape, golden vector elements, fantasy magic, dark light night, intricate, elegant, sharp focus, illustration, highly detailed, digital painting, concept art, matte, art by WLOP and Artgerm and Albert Bierstadt, masterpiece"

	generator = torch.Generator(device="cuda").manual_seed(0)
	images = []
	for i in range(4):
	image = guided_pipeline(
	prompt,
	num_inference_steps=50,
	guidance_scale=7.5,
	clip_guidance_scale=100,
	num_cutouts=4,
	use_cutouts=False,
	generator=generator,
	).images[0]
	images.append(image)

	# save images locally
	for i, img in enumerate(images):
	img.save(f"./clip_guided_sd/image_{i}.png")
	```

	The `images` list contains a list of PIL images that can be saved locally or displayed directly in a google colab.
	Generated images tend to be of higher qualtiy than natively using stable diffusion. E.g. the above script generates the following images:

	![clip_guidance](https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/clip_guidance/merged_clip_guidance.jpg).

	### One Step Unet

	The dummy "one-step-unet" can be run as follows:

	```python
	from diffusers import DiffusionPipeline

	pipe = DiffusionPipeline.from_pretrained("google/ddpm-cifar10-32", custom_pipeline="one_step_unet")
	pipe()
	```

	Note: This community pipeline is not useful as a feature, but rather just serves as an example of how community pipelines can be added (see https://github.com/huggingface/diffusers/issues/841).

	### Stable Diffusion Interpolation

	The following code can be run on a GPU of at least 8GB VRAM and should take approximately 5 minutes.

	```python
	from diffusers import DiffusionPipeline
	import torch

	pipe = DiffusionPipeline.from_pretrained(
	"CompVis/stable-diffusion-v1-4",
	revision='fp16',
	torch_dtype=torch.float16,
	safety_checker=None, # Very important for videos...lots of false positives while interpolating
	custom_pipeline="interpolate_stable_diffusion",
	).to('cuda')
	pipe.enable_attention_slicing()

	frame_filepaths = pipe.walk(
	prompts=['a dog', 'a cat', 'a horse'],
	seeds=[42, 1337, 1234],
	num_interpolation_steps=16,
	output_dir='./dreams',
	batch_size=4,
	height=512,
	width=512,
	guidance_scale=8.5,
	num_inference_steps=50,
	)
	```

	The output of the `walk(...)` function returns a list of images saved under the folder as defined in `output_dir`. You can use these images to create videos of stable diffusion.

	> Please have a look at https://github.com/nateraw/stable-diffusion-videos for more in-detail information on how to create videos using stable diffusion as well as more feature-complete functionality.

	### Stable Diffusion Mega

	The Stable Diffusion Mega Pipeline lets you use the main use cases of the stable diffusion pipeline in a single class.

	```python
	#!/usr/bin/env python3
	from diffusers import DiffusionPipeline
	import PIL
	import requests
	from io import BytesIO
	import torch


	def download_image(url):
	response = requests.get(url)
	return PIL.Image.open(BytesIO(response.content)).convert("RGB")

	pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", custom_pipeline="stable_diffusion_mega", torch_dtype=torch.float16, revision="fp16")
	pipe.to("cuda")
	pipe.enable_attention_slicing()


	### Text-to-Image

	images = pipe.text2img("An astronaut riding a horse").images

	### Image-to-Image

	init_image = download_image("https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg")

	prompt = "A fantasy landscape, trending on artstation"

	images = pipe.img2img(prompt=prompt, image=init_image, strength=0.75, guidance_scale=7.5).images

	### Inpainting

	img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
	mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
	init_image = download_image(img_url).resize((512, 512))
	mask_image = download_image(mask_url).resize((512, 512))

	prompt = "a cat sitting on a bench"
	images = pipe.inpaint(prompt=prompt, image=init_image, mask_image=mask_image, strength=0.75).images
	```

	As shown above this one pipeline can run all both "text-to-image", "image-to-image", and "inpainting" in one pipeline.

	### Long Prompt Weighting Stable Diffusion
	Features of this custom pipeline:
	- Input a prompt without the 77 token length limit.
	- Includes tx2img, img2img. and inpainting pipelines.
	- Emphasize/weigh part of your prompt with parentheses as so: `a baby deer with (big eyes)`
	- De-emphasize part of your prompt as so: `a [baby] deer with big eyes`
	- Precisely weigh part of your prompt as so: `a baby deer with (big eyes:1.3)`

	Prompt weighting equivalents:
	- `a baby deer with` == `(a baby deer with:1.0)`
	- `(big eyes)` == `(big eyes:1.1)`
	- `((big eyes))` == `(big eyes:1.21)`
	- `[big eyes]` == `(big eyes:0.91)`

	You can run this custom pipeline as so:

	#### pytorch

	```python
	from diffusers import DiffusionPipeline
	import torch

	pipe = DiffusionPipeline.from_pretrained(
	'hakurei/waifu-diffusion',
	custom_pipeline="lpw_stable_diffusion",

	torch_dtype=torch.float16
	)
	pipe=pipe.to("cuda")

	prompt = "best_quality (1girl:1.3) bow bride brown_hair closed_mouth frilled_bow frilled_hair_tubes frills (full_body:1.3) fox_ear hair_bow hair_tubes happy hood japanese_clothes kimono long_sleeves red_bow smile solo tabi uchikake white_kimono wide_sleeves cherry_blossoms"
	neg_prompt = "lowres, bad_anatomy, error_body, error_hair, error_arm, error_hands, bad_hands, error_fingers, bad_fingers, missing_fingers, error_legs, bad_legs, multiple_legs, missing_legs, error_lighting, error_shadow, error_reflection, text, error, extra_digit, fewer_digits, cropped, worst_quality, low_quality, normal_quality, jpeg_artifacts, signature, watermark, username, blurry"

	pipe.text2img(prompt, negative_prompt=neg_prompt, width=512,height=512,max_embeddings_multiples=3).images[0]

	```

	#### onnxruntime

	```python
	from diffusers import DiffusionPipeline
	import torch

	pipe = DiffusionPipeline.from_pretrained(
	'CompVis/stable-diffusion-v1-4',
	custom_pipeline="lpw_stable_diffusion_onnx",
	revision="onnx",
	provider="CUDAExecutionProvider"
	)

	prompt = "a photo of an astronaut riding a horse on mars, best quality"
	neg_prompt = "lowres, bad anatomy, error body, error hair, error arm, error hands, bad hands, error fingers, bad fingers, missing fingers, error legs, bad legs, multiple legs, missing legs, error lighting, error shadow, error reflection, text, error, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry"

	pipe.text2img(prompt,negative_prompt=neg_prompt, width=512, height=512, max_embeddings_multiples=3).images[0]

	```

	if you see `Token indices sequence length is longer than the specified maximum sequence length for this model ( *** > 77 ) . Running this sequence through the model will result in indexing errors`. Do not worry, it is normal.

	### Speech to Image

	The following code can generate an image from an audio sample using pre-trained OpenAI whisper-small and Stable Diffusion.

