add lora and edit some parameters

app.py

@@ -2,12 +2,14 @@ import gradio as gr
 import numpy as np
 import random
 
-# import spaces #[uncomment to use ZeroGPU]
+import spaces #[uncomment to use ZeroGPU]
 from diffusers import DiffusionPipeline
 import torch
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
+model_repo_id = "black-forest-labs/FLUX.1-schnell"  # Replace to the model you would like to use
+lora_path = "matteomarjanovic/flatsketcher"
+weigths_file = "lora.safetensors"
 
 if torch.cuda.is_available():
     torch_dtype = torch.float16
@@ -16,12 +18,13 @@ else:
 
 pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
 pipe = pipe.to(device)
+pipe.load_lora_weights(lora_path, weight_name=weigths_file)
 
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
 
 
-# @spaces.GPU #[uncomment to use ZeroGPU]
+@spaces.GPU #[uncomment to use ZeroGPU]
 def infer(
     prompt,
     negative_prompt,
@@ -41,11 +44,11 @@ def infer(
     image = pipe(
         prompt=prompt,
         negative_prompt=negative_prompt,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        width=width,
-        height=height,
-        generator=generator,
+        guidance_scale=0.,
+        num_inference_steps=4,
+        width=1920,
+        height=1080,
+        max_sequence_length=256,
     ).images[0]
 
     return image, seed