import gradio as gr import torch import numpy as np import requests import random from io import BytesIO from utils import * from constants import * from pipeline_semantic_stable_diffusion_img2img_solver import SemanticStableDiffusionImg2ImgPipeline_DPMSolver from torch import autocast, inference_mode from diffusers import StableDiffusionPipeline, AutoencoderKL from diffusers.schedulers import DDIMScheduler from scheduling_dpmsolver_multistep_inject import DPMSolverMultistepSchedulerInject from transformers import AutoProcessor, BlipForConditionalGeneration from share_btn import community_icon_html, loading_icon_html, share_js # load pipelines # sd_model_id = "runwayml/stable-diffusion-v1-5" sd_model_id = "stabilityai/stable-diffusion-2-1-base" device = torch.device("cuda" if torch.cuda.is_available() else "cpu") vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse", torch_dtype=torch.float16) pipe = SemanticStableDiffusionImg2ImgPipeline_DPMSolver.from_pretrained(sd_model_id,vae=vae,torch_dtype=torch.float16, safety_checker=None, requires_safety_checker=False).to(device) pipe.scheduler = DPMSolverMultistepSchedulerInject.from_pretrained(sd_model_id, subfolder="scheduler" , algorithm_type="sde-dpmsolver++", solver_order=2) blip_processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-base") blip_model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base",torch_dtype=torch.float16).to(device) ## IMAGE CPATIONING ## def caption_image(input_image): inputs = blip_processor(images=input_image, return_tensors="pt").to(device, torch.float16) pixel_values = inputs.pixel_values generated_ids = blip_model.generate(pixel_values=pixel_values, max_length=50) generated_caption = blip_processor.batch_decode(generated_ids, skip_special_tokens=True)[0] return generated_caption, generated_caption def sample(zs, wts, attention_store, text_cross_attention_maps, prompt_tar="", cfg_scale_tar=15, skip=36, eta=1): latents = wts[-1].expand(1, -1, -1, -1) img, attention_store, text_cross_attention_maps = pipe( prompt=prompt_tar, init_latents=latents, guidance_scale=cfg_scale_tar, # num_images_per_prompt=1, # num_inference_steps=steps, # use_ddpm=True, # wts=wts.value, attention_store = attention_store, text_cross_attention_maps=text_cross_attention_maps, zs=zs, ) return img.images[0], attention_store, text_cross_attention_maps def reconstruct( tar_prompt, image_caption, tar_cfg_scale, skip, wts, zs, attention_store, text_cross_attention_maps, do_reconstruction, reconstruction, reconstruct_button, ): if reconstruct_button == "Hide Reconstruction": return ( reconstruction, reconstruction, gr.update(visible=False), do_reconstruction, "Show Reconstruction", ) else: if do_reconstruction: if ( image_caption.lower() == tar_prompt.lower() ): # if image caption was not changed, run actual reconstruction tar_prompt = "" latents = wts[-1].expand(1, -1, -1, -1) reconstruction, attention_store, text_cross_attention_maps = sample( zs, wts, attention_store=attention_store, text_cross_attention_maps=text_cross_attention_maps,prompt_tar=tar_prompt, skip=skip, cfg_scale_tar=tar_cfg_scale ) do_reconstruction = False return ( reconstruction, reconstruction, gr.update(visible=True), do_reconstruction, "Hide Reconstruction", ) def load_and_invert( input_image, do_inversion, seed, randomize_seed, wts, zs, src_prompt="", # tar_prompt="", steps=30, src_cfg_scale=3.5, skip=15, tar_cfg_scale=15, progress=gr.Progress(track_tqdm=True), ): # x0 = load_512(input_image, device=device).to(torch.float16) if do_inversion or randomize_seed: seed = randomize_seed_fn(seed, randomize_seed) seed_everything(seed) # invert