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Linoy Tsaban
commited on
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4697625
1
Parent(s):
acc80f0
Create app.py
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app.py
ADDED
@@ -0,0 +1,259 @@
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1 |
+
import gradio as gr
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import torch
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import requests
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from io import BytesIO
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from diffusers import StableDiffusionPipeline
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from diffusers import DDIMScheduler
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from utils import *
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from inversion_utils import *
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from torch import autocast, inference_mode
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import re
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def invert(x0, prompt_src="", num_diffusion_steps=100, cfg_scale_src = 3.5, eta = 1):
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# inverts a real image according to Algorihm 1 in https://arxiv.org/pdf/2304.06140.pdf,
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# based on the code in https://github.com/inbarhub/DDPM_inversion
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# returns wt, zs, wts:
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# wt - inverted latent
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# wts - intermediate inverted latents
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# zs - noise maps
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sd_pipe.scheduler.set_timesteps(num_diffusion_steps)
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# vae encode image
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with autocast("cuda"), inference_mode():
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w0 = (sd_pipe.vae.encode(x0).latent_dist.mode() * 0.18215).float()
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# find Zs and wts - forward process
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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)
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return wt, zs, wts
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def sample(wt, zs, wts, prompt_tar="", cfg_scale_tar=15, skip=36, eta = 1):
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# reverse process (via Zs and wT)
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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:])
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# vae decode image
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with autocast("cuda"), inference_mode():
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x0_dec = sd_pipe.vae.decode(1 / 0.18215 * w0).sample
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if x0_dec.dim()<4:
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x0_dec = x0_dec[None,:,:,:]
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img = image_grid(x0_dec)
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return img
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# load pipelines
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# sd_model_id = "runwayml/stable-diffusion-v1-5"
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sd_model_id = "CompVis/stable-diffusion-v1-4"
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# sd_model_id = "stabilityai/stable-diffusion-2-base"
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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sd_pipe = StableDiffusionPipeline.from_pretrained(sd_model_id).to(device)
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sd_pipe.scheduler = DDIMScheduler.from_config(sd_model_id, subfolder = "scheduler")
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+
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def get_example():
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case = [
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[
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'examples/source_a_man_wearing_a_brown_hoodie_in_a_crowded_street.jpeg',
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'a man wearing a brown hoodie in a crowded street',
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'a robot wearing a brown hoodie in a crowded street',
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100,
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36,
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15,
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'+painting',
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10,
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1,
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'examples/ddpm_a_robot_wearing_a_brown_hoodie_in_a_crowded_street.png',
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'examples/ddpm_sega_painting_of_a_robot_wearing_a_brown_hoodie_in_a_crowded_street.png'
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],
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[
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'examples/source_wall_with_framed_photos.jpeg',
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'',
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'',
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100,
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36,
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15,
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'+pink drawings of muffins',
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10,
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1,
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'examples/ddpm_wall_with_framed_photos.png',
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'examples/ddpm_sega_plus_pink_drawings_of_muffins.png'
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],
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[
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'examples/source_an_empty_room_with_concrete_walls.jpg',
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'an empty room with concrete walls',
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'glass walls',
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100,
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36,
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17,
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'+giant elephant',
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10,
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1,
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'examples/ddpm_glass_walls.png',
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'examples/ddpm_sega_glass_walls_gian_elephant.png'
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]]
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return case
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inversion_map = dict()
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def invert(input_image,
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src_prompt ="",
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steps=100,
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src_cfg_scale = 3.5,
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left = 0,
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right = 0,
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top = 0,
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bottom = 0
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):
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# offsets=(0,0,0,0)
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x0 = load_512(input_image, left,right, top, bottom, device)
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# invert
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wt, zs, wts = invert(x0 =x0 , prompt_src=src_prompt, num_diffusion_steps=steps, cfg_scale_src=src_cfg_scale)
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latnets = wts[skip].expand(1, -1, -1, -1)
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inversion_map['latnets'] = latnets
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inversion_map['zs'] = zs
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inversion_map['wts'] = wts
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return
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def edit(tar_prompt="",
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steps=100,
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skip=36,
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tar_cfg_scale=15,
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):
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outputs = []
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num_generations = 1
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for i in range(num_generations):
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out = sample(wt, zs, wts, prompt_tar=tar_prompt,
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cfg_scale_tar=tar_cfg_scale, skip=skip)
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outputs.append(out)
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return outputs
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def reset():
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inversion_map.clear()
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########
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# demo #
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########
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intro = """
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<h1 style="font-weight: 1400; text-align: center; margin-bottom: 7px;">
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Edit Friendly DDPM Inversion
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</h1>
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<p style="font-size: 0.9rem; text-align: center; margin: 0rem; line-height: 1.2em; margin-top:1em">
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<a href="https://arxiv.org/abs/2301.12247" style="text-decoration: underline;" target="_blank">An Edit Friendly DDPM Noise Space:
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Inversion and Manipulations </a>
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<p/>
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<p style="font-size: 0.9rem; margin: 0rem; line-height: 1.2em; margin-top:1em">
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For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings.
