app.py

import gradio as gr
import torch
import numpy as np
import requests
import random
from io import BytesIO
from diffusers import StableDiffusionPipeline
from diffusers import DDIMScheduler
from utils import *
from inversion_utils import *
from modified_pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline
from torch import autocast, inference_mode
import re



def randomize_seed_fn(seed, randomize_seed):
    if randomize_seed:
        seed = random.randint(0, np.iinfo(np.int32).max)
    torch.manual_seed(seed)
    return seed


def invert(x0, prompt_src="", num_diffusion_steps=100, cfg_scale_src = 3.5, eta = 1):

  #  inverts a real image according to Algorihm 1 in https://arxiv.org/pdf/2304.06140.pdf, 
  #  based on the code in https://github.com/inbarhub/DDPM_inversion
   
  #  returns wt, zs, wts:
  #  wt - inverted latent
  #  wts - intermediate inverted latents
  #  zs - noise maps

  sd_pipe.scheduler.set_timesteps(num_diffusion_steps)

  # vae encode image
  with autocast("cuda"), inference_mode():
      w0 = (sd_pipe.vae.encode(x0).latent_dist.mode() * 0.18215).float()

  # find Zs and wts - forward process
  wt, zs, wts = inversion_forward_process(sd_pipe, w0, etas=eta, prompt=prompt_src, cfg_scale=cfg_scale_src, prog_bar=True, num_inference_steps=num_diffusion_steps)
  return zs, wts



def sample(zs, wts, prompt_tar="", cfg_scale_tar=15, skip=36, eta = 1):

    # reverse process (via Zs and wT)
    w0, _ = inversion_reverse_process(sd_pipe, xT=wts[skip], etas=eta, prompts=[prompt_tar], cfg_scales=[cfg_scale_tar], prog_bar=True, zs=zs[skip:])
    
    # vae decode image
    with autocast("cuda"), inference_mode():
        x0_dec = sd_pipe.vae.decode(1 / 0.18215 * w0).sample
    if x0_dec.dim()<4:
        x0_dec = x0_dec[None,:,:,:]
    img = image_grid(x0_dec)
    return img

# load pipelines
sd_model_id = "runwayml/stable-diffusion-v1-5"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
sd_pipe = StableDiffusionPipeline.from_pretrained(sd_model_id).to(device)
sd_pipe.scheduler = DDIMScheduler.from_config(sd_model_id, subfolder = "scheduler")
sem_pipe = SemanticStableDiffusionPipeline.from_pretrained(sd_model_id).to(device)


def get_example():
    case = [
        [
            'examples/source_a_cat_sitting_next_to_a_mirror.jpeg', 
            'a cat sitting next to a mirror',
            'watercolor painting of a cat sitting next to a mirror',
            100,
            36,
            15,
            'Schnauzer dog', 'cat',
            5.5,
            1,
            'examples/ddpm_sega_watercolor_painting_a_cat_sitting_next_to_a_mirror_plus_dog_minus_cat.png'
             ],
        [
            'examples/source_a_man_wearing_a_brown_hoodie_in_a_crowded_street.jpeg', 
            'a man wearing a brown hoodie in a crowded street',
            'a robot wearing a brown hoodie in a crowded street',
            100,
            36,
            15,
            'painting','',
            10,
            1,
            'examples/ddpm_sega_painting_of_a_robot_wearing_a_brown_hoodie_in_a_crowded_street.png'
             ],
    [
            'examples/source_wall_with_framed_photos.jpeg', 
            '',
            '',
            100,
            36,
            15,
            'pink drawings of muffins','',
            10,
            1,
            'examples/ddpm_sega_plus_pink_drawings_of_muffins.png'
             ],
    [
            'examples/source_an_empty_room_with_concrete_walls.jpg', 
            'an empty room with concrete walls',
            'glass walls',
            100,
            36,
            17,
            'giant elephant','',
            10,
            1,
            'examples/ddpm_sega_glass_walls_gian_elephant.png'
             ]]
    return case


def invert_and_reconstruct(
                    input_image, 
                    do_inversion,
                    seed, randomize_seed,
                    wts, zs, 
                    src_prompt ="", 
                    tar_prompt="", 
                    steps=100,
                    src_cfg_scale = 3.5,
                    skip=36,
                    tar_cfg_scale=15,
                    
