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| import gradio as gr | |
| import numpy as np | |
| import random | |
| from diffusers import DiffusionPipeline | |
| import torch | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| base_model = "stabilityai/stable-diffusion-xl-base-1.0" | |
| if torch.cuda.is_available(): | |
| torch.cuda.max_memory_allocated(device=device) | |
| pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.float16, variant="fp16", use_safetensors=True) | |
| pipe.enable_xformers_memory_efficient_attention() | |
| pipe = pipe.to(device) | |
| pipe.load_lora_weights("Fer14/sdxl-coffe-machines", weights_name ="pytorch_lora_weights.safetensors") | |
| else: | |
| pipe = DiffusionPipeline.from_pretrained(base_model, use_safetensors=True) | |
| pipe = pipe.to(device) | |
| pipe.load_lora_weights("Fer14/sdxl-coffe-machines", weights_name ="pytorch_lora_weights.safetensors") | |
| MAX_SEED = np.iinfo(np.int32).max | |
| MAX_IMAGE_SIZE = 1024 | |
| def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps): | |
| 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 | |
| examples = [ | |
| "A cream espresso machine with steam wand for milk frothing, retro-shaped, with no screen and three silver buttons", | |
| "A beige espresso coffee machine with a wooden handle, rectangular shaped, with no screen and three silver buttons", | |
| "A red espresso coffee machine with portafilter handle and milk frother, cylindrical shaped, with no screen and three silver buttons", | |
| ] | |
| css=""" | |
| #col-container { | |
| margin: 0 auto; | |
| max-width: 520px; | |
| } | |
| """ | |
| if torch.cuda.is_available(): | |
| power_device = "GPU" | |
| else: | |
| power_device = "CPU" | |
| with gr.Blocks(css=css) as demo: | |
| with gr.Column(elem_id="col-container"): | |
| gr.Markdown(f""" | |
| # Coffee Machine Generator ☕ | |
| Currently running on {power_device}. | |
| """) | |
| 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=540, | |
| maximum=MAX_IMAGE_SIZE, | |
| step=32, | |
| value=512, | |
| ) | |
| height = gr.Slider( | |
| label="Height", | |
| minimum=540, | |
| maximum=MAX_IMAGE_SIZE, | |
| step=32, | |
| value=512, | |
| ) | |
| with gr.Row(): | |
| guidance_scale = gr.Slider( | |
| label="Guidance scale", | |
| minimum=0.0, | |
| maximum=10.0, | |
| step=0.1, | |
| value=7.5, | |
| ) | |
| num_inference_steps = gr.Slider( | |
| label="Number of inference steps", | |
| minimum=1, | |
| maximum=40, | |
| step=1, | |
| value=30, | |
| ) | |
| gr.Examples( | |
| examples = examples, | |
| inputs = [prompt] | |
| ) | |
| run_button.click( | |
| fn = infer, | |
| inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps], | |
| outputs = [result] | |
| ) | |
| demo.queue().launch() |