Create app.py

app.py

@@ -0,0 +1,215 @@
+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()