Spaces:
Running
on
Zero
Running
on
Zero
File size: 4,294 Bytes
0a359f0 15b0708 0a359f0 e5e699f 0a359f0 15b0708 0a359f0 15b0708 0a359f0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 |
#!/usr/bin/env python
import gradio as gr
import PIL.Image
import spaces
import torch
from controlnet_aux import CannyDetector
from diffusers.pipelines import BlipDiffusionControlNetPipeline
from settings import CACHE_EXAMPLES, DEFAULT_NEGATIVE_PROMPT, MAX_INFERENCE_STEPS
from utils import MAX_SEED, randomize_seed_fn
canny_detector = CannyDetector()
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
if torch.cuda.is_available():
pipe = BlipDiffusionControlNetPipeline.from_pretrained(
"Salesforce/blipdiffusion-controlnet", torch_dtype=torch.float16
).to(device)
else:
pipe = None
@spaces.GPU
def run(
condition_image: PIL.Image.Image,
style_image: PIL.Image.Image,
condition_subject: str,
style_subject: str,
prompt: str,
negative_prompt: str = DEFAULT_NEGATIVE_PROMPT,
seed: int = 0,
guidance_scale: float = 7.5,
num_inference_steps: int = 25,
) -> PIL.Image.Image:
if num_inference_steps > MAX_INFERENCE_STEPS:
raise gr.Error(f"Number of inference steps must be less than {MAX_INFERENCE_STEPS}")
condition_image = canny_detector(condition_image, 30, 70, output_type="pil")
return pipe(
prompt,
style_image,
condition_image,
style_subject,
condition_subject,
generator=torch.Generator(device=device).manual_seed(seed),
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
neg_prompt=negative_prompt,
height=512,
width=512,
).images[0]
with gr.Blocks() as demo:
with gr.Row():
with gr.Column():
condition_image = gr.Image(label="Condition Image")
style_image = gr.Image(label="Style Image")
condition_subject = gr.Textbox(label="Condition Subject")
style_subject = gr.Textbox(label="Style Subject")
prompt = gr.Textbox(label="Prompt")
run_button = gr.Button()
with gr.Accordion(label="Advanced options", open=False):
negative_prompt = gr.Textbox(label="Negative Prompt", value=DEFAULT_NEGATIVE_PROMPT)
seed = gr.Slider(
label="Seed",
minimum=0,
maximum=MAX_SEED,
step=1,
value=0,
)
randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
guidance_scale = gr.Slider(
label="Guidance Scale",
minimum=0,
maximum=10,
step=0.1,
value=7.5,
)
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=1,
maximum=MAX_INFERENCE_STEPS,
step=1,
value=25,
)
with gr.Column():
result = gr.Image(label="Result")
gr.Examples(
examples=[
[
"images/kettle.jpg",
"images/flower.jpg",
"teapot",
"flower",
"on a marble table",
],
],
inputs=[
condition_image,
style_image,
condition_subject,
style_subject,
prompt,
],
outputs=result,
fn=run,
cache_examples=CACHE_EXAMPLES,
)
inputs = [
condition_image,
style_image,
condition_subject,
style_subject,
prompt,
negative_prompt,
seed,
guidance_scale,
num_inference_steps,
]
gr.on(
triggers=[
condition_subject.submit,
style_subject.submit,
prompt.submit,
negative_prompt.submit,
run_button.click,
],
fn=randomize_seed_fn,
inputs=[seed, randomize_seed],
outputs=seed,
api_name=False,
concurrency_limit=None,
).then(
fn=run,
inputs=inputs,
outputs=result,
api_name="run-stylization",
concurrency_id="gpu",
concurrency_limit=1,
)
if __name__ == "__main__":
demo.queue(max_size=20).launch()
|