from diffusers import DiffusionPipeline, LCMScheduler, AutoencoderTiny from compel import Compel, ReturnedEmbeddingsType 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 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 model_id = "stabilityai/stable-diffusion-xl-base-1.0" if SAFETY_CHECKER == "True": pipe = DiffusionPipeline.from_pretrained(model_id) else: pipe = DiffusionPipeline.from_pretrained(model_id, safety_checker=None) pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config) pipe.to(device=torch_device, dtype=torch_dtype).to(device) pipe.unet.to(memory_format=torch.channels_last) # check if computer has less than 64GB of RAM using sys or os if psutil.virtual_memory().total < 64 * 1024**3: pipe.enable_attention_slicing() if TORCH_COMPILE: pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True) pipe.vae = torch.compile(pipe.vae, mode="reduce-overhead", fullgraph=True) pipe(prompt="warmup", num_inference_steps=1, guidance_scale=8.0) # Load LCM LoRA pipe.load_lora_weights( "lcm-sd/lcm-sdxl-lora", weight_name="lcm_sdxl_lora.safetensors", #adapter_name="lcm", use_auth_token=HF_TOKEN, ) ## Load papercut LoRA #pipe.load_lora_weights( # "TheLastBen/Papercut_SDXL", # weight_name="papercut.safetensors", # adapter_name="papercut", #) # Mix the LoRAs #pipe.set_adapters(["lcm", "papercut"], adapter_weights=[1.0, 0.8]) compel_proc = Compel( tokenizer=[pipe.tokenizer, pipe.tokenizer_2], text_encoder=[pipe.text_encoder, pipe.text_encoder_2], returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED, requires_pooled=[False, True], ) def predict( prompt, guidance, steps, seed=1231231, progress=gr.Progress(track_tqdm=True) ): generator = torch.manual_seed(seed) prompt_embeds, pooled_prompt_embeds = compel_proc(prompt) results = pipe( prompt_embeds=prompt_embeds, pooled_prompt_embeds=pooled_prompt_embeds, generator=generator, num_inference_steps=steps, guidance_scale=guidance, width=1024, height=1024, # original_inference_steps=params.lcm_steps, output_type="pil", ) nsfw_content_detected = ( results.nsfw_content_detected[0] if "nsfw_content_detected" in results else False ) if nsfw_content_detected: raise gr.Error("NSFW content detected.") return results.images[0] css = """ #container{ margin: 0 auto; max-width: 50rem; } #intro{ max-width: 100%; text-align: center; margin: 0 auto; } """ with gr.Blocks(css=css) as demo: with gr.Column(elem_id="container"): gr.Markdown( """# Ultra-Fast SDXL with Latent Consistency LoRA SDXL is loaded with a LCM-LoRA, giving it the super power of doing inference in as little as 4 steps. [Learn more on our blog](#) or [technical report](#). """, elem_id="intro", ) with gr.Row(): with gr.Row(): prompt = gr.Textbox( placeholder="Insert your prompt here:", value="papercut style of a cute monster", scale=5, container=False ) generate_bt = gr.Button("Generate", scale=1) image = gr.Image(type="filepath") with gr.Accordion("Advanced options", open=False): guidance = gr.Slider( label="Guidance", minimum=0.0, maximum=5, value=0.3, step=0.001 ) steps = gr.Slider(label="Steps", value=4, minimum=2, maximum=10, step=1) seed = gr.Slider( randomize=True, minimum=0, maximum=12013012031030, label="Seed", step=1 ) with gr.Group(): gr.Markdown('''## Using it with `diffusers` ```py from diffusers import DiffusionPipeline, LCMScheduler pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0").to("cuda") pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config) pipe.load_lora_weights("lcm-sd/lcm-sdxl-lora") results = pipe( prompt="The spirit of a tamagotchi wandering in the city of Vienna", num_inference_steps=4, guidance_scale=0.5, ) results.images[0] ``` ''') inputs = [prompt, guidance, steps, seed] generate_bt.click(fn=predict, inputs=inputs, outputs=image) demo.queue() demo.launch()