import gradio as gr from pair_diff_demo import ImageComp # torch.cuda.set_per_process_memory_fraction(0.6) def init_input_canvas_wrapper(obj, *args): return obj.init_input_canvas(*args) def init_ref_canvas_wrapper(obj, *args): return obj.init_ref_canvas(*args) def select_input_object_wrapper(obj, evt: gr.SelectData): return obj.select_input_object(evt) def select_ref_object_wrapper(obj, evt: gr.SelectData): return obj.select_ref_object(evt) def process_wrapper(obj, *args): return obj.process(*args) def set_multi_modal_wrapper(obj, *args): return obj.set_multi_modal(*args) def save_result_wrapper(obj, *args): return obj.save_result(*args) def return_input_img_wrapper(obj): return obj.return_input_img() def get_caption_wrapper(obj, *args): return obj.get_caption(*args) def multimodal_params(b): if b: return 10, 3, 6 else: return 6, 8, 9 theme = gr.themes.Soft( primary_hue="purple", font_mono=[gr.themes.GoogleFont("IBM Plex Mono"), "ui-monospace", "Consolas", 'monospace'], ).set( block_label_background_fill_dark='*neutral_800' ) css = """ #customized_imbox { min-height: 450px; } #customized_imbox>div[data-testid="image"] { min-height: 450px; } #customized_imbox>div[data-testid="image"]>div { min-height: 450px; } #customized_imbox>div[data-testid="image"]>iframe { min-height: 450px; } #customized_imbox>div.unpadded_box { min-height: 450px; } #myinst { font-size: 0.8rem; margin: 0rem; color: #6B7280; } #maskinst { text-align: justify; min-width: 1200px; } #maskinst>img { min-width:399px; max-width:450px; vertical-align: top; display: inline-block; } #maskinst:after { content: ""; width: 100%; display: inline-block; } """ def create_app_demo(): with gr.Row(): gr.Markdown("## Object Level Appearance Editing") with gr.Row(): gr.HTML( """

