import torch import gradio as gr from PIL import Image import random from diffusers import ( DiffusionPipeline, AutoencoderKL, StableDiffusionControlNetPipeline, ControlNetModel, StableDiffusionControlNetImg2ImgPipeline, DPMSolverMultistepScheduler, EulerDiscreteScheduler ) import tempfile import time from share_btn import community_icon_html, loading_icon_html, share_js import user_history from illusion_style import css import os BASE_MODEL = "SG161222/Realistic_Vision_V5.1_noVAE" # Initialize both pipelines on CPU with float32 # Using float32 because CPU doesn't support half-precision (float16) well device = "cpu" torch_dtype = torch.float32 vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse", torch_dtype=torch_dtype) controlnet = ControlNetModel.from_pretrained("monster-labs/control_v1p_sd15_qrcode_monster", torch_dtype=torch_dtype) # Safety checker disabled by default to save memory/CPU cycles SAFETY_CHECKER_ENABLED = False main_pipe = StableDiffusionControlNetPipeline.from_pretrained( BASE_MODEL, controlnet=controlnet, vae=vae, safety_checker=None, feature_extractor=None, torch_dtype=torch_dtype, ).to(device) image_pipe = StableDiffusionControlNetImg2ImgPipeline(**main_pipe.components) # Sampler map 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 center_crop_resize(img, output_size=(512, 512)): width, height = img.size new_dimension = min(width, height) left = (width - new_dimension)/2 top = (height - new_dimension)/2 right = (width + new_dimension)/2 bottom = (height + new_dimension)/2 img = img.crop((left, top, right, bottom)) img = img.resize(output_size) return img def common_upscale(samples, width, height, upscale_method, crop=False): return torch.nn.functional.interpolate(samples, size=(height, width), mode=upscale_method) def upscale(samples, upscale_method, scale_by): width = round(samples.shape[3] * scale_by) height = round(samples.shape[2] * scale_by) s = common_upscale(samples, width, height, upscale_method) return s def check_inputs(prompt: str, control_image: Image.Image): if control_image is None: raise gr.Error("Please select or upload an Input Illusion") if prompt is None or prompt == "": raise gr.Error("Prompt is required") # Inference function def inference( control_image: Image.Image, prompt: str, negative_prompt: str, guidance_scale: float = 8.0, controlnet_conditioning_scale: float = 1, control_guidance_start: float = 1, control_guidance_end: float = 1, upscaler_strength: float = 0.5, seed: int = -1, sampler = "DPM++ Karras SDE", progress = gr.Progress(track_tqdm=True), profile: gr.OAuthProfile | None = None, ): start_time = time.time() control_image_small = center_crop_resize(control_image) control_image_large = center_crop_resize(control_image, (1024, 1024)) main_pipe.scheduler = SAMPLER_MAP[sampler](main_pipe.scheduler.config) my_seed = random.randint(0, 2**32 - 1) if seed == -1 else seed generator = torch.Generator(device=device).manual_seed(my_seed) # Reducing steps for CPU performance out = main_pipe( prompt=prompt, negative_prompt=negative_prompt, image=control_image_small, guidance_scale=float(guidance_scale), controlnet_conditioning_scale=float(controlnet_conditioning_scale), generator=generator, control_guidance_start=float(control_guidance_start), control_guidance_end=float(control_guidance_end), num_inference_steps=12, # Dropped steps for speed output_type="latent" ) upscaled_latents = upscale(out.images if hasattr(out, 'images') else out[0], "nearest-exact", 2) out_image = image_pipe( prompt=prompt, negative_prompt=negative_prompt, control_image=control_image_large, image=upscaled_latents, guidance_scale=float(guidance_scale), generator=generator, num_inference_steps=15, # Dropped steps for speed strength=upscaler_strength, control_guidance_start=float(control_guidance_start), control_guidance_end=float(control_guidance_end), controlnet_conditioning_scale=float(controlnet_conditioning_scale) ) end_time = time.time() print(f"Inference took {end_time-start_time}s") user_history.save_image( label=prompt, image=out_image["images"][0], profile=profile, metadata={"prompt": prompt, "seed": my_seed}, ) return out_image["images"][0], gr.update(visible=True), gr.update(visible=True), my_seed with gr.Blocks(css=css) as app: gr.Markdown("

Illusion Diffusion CPU 🌀

") with gr.Row(): with gr.Column(): control_image = gr.Image(label="Input Illusion", type="pil") controlnet_conditioning_scale = gr.Slider(minimum=0.0, maximum=5.0, step=0.01, value=0.8, label="Illusion strength") prompt = gr.Textbox(label="Prompt", placeholder="Medieval village scene") negative_prompt = gr.Textbox(label="Negative Prompt", value="low quality") with gr.Accordion(label="Advanced Options", open=False): 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="Euler") control_start = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, value=0, label="Start of ControlNet") control_end = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, value=1, label="End of ControlNet") strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, value=1, label="Strength of the upscaler") seed = gr.Slider(minimum=-1, maximum=9999999999, step=1, value=-1, label="Seed") used_seed = gr.Number(label="Last seed used", interactive=False) run_btn = gr.Button("Run") with gr.Column(): result_image = gr.Image(label="Output", interactive=False) with gr.Group(visible=False) as share_group: share_button = gr.Button("Share to community") run_btn.click( check_inputs, inputs=[prompt, control_image], queue=False ).success( inference, inputs=[control_image, prompt, negative_prompt, guidance_scale, controlnet_conditioning_scale, control_start, control_end, strength, seed, sampler], outputs=[result_image, result_image, share_group, used_seed]) app.queue(max_size=10).launch()