hyper sdxl

app.py

@@ -5,17 +5,11 @@ import tempfile
 import numpy as np
 from pathlib import Path
 from PIL import Image
-from diffusers import (
- ControlNetModel,
- StableDiffusionXLControlNetPipeline,
- UNet2DConditionModel,
- EulerDiscreteScheduler,
-)
+from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, TCDScheduler
 import spaces
 import gradio as gr
 from huggingface_hub import hf_hub_download, snapshot_download
 from ip_adapter import IPAdapterXL
-from safetensors.torch import load_file
 
 snapshot_download(
  repo_id="h94/IP-Adapter", allow_patterns="sdxl_models/*", local_dir="."
@@ -36,7 +30,7 @@ controlnet = ControlNetModel.from_pretrained(
  controlnet_path, use_safetensors=False, torch_dtype=torch.float16
 ).to(device)
 
-# load SDXL lightnining
+# load Hyper SD
 
 pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
  base_model_path,
@@ -46,17 +40,11 @@ pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
  add_watermarker=False,
 ).to(device)
 pipe.set_progress_bar_config(disable=True)
-pipe.scheduler = EulerDiscreteScheduler.from_config(
- pipe.scheduler.config, timestep_spacing="trailing", prediction_type="epsilon"
-)
-pipe.unet.load_state_dict(
- load_file(
- hf_hub_download(
- "ByteDance/SDXL-Lightning", "sdxl_lightning_2step_unet.safetensors"
- ),
- device="cuda",
- )
+pipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config)
+pipe.load_lora_weights(
+ hf_hub_download("ByteDance/Hyper-SD", "Hyper-SDXL-1step-lora.safetensors")
 )
+eta = 1.0
 
 # load ip-adapter
 # target_blocks=["block"] for original IP-Adapter
@@ -225,6 +213,7 @@ def create_image(
  controlnet_conditioning_scale=float(control_scale),
  neg_content_prompt=neg_content_prompt,
  neg_content_scale=neg_content_scale,
+ eta=1.0,
  )
  else:
  images = ip_model.generate(
@@ -238,6 +227,7 @@ def create_image(
  seed=seed,
  image=canny_map,
  controlnet_conditioning_scale=float(control_scale),
+ eta=1.0,
  )
  image = images[0]
  with tempfile.NamedTemporaryFile(suffix=".jpg", delete=False) as tmpfile:
@@ -258,7 +248,7 @@ title = r"""
 
 description = r"""
 <b>Forked from <a href='https://github.com/InstantStyle/InstantStyle' target='_blank'>InstantStyle: Free Lunch towards Style-Preserving in Text-to-Image Generation</a>.<br>
-<b>Model by <a href='https://huggingface.co/ByteDance/SDXL-Lightning' target='_blank'>SDXL Lightning</a> and <a href='https://huggingface.co/h94/IP-Adapter' target='_blank'>IP-Adapter</a>.</b><br>
+<b>Model by <a href='https://huggingface.co/ByteDance/Hyper-SD' target='_blank'>Hyper-SD</a> and <a href='https://huggingface.co/h94/IP-Adapter' target='_blank'>IP-Adapter</a>.</b><br>
 """
 
 article = r"""
@@ -347,10 +337,10 @@ with block:
  label="guidance scale",
  )
  num_inference_steps = gr.Slider(
- minimum=2,
- maximum=50.0,
+ minimum=1,
+ maximum=10.0,
  step=1.0,
- value=2,
+ value=1,
  label="num inference steps",
  )
  seed = gr.Slider(
@@ -390,6 +380,10 @@ with block:
  scale.input,
  control_scale.input,
  seed.input,
+ num_inference_steps.input,
+ target.input,
+ neg_content_prompt.input,
+ neg_content_scale.input,
  ],
  fn=create_image,
  inputs=inputs,