Instructions

  1. Upload an Input Image.
  2. Mark one of segmented objects in the Select Object to Edit tab.
  3. Upload an Reference Image.
  4. Mark one of segmented objects in the Select Reference Object tab, whose appearance needs to used in the selected input object.
  5. Enter a prompt and press Run button. (A very simple would also work)
""") with gr.Column(): with gr.Row(): img_edit = gr.State(ImageComp('edit_app')) with gr.Column(): input_image = gr.Image(source='upload', label='Input Image', type="numpy",) with gr.Column(): input_mask = gr.Image(source="upload", label='Select Object in Input Image', type="numpy",) with gr.Column(): ref_img = gr.Image(source='upload', label='Reference Image', type="numpy") with gr.Column(): reference_mask = gr.Image(source="upload", label='Select Object in Refernce Image', type="numpy") with gr.Row(): with gr.Column(): prompt = gr.Textbox(label="Prompt", value='A picture of truck') mulitmod = gr.Checkbox(label='Multi-Modal', value=False) mulitmod.change(fn=set_multi_modal_wrapper, inputs=[img_edit, mulitmod]) input_image.change(fn=init_input_canvas_wrapper, inputs=[img_edit, input_image], outputs=[input_image], show_progress=True) input_image.select(fn=select_input_object_wrapper, inputs=[img_edit], outputs=[input_mask, prompt]) ref_img.change(fn=init_ref_canvas_wrapper, inputs=[img_edit, ref_img], outputs=[ref_img], show_progress=True) ref_img.select(fn=select_ref_object_wrapper, inputs=[img_edit], outputs=[reference_mask]) with gr.Column(): interpolation = gr.Slider(label="Mixing ratio of appearance from reference object", minimum=0.1, maximum=1, value=1.0, step=0.1) whole_ref = gr.Checkbox(label='Use whole reference Image for appearance (Only useful for style transfers)', visible=False) # clear_button.click(fn=img_edit.clear_points, inputs=[], outputs=[input_mask, reference_mask]) with gr.Row(): run_button = gr.Button(label="Run") save_button = gr.Button("Save") with gr.Row(): result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery").style(grid=4, height='auto') with gr.Accordion("Advanced options", open=False): num_samples = gr.Slider(label="Images", minimum=1, maximum=12, value=4, step=1) image_resolution = gr.Slider(label="Image Resolution", minimum=512, maximum=512, value=512, step=64) strength = gr.Slider(label="Control Strength", minimum=0.0, maximum=2.0, value=1.0, step=0.01) guess_mode = gr.Checkbox(label='Guess Mode', value=False) ddim_steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=20, step=1) scale_t = gr.Slider(label="Guidance Scale Text", minimum=0., maximum=30.0, value=6.0, step=0.1) scale_f = gr.Slider(label="Guidance Scale Appearance", minimum=0., maximum=30.0, value=8.0, step=0.1) scale_s = gr.Slider(label="Guidance Scale Structure", minimum=0., maximum=30.0, value=9.0, step=0.1) seed = gr.Slider(label="Seed", minimum=-1, maximum=2147483647, step=1, randomize=True) eta = gr.Number(label="eta (DDIM)", value=0.0) masking = gr.Checkbox(label='Only edit the local region', value=True) a_prompt = gr.Textbox(label="Added Prompt", value='best quality, extremely detailed') n_prompt = gr.Textbox(label="Negative Prompt", value='longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality') dil = gr.Slider(label="Merging region around Edge", minimum=0, maximum=0, value=0, step=0) with gr.Column(): gr.Examples( examples=[['assets/examples/car.jpeg','assets/examples/ian.jpeg', '', 709736989, 6, 8, 9], ['assets/examples/ian.jpeg','assets/examples/car.jpeg', '', 709736989, 6, 8, 9], ['assets/examples/car.jpeg','assets/examples/ran.webp', '', 709736989, 6, 8, 9], ['assets/examples/car.jpeg','assets/examples/car1.webp', '', 709736989, 6, 8, 9], ['assets/examples/car1.webp','assets/examples/car.jpeg', '', 709736989, 6, 8, 9], ['assets/examples/chair.jpeg','assets/examples/chair1.jpeg', '', 1106204668, 6, 8, 9], ['assets/examples/house.jpeg','assets/examples/house2.jpeg', '', 1106204668, 6, 8, 9], ['assets/examples/house2.jpeg','assets/examples/house.jpeg', '', 1106204668, 6, 8, 9], ['assets/examples/park.webp','assets/examples/grasslands-national-park.jpeg', '', 1106204668, 6, 8, 9], ['assets/examples/door.jpeg','assets/examples/door2.jpeg', '', 709736989, 6, 8, 9]], inputs=[input_image, ref_img, prompt, seed, scale_t, scale_f, scale_s], cache_examples=False, ) mulitmod.change(fn=multimodal_params, inputs=[mulitmod], outputs=[scale_t, scale_f, scale_s]) ips = [input_mask, reference_mask, prompt, a_prompt, n_prompt, num_samples, ddim_steps, guess_mode, strength, scale_s, scale_f, scale_t, seed, eta, dil, masking, whole_ref, interpolation] ips_save = [input_mask, prompt, a_prompt, n_prompt, ddim_steps, scale_s, scale_f, scale_t, seed, dil, interpolation] run_button.click(fn=process_wrapper, inputs=[img_edit, *ips], outputs=[result_gallery]) save_button.click(fn=save_result_wrapper, inputs=[img_edit, *ips_save]) def create_add_obj_demo(): with gr.Row(): gr.Markdown("## Add Objects to Image") with gr.Row(): gr.HTML( """

