| |
|
|
| from __future__ import annotations |
|
|
| import os |
| import random |
|
|
| import gradio as gr |
| import numpy as np |
| import PIL.Image |
| import torch |
| from diffusers import DiffusionPipeline |
|
|
| DESCRIPTION = '# SD-XL' |
| if not torch.cuda.is_available(): |
| DESCRIPTION += '\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>' |
|
|
| MAX_SEED = np.iinfo(np.int32).max |
| CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv( |
| 'CACHE_EXAMPLES') == '1' |
| MAX_IMAGE_SIZE = int(os.getenv('MAX_IMAGE_SIZE', '1024')) |
| USE_TORCH_COMPILE = os.getenv('USE_TORCH_COMPILE') == '1' |
| ENABLE_CPU_OFFLOAD = os.getenv('ENABLE_CPU_OFFLOAD') == '1' |
|
|
| device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') |
| if torch.cuda.is_available(): |
| pipe = DiffusionPipeline.from_pretrained( |
| 'stabilityai/stable-diffusion-xl-base-1.0', |
| torch_dtype=torch.float16, |
| use_safetensors=True, |
| variant='fp16') |
| refiner = DiffusionPipeline.from_pretrained( |
| 'stabilityai/stable-diffusion-xl-refiner-1.0', |
| torch_dtype=torch.float16, |
| use_safetensors=True, |
| variant='fp16') |
|
|
| if ENABLE_CPU_OFFLOAD: |
| pipe.enable_model_cpu_offload() |
| refiner.enable_model_cpu_offload() |
| else: |
| pipe.to(device) |
| refiner.to(device) |
|
|
| if USE_TORCH_COMPILE: |
| pipe.unet = torch.compile(pipe.unet, |
| mode='reduce-overhead', |
| fullgraph=True) |
| else: |
| pipe = None |
| refiner = None |
|
|
|
|
| def randomize_seed_fn(seed: int, randomize_seed: bool) -> int: |
| if randomize_seed: |
| seed = random.randint(0, MAX_SEED) |
| return seed |
|
|
|
|
| def generate(prompt: str, |
| negative_prompt: str = '', |
| prompt_2: str = '', |
| negative_prompt_2: str = '', |
| use_negative_prompt: bool = False, |
| use_prompt_2: bool = False, |
| use_negative_prompt_2: bool = False, |
| seed: int = 0, |
| width: int = 1024, |
| height: int = 1024, |
| guidance_scale_base: float = 5.0, |
| guidance_scale_refiner: float = 5.0, |
| num_inference_steps_base: int = 50, |
| num_inference_steps_refiner: int = 50, |
| apply_refiner: bool = False) -> PIL.Image.Image: |
| generator = torch.Generator().manual_seed(seed) |
|
|
| if not use_negative_prompt: |
| negative_prompt = None |
| if not use_prompt_2: |
| prompt_2 = None |
| if not use_negative_prompt_2: |
| negative_prompt_2 = None |
|
|
| if not apply_refiner: |
| return pipe(prompt=prompt, |
| negative_prompt=negative_prompt, |
| prompt_2=prompt_2, |
| negative_prompt_2=negative_prompt_2, |
| width=width, |
| height=height, |
| guidance_scale=guidance_scale_base, |
| num_inference_steps=num_inference_steps_base, |
| generator=generator, |
| output_type='pil').images[0] |
| else: |
| latents = pipe(prompt=prompt, |
| negative_prompt=negative_prompt, |
| prompt_2=prompt_2, |
| negative_prompt_2=negative_prompt_2, |
| width=width, |
| height=height, |
| guidance_scale=guidance_scale_base, |
| num_inference_steps=num_inference_steps_base, |
| generator=generator, |
| output_type='latent').images |
| image = refiner(prompt=prompt, |
| negative_prompt=negative_prompt, |
| prompt_2=prompt_2, |
| negative_prompt_2=negative_prompt_2, |
| guidance_scale=guidance_scale_refiner, |
| num_inference_steps=num_inference_steps_refiner, |
| image=latents, |
| generator=generator).images[0] |
| return image |
|
|
|
|
| examples = [ |
| 'Astronaut in a jungle, cold color palette, muted colors, detailed, 8k', |
| 'An astronaut riding a green horse', |
| ] |
|
|
| with gr.Blocks(css='style.css') as demo: |
| gr.Markdown(DESCRIPTION) |
| gr.DuplicateButton(value='Duplicate Space for private use', |
| elem_id='duplicate-button', |
| visible=os.getenv('SHOW_DUPLICATE_BUTTON') == '1') |
| with gr.Box(): |
| 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 options', open=False): |
| with gr.Row(): |
