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Merged
anotherjesse merged 1 commit into from
Dec 23, 2022
Merged

add back schedulers for v2.1 #63

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4 changes: 2 additions & 2 deletions README.md
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Original file line number Diff line number Diff line change
@@ -1,8 +1,8 @@
# Stable Diffusion v2 Cog model

[![Replicate](https://replicate.com/cjwbw/stable-diffusion-v2/badge)](https://replicate.com/cjwbw/stable-diffusion-v2)
[![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](https://huggingface.co/stabilityai/stable-diffusion-2) as a Cog model. [Cog packages machine learning models as standard containers.](https://github.com/replicate/cog)
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:

Expand Down
17 changes: 13 additions & 4 deletions predict.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,10 +4,13 @@
import torch
from cog import BasePredictor, Input, Path
from diffusers import (
StableDiffusionPipeline,
PNDMScheduler,
LMSDiscreteScheduler,
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerDiscreteScheduler,
StableDiffusionPipeline,
EulerAncestralDiscreteScheduler,
DPMSolverMultistepScheduler,
)

MODEL_ID = "stabilityai/stable-diffusion-2-1"
Expand Down Expand Up @@ -62,13 +65,16 @@ def predict(
description="Scale for classifier-free guidance", ge=1, le=20, default=7.5
),
scheduler: str = Input(
default="K_EULER",
default="DPMSolverMultistep",
choices=[
"DDIM",
"K_EULER",
"DPMSolverMultistep",
"K_EULER_ANCESTRAL",
"PNDM",
"KLMS",
],
description="Choose a scheduler",
description="Choose a scheduler.",
),
seed: int = Input(
description="Random seed. Leave blank to randomize the seed", default=None
Expand Down Expand Up @@ -110,7 +116,10 @@ def predict(

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]