Instructions

  1. Upload an Input Image.
  2. Draw the precise shape of object in the image where you want to add object in Draw Object tab.
  3. Upload an Reference Image.
  4. Click on the object in the Reference Image tab that you want to add in the Input Image.
  5. Enter a prompt and press Run button. (A very simple would also work)
""") with gr.Column(): with gr.Row(): img_edit = gr.State(ImageComp('add_obj')) with gr.Column(): input_image = gr.Image(source='upload', label='Input Image', type="numpy",) with gr.Column(): input_mask = gr.Image(source="upload", label='Draw the desired Object', type="numpy", tool="sketch") input_image.change(fn=init_input_canvas_wrapper, inputs=[img_edit, input_image], outputs=[input_image]) input_image.change(fn=return_input_img_wrapper, inputs=[img_edit], outputs=[input_mask], queue=False) with gr.Column(): ref_img = gr.Image(source='upload', label='Reference Image', type="numpy") with gr.Column(): reference_mask = gr.Image(source="upload", label='Selected Object in Refernce Image', type="numpy") ref_img.change(fn=init_ref_canvas_wrapper, inputs=[img_edit, ref_img], outputs=[ref_img], queue=False) # ref_img.upload(fn=img_edit.init_ref_canvas, inputs=[ref_img], outputs=[ref_img]) ref_img.select(fn=select_ref_object_wrapper, inputs=[img_edit], outputs=[reference_mask]) with gr.Row(): prompt = gr.Textbox(label="Prompt", value='A picture of truck') mulitmod = gr.Checkbox(label='Multi-Modal', value=False, visible=False) mulitmod.change(fn=set_multi_modal_wrapper, inputs=[img_edit, mulitmod]) with gr.Row(): run_button = gr.Button(label="Run") save_button = gr.Button("Save") with gr.Row(): result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery").style(grid=4, height='auto') with gr.Accordion("Advanced options", open=False): num_samples = gr.Slider(label="Images", minimum=1, maximum=12, value=4, step=1) # image_resolution = gr.Slider(label="Image Resolution", minimum=512, maximum=512, value=512, step=64) strength = gr.Slider(label="Control Strength", minimum=0.0, maximum=2.0, value=1.0, step=0.01) guess_mode = gr.Checkbox(label='Guess Mode', value=False) ddim_steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=20, step=1) dil = gr.Slider(label="Merging region around Edge", minimum=0, maximum=5, value=2, step=1) scale_t = gr.Slider(label="Guidance Scale Text", minimum=0., maximum=30.0, value=6.0, step=0.1) scale_f = gr.Slider(label="Guidance Scale Appearance", minimum=0., maximum=30.0, value=8.0, step=0.1) scale_s = gr.Slider(label="Guidance Scale Structure", minimum=0., maximum=30.0, value=9.0, step=0.1) seed = gr.Slider(label="Seed", minimum=-1, maximum=2147483647, step=1, randomize=True) eta = gr.Number(label="eta (DDIM)", value=0.0) masking = gr.Checkbox(label='Only edit the local region', value=True) a_prompt = gr.Textbox(label="Added Prompt", value='best quality, extremely detailed') n_prompt = gr.Textbox(label="Negative Prompt", value='longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality') mulitmod.change(fn=multimodal_params, inputs=[mulitmod], outputs=[scale_t, scale_f, scale_s]) with gr.Column(): gr.Examples( examples=[['assets/examples/chair.jpeg','assets/examples/carpet2.webp', 'A picture of living room with carpet', 892905419, 6, 8, 9], ['assets/examples/chair.jpeg','assets/examples/chair1.jpeg', 'A picture of living room with a orange and white sofa', 892905419, 6, 8, 9], ['assets/examples/park.webp','assets/examples/dog.jpeg', 'A picture of dog in the park', 892905419, 6, 8, 9]], inputs=[input_image, ref_img, prompt, seed, scale_t, scale_f, scale_s], outputs=None, fn=None, cache_examples=False, ) ips = [input_mask, reference_mask, prompt, a_prompt, n_prompt, num_samples, ddim_steps, guess_mode, strength, scale_s, scale_f, scale_t, seed, eta, dil, masking] ips_save = [input_mask, prompt, a_prompt, n_prompt, ddim_steps, scale_s, scale_f, scale_t, seed, dil] run_button.click(fn=process_wrapper, inputs=[img_edit, *ips], outputs=[result_gallery]) save_button.click(fn=save_result_wrapper, inputs=[img_edit, *ips_save]) def create_obj_variation_demo(): with gr.Row(): gr.Markdown("## Objects Variation") with gr.Row(): gr.HTML( """

