Spaces:
Running
on
Zero
Running
on
Zero
import spaces | |
def load_pipeline(): | |
from diffusers import DiffusionPipeline | |
import torch | |
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") | |
pipe = DiffusionPipeline.from_pretrained( | |
"John6666/rae-diffusion-xl-v2-sdxl-spo-pcm", | |
custom_pipeline="lpw_stable_diffusion_xl", | |
torch_dtype=torch.float16, | |
) | |
pipe.to(device) | |
return pipe | |
def save_image(image, metadata, output_dir): | |
import os | |
import uuid | |
import json | |
from PIL import PngImagePlugin | |
filename = str(uuid.uuid4()) + ".png" | |
os.makedirs(output_dir, exist_ok=True) | |
filepath = os.path.join(output_dir, filename) | |
metadata_str = json.dumps(metadata) | |
info = PngImagePlugin.PngInfo() | |
info.add_text("metadata", metadata_str) | |
image.save(filepath, "PNG", pnginfo=info) | |
return filepath | |
pipe = load_pipeline() | |
def generate_image(prompt, neg_prompt): | |
metadata = { | |
"prompt": prompt, | |
"negative_prompt": neg_prompt, | |
"resolution": f"{1024} x {1024}", | |
"guidance_scale": 7.5, | |
"num_inference_steps": 16, | |
"sampler": "Euler", | |
} | |
try: | |
images = pipe( | |
prompt=prompt + ", masterpiece, best quality, very aesthetic, absurdres", | |
negative_prompt=neg_prompt + ", lowres, (bad), text, error, fewer, extra, missing, worst quality, jpeg artifacts, low quality, watermark, unfinished, displeasing, oldest, early, chromatic aberration, signature, extra digits, artistic error, username, scan, [abstract], photo, deformed, disfigured, low contrast, photo, deformed, disfigured, low contrast", | |
width=1024, | |
height=1024, | |
guidance_scale=7.5, | |
num_inference_steps=16, | |
output_type="pil", | |
clip_skip=1, | |
).images | |
if images: | |
image_paths = [ | |
save_image(image, metadata, "./outputs") | |
for image in images | |
] | |
return image_paths | |
except Exception as e: | |
return [] | |