# ------------------------------------------------------------------------------ # Copyright (c) 2023, Andres Chait. All rights reserved. # ------------------------------------------------------------------------------ from __future__ import annotations import math import cv2 import random from fnmatch import fnmatch import numpy as np import gradio as gr import torch from PIL import Image, ImageOps from diffusers import StableDiffusionInstructPix2PixPipeline title = "Gradio-TTI" description = """

Andres Chait, Tamir Babil, Yaron Schnitman and Avi Rotem
Project Page | Paper | Code

Demo for Gradio-TTI: Instruction-Tuned Text-to-Image Diffusion Models. \n Please upload a new image and provide an instruction outlining the specific vision task you wish Gradio-TTI to perform (e.g., “Segment the dog”, “Detect the dog”, “Estimate the depth map of this image”, etc.). \n """ # noqa example_instructions = [ "Please help me detect Buzz.", "Please help me detect Woody's face.", "Create a monocular depth map.", ] model_id = "andreschait/Kapara-K9" def main(): # pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16, safety_checker=None).to("cpu") pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16, safety_checker=None).to("cuda") example_image = Image.open("imgs/example2.jpg").convert("RGB") def load_example( seed: int, randomize_seed: bool, text_cfg_scale: float, image_cfg_scale: float, ): example_instruction = random.choice(example_instructions) return [example_image, example_instruction] + generate( example_image, example_instruction, seed, 0, text_cfg_scale, image_cfg_scale, ) def generate( input_image: Image.Image, instruction: str, seed: int, randomize_seed:bool, text_cfg_scale: float, image_cfg_scale: float, ): seed = random.randint(0, 100000) if randomize_seed else seed text_cfg_scale = text_cfg_scale image_cfg_scale = image_cfg_scale width, height = input_image.size factor = 512 / max(width, height) factor = math.ceil(min(width, height) * factor / 64) * 64 / min(width, height) width = int((width * factor) // 64) * 64 height = int((height * factor) // 64) * 64 input_image = ImageOps.fit(input_image, (width, height), method=Image.Resampling.LANCZOS) if instruction == "": return [input_image] generator = torch.manual_seed(seed) edited_image = pipe( instruction, image=input_image, guidance_scale=text_cfg_scale, image_guidance_scale=image_cfg_scale, num_inference_steps=25, generator=generator, ).images[0] instruction_ = instruction.lower() if fnmatch(instruction_, "*segment*") or fnmatch(instruction_, "*split*") or fnmatch(instruction_, "*divide*"): input_image = cv2.cvtColor(np.array(input_image), cv2.COLOR_RGB2BGR) #numpy.ndarray edited_image = cv2.cvtColor(np.array(edited_image), cv2.COLOR_RGB2GRAY) ret, thresh = cv2.threshold(edited_image, 127, 255, cv2.THRESH_BINARY) img2 = input_image.copy() seed_seg = np.random.randint(0,10000) np.random.seed(seed_seg) colors = np.random.randint(0,255,(3)) colors2 = np.random.randint(0,255,(3)) contours,_ = cv2.findContours(thresh,cv2.RETR_LIST,cv2.CHAIN_APPROX_NONE) edited_image = cv2.drawContours(input_image,contours,-1,(int(colors[0]),int(colors[1]),int(colors[2])),3) for j in range(len(contours)): edited_image_2 = cv2.fillPoly(img2, [contours[j]], (int(colors2[0]),int(colors2[1]),int(colors2[2]))) img_merge = cv2.addWeighted(edited_image, 0.5,edited_image_2, 0.5, 0) edited_image = Image.fromarray(cv2.cvtColor(img_merge, cv2.COLOR_BGR2RGB)) if fnmatch(instruction_, "*depth*"): edited_image = cv2.cvtColor(np.array(edited_image), cv2.COLOR_RGB2GRAY) n_min = np.min(edited_image) n_max = np.max(edited_image) edited_image = (edited_image-n_min)/(n_max-n_min+1e-8) edited_image = (255*edited_image).astype(np.uint8) edited_image = cv2.applyColorMap(edited_image, cv2.COLORMAP_JET) edited_image = Image.fromarray(cv2.cvtColor(edited_image, cv2.COLOR_BGR2RGB)) # text_cfg_scale = 7.5 # image_cfg_scale = 1.5 return [seed, text_cfg_scale, image_cfg_scale, edited_image] with gr.Blocks() as demo: # gr.HTML("""

# InstructCV: Towards Universal Text-to-Image Vision Generalists #

""") gr.Markdown("

" + title + "

") gr.Markdown(description) with gr.Row(): with gr.Column(scale=1.5, min_width=100): generate_button = gr.Button("Generate result") with gr.Column(scale=1.5, min_width=100): load_button = gr.Button("Load example") with gr.Column(scale=3): instruction = gr.Textbox(lines=1, label="Instruction", interactive=True) with gr.Row(): input_image = gr.Image(label="Input Image", type="pil", interactive=True) edited_image = gr.Image(label=f"Output Image", type="pil", interactive=False) input_image.style(height=512, width=512) edited_image.style(height=512, width=512) with gr.Row(): randomize_seed = gr.Radio( ["Fix Seed", "Randomize Seed"], value="Randomize Seed", type="index", show_label=False, interactive=True, ) seed = gr.Number(value=90, precision=0, label="Seed", interactive=True) text_cfg_scale = gr.Number(value=7.5, label=f"Text weight", interactive=True) image_cfg_scale = gr.Number(value=1.5, label=f"Image weight", interactive=True) # gr.Markdown(Intro_text) load_button.click( fn=load_example, inputs=[ seed, randomize_seed, text_cfg_scale, image_cfg_scale, ], outputs=[input_image, instruction, seed, text_cfg_scale, image_cfg_scale, edited_image], ) generate_button.click( fn=generate, inputs=[ input_image, instruction, seed, randomize_seed, text_cfg_scale, image_cfg_scale, ], outputs=[seed, text_cfg_scale, image_cfg_scale, edited_image], ) demo.queue(concurrency_count=1) demo.launch(share=False) if __name__ == "__main__": main()