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from utils import place_lora, get_exif_data |
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from css import css |
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from grutils import * |
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import inference |
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lora_list = pipe.constant("/sd/loras") |
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samplers = pipe.constant("/sd/samplers") |
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with gr.Blocks(css=css, theme="zenafey/prodia-web") as demo: |
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model = gr.Dropdown(interactive=True, value="anything-v4.5-pruned.ckpt [65745d25]", show_label=True, label="Stable Diffusion Checkpoint", |
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choices=model_list, elem_id="model_dd") |
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with gr.Tabs() as tabs: |
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with gr.Tab("txt2img", id='t2i'): |
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with gr.Row(): |
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with gr.Column(scale=6, min_width=600): |
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prompt = gr.Textbox("space warrior, beautiful, female, ultrarealistic, soft lighting, 8k", |
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placeholder="Prompt", show_label=False, lines=3) |
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negative_prompt = gr.Textbox(placeholder="Negative Prompt", show_label=False, lines=3, |
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value="3d, cartoon, anime, (deformed eyes, nose, ears, nose), bad anatomy, ugly") |
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with gr.Row(): |
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t2i_generate_btn = gr.Button("Generate", variant='primary', elem_id="generate") |
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t2i_stop_btn = gr.Button("Cancel", variant="stop", elem_id="generate", visible=False) |
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with gr.Row(): |
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with gr.Column(): |
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with gr.Tab("Generation"): |
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with gr.Row(): |
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with gr.Column(scale=1): |
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sampler = gr.Dropdown(value="DPM++ 2M Karras", show_label=True, label="Sampling Method", |
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choices=samplers) |
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with gr.Column(scale=1): |
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steps = gr.Slider(label="Sampling Steps", minimum=1, maximum=60, value=25, step=0.5) |
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with gr.Row(): |
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with gr.Column(scale=8): |
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width = gr.Slider(label="Width", maximum=1024, value=512, step=8) |
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height = gr.Slider(label="Height", maximum=1024, value=512, step=8) |
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with gr.Column(scale=1): |
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batch_size = gr.Slider(label="Batch Size", maximum=1, value=1) |
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batch_count = gr.Slider(label="Batch Count", minimum=1, maximum=50, value=1, step=1) |
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cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, value=7, step=0.5) |
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seed = gr.Number(label="Seed", value=-1) |
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with gr.Tab("Lora"): |
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with gr.Row(): |
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for lora in lora_list: |
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lora_btn = gr.Button(lora, size="sm") |
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lora_btn.click(place_lora, inputs=[prompt, lora_btn], outputs=prompt, queue=False) |
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with gr.Column(): |
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image_output = gr.Gallery(columns=3, |
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value=["https://images.prodia.xyz/8ede1a7c-c0ee-4ded-987d-6ffed35fc477.png"]) |
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with gr.Tab("img2img", id='i2i'): |
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with gr.Row(): |
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with gr.Column(scale=6, min_width=600): |
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i2i_prompt = gr.Textbox("space warrior, beautiful, female, ultrarealistic, soft lighting, 8k", |
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placeholder="Prompt", show_label=False, lines=3) |
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i2i_negative_prompt = gr.Textbox(placeholder="Negative Prompt", show_label=False, lines=3, |
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value="3d, cartoon, anime, (deformed eyes, nose, ears, nose), bad anatomy, ugly") |
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with gr.Row(): |
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i2i_generate_btn = gr.Button("Generate", variant='primary', elem_id="generate") |
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i2i_stop_btn = gr.Button("Cancel", variant="stop", elem_id="generate", visible=False) |
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with gr.Row(): |
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with gr.Column(scale=1): |
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with gr.Tab("Generation"): |
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i2i_image_input = gr.Image(type="pil") |
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with gr.Row(): |
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with gr.Column(scale=1): |
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i2i_sampler = gr.Dropdown(value="DPM++ 2M Karras", show_label=True, |
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label="Sampling Method", choices=samplers) |
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with gr.Column(scale=1): |
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i2i_steps = gr.Slider(label="Sampling Steps", minimum=1, maximum=60, value=25, step=0.5) |
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with gr.Row(): |
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with gr.Column(scale=6): |
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i2i_width = gr.Slider(label="Width", maximum=1024, value=512, step=8) |
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i2i_height = gr.Slider(label="Height", maximum=1024, value=512, step=8) |
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with gr.Column(scale=1): |
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i2i_batch_size = gr.Slider(label="Batch Size", maximum=1, value=1) |
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i2i_batch_count = gr.Slider(label="Batch Count", minimum=1, maximum=50, value=1, step=1) |
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i2i_cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, value=7, step=1) |
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i2i_denoising = gr.Slider(label="Denoising Strength", minimum=0, maximum=1, value=0.5, step=0.05) |
