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import spaces
import gradio as gr
import numpy as np
import torch
from PIL import Image
from diffusers import DDPMScheduler, StableDiffusionPipeline, DDIMScheduler, UNet2DConditionModel
from diffusers import StableDiffusionInstructPix2PixPipeline, LCMScheduler
# InstructPix2Pix with LCM specified scheduler
pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(
"timbrooks/instruct-pix2pix", torch_dtype=torch.float16
)
pipe = pipe.to("cuda")
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
# Adapt the InstructPix2Pix model using the LoRA parameters
adapter_id = "latent-consistency/lcm-lora-sdv1-5"
pipe.load_lora_weights(adapter_id)
pipe.to('cuda')
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024
@spaces.GPU(duration=30)
def infer(image, edit_instruction, guidance_scale, n_steps):
image = Image.fromarray(image).resize((512, 512))
image = pipe(prompt=edit_instruction,
image=image,
num_inference_steps=n_steps,
guidance_scale=guidance_scale,
image_guidance_scale=1.0
).images[0]
return image
css="""
#col-container {
margin: 0 auto;
max-width: 1024px;
}
"""
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"""
# ⚡ Instruct-pix2pix with Consistency Distillation⚡
Currently running on {power_device}
"""
)
gr.Markdown(
"If you enjoy the space, feel free to give a ⭐ to the <a href='https://github.com/yandex-research/invertible-cd' target='_blank'>Github Repo</a>. [](https://github.com/quickjkee/instruct-pix2pix-distill)"
)
with gr.Row():
edit_instruction = gr.Text(
label="Edit instruction",
max_lines=1,
placeholder="Enter your prompt",
)
with gr.Row():
with gr.Column():
image = gr.Image(label="Input image", height=512, width=512, show_label=False)
with gr.Column():
result = gr.Image(label="Result", height=512, width=512, show_label=False)
with gr.Accordion("Advanced Settings", open=True):
with gr.Row():
guidance_scale = gr.Slider(
label="guidance scale",
minimum=1.0,
maximum=5.0,
step=1.0,
value=2.0,
)
n_steps = gr.Slider(
label="inference steps",
minimum=1.0,
maximum=10.0,
step=1.0,
value=4.0,
)
with gr.Row():
run_button = gr.Button("Edit", scale=0)
with gr.Row():
examples = [
[
"examples/orig_3.jpg", #input_image
"turn apples into oranges", #tgt_prompt
2, #guidance_scale
4
],
[
"examples/orig_1.jpg", #input_image
"Make it a Modigliani painting", #tgt_prompt
2, #guidance_scale
4
],
[
"examples/orig_2.jpg", #input_image
"Turn a teddy bear into panda", #tgt_prompt
2, #guidance_scale
4
],
]
gr.Examples(
examples = examples,
inputs =[image, edit_instruction, guidance_scale, n_steps],
outputs=[
result
],
fn=infer, cache_examples=True
)
run_button.click(
fn = infer,
inputs=[image, edit_instruction, guidance_scale, n_steps],
outputs = [result]
)
demo.queue().launch()
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