import os import random import uuid from typing import Tuple import gradio as gr import numpy as np from PIL import Image import spaces import torch from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler DESCRIPTIONz= """## LoRA SD 🙀 """ 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 MAX_SEED = np.iinfo(np.int32).max if not torch.cuda.is_available(): DESCRIPTIONz += "\n

⚠️Running on CPU, This may not work on CPU.

" USE_TORCH_COMPILE = 0 ENABLE_CPU_OFFLOAD = 0 if torch.cuda.is_available(): pipe = StableDiffusionXLPipeline.from_pretrained( "SG161222/RealVisXL_V4.0_Lightning", torch_dtype=torch.float16, use_safetensors=True, ) pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config) LORA_OPTIONS = { "Realism": ("prithivMLmods/Canopus-Realism-LoRA", "Canopus-Realism-LoRA.safetensors", "rlms"), "PIXAR": ("prithivMLmods/Canopus-Pixar-Art", "Canopus-Pixar-Art.safetensors", "pixar"), "PhotoShoot": ("prithivMLmods/Canopus-Photo-Shoot-Mini-LoRA", "Canopus-Photo-Shoot-Mini-LoRA.safetensors", "photo"), "Interior Architecture": ("prithivMLmods/Canopus-Interior-Architecture-0.1", "Canopus-Interior-Architecture-0.1δ.safetensors", "arch"), "Fashion Product": ("prithivMLmods/Canopus-Fashion-Product-Dilation", "Canopus-Fashion-Product-Dilation.safetensors", "fashion"), } for model_name, weight_name, adapter_name in LORA_OPTIONS.values(): pipe.load_lora_weights(model_name, weight_name=weight_name, adapter_name=adapter_name) pipe.to("cuda") style_list = [ { "name": "3840 x 2160", "prompt": "hyper-realistic 8K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic", "negative_prompt": "cartoonish, low resolution, blurry, simplistic, abstract, deformed, ugly", }, { "name": "2560 x 1440", "prompt": "hyper-realistic 4K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic", "negative_prompt": "cartoonish, low resolution, blurry, simplistic, abstract, deformed, ugly", }, { "name": "HD+", "prompt": "hyper-realistic 2K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic", "negative_prompt": "cartoonish, low resolution, blurry, simplistic, abstract, deformed, ugly", }, { "name": "Style Zero", "prompt": "{prompt}", "negative_prompt": "", }, ] styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list} DEFAULT_STYLE_NAME = "3840 x 2160" STYLE_NAMES = list(styles.keys()) def apply_style(style_name: str, positive: str, negative: str = "") -> Tuple[str, str]: if style_name in styles: p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME]) else: p, n = styles[DEFAULT_STYLE_NAME] if not negative: negative = "" return p.replace("{prompt}", positive), n + negative @spaces.GPU(duration=60, enable_queue=True) 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, style_name: str = DEFAULT_STYLE_NAME, lora_model: str = "Realism", progress=gr.Progress(track_tqdm=True), ): seed = int(randomize_seed_fn(seed, randomize_seed)) positive_prompt, effective_negative_prompt = apply_style(style_name, prompt, negative_prompt) if not use_negative_prompt: effective_negative_prompt = "" # type: ignore model_name, weight_name, adapter_name = LORA_OPTIONS[lora_model] pipe.set_adapters(adapter_name) images = pipe( prompt=positive_prompt, negative_prompt=effective_negative_prompt, width=width, height=height, guidance_scale=guidance_scale, num_inference_steps=20, num_images_per_prompt=1, cross_attention_kwargs={"scale": 0.65}, output_type="pil", ).images image_paths = [save_image(img) for img in images] return image_paths, seed examples = [ "A man in ski mask, in the style of smokey background, androgynous, imaginative prison scenes, light indigo and black, close-up, michelangelo, street-savvy --ar 125:187 --v 5.1 --style raw", "Photography, front view, dynamic range, female model, upper-body, black T-shirt, dark khaki cargo pants, urban backdrop, dusk, dramatic sunlights, bokeh, cityscape, photorealism, natural, UHD --ar 9:16 --stylize 300" ] css = ''' .gradio-container{max-width: 545px !important} h1{text-align:center} footer { visibility: hidden } ''' with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo: gr.Markdown(DESCRIPTIONz) with gr.Group(): with gr.Row(): prompt = gr.Text( label="Prompt", show_label=False, max_lines=1, placeholder="Enter your prompt with realism tag!", container=False, ) run_button = gr.Button("Run", scale=0) result = gr.Gallery(label="Result", columns=1, preview=True, show_label=False) with gr.Accordion("Advanced options", open=False, visible=False): use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=True) negative_prompt = gr.Text( label="Negative prompt", lines=4, max_lines=6, value="(deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers:1.4), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation", placeholder="Enter a negative prompt", visible=True, ) seed = gr.Slider( label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, visible=True ) randomize_seed = gr.Checkbox(label="Randomize seed", value=True) with gr.Row(visible=True): width = gr.Slider( label="Width", minimum=512, maximum=2048, step=8, value=1024, ) height = gr.Slider( label="Height", minimum=512, maximum=2048, step=8, value=1024, ) with gr.Row(): guidance_scale = gr.Slider( label="Guidance Scale", minimum=0.1, maximum=20.0, step=0.1, value=3.0, ) style_selection = gr.Radio( show_label=True, container=True, interactive=True, choices=STYLE_NAMES, value=DEFAULT_STYLE_NAME, label="Quality Style", ) with gr.Row(visible=True): model_choice = gr.Dropdown( label="LoRA Selection", choices=list(LORA_OPTIONS.keys()), value="Realism" ) gr.Examples( examples=examples, inputs=prompt, outputs=[result, seed], fn=generate, cache_examples=False, ) 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, style_selection, model_choice, ], outputs=[result, seed], api_name="run", ) if __name__ == "__main__": demo.queue(max_size=30).launch()