model card added

README.md

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+---
+license: openrail++
+language:
+- en
+library_name: diffusers
+tags:
+- text-to-image
+- prior
+- unclip
+- kandinskyv2.2
+---
+
+
+# Introduction
+
+This ECLIPSE model weight is a tiny (33M parameter) non-diffusion text-to-image prior model trained on 5M LAION-HighRes subset data.
+
+Despite being so small and trained on limited amount of data, ECLIPSE priors achieves results that of 1 Billion parameter T2I prior models trained on millions of image-text pairs.
+
+## Installation
+```bash
+git clone git@github.com:eclipse-t2i/eclipse-inference.git
+
+conda create -p ./venv python=3.9
+pip install -r requirements.txt
+```
+
+## Run Inference
+
+This repository supports two pre-trained image decoders: [Karlo-v1-alpha](https://huggingface.co/kakaobrain/karlo-v1-alpha) and [Kandinsky-v2.2](https://huggingface.co/kandinsky-community/kandinsky-2-2-decoder).
+Note: ECLIPSE prior is not a diffusion model -- while image decoders are.
+
+### Karlo Inference
+```python
+from src.pipelines.pipeline_unclip import UnCLIPPipeline
+from src.priors.prior_transformer import PriorTransformer
+
+prior = PriorTransformer.from_pretrained("ECLIPSE-Community/ECLIPSE_Karlo_Prior")
+pipe = UnCLIPPipeline.from_pretrained("kakaobrain/karlo-v1-alpha", prior=prior).to("cuda")
+
+prompt="black apples in the basket"
+images = pipe(prompt, decoder_guidance_scale=7.5).images
+
+images[0]
+```
+
+### Kandinsky Inference
+```python
+from src.pipelines.pipeline_kandinsky_prior import KandinskyPriorPipeline
+from src.priors.prior_transformer import PriorTransformer
+from diffusers import DiffusionPipeline
+
+prior = PriorTransformer.from_pretrained("ECLIPSE-Community/ECLIPSE_KandinskyV22_Prior")
+pipe_prior = KandinskyPriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior", prior=prior).to("cuda")
+
+pipe = DiffusionPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder").to("cuda")
+
+prompt = "black apples in the basket"
+image_embeds, negative_image_embeds = pipe_prior(prompt).to_tuple()
+images = pipe(
+    num_inference_steps=50,
+    image_embeds=image_embeds,
+    negative_image_embeds=negative_image_embeds,
+).images
+
+images[0]
+```