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from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
from segment_anything import sam_model_registry, SamAutomaticMaskGenerator
from PIL import Image
import gradio as gr
import numpy as np
import requests
import torch
import gc
device = "cuda" if torch.cuda.is_available() else "cpu"
# Download and Create SAM Model
print("[Downloading SAM Weights]")
SAM_URL = "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth"
r = requests.get(SAM_URL, allow_redirects=True)
print("[Writing SAM Weights]")
with open("./sam_vit_h_4b8939.pth", "wb") as sam_weights:
sam_weights.write(r.content)
del r
gc.collect()
sam = sam_model_registry["vit_h"](checkpoint="./sam_vit_h_4b8939.pth").to(device)
mask_generator = SamAutomaticMaskGenerator(sam)
gc.collect()
# Create ControlNet Pipeline
print("Creating ControlNet Pipeline")
controlnet = ControlNetModel.from_pretrained(
"mfidabel/controlnet-segment-anything", torch_dtype=torch.float16
).to(device)
pipe = StableDiffusionControlNetPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16, safety_check=None
).to(device)
# Description
title = "# 🧨 ControlNet on Segment Anything 🤗"
description = """This is a demo on 🧨 ControlNet based on Meta's [Segment Anything Model](https://segment-anything.com/).
Upload an Image, Segment it with Segment Anything, write a prompt, and generate images 🤗
⌛️ It takes about 20~ seconds to generate 4 samples, to get faster results, don't forget to reduce the Nº Samples to 1.
You can obtain the Segmentation Map of any Image through this Colab: [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mfidabel/JAX_SPRINT_2023/blob/main/Segment_Anything_JAX_SPRINT.ipynb)
A huge thanks goes out to @GoogleCloud, for providing us with powerful TPUs that enabled us to train this model; and to the @HuggingFace Team for organizing the sprint.
Check out our [Model Card 🧨](https://huggingface.co/mfidabel/controlnet-segment-anything)
"""
about = """
# 👨‍💻 About the model
This [model](https://huggingface.co/mfidabel/controlnet-segment-anything) is based on the [ControlNet Model](https://huggingface.co/blog/controlnet), which allow us to generate Images using some sort of condition image. For this model, we selected the segmentation maps produced by Meta's new segmentation model called [Segment Anything Model](https://github.com/facebookresearch/segment-anything) as the condition image. We then trained the model to generate images based on the structure of the segmentation maps and the text prompts given.
# 💾 About the dataset
For the training, we generated a segmented dataset based on the [COYO-700M](https://huggingface.co/datasets/kakaobrain/coyo-700m) dataset. The dataset provided us with the images, and the text prompts. For the segmented images, we used [Segment Anything Model](https://github.com/facebookresearch/segment-anything). We then created 8k samples to train our model on, which isn't a lot, but as a team, we have been very busy with many other responsibilities and time constraints, which made it challenging to dedicate a lot of time to generating a larger dataset. Despite the constraints we faced, we have still managed to achieve some nice results 🙌
You can check the generated datasets below ⬇️
- [sam-coyo-2k](https://huggingface.co/datasets/mfidabel/sam-coyo-2k)
- [sam-coyo-2.5k](https://huggingface.co/datasets/mfidabel/sam-coyo-2.5k)
- [sam-coyo-3k](https://huggingface.co/datasets/mfidabel/sam-coyo-3k)
"""
gif_html = """ <img src="https://github.com/mfidabel/JAX_SPRINT_2023/blob/8632f0fde7388d7a4fc57225c96ef3b8411b3648/EX_1.gif?raw=true" alt= “” height="50%" class="about"> """
examples = [["photo of a futuristic dining table, high quality, tricolor", "low quality, deformed, blurry, points", "examples/condition_image_1.jpeg"],
["a monochrome photo of henry cavil using a shirt, high quality", "low quality, low res, deformed", "examples/condition_image_2.jpeg"],
["photo of a japanese living room, high quality, coherent", "low quality, colors, saturation, extreme brightness, blurry, low res", "examples/condition_image_3.jpeg"],
["living room, detailed, high quality", "low quality, low resolution, render, oversaturated, low contrast", "examples/condition_image_4.jpeg"],
