|
import gradio as gr |
|
import random |
|
import os |
|
import io, base64 |
|
from PIL import Image |
|
import numpy |
|
import shortuuid |
|
|
|
latent = gr.Interface.load("spaces/multimodalart/latentdiffusion") |
|
rudalle = gr.Interface.load("spaces/multimodalart/rudalle") |
|
diffusion = gr.Interface.load("spaces/multimodalart/diffusion") |
|
print(diffusion) |
|
vqgan = gr.Interface.load("spaces/multimodalart/vqgan") |
|
|
|
def text2image_latent(text,steps,width,height,images,diversity): |
|
results = latent(text, steps, width, height, images, diversity) |
|
image_paths = [] |
|
image_arrays = [] |
|
for image in results[1]: |
|
image_str = image[0] |
|
image_str = image_str.replace("data:image/png;base64,","") |
|
decoded_bytes = base64.decodebytes(bytes(image_str, "utf-8")) |
|
img = Image.open(io.BytesIO(decoded_bytes)) |
|
url = shortuuid.uuid() |
|
temp_dir = './tmp' |
|
if not os.path.exists(temp_dir): |
|
os.makedirs(temp_dir, exist_ok=True) |
|
image_path = f'{temp_dir}/{url}.png' |
|
img.save(f'{temp_dir}/{url}.png') |
|
image_paths.append(image_path) |
|
return(image_paths) |
|
|
|
def text2image_rudalle(text,aspect,model): |
|
image = rudalle(text,aspect,model)[0] |
|
return([image]) |
|
|
|
def text2image_vqgan(text,width,height,style,steps,flavor): |
|
results = vqgan(text,width,height,style,steps,flavor) |
|
return([results]) |
|
|
|
def text2image_diffusion(text,steps_diff, images_diff, weight, clip): |
|
results = diffusion(text, steps_diff, images_diff, weight, clip) |
|
image_paths = [] |
|
image_arrays = [] |
|
for image in results: |
|
image_str = image |
|
image_str = image_str.replace("data:image/png;base64,","") |
|
decoded_bytes = base64.decodebytes(bytes(image_str, "utf-8")) |
|
img = Image.open(io.BytesIO(decoded_bytes)) |
|
url = shortuuid.uuid() |
|
temp_dir = './tmp' |
|
if not os.path.exists(temp_dir): |
|
os.makedirs(temp_dir, exist_ok=True) |
|
image_path = f'{temp_dir}/{url}.png' |
|
img.save(f'{temp_dir}/{url}.png') |
|
image_paths.append(image_path) |
|
return(image_paths) |
|
|
|
css_mt = {"margin-top": "1em"} |
|
|
|
empty = gr.outputs.HTML() |
|
|
|
with gr.Blocks() as mindseye: |
|
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>") |
|
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>") |
|
text = gr.inputs.Textbox(placeholder="Type your prompt to generate an image", label="Prompt - try adding increments to your prompt such as 'oil on canvas', 'a painting'", default="A giant mecha robot in Rio de Janeiro, oil on canvas") |
|
with gr.Column(): |
|
with gr.Row(): |
|
with gr.Tabs(): |
|
with gr.TabItem("Latent Diffusion"): |
|
gr.Markdown("Latent Diffusion is the state of the art of open source text-to-image models, superb in text synthesis. Sometimes struggles with complex prompts") |
|
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) |
|
width = gr.inputs.Slider(label="Width", default=256, step=32, maximum=256, minimum=32) |
|
height = gr.inputs.Slider(label="Height", default=256, step=32, maximum = 256, minimum=32) |
|
images = gr.inputs.Slider(label="Images - How many images you wish to generate", default=2, step=1, minimum=1, maximum=4) |
|
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) |
|
get_image_latent = gr.Button("Generate Image",css=css_mt) |
|
with gr.TabItem("ruDALLE"): |
|
gr.Markdown("ruDALLE is a replication of DALL-E 1 in the russian language. No worries, your prompts will be translated automatically to russian. In case you see an error, try again a few times") |
|
aspect = gr.inputs.Radio(label="Aspect Ratio", choices=["Square", "Horizontal", "Vertical"],default="Square") |
|
model = gr.inputs.Dropdown(label="Model", choices=["Surrealism","Realism", "Emoji"], default="Surrealism") |
|
get_image_rudalle = gr.Button("Generate Image",css=css_mt) |
|
with gr.TabItem("VQGAN+CLIP"): |
|
gr.Markdown("VQGAN+CLIP is the most famous text-to-image generator. Can produce good artistic results") |
|
width_vq = gr.inputs.Slider(label="Width", default=256, minimum=32, step=32, maximum=512) |
|
height_vq= gr.inputs.Slider(label="Height", default=256, minimum=32, step=32, maximum=512) |
|
style = gr.inputs.Dropdown(label="Style - Hyper Fast Results is fast but compromises a bit of the quality",choices=["Default","Balanced","Detailed","Consistent Creativity","Realistic","Smooth","Subtle MSE","Hyper Fast Results"],default="Hyper Fast Results") |
|
steps_vq = gr.inputs.Slider(label="Steps - more steps can increase quality but will take longer to generate. All styles that are not Hyper Fast need at least 200 steps",default=50,maximum=300,minimum=1,step=1) |
|
flavor = gr.inputs.Dropdown(label="Flavor - pick a flavor for the style of the images, based on the images below",choices=["ginger", "cumin", "holywater", "zynth", "wyvern", "aaron", "moth", "juu"]) |
|
get_image_vqgan = gr.Button("Generate Image",css=css_mt) |
|
with gr.TabItem("Guided Diffusion"): |
|
gr.Markdown("Guided Diffusion models produce superb quality results. V-Diffusion is its latest implementation") |
|
steps_diff = gr.inputs.Slider(label="Steps - more steps can increase quality but will take longer to generate",default=40,maximum=80,minimum=1,step=1) |
|
images_diff = gr.inputs.Slider(label="Number of images in parallel", default=2, maximum=4, minimum=1, step=1) |
|
weight = gr.inputs.Slider(label="Weight - how closely the image should resemble the prompt", default=5, maximum=15, minimum=0, step=1) |
|
clip = gr.inputs.Checkbox(label="CLIP Guided - improves coherence with complex prompts, makes it slower") |
|
get_image_diffusion = gr.Button("Generate Image",css=css_mt) |
|
with gr.Row(): |
|
with gr.Tabs(): |
|
|
|
|
|
with gr.TabItem("Gallery output"): |
|
gallery = gr.Gallery(label="Individual images") |
|
|
|
get_image_latent.click(text2image_latent, inputs=[text,steps,width,height,images,diversity], outputs=gallery) |
|
get_image_rudalle.click(text2image_rudalle, inputs=[text,aspect,model], outputs=gallery) |
|
get_image_vqgan.click(text2image_vqgan, inputs=[text,width_vq,height_vq,style,steps_vq,flavor],outputs=gallery) |
|
get_image_diffusion.click(text2image_diffusion, inputs=[text, steps_diff, images_diff, weight, clip],outputs=gallery) |
|
mindseye.launch() |