	```Python
	import torch

	import matplotlib.pyplot as plt
	from datasets import load_dataset
	from diffusers import DiffusionPipeline
	from transformers import (
	WhisperForConditionalGeneration,
	WhisperProcessor,
	)


	device = "cuda" if torch.cuda.is_available() else "cpu"

	ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")

	audio_sample = ds[3]

	text = audio_sample["text"].lower()
	speech_data = audio_sample["audio"]["array"]

	model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small").to(device)
	processor = WhisperProcessor.from_pretrained("openai/whisper-small")

	diffuser_pipeline = DiffusionPipeline.from_pretrained(
	"CompVis/stable-diffusion-v1-4",
	custom_pipeline="speech_to_image_diffusion",
	speech_model=model,
	speech_processor=processor,

	torch_dtype=torch.float16,
	)

	diffuser_pipeline.enable_attention_slicing()
	diffuser_pipeline = diffuser_pipeline.to(device)

	output = diffuser_pipeline(speech_data)
	plt.imshow(output.images[0])
	```
	This example produces the following image:

	![image](https://user-images.githubusercontent.com/45072645/196901736-77d9c6fc-63ee-4072-90b0-dc8b903d63e3.png)

	### Wildcard Stable Diffusion
	Following the great examples from https://github.com/jtkelm2/stable-diffusion-webui-1/blob/master/scripts/wildcards.py and https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Scripts#wildcards, here's a minimal implementation that allows for users to add "wildcards", denoted by `__wildcard__` to prompts that are used as placeholders for randomly sampled values given by either a dictionary or a `.txt` file. For example:

	Say we have a prompt:

	```
	prompt = "__animal__ sitting on a __object__ wearing a __clothing__"
	```

	We can then define possible values to be sampled for `animal`, `object`, and `clothing`. These can either be from a `.txt` with the same name as the category.

	The possible values can also be defined / combined by using a dictionary like: `{"animal":["dog", "cat", mouse"]}`.

	The actual pipeline works just like `StableDiffusionPipeline`, except the `__call__` method takes in:

	`wildcard_files`: list of file paths for wild card replacement
	`wildcard_option_dict`: dict with key as `wildcard` and values as a list of possible replacements
	`num_prompt_samples`: number of prompts to sample, uniformly sampling wildcards

	A full example:

	create `animal.txt`, with contents like:

	```
	dog
	cat
	mouse
	```

	create `object.txt`, with contents like:

	```
	chair
	sofa
	bench
	```

	```python
	from diffusers import DiffusionPipeline
	import torch

	pipe = DiffusionPipeline.from_pretrained(
	"CompVis/stable-diffusion-v1-4",
	custom_pipeline="wildcard_stable_diffusion",

	torch_dtype=torch.float16,
	)
	prompt = "__animal__ sitting on a __object__ wearing a __clothing__"
	out = pipe(
	prompt,
	wildcard_option_dict={
	"clothing":["hat", "shirt", "scarf", "beret"]
	},
	wildcard_files=["object.txt", "animal.txt"],
	num_prompt_samples=1
	)
	```

	### Composable Stable diffusion

	[Composable Stable Diffusion](https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/) proposes conjunction and negation (negative prompts) operators for compositional generation with conditional diffusion models.

	```python
	import torch as th
	import numpy as np
	import torchvision.utils as tvu

	from diffusers import DiffusionPipeline

	import argparse

	parser = argparse.ArgumentParser()
	parser.add_argument("--prompt", type=str, default="mystical trees \| A magical pond \| dark",
	help="use '\|' as the delimiter to compose separate sentences.")
	parser.add_argument("--steps", type=int, default=50)
	parser.add_argument("--scale", type=float, default=7.5)
	parser.add_argument("--weights", type=str, default="7.5 \| 7.5 \| -7.5")
	parser.add_argument("--seed", type=int, default=2)
	parser.add_argument("--model_path", type=str, default="CompVis/stable-diffusion-v1-4")
	parser.add_argument("--num_images", type=int, default=1)
	args = parser.parse_args()

	has_cuda = th.cuda.is_available()
	device = th.device('cpu' if not has_cuda else 'cuda')

	prompt = args.prompt
	scale = args.scale
	steps = args.steps

	pipe = DiffusionPipeline.from_pretrained(
	args.model_path,
	custom_pipeline="composable_stable_diffusion",
	).to(device)

	pipe.safety_checker = None

	images = []
	generator = th.Generator("cuda").manual_seed(args.seed)
	for i in range(args.num_images):
	image = pipe(prompt, guidance_scale=scale, num_inference_steps=steps,
	weights=args.weights, generator=generator).images[0]
	images.append(th.from_numpy(np.array(image)).permute(2, 0, 1) / 255.)
	grid = tvu.make_grid(th.stack(images, dim=0), nrow=4, padding=0)
	tvu.save_image(grid, f'{prompt}_{args.weights}' + '.png')

	```

	### Imagic Stable Diffusion
	Allows you to edit an image using stable diffusion.

	```python
	import requests
	from PIL import Image
	from io import BytesIO
	import torch
	import os
	from diffusers import DiffusionPipeline, DDIMScheduler
	has_cuda = torch.cuda.is_available()
	device = torch.device('cpu' if not has_cuda else 'cuda')
	pipe = DiffusionPipeline.from_pretrained(
	"CompVis/stable-diffusion-v1-4",
	safety_checker=None,
	use_auth_token=True,
	custom_pipeline="imagic_stable_diffusion",
	scheduler = DDIMScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", clip_sample=False, set_alpha_to_one=False)
	).to(device)
	generator = torch.Generator("cuda").manual_seed(0)
	seed = 0
	prompt = "A photo of Barack Obama smiling with a big grin"
	url = 'https://www.dropbox.com/s/6tlwzr73jd1r9yk/obama.png?dl=1'
	response = requests.get(url)
	init_image = Image.open(BytesIO(response.content)).convert("RGB")
	init_image = init_image.resize((512, 512))
	res = pipe.train(
	prompt,
	image=init_image,
	generator=generator)
	res = pipe(alpha=1, guidance_scale=7.5, num_inference_steps=50)
	os.makedirs("imagic", exist_ok=True)
	image = res.images[0]
	image.save('./imagic/imagic_image_alpha_1.png')
	res = pipe(alpha=1.5, guidance_scale=7.5, num_inference_steps=50)
	image = res.images[0]
	image.save('./imagic/imagic_image_alpha_1_5.png')
	res = pipe(alpha=2, guidance_scale=7.5, num_inference_steps=50)
	image = res.images[0]
	image.save('./imagic/imagic_image_alpha_2.png')
	```

	### Seed Resizing
	Test seed resizing. Originally generate an image in 512 by 512, then generate image with same seed at 512 by 592 using seed resizing. Finally, generate 512 by 592 using original stable diffusion pipeline.

	```python
	import torch as th
	import numpy as np
	from diffusers import DiffusionPipeline

	has_cuda = th.cuda.is_available()
	device = th.device('cpu' if not has_cuda else 'cuda')

	pipe = DiffusionPipeline.from_pretrained(
	"CompVis/stable-diffusion-v1-4",
	use_auth_token=True,
	custom_pipeline="seed_resize_stable_diffusion"
	).to(device)

	def dummy(images, **kwargs):
	return images, False

	pipe.safety_checker = dummy


	images = []
	th.manual_seed(0)
	generator = th.Generator("cuda").manual_seed(0)

	seed = 0
	prompt = "A painting of a futuristic cop"

	width = 512
	height = 512

	res = pipe(
	prompt,
	guidance_scale=7.5,
	num_inference_steps=50,
	height=height,
	width=width,
	generator=generator)
	image = res.images[0]
	image.save('./seed_resize/seed_resize_{w}_{h}_image.png'.format(w=width, h=height))


	th.manual_seed(0)
	generator = th.Generator("cuda").manual_seed(0)

	pipe = DiffusionPipeline.from_pretrained(
	"CompVis/stable-diffusion-v1-4",
	use_auth_token=True,
	custom_pipeline="/home/mark/open_source/diffusers/examples/community/"
	).to(device)

	width = 512
	height = 592

	res = pipe(
	prompt,
	guidance_scale=7.5,
	num_inference_steps=50,
	height=height,
	width=width,
	generator=generator)
	image = res.images[0]
	image.save('./seed_resize/seed_resize_{w}_{h}_image.png'.format(w=width, h=height))

	pipe_compare = DiffusionPipeline.from_pretrained(
	"CompVis/stable-diffusion-v1-4",
	use_auth_token=True,
	custom_pipeline="/home/mark/open_source/diffusers/examples/community/"
	).to(device)

	res = pipe_compare(
	prompt,
	guidance_scale=7.5,
	num_inference_steps=50,
	height=height,
	width=width,
	generator=generator
	)

	image = res.images[0]
	image.save('./seed_resize/seed_resize_{w}_{h}_image_compare.png'.format(w=width, h=height))
	```

	### Multilingual Stable Diffusion Pipeline

	The following code can generate an images from texts in different languages using the pre-trained [mBART-50 many-to-one multilingual machine translation model](https://huggingface.co/facebook/mbart-large-50-many-to-one-mmt) and Stable Diffusion.

