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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 spaces |
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import torch |
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from diffusers import AutoencoderKL, DiffusionPipeline |
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DESCRIPTION = "# SDXL" |
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if not torch.cuda.is_available(): |
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DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>" |
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MAX_SEED = np.iinfo(np.int32).max |
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CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES") == "1" |
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MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "1824")) |
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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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ENABLE_REFINER = os.getenv("ENABLE_REFINER", "1") == "1" |
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ENABLE_USE_LORA = os.getenv("ENABLE_USE_LORA", "1") == "1" |
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ENABLE_USE_VAE = os.getenv("ENABLE_USE_VAE", "1") == "1" |
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") |
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models = ["runwayml/stable-diffusion-v1-5", |
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"stabilityai/stable-diffusion-xl-base-1.0", |
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"stablediffusionapi/juggernaut-xl-v8", |
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"emilianJR/epiCRealism", |
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"SG161222/Realistic_Vision_V5.1_noVAE", |
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"cagliostrolab/animagine-xl-3.0", |
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"misri/cyberrealistic_v41BackToBasics", |
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"malcolmrey/serenity", |
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"SG161222/RealVisXL_V3.0", |
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"stablediffusionapi/realistic-stock-photo-v2", |
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"stablediffusionapi/pixel-art-diffusion-xl", |
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"playgroundai/playground-v2-1024px-aesthetic", |
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"dataautogpt3/ProteusV0.3", |
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"stablediffusionapi/disney-pixar-cartoon", |
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"RunDiffusion/Juggernaut-XL-Lightning"] |
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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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@spaces.GPU |
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def generate( |
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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 = 25, |
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num_inference_steps_refiner: int = 25, |
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use_vae: bool = False, |
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use_lora: bool = False, |
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apply_refiner: bool = False, |
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dropdown_model = 'cagliostrolab/animagine-xl-3.0', |
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vaecall = 'stabilityai/sd-vae-ft-mse', |
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lora = 'amazonaws-la/juliette', |
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lora_scale: float = 0.7, |
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) -> PIL.Image.Image: |
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if torch.cuda.is_available(): |
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if not use_vae: |
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pipe = DiffusionPipeline.from_pretrained(dropdown_model, torch_dtype=torch.float16) |
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if use_vae: |
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vae = AutoencoderKL.from_pretrained(vaecall, torch_dtype=torch.float16) |
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pipe = DiffusionPipeline.from_pretrained(dropdown_model, vae=vae, torch_dtype=torch.float16) |
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if use_lora: |
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pipe.load_lora_weights(lora) |
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pipe.fuse_lora(lora_scale=0.7) |
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if ENABLE_CPU_OFFLOAD: |
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pipe.enable_model_cpu_offload() |
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else: |
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pipe.to(device) |
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if USE_TORCH_COMPILE: |
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pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True) |
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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( |
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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", |
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).images[0] |
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else: |
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latents = pipe( |
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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", |
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).images |
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image = refiner( |
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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, |
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).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( |
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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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) |
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with gr.Group(): |
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dropdown_model = gr.Dropdown(label='Model', value='cagliostrolab/animagine-xl-3.0', choices=models) |
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vaecall = gr.Text(label='VAE') |
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lora = gr.Text(label='LoRA') |
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lora_scale = gr.Slider( |
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label="Lora Scale", |
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minimum=0.01, |
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maximum=1, |
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step=0.01, |
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value=0.7, |
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) |
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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", 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(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( |
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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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) |
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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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use_vae = gr.Checkbox(label='Use VAE', value=False, visible=ENABLE_USE_VAE) |
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use_lora = gr.Checkbox(label='Use Lora', value=False, visible=ENABLE_USE_LORA) |
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apply_refiner = gr.Checkbox(label="Apply refiner", value=False, visible=ENABLE_REFINER) |
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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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) |
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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=25, |
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) |
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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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) |
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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=25, |
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) |
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gr.Examples( |
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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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) |
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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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use_vae.change( |
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fn=lambda x: gr.update(visible=x), |
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inputs=use_vae, |
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outputs=vaecall, |
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queue=False, |
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api_name=False, |
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) |
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use_lora.change( |
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fn=lambda x: gr.update(visible=x), |
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inputs=use_lora, |
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outputs=lora, |
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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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gr.on( |
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triggers=[ |
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prompt.submit, |
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negative_prompt.submit, |
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prompt_2.submit, |
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negative_prompt_2.submit, |
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run_button.click, |
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], |
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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=[ |
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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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use_vae, |
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use_lora, |
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apply_refiner, |
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dropdown_model, |
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vaecall, |
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lora, |
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lora_scale, |
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], |
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outputs=result, |
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api_name="run", |
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) |
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css = """ |
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.svelte-vt1mxs.gap { |
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border-radius: 20px; |
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} |
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div#component-6 {padding: 26px;} |
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button#generate { |
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background: radial-gradient(#ff7300, #ffffff9e); |
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border-radius: 40px; |
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padding: 16px; |
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color: #FFF; |
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FONT-SIZE: large; |
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border: 2px solid #ffffff2e; |
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border-top: 0px solid; |
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box-shadow: 0px 18px 10px -10px #ff5400; |
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backdrop-filter: blur(12px); |
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} |
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.wrap.default.full.svelte-zlszon { |
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background: url(https://vivawaves.com/spaces.gif) center center no-repeat; |
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background-color: black; |
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} |
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.eta-bar.svelte-zlszon.svelte-zlszon { |
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background: #484848; |
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} |
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""" |
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if __name__ == "__main__": |
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demo.queue(max_size=20).launch() |
