Update app.py

app.py

@@ -3,40 +3,33 @@ import torch
 import numpy as np
 import modin.pandas as pd
 from PIL import Image
-from diffusers import DiffusionPipeline, FluxPipeline #CogView4Pipeline #, StableDiffusion3Pipeline from diffusers import CogView4Pipeline
+from diffusers import FluxPipeline #CogView4Pipeline #, StableDiffusion3Pipeline from diffusers import CogView4Pipeline
 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 = CogView4Pipeline.from_pretrained("THUDM/CogView4-6B", 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, height, width, scale, steps, seed):
     generator = np.random.seed(0) if seed == 0 else torch.manual_seed(seed)
        
-    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
+    image = pipe(
+        prompt=Prompt, negative_prompt=negative_prompt,
+        guidance_scale=scale,
+        num_images_per_prompt=1,
+        num_inference_steps=steps,
+        width=width,
+        height=height,).images[0]
 
     return image
     
-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.Interface(fn=genie, inputs=[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'),