	```python
	from PIL import Image

	import torch

	from diffusers import DiffusionPipeline
	from transformers import (
	pipeline,
	MBart50TokenizerFast,
	MBartForConditionalGeneration,
	)
	device = "cuda" if torch.cuda.is_available() else "cpu"
	device_dict = {"cuda": 0, "cpu": -1}

	# helper function taken from: https://huggingface.co/blog/stable_diffusion
	def image_grid(imgs, rows, cols):
	assert len(imgs) == rows*cols

	w, h = imgs[0].size
	grid = Image.new('RGB', size=(colsw, rowsh))
	grid_w, grid_h = grid.size

	for i, img in enumerate(imgs):
	grid.paste(img, box=(i%colsw, i//colsh))
	return grid

	# Add language detection pipeline
	language_detection_model_ckpt = "papluca/xlm-roberta-base-language-detection"
	language_detection_pipeline = pipeline("text-classification",
	model=language_detection_model_ckpt,
	device=device_dict[device])

	# Add model for language translation
	trans_tokenizer = MBart50TokenizerFast.from_pretrained("facebook/mbart-large-50-many-to-one-mmt")
	trans_model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-one-mmt").to(device)

	diffuser_pipeline = DiffusionPipeline.from_pretrained(
	"CompVis/stable-diffusion-v1-4",
	custom_pipeline="multilingual_stable_diffusion",
	detection_pipeline=language_detection_pipeline,
	translation_model=trans_model,
	translation_tokenizer=trans_tokenizer,

	torch_dtype=torch.float16,
	)

	diffuser_pipeline.enable_attention_slicing()
	diffuser_pipeline = diffuser_pipeline.to(device)

	prompt = ["a photograph of an astronaut riding a horse",
	"Una casa en la playa",
	"Ein Hund, der Orange isst",
	"Un restaurant parisien"]

	output = diffuser_pipeline(prompt)

	images = output.images

	grid = image_grid(images, rows=2, cols=2)
	```

	This example produces the following images:
	![image](https://user-images.githubusercontent.com/4313860/198328706-295824a4-9856-4ce5-8e66-278ceb42fd29.png)

	### Image to Image Inpainting Stable Diffusion

	Similar to the standard stable diffusion inpainting example, except with the addition of an `inner_image` argument.

	`image`, `inner_image`, and `mask` should have the same dimensions. `inner_image` should have an alpha (transparency) channel.

	The aim is to overlay two images, then mask out the boundary between `image` and `inner_image` to allow stable diffusion to make the connection more seamless.
	For example, this could be used to place a logo on a shirt and make it blend seamlessly.

	```python
	import PIL
	import torch

	from diffusers import DiffusionPipeline

	image_path = "./path-to-image.png"
	inner_image_path = "./path-to-inner-image.png"
	mask_path = "./path-to-mask.png"

	init_image = PIL.Image.open(image_path).convert("RGB").resize((512, 512))
	inner_image = PIL.Image.open(inner_image_path).convert("RGBA").resize((512, 512))
	mask_image = PIL.Image.open(mask_path).convert("RGB").resize((512, 512))

	pipe = DiffusionPipeline.from_pretrained(
	"runwayml/stable-diffusion-inpainting",
	custom_pipeline="img2img_inpainting",

	torch_dtype=torch.float16
	)
	pipe = pipe.to("cuda")

	prompt = "Your prompt here!"
	image = pipe(prompt=prompt, image=init_image, inner_image=inner_image, mask_image=mask_image).images[0]
	```

	![2 by 2 grid demonstrating image to image inpainting.](https://user-images.githubusercontent.com/44398246/203506577-ec303be4-887e-4ebd-a773-c83fcb3dd01a.png)

	### Text Based Inpainting Stable Diffusion

	Use a text prompt to generate the mask for the area to be inpainted.
	Currently uses the CLIPSeg model for mask generation, then calls the standard Stable Diffusion Inpainting pipeline to perform the inpainting.

	```python
	from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation
	from diffusers import DiffusionPipeline

	from PIL import Image
	import requests

	processor = CLIPSegProcessor.from_pretrained("CIDAS/clipseg-rd64-refined")
	model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64-refined")

	pipe = DiffusionPipeline.from_pretrained(
	"runwayml/stable-diffusion-inpainting",
	custom_pipeline="text_inpainting",
	segmentation_model=model,
	segmentation_processor=processor
	)
	pipe = pipe.to("cuda")


	url = "https://github.com/timojl/clipseg/blob/master/example_image.jpg?raw=true"
	image = Image.open(requests.get(url, stream=True).raw).resize((512, 512))
	text = "a glass" # will mask out this text
	prompt = "a cup" # the masked out region will be replaced with this

	image = pipe(image=image, text=text, prompt=prompt).images[0]
	```

	### Bit Diffusion
	Based https://arxiv.org/abs/2208.04202, this is used for diffusion on discrete data - eg, discreate image data, DNA sequence data. An unconditional discreate image can be generated like this:

	```python
	from diffusers import DiffusionPipeline
	pipe = DiffusionPipeline.from_pretrained("google/ddpm-cifar10-32", custom_pipeline="bit_diffusion")
	image = pipe().images[0]

	```

	### Stable Diffusion with K Diffusion

	Make sure you have @crowsonkb's https://github.com/crowsonkb/k-diffusion installed:

	```
	pip install k-diffusion
	```

	You can use the community pipeline as follows:

	```python
	from diffusers import DiffusionPipeline

	pipe = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", custom_pipeline="sd_text2img_k_diffusion")
	pipe = pipe.to("cuda")

	prompt = "an astronaut riding a horse on mars"
	pipe.set_scheduler("sample_heun")
	generator = torch.Generator(device="cuda").manual_seed(seed)
	image = pipe(prompt, generator=generator, num_inference_steps=20).images[0]

	image.save("./astronaut_heun_k_diffusion.png")
	```

	To make sure that K Diffusion and `diffusers` yield the same results:

	Diffusers:
	```python
	from diffusers import DiffusionPipeline, EulerDiscreteScheduler

	seed = 33

	pipe = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
	pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
	pipe = pipe.to("cuda")

	generator = torch.Generator(device="cuda").manual_seed(seed)
	image = pipe(prompt, generator=generator, num_inference_steps=50).images[0]
	```

	![diffusers_euler](https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/k_diffusion/astronaut_euler.png)

	K Diffusion:
	```python
	from diffusers import DiffusionPipeline, EulerDiscreteScheduler

	seed = 33

	pipe = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", custom_pipeline="sd_text2img_k_diffusion")
	pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
	pipe = pipe.to("cuda")

	pipe.set_scheduler("sample_euler")
	generator = torch.Generator(device="cuda").manual_seed(seed)
	image = pipe(prompt, generator=generator, num_inference_steps=50).images[0]
	```

	![diffusers_euler](https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/k_diffusion/astronaut_euler_k_diffusion.png)

	### Checkpoint Merger Pipeline
	Based on the AUTOMATIC1111/webui for checkpoint merging. This is a custom pipeline that merges upto 3 pretrained model checkpoints as long as they are in the HuggingFace model_index.json format.

	The checkpoint merging is currently memory intensive as it modifies the weights of a DiffusionPipeline object in place. Expect atleast 13GB RAM Usage on Kaggle GPU kernels and
	on colab you might run out of the 12GB memory even while merging two checkpoints.

	Usage:-
	```python
	from diffusers import DiffusionPipeline

	#Return a CheckpointMergerPipeline class that allows you to merge checkpoints.
	#The checkpoint passed here is ignored. But still pass one of the checkpoints you plan to
	#merge for convenience
	pipe = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", custom_pipeline="checkpoint_merger")

	#There are multiple possible scenarios:
	#The pipeline with the merged checkpoints is returned in all the scenarios

	#Compatible checkpoints a.k.a matched model_index.json files. Ignores the meta attributes in model_index.json during comparision.( attrs with _ as prefix )
	merged_pipe = pipe.merge(["CompVis/stable-diffusion-v1-4","CompVis/stable-diffusion-v1-2"], interp = "sigmoid", alpha = 0.4)

