Use replicate-weights

.dockerignore

@@ -1 +1,20 @@
+# The .dockerignore file excludes files from the container build process.
+#
+# https://docs.docker.com/engine/reference/builder/#dockerignore-file
+
+# Exclude Git files
 .git
+.github
+.gitignore
+
+# Exclude Python cache files
+__pycache__
+.mypy_cache
+.pytest_cache
+.ruff_cache
+
+# Exclude weights
+diffusers-cache
+
+# Exclude Python virtual environment
+/venv

.gitignore

@@ -1,3 +1,4 @@
-.cog/
+.vscode/
 __pycache__/
-diffusers-cache/
+diffusers-cache/
+.cog/

README.md

@@ -4,10 +4,12 @@
 
 This is an implementation of the [Diffusers Stable Diffusion v2.1](https://huggingface.co/stabilityai/stable-diffusion-2-1) as a Cog model. [Cog packages machine learning models as standard containers.](https://github.com/replicate/cog)
 
-First, download the pre-trained weights:
-
-    cog run script/download-weights 
-
-Then, you can run predictions:
-
-    cog predict -i prompt="monkey scuba diving"
+Make single prediction:
+```bash
+cog predict -i prompt="monkey scuba diving"
+```
+
+Run HTTP API for making predictions:
+```bash
+cog run -p 5000 python -m cog.server.http
+```

cog.yaml

@@ -11,4 +11,7 @@ build:
     - "accelerate==0.15.0"
     - "huggingface-hub==0.13.2"
 
+  run:
+    - curl -o /usr/local/bin/pget -L "https://github.com/replicate/pget/releases/download/v0.0.6/pget" && chmod +x /usr/local/bin/pget
+
 predict: "predict.py:Predictor"

predict.py

@@ -4,33 +4,48 @@
 import torch
 from cog import BasePredictor, Input, Path
 from diffusers import (
-    StableDiffusionPipeline,
-    PNDMScheduler,
-    LMSDiscreteScheduler,
     DDIMScheduler,
-    EulerDiscreteScheduler,
-    EulerAncestralDiscreteScheduler,
     DPMSolverMultistepScheduler,
+    EulerAncestralDiscreteScheduler,
+    EulerDiscreteScheduler,
+    LMSDiscreteScheduler,
+    PNDMScheduler,
+    StableDiffusionPipeline,
 )
 from diffusers.pipelines.stable_diffusion.safety_checker import (
     StableDiffusionSafetyChecker,
 )
 
+from weights_downloader import WeightsDownloader
+
 # MODEL_ID refers to a diffusers-compatible model on HuggingFace
 # e.g. prompthero/openjourney-v2, wavymulder/Analog-Diffusion, etc
-MODEL_ID = "stabilityai/stable-diffusion-2-1"
 MODEL_CACHE = "diffusers-cache"
+
+SD_MODEL_CACHE = os.path.join(MODEL_CACHE, "models--stabilityai--stable-diffusion-2-1")
+MODEL_ID = "stabilityai/stable-diffusion-2-1"
+SD_URL = "https://weights.replicate.delivery/default/stable-diffusion/stable-diffusion-2-1.tar"
+
+SAFETY_CACHE = os.path.join(
+    MODEL_CACHE, "models--CompVis--stable-diffusion-safety-checker"
+)
 SAFETY_MODEL_ID = "CompVis/stable-diffusion-safety-checker"
+SAFETY_URL = "https://weights.replicate.delivery/default/stable-diffusion/stable-diffusion-safety-checker.tar"
+
 
 class Predictor(BasePredictor):
     def setup(self):
         """Load the model into memory to make running multiple predictions efficient"""
+
         print("Loading pipeline...")
+        WeightsDownloader.download_if_not_exists(SAFETY_URL, SAFETY_CACHE)
         safety_checker = StableDiffusionSafetyChecker.from_pretrained(
             SAFETY_MODEL_ID,
             cache_dir=MODEL_CACHE,
             local_files_only=True,
         )
+
+        WeightsDownloader.download_if_not_exists(SD_URL, SD_MODEL_CACHE)
         self.pipe = StableDiffusionPipeline.from_pretrained(
             MODEL_ID,
             safety_checker=safety_checker,

weights_downloader.py

@@ -0,0 +1,18 @@
+import os
+import subprocess
+import time
+
+
+class WeightsDownloader:
+    @staticmethod
+    def download_if_not_exists(url, dest):
+        if not os.path.exists(dest):
+            WeightsDownloader.download(url, dest)
+
+    @staticmethod
+    def download(url, dest):
+        start = time.time()
+        print("downloading url: ", url)
+        print("downloading to: ", dest)
+        subprocess.check_call(["pget", "-x", url, dest], close_fds=False)
+        print("downloading took: ", time.time() - start)