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import gradio as gr |
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import random |
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import os |
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import io, base64 |
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from PIL import Image |
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import numpy |
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import shortuuid |
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latent = gr.Interface.load("spaces/multimodalart/latentdiffusion") |
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rudalle = gr.Interface.load("spaces/multimodalart/rudalle") |
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def text2image_latent(text,steps,width,height,images,diversity): |
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results = latent(text, steps, width, height, images, diversity) |
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image_paths = [] |
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image_arrays = [] |
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for image in results[1]: |
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image_str = image[0] |
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image_str = image_str.replace("data:image/png;base64,","") |
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decoded_bytes = base64.decodebytes(bytes(image_str, "utf-8")) |
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img = Image.open(io.BytesIO(decoded_bytes)) |
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url = shortuuid.uuid() |
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temp_dir = './tmp' |
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if not os.path.exists(temp_dir): |
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os.makedirs(temp_dir, exist_ok=True) |
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image_path = f'{temp_dir}/{url}.png' |
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img.save(f'{temp_dir}/{url}.png') |
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image_paths.append(image_path) |
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return(results[0],image_paths) |
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def text2image_rudalle(text,aspect,model): |
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image = rudalle(text,aspect,model)[0] |
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return(image) |
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css_mt = {"margin-top": "1em"} |
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empty = gr.outputs.HTML() |
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with gr.Blocks() as mindseye: |
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gr.Markdown("<h1>MindsEye Lite <small><small>run multiple text-to-image models in one place</small></small></h1><p>MindsEye Lite orchestrates multiple text-to-image Hugging Face Spaces in one convenient space, so you can try different models. This work carries the spirit of <a href='https://multimodal.art/mindseye' target='_blank'>MindsEye Beta</a>, a tool to run multiple models with a single UI, but adjusted to the current hardware limitations of Spaces. MindsEye Lite was created by <a style='color: rgb(99, 102, 241);font-weight:bold' href='https://twitter.com/multimodalart' target='_blank'>@multimodalart</a>, keep up with the <a style='color: rgb(99, 102, 241);' href='https://multimodal.art/news' target='_blank'>latest multimodal ai art news here</a> and consider <a style='color: rgb(99, 102, 241);' href='https://www.patreon.com/multimodalart' target='_blank'>supporting us on Patreon</a></div></p>") |
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gr.Markdown("<style>.mx-auto.container .gr-form-gap {flex-direction: row; gap: calc(1rem * calc(1 - var(--tw-space-y-reverse)));} .mx-auto.container .gr-form-gap .flex-col, .mx-auto.container .gr-form-gap .gr-box{width: 100%}</style>") |
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text = gr.inputs.Textbox(placeholder="Try writing something..", label="Prompt") |
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with gr.Column(): |
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with gr.Row(): |
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with gr.Tabs(): |
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with gr.TabItem("Latent Diffusion"): |
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steps = gr.inputs.Slider(label="Steps - more steps can increase quality but will take longer to generate",default=45,maximum=50,minimum=1,step=1) |
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width = gr.inputs.Slider(label="Width", default=256, step=32, maximum=256, minimum=32) |
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height = gr.inputs.Slider(label="Height", default=256, step=32, maximum = 256, minimum=32) |
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images = gr.inputs.Slider(label="Images - How many images you wish to generate", default=2, step=1, minimum=1, maximum=4) |
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diversity = gr.inputs.Slider(label="Diversity scale - How different from one another you wish the images to be",default=5.0, minimum=1.0, maximum=15.0) |
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get_image_latent = gr.Button("Generate Image",css=css_mt) |
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with gr.TabItem("ruDALLE"): |
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aspect = gr.inputs.Radio(label="Aspect Ratio", choices=["Square", "Horizontal", "Vertical"],default="Square") |
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model = gr.inputs.Dropdown(label="Model", choices=["Surrealism","Realism", "Emoji"], default="Surrealism") |
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get_image_rudalle = gr.Button("Generate Image",css=css_mt) |
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with gr.TabItem("VQGAN+CLIP"): |
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pass |
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with gr.TabItem("V-Diffusion"): |
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pass |
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with gr.Row(): |
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with gr.Tabs(): |
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with gr.TabItem("Image output"): |
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image = gr.outputs.Image() |
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with gr.TabItem("Gallery output"): |
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gallery = gr.Gallery(label="Individual images") |
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get_image_latent.click(text2image_latent, inputs=[text,steps,width,height,images,diversity], outputs=[image,gallery]) |
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get_image_rudalle.click(text2image_rudalle, inputs=[text,aspect,model], outputs=image) |
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mindseye.launch() |