["painting of the bodiam castle, Vicent Van Gogh style, Starry Night", "low quality, low resolution, render, oversaturated, low contrast", "examples/condition_image_5.jpeg"],
["painting of food, olive oil can, purple wine, green cabbage, chili peppers, pablo picasso style, high quality", "low quality, low resolution, render, oversaturated, low contrast, realistic", "examples/condition_image_6.jpeg"],
["Katsushika Hokusai painting of mountains, a sky and desert landscape, The Great Wave off Kanagawa style, colorful",
"low quality, low resolution, render, oversaturated, low contrast, realistic", "examples/condition_image_7.jpeg"]]
default_example = examples[4]
examples = examples[::-1]
css = "h1 { text-align: center } .about { text-align: justify; padding-left: 10%; padding-right: 10%; }"
# Inference Function
def show_anns(anns):
if len(anns) == 0:
return
sorted_anns = sorted(anns, key=(lambda x: x['area']), reverse=True)
h, w = anns[0]['segmentation'].shape
final_img = Image.fromarray(np.zeros((h, w, 3), dtype=np.uint8), mode="RGB")
for ann in sorted_anns:
m = ann['segmentation']
img = np.empty((m.shape[0], m.shape[1], 3), dtype=np.uint8)
for i in range(3):
img[:,:,i] = np.random.randint(255, dtype=np.uint8)
final_img.paste(Image.fromarray(img, mode="RGB"), (0, 0), Image.fromarray(np.uint8(m*255)))
return final_img
def segment_image(image, seed = 0):
# Generate Masks
np.random.seed(int(seed))
masks = mask_generator.generate(image)
torch.cuda.empty_cache()
# Create map
map = show_anns(masks)
del masks
gc.collect()
torch.cuda.empty_cache()
return map
def infer(prompts, negative_prompts, image, num_inference_steps = 50, seed = 4, num_samples = 4):
try:
# Segment Image
print("Segmenting Everything")
segmented_map = segment_image(image, seed)
yield segmented_map, [Image.fromarray(np.zeros((512, 512, 3), dtype=np.uint8))] * num_samples
# Generate
rng = torch.Generator(device="cpu").manual_seed(seed)
num_inference_steps = int(num_inference_steps)
print(f"Generating Prompt: {prompts} \nNegative Prompt: {negative_prompts} \nSamples:{num_samples}")
output = pipe([prompts] * num_samples,
[segmented_map] * num_samples,
negative_prompt = [negative_prompts] * num_samples,
generator = rng,
num_inference_steps = num_inference_steps)
final_image = output.images
del output
except Exception as e:
print("Error: " + str(e))
final_image = segmented_map = [np.zeros((512, 512, 3), dtype=np.uint8)] * num_samples
finally:
gc.collect()
torch.cuda.empty_cache()
yield segmented_map, final_image
cond_img = gr.Image(label="Input", shape=(512, 512), value=default_example[2])\
.style(height=400)
segm_img = gr.Image(label="Segmented Image", shape=(512, 512), interactive=False)\
.style(height=400)
output = gr.Gallery(label="Generated images")\
.style(height=200, rows=[2], columns=[2], object_fit="contain")
prompt = gr.Textbox(lines=1, label="Prompt", value=default_example[0])
negative_prompt = gr.Textbox(lines=1, label="Negative Prompt", value=default_example[1])
with gr.Blocks(css=css) as demo:
with gr.Row():
with gr.Column():
# Title
gr.Markdown(title)
# Description
gr.Markdown(description)
with gr.Column():
# Examples
gr.Markdown(gif_html)
# Images
with gr.Row(variant="panel"):
with gr.Column(scale=1):
cond_img.render()
with gr.Column(scale=1):
segm_img.render()
with gr.Column(scale=1):
output.render()
# Submit & Clear
with gr.Row():
with gr.Column():
prompt.render()
negative_prompt.render()
with gr.Column():
with gr.Accordion("Advanced options", open=False):
num_steps = gr.Slider(10, 60, 50, step=1, label="Steps")
seed = gr.Slider(0, 1024, 4, step=1, label="Seed")
num_samples = gr.Slider(1, 4, 4, step=1, label="Nº Samples")
segment_btn = gr.Button("Segment")
submit = gr.Button("Segment & Generate Images")
# TODO: Download Button
with gr.Row():
with gr.Column():
gr.Markdown("Try some of the examples below ⬇️")
gr.Examples(examples=examples,
inputs=[prompt, negative_prompt, cond_img],
outputs=output,
fn=infer,
examples_per_page=4)
with gr.Column():
gr.Markdown(about, elem_classes="about")
submit.click(infer,
inputs=[prompt, negative_prompt, cond_img, num_steps, seed, num_samples],
outputs = [segm_img, output])
segment_btn.click(segment_image,
inputs=[cond_img, seed],
outputs=segm_img)
demo.queue()
demo.launch()