inference code added

README.md

@@ -12,6 +12,7 @@ tags:
 ---
 
 # Introduction
+<a href="https://colab.research.google.com/drive/1VcqzXZmilntec3AsIyzCqlstEhX4Pa1o?usp=sharing" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
 
 The &lambda;-ECLIPSE model is a light weight support for multi-concept personalization. &lambda;-ECLIPSE is tiny T2I prior model designed for Kandinsky v2.2 diffusion image generator.
 
@@ -32,14 +33,61 @@ More examples at: [Gallery](https://eclipse-t2i.github.io/Lambda-ECLIPSE/gallery
 
 ## Installation
 ```bash
-git clone git@github.com:eclipse-t2i/eclipse-inference.git
+git clone https://github.com/eclipse-t2i/lambda-eclipse-inference.git
 conda create -p ./venv python=3.9
 pip install -r requirements.txt
 ```
 
 ## Run Inference
+<a href="https://colab.research.google.com/drive/1VcqzXZmilntec3AsIyzCqlstEhX4Pa1o?usp=sharing" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
 
-TBD
+```bash
+import os
+import torch
+from transformers import (
+    CLIPTextModelWithProjection,
+    CLIPTokenizer,
+)
+from src.pipelines.pipeline_kandinsky_subject_prior import KandinskyPriorPipeline
+from src.priors.lambda_prior_transformer import PriorTransformer
+from diffusers import DiffusionPipeline
+
+text_encoder = CLIPTextModelWithProjection.from_pretrained(
+    "laion/CLIP-ViT-bigG-14-laion2B-39B-b160k",
+    projection_dim=1280,
+    torch_dtype=torch.float32,
+)
+tokenizer = CLIPTokenizer.from_pretrained("laion/CLIP-ViT-bigG-14-laion2B-39B-b160k")
+
+prior = PriorTransformer.from_pretrained("ECLIPSE-Community/Lambda-ECLIPSE-Prior-v1.0")
+pipe_prior = KandinskyPriorPipeline.from_pretrained(
+    "kandinsky-community/kandinsky-2-2-prior",
+    prior=prior,
+    text_encoder=text_encoder,
+    tokenizer=tokenizer,
+).to("cuda")
+
+pipe = DiffusionPipeline.from_pretrained(
+    "kandinsky-community/kandinsky-2-2-decoder"
+).to("cuda")
+
+raw_data = {
+    "prompt": args.prompt,
+    "subject_images": [args.subject1_path, args.subject2_path],
+    "subject_keywords": [args.subject1_name, args.subject2_name]
+}
+image_emb, negative_image_emb = pipe_prior(
+    raw_data=raw_data,
+).to_tuple()
+image = pipe(
+    image_embeds=image_emb,
+    negative_image_embeds=negative_image_emb,
+    num_inference_steps=50,
+    guidance_scale=7.5,
+).images
+
+image[0]
+```
 
 ## Important Notes (and limitations):