and retrieve noise maps and latent zs_tensor, wts_tensor = pipe.invert( image_path=input_image, source_prompt=src_prompt, source_guidance_scale=src_cfg_scale, num_inversion_steps=steps, skip=skip, eta=1.0, ) wts = wts_tensor zs = zs_tensor do_inversion = False return wts, zs, do_inversion, gr.update(visible=False) ## SEGA ## def edit(input_image, wts, zs, attention_store, text_cross_attention_maps, tar_prompt, image_caption, 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, do_reconstruction, reconstruction, # for inversion in case it needs to be re computed (and avoid delay): do_inversion, seed, randomize_seed, src_prompt, src_cfg_scale, mask_type, progress=gr.Progress(track_tqdm=True)): show_share_button = gr.update(visible=True) if(mask_type == "No mask"): use_cross_attn_mask = False use_intersect_mask = False elif(mask_type=="Cross Attention Mask"): use_cross_attn_mask = True use_intersect_mask = False elif(mask_type=="Intersect Mask"): use_cross_attn_mask = False use_intersect_mask = True if randomize_seed: seed = randomize_seed_fn(seed, randomize_seed) seed_everything(seed) if do_inversion or randomize_seed: zs_tensor, wts_tensor = pipe.invert( image_path = input_image, source_prompt =src_prompt, source_guidance_scale= src_cfg_scale, num_inversion_steps = steps, skip = skip, eta = 1.0, ) wts = wts_tensor zs = zs_tensor do_inversion = False if image_caption.lower() == tar_prompt.lower(): # if image caption was not changed, run pure sega tar_prompt = "" if edit_concept_1 != "" or edit_concept_2 != "" or edit_concept_3 != "": 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, edit_mom_beta=0, eta=1, use_cross_attn_mask=use_cross_attn_mask, use_intersect_mask=use_intersect_mask ) latnets = wts[-1].expand(1, -1, -1, -1) sega_out, attention_store, text_cross_attention_maps = pipe(prompt=tar_prompt, init_latents=latnets, guidance_scale = tar_cfg_scale, # num_images_per_prompt=1, # num_inference_steps=steps, # use_ddpm=True, # wts=wts.value, zs=zs, attention_store=attention_store, text_cross_attention_maps=text_cross_attention_maps, **editing_args) return sega_out.images[0], gr.update(visible=True), do_reconstruction, reconstruction, wts, zs, attention_store, text_cross_attention_maps, do_inversion, show_share_button else: # if sega concepts were not added, performs regular ddpm sampling if do_reconstruction: # if ddpm sampling wasn't computed pure_ddpm_img, attention_store, text_cross_attention_maps = sample(zs, wts, attention_store=attention_store, text_cross_attention_maps=text_cross_attention_maps, prompt_tar=tar_prompt, skip=skip, cfg_scale_tar=tar_cfg_scale) reconstruction = pure_ddpm_img do_reconstruction = False return pure_ddpm_img, gr.update(visible=False), do_reconstruction, reconstruction, wts, zs, attention_store, text_cross_attention_maps, do_inversion, show_share_button return reconstruction, gr.update(visible=False), do_reconstruction, reconstruction, wts, zs, attention_store, text_cross_attention_maps, do_inversion, show_share_button def randomize_seed_fn(seed, is_random): if is_random: seed = random.randint(0, np.iinfo(np.int32).max) return seed def seed_everything(seed): torch.manual_seed(seed) torch.cuda.manual_seed(seed) random.seed(seed) np.random.seed(seed) def crop_image(image): h, w, c = image.shape if h < w: offset = (w - h) // 2 image = image[:, offset:offset + h] elif w < h: offset = (h - w) // 2 image = image[offset:offset + w] image = np.array(Image.fromarray(image).resize((512, 512))) return image def get_example(): case = [ [ 'examples/car_input.png', # '', 'cherry blossom', 'green cabriolet','yellow car', 