Update app.py

app.py

@@ -6,11 +6,10 @@ import gradio as gr
 import numpy as np
 import torch
 from PIL import Image
-from diffusers import StableDiffusionXLPipeline, EDMEulerScheduler, StableDiffusionXLInstructPix2PixPipeline, AutoencoderKL
+from diffusers import StableDiffusionXLPipeline, EDMEulerScheduler, StableDiffusionXLInstructPix2PixPipeline, AutoencoderKL, EulerAncestralDiscreteScheduler
 from custom_pipeline import CosStableDiffusionXLInstructPix2PixPipeline
 from huggingface_hub import hf_hub_download
 from huggingface_hub import InferenceClient
-from diffusers import StableDiffusion3Pipeline, SD3Transformer2DModel, FlowMatchEulerDiscreteScheduler
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
 dtype = torch.float16
@@ -19,17 +18,20 @@ vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype
 repo = "fluently/Fluently-XL-Final"
 
 pipe_best = StableDiffusionXLPipeline.from_pretrained(repo, torch_dtype=torch.float16, vae=vae)
+pipe_best.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe_best.scheduler.config)
 pipe_best.load_lora_weights("ehristoforu/dalle-3-xl-v2", weight_name="dalle-3-xl-lora-v2.safetensors", adapter_name="dalle")
 pipe_best.load_lora_weights("KingNish/Better-Image-XL-Lora", weight_name="example-03.safetensors", adapter_name="lora")
 pipe_best.set_adapters(["lora","dalle"], adapter_weights=[1.5, 0.7])
 pipe_best.to("cuda")
 
 pipe_3D = StableDiffusionXLPipeline.from_pretrained(repo, torch_dtype=torch.float16, vae=vae)
+pipe_3D.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe_3D.scheduler.config)
 pipe_3D.load_lora_weights("artificialguybr/3DRedmond-V1", weight_name="3DRedmond-3DRenderStyle-3DRenderAF.safetensors", adapter_name="3D")
 pipe_3D.set_adapters(["3D"])
 pipe_3D.to("cuda")
 
 pipe_logo = StableDiffusionXLPipeline.from_pretrained(repo, torch_dtype=torch.float16, vae=vae)
+pipe_logo.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe_logo.scheduler.config)
 pipe_logo.load_lora_weights("artificialguybr/LogoRedmond-LogoLoraForSDXL", weight_name="LogoRedmond_LogoRedAF.safetensors", adapter_name="logo")
 pipe_logo.set_adapters(["logo"])
 pipe_logo.to("cuda")
@@ -60,10 +62,11 @@ def set_timesteps_patched(self, num_inference_steps: int, device = None):
 # Image Editor
 edit_file = hf_hub_download(repo_id="stabilityai/cosxl", filename="cosxl_edit.safetensors")
 EDMEulerScheduler.set_timesteps = set_timesteps_patched
-pipe_edit = StableDiffusionXLInstructPix2PixPipeline.from_single_file(
-    edit_file, num_in_channels=8, is_cosxl_edit=True, vae=vae, torch_dtype=torch.float16,
-)
-pipe_edit.scheduler = EDMEulerScheduler(sigma_min=0.002, sigma_max=120.0, sigma_data=1.0, prediction_type="v_prediction")
+pipe_edit = StableDiffusionXLInstructPix2PixPipeline.from_single_file( edit_file, num_in_channels=8, is_cosxl_edit=True, vae=vae, torch_dtype=torch.float16,)
+pipe_edit.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe_edit.scheduler.config)
+pipe_edit.load_lora_weights("ehristoforu/dalle-3-xl-v2", weight_name="dalle-3-xl-lora-v2.safetensors", adapter_name="dalle")
+pipe_edit.load_lora_weights("KingNish/Better-Image-XL-Lora", weight_name="example-03.safetensors", adapter_name="lora")
+pipe_edit.set_adapters(["lora","dalle"], adapter_weights=[1.5, 0.7])
 pipe_edit.to("cuda")
 
 # Generator