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