Delete app.py

app.py

@@ -1,101 +0,0 @@
-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],
-}
-
-
-# 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")
-        
-    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()