Update app.py

Bringing in FXL

app.py

@@ -3,28 +3,48 @@ import torch
 import numpy as np
 import modin.pandas as pd
 from PIL import Image
+from diffusers import DiffusionPipeline, StableDiffusion3Pipeline
 from huggingface_hub import hf_hub_download
-from diffusers import StableDiffusion3Pipeline
 
-device = 'cuda' #if torch.cuda.is_available() else 'cpu'
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
 torch.cuda.max_memory_allocated(device=device)
 torch.cuda.empty_cache()
-SD3 = StableDiffusion3Pipeline.from_pretrained("stabilityai/stable-diffusion-3-medium-diffusers", torch_dtype=torch.float16).to(device)
-                   
-def genie (Prompt, negative_prompt, height, width, scale, steps, seed):
+
+def genie (Model, Prompt, negative_prompt, height, width, scale, steps, seed):
     generator = np.random.seed(0) if seed == 0 else torch.manual_seed(seed)
-    torch.cuda.empty_cache()
-    image=SD3(
-    prompt=Prompt,
-    height=height,
-    width=width,
-    negative_prompt=negative_prompt,
-    guidance_scale=scale,
-    num_images_per_prompt=1,
-    num_inference_steps=steps).images[0]
+    if Model == "SD3":
+        
+        torch.cuda.empty_cache()
+        image=SD3(
+        prompt=Prompt,
+        height=height,
+        width=width,
+        negative_prompt=negative_prompt,
+        guidance_scale=scale,
+        num_images_per_prompt=1,
+        num_inference_steps=steps).images[0]
+    if Model == "FXL":
+
+        torch.cuda.empty_cache()
+        torch.cuda.max_memory_allocated(device=device)
+        pipe = DiffusionPipeline.from_pretrained("circulus/canvers-fusionXL-v1", torch_dtype=torch.float16, safety_checker=None) if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("circulus/canvers-real-v3.8.1")
+        pipe.enable_xformers_memory_efficient_attention()
+        pipe = pipe.to(device)
+        torch.cuda.empty_cache()
+
+        torch.cuda.max_memory_allocated(device=device)
+        int_image = pipe(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale, output_type="latent").images
+        pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", use_safetensors=True, torch_dtype=torch.float16, variant="fp16") if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0")
+        pipe.enable_xformers_memory_efficient_attention()
+        pipe = pipe.to(device)
+        torch.cuda.empty_cache()
+        image = pipe(Prompt, negative_prompt=negative_prompt, image=int_image, denoising_start=high_noise_frac).images[0]
+        torch.cuda.empty_cache()
+        
     return image
     
-gr.Interface(fn=genie, inputs=[gr.Textbox(label='What you want the AI to generate. 77 Token Limit.'), 
+gr.Interface(fn=genie, inputs=[gr.Radio(["SD3", "FXL"], value='SD3', label='Choose Model'),
+                               gr.Textbox(label='What you want the AI to generate. 77 Token Limit.'), 
                                gr.Textbox(label='What you Do Not want the AI to generate. 77 Token Limit'),
                                gr.Slider(512, 1536, 1024, step=128, label='Height'),
                                gr.Slider(512, 1536, 1024, step=128, label='Width'),