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import os
import io
import base64
import torch
from torch.cuda import amp
import numpy as np
from PIL import Image
from diffusers import AutoPipelineForText2Image, AutoencoderKL, DPMSolverMultistepScheduler
pipe = None
def load_model(_model = None, _vae = None, loras = []):
global pipe
_model = _model or 'cagliostrolab/animagine-xl-3.0'
if torch.cuda.is_available():
torch_dtype = torch.float16
else:
torch_dtype = torch.float32
if _vae:
# "stabilityai/sdxl-vae"
vae = AutoencoderKL.from_pretrained(_vae, torch_dtype=torch_dtype)
pipe = AutoPipelineForText2Image.from_pretrained(
_model,
torch_dtype=torch_dtype,
vae=vae,
)
else:
pipe = AutoPipelineForText2Image.from_pretrained(
_model,
torch_dtype=torch_dtype,
)
# DPM++ 2M Karras
pipe.scheduler = DPMSolverMultistepScheduler.from_config(
pipe.scheduler.config,
algorithm_type="sde-dpmsolver++",
use_karras_sigmas=True
)
for lora in loras:
pipe.load_lora_weights(".", weight_name=lora + ".safetensors")
if torch.cuda.is_available():
pipe.to("cuda")
pipe.enable_vae_slicing()
def pil_to_webp(img):
buffer = io.BytesIO()
img.save(buffer, 'webp')
return buffer.getvalue()
def bin_to_base64(bin):
return base64.b64encode(bin).decode('ascii')
def run(prompt = None, negative_prompt = None, model = None, guidance_scale = None, steps = None, seed = None):
global pipe
if not pipe:
load_model(model)
_prompt = "masterpiece, best quality, 1girl, portrait"
_negative_prompt = "lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, artist name"
prompt = prompt or _prompt
negative_prompt = negative_prompt or _negative_prompt
guidance_scale = float(guidance_scale) if guidance_scale else 5.0
steps = int(steps) if steps else 20
seed = int(seed) if seed else -1
generator = None
if seed != -1:
generator = torch.manual_seed(seed)
image = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
guidance_scale=guidance_scale,
num_inference_steps=steps,
clip_skip=2,
generator=generator,
).images[0]
return image
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