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.dockerignore

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+.git

.gitignore

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+.cog/
+__pycache__/
+diffusers-cache/

LICENSE

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README.md

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+# Stable Diffusion v2 Cog model
+
+[![Replicate](https://replicate.com/stability-ai/stable-diffusion/badge)](https://replicate.com/stability-ai/stable-diffusion) 
+
+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"

cog.yaml

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+build:
+  gpu: true
+  cuda: "11.8"
+  python_version: "3.11.1"
+  python_packages:
+    - "diffusers==0.11.1"
+    - "torch==1.13.0"
+    - "ftfy==6.1.1"
+    - "scipy==1.9.3"
+    - "transformers==4.25.1"
+    - "accelerate==0.15.0"
+    - "huggingface-hub==0.13.2"
+
+predict: "predict.py:Predictor"

download-weights

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+#!/usr/bin/env python
+
+import os
+import shutil
+import sys
+
+from diffusers import StableDiffusionPipeline
+from diffusers.pipelines.stable_diffusion.safety_checker import \
+    StableDiffusionSafetyChecker
+
+# append project directory to path so predict.py can be imported
+sys.path.append('.')
+
+from predict import MODEL_CACHE, MODEL_ID, SAFETY_MODEL_ID
+
+if os.path.exists(MODEL_CACHE):
+    shutil.rmtree(MODEL_CACHE)
+os.makedirs(MODEL_CACHE, exist_ok=True)
+
+saftey_checker = StableDiffusionSafetyChecker.from_pretrained(
+    SAFETY_MODEL_ID,
+    cache_dir=MODEL_CACHE,
+)
+
+pipe = StableDiffusionPipeline.from_pretrained(
+    MODEL_ID,
+    cache_dir=MODEL_CACHE,
+)

predict.py

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+import os
+from typing import List
+
+import torch
+from cog import BasePredictor, Input, Path
+from diffusers import (
+    StableDiffusionPipeline,
+    PNDMScheduler,
+    LMSDiscreteScheduler,
+    DDIMScheduler,
+    EulerDiscreteScheduler,
+    EulerAncestralDiscreteScheduler,
+    DPMSolverMultistepScheduler,
+)
+from diffusers.pipelines.stable_diffusion.safety_checker import (
+    StableDiffusionSafetyChecker,
+)
+
+# 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"
+SAFETY_MODEL_ID = "CompVis/stable-diffusion-safety-checker"
+
+class Predictor(BasePredictor):
+    def setup(self):
+        """Load the model into memory to make running multiple predictions efficient"""
+        print("Loading pipeline...")
+        safety_checker = StableDiffusionSafetyChecker.from_pretrained(
+            SAFETY_MODEL_ID,
+            cache_dir=MODEL_CACHE,
+            local_files_only=True,
+        )
+        self.pipe = StableDiffusionPipeline.from_pretrained(
+            MODEL_ID,
+            safety_checker=safety_checker,
+            cache_dir=MODEL_CACHE,
+            local_files_only=True,
+        ).to("cuda")
+
+    @torch.inference_mode()
+    def predict(
+        self,
+        prompt: str = Input(
+            description="Input prompt",
+            default="a photo of an astronaut riding a horse on mars",
+        ),
+        negative_prompt: str = Input(
+            description="Specify things to not see in the output",
+            default=None,
+        ),
+        width: int = Input(
+            description="Width of output image. Maximum size is 1024x768 or 768x1024 because of memory limits",
+            choices=[128, 256, 384, 448, 512, 576, 640, 704, 768, 832, 896, 960, 1024],
+            default=768,
+        ),
+        height: int = Input(
+            description="Height of output image. Maximum size is 1024x768 or 768x1024 because of memory limits",
+            choices=[128, 256, 384, 448, 512, 576, 640, 704, 768, 832, 896, 960, 1024],
+            default=768,
+        ),
+        num_outputs: int = Input(
+            description="Number of images to output.",
+            ge=1,
+            le=4,
+            default=1,
+        ),
+        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=7.5
+        ),
+        scheduler: str = Input(
+            default="DPMSolverMultistep",
+            choices=[
+                "DDIM",
+                "K_EULER",
+                "DPMSolverMultistep",
+                "K_EULER_ANCESTRAL",
+                "PNDM",
+                "KLMS",
+            ],
+            description="Choose a scheduler.",
+        ),
+        seed: int = Input(
+            description="Random seed. Leave blank to randomize the seed", default=None
+        ),
+    ) -> List[Path]:
+        """Run a single prediction on the model"""
+        if seed is None:
+            seed = int.from_bytes(os.urandom(2), "big")
+        print(f"Using seed: {seed}")
+
+        if width * height > 786432:
+            raise ValueError(
+                "Maximum size is 1024x768 or 768x1024 pixels, because of memory limits. Please select a lower width or height."
+            )
+
+        self.pipe.scheduler = make_scheduler(scheduler, self.pipe.scheduler.config)
+
+        generator = torch.Generator("cuda").manual_seed(seed)
+        output = self.pipe(
+            prompt=[prompt] * num_outputs if prompt is not None else None,
+            negative_prompt=[negative_prompt] * num_outputs
+            if negative_prompt is not None
+            else None,
+            width=width,
+            height=height,
+            guidance_scale=guidance_scale,
+            generator=generator,
+            num_inference_steps=num_inference_steps,
+        )
+
+        output_paths = []
+        for i, sample in enumerate(output.images):
+            if output.nsfw_content_detected and output.nsfw_content_detected[i]:
+                continue
+
+            output_path = f"/tmp/out-{i}.png"
+            sample.save(output_path)
+            output_paths.append(Path(output_path))
+
+        if len(output_paths) == 0:
+            raise Exception(
+                f"NSFW content detected. Try running it again, or try a different prompt."
+            )
+
+        return output_paths
+
+
+def make_scheduler(name, config):
+    return {
+        "PNDM": PNDMScheduler.from_config(config),
+        "KLMS": LMSDiscreteScheduler.from_config(config),
+        "DDIM": DDIMScheduler.from_config(config),
+        "K_EULER": EulerDiscreteScheduler.from_config(config),
+        "K_EULER_ANCESTRAL": EulerAncestralDiscreteScheduler.from_config(config),
+        "DPMSolverMultistep": DPMSolverMultistepScheduler.from_config(config),
+    }[name]

settings.json

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+{
+    "python.formatting.provider": "black"
+}