Add Replicate demo and API

README.md

@@ -63,6 +63,7 @@ images = model.generate_text2img(
 
 [![Framework: PyTorch](https://img.shields.io/badge/Framework-PyTorch-orange.svg)](https://pytorch.org/) [![Huggingface space](https://img.shields.io/badge/🤗-Huggingface-yello.svg)](https://huggingface.co/sberbank-ai/Kandinsky_2.1)
 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1xSbu-b-EwYd6GdaFPRVgvXBX_mciZ41e?usp=sharing)
+[![Replicate](https://replicate.com/cjwbw/kandinsky-2/badge)](https://replicate.com/cjwbw/kandinsky-2) 
 
 
 [Habr post](https://habr.com/ru/company/sberbank/blog/725282/)

cog.yaml

@@ -0,0 +1,28 @@
+# Configuration for Cog ⚙️
+# Reference: https://github.com/replicate/cog/blob/main/docs/yaml.md
+
+build:
+  gpu: true
+  cuda: "11.6"
+  python_version: "3.10"
+  system_packages:
+    - "libgl1-mesa-glx"
+    - "libglib2.0-0"
+  python_packages:
+    - "torch==1.13.1"
+    - "sentencepiece==0.1.97"
+    - "accelerate==0.16.0"
+    - "Pillow==9.5.0"
+    - "attrs==22.2.0"
+    - "opencv-python==4.7.0.72"
+    - git+https://github.com/openai/CLIP.git
+    - "tqdm==4.65.0"
+    - "ftfy==6.1.1"
+    - "blobfile==2.0.1"
+    - "transformers==4.23.1"
+    - "torchvision==0.14.1"
+    - "omegaconf==2.3.0"
+    - "pytorch_lightning==2.0.1"
+    - "einops==0.6.0"
+
+predict: "predict.py:Predictor"

predict.py

@@ -0,0 +1,65 @@
+from typing import List
+from cog import BasePredictor, Input, Path
+from kandinsky2 import get_kandinsky2
+
+
+class Predictor(BasePredictor):
+    def setup(self):
+        self.model = get_kandinsky2(
+            "cuda",
+            task_type="text2img",
+            cache_dir="./kandinsky2-weights",
+            model_version="2.1",
+            use_flash_attention=False,
+        )
+
+    def predict(
+        self,
+        prompt: str = Input(description="Input Prompt", default="red cat, 4k photo"),
+        num_inference_steps: int = Input(
+            description="Number of denoising steps", ge=1, le=500, default=50
+        ),
+        guidance_scale: float = Input(
+            description="Scale for classifier-free guidance", ge=1, le=20, default=4
+        ),
+        scheduler: str = Input(
+            description="Choose a scheduler",
+            default="p_sampler",
+            choices=["ddim_sampler", "p_sampler", "plms_sampler"],
+        ),
+        prior_cf_scale: int = Input(default=4),
+        prior_steps: str = Input(default="5"),
+        width: int = Input(
+            description="Choose width. Lower the setting if out of memory.",
+            default=512,
+            choices=[256, 288, 432, 512, 576, 768, 1024],
+        ),
+        height: int = Input(
+            description="Choose height. Lower the setting if out of memory.",
+            default=512,
+            choices=[256, 288, 432, 512, 576, 768, 1024],
+        ),
+        batch_size: int = Input(
+            description="Choose batch size. Lower the setting if out of memory.",
+            default=1,
+            choices=[1, 2, 3, 4],
+        ),
+    ) -> List[Path]:
+        images = self.model.generate_text2img(
+            prompt,
+            num_steps=num_inference_steps,
+            batch_size=batch_size,
+            guidance_scale=guidance_scale,
+            h=height,
+            w=width,
+            sampler=scheduler,
+            prior_cf_scale=prior_cf_scale,
+            prior_steps=prior_steps,
+        )
+        output = []
+        for i, im in enumerate(images):
+            out = f"/tmp/out_{i}.png"
+            im.save(out)
+            im.save(f"out_{i}.png")
+            output.append(Path(out))
+        return output