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Running
on
Zero
File size: 3,660 Bytes
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import gradio as gr
import numpy as np
import random
from diffusers import StableDiffusionPipeline, LCMScheduler
import torch
device = "cuda" if torch.cuda.is_available() else "cpu"
adapter_id = "jasperai/flash-sd"
if torch.cuda.is_available():
torch.cuda.max_memory_allocated(device=device)
pipe = StableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5",
use_safetensors=True,
)
pipe.enable_xformers_memory_efficient_attention()
pipe = pipe.to(device)
else:
pipe = StableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5",
use_safetensors=True,
)
pipe = pipe.to(device)
pipe.scheduler = LCMScheduler.from_pretrained(
"runwayml/stable-diffusion-v1-5",
subfolder="scheduler",
timestep_spacing="trailing",
)
pipe.load_lora_weights(adapter_id)
pipe.fuse_lora()
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024
def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
if randomize_seed:
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)
image = pipe(
prompt = prompt,
negative_prompt = negative_prompt,
guidance_scale = guidance_scale,
num_inference_steps = num_inference_steps,
width = width,
height = height,
generator = generator
).images[0]
return image
examples = [
"The image showcases a freshly baked bread, possibly focaccia, with rosemary sprigs and red pepper flakes sprinkled on top. It's sliced and placed on a wire cooling rack, with a bowl of mixed peppercorns beside it.",
"A raccoon reading a book in a lush forest.",
"A serene landscape showcases a winding road alongside a vast, turquoise lake, flanked by majestic snow-capped mountains under a partly cloudy sky.",
]
css="""
#col-container {
margin: 0 auto;
max-width: 520px;
}
"""
if torch.cuda.is_available():
power_device = "GPU"
else:
power_device = "CPU"
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown(f"""
# Text-to-Image Gradio Template
Currently running on {power_device}.
""")
with gr.Row():
prompt = gr.Text(
label="Prompt",
show_label=False,
max_lines=1,
placeholder="Enter your prompt",
container=False,
)
run_button = gr.Button("Run", scale=0)
result = gr.Image(label="Result", show_label=False)
with gr.Accordion("Advanced Settings", open=False):
seed = gr.Slider(
label="Seed",
minimum=0,
maximum=MAX_SEED,
step=1,
value=0,
)
randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
with gr.Row():
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=1,
maximum=12,
step=1,
value=2,
)
gr.Examples(
examples = examples,
inputs = [prompt]
)
run_button.click(
fn = infer,
inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
outputs = [result]
)
demo.queue().launch() |