	#Incompatible checkpoints in model_index.json but merge might be possible. Use force = True to ignore model_index.json compatibility
	merged_pipe_1 = pipe.merge(["CompVis/stable-diffusion-v1-4","hakurei/waifu-diffusion"], force = True, interp = "sigmoid", alpha = 0.4)

	#Three checkpoint merging. Only "add_difference" method actually works on all three checkpoints. Using any other options will ignore the 3rd checkpoint.
	merged_pipe_2 = pipe.merge(["CompVis/stable-diffusion-v1-4","hakurei/waifu-diffusion","prompthero/openjourney"], force = True, interp = "add_difference", alpha = 0.4)

	prompt = "An astronaut riding a horse on Mars"

	image = merged_pipe(prompt).images[0]

	```
	Some examples along with the merge details:

	1. "CompVis/stable-diffusion-v1-4" + "hakurei/waifu-diffusion" ; Sigmoid interpolation; alpha = 0.8

	![Stable plus Waifu Sigmoid 0.8](https://huggingface.co/datasets/NagaSaiAbhinay/CheckpointMergerSamples/resolve/main/stability_v1_4_waifu_sig_0.8.png)

	2. "hakurei/waifu-diffusion" + "prompthero/openjourney" ; Inverse Sigmoid interpolation; alpha = 0.8

	![Stable plus Waifu Sigmoid 0.8](https://huggingface.co/datasets/NagaSaiAbhinay/CheckpointMergerSamples/resolve/main/waifu_openjourney_inv_sig_0.8.png)


	3. "CompVis/stable-diffusion-v1-4" + "hakurei/waifu-diffusion" + "prompthero/openjourney"; Add Difference interpolation; alpha = 0.5

	![Stable plus Waifu plus openjourney add_diff 0.5](https://huggingface.co/datasets/NagaSaiAbhinay/CheckpointMergerSamples/resolve/main/stable_waifu_openjourney_add_diff_0.5.png)


	### Stable Diffusion Comparisons

	This Community Pipeline enables the comparison between the 4 checkpoints that exist for Stable Diffusion. They can be found through the following links:
	1. [Stable Diffusion v1.1](https://huggingface.co/CompVis/stable-diffusion-v1-1)
	2. [Stable Diffusion v1.2](https://huggingface.co/CompVis/stable-diffusion-v1-2)
	3. [Stable Diffusion v1.3](https://huggingface.co/CompVis/stable-diffusion-v1-3)
	4. [Stable Diffusion v1.4](https://huggingface.co/CompVis/stable-diffusion-v1-4)

	```python
	from diffusers import DiffusionPipeline
	import matplotlib.pyplot as plt

	pipe = DiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4', custom_pipeline='suvadityamuk/StableDiffusionComparison')
	pipe.enable_attention_slicing()
	pipe = pipe.to('cuda')
	prompt = "an astronaut riding a horse on mars"
	output = pipe(prompt)

	plt.subplots(2,2,1)
	plt.imshow(output.images[0])
	plt.title('Stable Diffusion v1.1')
	plt.axis('off')
	plt.subplots(2,2,2)
	plt.imshow(output.images[1])
	plt.title('Stable Diffusion v1.2')
	plt.axis('off')
	plt.subplots(2,2,3)
	plt.imshow(output.images[2])
	plt.title('Stable Diffusion v1.3')
	plt.axis('off')
	plt.subplots(2,2,4)
	plt.imshow(output.images[3])
	plt.title('Stable Diffusion v1.4')
	plt.axis('off')

	plt.show()
	```

	As a result, you can look at a grid of all 4 generated images being shown together, that captures a difference the advancement of the training between the 4 checkpoints.

	### Magic Mix

	Implementation of the [MagicMix: Semantic Mixing with Diffusion Models](https://arxiv.org/abs/2210.16056) paper. This is a Diffusion Pipeline for semantic mixing of an image and a text prompt to create a new concept while preserving the spatial layout and geometry of the subject in the image. The pipeline takes an image that provides the layout semantics and a prompt that provides the content semantics for the mixing process.

	There are 3 parameters for the method-
	- `mix_factor`: It is the interpolation constant used in the layout generation phase. The greater the value of `mix_factor`, the greater the influence of the prompt on the layout generation process.
	- `kmax` and `kmin`: These determine the range for the layout and content generation process. A higher value of kmax results in loss of more information about the layout of the original image and a higher value of kmin results in more steps for content generation process.

	Here is an example usage-

	```python
	from diffusers import DiffusionPipeline, DDIMScheduler
	from PIL import Image

	pipe = DiffusionPipeline.from_pretrained(
	"CompVis/stable-diffusion-v1-4",
	custom_pipeline="magic_mix",
	scheduler = DDIMScheduler.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="scheduler"),
	).to('cuda')

	img = Image.open('phone.jpg')
	mix_img = pipe(
	img,
	prompt = 'bed',
	kmin = 0.3,
	kmax = 0.5,
	mix_factor = 0.5,
	)
	mix_img.save('phone_bed_mix.jpg')
	```
	The `mix_img` is a PIL image that can be saved locally or displayed directly in a google colab. Generated image is a mix of the layout semantics of the given image and the content semantics of the prompt.

	E.g. the above script generates the following image:

	`phone.jpg`

	![206903102-34e79b9f-9ed2-4fac-bb38-82871343c655](https://user-images.githubusercontent.com/59410571/209578593-141467c7-d831-4792-8b9a-b17dc5e47816.jpg)

	`phone_bed_mix.jpg`

	![206903104-913a671d-ef53-4ae4-919d-64c3059c8f67](https://user-images.githubusercontent.com/59410571/209578602-70f323fa-05b7-4dd6-b055-e40683e37914.jpg)

	For more example generations check out this [demo notebook](https://github.com/daspartho/MagicMix/blob/main/demo.ipynb).


	### Stable UnCLIP

	UnCLIPPipeline("kakaobrain/karlo-v1-alpha") provide a prior model that can generate clip image embedding from text.
	StableDiffusionImageVariationPipeline("lambdalabs/sd-image-variations-diffusers") provide a decoder model than can generate images from clip image embedding.

	```python
	import torch
	from diffusers import DiffusionPipeline

	device = torch.device("cpu" if not torch.cuda.is_available() else "cuda")

	pipeline = DiffusionPipeline.from_pretrained(
	"kakaobrain/karlo-v1-alpha",
	torch_dtype=torch.float16,
	custom_pipeline="stable_unclip",
	decoder_pipe_kwargs=dict(
	image_encoder=None,
	),
	)
	pipeline.to(device)

	prompt = "a shiba inu wearing a beret and black turtleneck"
	random_generator = torch.Generator(device=device).manual_seed(1000)
	output = pipeline(
	prompt=prompt,
	width=512,
	height=512,
	generator=random_generator,
	prior_guidance_scale=4,
	prior_num_inference_steps=25,
	decoder_guidance_scale=8,
	decoder_num_inference_steps=50,
	)

	image = output.images[0]
	image.save("./shiba-inu.jpg")

	# debug

	# `pipeline.decoder_pipe` is a regular StableDiffusionImageVariationPipeline instance.
	# It is used to convert clip image embedding to latents, then fed into VAE decoder.
	print(pipeline.decoder_pipe.__class__)
	# <class 'diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_image_variation.StableDiffusionImageVariationPipeline'>

	# this pipeline only use prior module in "kakaobrain/karlo-v1-alpha"
	# It is used to convert clip text embedding to clip image embedding.
	print(pipeline)
	# StableUnCLIPPipeline {
	# "_class_name": "StableUnCLIPPipeline",
	# "_diffusers_version": "0.12.0.dev0",
	# "prior": [
	# "diffusers",
	# "PriorTransformer"
	# ],
	# "prior_scheduler": [
	# "diffusers",
	# "UnCLIPScheduler"
	# ],
	# "text_encoder": [
	# "transformers",
	# "CLIPTextModelWithProjection"
	# ],
	# "tokenizer": [
	# "transformers",
	# "CLIPTokenizer"
	# ]
	# }

	# pipeline.prior_scheduler is the scheduler used for prior in UnCLIP.
	print(pipeline.prior_scheduler)
	# UnCLIPScheduler {
	# "_class_name": "UnCLIPScheduler",
	# "_diffusers_version": "0.12.0.dev0",
	# "clip_sample": true,
	# "clip_sample_range": 5.0,
	# "num_train_timesteps": 1000,
	# "prediction_type": "sample",
	# "variance_type": "fixed_small_log"
	# }
	```


	`shiba-inu.jpg`


	![shiba-inu](https://user-images.githubusercontent.com/16448529/209185639-6e5ec794-ce9d-4883-aa29-bd6852a2abad.jpg)

	### UnCLIP Text Interpolation Pipeline

	This Diffusion Pipeline takes two prompts and interpolates between the two input prompts using spherical interpolation ( slerp ). The input prompts are converted to text embeddings by the pipeline's text_encoder and the interpolation is done on the resulting text_embeddings over the number of steps specified. Defaults to 5 steps.

