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| #!/usr/bin/env python | |
| 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') | |
| # LoRA for 360° images | |
| pipe.load_lora_weights('artificialguybr/360Redmond', weight_name='View360.safetensors') | |
| 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 # type: ignore | |
| if not use_prompt_2: | |
| prompt_2 = None # type: ignore | |
| if not use_negative_prompt_2: | |
| negative_prompt_2 = None # type: ignore | |
| 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, | |
| ) | |
| 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() | |