File size: 2,921 Bytes
a67f088 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 |
#!/usr/bin/env python3
from diffusers import DiffusionPipeline, EulerDiscreteScheduler, StableDiffusionPipeline, KDPM2DiscreteScheduler, StableDiffusionImg2ImgPipeline, HeunDiscreteScheduler, KDPM2AncestralDiscreteScheduler, DDIMScheduler
from diffusers import StableDiffusionXLPipeline, StableDiffusionXLImg2ImgPipeline
import time
from pytorch_lightning import seed_everything
import os
from huggingface_hub import HfApi
# from compel import Compel
import torch
import sys
from pathlib import Path
import requests
from PIL import Image
from io import BytesIO
api = HfApi()
start_time = time.time()
use_refiner = bool(int(sys.argv[1]))
use_diffusers = True
if use_diffusers:
start_time = time.time()
pipe = StableDiffusionXLPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-0.9", torch_dtype=torch.float16, variant="fp16", use_safetensors=True, local_files_only=True)
pipe.to("cuda")
if use_refiner:
refiner = StableDiffusionXLImg2ImgPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-0.9", torch_dtype=torch.float16, use_safetensors=True, variant="fp16")
refiner.to("cuda")
# refiner.enable_sequential_cpu_offload()
else:
pipe = StableDiffusionXLPipeline.from_single_file("https://huggingface.co/stabilityai/stable-diffusion-xl-base-0.9/blob/main/sd_xl_base_0.9.safetensors", torch_dtype=torch.float16, use_safetensors=True)
pipe.to("cuda")
if use_refiner:
refiner = StableDiffusionXLImg2ImgPipeline.from_single_file("https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-0.9/blob/main/sd_xl_refiner_0.9.safetensors", torch_dtype=torch.float16, use_safetensors=True)
refiner.to("cuda")
prompt = "An astronaut riding a green horse on Mars"
for steps in [24, 27, 31]:
for denoising_end in [0.63, 0.66, 0.67, 0.71]:
seed = 0
seed_everything(seed)
image = pipe(prompt=prompt, num_inference_steps=40, denoising_end=0.675, output_type="latent" if use_refiner else "pil").images[0]
# image = pipe(prompt=prompt, output_type="latent" if use_refiner else "pil").images[0]
if use_refiner:
image = refiner(prompt=prompt, num_inference_steps=40, denoising_start=0.675, image=image[None, :]).images[0]
# pipe.unet.to(memory_format=torch.channels_last)
# pipe(prompt=prompt, num_inference_steps=2).images[0]
# image = pipe(prompt=prompt, num_images_per_prompt=1, num_inference_steps=40, output_type="latent").images
file_name = f"aaa_{seed}"
path = os.path.join(Path.home(), "images", "ediffi_sdxl", f"{file_name}.png")
image.save(path)
api.upload_file(
path_or_fileobj=path,
path_in_repo=path.split("/")[-1],
repo_id="patrickvonplaten/images",
repo_type="dataset",
)
print(f"https://huggingface.co/datasets/patrickvonplaten/images/blob/main/ediffi_sdxl/{file_name}.png")
|