├── .dockerignore ├── .gitignore ├── .vscode └── settings.json ├── LICENSE ├── README.md ├── cog.yaml ├── predict.py └── script └── download-weights /.dockerignore: -------------------------------------------------------------------------------- 1 | .git 2 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | .cog/ 2 | __pycache__/ 3 | diffusers-cache/ -------------------------------------------------------------------------------- /.vscode/settings.json: -------------------------------------------------------------------------------- 1 | { 2 | "python.formatting.provider": "black" 3 | } 4 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | 2 | Apache License 3 | Version 2.0, January 2004 4 | http://www.apache.org/licenses/ 5 | 6 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 7 | 8 | 1. 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We also recommend that a 186 | file or class name and description of purpose be included on the 187 | same "printed page" as the copyright notice for easier 188 | identification within third-party archives. 189 | 190 | Copyright 2022, Replicate, Inc. 191 | 192 | Licensed under the Apache License, Version 2.0 (the "License"); 193 | you may not use this file except in compliance with the License. 194 | You may obtain a copy of the License at 195 | 196 | http://www.apache.org/licenses/LICENSE-2.0 197 | 198 | Unless required by applicable law or agreed to in writing, software 199 | distributed under the License is distributed on an "AS IS" BASIS, 200 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 201 | See the License for the specific language governing permissions and 202 | limitations under the License. 203 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Stable Diffusion v2 Cog model 2 | 3 | [![Replicate](https://replicate.com/stability-ai/stable-diffusion/badge)](https://replicate.com/stability-ai/stable-diffusion) 4 | 5 | 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) 6 | 7 | First, download the pre-trained weights: 8 | 9 | cog run script/download-weights 10 | 11 | Then, you can run predictions: 12 | 13 | cog predict -i prompt="monkey scuba diving" 14 | -------------------------------------------------------------------------------- /cog.yaml: -------------------------------------------------------------------------------- 1 | build: 2 | gpu: true 3 | cuda: "11.8" 4 | python_version: "3.11.1" 5 | python_packages: 6 | - "diffusers==0.11.1" 7 | - "torch==1.13.0" 8 | - "ftfy==6.1.1" 9 | - "scipy==1.9.3" 10 | - "transformers==4.25.1" 11 | - "accelerate==0.15.0" 12 | - "huggingface-hub==0.13.2" 13 | 14 | predict: "predict.py:Predictor" 15 | -------------------------------------------------------------------------------- /predict.py: -------------------------------------------------------------------------------- 1 | import os 2 | from typing import List 3 | 4 | import torch 5 | from cog import BasePredictor, Input, Path 6 | from diffusers import ( 7 | StableDiffusionPipeline, 8 | PNDMScheduler, 9 | LMSDiscreteScheduler, 10 | DDIMScheduler, 11 | EulerDiscreteScheduler, 12 | EulerAncestralDiscreteScheduler, 13 | DPMSolverMultistepScheduler, 14 | ) 15 | from diffusers.pipelines.stable_diffusion.safety_checker import ( 16 | StableDiffusionSafetyChecker, 17 | ) 18 | 19 | # MODEL_ID refers to a diffusers-compatible model on HuggingFace 20 | # e.g. prompthero/openjourney-v2, wavymulder/Analog-Diffusion, etc 21 | MODEL_ID = "stabilityai/stable-diffusion-2-1" 22 | MODEL_CACHE = "diffusers-cache" 23 | SAFETY_MODEL_ID = "CompVis/stable-diffusion-safety-checker" 24 | 25 | class Predictor(BasePredictor): 26 | def setup(self): 27 | """Load the model into memory to make running multiple predictions efficient""" 28 | print("Loading pipeline...") 