Spaces:
Sleeping
Sleeping
| import gradio as gr | |
| import numpy as np | |
| import random | |
| from diffusers import DiffusionPipeline, DDPMPipeline, DDPMScheduler | |
| import torch | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| noise_scheduler = DDPMScheduler(num_train_timesteps=1000) | |
| if torch.cuda.is_available(): | |
| torch.cuda.max_memory_allocated(device=device) | |
| pipe = DDPMPipeline.from_pretrained("FrozenScar/cartoon_face", torch_dtype=torch.float16, variant="fp16", use_safetensors=True,scheduler=noise_scheduler) | |
| pipe.enable_xformers_memory_efficient_attention() | |
| pipe = pipe.to(device) | |
| else: | |
| pipe = DDPMPipeline.from_pretrained("FrozenScar/cartoon_face", scheduler=noise_scheduler, use_safetensors=True) | |
| pipe = pipe.to(device) | |
| MAX_SEED = np.iinfo(np.int32).max | |
| MAX_IMAGE_SIZE = 1024 | |
| def infer(num_inference_steps,prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale): | |
| #if randomize_seed: | |
| seed = random.randint(0, MAX_SEED) | |
| generator = torch.Generator().manual_seed(seed) | |
| image = pipe(generator=generator,num_inference_steps=num_inference_steps).images[0] | |
| return image | |
| examples = [ | |
| "OK broo", | |
| "Nothing brooo" | |
| ] | |
| 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""" | |
| # FACE GENERATOR | |
| Currently running on {power_device}. | |
| """) | |
| with gr.Row(): | |
| num_inference_steps = gr.Slider( | |
| label="Number of inference steps", | |
| minimum=1, | |
| maximum=20, | |
| step=1, | |
| value=6, | |
| ) | |
| 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=512, | |
| # ) | |
| # height = gr.Slider( | |
| # label="Height", | |
| # minimum=256, | |
| # 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=0.0, | |
| # ) | |
| # num_inference_steps = gr.Slider( | |
| # label="Number of inference steps", | |
| # minimum=1, | |
| # maximum=120, | |
| # step=1, | |
| # value=2, | |
| # ) | |
| # gr.Examples( | |
| # examples = examples, | |
| # inputs = [prompt] | |
| # ) | |
| run_button.click( | |
| fn = infer, | |
| inputs = [ num_inference_steps], | |
| outputs = [result] | |
| ) | |
| demo.queue().launch() |