'examples/car_output.png', 13,11,7, 2,2,2, False, False, True, 50, 25, 7.5, 0.65, 0.8, 0.8, 890000000 ], [ 'examples/girl_with_pearl_earring_input.png', # '', 'glasses', '','', 'examples/girl_with_pearl_earring_output.png', 4,7,0, 3,2,2, False,False,False, 50, 25, 5, 0.97, 0.95,0.95, 1900000000 ], [ 'examples/flower_field_input.jpg', # '', 'pink tulips', 'red flowers', 'van gogh painting', 'examples/flower_field_output.png', 20,7,10, 1,1,1, False,True,False, 50, 25, 7, 0.9, 0.9,0.8, 1900000000 ], ] return case def swap_visibilities(input_image, edit_concept_1, edit_concept_2, edit_concept_3, sega_edited_image, guidnace_scale_1, guidnace_scale_2, guidnace_scale_3, warmup_1, warmup_2, warmup_3, neg_guidance_1, neg_guidance_2, neg_guidance_3, steps, skip, tar_cfg_scale, threshold_1, threshold_2, threshold_3, sega_concepts_counter ): sega_concepts_counter=0 concept1_update = update_display_concept("Remove" if neg_guidance_1 else "Add", edit_concept_1, neg_guidance_1, sega_concepts_counter) if(edit_concept_2 != ""): concept2_update = update_display_concept("Remove" if neg_guidance_2 else "Add", edit_concept_2, neg_guidance_2, sega_concepts_counter+1) else: concept2_update = gr.update(visible=False), gr.update(visible=False),gr.update(visible=False), gr.update(value=neg_guidance_2),gr.update(visible=True),gr.update(visible=False),sega_concepts_counter+1 return (gr.update(visible=True), *concept1_update[:-1], *concept2_update) ######## # demo # ######## intro = """

LEDITS++

Limitless Image Editing using Text-to-Image Models

project page | paper | Duplicate Space

""" with gr.Blocks(css="style.css") as demo: def update_counter(sega_concepts_counter, concept1, concept2, concept3): if sega_concepts_counter == "": sega_concepts_counter = sum(1 for concept in (concept1, concept2, concept3) if concept != '') return sega_concepts_counter def remove_concept(sega_concepts_counter, row_triggered): sega_concepts_counter -= 1 rows_visibility = [gr.update(visible=False) for _ in range(4)] if(row_triggered-1 > sega_concepts_counter): rows_visibility[sega_concepts_counter] = gr.update(visible=True) else: rows_visibility[row_triggered-1] = gr.update(visible=True) row1_visibility, row2_visibility, row3_visibility, row4_visibility = rows_visibility guidance_scale_label = "Concept Guidance Scale" # enable_interactive = gr.update(interactive=True) return (gr.update(visible=False), gr.update(visible=False, value="",), gr.update(interactive=True, value=""), gr.update(visible=False,label = guidance_scale_label), gr.update(interactive=True, value =False), gr.update(value=DEFAULT_WARMUP_STEPS), gr.update(value=DEFAULT_THRESHOLD), gr.update(visible=True), gr.update(interactive=True, value="custom"), row1_visibility, row2_visibility, row3_visibility, row4_visibility, sega_concepts_counter ) def update_display_concept(button_label, edit_concept, neg_guidance, sega_concepts_counter): sega_concepts_counter += 1 guidance_scale_label = "Concept Guidance Scale" if(button_label=='Remove'): neg_guidance = True guidance_scale_label = "Negative Guidance Scale" return (gr.update(visible=True), #boxn gr.update(visible=True, value=edit_concept), #concept_n gr.update(visible=True,label = guidance_scale_label), #guidance_scale_n gr.update(value=neg_guidance),#neg_guidance_n gr.update(visible=False), #row_n gr.update(visible=True), #row_n+1 sega_concepts_counter ) def display_editing_options(run_button, clear_button, sega_tab): return gr.update(visible=True), gr.update(visible=True), gr.update(visible=True) def update_interactive_mode(add_button_label): if add_button_label == "Clear": return