[Community Pipeline] UnCLIP Text Interpolation Pipeline

examples/community/README.md

@@ -27,6 +27,8 @@ If a community doesn't work as expected, please open an issue and ping the autho
 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/) | 
+
 
 
 
@@ -951,3 +953,39 @@ print(pipeline.prior_scheduler)
 
 ![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 a frog"
+end_prompt = "A painting of a horse"
+#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/frog_horse_0.png)
+![result_1](https://huggingface.co/datasets/NagaSaiAbhinay/UnCLIPTextInterpolationSamples/resolve/main/frog_horse_1.png)
+![result_2](https://huggingface.co/datasets/NagaSaiAbhinay/UnCLIPTextInterpolationSamples/resolve/main/frog_horse_2.png)
+![result_3](https://huggingface.co/datasets/NagaSaiAbhinay/UnCLIPTextInterpolationSamples/resolve/main/frog_horse_3.png)
+![result_4](https://huggingface.co/datasets/NagaSaiAbhinay/UnCLIPTextInterpolationSamples/resolve/main/frog_horse_4.png)
+![result_5](https://huggingface.co/datasets/NagaSaiAbhinay/UnCLIPTextInterpolationSamples/resolve/main/frog_horse_5.png)