first

.gitignore

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+venv/
+__pycache__/
+*.py[cod]

app.py

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+from diffusers import DiffusionPipeline
+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
+
+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":
+    pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", revision="pr/4")
+else:
+    pipe = DiffusionPipeline.from_pretrained(
+        "stabilityai/sdxl-turbo", revision="pr/4", safety_checker=None
+    )
+
+
+pipe.to(device=torch_device, dtype=torch_dtype).to(device)
+pipe.unet.to(memory_format=torch.channels_last)
+pipe.set_progress_bar_config(disable=True)
+
+
+def predict(prompt, steps, seed=1231231):
+    generator = torch.manual_seed(seed)
+    last_time = time.time()
+    results = pipe(
+        prompt=prompt,
+        generator=generator,
+        num_inference_steps=steps,
+        guidance_scale=0.0,
+        width=512,
+        height=512,
+        # original_inference_steps=params.lcm_steps,
+        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: 40rem;
+}
+#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(
+            """# SDXL Turbo - 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():
+            with gr.Row():
+                prompt = gr.Textbox(
+                    placeholder="Insert your prompt here:", scale=5, container=False
+                )
+                generate_bt = gr.Button("Generate", scale=1)
+
+        image = gr.Image(type="filepath")
+        with gr.Accordion("Advanced options", open=False):
+            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", revision="refs/pr/4"
+            ).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 = [prompt, 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)
+
+demo.queue()
+demo.launch()

requirements.txt

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+diffusers==0.23.1
+transformers
+gradio==4.7.1
+--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
+xformers
+hf_transfer