first

.gitignore

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+__pycache__
+venv

app.py

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+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",
+    token=HF_TOKEN,
+)
+
+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: 32rem;
+    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 LoRAs borrowed from Latent Consistency Models
+
+            """,
+            elem_id="intro",
+        )
+        with gr.Row():
+            with gr.Row():
+                prompt = gr.Textbox(
+                    placeholder="Insert your prompt here", scale=5, container=False
+                )
+                generate_bt = gr.Button("Generate", scale=1)
+        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"
+            )
+        image = gr.Image(type="filepath")
+
+        inputs = [prompt, guidance, steps, seed]
+        generate_bt.click(fn=predict, inputs=inputs, outputs=image)
+
+demo.queue()
+demo.launch()

requirements.txt

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+# diffusers==0.22.2
+git+https://github.com/huggingface/diffusers.git@6110d7c95f630479cf01340cc8a8141c1e359f09
+transformers==4.34.1
+gradio==4.1.2
+--extra-index-url https://download.pytorch.org/whl/cu121
+torch==2.1.0
+fastapi==0.104.0
+uvicorn==0.23.2
+Pillow==10.1.0
+accelerate==0.24.0
+compel==2.0.2
+controlnet-aux==0.0.7
+peft==0.6.0
+bitsandbytes