import torch import os import gradio as gr from PIL import Image from diffusers import ( StableDiffusionPipeline, StableDiffusionControlNetImg2ImgPipeline, ControlNetModel, DDIMScheduler, DPMSolverMultistepScheduler, DEISMultistepScheduler, HeunDiscreteScheduler, EulerDiscreteScheduler, ) # Initialize ControlNet model controlnet = ControlNetModel.from_pretrained( "DionTimmer/controlnet_qrcode-control_v1p_sd15", torch_dtype=torch.float16 ) # Initialize pipeline pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained( "XpucT/Deliberate", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16, ).to("cuda") pipe.enable_xformers_memory_efficient_attention() # Sampler configurations 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), } # Inference function def inference( input_image: Image.Image, prompt: str, negative_prompt: str, guidance_scale: float = 10.0, controlnet_conditioning_scale: float = 1.0, strength: float = 0.8, seed: int = -1, sampler = "DPM++ Karras SDE", ): if prompt is None or prompt == "": raise gr.Error("Prompt is required") 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=input_image, control_image=input_image, # type: ignore width=512, # type: ignore height=512, # type: ignore guidance_scale=float(guidance_scale), controlnet_conditioning_scale=float(controlnet_conditioning_scale), # type: ignore generator=generator, strength=float(strength), num_inference_steps=40, ) return out.images[0] # type: ignore # Gradio UI with gr.Blocks() as app: gr.Markdown( ''' # Illusion Diffusion ## A simple UI for generating beatiful illusion art with Stable Diffusion 1.5 ''' ) with gr.Row(): with gr.Column(): input_image = gr.Image(label="Input Illusion", type="pil") prompt = gr.Textbox(label="Prompt", info="Prompt that guides the generation towards") 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=[input_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)