| # import gradio as gr | |
| # import numpy as np | |
| # import random | |
| # #import spaces #[uncomment to use ZeroGPU] | |
| # from diffusers import DiffusionPipeline | |
| # import torch | |
| # | |
| # device = "cuda" if torch.cuda.is_available() else "cpu" | |
| # model_repo_id = "stabilityai/sdxl-turbo" #Replace to the model you would like to use | |
| # | |
| # if torch.cuda.is_available(): | |
| # torch_dtype = torch.float16 | |
| # else: | |
| # torch_dtype = torch.float32 | |
| # | |
| # pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype) | |
| # pipe = pipe.to(device) | |
| # | |
| # MAX_SEED = np.iinfo(np.int32).max | |
| # MAX_IMAGE_SIZE = 1024 | |
| # | |
| # #@spaces.GPU #[uncomment to use ZeroGPU] | |
| # def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, progress=gr.Progress(track_tqdm=True)): | |
| # | |
| # if randomize_seed: | |
| # seed = random.randint(0, MAX_SEED) | |
| # | |
| # generator = torch.Generator().manual_seed(seed) | |
| # | |
| # image = pipe( | |
| # prompt = prompt, | |
| # negative_prompt = negative_prompt, | |
| # guidance_scale = guidance_scale, | |
| # num_inference_steps = num_inference_steps, | |
| # width = width, | |
| # height = height, | |
| # generator = generator | |
| # ).images[0] | |
| # | |
| # return image, seed | |
| # | |
| # examples = [ | |
| # "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k", | |
| # "An astronaut riding a green horse", | |
| # "A delicious ceviche cheesecake slice", | |
| # ] | |
| # | |
| # css=""" | |
| # #col-container { | |
| # margin: 0 auto; | |
| # max-width: 640px; | |
| # } | |
| # """ | |
| # | |
| # with gr.Blocks(css=css) as demo: | |
| # | |
| # with gr.Column(elem_id="col-container"): | |
| # gr.Markdown(f""" | |
| # # Text-to-Image Gradio Template | |
| # """) | |
| # | |
| # with gr.Row(): | |
| # | |
| # prompt = gr.Text( | |
| # label="Prompt", | |
| # show_label=False, | |
| # max_lines=1, | |
| # placeholder="Enter your prompt", | |
| # container=False, | |
| # ) | |
| # | |
| # run_button = gr.Button("Run", scale=0) | |
| # | |
| # result = gr.Image(label="Result", show_label=False) | |
| # | |
| # with gr.Accordion("Advanced Settings", open=False): | |
| # | |
| # negative_prompt = gr.Text( | |
| # label="Negative prompt", | |
| # max_lines=1, | |
| # placeholder="Enter a negative prompt", | |
| # visible=False, | |
| # ) | |
| # | |
| # seed = gr.Slider( | |
| # label="Seed", | |
| # minimum=0, | |
| # maximum=MAX_SEED, | |
| # step=1, | |
| # value=0, | |
| # ) | |
| # | |
| # randomize_seed = gr.Checkbox(label="Randomize seed", value=True) | |
| # | |
| # with gr.Row(): | |
| # | |
| # width = gr.Slider( | |
| # label="Width", | |
| # minimum=256, | |
| # maximum=MAX_IMAGE_SIZE, | |
| # step=32, | |
| # value=1024, #Replace with defaults that work for your model | |
| # ) | |
| # | |
| # height = gr.Slider( | |
| # label="Height", | |
| # minimum=256, | |
| # maximum=MAX_IMAGE_SIZE, | |
| # step=32, | |
| # value=1024, #Replace with defaults that work for your model | |
| # ) | |
| # | |
| # with gr.Row(): | |
| # | |
| # guidance_scale = gr.Slider( | |
| # label="Guidance scale", | |
| # minimum=0.0, | |
| # maximum=10.0, | |
| # step=0.1, | |
| # value=0.0, #Replace with defaults that work for your model | |
| # ) | |
| # | |
| # num_inference_steps = gr.Slider( | |
| # label="Number of inference steps", | |
| # minimum=1, | |
| # maximum=50, | |
| # step=1, | |
| # value=2, #Replace with defaults that work for your model | |
| # ) | |
| # | |
| # gr.Examples( | |
| # examples = examples, | |
| # inputs = [prompt] | |
| # ) | |
| # gr.on( | |
| # triggers=[run_button.click, prompt.submit], | |
| # fn = infer, | |
| # inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps], | |
| # outputs = [result, seed] | |
| # ) | |
| # | |
| # demo.queue().launch() | |
| import gradio as gr | |
| gr.load("models/nerijs/dark-fantasy-illustration-flux").launch() | |