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import os |
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import random |
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import uuid |
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from typing import Tuple |
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import gradio as gr |
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import numpy as np |
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from PIL import Image |
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import spaces |
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import torch |
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from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler |
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DESCRIPTIONz= """## LoRA SD 🙀 |
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""" |
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def save_image(img): |
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unique_name = str(uuid.uuid4()) + ".png" |
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img.save(unique_name) |
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return unique_name |
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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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MAX_SEED = np.iinfo(np.int32).max |
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if not torch.cuda.is_available(): |
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DESCRIPTIONz += "\n<p>⚠️Running on CPU, This may not work on CPU.</p>" |
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USE_TORCH_COMPILE = 0 |
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ENABLE_CPU_OFFLOAD = 0 |
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if torch.cuda.is_available(): |
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pipe = StableDiffusionXLPipeline.from_pretrained( |
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"SG161222/RealVisXL_V4.0_Lightning", |
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torch_dtype=torch.float16, |
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use_safetensors=True, |
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) |
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pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config) |
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LORA_OPTIONS = { |
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"Realism": ("prithivMLmods/Canopus-Realism-LoRA", "Canopus-Realism-LoRA.safetensors", "rlms"), |
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"PIXAR": ("prithivMLmods/Canopus-Pixar-Art", "Canopus-Pixar-Art.safetensors", "pixar"), |
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"PhotoShoot": ("prithivMLmods/Canopus-Photo-Shoot-Mini-LoRA", "Canopus-Photo-Shoot-Mini-LoRA.safetensors", "photo"), |
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"Interior Architecture": ("prithivMLmods/Canopus-Interior-Architecture-0.1", "Canopus-Interior-Architecture-0.1δ.safetensors", "arch"), |
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"Fashion Product": ("prithivMLmods/Canopus-Fashion-Product-Dilation", "Canopus-Fashion-Product-Dilation.safetensors", "fashion"), |
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} |
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for model_name, weight_name, adapter_name in LORA_OPTIONS.values(): |
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pipe.load_lora_weights(model_name, weight_name=weight_name, adapter_name=adapter_name) |
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pipe.to("cuda") |
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style_list = [ |
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{ |
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"name": "3840 x 2160", |
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"prompt": "hyper-realistic 8K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic", |
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"negative_prompt": "cartoonish, low resolution, blurry, simplistic, abstract, deformed, ugly", |
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}, |
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{ |
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"name": "2560 x 1440", |
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"prompt": "hyper-realistic 4K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic", |
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"negative_prompt": "cartoonish, low resolution, blurry, simplistic, abstract, deformed, ugly", |
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}, |
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{ |
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"name": "HD+", |
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"prompt": "hyper-realistic 2K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic", |
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"negative_prompt": "cartoonish, low resolution, blurry, simplistic, abstract, deformed, ugly", |
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}, |
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{ |
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"name": "Style Zero", |
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"prompt": "{prompt}", |
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"negative_prompt": "", |
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}, |
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] |
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styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list} |
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DEFAULT_STYLE_NAME = "3840 x 2160" |
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STYLE_NAMES = list(styles.keys()) |
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def apply_style(style_name: str, positive: str, negative: str = "") -> Tuple[str, str]: |
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if style_name in styles: |
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p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME]) |
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else: |
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p, n = styles[DEFAULT_STYLE_NAME] |
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if not negative: |
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negative = "" |
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return p.replace("{prompt}", positive), n + negative |
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@spaces.GPU(duration=60, enable_queue=True) |
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def generate( |
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prompt: str, |
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negative_prompt: str = "", |
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use_negative_prompt: 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: float = 3, |
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randomize_seed: bool = False, |
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style_name: str = DEFAULT_STYLE_NAME, |
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lora_model: str = "Realism", |
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progress=gr.Progress(track_tqdm=True), |
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): |
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seed = int(randomize_seed_fn(seed, randomize_seed)) |
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positive_prompt, effective_negative_prompt = apply_style(style_name, prompt, negative_prompt) |
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if not use_negative_prompt: |
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effective_negative_prompt = "" |
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model_name, weight_name, adapter_name = LORA_OPTIONS[lora_model] |
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pipe.set_adapters(adapter_name) |
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images = pipe( |
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prompt=positive_prompt, |
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negative_prompt=effective_negative_prompt, |
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width=width, |
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height=height, |
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guidance_scale=guidance_scale, |
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num_inference_steps=20, |
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num_images_per_prompt=1, |
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cross_attention_kwargs={"scale": 0.65}, |
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output_type="pil", |
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).images |
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image_paths = [save_image(img) for img in images] |
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return image_paths, seed |
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examples = [ |
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"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", |
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"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" |
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] |
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css = ''' |
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.gradio-container{max-width: 545px !important} |
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h1{text-align:center} |
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footer { |
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visibility: hidden |
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} |
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''' |
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with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo: |
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gr.Markdown(DESCRIPTIONz) |
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with gr.Group(): |
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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 with realism tag!", |
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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.Gallery(label="Result", columns=1, preview=True, show_label=False) |
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with gr.Accordion("Advanced options", open=False, visible=False): |
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use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=True) |
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negative_prompt = gr.Text( |
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label="Negative prompt", |
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lines=4, |
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max_lines=6, |
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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", |
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placeholder="Enter a negative prompt", |
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visible=True, |
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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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visible=True |
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) |
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randomize_seed = gr.Checkbox(label="Randomize seed", value=True) |
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with gr.Row(visible=True): |
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width = gr.Slider( |
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label="Width", |
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minimum=512, |
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maximum=2048, |
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step=8, |
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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=512, |
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maximum=2048, |
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step=8, |
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value=1024, |
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) |
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with gr.Row(): |
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guidance_scale = gr.Slider( |
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label="Guidance Scale", |
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minimum=0.1, |
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maximum=20.0, |
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step=0.1, |
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value=3.0, |
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) |
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style_selection = gr.Radio( |
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show_label=True, |
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container=True, |
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interactive=True, |
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choices=STYLE_NAMES, |
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value=DEFAULT_STYLE_NAME, |
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label="Quality Style", |
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) |
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with gr.Row(visible=True): |
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model_choice = gr.Dropdown( |
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label="LoRA Selection", |
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choices=list(LORA_OPTIONS.keys()), |
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value="Realism" |
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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, seed], |
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fn=generate, |
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cache_examples=False, |
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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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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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run_button.click, |
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], |
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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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use_negative_prompt, |
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seed, |
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width, |
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height, |
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guidance_scale, |
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randomize_seed, |
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style_selection, |
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model_choice, |
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], |
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outputs=[result, seed], |
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api_name="run", |
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) |
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if __name__ == "__main__": |
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demo.queue(max_size=30).launch() |