	```python
	import torch
	from diffusers import DiffusionPipeline

	device = torch.device("cpu" if not torch.cuda.is_available() else "cuda")

	pipe = DiffusionPipeline.from_pretrained(
	"kakaobrain/karlo-v1-alpha",
	torch_dtype=torch.float16,
	custom_pipeline="unclip_text_interpolation"
	)
	pipe.to(device)

	start_prompt = "A photograph of an adult lion"
	end_prompt = "A photograph of a lion cub"
	#For best results keep the prompts close in length to each other. Of course, feel free to try out with differing lengths.
	generator = torch.Generator(device=device).manual_seed(42)

	output = pipe(start_prompt, end_prompt, steps = 6, generator = generator, enable_sequential_cpu_offload=False)

	for i,image in enumerate(output.images):
	img.save('result%s.jpg' % i)
	```

	The resulting images in order:-

	![result_0](https://huggingface.co/datasets/NagaSaiAbhinay/UnCLIPTextInterpolationSamples/resolve/main/lion_to_cub_0.png)
	![result_1](https://huggingface.co/datasets/NagaSaiAbhinay/UnCLIPTextInterpolationSamples/resolve/main/lion_to_cub_1.png)
	![result_2](https://huggingface.co/datasets/NagaSaiAbhinay/UnCLIPTextInterpolationSamples/resolve/main/lion_to_cub_2.png)
	![result_3](https://huggingface.co/datasets/NagaSaiAbhinay/UnCLIPTextInterpolationSamples/resolve/main/lion_to_cub_3.png)
	![result_4](https://huggingface.co/datasets/NagaSaiAbhinay/UnCLIPTextInterpolationSamples/resolve/main/lion_to_cub_4.png)
	![result_5](https://huggingface.co/datasets/NagaSaiAbhinay/UnCLIPTextInterpolationSamples/resolve/main/lion_to_cub_5.png)

	### UnCLIP Image Interpolation Pipeline

	This Diffusion Pipeline takes two images or an image_embeddings tensor of size 2 and interpolates between their embeddings using spherical interpolation ( slerp ). The input images/image_embeddings are converted to image embeddings by the pipeline's image_encoder and the interpolation is done on the resulting image_embeddings over the number of steps specified. Defaults to 5 steps.

	```python
	import torch
	from diffusers import DiffusionPipeline
	from PIL import Image

	device = torch.device("cpu" if not torch.cuda.is_available() else "cuda")
	dtype = torch.float16 if torch.cuda.is_available() else torch.bfloat16

	pipe = DiffusionPipeline.from_pretrained(
	"kakaobrain/karlo-v1-alpha-image-variations",
	torch_dtype=dtype,
	custom_pipeline="unclip_image_interpolation"
	)
	pipe.to(device)

	images = [Image.open('./starry_night.jpg'), Image.open('./flowers.jpg')]
	#For best results keep the prompts close in length to each other. Of course, feel free to try out with differing lengths.
	generator = torch.Generator(device=device).manual_seed(42)

	output = pipe(image = images ,steps = 6, generator = generator)

	for i,image in enumerate(output.images):
	image.save('starry_to_flowers_%s.jpg' % i)
	```
	The original images:-

	![starry](https://huggingface.co/datasets/NagaSaiAbhinay/UnCLIPImageInterpolationSamples/resolve/main/starry_night.jpg)
	![flowers](https://huggingface.co/datasets/NagaSaiAbhinay/UnCLIPImageInterpolationSamples/resolve/main/flowers.jpg)

	The resulting images in order:-

	![result0](https://huggingface.co/datasets/NagaSaiAbhinay/UnCLIPImageInterpolationSamples/resolve/main/starry_to_flowers_0.png)
	![result1](https://huggingface.co/datasets/NagaSaiAbhinay/UnCLIPImageInterpolationSamples/resolve/main/starry_to_flowers_1.png)
	![result2](https://huggingface.co/datasets/NagaSaiAbhinay/UnCLIPImageInterpolationSamples/resolve/main/starry_to_flowers_2.png)
	![result3](https://huggingface.co/datasets/NagaSaiAbhinay/UnCLIPImageInterpolationSamples/resolve/main/starry_to_flowers_3.png)
	![result4](https://huggingface.co/datasets/NagaSaiAbhinay/UnCLIPImageInterpolationSamples/resolve/main/starry_to_flowers_4.png)
	![result5](https://huggingface.co/datasets/NagaSaiAbhinay/UnCLIPImageInterpolationSamples/resolve/main/starry_to_flowers_5.png)

	### DDIM Noise Comparative Analysis Pipeline
	#### Research question: What visual concepts do the diffusion models learn from each noise level during training?
	The [P2 weighting (CVPR 2022)](https://arxiv.org/abs/2204.00227) paper proposed an approach to answer the above question, which is their second contribution.
	The approach consists of the following steps:

	1. The input is an image x0.
	2. Perturb it to xt using a diffusion process q(xt\|x0).
	- `strength` is a value between 0.0 and 1.0, that controls the amount of noise that is added to the input image. Values that approach 1.0 allow for lots of variations but will also produce images that are not semantically consistent with the input.
	3. Reconstruct the image with the learned denoising process pθ(ˆx0\|xt).
	4. Compare x0 and ˆx0 among various t to show how each step contributes to the sample.
	The authors used [openai/guided-diffusion](https://github.com/openai/guided-diffusion) model to denoise images in FFHQ dataset. This pipeline extends their second contribution by investigating DDIM on any input image.

	```python
	import torch
	from PIL import Image
	import numpy as np

	image_path = "path/to/your/image" # images from CelebA-HQ might be better
	image_pil = Image.open(image_path)
	image_name = image_path.split("/")[-1].split(".")[0]

	device = torch.device("cpu" if not torch.cuda.is_available() else "cuda")
	pipe = DiffusionPipeline.from_pretrained(
	"google/ddpm-ema-celebahq-256",
	custom_pipeline="ddim_noise_comparative_analysis",
	)
	pipe = pipe.to(device)

	for strength in np.linspace(0.1, 1, 25):
	denoised_image, latent_timestep = pipe(
	image_pil, strength=strength, return_dict=False
	)
	denoised_image = denoised_image[0]
	denoised_image.save(
	f"noise_comparative_analysis_{image_name}_{latent_timestep}.png"
	)
	```

	Here is the result of this pipeline (which is DDIM) on CelebA-HQ dataset.

	![noise-comparative-analysis](https://user-images.githubusercontent.com/67547213/224677066-4474b2ed-56ab-4c27-87c6-de3c0255eb9c.jpeg)

	### CLIP Guided Img2Img Stable Diffusion

	CLIP guided Img2Img stable diffusion can help to generate more realistic images with an initial image
	by guiding stable diffusion at every denoising step with an additional CLIP model.

	The following code requires roughly 12GB of GPU RAM.

