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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, image_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=image_guidance_scale,
).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=8.0,
step=1.0,
value=2.0,
)
image_guidance_scale = gr.Slider(
label="image guidance scale",
minimum=1.0,
maximum=8.0,
step=1.0,
value=1.0,
)
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
"a photo of a basket of apples", #src_prompt
"a photo of a basket of oranges", #tgt_prompt
20, #guidance_scale
0.6, #tau
0.4, #crs
0.6, #srs
1, #amplify factor
'oranges', # amplify word
'', #orig blend
'oranges', #edited blend
False #replacement
],
[
"examples/orig_3.jpg", #input_image
"a photo of a basket of apples", #src_prompt
"a photo of a basket of puppies", #tgt_prompt
20, #guidance_scale
0.6, #tau
0.4, #crs
0.1, #srs
2, #amplify factor
'puppies', # amplify word
'', #orig blend
'puppies', #edited blend
True #replacement
],
[
"examples/orig_3.jpg", #input_image
"a photo of a basket of apples", #src_prompt
"a photo of a basket of apples under snowfall", #tgt_prompt
20, #guidance_scale
0.6, #tau
0.4, #crs
0.4, #srs
30, #amplify factor
'snowfall', # amplify word
'', #orig blend
'snowfall', #edited blend
False #replacement
],
[
"examples/orig_1.jpg", #input_image
"a photo of an owl", #src_prompt
"a photo of an yellow owl", #tgt_prompt
20, #guidance_scale
0.6, #tau
0.9, #crs
0.9, #srs
20, #amplify factor
'yellow', # amplify word
'owl', #orig blend
'yellow', #edited blend
False #replacement
],
[
"examples/orig_1.jpg", #input_image
"a photo of an owl", #src_prompt
"an anime-style painting of an owl", #tgt_prompt
20, #guidance_scale
0.8, #tau
0.6, #crs
0.3, #srs
10, #amplify factor
'anime-style', # amplify word
'painting', #orig blend
'anime-style', #edited blend
False #replacement
],
[
"examples/orig_1.jpg", #input_image
"a photo of an owl", #src_prompt
"a photo of an owl underwater with many fishes nearby", #tgt_prompt
20, #guidance_scale
0.8, #tau
0.4, #crs
0.4, #srs
18, #amplify factor
'fishes', # amplify word
'', #orig blend
'fishes', #edited blend
False #replacement
],
[
"examples/orig_2.jpg", #input_image
"a photograph of a teddy bear sitting on a wall", #src_prompt
"a photograph of a teddy bear sitting on a wall surrounded by roses", #tgt_prompt
20, #guidance_scale
0.6, #tau
0.4, #crs
0.1, #srs
25, #amplify factor
'roses', # amplify word
'', #orig blend
'roses', #edited blend
False #replacement
],
[
"examples/orig_2.jpg", #input_image
"a photograph of a teddy bear sitting on a wall", #src_prompt
"a photograph of a wooden bear sitting on a wall", #tgt_prompt
20, #guidance_scale
0.8, #tau
0.5, #crs
0.5, #srs
14, #amplify factor
'wooden', # amplify word
'', #orig blend
'wooden', #edited blend
True #replacement
],
[
"examples/orig_2.jpg", #input_image
"a photograph of a teddy bear sitting on a wall", #src_prompt
"a photograph of a teddy rabbit sitting on a wall", #tgt_prompt
20, #guidance_scale
0.8, #tau
0.4, #crs
0.4, #srs
3, #amplify factor
'rabbit', # amplify word
'', #orig blend
'rabbit', #edited blend
True #replacement
],
]
#gr.Examples(
# examples = examples,
# inputs =[input_image, input_prompt, prompt,
# guidance_scale, tau, crs, srs, amplify_factor, amplify_word,
# blend_orig, blend_edited, is_replacement],
# outputs=[
# result
# ],
# fn=infer, cache_examples=True
#)
run_button.click(
fn = infer,
inputs=[image, edit_instruction, guidance_scale, image_guidance_scale, n_steps],
outputs = [result]
)
demo.queue().launch()
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