import torch import os import gradio as gr from PIL import Image from diffusers import ( StableDiffusionPipeline, StableDiffusionControlNetImg2ImgPipeline, ControlNetModel, DDIMScheduler, DPMSolverMultistepScheduler, DEISMultistepScheduler, HeunDiscreteScheduler, EulerDiscreteScheduler, ) # Load controlnet model in float16 precision controlnet = ControlNetModel.from_pretrained( "monster-labs/control_v1p_sd15_qrcode_monster", torch_dtype=torch.float16 ) # Load the pipeline in float16 precision pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained( "SG161222/Realistic_Vision_V2.0", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16, ).to("cuda") pipe.enable_xformers_memory_efficient_attention() SAMPLER_MAP = { "DPM++ Karras SDE": lambda config: DPMSolverMultistepScheduler.from_config(config, use_karras=True, algorithm_type="sde-dpmsolver++"), "Euler": lambda config: EulerDiscreteScheduler.from_config(config), } def inference( control_image: Image.Image, prompt: str, negative_prompt: str, guidance_scale: float = 8.0, controlnet_conditioning_scale: float = 1, strength: float = 0.9, seed: int = -1, sampler = "DPM++ Karras SDE", ): if prompt is None or prompt == "": raise gr.Error("Prompt is required") # Generate init_image using the "Realistic Vision V2.0" model init_image = pipe(prompt, height=512, width=512).images[0] control_image = control_image.resize((512, 512)) pipe.scheduler = SAMPLER_MAP[sampler](pipe.scheduler.config) generator = torch.manual_seed(seed) if seed != -1 else torch.Generator() out = pipe( prompt=prompt, negative_prompt=negative_prompt, image=init_image, control_image=control_image, guidance_scale=guidance_scale, controlnet_conditioning_scale=controlnet_conditioning_scale, generator=generator, strength=strength, num_inference_steps=30, ) return out.images[0] with gr.Blocks() as app: gr.Markdown( ''' # Illusion Diffusion 🌀 ## Generate stunning illusion artwork with Stable Diffusion **[Follow me on Twitter](https://twitter.com/angrypenguinPNG)** ''' ) with gr.Row(): with gr.Column(): control_image = gr.Image(label="Input Illusion", type="pil") prompt = gr.Textbox(label="Prompt") negative_prompt = gr.Textbox(label="Negative Prompt", value="ugly, disfigured, low quality, blurry, nsfw") with gr.Accordion(label="Advanced Options", open=False): controlnet_conditioning_scale = gr.Slider(minimum=0.0, maximum=5.0, step=0.01, value=1.1, label="Controlnet Conditioning Scale") strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, value=0.9, label="Strength") guidance_scale = gr.Slider(minimum=0.0, maximum=50.0, step=0.25, value=7.5, label="Guidance Scale") sampler = gr.Dropdown(choices=list(SAMPLER_MAP.keys()), value="DPM++ Karras SDE") seed = gr.Slider(minimum=-1, maximum=9999999999, step=1, value=2313123, label="Seed", randomize=True) run_btn = gr.Button("Run") with gr.Column(): result_image = gr.Image(label="Illusion Diffusion Output") run_btn.click( inference, inputs=[control_image, prompt, negative_prompt, guidance_scale, controlnet_conditioning_scale, strength, seed, sampler], outputs=[result_image] ) app.queue(concurrency_count=4, max_size=20) if __name__ == "__main__": app.launch(debug=True)