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import os |
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import random |
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import uuid |
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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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from typing import Tuple |
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import torch |
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from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler |
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DESCRIPTION = """# InterDiffusion-3.8 |
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### [https://huggingface.co/cutycat2000x/InterDiffusion-3.8](https://huggingface.co/cutycat2000x/InterDiffusion-3.8)""" |
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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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DESCRIPTION += "\n<p>Running on CPU, This may not work on CPU.</p>" |
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MAX_SEED = np.iinfo(np.int32).max |
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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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"cutycat2000x/InterDiffusion-3.8", |
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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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pipe.load_lora_weights("cutycat2000x/LoRA", weight_name="lora.safetensors", adapter_name="adapt") |
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pipe.set_adapters("adapt") |
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pipe.to("cuda") |
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style_list = [ |
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{ |
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"name": "(LoRA)", |
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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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STYLE_NAMES = list(styles.keys()) |
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DEFAULT_STYLE_NAME = "(No style)" |
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def apply_style(style_name: str, positive: str, negative: str = "") -> Tuple[str, str]: |
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p, n = styles.get(style_name, 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(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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style: str = DEFAULT_STYLE_NAME, |
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use_negative_prompt: bool = False, |
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num_inference_steps: int = 30, |
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num_images_per_prompt: int = 2, |
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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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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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if not use_negative_prompt: |
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negative_prompt = "" |
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prompt, negative_prompt = apply_style(style, prompt, negative_prompt) |
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images = pipe( |
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prompt=prompt, |
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negative_prompt=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=num_inference_steps, |
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num_images_per_prompt=num_images_per_prompt, |
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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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print(image_paths) |
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return image_paths, seed |
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examples = [ |
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'a smiling girl with sparkles in her eyes, walking in a garden, in the morning --style anime', |
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'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', |
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'Samsung Galaxy S9', |
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'cat, 4k, 8k, hyperrealistic, realistic, High-resolution, unreal engine 5, rtx, 16k, taken on a sony camera, Cinematic, dramatic lighting', |
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'cinimatic closeup of burning skull', |
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'frozen elsa', |
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'A rainbow tree, anime style, tree in focus', |
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'A cat holding a sign that reads "Hello World" in cursive text', |
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'A birthday card for "Meow"' |
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] |
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css = ''' |
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.gradio-container{max-width: 560px !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="xiaobaiyuan/theme_brief") as demo: |
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gr.Markdown(DESCRIPTION) |
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gr.DuplicateButton( |
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value="Duplicate Space for private use", |
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elem_id="duplicate-button", |
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visible=False, |
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) |
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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", |
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container=False, |
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) |
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run_button = gr.Button("Run") |
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result = gr.Gallery(label="Result", columns=1, preview=True) |
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with gr.Accordion("Advanced options", open=False): |
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use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=False, visible=True) |
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negative_prompt = gr.Text( |
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label="Negative prompt", |
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max_lines=1, |
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placeholder="Enter a negative prompt", |
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visible=True, |
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) |
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with gr.Row(): |
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num_inference_steps = gr.Slider( |
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label="Steps", |
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minimum=10, |
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maximum=60, |
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step=1, |
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value=30, |
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) |
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with gr.Row(): |
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num_images_per_prompt = gr.Slider( |
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label="Images", |
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minimum=1, |
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maximum=5, |
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step=1, |
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value=2, |
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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=6, |
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) |
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with gr.Row(visible=True): |
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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="Image Style", |
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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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style_selection, |
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use_negative_prompt, |
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num_inference_steps, |
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num_images_per_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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], |
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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=20).launch(show_api=False, debug=False) |