	```python
	from io import BytesIO
	import requests
	import torch
	from diffusers import DiffusionPipeline
	from PIL import Image
	from transformers import CLIPFeatureExtractor, CLIPModel
	feature_extractor = CLIPFeatureExtractor.from_pretrained(
	"laion/CLIP-ViT-B-32-laion2B-s34B-b79K"
	)
	clip_model = CLIPModel.from_pretrained(
	"laion/CLIP-ViT-B-32-laion2B-s34B-b79K", torch_dtype=torch.float16
	)
	guided_pipeline = DiffusionPipeline.from_pretrained(
	"CompVis/stable-diffusion-v1-4",
	# custom_pipeline="clip_guided_stable_diffusion",
	custom_pipeline="/home/njindal/diffusers/examples/community/clip_guided_stable_diffusion.py",
	clip_model=clip_model,
	feature_extractor=feature_extractor,
	torch_dtype=torch.float16,
	)
	guided_pipeline.enable_attention_slicing()
	guided_pipeline = guided_pipeline.to("cuda")
	prompt = "fantasy book cover, full moon, fantasy forest landscape, golden vector elements, fantasy magic, dark light night, intricate, elegant, sharp focus, illustration, highly detailed, digital painting, concept art, matte, art by WLOP and Artgerm and Albert Bierstadt, masterpiece"
	url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
	response = requests.get(url)
	init_image = Image.open(BytesIO(response.content)).convert("RGB")
	image = guided_pipeline(
	prompt=prompt,
	num_inference_steps=30,
	image=init_image,
	strength=0.75,
	guidance_scale=7.5,
	clip_guidance_scale=100,
	num_cutouts=4,
	use_cutouts=False,
	).images[0]
	display(image)
	```

	Init Image

	![img2img_init_clip_guidance](https://huggingface.co/datasets/njindal/images/resolve/main/clip_guided_img2img_init.jpg)

	Output Image

	![img2img_clip_guidance](https://huggingface.co/datasets/njindal/images/resolve/main/clip_guided_img2img.jpg)

	### TensorRT Text2Image Stable Diffusion Pipeline

	The TensorRT Pipeline can be used to accelerate the Text2Image Stable Diffusion Inference run.

	NOTE: The ONNX conversions and TensorRT engine build may take up to 30 minutes.

	```python
	import torch
	from diffusers import DDIMScheduler
	from diffusers.pipelines.stable_diffusion import StableDiffusionPipeline

	# Use the DDIMScheduler scheduler here instead
	scheduler = DDIMScheduler.from_pretrained("stabilityai/stable-diffusion-2-1",
	subfolder="scheduler")

	pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1",
	custom_pipeline="stable_diffusion_tensorrt_txt2img",
	revision='fp16',
	torch_dtype=torch.float16,
	scheduler=scheduler,)

	# re-use cached folder to save ONNX models and TensorRT Engines
	pipe.set_cached_folder("stabilityai/stable-diffusion-2-1", revision='fp16',)

	pipe = pipe.to("cuda")

	prompt = "a beautiful photograph of Mt. Fuji during cherry blossom"
	image = pipe(prompt).images[0]
	image.save('tensorrt_mt_fuji.png')
	```

	### EDICT Image Editing Pipeline

	This pipeline implements the text-guided image editing approach from the paper [EDICT: Exact Diffusion Inversion via Coupled Transformations](https://arxiv.org/abs/2211.12446). You have to pass:
	- (`PIL`) `image` you want to edit.
	- `base_prompt`: the text prompt describing the current image (before editing).
	- `target_prompt`: the text prompt describing with the edits.

	```python
	from diffusers import DiffusionPipeline, DDIMScheduler
	from transformers import CLIPTextModel
	import torch, PIL, requests
	from io import BytesIO
	from IPython.display import display

	def center_crop_and_resize(im):

	width, height = im.size
	d = min(width, height)
	left = (width - d) / 2
	upper = (height - d) / 2
	right = (width + d) / 2
	lower = (height + d) / 2

	return im.crop((left, upper, right, lower)).resize((512, 512))

	torch_dtype = torch.float16
	device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

	# scheduler and text_encoder param values as in the paper
	scheduler = DDIMScheduler(
	num_train_timesteps=1000,
	beta_start=0.00085,
	beta_end=0.012,
	beta_schedule="scaled_linear",
	set_alpha_to_one=False,
	clip_sample=False,
	)

	text_encoder = CLIPTextModel.from_pretrained(
	pretrained_model_name_or_path="openai/clip-vit-large-patch14",
	torch_dtype=torch_dtype,
	)

	# initialize pipeline
	pipeline = DiffusionPipeline.from_pretrained(
	pretrained_model_name_or_path="CompVis/stable-diffusion-v1-4",
	custom_pipeline="edict_pipeline",
	revision="fp16",
	scheduler=scheduler,
	text_encoder=text_encoder,
	leapfrog_steps=True,
	torch_dtype=torch_dtype,
	).to(device)

	# download image
	image_url = "https://huggingface.co/datasets/Joqsan/images/resolve/main/imagenet_dog_1.jpeg"
	response = requests.get(image_url)
	image = PIL.Image.open(BytesIO(response.content))

	# preprocess it
	cropped_image = center_crop_and_resize(image)

	# define the prompts
	base_prompt = "A dog"
	target_prompt = "A golden retriever"

	# run the pipeline
	result_image = pipeline(
	base_prompt=base_prompt,
	target_prompt=target_prompt,
	image=cropped_image,
	)

	display(result_image)
	```

	Init Image

	![img2img_init_edict_text_editing](https://huggingface.co/datasets/Joqsan/images/resolve/main/imagenet_dog_1.jpeg)

	Output Image

	![img2img_edict_text_editing](https://huggingface.co/datasets/Joqsan/images/resolve/main/imagenet_dog_1_cropped_generated.png)

	### Stable Diffusion RePaint

	This pipeline uses the [RePaint](https://arxiv.org/abs/2201.09865) logic on the latent space of stable diffusion. It can
	be used similarly to other image inpainting pipelines but does not rely on a specific inpainting model. This means you can use
	models that are not specifically created for inpainting.

	Make sure to use the ```RePaintScheduler``` as shown in the example below.

	Disclaimer: The mask gets transferred into latent space, this may lead to unexpected changes on the edge of the masked part.
	The inference time is a lot slower.

	```py
	import PIL
	import requests
	import torch
	from io import BytesIO
	from diffusers import StableDiffusionPipeline, RePaintScheduler
	def download_image(url):
	response = requests.get(url)
	return PIL.Image.open(BytesIO(response.content)).convert("RGB")
	img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
	mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
	init_image = download_image(img_url).resize((512, 512))
	mask_image = download_image(mask_url).resize((512, 512))
	mask_image = PIL.ImageOps.invert(mask_image)
	pipe = StableDiffusionPipeline.from_pretrained(
	"CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16, custom_pipeline="stable_diffusion_repaint",
	)
	pipe.scheduler = RePaintScheduler.from_config(pipe.scheduler.config)
	pipe = pipe.to("cuda")
	prompt = "Face of a yellow cat, high resolution, sitting on a park bench"
	image = pipe(prompt=prompt, image=init_image, mask_image=mask_image).images[0]
	```

	### TensorRT Image2Image Stable Diffusion Pipeline

	The TensorRT Pipeline can be used to accelerate the Image2Image Stable Diffusion Inference run.

	NOTE: The ONNX conversions and TensorRT engine build may take up to 30 minutes.

	```python
	import requests
	from io import BytesIO
	from PIL import Image
	import torch
	from diffusers import DDIMScheduler
	from diffusers.pipelines.stable_diffusion import StableDiffusionImg2ImgPipeline

	# Use the DDIMScheduler scheduler here instead
	scheduler = DDIMScheduler.from_pretrained("stabilityai/stable-diffusion-2-1",
	subfolder="scheduler")


	pipe = StableDiffusionImg2ImgPipeline.from_pretrained("stabilityai/stable-diffusion-2-1",
	custom_pipeline="stable_diffusion_tensorrt_img2img",
	revision='fp16',
	torch_dtype=torch.float16,
	scheduler=scheduler,)

	# re-use cached folder to save ONNX models and TensorRT Engines
	pipe.set_cached_folder("stabilityai/stable-diffusion-2-1", revision='fp16',)

	pipe = pipe.to("cuda")

	url = "https://pajoca.com/wp-content/uploads/2022/09/tekito-yamakawa-1.png"
	response = requests.get(url)
	input_image = Image.open(BytesIO(response.content)).convert("RGB")

	prompt = "photorealistic new zealand hills"
	image = pipe(prompt, image=input_image, strength=0.75,).images[0]
	image.save('tensorrt_img2img_new_zealand_hills.png')
	```

	### Stable Diffusion Reference

	This pipeline uses the Reference Control. Refer to the [sd-webui-controlnet discussion: Reference-only Control](https://github.com/Mikubill/sd-webui-controlnet/discussions/1236)[sd-webui-controlnet discussion: Reference-adain Control](https://github.com/Mikubill/sd-webui-controlnet/discussions/1280).

	Based on [this issue](https://github.com/huggingface/diffusers/issues/3566),
	- `EulerAncestralDiscreteScheduler` got poor results.