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i2i_seed = gr.Number(label="Seed", value=-1) |
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with gr.Tab("Lora"): |
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with gr.Row(): |
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for lora in lora_list: |
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lora_btn = gr.Button(lora, size="sm") |
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lora_btn.click(place_lora, inputs=[i2i_prompt, lora_btn], outputs=i2i_prompt, queue=False) |
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with gr.Column(scale=1): |
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i2i_image_output = gr.Gallery(columns=3, |
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value=["https://images.prodia.xyz/8ede1a7c-c0ee-4ded-987d-6ffed35fc477.png"]) |
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with gr.Tab("Extras"): |
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with gr.Row(): |
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with gr.Tab("Single Image"): |
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with gr.Column(): |
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upscale_image_input = gr.Image(type="pil") |
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upscale_btn = gr.Button("Generate", variant="primary") |
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upscale_stop_btn = gr.Button("Stop", variant="stop", visible=False) |
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with gr.Tab("Scale by"): |
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upscale_scale = gr.Radio([2, 4], value=2, label="Resize") |
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upscale_output = gr.Image() |
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with gr.Tab("PNG Info"): |
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with gr.Row(): |
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with gr.Column(): |
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image_input = gr.Image(type="pil") |
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with gr.Column(): |
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exif_output = gr.HTML(label="EXIF Data") |
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send_to_txt2img_btn = gr.Button("Send to txt2img") |
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with gr.Tab("Past generations"): |
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inference.gr_user_history.render() |
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t2i_event_start = t2i_generate_btn.click( |
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update_btn_start, |
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outputs=[t2i_generate_btn, t2i_stop_btn], |
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queue=False |
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) |
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t2i_event = t2i_event_start.then( |
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inference.txt2img, |
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inputs=[prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, seed, batch_count], |
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outputs=[image_output] |
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) |
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t2i_event_end = t2i_event.then( |
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update_btn_end, |
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outputs=[t2i_generate_btn, t2i_stop_btn], |
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queue=False |
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) |
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t2i_stop_btn.click(fn=update_btn_end, outputs=[t2i_generate_btn, t2i_stop_btn], cancels=[t2i_event], queue=False) |
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image_input.upload(get_exif_data, inputs=[image_input], outputs=exif_output) |
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send_to_txt2img_btn.click( |
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fn=switch_to_t2i, |
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outputs=[tabs], |
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queue=False |
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).then( |
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fn=send_to_txt2img, |
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inputs=[image_input], |
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outputs=[prompt, negative_prompt, steps, seed, model, sampler, width, height, cfg_scale], |
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queue=False |
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) |
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i2i_event_start = i2i_generate_btn.click( |
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update_btn_start, |
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outputs=[i2i_generate_btn, i2i_stop_btn], |
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queue=False |
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) |
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i2i_event = i2i_event_start.then(inference.img2img, |
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inputs=[i2i_image_input, i2i_denoising, i2i_prompt, i2i_negative_prompt, |
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model, i2i_steps, i2i_sampler, i2i_cfg_scale, i2i_width, i2i_height, |
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i2i_seed, i2i_batch_count], |
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outputs=[i2i_image_output]) |
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i2i_event_end = i2i_event.then( |
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update_btn_end, |
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outputs=[i2i_generate_btn, i2i_stop_btn], |
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queue=False |
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) |
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i2i_stop_btn.click(fn=update_btn_end, outputs=[i2i_generate_btn, i2i_stop_btn], cancels=[i2i_event], queue=False) |
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upscale_event_start = upscale_btn.click( |
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fn=update_btn_start, |
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outputs=[upscale_btn, upscale_stop_btn], |
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queue=False |
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) |
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upscale_event = upscale_event_start.then( |
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fn=inference.upscale, |
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inputs=[upscale_image_input, upscale_scale], |
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outputs=[upscale_output] |
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) |
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upscale_event_end = upscale_event.then( |
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fn=update_btn_end, |
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outputs=[upscale_btn, upscale_stop_btn], |
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queue=False |
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) |
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upscale_stop_btn.click(fn=update_btn_end, outputs=[upscale_btn, upscale_stop_btn], cancels=[upscale_event], queue=False) |
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demo.queue(max_size=20, api_open=False).launch(max_threads=400) |