gr.update(interactive=False), gr.update(interactive=False) else: return gr.update(interactive=True), gr.update(interactive=True) def update_dropdown_parms(dropdown): if dropdown == 'custom': return DEFAULT_SEGA_CONCEPT_GUIDANCE_SCALE,DEFAULT_WARMUP_STEPS, DEFAULT_THRESHOLD elif dropdown =='style': return STYLE_SEGA_CONCEPT_GUIDANCE_SCALE,STYLE_WARMUP_STEPS, STYLE_THRESHOLD elif dropdown =='object': return OBJECT_SEGA_CONCEPT_GUIDANCE_SCALE,OBJECT_WARMUP_STEPS, OBJECT_THRESHOLD elif dropdown =='faces': return FACE_SEGA_CONCEPT_GUIDANCE_SCALE,FACE_WARMUP_STEPS, FACE_THRESHOLD def reset_do_inversion(): return True def reset_do_reconstruction(): do_reconstruction = True return do_reconstruction def reset_image_caption(): return "" def update_inversion_progress_visibility(input_image, do_inversion): if do_inversion and not input_image is None: return gr.update(visible=True) else: return gr.update(visible=False) def update_edit_progress_visibility(input_image, do_inversion): # if do_inversion and not input_image is None: # return inversion_progress.update(visible=True) # else: return gr.update(visible=True) gr.HTML(intro) wts = gr.State() zs = gr.State() attention_store=gr.State() text_cross_attention_maps = gr.State() reconstruction = gr.State() do_inversion = gr.State(value=True) do_reconstruction = gr.State(value=True) sega_concepts_counter = gr.State(0) image_caption = gr.State(value="") with gr.Row(): input_image = gr.Image(label="Input Image", interactive=True, elem_id="input_image") ddpm_edited_image = gr.Image(label=f"Pure DDPM Inversion Image", interactive=False, visible=False) sega_edited_image = gr.Image(label=f"LEDITS Edited Image", interactive=False, elem_id="output_image") with gr.Group(visible=False, elem_id="share-btn-wrapper") as share_btn_container: with gr.Group(elem_id="share-btn-container"): community_icon = gr.HTML(community_icon_html, visible=True) loading_icon = gr.HTML(loading_icon_html, visible=False) share_button = gr.Button("Share to community", elem_id="share-btn", visible=True) with gr.Row(): with gr.Group(visible=False, elem_id="box1") as box1: with gr.Row(): concept_1 = gr.Button(scale=3, value="") remove_concept1 = gr.Button("x", scale=1, min_width=10) with gr.Row(): guidnace_scale_1 = gr.Slider(label='Concept Guidance Scale', minimum=1, maximum=30, info="How strongly the concept should modify the image", value=DEFAULT_SEGA_CONCEPT_GUIDANCE_SCALE, step=0.5, interactive=True) with gr.Group(visible=False, elem_id="box2") as box2: with gr.Row(): concept_2 = gr.Button(scale=3, value="") remove_concept2 = gr.Button("x", scale=1, min_width=10) with gr.Row(): guidnace_scale_2 = gr.Slider(label='Concept Guidance Scale', minimum=1, maximum=30, info="How strongly the concept should modify the image", value=DEFAULT_SEGA_CONCEPT_GUIDANCE_SCALE, step=0.5, interactive=True) with gr.Group(visible=False, elem_id="box3") as box3: with gr.Row(): concept_3 = gr.Button(scale=3, value="") remove_concept3 = gr.Button("x", scale=1, min_width=10) with gr.Row(): guidnace_scale_3 = gr.Slider(label='Concept Guidance Scale', minimum=1, maximum=30, info="How strongly the concept should modify the image", value=DEFAULT_SEGA_CONCEPT_GUIDANCE_SCALE, step=0.5, interactive=True) with gr.Row(): inversion_progress = gr.Textbox(visible=False, label="Inversion progress") with gr.Group(): intro_segs = gr.Markdown("Add/Remove Concepts from your Image with Semantic Guidance") # 1st SEGA concept with gr.Row() as row1: with gr.Column(scale=3, min_width=100): with gr.Row(): # with gr.Column(scale=3, min_width=100): edit_concept_1 = gr.Textbox( label="Concept", show_label=True, max_lines=1, value="", placeholder="E.g.: Sunglasses", ) # with gr.Column(scale=2, min_width=100):# better mobile ui dropdown1 = gr.Dropdown(label = "Edit Type", value ='custom' , choices=['custom','style', 'object', 'faces']) with gr.Column(scale=1, min_width=100, visible=False): neg_guidance_1 = gr.Checkbox( label='Remove Concept?') with gr.Column(scale=1, min_width=100): with gr.Row(): # better mobile ui with gr.Column(): add_1 = gr.Button('Add') remove_1 = gr.Button('Remove') # 2nd SEGA concept with gr.Row(visible=False) as row2: with gr.Column(scale=3, min_width=100): with gr.Row(): #better mobile UI # with gr.Column(scale=3, min_width=100): edit_concept_2 = gr.Textbox( label="Concept", show_label=True, max_lines=1, placeholder="E.g.: Realistic", ) # with gr.Column(scale=2, min_width=100):# better mobile ui dropdown2 = gr.Dropdown(label = "Edit Type", value ='custom' , choices=['custom','style', 'object', 'faces']) with gr.Column(scale=1, min_width=100, visible=False): neg_guidance_2 = gr.Checkbox( label='Remove Concept?') with gr.Column(scale=1, min_width=100): with gr.Row(): # better mobile ui with gr.Column(): add_2 = gr.Button('Add') remove_2 = gr.Button('Remove') # 3rd SEGA concept with gr.Row(visible=False) as row3: with gr.Column(scale=3, min_width=100): with gr.Row(): #better mobile UI # with gr.Column(scale=3, min_width=100): edit_concept_3 = gr.Textbox( label="Concept", show_label=True, max_lines=1, placeholder="E.g.: orange", ) # with gr.Column(scale=2, min_width=100): dropdown3 = gr.Dropdown(label = "Edit Type", value ='custom' , choices=['custom','style', 'object', 'faces']) with gr.Column(scale=1, min_width=100, visible=False): neg_guidance_3 = gr.Checkbox( label='Remove Concept?',visible=True) with gr.Column(scale=1, min_width=100): with gr.Row(): # better mobile ui with gr.Column(): add_3 = gr.Button('Add') remove_3 = gr.Button('Remove') with gr.Row(visible=False) as row4: gr.Markdown("### Max of 3 concepts reached. Remove a concept to add more") #with gr.Row(visible=False).style(mobile_collapse=False, equal_height=True): # add_concept_button = gr.Button("+1 concept") # caption_button = gr.Button("Caption Image", scale=1) with gr.Row(): run_button = gr.Button("Edit your image!", visible=True) with gr.Accordion("Advanced Options", open=False): with gr.Row(): tar_prompt = gr.Textbox( label="Describe your edited image (optional)", elem_id="target_prompt", # show_label=False, max_lines=1, value="", scale=3, placeholder="Target prompt, DDPM Inversion", info = "DPM Solver++ Inversion Prompt. Can help with global changes, modify to what you would like to see" ) with gr.Tabs() as tabs: with gr.TabItem('General options', id=2): with gr.Row(): with gr.Column(min_width=100): clear_button = gr.Button("Clear", visible=True) src_prompt = gr.Textbox(lines=1, label="Source Prompt", interactive=True, placeholder="") steps = gr.Number(value=50, precision=0, label="Num Diffusion Steps", interactive=True) src_cfg_scale = gr.Number(value=3.5, label=f"Source Guidance Scale", interactive=True) mask_type = gr.Radio(choices=["No mask", "Cross Attention Mask", "Intersect Mask"], value="Intersect Mask", label="Mask type") with gr.Column(min_width=100): reconstruct_button = gr.Button("Show Reconstruction", visible=False) skip = gr.Slider(minimum=0, maximum=95, value=25, step=1, label="Skip Steps", interactive=True, info = "Percentage of skipped denoising steps. Bigger values increase fidelity to input image") tar_cfg_scale = gr.Slider(minimum=1, maximum=30,value=7.5, label=f"Guidance Scale", interactive=True) seed = gr.Slider(minimum=0, maximum=np.iinfo(np.int32).max, label="Seed", interactive=True, randomize=True) randomize_seed = gr.Checkbox(label='Randomize seed', value=False) with gr.TabItem('SEGA options', id=3) as sega_advanced_tab: # 1st SEGA concept gr.Markdown("1st concept") with gr.Row(): warmup_1 = gr.Slider(label='Warmup', minimum=0, maximum=50, value=DEFAULT_WARMUP_STEPS, step=1, interactive=True, info="At which step to start applying semantic guidance. Bigger values reduce edit concept's effect") threshold_1 = gr.Slider(label='Threshold', minimum=0, maximum=0.99, value=DEFAULT_THRESHOLD, step=0.01, interactive=True, info = "Lower the threshold for more effect (e.g. ~0.9 for style transfer)") # 2nd SEGA concept gr.Markdown("2nd concept") with gr.Row() as row2_advanced: warmup_2 = gr.Slider(label='Warmup', minimum=0, maximum=50, value=DEFAULT_WARMUP_STEPS, step=1, interactive=True, info="At which step to start applying semantic guidance. Bigger values reduce edit concept's effect") threshold_2 = gr.Slider(label='Threshold', minimum=0, maximum=0.99, value=DEFAULT_THRESHOLD, step=0.01, interactive=True, info = "Lower the threshold for more effect (e.g. ~0.9 for style transfer)") # 3rd SEGA concept gr.Markdown("3rd concept") with gr.Row() as row3_advanced: warmup_3 = gr.Slider(label='Warmup', minimum=0, maximum=50, value=DEFAULT_WARMUP_STEPS, step=1, interactive=True, info="At which step to start applying semantic guidance. Bigger values reduce edit concept's effect") threshold_3 = gr.Slider(label='Threshold', minimum=0, maximum=0.99, value=DEFAULT_THRESHOLD, step=0.01, interactive=True, info = "Lower the threshold for more effect (e.g. ~0.9 for style transfer)") # caption_button.click( # fn = caption_image, # inputs = [input_image], # outputs = [tar_prompt] # ) #neg_guidance_1.change(fn = update_label, inputs=[neg_guidance_1], outputs=[add_1]) #neg_guidance_2.change(fn = update_label, inputs=[neg_guidance_2], outputs=[add_2]) #neg_guidance_3.change(fn = update_label, inputs=[neg_guidance_3], outputs=[add_3]) add_1.click(fn=update_counter, inputs=[sega_concepts_counter,edit_concept_1,edit_concept_2,edit_concept_3], outputs=sega_concepts_counter,queue=False).then(fn = update_display_concept, inputs=[add_1, edit_concept_1, neg_guidance_1, sega_concepts_counter], outputs=[box1, concept_1, guidnace_scale_1,neg_guidance_1,row1, row2, sega_concepts_counter],queue=False) add_2.click(fn=update_counter,inputs=[sega_concepts_counter,edit_concept_1,edit_concept_2,edit_concept_3], outputs=sega_concepts_counter,queue=False).then(fn = update_display_concept, inputs=[add_2, edit_concept_2, neg_guidance_2, sega_concepts_counter], outputs=[box2, concept_2, guidnace_scale_2,neg_guidance_2,row2, row3, sega_concepts_counter],queue=False) add_3.click(fn=update_counter,inputs=[sega_concepts_counter,edit_concept_1,edit_concept_2,edit_concept_3], outputs=sega_concepts_counter,queue=False).then(fn = update_display_concept, inputs=[add_3, edit_concept_3, neg_guidance_3, sega_concepts_counter], outputs=[box3, concept_3, guidnace_scale_3,neg_guidance_3,row3, row4, sega_concepts_counter],queue=False) remove_1.click(fn = update_display_concept, inputs=[remove_1, edit_concept_1, neg_guidance_1, sega_concepts_counter], outputs=[box1, concept_1, guidnace_scale_1,neg_guidance_1,row1, row2, sega_concepts_counter],queue=False) remove_2.click(fn = update_display_concept, inputs=[remove_2, edit_concept_2, neg_guidance_2 ,sega_concepts_counter], outputs=[box2, concept_2, guidnace_scale_2,neg_guidance_2,row2, row3,sega_concepts_counter],queue=False) remove_3.click(fn = update_display_concept, inputs=[remove_3, edit_concept_3, neg_guidance_3, sega_concepts_counter], outputs=[box3, concept_3, guidnace_scale_3,neg_guidance_3, row3, row4, sega_concepts_counter],queue=False) remove_concept1.click( fn=update_counter,inputs=[sega_concepts_counter,edit_concept_1,edit_concept_2,edit_concept_3], outputs=sega_concepts_counter,queue=False).then( fn = remove_concept, inputs=[sega_concepts_counter,gr.State(1)], outputs= [box1, concept_1, edit_concept_1, guidnace_scale_1,neg_guidance_1,warmup_1, threshold_1, add_1, dropdown1, row1, row2, row3, row4, sega_concepts_counter],queue=False) remove_concept2.click( fn=update_counter,inputs=[sega_concepts_counter,edit_concept_1,edit_concept_2,edit_concept_3], outputs=sega_concepts_counter,queue=False).then( fn = remove_concept, inputs=[sega_concepts_counter,gr.State(2)], outputs=[box2, concept_2, edit_concept_2, guidnace_scale_2,neg_guidance_2, warmup_2, threshold_2, add_2 , dropdown2, row1, row2, row3, row4, sega_concepts_counter],queue=False) remove_concept3.click( fn=update_counter,inputs=[sega_concepts_counter,edit_concept_1,edit_concept_2,edit_concept_3], outputs=sega_concepts_counter,queue=False).then( fn = remove_concept,inputs=[sega_concepts_counter,gr.State(3)], outputs=[box3, concept_3, edit_concept_3, guidnace_scale_3,neg_guidance_3,warmup_3, threshold_3, add_3, dropdown3, row1, row2, row3, row4, sega_concepts_counter],queue=False) #add_concept_button.click(fn = update_display_concept, inputs=sega_concepts_counter, # outputs= [row2, row2_advanced, row3, row3_advanced, add_concept_button, sega_concepts_counter], queue = False) run_button.click( fn=edit, inputs=[input_image, wts, zs, attention_store, text_cross_attention_maps, tar_prompt, image_caption, 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, do_reconstruction, reconstruction, do_inversion, seed, randomize_seed, src_prompt, src_cfg_scale, mask_type ], outputs=[sega_edited_image, reconstruct_button, do_reconstruction, reconstruction, wts, zs,attention_store, text_cross_attention_maps, do_inversion, share_btn_container] ) # .success(fn=update_gallery_display, inputs= [prev_output_image, sega_edited_image], outputs = [gallery, gallery, prev_output_image]) input_image.change( fn = reset_do_inversion, outputs = [do_inversion], queue=False, concurrency_limit=None ).then( fn = randomize_seed_fn, inputs = [seed, randomize_seed], outputs = [seed], queue=False, concurrency_limit=None ) # Automatically start inverting upon input_image change input_image.upload( fn = crop_image, inputs = [input_image], outputs = [input_image], queue=False, concurrency_limit=None, ).then( fn = reset_do_inversion, outputs = [do_inversion], queue=False, concurrency_limit=None ).then( fn = randomize_seed_fn, inputs = [seed, randomize_seed], outputs = [seed], queue=False, concurrency_limit=None ).then(fn = caption_image, inputs = [input_image], outputs = [tar_prompt, image_caption], queue=False, concurrency_limit=None ) # Repeat inversion (and reconstruction) when these params are changed: src_prompt.change( fn = reset_do_inversion, outputs = [do_inversion], queue = False ).then( fn = reset_do_reconstruction, outputs = [do_reconstruction], queue = False ) steps.change( fn = reset_do_inversion, outputs = [do_inversion], queue = False ).then( fn = reset_do_reconstruction, outputs = [do_reconstruction], queue = False ) src_cfg_scale.change( fn = reset_do_inversion, outputs = [do_inversion], queue = False ).then( fn = reset_do_reconstruction, outputs = [do_reconstruction], queue = False ) # Repeat only reconstruction these params are changed: tar_prompt.change( fn = reset_do_reconstruction, outputs = [do_reconstruction], queue = False ) tar_cfg_scale.change( fn = reset_do_reconstruction, outputs = [do_reconstruction], queue = False ) skip.change( fn = reset_do_inversion, outputs = [do_inversion], queue = False ).then( fn = reset_do_reconstruction, outputs = [do_reconstruction], queue = False ) seed.change( fn=reset_do_inversion, outputs=[do_inversion], queue=False ).then( fn=reset_do_reconstruction, outputs=[do_reconstruction], queue=False ) dropdown1.change(fn=update_dropdown_parms, inputs = [dropdown1], outputs = [guidnace_scale_1,warmup_1, threshold_1], queue=False) dropdown2.change(fn=update_dropdown_parms, inputs = [dropdown2], outputs = [guidnace_scale_2,warmup_2, threshold_2], queue=False) dropdown3.change(fn=update_dropdown_parms, inputs = [dropdown3], outputs = [guidnace_scale_3,warmup_3, threshold_3], queue=False) clear_components = [input_image,ddpm_edited_image,ddpm_edited_image,sega_edited_image, do_inversion, src_prompt, steps, src_cfg_scale, seed, tar_prompt, skip, tar_cfg_scale, reconstruct_button,reconstruct_button, edit_concept_1, guidnace_scale_1,guidnace_scale_1,warmup_1, threshold_1, neg_guidance_1,dropdown1, concept_1, concept_1, row1, edit_concept_2, guidnace_scale_2,guidnace_scale_2,warmup_2, threshold_2, neg_guidance_2,dropdown2, concept_2, concept_2, row2, edit_concept_3, guidnace_scale_3,guidnace_scale_3,warmup_3, threshold_3, neg_guidance_3,dropdown3, concept_3,concept_3, row3, row4,sega_concepts_counter, box1, box2, box3 ] clear_components_output_vals = [None, None,gr.update(visible=False), None, True, "", DEFAULT_DIFFUSION_STEPS, DEFAULT_SOURCE_GUIDANCE_SCALE, DEFAULT_SEED, "", DEFAULT_SKIP_STEPS, DEFAULT_TARGET_GUIDANCE_SCALE, gr.update(value="Show Reconstruction"),gr.update(visible=False), "", DEFAULT_SEGA_CONCEPT_GUIDANCE_SCALE,gr.update(visible=False), DEFAULT_WARMUP_STEPS, DEFAULT_THRESHOLD, DEFAULT_NEGATIVE_GUIDANCE, "custom","", gr.update(visible=False), gr.update(visible=True), "", DEFAULT_SEGA_CONCEPT_GUIDANCE_SCALE,gr.update(visible=False), DEFAULT_WARMUP_STEPS, DEFAULT_THRESHOLD, DEFAULT_NEGATIVE_GUIDANCE, "custom","", gr.update(visible=False), gr.update(visible=False), "", DEFAULT_SEGA_CONCEPT_GUIDANCE_SCALE,gr.update(visible=False), DEFAULT_WARMUP_STEPS, DEFAULT_THRESHOLD, DEFAULT_NEGATIVE_GUIDANCE, "custom","",gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(value=0), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)] clear_button.click(lambda: clear_components_output_vals, outputs = clear_components) reconstruct_button.click(lambda: ddpm_edited_image.update(visible=True), outputs=[ddpm_edited_image]).then(fn = reconstruct, inputs = [tar_prompt, image_caption, tar_cfg_scale, skip, wts, zs, do_reconstruction, reconstruction, reconstruct_button], outputs = [ddpm_edited_image,reconstruction, ddpm_edited_image, do_reconstruction, reconstruct_button]) randomize_seed.change( fn = randomize_seed_fn, inputs = [seed, randomize_seed], outputs = [seed], queue = False) share_button.click(None, [], [], js=share_js) gr.Examples( label='Examples', fn=swap_visibilities, run_on_click=True, examples=get_example(), inputs=[input_image, edit_concept_1, edit_concept_2, edit_concept_3, sega_edited_image, guidnace_scale_1, guidnace_scale_2, guidnace_scale_3, warmup_1, warmup_2, warmup_3, neg_guidance_1, neg_guidance_2, neg_guidance_3, steps, skip, tar_cfg_scale, threshold_1, threshold_2, threshold_3, seed, sega_concepts_counter ], outputs=[share_btn_container, box1, concept_1, guidnace_scale_1,neg_guidance_1, row1, row2,box2, concept_2, guidnace_scale_2,neg_guidance_2,row2, row3,sega_concepts_counter], cache_examples=True ) demo.queue(default_concurrency_limit=1) demo.launch()