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| 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 | |
| 8, # 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) |