Update requirements.txt

requirements.txt

@@ -1,58 +0,0 @@
-import torch
-from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler
-from PIL import Image
-import gradio as gr
-import random
-import utils
-
-class Model:
-    def __init__(self, name, path="", prefix=""):
-        self.name = name
-        self.path = path
-        self.prefix = prefix
-
-class DiffusionApp:
-    def __init__(self, models):
-        self.models = models
-        self.current_model = self.models[0]
-        self.current_steps = 25
-        self.is_colab = utils.is_google_colab()
-        self.device = "cuda" if torch.cuda.is_available() else "cpu"
-        self.pipe = self.initialize_pipe(self.current_model.path)
-
-    def initialize_pipe(self, model_path):
-        pipe = StableDiffusionPipeline.from_pretrained(
-            model_path,
-            torch_dtype=torch.float16,
-            scheduler=DPMSolverMultistepScheduler.from_pretrained(model_path, subfolder="scheduler")
-        )
-        pipe = pipe.to(self.device)
-        if self.device == "cuda":
-            pipe.enable_xformers_memory_efficient_attention()
-        return pipe
-
-    def update_model(self, model_name):
-        for model in self.models:
-            if model.name == model_name:
-                self.current_model = model
-                self.pipe = self.initialize_pipe(self.current_model.path)
-
-    def inference(self, model_name, prompt, guidance, steps, n_images=1, width=512, height=512, seed=0, img=None, strength=0.5, neg_prompt=""):
-        self.update_model(model_name)
-        # Rest of your inference code here...
-
-# Initialize your models
-models = [
-    Model("Vivid Watercolors", "Evel/VividWatercolors", "watercolor style"),
-    Model("Loving Vincent (Van Gogh)", "dallinmackay/Van-Gogh-diffusion", "lvngvncnt "),
-    # Add the rest of your models here...
-]
-
-# Initialize your app
-app = DiffusionApp(models)
-
-# Use your app in your Gradio interface
-# For example:
-inputs = [gr.Dropdown(label="Model", choices=[m.name for m in app.models], value=app.current_model.name)]
-outputs = [gr.Image(height=512)]
-gr.Interface(app.inference, inputs, outputs).launch()