Spaces:
Running
Running
File size: 3,416 Bytes
39b3d5c 33b0931 2053864 33b0931 2053864 39b3d5c 33b0931 39b3d5c 2162e3e 33b0931 39b3d5c c1a9a8e ba00eb1 39b3d5c aec599b 39b3d5c d77c785 39b3d5c 1bb2af8 35a3dd9 67e0c9d 1bb2af8 2053864 1bb2af8 39b3d5c 1bb2af8 2053864 39b3d5c a2ddcd6 e6af018 39b3d5c cefcc96 1bb2af8 2053864 39b3d5c 1bb2af8 52f87fe 2053864 1bb2af8 145dc43 219f0c6 4cda995 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 |
import gradio as gr
from gradio_client import Client
import os
import numpy as np
import random
hf_token = os.environ.get("HF_TKN")
MAX_SEED = np.iinfo(np.int32).max
def get_caption(image_in):
client = Client("https://fffiloni-moondream1.hf.space/", hf_token=hf_token)
result = client.predict(
image_in, # filepath in 'image' Image component
"Describe the image", # str in 'Question' Textbox component
api_name="/predict"
)
print(result)
return result
def get_lcm(prompt):
client = Client("https://latent-consistency-lcm-lora-for-sdxl.hf.space/")
result = client.predict(
prompt, # str in 'parameter_5' Textbox component
0.3, # float (numeric value between 0.0 and 5) in 'Guidance' Slider component
8, # float (numeric value between 2 and 10) in 'Steps' Slider component
0, # float (numeric value between 0 and 12013012031030) in 'Seed' Slider component
True, # bool in 'Randomize' Checkbox component
api_name="/predict"
)
print(result)
return result[0]
def get_sdxl_lightning(prompt):
client = Client("AP123/SDXL-Lightning")
result = client.predict(
prompt, # str in 'parameter_1' Textbox component
"4-Step",
api_name="/generate_image"
)
print(result)
return result
def get_turbo(prompt):
seed = random.randint(0, MAX_SEED)
print(f"SEED: {seed}")
client = Client("https://diffusers-unofficial-sdxl-turbo-i2i-t2i.hf.space/")
result = client.predict(
None, # filepath in 'Webcam' Image component
prompt, # str in 'parameter_5' Textbox component
0.7, # float (numeric value between 0.0 and 1.0) in 'Strength' Slider component
4, # float (numeric value between 1 and 10) in 'Steps' Slider component
seed, # float (numeric value between 0 and MAX_SEED) in 'Seed' Slider component
api_name="/predict"
)
print(result)
return result
def infer(image_in, chosen_method):
caption = get_caption(image_in)
if chosen_method == "LCM" :
img_var = get_lcm(caption)
elif chosen_method == "SDXL Lightning" :
img_var = get_sdxl_lightning(caption)
elif chosen_method == "SDXL Turbo" :
img_var = get_turbo(caption)
return img_var
gr.Interface(
title = "Supa Fast Image Variation",
description = "Get quick image variation from image input, using <a href='https://huggingface.co/vikhyatk/moondream1' target='_blank'>moondream1</a> for caption, and <a href='https://huggingface.co/spaces/latent-consistency/lcm-lora-for-sdxl' target='_blank'>LCM SDXL</a>, <a href='https://huggingface.co/spaces/AP123/SDXL-Lightning' target='_blank'>SDXL Lightning</a> or <a href='https://huggingface.co/spaces/diffusers/unofficial-SDXL-Turbo-i2i-t2i' target='_blank'>SDXL Turbo</a> for image generation",
fn = infer,
inputs = [
gr.Image(type="filepath", label="Image input"),
gr.Dropdown(label="Choose a model", choices=["LCM", "SDXL Lightning", "SDXL Turbo"], value="SDXL Lightning")
],
outputs = [
gr.Image(label="Image variation")
],
examples = [
["examples/frog_clean.jpg", "LCM"],
["examples/martin_pecheur.jpeg", "SDXL Turbo"],
["examples/forest_deer.png", "SDXL Lightning"]
],
cache_examples = False,
concurrency_limit = 2
).queue(max_size=25).launch(show_api=False, show_error=True) |