| use_negative_prompt = gr.Checkbox(label='Use negative prompt', |
| value=False) |
| use_prompt_2 = gr.Checkbox(label='Use prompt 2', value=False) |
| use_negative_prompt_2 = gr.Checkbox( |
| label='Use negative prompt 2', value=False) |
| negative_prompt = gr.Text( |
| label='Negative prompt', |
| max_lines=1, |
| placeholder='Enter a negative prompt', |
| visible=False, |
| ) |
| prompt_2 = gr.Text( |
| label='Prompt 2', |
| max_lines=1, |
| placeholder='Enter your prompt', |
| visible=False, |
| ) |
| negative_prompt_2 = gr.Text( |
| label='Negative prompt 2', |
| 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, |
| ) |
| height = gr.Slider( |
| label='Height', |
| minimum=256, |
| maximum=MAX_IMAGE_SIZE, |
| step=32, |
| value=1024, |
| ) |
| apply_refiner = gr.Checkbox(label='Apply refiner', value=False) |
| with gr.Row(): |
| guidance_scale_base = gr.Slider( |
| label='Guidance scale for base', |
| minimum=1, |
| maximum=20, |
| step=0.1, |
| value=5.0) |
| num_inference_steps_base = gr.Slider( |
| label='Number of inference steps for base', |
| minimum=10, |
| maximum=100, |
| step=1, |
| value=50) |
| with gr.Row(visible=False) as refiner_params: |
| guidance_scale_refiner = gr.Slider( |
| label='Guidance scale for refiner', |
| minimum=1, |
| maximum=20, |
| step=0.1, |
| value=5.0) |
| num_inference_steps_refiner = gr.Slider( |
| label='Number of inference steps for refiner', |
| minimum=10, |
| maximum=100, |
| step=1, |
| value=50) |
|
|
| gr.Examples(examples=examples, |
| inputs=prompt, |
| outputs=result, |
| fn=generate, |
| cache_examples=CACHE_EXAMPLES) |
|
|
| use_negative_prompt.change( |
| fn=lambda x: gr.update(visible=x), |
| inputs=use_negative_prompt, |
| outputs=negative_prompt, |
| queue=False, |
| api_name=False, |
| ) |
| use_prompt_2.change( |
| fn=lambda x: gr.update(visible=x), |
| inputs=use_prompt_2, |
| outputs=prompt_2, |
| queue=False, |
| api_name=False, |
| ) |
| use_negative_prompt_2.change( |
| fn=lambda x: gr.update(visible=x), |
| inputs=use_negative_prompt_2, |
| outputs=negative_prompt_2, |
| queue=False, |
| api_name=False, |
| ) |
| apply_refiner.change( |
| fn=lambda x: gr.update(visible=x), |
| inputs=apply_refiner, |
| outputs=refiner_params, |
| queue=False, |
| api_name=False, |
| ) |
|
|
| inputs = [ |
| prompt, |
| negative_prompt, |
| prompt_2, |
| negative_prompt_2, |
| use_negative_prompt, |
| use_prompt_2, |
| use_negative_prompt_2, |
| seed, |
| width, |
| height, |
| guidance_scale_base, |
| guidance_scale_refiner, |
| num_inference_steps_base, |
| num_inference_steps_refiner, |
| apply_refiner, |
| ] |
| prompt.submit( |
| fn=randomize_seed_fn, |
| inputs=[seed, randomize_seed], |
| outputs=seed, |
| queue=False, |
| api_name=False, |
| ).then( |
| fn=generate, |
| inputs=inputs, |
| outputs=result, |
| api_name='run', |
| ) |
| negative_prompt.submit( |
| fn=randomize_seed_fn, |
| inputs=[seed, randomize_seed], |
| outputs=seed, |
| queue=False, |
| api_name=False, |
| ).then( |
| fn=generate, |
| inputs=inputs, |
| outputs=result, |
| api_name=False, |
| ) |
| prompt_2.submit( |
| fn=randomize_seed_fn, |
| inputs=[seed, randomize_seed], |
| outputs=seed, |
| queue=False, |
| api_name=False, |
| ).then( |
| fn=generate, |
| inputs=inputs, |
| outputs=result, |
| api_name=False, |
| ) |
| negative_prompt_2.submit( |
| fn=randomize_seed_fn, |
| inputs=[seed, randomize_seed], |
| outputs=seed, |
| queue=False, |
| api_name=False, |
| ).then( |
| fn=generate, |
| inputs=inputs, |
| outputs=result, |
| api_name=False, |
| ) |
| run_button.click( |
| fn=randomize_seed_fn, |
| inputs=[seed, randomize_seed], |
| outputs=seed, |
| queue=False, |
| api_name=False, |
| ).then( |
| fn=generate, |
| inputs=inputs, |
| outputs=result, |
| api_name=False, |
| ) |
| demo.queue(max_size=20).launch() |
|
|
