Commit
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84f6f2e
1
Parent(s):
071944b
new stuff
Browse files- README.md +1 -1
- app.py +54 -36
- gradio_queue.db +0 -0
- requirements.txt +1 -0
README.md
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@@ -4,7 +4,7 @@ emoji: 💻
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colorFrom: pink
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colorTo: yellow
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sdk: gradio
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sdk_version: 2.
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app_file: app.py
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pinned: false
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license: mit
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colorFrom: pink
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colorTo: yellow
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sdk: gradio
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sdk_version: 2.9b21
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app_file: app.py
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pinned: false
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license: mit
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app.py
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from turtle import width
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import gradio as gr
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import random
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import
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latent = gr.Interface.load("spaces/multimodalart/latentdiffusion")
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print(latent)
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rudalle = gr.Interface.load("spaces/multimodalart/rudalle")
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#guided = gr.Interface.load("spaces/EleutherAI/clip-guided-diffusion")
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#print(guided)
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def text2image_latent(text,steps,width,height,images,diversity):
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def text2image_rudalle(text,aspect,model):
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print(text)
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print(aspect)
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print(model)
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image = rudalle(text,aspect,model)[0]
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print(image)
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return(image)
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#def text2image_guided(text):
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# image = guided(text, None, 10, 600, 0, 0, 0, random.randint(0,2147483647), None, 50, 32)
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# print(image)
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# image = image[0]
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# return(image)
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with gr.Blocks() as mindseye:
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with gr.Column():
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with gr.Row():
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get_image_latent.click(text2image_latent, inputs=[text,steps,width,height,images,diversity], outputs=image)
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get_image_rudalle.click(text2image_rudalle, inputs=[text,aspect,model], outputs=image)
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mindseye.launch()
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from turtle import width
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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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#print(rudalle)
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#guided = gr.Interface.load("spaces/EleutherAI/clip-guided-diffusion")
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#print(guided)
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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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image_arrays.append(numpy.asarray(img))
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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_arrays)
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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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#def text2image_guided(text):
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# image = guided(text, None, 10, 600, 0, 0, 0, random.randint(0,2147483647), None, 50, 32)
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# print(image)
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# image = image[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("# MindsEye Lite")
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gr.Markdown("### Run multiple text-to-image models in one place")
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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.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.outputs.Carousel(label="Individual images",components=["image"])
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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(enable_queue=True)
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gradio_queue.db
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Binary file (16.4 kB). View file
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requirements.txt
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@@ -0,0 +1 @@
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shortuuid
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