Update app.py

app.py

@@ -1,70 +1,119 @@
+import os
+import random
+import uuid
+import json
+
 import gradio as gr
 import numpy as np
-import random
-from diffusers import DiffusionPipeline
-from diffusers import StableDiffusionXLPipeline, DPMSolverSinglestepScheduler
-import torch
+from PIL import Image
 import spaces
+import torch
+from diffusers import DiffusionPipeline
 
-device = "cuda"
-
-torch.cuda.max_memory_allocated(device=device)
-pipe = StableDiffusionXLPipeline.from_pretrained("sd-community/sdxl-flash")
-pipe = pipe.to(device)
-pipe.scheduler = DPMSolverSinglestepScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
+DESCRIPTION = """# SDXL Flash"""
+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 = 4096
-
-@spaces.GPU(duration=120, queue=False)
-def infer(
-        prompt: str,
-        negative_prompt: str = "",
-        seed: int = 24,
-        randomize_seed: bool = False,
-        width: int = 1024,
-        height: int = 1024,
-        guidance_scale = 3,
-        num_inference_steps: int = 9,
-        progress=gr.Progress(track_tqdm=True)):
+CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES", "0") == "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")
+
+NUM_IMAGES_PER_PROMPT = 1
+
+if torch.cuda.is_available():
+    pipe = DiffusionPipeline.from_pretrained(
+        "sd-community/sdxl-flash",
+        torch_dtype=torch.float16,
+        use_safetensors=True,
+        add_watermarker=False,
+        variant="fp16"
+    )
+    if ENABLE_CPU_OFFLOAD:
+        pipe.enable_model_cpu_offload()
+    else:
+        pipe.to(device)       
+        print("Loaded on Device!")
+    
+    if USE_TORCH_COMPILE:
+        pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
+        pipe2.unet = torch.compile(pipe2.unet, mode="reduce-overhead", fullgraph=True)
+        print("Model Compiled!")
+
+
+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)        
-    generator = torch.Generator().manual_seed(seed)    
-    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]     
-    return image
+        seed = random.randint(0, MAX_SEED)
+    return seed
+
+@spaces.GPU(enable_queue=False)
+def generate(
+    prompt: str,
+    negative_prompt: str = "",
+    use_negative_prompt: bool = False,
+    seed: int = 0,
+    width: int = 1024,
+    height: int = 1024,
+    guidance_scale: float = 3,
+    num_inference_steps: int = 9,
+    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)
+
+    if not use_negative_prompt:
+        negative_prompt = ""  # type: ignore
+    negative_prompt += default_negative    
+
+    options = {
+        "prompt":prompt,
+        "negative_prompt":negative_prompt,
+        "width":width,
+        "height":height,
+        "guidance_scale":guidance_scale,
+        "num_inference_steps":num_inference_steps,
+        "generator":generator,
+        "num_images_per_prompt":NUM_IMAGES_PER_PROMPT,
+        "use_resolution_binning":use_resolution_binning,
+        "output_type":"pil",
+
+    }
+    
+    images = pipe(**options).images
+
+    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",
-    "An alien grasping a sign board contain word 'Flash'",
-    "Kids going to school, Anime style"
+    "neon holography crystal cat",
+    "a cat eating a piece of cheese",
+    "an astronaut riding a horse in space",
+    "a cartoon of a boy playing with a tiger",
+    "a cute robot artist painting on an easel, concept art",
+    #"a close up of a woman wearing a transparent, prismatic, elaborate nemeses headdress, over the should pose, brown skin-tone"
 ]
 
 css = '''
 .gradio-container{max-width: 560px !important}
 h1{text-align:center}
-footer {
-    visibility: hidden
-}
 '''
-
-with gr.Blocks(title="SDXL Flash", css=css) as demo:
-    with gr.Column():
-        gr.Markdown("""# SDXL Flash
-        ### Super fast text to Image Generator.
-        ### <span style='color: red;'>You may change the steps from 5 to 8 or 10, if you didn't get satisfied results.
-        ### First Image processing takes time then images generate faster.""")    
+with gr.Blocks(css=css) as demo:
+    gr.Markdown(DESCRIPTION)
+    with gr.Group():
         with gr.Row():
-            
             prompt = gr.Text(
                 label="Prompt",
                 show_label=False,
@@ -72,79 +121,93 @@ with gr.Blocks(title="SDXL Flash", css=css) as demo:
                 placeholder="Enter your prompt",
                 container=False,
             )
-            
             run_button = gr.Button("Run", scale=0)
-        
-        result = gr.Image(label="Result")
-
-        with gr.Accordion("Advanced Settings", open=False):
-            
+        result = gr.Gallery(label="Result", columns=NUM_IMAGES_PER_PROMPT, show_label=False)
+    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=5,
-                lines=4,
+                max_lines=1,
                 placeholder="Enter a negative prompt",
-                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",
+                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,
+            step=1,
+            value=0,
+        )
+        randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+        with gr.Row(visible=True):
+            width = gr.Slider(
+                label="Width",
+                minimum=256,
+                maximum=MAX_IMAGE_SIZE,
+                step=32,
+                value=1024,
+            )
+            height = gr.Slider(
+                label="Height",
+                minimum=256,
+                maximum=MAX_IMAGE_SIZE,
+                step=32,
+                value=1024,
             )
-            
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
+        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=5,
             )
-            
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-            
-            with gr.Row():
-                
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=8,
-                    value=1024,
-                )
-                
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=8,
-                    value=1024,
-                )
-            
-            with gr.Row():
-                
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=1.0,
-                    maximum=6.0,
-                    step=0.1,
-                    value=3.0,
-                )
-                
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=15,
-                    step=1,
-                    value=5,
-                )
-        
-        gr.Examples(
-            examples = examples,
-            inputs = prompt,
-            outputs = result,
-            fn=infer,
-            cache_examples=True
-        )
 
-    run_button.click(
-        fn = infer,
-        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
-        outputs = [result]
+    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=[
+            prompt.submit,
+            negative_prompt.submit,
+            run_button.click,
+        ],
+        fn=generate,
+        inputs=[
+            prompt,
+            negative_prompt,
+            use_negative_prompt,
+            seed,
+            width,
+            height,
+            guidance_scale,
+            num_inference_steps,
+            randomize_seed,
+        ],
+        outputs=[result, seed],
+        api_name="run",
     )
 
-demo.queue(max_size=20).launch()
+if __name__ == "__main__":
+    demo.queue(max_size=20).launch()