):

    
    x0 = load_512(input_image, device=device)
    
    if do_inversion or randomize_seed:
        # invert and retrieve noise maps and latent
        zs_tensor, wts_tensor = invert(x0 =x0 , prompt_src=src_prompt, num_diffusion_steps=steps, cfg_scale_src=src_cfg_scale)
        wts = gr.State(value=wts_tensor)
        zs = gr.State(value=zs_tensor)
        do_inversion = False

    # output = sample(zs.value, wts.value, prompt_tar=tar_prompt, skip=skip, cfg_scale_tar=tar_cfg_scale)

    # return output, wts, zs, do_inversion
    return wts, zs, do_inversion

    
def edit(input_image,
            wts, zs, 
            tar_prompt, 
            steps,
            skip,
            tar_cfg_scale,
            edit_concept_1,edit_concept_2,edit_concept_3,
            guidnace_scale_1,guidnace_scale_2,guidnace_scale_3,
            warmup_1, warmup_2, warmup_3,
            neg_guidance_1, neg_guidance_2, neg_guidance_3,
            threshold_1, threshold_2, threshold_3

   ):
       
    # SEGA
    # parse concepts and neg guidance 

    
    
    editing_args = dict(
    editing_prompt = [edit_concept_1,edit_concept_2,edit_concept_3],
    reverse_editing_direction = [ neg_guidance_1, neg_guidance_2, neg_guidance_3,],
    edit_warmup_steps=[warmup_1, warmup_2, warmup_3,],
    edit_guidance_scale=[guidnace_scale_1,guidnace_scale_2,guidnace_scale_3], 
    edit_threshold=[threshold_1, threshold_2, threshold_3],
    edit_momentum_scale=0.5, 
    edit_mom_beta=0.6,
    eta=1,
  )
    latnets = wts.value[skip].expand(1, -1, -1, -1)
    sega_out = sem_pipe(prompt=tar_prompt, latents=latnets, guidance_scale = tar_cfg_scale,
                        num_images_per_prompt=1,  
                        num_inference_steps=steps, 
                        use_ddpm=True,  wts=wts.value, zs=zs.value[skip:], **editing_args)
    return sega_out.images[0]




########
# demo #
########
                        
intro = """
<h1 style="font-weight: 1400; text-align: center; margin-bottom: 7px;">
   Edit Friendly DDPM X Semantic Guidance
</h1>
<p style="font-size: 0.9rem; text-align: center; margin: 0rem; line-height: 1.2em; margin-top:1em">
<a href="https://arxiv.org/abs/2301.12247" style="text-decoration: underline;" target="_blank">An Edit Friendly DDPM Noise Space:
Inversion and Manipulations </a> X
<a href="https://arxiv.org/abs/2301.12247" style="text-decoration: underline;" target="_blank">SEGA: Instructing Diffusion using Semantic Dimensions</a>
<p/>
<p style="font-size: 0.9rem; margin: 0rem; line-height: 1.2em; margin-top:1em">
For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings.
<a href="https://huggingface.co/spaces/LinoyTsaban/ddpm_sega?duplicate=true">
<img style="margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>
<p/>"""
with gr.Blocks(css='style.css') as demo:
    
    def add_concept(sega_concepts_counter):
      if sega_concepts_counter == 1:
        return row2.update(visible=True), row3.update(visible=False), plus.update(visible=True), 2
      else:
        return row2.update(visible=True), row3.update(visible=True), plus.update(visible=False), 3


    def reset_do_inversion():
        do_inversion = True
        return do_inversion

