Create app.py

app.py

@@ -0,0 +1,53 @@
+import gradio as gr
+from diffusers import StableDiffusionXLPipeline, UNet2DConditionModel, EulerDiscreteScheduler, LCMScheduler
+import torch
+from huggingface_hub import hf_hub_download
+from safetensors.torch import load_file
+import spaces
+
+### SDXL Turbo #### 
+
+pipe_turbo = StableDiffusionXLPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16")
+pipe_turbo.to("cuda")
+
+### SDXL Lightning ### 
+
+base = "stabilityai/stable-diffusion-xl-base-1.0"
+repo = "ByteDance/SDXL-Lightning"
+ckpt = "sdxl_lightning_1step_unet_x0.safetensors" 
+
+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_lightning = StableDiffusionXLPipeline.from_pretrained(base, unet=unet, torch_dtype=torch.float16, variant="fp16").to("cuda")
+
+pipe_lightning.scheduler = EulerDiscreteScheduler.from_config(pipe_lightning.scheduler.config, timestep_spacing="trailing", prediction_type="sample")
+pipe_lightning.to("cuda")
+
+### Hyper SDXL ### 
+repo_name = "ByteDance/Hyper-SD"
+ckpt_name = "Hyper-SDXL-1step-Unet.safetensors"
+
+unet = UNet2DConditionModel.from_config(base, subfolder="unet").to("cuda", torch.float16)
+unet.load_state_dict(load_file(hf_hub_download(repo_name, ckpt_name), device="cuda"))
+pipe_hyper = DiffusionPipeline.from_pretrained(base_model_id, unet=unet, torch_dtype=torch.float16, variant="fp16").to("cuda")
+pipe_hyper.scheduler = LCMScheduler.from_config(pipe_hyper.scheduler.config)
+pipe_hyper.to("cuda")
+
+def run(prompt):
+    image_turbo=pipe_turbo(prompt=prompt, num_inference_steps=1, guidance_scale=0).images[0]
+    image_lightning=pipe_lightning(prompt=prompt, num_inference_steps=1, guidance_scale=0).images[0]
+    image_hyper=pipe_hyper(prompt=prompt, num_inference_steps=1, guidance_scale=0, timesteps=[800]).images[0]
+    return image_turbo, image_lightning, image_hyper
+css = '''
+.gradio-container{max-width: 768px !important}
+'''
+
+@spaces.GPU
+with gr.Blocks(css=css) as demo:
+    prompt = gr.Textbox(label="Prompt")
+    run = gr.Button("Run")
+    with gr.Row():
+        image_turbo = gr.Image(label="SDXL Turbo")
+        image_lightning = gr.Image(label="SDXL Lightning")
+        image_hyper = gr.Image("Hyper SDXL")
+    run.click(fn=run, inputs=prompt, outputs=[image_turbo, image_lightning, image_hyper])