Update app.py

app.py

@@ -1,68 +1,104 @@
-import gradio as gr
-import numpy as np
+import os
 import random
+import uuid
+import json
 
-import spaces #[uncomment to use ZeroGPU]
-from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
+import gradio as gr
+import numpy as np
+from PIL import Image
+import spaces
 import torch
+from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
 
-device = "cuda"
-model_repo_id = "anon4ik/noobaiXLNAIXL_epsilonPred05Version"  # Replace to the model you would like to use
-
-pipe = StableDiffusionXLPipeline.from_pretrained(model_repo_id, torch_dtype=torch.float16)
-pipe = pipe.to(device)
+if not torch.cuda.is_available():
+    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo may not work on CPU.</p>"
 
 MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-
-@spaces.GPU #[uncomment to use ZeroGPU]
-def infer(
-    prompt,
-    negative_prompt,
-    seed,
-    randomize_seed,
-    width,
-    height,
-    guidance_scale,
-    num_inference_steps,
-    progress=gr.Progress(track_tqdm=True),
-):
+CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES", "1") == "1"
+MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "4096"))
+USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE", "0") == "1"
+ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "0") == "1"
+
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+if torch.cuda.is_available():
+    pipe = StableDiffusionXLPipeline.from_pretrained(
+        "anon4ik/noobaiXLNAIXL_epsilonPred05Version",
+        torch_dtype=torch.float16,
+        use_safetensors=True,
+        add_watermarker=False
+    )
+    pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
+    pipe.to("cuda")
+
+
+def save_image(img):
+    unique_name = str(uuid.uuid4()) + ".png"
+    img.save(unique_name)
+    return unique_name
+
+def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
+    return seed
+
+@spaces.GPU(queue=False,duration=30)
+def generate(
+    prompt: str,
+    negative_prompt: str = "",
+    use_negative_prompt: bool = False,
+    seed: int = 1,
+    width: int = 1024,
+    height: int = 1024,
+    guidance_scale: float = 3,
+    num_inference_steps: int = 30,
+    randomize_seed: bool = False,
+    use_resolution_binning: bool = True,
+    progress=gr.Progress(track_tqdm=True),
+):
+    pipe.to(device)
+    seed = int(randomize_seed_fn(seed, randomize_seed))
+    generator = torch.Generator().manual_seed(seed)   
 
-    generator = torch.Generator().manual_seed(seed)
+    options = {
+        "prompt":prompt,
+        "negative_prompt":negative_prompt,
+        "width":width,
+        "height":height,
+        "guidance_scale":guidance_scale,
+        "num_inference_steps":num_inference_steps,
+        "generator":generator,
+        "use_resolution_binning":use_resolution_binning,
+        "output_type":"pil",
 
-    image = pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        width=width,
-        height=height,
-        generator=generator,
-    ).images[0]
+    }
+    
+    images = pipe(**options).images
 
-    return image, seed
+    image_paths = [save_image(img) for img in images]
+    return image_paths, seed
 
 
 examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
+    "a cat eating a piece of cheese",
+    "a ROBOT riding a BLUE horse on Mars, photorealistic, 4k",
+    "Ironman VS Hulk, ultrarealistic",
+    "Astronaut in a jungle, cold color palette, oil pastel, detailed, 8k",
+    "An alien holding sign board contain word 'Flash', futuristic, neonpunk",
+    "Kids going to school, Anime style"
 ]
 
-css = """
-#col-container {
-    margin: 0 auto;
-    max-width: 640px;
+css = '''
+.gradio-container{max-width: 560px !important}
+h1{text-align:center}
+footer {
+    visibility: hidden
 }
-"""
-
+'''
 with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
-
+    gr.Markdown("""# SDXL Flash
+        ### First Image processing takes time then images generate faster.""")
+    with gr.Group():
         with gr.Row():
             prompt = gr.Text(
                 label="Prompt",
@@ -71,79 +107,94 @@ with gr.Blocks(css=css) as demo:
                 placeholder="Enter your prompt",
                 container=False,
             )
-
-            run_button = gr.Button("Run", scale=0, variant="primary")
-
-        result = gr.Image(label="Result", show_label=False)
-
-        with gr.Accordion("Advanced Settings", open=True):
+            run_button = gr.Button("Run", scale=0)
+        result = gr.Gallery(label="Result", columns=1)
+    with gr.Accordion("Advanced options", open=False):
+        with gr.Row():
+            use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=True)
             negative_prompt = gr.Text(
                 label="Negative prompt",
-                max_lines=1,
+                max_lines=5,
+                lines=4,
                 placeholder="Enter a negative prompt",
-                visible=False,
+                value="(deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers:1.4), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation, NSFW",
+                visible=True,
             )
-
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
+        seed = gr.Slider(
+            label="Seed",
+            minimum=0,
+            maximum=MAX_SEED,
+            step=1,
+            value=0,
+        )
+        randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+        with gr.Row(visible=True):
+            width = gr.Slider(
+                label="Width",
+                minimum=512,
+                maximum=MAX_IMAGE_SIZE,
+                step=64,
+                value=1024,
+            )
+            height = gr.Slider(
+                label="Height",
+                minimum=512,
+                maximum=MAX_IMAGE_SIZE,
+                step=64,
+                value=1024,
+            )
+        with gr.Row():
+            guidance_scale = gr.Slider(
+                label="Guidance Scale",
+                minimum=0.1,
+                maximum=6,
+                step=0.1,
+                value=3.0,
+            )
+            num_inference_steps = gr.Slider(
+                label="Number of inference steps",
+                minimum=1,
+                maximum=15,
                 step=1,
-                value=0,
+                value=8,
             )
 
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-
-            with gr.Row():
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=512,  # Replace with defaults that work for your model
-                )
-
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=768,  # Replace with defaults that work for your model
-                )
-
-            with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=1,  # Replace with defaults that work for your model
-                )
-
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=28,  # Replace with defaults that work for your model
-                )
-
-        gr.Examples(examples=examples, inputs=[prompt])
+    gr.Examples(
+        examples=examples,
+        inputs=prompt,
+        outputs=[result, seed],
+        fn=generate,
+        cache_examples=CACHE_EXAMPLES,
+    )
+
+    use_negative_prompt.change(
+        fn=lambda x: gr.update(visible=x),
+        inputs=use_negative_prompt,
+        outputs=negative_prompt,
+        api_name=False,
+    )
+
     gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn=infer,
+        triggers=[
+            prompt.submit,
+            negative_prompt.submit,
+            run_button.click,
+        ],
+        fn=generate,
         inputs=[
             prompt,
             negative_prompt,
+            use_negative_prompt,
             seed,
-            randomize_seed,
             width,
             height,
             guidance_scale,
             num_inference_steps,
+            randomize_seed,
         ],
         outputs=[result, seed],
+        api_name="run",
     )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.queue(max_size=20).launch()