copy

app.py

@@ -1,30 +1,111 @@
-# import gradio as gr
-# import torch
-# from diffusers import StableDiffusionXLPipeline, UNet2DConditionModel, EulerDiscreteScheduler
-# from huggingface_hub import hf_hub_download
-# from safetensors.torch import load_file
-#
-# base = "stabilityai/stable-diffusion-xl-base-1.0"
-# repo = "ByteDance/SDXL-Lightning"
-# ckpt = "sdxl_lightning_4step_unet.safetensors" # Use the correct ckpt for your step setting!
-#
-# # Load model.
-# unet = UNet2DConditionModel.from_config(base, subfolder="unet").to("cuda", torch.float16)
-# unet.load_state_dict(load_file(hf_hub_download(repo, ckpt), device="cuda"))
-# pipe = StableDiffusionXLPipeline.from_pretrained(base, unet=unet, torch_dtype=torch.float16, variant="fp16").to("cuda")
-#
-# # Ensure sampler uses "trailing" timesteps.
-# pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
-#
-# # Ensure using the same inference steps as the loaded model and CFG set to 0.
-# pipe("A girl smiling", num_inference_steps=4, guidance_scale=0).images[0].save("output.png")
+import gradio as gr
+import torch
+from diffusers import StableDiffusionXLPipeline, EulerDiscreteScheduler
+from huggingface_hub import hf_hub_download
+from safetensors.torch import load_file
+import spaces
+import os
+from PIL import Image
 
+SAFETY_CHECKER = os.environ.get("SAFETY_CHECKER", "0") == "1"
 
+# Constants
+base = "stabilityai/stable-diffusion-xl-base-1.0"
+repo = "ByteDance/SDXL-Lightning"
+checkpoints = {
+    "1-Step" : ["sdxl_lightning_1step_unet_x0.safetensors", 1],
+    "2-Step" : ["sdxl_lightning_2step_unet.safetensors", 2],
+    "4-Step" : ["sdxl_lightning_4step_unet.safetensors", 4],
+    "8-Step" : ["sdxl_lightning_8step_unet.safetensors", 8],
+}
 
 
-import gradio as gr
-def greet(name):
-    return "Hello " + name + "!!"
+# 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")
+
+if SAFETY_CHECKER:
+    from safety_checker import StableDiffusionSafetyChecker
+    from transformers import CLIPFeatureExtractor
+
+    safety_checker = StableDiffusionSafetyChecker.from_pretrained(
+        "CompVis/stable-diffusion-safety-checker"
+    ).to("cuda")
+    feature_extractor = CLIPFeatureExtractor.from_pretrained(
+        "openai/clip-vit-base-patch32"
+    )
+
+    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):
+
+    checkpoint = checkpoints[ckpt][0]
+    num_inference_steps = checkpoints[ckpt][1]
+
+    if num_inference_steps==1:
+        # Ensure sampler uses "trailing" timesteps and "sample" prediction type for 1-step inference.
+        pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing", prediction_type="sample")
+    else:
+        # Ensure sampler uses "trailing" timesteps.
+        pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+    pipe.unet.load_state_dict(load_file(hf_hub_download(repo, checkpoint), device="cuda"))
+    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
+description = """
+This demo utilizes the SDXL-Lightning model by ByteDance, which is a lightning-fast text-to-image generative model capable of producing high-quality images in 4 steps.
+As a community effort, this demo was put together by AngryPenguin. Link to model: https://huggingface.co/ByteDance/SDXL-Lightning
+"""
+
+with gr.Blocks(css="style.css") as demo:
+    gr.HTML("<h1><center>Text-to-Image with SDXL-Lightning ⚡</center></h1>")
+    gr.Markdown(description)
+    with gr.Group():
+        with gr.Row():
+            prompt = gr.Textbox(label='Enter you image prompt:', 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()
+
+
+
+
+# import gradio as gr
+# def greet(name):
+#     return "Hello " + name + "!!"
+#
+# iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+# iface.launch()

requirements.txt

@@ -0,0 +1,5 @@
+transformers
+diffusers
+torch
+accelerate
+gradio