Instructions

  1. Upload an Input Image.
  2. Click on object to have variations
  3. Press Run button
""") with gr.Column(): with gr.Row(): img_edit = gr.State(ImageComp('edit_app')) with gr.Column(): input_image = gr.Image(source='upload', label='Input Image', type="numpy",) with gr.Column(): input_mask = gr.Image(source="upload", label='Select Object in Input Image', type="numpy",) with gr.Row(): prompt = gr.Textbox(label="Prompt", value='') mulitmod = gr.Checkbox(label='Multi-Modal', value=False) mulitmod.change(fn=set_multi_modal_wrapper, inputs=[img_edit, mulitmod]) input_image.change(fn=init_input_canvas_wrapper, inputs=[img_edit, input_image], outputs=[input_image]) input_image.select(fn=select_input_object_wrapper, inputs=[img_edit], outputs=[input_mask, prompt]) input_image.change(fn=init_ref_canvas_wrapper, inputs=[img_edit, input_image], outputs=[], queue=False) input_image.select(fn=select_ref_object_wrapper, inputs=[img_edit], outputs=[]) with gr.Row(): run_button = gr.Button(label="Run") save_button = gr.Button("Save") with gr.Row(): result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery").style(grid=4, height='auto') with gr.Accordion("Advanced options", open=False): num_samples = gr.Slider(label="Images", minimum=1, maximum=12, value=4, step=2) # image_resolution = gr.Slider(label="Image Resolution", minimum=512, maximum=512, value=512, step=64) strength = gr.Slider(label="Control Strength", minimum=0.0, maximum=2.0, value=1.0, step=0.01) guess_mode = gr.Checkbox(label='Guess Mode', value=False) ddim_steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=20, step=1) dil = gr.Slider(label="Merging region around Edge", minimum=0, maximum=5, value=2, step=1) scale_t = gr.Slider(label="Guidance Scale Text", minimum=0.0, maximum=30.0, value=6.0, step=0.1) scale_f = gr.Slider(label="Guidance Scale Appearance", minimum=0.0, maximum=30.0, value=8.0, step=0.1) scale_s = gr.Slider(label="Guidance Scale Structure", minimum=0.0, maximum=30.0, value=9.0, step=0.1) seed = gr.Slider(label="Seed", minimum=-1, maximum=2147483647, step=1, randomize=True) eta = gr.Number(label="eta (DDIM)", value=0.0) masking = gr.Checkbox(label='Only edit the local region', value=True) a_prompt = gr.Textbox(label="Added Prompt", value='best quality, extremely detailed') n_prompt = gr.Textbox(label="Negative Prompt", value='longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality') mulitmod.change(fn=multimodal_params, inputs=[mulitmod], outputs=[scale_t, scale_f, scale_s]) with gr.Column(): gr.Examples( examples=[['assets/examples/chair.jpeg' , 892905419, 6, 8, 9], ['assets/examples/chair1.jpeg', 892905419, 6, 8, 9], ['assets/examples/park.webp', 892905419, 6, 8, 9], ['assets/examples/car.jpeg', 709736989, 6, 8, 9], ['assets/examples/ian.jpeg', 709736989, 6, 8, 9], ['assets/examples/chair.jpeg', 1106204668, 6, 8, 9], ['assets/examples/door.jpeg', 709736989, 6, 8, 9], ['assets/examples/carpet2.webp', 892905419, 6, 8, 9], ['assets/examples/house.jpeg', 709736989, 6, 8, 9], ['assets/examples/house2.jpeg', 709736989, 6, 8, 9],], inputs=[input_image, seed, scale_t, scale_f, scale_s], outputs=None, fn=None, cache_examples=False, ) ips = [input_mask, input_mask, prompt, a_prompt, n_prompt, num_samples, ddim_steps, guess_mode, strength, scale_s, scale_f, scale_t, seed, eta, dil, masking] ips_save = [input_mask, prompt, a_prompt, n_prompt, ddim_steps, scale_s, scale_f, scale_t, seed, dil] run_button.click(fn=process_wrapper, inputs=[img_edit, *ips], outputs=[result_gallery]) save_button.click(fn=save_result_wrapper, inputs=[img_edit, *ips_save]) def create_free_form_obj_variation_demo(): with gr.Row(): gr.Markdown("## Objects Variation") with gr.Row(): gr.HTML( """

