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from diffusers import StableDiffusionPipeline, DDIMScheduler
import gradio as gr
import torch
stable_model_list = [
"runwayml/stable-diffusion-v1-5",
"stabilityai/stable-diffusion-2-1",
"sd-dreambooth-library/disco-diffusion-style",
"prompthero/openjourney-v2",
"andite/anything-v4.0",
"Lykon/DreamShaper",
"nitrosocke/Nitro-Diffusion",
"dreamlike-art/dreamlike-diffusion-1.0"
]
stable_prompt_list = [
"a photo of a man.",
"a photo of a girl."
]
stable_negative_prompt_list = [
"bad, ugly",
"deformed"
]
def stable_diffusion_text2img(
model_path:str,
prompt:str,
negative_prompt:str,
guidance_scale:int,
num_inference_step:int,
height:int,
width:int,
):
pipe = StableDiffusionPipeline.from_pretrained(
model_path,
safety_checker=None,
torch_dtype=torch.float16
).to("cuda")
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
pipe.enable_xformers_memory_efficient_attention()
images = pipe(
prompt,
height=height,
width=width,
negative_prompt=negative_prompt,
num_inference_steps=num_inference_step,
guidance_scale=guidance_scale,
).images
return images[0]
def stable_diffusion_text2img_app():
with gr.Blocks():
with gr.Row():
with gr.Column():
text2image_model_path = gr.Dropdown(
choices=stable_model_list,
value=stable_model_list[0],
label='Text-Image Model Id'
)
text2image_prompt = gr.Textbox(
lines=1,
value=stable_prompt_list[0],
label='Prompt'
)
text2image_negative_prompt = gr.Textbox(
lines=1,
value=stable_negative_prompt_list[0],
label='Negative Prompt'
)
with gr.Accordion("Advanced Options", open=False):
text2image_guidance_scale = gr.Slider(
minimum=0.1,
maximum=15,
step=0.1,
value=7.5,
label='Guidance Scale'
)
text2image_num_inference_step = gr.Slider(
minimum=1,
maximum=100,
step=1,
value=50,
label='Num Inference Step'
)
text2image_height = gr.Slider(
minimum=128,
maximum=1280,
step=32,
value=512,
label='Image Height'
)
text2image_width = gr.Slider(
minimum=128,
maximum=1280,
step=32,
value=768,
label='Image Height'
)
text2image_predict = gr.Button(value='Generator')
with gr.Column():
output_image = gr.Image(label='Output')
text2image_predict.click(
fn=stable_diffusion_text2img,
inputs=[
text2image_model_path,
text2image_prompt,
text2image_negative_prompt,
text2image_guidance_scale,
text2image_num_inference_step,
text2image_height,
text2image_width,
],
outputs=output_image
)
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