	```py
	import torch
	from diffusers import UniPCMultistepScheduler
	from diffusers.utils import load_image

	input_image = load_image("https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png")

	pipe = StableDiffusionReferencePipeline.from_pretrained(
	"runwayml/stable-diffusion-v1-5",
	safety_checker=None,
	torch_dtype=torch.float16
	).to('cuda:0')

	pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)

	result_img = pipe(ref_image=input_image,
	prompt="1girl",
	num_inference_steps=20,
	reference_attn=True,
	reference_adain=True).images[0]
	```

	Reference Image

	![reference_image](https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png)

	Output Image of `reference_attn=True` and `reference_adain=False`

	![output_image](https://github.com/huggingface/diffusers/assets/24734142/813b5c6a-6d89-46ba-b7a4-2624e240eea5)

	Output Image of `reference_attn=False` and `reference_adain=True`

	![output_image](https://github.com/huggingface/diffusers/assets/24734142/ffc90339-9ef0-4c4d-a544-135c3e5644da)

	Output Image of `reference_attn=True` and `reference_adain=True`

	![output_image](https://github.com/huggingface/diffusers/assets/24734142/3c5255d6-867d-4d35-b202-8dfd30cc6827)

	### Stable Diffusion ControlNet Reference

	This pipeline uses the Reference Control with ControlNet. Refer to the [sd-webui-controlnet discussion: Reference-only Control](https://github.com/Mikubill/sd-webui-controlnet/discussions/1236)[sd-webui-controlnet discussion: Reference-adain Control](https://github.com/Mikubill/sd-webui-controlnet/discussions/1280).

	Based on [this issue](https://github.com/huggingface/diffusers/issues/3566),
	- `EulerAncestralDiscreteScheduler` got poor results.
	- `guess_mode=True` works well for ControlNet v1.1

	```py
	import cv2
	import torch
	import numpy as np
	from PIL import Image
	from diffusers import UniPCMultistepScheduler
	from diffusers.utils import load_image

	input_image = load_image("https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png")

	# get canny image
	image = cv2.Canny(np.array(input_image), 100, 200)
	image = image[:, :, None]
	image = np.concatenate([image, image, image], axis=2)
	canny_image = Image.fromarray(image)

	controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16)
	pipe = StableDiffusionControlNetReferencePipeline.from_pretrained(
	"runwayml/stable-diffusion-v1-5",
	controlnet=controlnet,
	safety_checker=None,
	torch_dtype=torch.float16
	).to('cuda:0')

	pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)

	result_img = pipe(ref_image=input_image,
	prompt="1girl",
	image=canny_image,
	num_inference_steps=20,
	reference_attn=True,
	reference_adain=True).images[0]
	```

	Reference Image

	![reference_image](https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png)

	Output Image

	![output_image](https://github.com/huggingface/diffusers/assets/24734142/7b9a5830-f173-4b92-b0cf-73d0e9c01d60)


	### Stable Diffusion on IPEX

	This diffusion pipeline aims to accelarate the inference of Stable-Diffusion on Intel Xeon CPUs with BF16/FP32 precision using [IPEX](https://github.com/intel/intel-extension-for-pytorch).

	To use this pipeline, you need to:
	1. Install [IPEX](https://github.com/intel/intel-extension-for-pytorch)

	Note: For each PyTorch release, there is a corresponding release of the IPEX. Here is the mapping relationship. It is recommended to install Pytorch/IPEX2.0 to get the best performance.

	\|PyTorch Version\|IPEX Version\|
	\|--\|--\|
	\|[v2.0.\](https://github.com/pytorch/pytorch/tree/v2.0.1 "v2.0.1")\|[v2.0.\](https://github.com/intel/intel-extension-for-pytorch/tree/v2.0.100+cpu)\|
	\|[v1.13.\](https://github.com/pytorch/pytorch/tree/v1.13.0 "v1.13.0")\|[v1.13.\](https://github.com/intel/intel-extension-for-pytorch/tree/v1.13.100+cpu)\|

	You can simply use pip to install IPEX with the latest version.
	```python
	python -m pip install intel_extension_for_pytorch
	```
	Note: To install a specific version, run with the following command:
	```
	python -m pip install intel_extension_for_pytorch==<version_name> -f https://developer.intel.com/ipex-whl-stable-cpu
	```

	2. After pipeline initialization, `prepare_for_ipex()` should be called to enable IPEX accelaration. Supported inference datatypes are Float32 and BFloat16.

	Note: The setting of generated image height/width for `prepare_for_ipex()` should be same as the setting of pipeline inference.
	```python
	pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", custom_pipeline="stable_diffusion_ipex")
	# For Float32
	pipe.prepare_for_ipex(prompt, dtype=torch.float32, height=512, width=512) #value of image height/width should be consistent with the pipeline inference
	# For BFloat16
	pipe.prepare_for_ipex(prompt, dtype=torch.bfloat16, height=512, width=512) #value of image height/width should be consistent with the pipeline inference
	```

	Then you can use the ipex pipeline in a similar way to the default stable diffusion pipeline.
	```python
	# For Float32
	image = pipe(prompt, num_inference_steps=20, height=512, width=512).images[0] #value of image height/width should be consistent with 'prepare_for_ipex()'
	# For BFloat16
	with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloat16):
	image = pipe(prompt, num_inference_steps=20, height=512, width=512).images[0] #value of image height/width should be consistent with 'prepare_for_ipex()'
	```

	The following code compares the performance of the original stable diffusion pipeline with the ipex-optimized pipeline.

	```python
	import torch
	import intel_extension_for_pytorch as ipex
	from diffusers import StableDiffusionPipeline
	import time

	prompt = "sailing ship in storm by Rembrandt"
	model_id = "runwayml/stable-diffusion-v1-5"
	# Helper function for time evaluation
	def elapsed_time(pipeline, nb_pass=3, num_inference_steps=20):
	# warmup
	for _ in range(2):
	images = pipeline(prompt, num_inference_steps=num_inference_steps, height=512, width=512).images
	#time evaluation
	start = time.time()
	for _ in range(nb_pass):
	pipeline(prompt, num_inference_steps=num_inference_steps, height=512, width=512)
	end = time.time()
	return (end - start) / nb_pass

	############## bf16 inference performance ###############

	# 1. IPEX Pipeline initialization
	pipe = DiffusionPipeline.from_pretrained(model_id, custom_pipeline="stable_diffusion_ipex")
	pipe.prepare_for_ipex(prompt, dtype=torch.bfloat16, height=512, width=512)

	# 2. Original Pipeline initialization
	pipe2 = StableDiffusionPipeline.from_pretrained(model_id)

	# 3. Compare performance between Original Pipeline and IPEX Pipeline
	with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloat16):
	latency = elapsed_time(pipe)
	print("Latency of StableDiffusionIPEXPipeline--bf16", latency)
	latency = elapsed_time(pipe2)
	print("Latency of StableDiffusionPipeline--bf16",latency)

	############## fp32 inference performance ###############

	# 1. IPEX Pipeline initialization
	pipe3 = DiffusionPipeline.from_pretrained(model_id, custom_pipeline="stable_diffusion_ipex")
	pipe3.prepare_for_ipex(prompt, dtype=torch.float32, height=512, width=512)