Instructions

  1. Upload an Input Image.
  2. Mask the region that you want to have variation
  3. Press Run button
""") with gr.Column(): with gr.Row(): img_edit = gr.State(ImageComp('edit_app')) with gr.Column(): input_image = gr.Image(source='upload', label='Input Image', type="numpy", ) with gr.Column(): input_mask = gr.Image(source="upload", label='Select Object in Input Image', type="numpy", tool="sketch") with gr.Row(): prompt = gr.Textbox(label="Prompt", value='') ignore_structure = gr.Checkbox(label='Ignore Structure (Please provide a good caption)', visible=False) mulitmod = gr.Checkbox(label='Multi-Modal', value=False) mulitmod.change(fn=set_multi_modal_wrapper, inputs=[img_edit, mulitmod]) input_image.change(fn=init_input_canvas_wrapper, inputs=[img_edit, input_image], outputs=[input_mask]) input_mask.edit(fn=get_caption_wrapper, inputs=[img_edit, input_mask], outputs=[prompt]) input_image.change(fn=init_ref_canvas_wrapper, inputs=[img_edit, input_image], outputs=[], queue=False) # input_image.select(fn=select_ref_object_wrapper, inputs=[img_edit], outputs=[]) # input_image.edit(fn=img_edit.vis_mask, inputs=[input_image], outputs=[input_mask]) with gr.Row(): run_button = gr.Button(label="Run") save_button = gr.Button("Save") with gr.Row(): result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery").style(grid=4, height='auto') with gr.Accordion("Advanced options", open=False): num_samples = gr.Slider(label="Images", minimum=1, maximum=12, value=4, step=2) # image_resolution = gr.Slider(label="Image Resolution", minimum=512, maximum=512, value=512, step=64) strength = gr.Slider(label="Control Strength", minimum=0.0, maximum=2.0, value=1.0, step=0.01) guess_mode = gr.Checkbox(label='Guess Mode', value=False) ddim_steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=20, step=1) dil = gr.Slider(label="Merging region around Edge", minimum=0, maximum=5, value=2, step=1) scale_t = gr.Slider(label="Guidance Scale Text", minimum=0.0, maximum=30.0, value=6.0, step=0.1) scale_f = gr.Slider(label="Guidance Scale Appearance", minimum=0.0, maximum=30.0, value=8.0, step=0.1) scale_s = gr.Slider(label="Guidance Scale Structure", minimum=0.0, maximum=30.0, value=9.0, step=0.1) seed = gr.Slider(label="Seed", minimum=-1, maximum=2147483647, step=1, randomize=True) eta = gr.Number(label="eta (DDIM)", value=0.0) masking = gr.Checkbox(label='Only edit the local region', value=True) free_form_obj_var = gr.Checkbox(label='', value=True) a_prompt = gr.Textbox(label="Added Prompt", value='best quality, extremely detailed') n_prompt = gr.Textbox(label="Negative Prompt", value='longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality') interpolation = gr.Slider(label="Mixing ratio of appearance from reference object", minimum=0.0, maximum=0.1, step=0.1) mulitmod.change(fn=multimodal_params, inputs=[mulitmod], outputs=[scale_t, scale_f, scale_s]) with gr.Column(): gr.Examples( examples=[['assets/examples/chair.jpeg' , 892905419, 6, 8, 9], ['assets/examples/chair1.jpeg', 892905419, 6, 8, 9], ['assets/examples/park.webp', 892905419, 6, 8, 9], ['assets/examples/car.jpeg', 709736989, 6, 8, 9], ['assets/examples/ian.jpeg', 709736989, 6, 8, 9], ['assets/examples/chair.jpeg', 1106204668, 6, 8, 9], ['assets/examples/door.jpeg', 709736989, 6, 8, 9], ['assets/examples/carpet2.webp', 892905419, 6, 8, 9], ['assets/examples/house.jpeg', 709736989, 6, 8, 9], ['assets/examples/house2.jpeg', 709736989, 6, 8, 9],], inputs=[input_image, seed, scale_t, scale_f, scale_s], outputs=None, fn=None, cache_examples=False, ) ips = [input_mask, input_mask, prompt, a_prompt, n_prompt, num_samples, ddim_steps, guess_mode, strength, scale_s, scale_f, scale_t, seed, eta, dil, masking, free_form_obj_var, dil, free_form_obj_var, ignore_structure] ips_save = [input_mask, prompt, a_prompt, n_prompt, ddim_steps, scale_s, scale_f, scale_t, seed, dil, interpolation, free_form_obj_var] run_button.click(fn=process_wrapper, inputs=[img_edit, *ips], outputs=[result_gallery]) save_button.click(fn=save_result_wrapper, inputs=[img_edit, *ips_save]) block = gr.Blocks(css=css, theme=theme).queue() with block: gr.HTML( """

PAIR Diffusion: A Comprehensive Multimodal Object-Level Image Editor

Picsart AI Research

PAIR diffusion provides comprehensive multi-modal editing capabilities to edit real images without the need of inverting them. The current suite contains Object Variation, Edit Appearance of any object using a reference image and text, Add any object from a reference image in the input image. This operations can be mixed with each other to develop new editing operations in future.

""") with gr.Tab('Edit Appearance'): create_app_demo() with gr.Tab('Object Variation Free Form Mask'): create_free_form_obj_variation_demo() with gr.Tab('Object Variation'): create_obj_variation_demo() with gr.Tab('Add Objects'): create_add_obj_demo() block.queue(max_size=20) block.launch(share=False)