Create backup.app.py

backup.app.py

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+import gradio as gr
+from diffusers import DiffusionPipeline, LCMScheduler, AutoencoderTiny
+import torch
+import os
+import datetime
+import time
+from PIL import Image
+import re
+import base64
+from io import BytesIO
+import pytz
+
+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("Lykon/dreamshaper-7")
+else:
+    pipe = DiffusionPipeline.from_pretrained("Lykon/dreamshaper-7", 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)
+pipe.set_progress_bar_config(disable=True)
+
+# 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("latent-consistency/lcm-lora-sdv1-5")
+pipe.fuse_lora()
+
+def safe_filename(text):
+    """Generate a safe filename from a string."""
+    safe_text = re.sub(r'\W+', '_', text)
+    timestamp = datetime.datetime.now().strftime("%Y%m%d")
+    return f"{safe_text}_{timestamp}.png"
+    
+def encode_image(image):
+    """Encode image to base64."""
+    buffered = BytesIO()
+    #image.save(buffered, format="PNG")
+    return base64.b64encode(buffered.getvalue()).decode()
+    
+def predict(prompt, guidance, steps, seed=1231231):
+    generator = torch.manual_seed(seed)
+    last_time = time.time()
+    results = pipe(
+        prompt=prompt,
+        generator=generator,
+        num_inference_steps=steps,
+        guidance_scale=guidance,
+        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:
+        nsfw=gr.Button("🕹️NSFW🎨", scale=1)
+
+    # Generate file name
+    #date_str = datetime.datetime.now().strftime("%Y%m%d")
+    #safe_prompt = prompt.replace(" ", "_")[:50]  # Truncate long prompts
+    #filename = f"{date_str}_{safe_prompt}.png"
+
+    central = pytz.timezone('US/Central')
+    safe_date_time = datetime.datetime.now().strftime("%Y%m%d")
+    replaced_prompt = prompt.replace(" ", "_").replace("\n", "_")
+    safe_prompt = "".join(x for x in replaced_prompt if x.isalnum() or x == "_")[:90]
+    filename = f"{safe_date_time}_{safe_prompt}.png"
+    
+
+    # Save the image
+    if len(results.images) > 0:
+        image_path = os.path.join("", filename)  # Specify your directory
+        results.images[0].save(image_path)
+        print(f"#Image saved as {image_path}")
+        #filename = safe_filename(prompt)
+        #image.save(filename)
+        encoded_image = encode_image(image)
+        html_link = f'<a href="data:image/png;base64,{encoded_image}" download="{filename}">Download Image</a>'
+        gr.Markdown(html_link)
+    
+
+
+    return results.images[0] if len(results.images) > 0 else None
+
+
+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(
+            """## 🕹️ Stable Diffusion 1.5 - Real Time 🎨 Image Generation Using 🌐 Latent Consistency LoRAs""",
+            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):
+            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.Accordion("Run with diffusers"):
+            gr.Markdown(
+                """## Running LCM-LoRAs it with `diffusers`
+            ```bash
+            pip install diffusers==0.23.0
+            ```
+            
+            ```py
+            from diffusers import DiffusionPipeline, LCMScheduler
+            pipe = DiffusionPipeline.from_pretrained("Lykon/dreamshaper-7").to("cuda") 
+            pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
+            pipe.load_lora_weights("latent-consistency/lcm-lora-sdv1-5") #yes, it's a normal LoRA
+            results = pipe(
+                prompt="ImageEditor",
+                num_inference_steps=4,
+                guidance_scale=0.0,
+            )
+            results.images[0]
+            ```
+            """
+            )
+
+        inputs = [prompt, guidance, 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)
+        guidance.change(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()