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| # 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) |