[1929]: Add CLIP guidance for Img2Img stable diffusion pipeline (#2723)

* [Img2Img]: Copyover img2img pipeline

* [Img2Img]: img2img pipeline

* [Img2Img]: img2img pipeline

* [Img2Img]: img2img pipeline

---------

Co-authored-by: njindal <njindal@adobe.com>

Author: nipunjindal

examples/community/README.md

@@ -30,6 +30,7 @@ MagicMix | Diffusion Pipeline for semantic mixing of an image and a text prompt
 | 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/) | 
 
 
 
@@ -1074,3 +1075,58 @@ for strength in np.linspace(0.1, 1, 25):
 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)