Spaces:
Runtime error
Runtime error
File size: 4,061 Bytes
ff88d5b |
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 |
from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, DPMSolverMultistepScheduler
import gradio as gr
import torch
from PIL import Image
from diffusers import OnnxStableDiffusionPipeline
import pipeline_openvino_stable_diffusion
model_id = 'OFA-Sys/small-stable-diffusion-v0'
prefix = ''
scheduler = DPMSolverMultistepScheduler.from_pretrained(model_id, subfolder="scheduler")
onnx_pipe = OnnxStableDiffusionPipeline.from_pretrained(
"OFA-Sys/small-stable-diffusion-v0",
revision="onnx",
provider="CPUExecutionProvider",
)
pipe = pipeline_openvino_stable_diffusion.OpenVINOStableDiffusionPipeline.from_onnx_pipeline(onnx_pipe)
def error_str(error, title="Error"):
return f"""#### {title}
{error}""" if error else ""
def inference(prompt, guidance, steps, width=512, height=512, seed=0, neg_prompt="", auto_prefix=False):
generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None
prompt = f"{prefix} {prompt}" if auto_prefix else prompt
try:
return txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator), None
except Exception as e:
return None, error_str(e)
def txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator):
result = pipe(
prompt,
negative_prompt = neg_prompt,
num_inference_steps = int(steps),
guidance_scale = guidance,
width = width,
height = height,
generator = generator)
return result.images[0]
css = """.main-div div{display:inline-flex;align-items:center;gap:.8rem;font-size:1.75rem}.main-div div h1{font-weight:900;margin-bottom:7px}.main-div p{margin-bottom:10px;font-size:94%}a{text-decoration:underline}.tabs{margin-top:0;margin-bottom:0}#gallery{min-height:20rem}
"""
with gr.Blocks(css=css) as demo:
gr.HTML(
f"""
<div class="main-div">
<div>
<h1>Small Stable Diffusion V0</h1>
</div>
<p>
Demo for <a href="https://huggingface.co/OFA-Sys/small-stable-diffusion-v0">Small Stable Diffusion V0</a> Stable Diffusion model.<br>
</p>
Running on CPUs with <a href="https://github.com/OFA-Sys/diffusion-deploy">diffusion-deploy</a> to speedup the inference.
</div>
"""
)
with gr.Row():
with gr.Column(scale=55):
with gr.Group():
with gr.Row():
prompt = gr.Textbox(label="Prompt", show_label=False, max_lines=2,placeholder=f"{prefix} [your prompt]").style(container=False)
generate = gr.Button(value="Generate").style(rounded=(False, True, True, False))
image_out = gr.Image(height=512)
error_output = gr.Markdown()
with gr.Column(scale=45):
with gr.Tab("Options"):
with gr.Group():
neg_prompt = gr.Textbox(label="Negative prompt", placeholder="What to exclude from the image")
with gr.Row():
guidance = gr.Slider(label="Guidance scale", value=7.5, maximum=15)
steps = gr.Slider(label="Steps", value=15, minimum=2, maximum=75, step=1)
with gr.Row():
width = gr.Slider(label="Width", value=512, minimum=64, maximum=1024, step=8)
height = gr.Slider(label="Height", value=512, minimum=64, maximum=1024, step=8)
seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1)
inputs = [prompt, guidance, steps, width, height, seed, neg_prompt, auto_prefix]
outputs = [image_out, error_output]
prompt.submit(inference, inputs=inputs, outputs=outputs)
generate.click(inference, inputs=inputs, outputs=outputs)
gr.HTML("""
<div style="border-top: 1px solid #303030;">
<br>
<p>This space was created using <a href="https://huggingface.co/spaces/anzorq/sd-space-creator">SD Space Creator</a>.</p>
</div>
""")
demo.queue(concurrency_count=1)
demo.launch()
|