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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 AutoencoderKL, StableDiffusionXLPipeline | |
| import uuid | |
| DESCRIPTION = '''# Segmind Stable Diffusion: SSD-1B | |
| #### [Segmind's SSD-1B](https://huggingface.co./segmind/SSD-1B) is a distilled, 50% smaller version of SDXL, offering up to 60% speedup | |
| ''' | |
| 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") == "1" | |
| MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "1024")) | |
| USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE", "1") == "1" | |
| ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "0") == "1" | |
| ENABLE_REFINER = os.getenv("ENABLE_REFINER", "0") == "1" | |
| device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") | |
| style_list = [ | |
| { | |
| "name": "(No style)", | |
| "prompt": "{prompt}", | |
| "negative_prompt": "", | |
| }, | |
| { | |
| "name": "Cinematic", | |
| "prompt": "cinematic still {prompt} . emotional, harmonious, vignette, highly detailed, high budget, bokeh, cinemascope, moody, epic, gorgeous, film grain, grainy", | |
| "negative_prompt": "anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured", | |
| }, | |
| { | |
| "name": "Photographic", | |
| "prompt": "cinematic photo {prompt} . 35mm photograph, film, bokeh, professional, 4k, highly detailed", | |
| "negative_prompt": "drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly", | |
| }, | |
| { | |
| "name": "Anime", | |
| "prompt": "anime artwork {prompt} . anime style, key visual, vibrant, studio anime, highly detailed", | |
| "negative_prompt": "photo, deformed, black and white, realism, disfigured, low contrast", | |
| }, | |
| { | |
| "name": "Manga", | |
| "prompt": "manga style {prompt} . vibrant, high-energy, detailed, iconic, Japanese comic style", | |
| "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, Western comic style", | |
| }, | |
| { | |
| "name": "Digital Art", | |
| "prompt": "concept art {prompt} . digital artwork, illustrative, painterly, matte painting, highly detailed", | |
| "negative_prompt": "photo, photorealistic, realism, ugly", | |
| }, | |
| { | |
| "name": "Pixel art", | |
| "prompt": "pixel-art {prompt} . low-res, blocky, pixel art style, 8-bit graphics", | |
| "negative_prompt": "sloppy, messy, blurry, noisy, highly detailed, ultra textured, photo, realistic", | |
| }, | |
| { | |
| "name": "Fantasy art", | |
| "prompt": "ethereal fantasy concept art of {prompt} . magnificent, celestial, ethereal, painterly, epic, majestic, magical, fantasy art, cover art, dreamy", | |
| "negative_prompt": "photographic, realistic, realism, 35mm film, dslr, cropped, frame, text, deformed, glitch, noise, noisy, off-center, deformed, cross-eyed, closed eyes, bad anatomy, ugly, disfigured, sloppy, duplicate, mutated, black and white", | |
| }, | |
| { | |
| "name": "Neonpunk", | |
| "prompt": "neonpunk style {prompt} . cyberpunk, vaporwave, neon, vibes, vibrant, stunningly beautiful, crisp, detailed, sleek, ultramodern, magenta highlights, dark purple shadows, high contrast, cinematic, ultra detailed, intricate, professional", | |
| "negative_prompt": "painting, drawing, illustration, glitch, deformed, mutated, cross-eyed, ugly, disfigured", | |
| }, | |
| { | |
| "name": "3D Model", | |
| "prompt": "professional 3d model {prompt} . octane render, highly detailed, volumetric, dramatic lighting", | |
| "negative_prompt": "ugly, deformed, noisy, low poly, blurry, painting", | |
| }, | |
| ] | |
| styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list} | |
| STYLE_NAMES = list(styles.keys()) | |
| DEFAULT_STYLE_NAME = "Cinematic" | |
| def apply_style(style_name: str, positive: str, negative: str = "") -> Tuple[str, str]: | |
| p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME]) | |
| if not negative: | |
| negative = "" | |
| return p.replace("{prompt}", positive), n + negative | |
| if torch.cuda.is_available(): | |
| vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16) | |
| pipe = StableDiffusionXLPipeline.from_pretrained( | |
| "segmind/SSD-1B", | |
| 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) | |
| print("Loaded on 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) | |
| 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 = "", | |
| style: str = DEFAULT_STYLE_NAME, | |
| 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, | |
| randomize_seed: bool = False, | |
| progress = gr.Progress(track_tqdm=True) | |
| ): | |
| seed = randomize_seed_fn(seed, randomize_seed) | |
| 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 | |
| prompt, negative_prompt = apply_style(style, prompt, negative_prompt) | |
| if not apply_refiner: | |
| image = 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] | |
| image_path = save_image(image) | |
| print(image_path) | |
| return [image_path], seed | |
| examples = ['3d digital art of an adorable ghost, glowing within, holding a heart shaped pumpkin, Halloween, super cute, spooky haunted house background', 'beautiful lady, freckles, big smile, blue eyes, short ginger hair, dark makeup, wearing a floral blue vest top, soft light, dark grey background', 'professional portrait photo of an anthropomorphic cat wearing fancy gentleman hat and jacket walking in autumn forest.', 'an astronaut sitting in a diner, eating fries, cinematic, analog film', 'Albert Einstein in a surrealist Cyberpunk 2077 world, hyperrealistic', 'cinematic film still of Futuristic hero with golden dark armour with machine gun, muscular body'] | |
| 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.Gallery(label="Result", columns=1, 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) | |
| style_selection = gr.Radio( | |
| show_label=True, container=True, interactive=True, | |
| choices=STYLE_NAMES, | |
| value=DEFAULT_STYLE_NAME, | |
| label='Image Style' | |
| ) | |
| 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(visible=False): | |
| 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=9.0, | |
| ) | |
| 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, 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, | |
| 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=generate, | |
| inputs=[ | |
| prompt, | |
| negative_prompt, | |
| style_selection, | |
| 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, | |
| randomize_seed | |
| ], | |
| outputs=[result, seed], | |
| api_name="run", | |
| ) | |
| if __name__ == "__main__": | |
| demo.queue(max_size=20).launch() |