from diffusers import AutoPipelineForImage2Image, AutoPipelineForText2Image import torch import os try: import intel_extension_for_pytorch as ipex except: pass from PIL import Image import numpy as np import gradio as gr import psutil import time import math SAFETY_CHECKER = os.environ.get("SAFETY_CHECKER", None) TORCH_COMPILE = os.environ.get("TORCH_COMPILE", None) HF_TOKEN = os.environ.get("HF_TOKEN", None) # check if MPS is available OSX only M1/M2/M3 chips mps_available = hasattr(torch.backends, "mps") and torch.backends.mps.is_available() xpu_available = hasattr(torch, "xpu") and torch.xpu.is_available() device = torch.device( "cuda" if torch.cuda.is_available() else "xpu" if xpu_available else "cpu" ) torch_device = device torch_dtype = torch.float16 print(f"SAFETY_CHECKER: {SAFETY_CHECKER}") print(f"TORCH_COMPILE: {TORCH_COMPILE}") print(f"device: {device}") if mps_available: device = torch.device("mps") torch_device = "cpu" torch_dtype = torch.float32 if SAFETY_CHECKER == "True": i2i_pipe = AutoPipelineForImage2Image.from_pretrained( "stabilityai/sdxl-turbo", torch_dtype=torch_dtype, variant="fp16" if torch_dtype == torch.float16 else "fp32", ) t2i_pipe = AutoPipelineForText2Image.from_pretrained( "stabilityai/sdxl-turbo", torch_dtype=torch_dtype, variant="fp16" if torch_dtype == torch.float16 else "fp32", ) else: i2i_pipe = AutoPipelineForImage2Image.from_pretrained( "stabilityai/sdxl-turbo", safety_checker=None, torch_dtype=torch_dtype, variant="fp16" if torch_dtype == torch.float16 else "fp32", ) t2i_pipe = AutoPipelineForText2Image.from_pretrained( "stabilityai/sdxl-turbo", safety_checker=None, torch_dtype=torch_dtype, variant="fp16" if torch_dtype == torch.float16 else "fp32", ) t2i_pipe.to(device=torch_device, dtype=torch_dtype).to(device) t2i_pipe.set_progress_bar_config(disable=True) i2i_pipe.to(device=torch_device, dtype=torch_dtype).to(device) i2i_pipe.set_progress_bar_config(disable=True) def resize_crop(image, size=512): image = image.convert("RGB") w, h = image.size image = image.resize((size, int(size * (h / w))), Image.BICUBIC) return image async def predict(init_image, prompt, strength, steps, seed=1231231): if init_image is not None: init_image = resize_crop(init_image) generator = torch.manual_seed(seed) last_time = time.time() if int(steps * strength) < 1: steps = math.ceil(1 / max(0.10, strength)) results = i2i_pipe( prompt=prompt, image=init_image, generator=generator, num_inference_steps=steps, guidance_scale=0.0, strength=strength, width=512, height=512, output_type="pil", ) else: generator = torch.manual_seed(seed) last_time = time.time() results = t2i_pipe( prompt=prompt, generator=generator, num_inference_steps=steps, guidance_scale=0.0, width=512, height=512, output_type="pil", ) print(f"Pipe took {time.time() - last_time} seconds") nsfw_content_detected = ( results.nsfw_content_detected[0] if "nsfw_content_detected" in results else False ) if nsfw_content_detected: gr.Warning("NSFW content detected.") return Image.new("RGB", (512, 512)) return results.images[0] css = """ #container{ margin: 0 auto; max-width: 80rem; } #intro{ max-width: 100%; text-align: center; margin: 0 auto; } """ with gr.Blocks(css=css) as demo: init_image_state = gr.State() with gr.Column(elem_id="container"): gr.Markdown( """# SDXL Turbo Image to Image/Text to Image ## Unofficial Demo SDXL Turbo model can generate high quality images in a single pass read more on [stability.ai post](https://stability.ai/news/stability-ai-sdxl-turbo). **Model**: https://huggingface.co/stabilityai/sdxl-turbo """, elem_id="intro", ) with gr.Row(): prompt = gr.Textbox( placeholder="Insert your prompt here:", scale=5, container=False, ) generate_bt = gr.Button("Generate", scale=1) with gr.Row(): with gr.Column(): image_input = gr.Image( sources=["upload", "webcam", "clipboard"], label="Webcam", type="pil", ) with gr.Column(): image = gr.Image(type="filepath") with gr.Accordion("Advanced options", open=False): strength = gr.Slider( label="Strength", value=0.7, minimum=0.0, maximum=1.0, step=0.001, ) steps = gr.Slider( label="Steps", value=2, minimum=1, maximum=10, step=1 ) seed = gr.Slider( randomize=True, minimum=0, maximum=12013012031030, label="Seed", step=1, ) with gr.Accordion("Run with diffusers"): gr.Markdown( """## Running SDXL Turbo with `diffusers` ```bash pip install diffusers==0.23.1 ``` ```py from diffusers import DiffusionPipeline pipe = DiffusionPipeline.from_pretrained( "stabilityai/sdxl-turbo" ).to("cuda") results = pipe( prompt="A cinematic shot of a baby racoon wearing an intricate italian priest robe", num_inference_steps=1, guidance_scale=0.0, ) imga = results.images[0] imga.save("image.png") ``` """ ) inputs = [image_input, prompt, strength, steps, seed] generate_bt.click(fn=predict, inputs=inputs, outputs=image, show_progress=False) prompt.input(fn=predict, inputs=inputs, outputs=image, show_progress=False) steps.change(fn=predict, inputs=inputs, outputs=image, show_progress=False) seed.change(fn=predict, inputs=inputs, outputs=image, show_progress=False) strength.change(fn=predict, inputs=inputs, outputs=image, show_progress=False) image_input.change( fn=lambda x: x, inputs=image_input, outputs=init_image_state, show_progress=False, queue=False, ) demo.queue() demo.launch()