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Running
on
Zero
| #!/usr/bin/env python | |
| from __future__ import annotations | |
| import os | |
| import random | |
| import gradio as gr | |
| import numpy as np | |
| import PIL.Image | |
| import spaces | |
| import torch | |
| from diffusers import AutoencoderKL, DiffusionPipeline | |
| DESCRIPTION = "# SDXL" | |
| 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 | |
| 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" | |
| ENABLE_REFINER = os.getenv("ENABLE_REFINER", "1") == "1" | |
| device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") | |
| if torch.cuda.is_available(): | |
| vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16) | |
| pipe = DiffusionPipeline.from_pretrained( | |
| "stabilityai/stable-diffusion-xl-base-1.0", | |
| vae=vae, | |
| torch_dtype=torch.float16, | |
| use_safetensors=True, | |
| variant="fp16", | |
| ) | |
| if ENABLE_REFINER: | |
| refiner = DiffusionPipeline.from_pretrained( | |
| "stabilityai/stable-diffusion-xl-refiner-1.0", | |
| vae=vae, | |
| torch_dtype=torch.float16, | |
| use_safetensors=True, | |
| variant="fp16", | |
| ) | |
| if ENABLE_CPU_OFFLOAD: | |
| pipe.enable_model_cpu_offload() | |
| if ENABLE_REFINER: | |
| refiner.enable_model_cpu_offload() | |
| else: | |
| pipe.to(device) | |
| if ENABLE_REFINER: | |
| refiner.to(device) | |
| if USE_TORCH_COMPILE: | |
| pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True) | |
| if ENABLE_REFINER: | |
| refiner.unet = torch.compile(refiner.unet, mode="reduce-overhead", fullgraph=True) | |
| 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 = 25, | |
| num_inference_steps_refiner: int = 25, | |
| 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.Group(): | |
| 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, visible=ENABLE_REFINER) | |
| 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=25, | |
| ) | |
| 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=25, | |
| ) | |
| gr.Examples( | |
| examples=examples, | |
| inputs=prompt, | |
| outputs=result, | |
| fn=generate, | |
| ) | |
| 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, | |
| ) | |
| gr.on( | |
| triggers=[ | |
| prompt.submit, | |
| negative_prompt.submit, | |
| prompt_2.submit, | |
| negative_prompt_2.submit, | |
| run_button.click, | |
| ], | |
| fn=randomize_seed_fn, | |
| inputs=[seed, randomize_seed], | |
| outputs=seed, | |
| queue=False, | |
| api_name=False, | |
| ).then( | |
| fn=generate, | |
| 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, | |
| ], | |
| outputs=result, | |
| api_name="run", | |
| ) | |
| if __name__ == "__main__": | |
| demo.queue(max_size=20).launch() | |