	# 2. Original Pipeline initialization
	pipe4 = StableDiffusionPipeline.from_pretrained(model_id)

	# 3. Compare performance between Original Pipeline and IPEX Pipeline
	latency = elapsed_time(pipe3)
	print("Latency of StableDiffusionIPEXPipeline--fp32", latency)
	latency = elapsed_time(pipe4)
	print("Latency of StableDiffusionPipeline--fp32",latency)

	```

	### CLIP Guided Images Mixing With Stable Diffusion

	![clip_guided_images_mixing_examples](https://huggingface.co/datasets/TheDenk/images_mixing/resolve/main/main.png)

	CLIP guided stable diffusion images mixing pipline allows to combine two images using standard diffusion models.
	This approach is using (optional) CoCa model to avoid writing image description.
	[More code examples](https://github.com/TheDenk/images_mixing)

	## Example Images Mixing (with CoCa)
	```python
	import requests
	from io import BytesIO

	import PIL
	import torch
	import open_clip
	from open_clip import SimpleTokenizer
	from diffusers import DiffusionPipeline
	from transformers import CLIPFeatureExtractor, CLIPModel


	def download_image(url):
	response = requests.get(url)
	return PIL.Image.open(BytesIO(response.content)).convert("RGB")

	# Loading additional models
	feature_extractor = CLIPFeatureExtractor.from_pretrained(
	"laion/CLIP-ViT-B-32-laion2B-s34B-b79K"
	)
	clip_model = CLIPModel.from_pretrained(
	"laion/CLIP-ViT-B-32-laion2B-s34B-b79K", torch_dtype=torch.float16
	)
	coca_model = open_clip.create_model('coca_ViT-L-14', pretrained='laion2B-s13B-b90k').to('cuda')
	coca_model.dtype = torch.float16
	coca_transform = open_clip.image_transform(
	coca_model.visual.image_size,
	is_train = False,
	mean = getattr(coca_model.visual, 'image_mean', None),
	std = getattr(coca_model.visual, 'image_std', None),
	)
	coca_tokenizer = SimpleTokenizer()

	# Pipline creating
	mixing_pipeline = DiffusionPipeline.from_pretrained(
	"CompVis/stable-diffusion-v1-4",
	custom_pipeline="clip_guided_images_mixing_stable_diffusion",
	clip_model=clip_model,
	feature_extractor=feature_extractor,
	coca_model=coca_model,
	coca_tokenizer=coca_tokenizer,
	coca_transform=coca_transform,
	torch_dtype=torch.float16,
	)
	mixing_pipeline.enable_attention_slicing()
	mixing_pipeline = mixing_pipeline.to("cuda")

	# Pipline running
	generator = torch.Generator(device="cuda").manual_seed(17)

	def download_image(url):
	response = requests.get(url)
	return PIL.Image.open(BytesIO(response.content)).convert("RGB")

	content_image = download_image("https://huggingface.co/datasets/TheDenk/images_mixing/resolve/main/boromir.jpg")
	style_image = download_image("https://huggingface.co/datasets/TheDenk/images_mixing/resolve/main/gigachad.jpg")

	pipe_images = mixing_pipeline(
	num_inference_steps=50,
	content_image=content_image,
	style_image=style_image,
	noise_strength=0.65,
	slerp_latent_style_strength=0.9,
	slerp_prompt_style_strength=0.1,
	slerp_clip_image_style_strength=0.1,
	guidance_scale=9.0,
	batch_size=1,
	clip_guidance_scale=100,
	generator=generator,
	).images
	```

	![image_mixing_result](https://huggingface.co/datasets/TheDenk/images_mixing/resolve/main/boromir_gigachad.png)

	### Stable Diffusion Mixture Tiling

	This pipeline uses the Mixture. Refer to the [Mixture](https://arxiv.org/abs/2302.02412) paper for more details.

	```python
	from diffusers import LMSDiscreteScheduler, DiffusionPipeline

	# Creater scheduler and model (similar to StableDiffusionPipeline)
	scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000)
	pipeline = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", scheduler=scheduler, custom_pipeline="mixture_tiling")
	pipeline.to("cuda")

	# Mixture of Diffusers generation
	image = pipeline(
	prompt=[[
	"A charming house in the countryside, by jakub rozalski, sunset lighting, elegant, highly detailed, smooth, sharp focus, artstation, stunning masterpiece",
	"A dirt road in the countryside crossing pastures, by jakub rozalski, sunset lighting, elegant, highly detailed, smooth, sharp focus, artstation, stunning masterpiece",
	"An old and rusty giant robot lying on a dirt road, by jakub rozalski, dark sunset lighting, elegant, highly detailed, smooth, sharp focus, artstation, stunning masterpiece"
	]],
	tile_height=640,
	tile_width=640,
	tile_row_overlap=0,
	tile_col_overlap=256,
	guidance_scale=8,
	seed=7178915308,
	num_inference_steps=50,
	)["images"][0]
	```
	![mixture_tiling_results](https://huggingface.co/datasets/kadirnar/diffusers_readme_images/resolve/main/mixture_tiling.png)

	### TensorRT Inpainting Stable Diffusion Pipeline

	The TensorRT Pipeline can be used to accelerate the Inpainting Stable Diffusion Inference run.

	NOTE: The ONNX conversions and TensorRT engine build may take up to 30 minutes.

	```python
	import requests
	from io import BytesIO
	from PIL import Image
	import torch
	from diffusers import PNDMScheduler
	from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline

	# Use the PNDMScheduler scheduler here instead
	scheduler = PNDMScheduler.from_pretrained("stabilityai/stable-diffusion-2-inpainting", subfolder="scheduler")


	pipe = StableDiffusionInpaintPipeline.from_pretrained("stabilityai/stable-diffusion-2-inpainting",
	custom_pipeline="stable_diffusion_tensorrt_inpaint",
	revision='fp16',
	torch_dtype=torch.float16,
	scheduler=scheduler,
	)

	# re-use cached folder to save ONNX models and TensorRT Engines
	pipe.set_cached_folder("stabilityai/stable-diffusion-2-inpainting", revision='fp16',)

	pipe = pipe.to("cuda")

	url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
	response = requests.get(url)
	input_image = Image.open(BytesIO(response.content)).convert("RGB")

	mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
	response = requests.get(mask_url)
	mask_image = Image.open(BytesIO(response.content)).convert("RGB")

	prompt = "a mecha robot sitting on a bench"
	image = pipe(prompt, image=input_image, mask_image=mask_image, strength=0.75,).images[0]
	image.save('tensorrt_inpaint_mecha_robot.png')
	```

	### Stable Diffusion Mixture Canvas

	This pipeline uses the Mixture. Refer to the [Mixture](https://arxiv.org/abs/2302.02412) paper for more details.

	```python
	from PIL import Image
	from diffusers import LMSDiscreteScheduler, DiffusionPipeline
	from diffusers.pipelines.pipeline_utils import Image2ImageRegion, Text2ImageRegion, preprocess_image


	# Load and preprocess guide image
	iic_image = preprocess_image(Image.open("input_image.png").convert("RGB"))

	# Creater scheduler and model (similar to StableDiffusionPipeline)
	scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000)
	pipeline = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", scheduler=scheduler).to("cuda:0", custom_pipeline="mixture_canvas")
	pipeline.to("cuda")

	# Mixture of Diffusers generation
	output = pipeline(
	canvas_height=800,
	canvas_width=352,
	regions=[
	Text2ImageRegion(0, 800, 0, 352, guidance_scale=8,
	prompt=f"best quality, masterpiece, WLOP, sakimichan, art contest winner on pixiv, 8K, intricate details, wet effects, rain drops, ethereal, mysterious, futuristic, UHD, HDR, cinematic lighting, in a beautiful forest, rainy day, award winning, trending on artstation, beautiful confident cheerful young woman, wearing a futuristic sleeveless dress, ultra beautiful detailed eyes, hyper-detailed face, complex, perfect, model, textured, chiaroscuro, professional make-up, realistic, figure in frame, "),
	Image2ImageRegion(352-800, 352, 0, 352, reference_image=iic_image, strength=1.0),
	],
	num_inference_steps=100,
	seed=5525475061,
	)["images"][0]
	```
	![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()
	```

	### 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

	```