# original code by zenafey from utils import place_lora, get_exif_data from css import css from grutils import * import inference lora_list = pipe.constant("/sd/loras") samplers = pipe.constant("/sd/samplers") with gr.Blocks(css=css, theme="zenafey/prodia-web") as demo: model = gr.Dropdown(interactive=True, value=model_list[0], show_label=True, label="Stable Diffusion Checkpoint", choices=model_list, elem_id="model_dd") with gr.Tabs() as tabs: with gr.Tab("txt2img", id='t2i'): with gr.Row(): with gr.Column(scale=6, min_width=600): prompt = gr.Textbox("space warrior, beautiful, female, ultrarealistic, soft lighting, 8k", placeholder="Prompt", show_label=False, lines=3) negative_prompt = gr.Textbox(placeholder="Negative Prompt", show_label=False, lines=3, value="3d, cartoon, anime, (deformed eyes, nose, ears, nose), bad anatomy, ugly") with gr.Row(): t2i_generate_btn = gr.Button("Generate", variant='primary', elem_id="generate") t2i_stop_btn = gr.Button("Cancel", variant="stop", elem_id="generate", visible=False) with gr.Row(): with gr.Column(): with gr.Tab("Generation"): with gr.Row(): with gr.Column(scale=1): sampler = gr.Dropdown(value="DPM++ 2M Karras", show_label=True, label="Sampling Method", choices=samplers) with gr.Column(scale=1): steps = gr.Slider(label="Sampling Steps", minimum=1, maximum=30, value=25, step=1) with gr.Row(): with gr.Column(scale=8): width = gr.Slider(label="Width", maximum=1024, value=512, step=8) height = gr.Slider(label="Height", maximum=1024, value=512, step=8) with gr.Column(scale=1): batch_size = gr.Slider(label="Batch Size", maximum=1, value=1) batch_count = gr.Slider(label="Batch Count", minimum=1, maximum=4, value=1, step=1) cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, value=7, step=1) seed = gr.Number(label="Seed", value=-1) with gr.Tab("Lora"): with gr.Row(): for lora in lora_list: lora_btn = gr.Button(lora, size="sm") lora_btn.click(place_lora, inputs=[prompt, lora_btn], outputs=prompt) with gr.Column(): image_output = gr.Gallery(columns=3, value=["https://images.prodia.xyz/8ede1a7c-c0ee-4ded-987d-6ffed35fc477.png"]) with gr.Tab("img2img", id='i2i'): with gr.Row(): with gr.Column(scale=6, min_width=600): i2i_prompt = gr.Textbox("space warrior, beautiful, female, ultrarealistic, soft lighting, 8k", placeholder="Prompt", show_label=False, lines=3) i2i_negative_prompt = gr.Textbox(placeholder="Negative Prompt", show_label=False, lines=3, value="3d, cartoon, anime, (deformed eyes, nose, ears, nose), bad anatomy, ugly") with gr.Row(): i2i_generate_btn = gr.Button("Generate", variant='primary', elem_id="generate") i2i_stop_btn = gr.Button("Cancel", variant="stop", elem_id="generate", visible=False) with gr.Row(): with gr.Column(scale=1): with gr.Tab("Generation"): i2i_image_input = gr.Image(type="pil") with gr.Row(): with gr.Column(scale=1): i2i_sampler = gr.Dropdown(value="DPM++ 2M Karras", show_label=True, label="Sampling Method", choices=samplers) with gr.Column(scale=1): i2i_steps = gr.Slider(label="Sampling Steps", minimum=1, maximum=30, value=25, step=1) with gr.Row(): with gr.Column(scale=6): i2i_width = gr.Slider(label="Width", maximum=1024, value=512, step=8) i2i_height = gr.Slider(label="Height", maximum=1024, value=512, step=8) with gr.Column(scale=1): i2i_batch_size = gr.Slider(label="Batch Size", maximum=1, value=1) i2i_batch_count = gr.Slider(label="Batch Count", minimum=1, maximum=4, value=1, step=1) i2i_cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, value=7, step=1) i2i_denoising = gr.Slider(label="Denoising Strength", minimum=0, maximum=1, value=0.7, step=0.1) i2i_seed = gr.Number(label="Seed", value=-1) with gr.Tab("Lora"): with gr.Row(): for lora in lora_list: lora_btn = gr.Button(lora, size="sm") lora_btn.click(place_lora, inputs=[i2i_prompt, lora_btn], outputs=i2i_prompt) with gr.Column(scale=1): i2i_image_output = gr.Gallery(columns=3, value=["https://images.prodia.xyz/8ede1a7c-c0ee-4ded-987d-6ffed35fc477.png"]) with gr.Tab("Extras"): with gr.Row(): with gr.Tab("Single Image"): with gr.Column(): upscale_image_input = gr.Image(type="pil") upscale_btn = gr.Button("Generate", variant="primary") upscale_stop_btn = gr.Button("Stop", variant="stop", visible=False) with gr.Tab("Scale by"): upscale_scale = gr.Radio([2, 4], value=2, label="Resize") upscale_output = gr.Image() with gr.Tab("PNG Info"): with gr.Row(): with gr.Column(): image_input = gr.Image(type="pil") with gr.Column(): exif_output = gr.HTML(label="EXIF Data") send_to_txt2img_btn = gr.Button("Send to txt2img") with gr.Tab("Past generations"): inference.gr_user_history.render() t2i_event_start = t2i_generate_btn.click( update_btn_start, outputs=[t2i_generate_btn, t2i_stop_btn] ) t2i_event = t2i_event_start.then( inference.txt2img, inputs=[prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, seed, batch_count], outputs=[image_output] ) t2i_event_end = t2i_event.then( update_btn_end, outputs=[t2i_generate_btn, t2i_stop_btn] ) t2i_stop_btn.click(fn=update_btn_end, outputs=[t2i_generate_btn, t2i_stop_btn], cancels=[t2i_event]) image_input.upload(get_exif_data, inputs=[image_input], outputs=exif_output) send_to_txt2img_btn.click( fn=switch_to_t2i, outputs=[tabs] ).then( fn=send_to_txt2img, inputs=[image_input], outputs=[prompt, negative_prompt, steps, seed, model, sampler, width, height, cfg_scale] ) i2i_event_start = i2i_generate_btn.click( update_btn_start, outputs=[i2i_generate_btn, i2i_stop_btn] ) i2i_event = i2i_event_start.then(inference.img2img, inputs=[i2i_image_input, i2i_denoising, i2i_prompt, i2i_negative_prompt, model, i2i_steps, i2i_sampler, i2i_cfg_scale, i2i_width, i2i_height, i2i_seed, i2i_batch_count], outputs=[i2i_image_output]) i2i_event_end = i2i_event.then( update_btn_end, outputs=[i2i_generate_btn, i2i_stop_btn] ) i2i_stop_btn.click(fn=update_btn_end, outputs=[i2i_generate_btn, i2i_stop_btn], cancels=[i2i_event]) upscale_event_start = upscale_btn.click( fn=update_btn_start, outputs=[upscale_btn, upscale_stop_btn] ) upscale_event = upscale_event_start.then( fn=inference.upscale, inputs=[upscale_image_input, upscale_scale], outputs=[upscale_output] ) upscale_event_end = upscale_event.then( fn=update_btn_end, outputs=[upscale_btn, upscale_stop_btn] ) upscale_stop_btn.click(fn=update_btn_end, outputs=[upscale_btn, upscale_stop_btn], cancels=[upscale_event]) demo.queue().launch(max_threads=256)