Spaces:
Running
on
Zero
Running
on
Zero
File size: 3,397 Bytes
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from PIL import Image
import torch
from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl import (
StableDiffusionXLPipeline,
)
from diffusers.schedulers.scheduling_euler_ancestral_discrete import (
EulerAncestralDiscreteScheduler,
)
from diffusers.models.attention_processor import AttnProcessor2_0
try:
import spaces
except ImportError:
class spaces:
def GPU(*args, **kwargs):
return lambda x: x
import gradio as gr
from utils import NEGATIVE_PROMPT, IMAGE_SIZE_OPTIONS, QUALITY_TAGS, IMAGE_SIZES
device = "cuda"
model_name: str = "cagliostrolab/animagine-xl-3.1"
pipe = StableDiffusionXLPipeline.from_pretrained(
model_name,
torch_dtype=torch.bfloat16,
use_safetensors=True,
add_watermarker=False,
custom_pipeline="lpw_stable_diffusion_xl",
)
pipe.scheduler = EulerAncestralDiscreteScheduler.from_pretrained(
model_name,
subfolder="scheduler",
)
# sdpa
pipe.unet.set_attn_processor(AttnProcessor2_0())
pipe.to(device)
def image_generation_config_ui():
with gr.Accordion(label="Image generation config", open=False) as accordion:
image_size = gr.Radio(
label="Image size",
choices=list(IMAGE_SIZE_OPTIONS.keys()),
value=list(IMAGE_SIZE_OPTIONS.keys())[3],
interactive=True,
)
quality_tags = gr.Textbox(
label="Quality tags",
placeholder=QUALITY_TAGS["default"],
value=QUALITY_TAGS["default"],
interactive=True,
)
negative_prompt = gr.Textbox(
label="Negative prompt",
placeholder=NEGATIVE_PROMPT["default"],
value=NEGATIVE_PROMPT["default"],
interactive=True,
)
num_inference_steps = gr.Slider(
label="Num inference steps",
minimum=20,
maximum=30,
step=1,
value=25,
interactive=True,
)
guidance_scale = gr.Slider(
label="Guidance scale",
minimum=0.0,
maximum=10.0,
step=0.5,
value=7.0,
interactive=True,
)
return accordion, [
image_size,
quality_tags,
negative_prompt,
num_inference_steps,
guidance_scale,
]
class ImageGenerator:
# pipe: StableDiffusionXLPipeline
def __init__(self):
pass
@spaces.GPU(duration=30)
def generate(
self,
prompt: str,
image_size: str = "768x1344",
quality_tags: str = QUALITY_TAGS["default"], # Light v3.1
negative_prompt: str = NEGATIVE_PROMPT["default"], # Light v3.1
num_inference_steps: int = 25,
guidance_scale: float = 7.0,
) -> Image.Image:
width, height = IMAGE_SIZES[image_size]
prompt = ", ".join([prompt, quality_tags])
print("prompt", prompt)
print("negative_prompt", negative_prompt)
print("height", height)
print("width", width)
print("num_inference_steps", num_inference_steps)
print("guidance_scale", guidance_scale)
return pipe(
prompt=prompt,
negative_prompt=negative_prompt,
height=height,
width=width,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
).images
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