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| #!/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-4.0 | |
| ### [https://huggingface.co/cutycat2000x/InterDiffusion-4.0](https://huggingface.co/cutycat2000x/InterDiffusion-4.0)""" | |
| 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<p>Running on CPU, This may not work on CPU.</p>" | |
| 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-4.0", | |
| torch_dtype=torch.float16, | |
| use_safetensors=True, | |
| ) | |
| pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config) | |
| pipe.load_lora_weights("cutycat2000x/LoRA2", 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 = "(LoRA)" | |
| 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 | |
| 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) |