Stable Diffusion

requirements.txt

# --index-url https://download.pytorch.org/whl/nightly/cpu
accelerate
diffusers @ git+https://github.com/kashif/diffusers.git@a3dc21385b7386beb3dab3a9845962ede6765887
huggingface
pillow
python-slugify[unidecode]
torch
transformers
peft

SDCascade.py

import os

from accelerate import Accelerator
from diffusers import StableCascadeDecoderPipeline, StableCascadePriorPipeline
from slugify import slugify
import torch

priorModelId = "stabilityai/stable-cascade-prior"
modelId = "stabilityai/stable-cascade"
dataType = torch.float32 #16 is buggy
outputDir = "Output"

# Use the Pipelines
prior = StableCascadePriorPipeline.from_pretrained(priorModelId, torch_dtype=dataType)
decoder = StableCascadeDecoderPipeline.from_pretrained(modelId, torch_dtype=dataType)

# Use Accelerator
accelerator = Accelerator()
prior = prior.to(accelerator.device)
decoder = decoder.to(accelerator.device)

# User Data
userPrompt = input("Enter a description of what you would like to Draw: ")
userPrompt = userPrompt or "A potato"
negativePrompt = input("Enter a description of what you would not like to Draw: ")
negativePrompt = negativePrompt or ""
inferenceSteps = 25 # Default of 50.
numImages = 4 # Should set to 1 for 1 Image.

# Start Generating
slug = slugify(userPrompt)
print("Using {} to generate image for Prompt: {} ...".format(accelerator.device, userPrompt))

priorOutput = prior(
    prompt=userPrompt,
    negative_prompt=negativePrompt,
    height=1024,
    width=1024,
    guidance_scale=4.0,
    num_images_per_prompt=numImages,
    num_inference_steps=inferenceSteps
)

imagesGenerated = decoder(
    image_embeddings=priorOutput.image_embeddings,
    prompt=userPrompt,
    negative_prompt=negativePrompt,
    guidance_scale=0.0,
    output_type="pil",
    num_inference_steps=inferenceSteps
)

if not os.path.exists(outputDir):
    os.makedirs(outputDir)

for idx, image in enumerate(imagesGenerated.images):
    fileToGenerate = os.path.join(outputDir, "SDC-{}-{}.png".format(slug, idx))
    image.save(fileToGenerate)

SDv2.py

import os

from accelerate import Accelerator
from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler
from slugify import slugify
import torch

modelId = "stabilityai/stable-diffusion-2-1"
outputDir = "Output"

# Use the DPMSolverMultistepScheduler (DPM-Solver++) scheduler here instead
pipe = StableDiffusionPipeline.from_pretrained(modelId)
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)

# Use Accelerator
accelerator = Accelerator()
pipe = pipe.to(accelerator.device)

# User Data
userPrompt = input("Enter a description of what you would like to Draw: ")
userPrompt = userPrompt or "A potato"
inferenceSteps = 25 # Default of 50.
numImages = 4 # Should set to 1 for 1 Image.

# Start Generating
slug = slugify(userPrompt)
print("Using {} to generate image for Prompt: {} ...".format(accelerator.device, userPrompt))

if not os.path.exists(outputDir):
    os.makedirs(outputDir)

for idx in range(numImages):
    imagesGenerated = pipe(prompt = userPrompt, num_inference_steps = inferenceSteps)
    for _, image in enumerate(imagesGenerated.images):
        fileToGenerate = os.path.join(outputDir, "SDv2-{}-{}.png".format(slug, idx))
        image.save(fileToGenerate)