        
    gr.HTML(intro)
    wts = gr.State()
    zs = gr.State()
    do_inversion = gr.State(value=True)
    sega_concepts_counter = gr.State(1)
    

    
    with gr.Row():
        input_image = gr.Image(label="Input Image", interactive=True)
        # ddpm_edited_image = gr.Image(label=f"DDPM Reconstructed Image", interactive=False, visible=False)
        sega_edited_image = gr.Image(label=f"DDPM + SEGA Edited Image", interactive=False)
        input_image.style(height=365, width=365)
        # ddpm_edited_image.style(height=512, width=512)
        sega_edited_image.style(height=365, width=365)

    with gr.Tabs() as tabs:
          with gr.TabItem('1. Describe the desired output', id=0):
            with gr.Row().style(mobile_collapse=False, equal_height=True):
              tar_prompt = gr.Textbox(
                                label="Edit Concept",
                                show_label=False,
                                max_lines=1,
                                placeholder="Enter your 1st edit prompt",
                            )
          with gr.TabItem('2. Add SEGA edit concepts', id=1):
            # with gr.Group():
              with gr.Row().style(mobile_collapse=False, equal_height=True):
                  # tar_prompt = gr.Textbox(lines=1, label="Target Prompt", interactive=True, placeholder="")
                  neg_guidance_1 = gr.Checkbox(
                      label='Negative Guidance')
                  warmup_1 = gr.Slider(label='Warmup', minimum=0, maximum=50, value=1, step=1, interactive=True)
                  guidnace_scale_1 = gr.Slider(label='Concept Guidance Scale', minimum=1, maximum=15, value=5, step=0.25, interactive=True)
                  threshold_1 = gr.Slider(label='Threshold', minimum=0.5, maximum=0.99, value=0.95, steps=0.01, interactive=True)
                  edit_concept_1 = gr.Textbox(
                                  label="Edit Concept",
                                  show_label=False,
                                  max_lines=1,
                                  placeholder="Enter your 1st edit prompt",
                              )
              
              with gr.Row(visible=False) as row2:
                 
                  # tar_prompt = gr.Textbox(lines=1, label="Target Prompt", interactive=True, placeholder="")
                  neg_guidance_2 = gr.Checkbox(
                      label='Negative Guidance',visible=True)
                  warmup_2 = gr.Slider(label='Warmup', minimum=0, maximum=50, value=1, step=1, visible=True,interactive=True)
                  guidnace_scale_2 = gr.Slider(label='Concept Guidance Scale', minimum=1, maximum=15, value=5, step=0.25,visible=True, interactive=True)
                  threshold_2 = gr.Slider(label='Threshold', minimum=0.5, maximum=0.99, value=0.95, steps=0.01,visible=True, interactive=True)
                  edit_concept_2 = gr.Textbox(
                                  label="Edit Concept",
                                  show_label=False,visible=True,
                                  max_lines=1,
                                  placeholder="Enter your 2st edit prompt",
                              )
              
              with gr.Row(visible=False) as row3:
                  
                  # tar_prompt = gr.Textbox(lines=1, label="Target Prompt", interactive=True, placeholder="")
                  neg_guidance_3 = gr.Checkbox(
                      label='Negative Guidance',visible=True)
                  warmup_3 = gr.Slider(label='Warmup', minimum=0, maximum=50, value=1, step=1, visible=True,interactive=True)
                  guidnace_scale_3 = gr.Slider(label='Concept Guidance Scale', minimum=1, maximum=15, value=5, step=0.25,visible=True, interactive=True)
                  threshold_3 = gr.Slider(label='Threshold', minimum=0.5, maximum=0.99, value=0.95, steps=0.01,visible=True, interactive=True)
                  edit_concept_3 = gr.Textbox(
                                  label="Edit Concept",
                                  show_label=False,visible=True,
                                  max_lines=1,
                                  placeholder="Enter your 3rd edit prompt",
                              )
              
              with gr.Row().style(mobile_collapse=False, equal_height=True):
                plus = gr.Button("+")

