Spaces:
Runtime error
Runtime error
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 | |
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, | |
).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>. [![GitHub Stars](https://img.shields.io/github/stars/quickjkee/instruct-pix2pix-distill?style=social)](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 | |
"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, n_steps], | |
outputs = [result] | |
) | |
demo.queue().launch() | |