Update app.py

app.py

@@ -1,92 +1,154 @@
 import gradio as gr
+import numpy as np
+import random
+
+import spaces #[uncomment to use ZeroGPU]
+from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
 import torch
-import spaces
-from diffusers import StableDiffusionXLPipeline, EulerDiscreteScheduler
-from huggingface_hub import hf_hub_download
-from safetensors.torch import load_file
-from PIL import Image
-
-SAFETY_CHECKER = False
-
-# Constants
-base = "stabilityai/stable-diffusion-xl-base-1.0"
-repo = "advokat/noobaiXLNAIXL_epsilonPred075"
-checkpoints = {
-    "1-Step" : ["noobaiXLNAIXL_epsilonPred075.safetensors", 1],
-}
-loaded = None
 
-# Ensure model and scheduler are initialized in GPU-enabled function
-if torch.cuda.is_available():
-    pipe = StableDiffusionXLPipeline.from_pretrained(base, torch_dtype=torch.float16, variant="fp16").to("cuda")
+device = "cuda"
+model_repo_id = "advokat/noobaiXLNAIXL_epsilonPred075"  # Replace to the model you would like to use
+
+pipe = StableDiffusionXLPipeline.from_pretrained(model_repo_id, torch_dtype=torch.float16)
+pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
+pipe.scheduler.register_to_config(
+    prediction_type="v_prediction",
+    rescale_betas_zero_snr=True,
+)
+pipe = pipe.to(device)
+
+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),
+):
+    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
+
+
+examples = [
+    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
+    "An astronaut riding a green horse",
+    "A delicious ceviche cheesecake slice",
+]
+
+css = """
+#col-container {
+    margin: 0 auto;
+    max-width: 640px;
+}
+"""
 
-if SAFETY_CHECKER:
-    from safety_checker import StableDiffusionSafetyChecker
-    from transformers import CLIPFeatureExtractor
+with gr.Blocks(css=css) as demo:
+    with gr.Column(elem_id="col-container"):
+        gr.Markdown(" # Text-to-Image Gradio Template")
 
-    safety_checker = StableDiffusionSafetyChecker.from_pretrained(
-        "CompVis/stable-diffusion-safety-checker"
-    ).to("cuda")
-    feature_extractor = CLIPFeatureExtractor.from_pretrained(
-        "openai/clip-vit-base-patch32"
+        with gr.Row():
+            prompt = gr.Text(
+                label="Prompt",
+                show_label=False,
+                max_lines=1,
+                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=False):
+            negative_prompt = gr.Text(
+                label="Negative prompt",
+                max_lines=1,
+                placeholder="Enter a negative prompt",
+                visible=False,
+            )
+
+            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():
+                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.on(
+        triggers=[run_button.click, prompt.submit],
+        fn=infer,
+        inputs=[
+            prompt,
+            negative_prompt,
+            seed,
+            randomize_seed,
+            width,
+            height,
+            guidance_scale,
+            num_inference_steps,
+        ],
+        outputs=[result, seed],
     )
 
-    def check_nsfw_images(
-        images: list[Image.Image],
-    ) -> tuple[list[Image.Image], list[bool]]:
-        safety_checker_input = feature_extractor(images, return_tensors="pt").to("cuda")
-        has_nsfw_concepts = safety_checker(
-            images=[images],
-            clip_input=safety_checker_input.pixel_values.to("cuda")
-        )
-
-        return images, has_nsfw_concepts
-
-# Function 
-@spaces.GPU(enable_queue=True)
-def generate_image(prompt, ckpt):
-    global loaded
-    print(prompt, ckpt)
-
-    checkpoint = checkpoints[ckpt][0]
-    num_inference_steps = checkpoints[ckpt][1]
-
-    if loaded != num_inference_steps:
-        pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing", prediction_type="sample" if num_inference_steps==1 else "epsilon")
-        pipe.unet.load_state_dict(load_file(hf_hub_download(repo, checkpoint), device="cuda"))
-        loaded = num_inference_steps
-        
-    results = pipe(prompt, num_inference_steps=num_inference_steps, guidance_scale=0)
-
-    if SAFETY_CHECKER:
-        images, has_nsfw_concepts = check_nsfw_images(results.images)
-        if any(has_nsfw_concepts):
-            gr.Warning("NSFW content detected.")
-            return Image.new("RGB", (512, 512))
-        return images[0]
-    return results.images[0]
-
-
-
-# Gradio Interface
-
-with gr.Blocks(css="style.css") as demo:
-    gr.HTML("<h1><center>SDXL-Lightning ⚡</center></h1>")
-    gr.HTML("<p><center>Lightning-fast text-to-image generation</center></p><p><center><a href='https://huggingface.co/ByteDance/SDXL-Lightning'>https://huggingface.co/ByteDance/SDXL-Lightning</a></center></p>")
-    with gr.Group():
-        with gr.Row():
-            prompt = gr.Textbox(label='Enter your prompt (English)', scale=8)
-            ckpt = gr.Dropdown(label='Select inference steps',choices=['1-Step', '2-Step', '4-Step', '8-Step'], value='4-Step', interactive=True)
-            submit = gr.Button(scale=1, variant='primary')
-    img = gr.Image(label='SDXL-Lightning Generated Image')
-
-    prompt.submit(fn=generate_image,
-                 inputs=[prompt, ckpt],
-                 outputs=img,
-                 )
-    submit.click(fn=generate_image,
-                 inputs=[prompt, ckpt],
-                 outputs=img,
-                 )
-    
-demo.queue().launch()
+if __name__ == "__main__":
+    demo.launch()