#!/usr/bin/env python import os import random import uuid import gradio as gr import numpy as np from PIL import Image import spaces from typing import Tuple import torch from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler DESCRIPTION = """# InterDiffusion-3.8 ### [https://huggingface.co/cutycat2000x/InterDiffusion-3.8](https://huggingface.co/cutycat2000x/InterDiffusion-3.8)""" 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(): DESCRIPTION += "\n

Running on CPU, This may not work on CPU.

" MAX_SEED = np.iinfo(np.int32).max USE_TORCH_COMPILE = 0 ENABLE_CPU_OFFLOAD = 0 if torch.cuda.is_available(): pipe = StableDiffusionXLPipeline.from_pretrained( "cutycat2000x/InterDiffusion-3.8", torch_dtype=torch.float16, use_safetensors=True, ) pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config) pipe.load_lora_weights("cutycat2000x/LoRA", weight_name="lora.safetensors", adapter_name="adapt") pipe.set_adapters("adapt") pipe.to("cuda") style_list = [ { "name": "(LoRA)", "prompt": "{prompt}", "negative_prompt": "", }, ] styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list} STYLE_NAMES = list(styles.keys()) DEFAULT_STYLE_NAME = "(No style)" 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 @spaces.GPU(enable_queue=True) def generate( prompt: str, negative_prompt: str = "", style: str = DEFAULT_STYLE_NAME, use_negative_prompt: bool = False, num_inference_steps: int = 30, num_images_per_prompt: int = 2, seed: int = 0, width: int = 1024, height: int = 1024, guidance_scale: float = 3, randomize_seed: bool = False, progress=gr.Progress(track_tqdm=True), ): seed = int(randomize_seed_fn(seed, randomize_seed)) if not use_negative_prompt: negative_prompt = "" # type: ignore prompt, negative_prompt = apply_style(style, prompt, negative_prompt) images = pipe( prompt=prompt, negative_prompt=negative_prompt, width=width, height=height, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps, num_images_per_prompt=num_images_per_prompt, cross_attention_kwargs={"scale": 0.65}, output_type="pil", ).images image_paths = [save_image(img) for img in images] print(image_paths) return image_paths, seed examples = [ 'a smiling girl with sparkles in her eyes, walking in a garden, in the morning --style anime', 'firewatch landscape, Graphic Novel, Pastel Art, Poster, Golden Hour, Electric Colors, 4k, RGB, Geometric, Volumetric, Lumen Global Illumination, Ray Tracing Reflections, Twisted Rays, Glowing Edges, RTX --raw', 'Samsung Galaxy S9', 'cat, 4k, 8k, hyperrealistic, realistic, High-resolution, unreal engine 5, rtx, 16k, taken on a sony camera, Cinematic, dramatic lighting', 'cinimatic closeup of burning skull', 'frozen elsa', 'A rainbow tree, anime style, tree in focus', 'A cat holding a sign that reads "Hello World" in cursive text', 'A birthday card for "Meow"' ] css = ''' .gradio-container{max-width: 560px !important} h1{text-align:center} footer { visibility: hidden } ''' with gr.Blocks(css=css, theme="xiaobaiyuan/theme_brief") as demo: gr.Markdown(DESCRIPTION) gr.DuplicateButton( value="Duplicate Space for private use", elem_id="duplicate-button", visible=False, ) 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") result = gr.Gallery(label="Result", columns=1, preview=True) with gr.Accordion("Advanced options", open=False): use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=False, visible=True) negative_prompt = gr.Text( label="Negative prompt", max_lines=1, placeholder="Enter a negative prompt", visible=True, ) with gr.Row(): num_inference_steps = gr.Slider( label="Steps", minimum=10, maximum=60, step=1, value=30, ) with gr.Row(): num_images_per_prompt = gr.Slider( label="Images", minimum=1, maximum=5, step=1, value=2, ) 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=6, ) with gr.Row(visible=True): style_selection = gr.Radio( show_label=True, container=True, interactive=True, choices=STYLE_NAMES, value=DEFAULT_STYLE_NAME, label="Image Style", ) 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, style_selection, use_negative_prompt, num_inference_steps, num_images_per_prompt, seed, width, height, guidance_scale, randomize_seed, ], outputs=[result, seed], api_name="run", ) if __name__ == "__main__": demo.queue(max_size=20).launch(show_api=False, debug=False)