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<a href="https://huggingface.co/spaces/LinoyTsaban/ddpm_sega?duplicate=true">
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<img style="margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>
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<p/>"""
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with gr.Blocks() as demo:
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gr.HTML(intro)
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with gr.Row():
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src_prompt = gr.Textbox(lines=1, label="Source Prompt", interactive=True, placeholder="optional: describe the original image")
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tar_prompt = gr.Textbox(lines=1, label="Target Prompt", interactive=True, placeholder="optional: describe the target image to edit with DDPM")
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with gr.Row():
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input_image = gr.Image(label="Input Image", interactive=True)
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input_image.style(height=512, width=512)
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output_image = gr.Image(label=f"Edited Image", interactive=False)
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output_image.style(height=512, width=512)
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with gr.Row():
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with gr.Column(scale=1, min_width=100):
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invert_button = gr.Button("Load & Invert")
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with gr.Column(scale=1, min_width=100):
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edit_button = gr.Button("Sample & Edit")
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with gr.Accordion("Advanced Options", open=False):
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with gr.Row():
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with gr.Column():
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#inversion
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steps = gr.Number(value=100, precision=0, label="Num Diffusion Steps", interactive=True)
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src_cfg_scale = gr.Slider(minimum=1, maximum=15, value=3.5, label=f"Source Guidance Scale", interactive=True)
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+
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# reconstruction
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skip = gr.Slider(minimum=0, maximum=40, value=36, precision=0, label="Skip Steps", interactive=True)
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tar_cfg_scale = gr.Slider(minimum=7, maximum=18,value=15, label=f"Target Guidance Scale", interactive=True)
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#shift
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with gr.Column():
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left = gr.Number(value=0, precision=0, label="Left Shift", interactive=True)
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right = gr.Number(value=0, precision=0, label="Right Shift", interactive=True)
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top = gr.Number(value=0, precision=0, label="Top Shift", interactive=True)
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bottom = gr.Number(value=0, precision=0, label="Bottom Shift", interactive=True)
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# gr.Markdown(help_text)
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invert_button.click(
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fn=invert,
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inputs=[input_image,
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src_prompt,
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steps,
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src_cfg_scale,
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left,
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right,
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top,
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bottom
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],
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outputs = [],
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)
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edit_button.click(
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fn=edit,
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inputs=[tar_prompt,
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steps,
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skip,
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tar_cfg_scale,
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],
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outputs=[output_image],
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)
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+
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input_image.change(
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fn = reset
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)
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# gr.Examples(
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# label='Examples',
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# examples=get_example(),
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# inputs=[input_image, src_prompt, tar_prompt, steps,
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# # src_cfg_scale,
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# skip,
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# tar_cfg_scale,
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# edit_concept,
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# sega_edit_guidance,
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# warm_up,
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# # neg_guidance,
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# ddpm_edited_image, sega_edited_image
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# ],
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# outputs=[ddpm_edited_image, sega_edited_image],
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# # fn=edit,
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# # cache_examples=True
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# )
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+
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demo.queue()
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demo.launch(share=False)
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