                      
    with gr.Row():
        with gr.Column(scale=1, min_width=100):
            run_button = gr.Button("Run")
        # with gr.Column(scale=1, min_width=100):
        #     edit_button = gr.Button("Edit")

    with gr.Accordion("Advanced Options", open=False):
            with gr.Row():
                with gr.Column():
                    src_prompt = gr.Textbox(lines=1, label="Source Prompt", interactive=True, placeholder="")
                    steps = gr.Number(value=100, precision=0, label="Num Diffusion Steps", interactive=True)
                    src_cfg_scale = gr.Number(value=3.5, label=f"Source Guidance Scale", interactive=True)
                    seed = gr.Number(value=0, precision=0, label="Seed", interactive=True)
                    randomize_seed = gr.Checkbox(label='Randomize seed', value=False)
                with gr.Column():    
                    skip = gr.Slider(minimum=0, maximum=60, value=36, label="Skip Steps", interactive=True)
                    tar_cfg_scale = gr.Slider(minimum=7, maximum=18,value=15, label=f"Guidance Scale", interactive=True)  




    # gr.Markdown(help_text)
    plus.click(fn = add_concept, inputs=sega_concepts_counter,
               outputs= [row2, row3, plus, sega_concepts_counter], queue = False)

    
    run_button.click(
        fn = randomize_seed_fn,
        inputs = [seed, randomize_seed],
        outputs = [seed], 
        queue = False).then(
        fn=invert_and_reconstruct,
        inputs=[input_image, 
                do_inversion,
                seed, randomize_seed,
                wts, zs, 
                src_prompt, 
                tar_prompt, 
                steps,
                src_cfg_scale,
                skip,
                tar_cfg_scale,          
        ],
        # outputs=[ddpm_edited_image, wts, zs, do_inversion],
        outputs=[wts, zs, do_inversion],
    ).success(
        fn=edit,
        inputs=[input_image, 
                wts, zs, 
                tar_prompt, 
                steps,
                skip,
                tar_cfg_scale,
                edit_concept_1,edit_concept_2,edit_concept_3,
                guidnace_scale_1,guidnace_scale_2,guidnace_scale_3,
                warmup_1, warmup_2, warmup_3,
                neg_guidance_1, neg_guidance_2, neg_guidance_3,
                threshold_1, threshold_2, threshold_3

        ],
        outputs=[sega_edited_image],     
    )

    # Automatically start inverting upon input_image change
    input_image.change(
        fn = reset_do_inversion,
        outputs = [do_inversion], queue = False
    ).then(
        fn=invert_and_reconstruct,
        inputs=[input_image, 
                do_inversion,
                seed, randomize_seed,
                wts, zs, 
                src_prompt, 
                tar_prompt, 
                steps,
                src_cfg_scale,
                skip,
                tar_cfg_scale,          
        ],
        # outputs=[ddpm_edited_image, wts, zs, do_inversion],
        outputs=[wts, zs, do_inversion],
    )

    # Repeat inversion when these params are changed:
    src_prompt.change(
        fn = reset_do_inversion,
        outputs = [do_inversion], queue = False
    )
    steps.change(fn = reset_do_inversion,
        outputs = [do_inversion], queue = False)

    src_cfg_scale.change(fn = reset_do_inversion,
        outputs = [do_inversion], queue = False)
    

    gr.Examples(
        label='Examples', 
        examples=get_example(), 
        inputs=[input_image, src_prompt, tar_prompt, steps,
                    # src_cfg_scale,
                    skip,
                    tar_cfg_scale,
                    edit_concept_1,
                    edit_concept_2,
                    guidnace_scale_1,
                    warmup_1,
                    # neg_guidance,
                    sega_edited_image
               ],
        outputs=[sega_edited_image],
        # fn=edit,
        # cache_examples=True
    )



demo.queue()
demo.launch(share=False)