29 | safety_checker = StableDiffusionSafetyChecker.from_pretrained( 30 | SAFETY_MODEL_ID, 31 | cache_dir=MODEL_CACHE, 32 | local_files_only=True, 33 | ) 34 | self.pipe = StableDiffusionPipeline.from_pretrained( 35 | MODEL_ID, 36 | safety_checker=safety_checker, 37 | cache_dir=MODEL_CACHE, 38 | local_files_only=True, 39 | ).to("cuda") 40 | 41 | @torch.inference_mode() 42 | def predict( 43 | self, 44 | prompt: str = Input( 45 | description="Input prompt", 46 | default="a photo of an astronaut riding a horse on mars", 47 | ), 48 | negative_prompt: str = Input( 49 | description="Specify things to not see in the output", 50 | default=None, 51 | ), 52 | width: int = Input( 53 | description="Width of output image. Maximum size is 1024x768 or 768x1024 because of memory limits", 54 | choices=[128, 256, 384, 448, 512, 576, 640, 704, 768, 832, 896, 960, 1024], 55 | default=768, 56 | ), 57 | height: int = Input( 58 | description="Height of output image. Maximum size is 1024x768 or 768x1024 because of memory limits", 59 | choices=[128, 256, 384, 448, 512, 576, 640, 704, 768, 832, 896, 960, 1024], 60 | default=768, 61 | ), 62 | num_outputs: int = Input( 63 | description="Number of images to output.", 64 | ge=1, 65 | le=4, 66 | default=1, 67 | ), 68 | num_inference_steps: int = Input( 69 | description="Number of denoising steps", ge=1, le=500, default=50 70 | ), 71 | guidance_scale: float = Input( 72 | description="Scale for classifier-free guidance", ge=1, le=20, default=7.5 73 | ), 74 | scheduler: str = Input( 75 | default="DPMSolverMultistep", 76 | choices=[ 77 | "DDIM", 78 | "K_EULER", 79 | "DPMSolverMultistep", 80 | "K_EULER_ANCESTRAL", 81 | "PNDM", 82 | "KLMS", 83 | ], 84 | description="Choose a scheduler.", 85 | ), 86 | seed: int = Input( 87 | description="Random seed. Leave blank to randomize the seed", default=None 88 | ), 89 | ) -> List[Path]: 90 | """Run a single prediction on the model""" 91 | if seed is None: 92 | seed = int.from_bytes(os.urandom(2), "big") 93 | print(f"Using seed: {seed}") 94 | 95 | if width * height > 786432: 96 | raise ValueError( 97 | "Maximum size is 1024x768 or 768x1024 pixels, because of memory limits. Please select a lower width or height." 98 | ) 99 | 100 | self.pipe.scheduler = make_scheduler(scheduler, self.pipe.scheduler.config) 101 | 102 | generator = torch.Generator("cuda").manual_seed(seed) 103 | output = self.pipe( 104 | prompt=[prompt] * num_outputs if prompt is not None else None, 105 | negative_prompt=[negative_prompt] * num_outputs 106 | if negative_prompt is not None 107 | else None, 108 | width=width, 109 | height=height, 110 | guidance_scale=guidance_scale, 111 | generator=generator, 112 | num_inference_steps=num_inference_steps, 113 | ) 114 | 115 | output_paths = [] 116 | for i, sample in enumerate(output.images): 117 | if output.nsfw_content_detected and output.nsfw_content_detected[i]: 118 | continue 119 | 120 | output_path = f"/tmp/out-{i}.png" 121 | sample.save(output_path) 122 | output_paths.append(Path(output_path)) 123 | 124 | if len(output_paths) == 0: 125 | raise Exception( 126 | f"NSFW content detected. Try running it again, or try a different prompt." 127 | ) 128 | 129 | return output_paths 130 | 131 | 132 | def make_scheduler(name, config): 133 | return { 134 | "PNDM": PNDMScheduler.from_config(config), 135 | "KLMS": LMSDiscreteScheduler.from_config(config), 136 | "DDIM": DDIMScheduler.from_config(config), 137 | "K_EULER": EulerDiscreteScheduler.from_config(config), 138 | "K_EULER_ANCESTRAL": EulerAncestralDiscreteScheduler.from_config(config), 139 | "DPMSolverMultistep": DPMSolverMultistepScheduler.from_config(config), 140 | }[name] 141 | -------------------------------------------------------------------------------- /script/download-weights: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | 3 | import os 4 | import shutil 5 | import sys 6 | 7 | from diffusers import StableDiffusionPipeline 8 | from diffusers.pipelines.stable_diffusion.safety_checker import \ 9 | StableDiffusionSafetyChecker 10 | 11 | # append project directory to path so predict.py can be imported 12 | sys.path.append('.') 13 | 14 | from predict import MODEL_CACHE, MODEL_ID, SAFETY_MODEL_ID 15 | 16 | if os.path.exists(MODEL_CACHE): 17 | shutil.rmtree(MODEL_CACHE) 18 | os.makedirs(MODEL_CACHE, exist_ok=True) 19 | 20 | saftey_checker = StableDiffusionSafetyChecker.from_pretrained( 21 | SAFETY_MODEL_ID, 22 | cache_dir=MODEL_CACHE, 23 | ) 24 | 25 | pipe = StableDiffusionPipeline.from_pretrained( 26 | MODEL_ID, 27 | cache_dir=MODEL_CACHE, 28 | ) 29 | --------------------------------------------------------------------------------