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Running
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T4
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from diffusers import AutoPipelineForText2Image, StableDiffusionImg2ImgPipeline
from PIL import Image
import gradio as gr
import random
import torch
import math
css = """
.btn-green {
background-image: linear-gradient(to bottom right, #6dd178, #00a613) !important;
border-color: #22c55e !important;
color: #166534 !important;
}
.btn-green:hover {
background-image: linear-gradient(to bottom right, #6dd178, #6dd178) !important;
}
"""
def generate(prompt, turbo_steps, samp_steps, seed, progress=gr.Progress(track_tqdm=True)):
print("prompt = ", prompt)
if seed < 0:
seed = random.randint(1,999999)
image = txt2img(
prompt,
num_inference_steps=turbo_steps,
guidance_scale=0.0,
generator=torch.manual_seed(seed),
).images[0]
upscaled_image = image.resize((1024,1024), 1)
final_image = img2img(
prompt,
upscaled_image,
num_inference_steps=samp_steps,
guidance_scale=5,
strength=1,
generator=torch.manual_seed(seed),
).images[0]
return [final_image], seed
def set_base_models():
txt2img = AutoPipelineForText2Image.from_pretrained(
"stabilityai/sdxl-turbo",
torch_dtype = torch.float16,
variant = "fp16"
)
txt2img.to("cuda")
img2img = StableDiffusionImg2ImgPipeline.from_pretrained(
"Lykon/dreamshaper-8",
torch_dtype = torch.float16,
variant = "fp16",
safety_checker=None
)
img2img.to("cuda")
return txt2img, img2img
with gr.Blocks(css=css) as demo:
with gr.Column():
prompt = gr.Textbox(label="Prompt")
submit_btn = gr.Button("Generate", elem_classes="btn-green")
with gr.Row():
turbo_steps = gr.Slider(1, 4, value=1, step=1, label="Turbo steps")
sampling_steps = gr.Slider(1, 6, value=3, step=1, label="Refiner steps")
seed = gr.Number(label="Seed", value=-1, minimum=-1, precision=0)
lastSeed = gr.Number(label="Last Seed", value=-1, interactive=False)
gallery = gr.Gallery(show_label=False, preview=True, container=False, height=1100)
submit_btn.click(generate, [prompt, turbo_steps, sampling_steps, seed], [gallery, lastSeed], queue=True)
txt2img, img2img = set_base_models()
demo.launch(debug=True) |