2 steps

app.py

@@ -18,7 +18,7 @@ from sfast.compilers.diffusion_pipeline_compiler import compile, CompilationConf
 BASE = "stabilityai/stable-diffusion-xl-base-1.0"
 REPO = "ByteDance/SDXL-Lightning"
 # 1-step
-CHECKPOINT = "sdxl_lightning_1step_unet_x0.safetensors"
+CHECKPOINT = "sdxl_lightning_2step_lora.safetensors"
 
 # {
 #     "1-Step": ["sdxl_lightning_1step_unet_x0.safetensors", 1],
@@ -38,17 +38,18 @@ torch_dtype = torch.float16
 print(f"TORCH_COMPILE: {TORCH_COMPILE}")
 print(f"device: {device}")
 
-# Load model.
-unet = UNet2DConditionModel.from_config(BASE, subfolder="unet").to(device, torch_dtype)
-unet.load_state_dict(load_file(hf_hub_download(REPO, CHECKPOINT), device="cuda"))
+
 pipe = StableDiffusionXLPipeline.from_pretrained(
-    BASE, unet=unet, torch_dtype=torch_dtype, variant="fp16"
-).to(device)
+    BASE, torch_dtype=torch.float16, variant="fp16"
+).to("cuda")
+pipe.load_lora_weights(hf_hub_download(REPO, CHECKPOINT))
+pipe.fuse_lora()
 
-# Ensure sampler uses "trailing" timesteps and "sample" prediction type.
+# Ensure sampler uses "trailing" timesteps.
 pipe.scheduler = EulerDiscreteScheduler.from_config(
-    pipe.scheduler.config, timestep_spacing="trailing", prediction_type="sample"
+    pipe.scheduler.config, timestep_spacing="trailing"
 )
+
 pipe.set_progress_bar_config(disable=True)
 config = CompilationConfig.Default()
 try:
@@ -77,7 +78,7 @@ def predict(prompt, seed=1231231):
     results = pipe(
         prompt=prompt,
         generator=generator,
-        num_inference_steps=1,
+        num_inference_steps=2,
         guidance_scale=0.0,
         # width=768,
         # height=768,
@@ -110,7 +111,7 @@ with gr.Blocks(css=css) as demo:
     with gr.Column(elem_id="container"):
         gr.Markdown(
             """
-# SDXL-Lightning- Text To Image 1-Step
+# SDXL-Lightning- Text To Image 2-Steps
 **Model**: https://huggingface.co/ByteDance/SDXL-Lightning
             """,
             elem_id="intro",
@@ -132,36 +133,23 @@ with gr.Blocks(css=css) as demo:
                 """## Running SDXL-Lightning with `diffusers`
 ```py
 import torch
-from diffusers import (
-    StableDiffusionXLPipeline,
-    UNet2DConditionModel,
-    EulerDiscreteScheduler,
-)
+from diffusers import StableDiffusionXLPipeline, 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_1step_unet_x0.safetensors"  # Use the correct ckpt for your step setting!
+ckpt = "sdxl_lightning_4step_lora.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")
+pipe = StableDiffusionXLPipeline.from_pretrained(base, torch_dtype=torch.float16, variant="fp16").to("cuda")
+pipe.load_lora_weights(hf_hub_download(repo, ckpt))
+pipe.fuse_lora()
 
-# Ensure sampler uses "trailing" timesteps and "sample" prediction type.
-pipe.scheduler = EulerDiscreteScheduler.from_config(
-    pipe.scheduler.config, timestep_spacing="trailing", prediction_type="sample"
-)
+# 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=1, guidance_scale=0).images[0].save(
-    "output.png"
-)
+pipe("A girl smiling", num_inference_steps=4, guidance_scale=0).images[0].save("output.png")
 ```
             """
             )