revert commit 65ec9a5

app.py

@@ -7,8 +7,6 @@ import numpy as np
 import PIL
 import spaces
 import torch
-
-import diffusers
 from diffusers.models import ControlNetModel
 from diffusers.utils import load_image
 from insightface.app import FaceAnalysis
@@ -55,11 +53,6 @@ pipe = StableDiffusionXLInstantIDPipeline.from_pretrained(
     safety_checker=None,
     feature_extractor=None,
 )
-
-# load and disable LCM
-pipe.load_lora_weights("latent-consistency/lcm-lora-sdxl")
-pipe.disable_lora()
-
 pipe.cuda()
 pipe.load_ip_adapter_instantid(face_adapter)
 pipe.image_proj_model.to("cuda")
@@ -214,14 +207,9 @@ def generate_image(
     seed,
     progress=gr.Progress(track_tqdm=True),
 ):
-
     if prompt is None:
         prompt = "a person"
 
-    # LCM Sceduler Callback
-    pipe.scheduler = diffusers.LCMScheduler.from_config(pipe.scheduler.config)
-    pipe.enable_lora()
-
     # apply the style template
     prompt, negative_prompt = apply_style(style_name, prompt, negative_prompt)
 
@@ -296,7 +284,6 @@ title = r"""
 
 description = r"""
 <b>Official 🤗 Gradio demo</b> for <a href='https://github.com/InstantID/InstantID' target='_blank'><b>InstantID: Zero-shot Identity-Preserving Generation in Seconds</b></a>.<br>
-
 How to use:<br>
 1. Upload a person image. For multiple person images, we will only detect the biggest face. Make sure face is not too small and not significantly blocked or blurred.
 2. (Optionally) upload another person image as reference pose. If not uploaded, we will use the first person image to extract landmarks. If you use a cropped face at step1, it is recommeneded to upload it to extract a new pose.
@@ -383,17 +370,17 @@ with gr.Blocks(css=css) as demo:
                 )
                 num_steps = gr.Slider(
                     label="Number of sample steps",
-                    minimum=2,
-                    maximum=20,
+                    minimum=20,
+                    maximum=100,
                     step=1,
-                    value=4,
+                    value=30,
                 )
                 guidance_scale = gr.Slider(
                     label="Guidance scale",
-                    minimum=1.0,
-                    maximum=2.0,
+                    minimum=0.1,
+                    maximum=10.0,
                     step=0.1,
-                    value=1.0,
+                    value=5,
                 )
                 seed = gr.Slider(
                     label="Seed",