Update app.py

app.py

@@ -3,36 +3,44 @@ import torch
 import numpy as np
 import modin.pandas as pd
 from PIL import Image
-from diffusers import FluxPipeline
+from diffusers import DiffusionPipeline #, StableDiffusion3Pipeline
 from huggingface_hub import hf_hub_download
 
 device = 'cuda' if torch.cuda.is_available() else 'cpu'
 torch.cuda.max_memory_allocated(device=device)
 torch.cuda.empty_cache()
-pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16)
 
-# Open it for reduce GPU memory usage
-pipe.enable_model_cpu_offload()
-pipe.vae.enable_slicing()
-pipe.vae.enable_tiling()
-
-def genie (Model, Prompt, negative_prompt, scale, steps, seed):
-    generator = np.random.seed(0) #if seed == 0 else torch.manual_seed(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)
        
-    image = pipe(
-        prompt=Prompt, negative_prompt=negative_prompt,
-        guidance_scale=scale,
-        num_images_per_prompt=1,
-        num_inference_steps=steps,
-        width=1024,
-        height=1024, generator=generator).images[0]
+    if Model == "PhotoReal":
+        pipe = DiffusionPipeline.from_pretrained("circulus/canvers-real-v3.9.1", torch_dtype=torch.float16, safety_checker=None) if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("circulus/canvers-real-v3.9.1")
+        pipe.enable_xformers_memory_efficient_attention()
+        pipe = pipe.to(device)
+        torch.cuda.empty_cache()
+    
+        image = pipe(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images[0]
+        torch.cuda.empty_cache()
+        return image
+    
+    if Model == "Animagine XL 4":
+        animagine = DiffusionPipeline.from_pretrained("cagliostrolab/animagine-xl-4.0", torch_dtype=torch.float16, safety_checker=None) if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("cagliostrolab/animagine-xl-4.0")
+        animagine.enable_xformers_memory_efficient_attention()
+        animagine = animagine.to(device)
+        torch.cuda.empty_cache()
+        
+        image = animagine(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images[0]
+        torch.cuda.empty_cache()
+        return image
 
+    
     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(['PhotoReal', 'Animagine XL 4',], value='PhotoReal', 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, 1024, 768, step=128, label='Height'),
-                               #gr.Slider(512, 1024, 768, step=128, label='Width'),
+                               gr.Slider(512, 1024, 768, step=128, label='Height'),
+                               gr.Slider(512, 1024, 768, step=128, label='Width'),
                                gr.Slider(3, maximum=12, value=5, step=.25, label='Guidance Scale', info="5-7 for PhotoReal and 7-10 for Animagine"), 
                                gr.Slider(25, maximum=50, value=25, step=25, label='Number of Iterations'), 
                                gr.Slider(minimum=0, step=1, maximum=9999999999999999, randomize=True, label='Seed: 0 is Random'),