Spaces:
Running
on
Zero
Running
on
Zero
| #!/usr/bin/env python | |
| import os | |
| import random | |
| import uuid | |
| import gradio as gr | |
| import numpy as np | |
| from PIL import Image | |
| import spaces | |
| import torch | |
| from diffusers import DiffusionPipeline | |
| DESCRIPTION = """# Playground v2.5""" | |
| if not torch.cuda.is_available(): | |
| DESCRIPTION += "\n<p>Running on CPU 🥶 This demo may not work on CPU.</p>" | |
| MAX_SEED = np.iinfo(np.int32).max | |
| CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES", "1") == "1" | |
| MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "1536")) | |
| USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE", "0") == "1" | |
| ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "0") == "1" | |
| device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") | |
| NUM_IMAGES_PER_PROMPT = 1 | |
| if torch.cuda.is_available(): | |
| pipe = DiffusionPipeline.from_pretrained( | |
| "playgroundai/playground-v2.5-1024px-aesthetic", | |
| torch_dtype=torch.float16, | |
| use_safetensors=True, | |
| add_watermarker=False, | |
| variant="fp16" | |
| ) | |
| if ENABLE_CPU_OFFLOAD: | |
| pipe.enable_model_cpu_offload() | |
| else: | |
| pipe.to(device) | |
| print("Loaded on Device!") | |
| if USE_TORCH_COMPILE: | |
| pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True) | |
| print("Model Compiled!") | |
| def save_image(img): | |
| unique_name = str(uuid.uuid4()) + ".png" | |
| img.save(unique_name) | |
| return unique_name | |
| 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 = "", | |
| use_negative_prompt: bool = False, | |
| seed: int = 0, | |
| width: int = 1024, | |
| height: int = 1024, | |
| guidance_scale: float = 3, | |
| randomize_seed: bool = False, | |
| use_resolution_binning: bool = True, | |
| progress=gr.Progress(track_tqdm=True), | |
| ): | |
| pipe.to(device) | |
| seed = int(randomize_seed_fn(seed, randomize_seed)) | |
| generator = torch.Generator().manual_seed(seed) | |
| if not use_negative_prompt: | |
| negative_prompt = None # type: ignore | |
| images = pipe( | |
| prompt=prompt, | |
| negative_prompt=negative_prompt, | |
| width=width, | |
| height=height, | |
| guidance_scale=guidance_scale, | |
| num_inference_steps=25, | |
| generator=generator, | |
| num_images_per_prompt=NUM_IMAGES_PER_PROMPT, | |
| use_resolution_binning=use_resolution_binning, | |
| output_type="pil", | |
| ).images | |
| image_paths = [save_image(img) for img in images] | |
| print(image_paths) | |
| return image_paths, seed | |
| examples = [ | |
| "neon holography crystal cat", | |
| "a cat eating a piece of cheese", | |
| "an astronaut riding a horse in space", | |
| "a cartoon of a boy playing with a tiger", | |
| "a cute robot artist painting on an easel, concept art", | |
| "a close up of a woman wearing a transparent, prismatic, elaborate nemeses headdress, over the should pose, brown skin-tone" | |
| ] | |
| css = ''' | |
| .gradio-container{max-width: 560px !important} | |
| h1{text-align:center} | |
| ''' | |
| with gr.Blocks(css=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.Gallery(label="Result", columns=NUM_IMAGES_PER_PROMPT, show_label=False) | |
| with gr.Accordion("Advanced options", open=False): | |
| with gr.Row(): | |
| use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=False) | |
| negative_prompt = gr.Text( | |
| label="Negative prompt", | |
| max_lines=1, | |
| placeholder="Enter a negative prompt", | |
| visible=True, | |
| ) | |
| 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(visible=True): | |
| 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, | |
| ) | |
| with gr.Row(): | |
| guidance_scale = gr.Slider( | |
| label="Guidance Scale", | |
| minimum=0.1, | |
| maximum=20, | |
| step=0.1, | |
| value=3.0, | |
| ) | |
| gr.Examples( | |
| examples=examples, | |
| inputs=prompt, | |
| outputs=[result, seed], | |
| fn=generate, | |
| cache_examples=CACHE_EXAMPLES, | |
| ) | |
| use_negative_prompt.change( | |
| fn=lambda x: gr.update(visible=x), | |
| inputs=use_negative_prompt, | |
| outputs=negative_prompt, | |
| api_name=False, | |
| ) | |
| gr.on( | |
| triggers=[ | |
| prompt.submit, | |
| negative_prompt.submit, | |
| run_button.click, | |
| ], | |
| fn=generate, | |
| inputs=[ | |
| prompt, | |
| negative_prompt, | |
| use_negative_prompt, | |
| seed, | |
| width, | |
| height, | |
| guidance_scale, | |
| randomize_seed, | |
| ], | |
| outputs=[result, seed], | |
| api_name="run", | |
| ) | |
| if __name__ == "__main__": | |
| demo.queue(max_size=20).launch() |