Update app.py

app.py

@@ -3,37 +3,50 @@ import torch
 import numpy as np
 import modin.pandas as pd
 from PIL import Image
-from diffusers import DiffusionPipeline, StableDiffusion3Pipeline
+from diffusers import DiffusionPipeline, QwenImagePipeline #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()
-#torch.cuda.max_memory_allocated(device=device)
-pipe = DiffusionPipeline.from_pretrained("circulus/canvers-fusionXL-v1", torch_dtype=torch.float16).to(device)
-pipe.enable_xformers_memory_efficient_attention()
-# Open it for reduce GPU memory usage
-#pipe.enable_model_cpu_offload()
-pipe.vae.enable_slicing()
-pipe.vae.enable_tiling()
-torch.cuda.empty_cache()
 
-#torch.cuda.max_memory_allocated(device=device)
-refiner = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", use_safetensors=True, torch_dtype=torch.float16, variant="fp16").to(device)
-refiner.enable_xformers_memory_efficient_attention()
-torch.cuda.empty_cache()
+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 == "SD3":
+        #torch.cuda.max_memory_allocated(device=device)
+        torch.cuda.empty_cache()
+        SD3 = QwenImagePipeline.from_pretrained("Qwen/Qwen-Image", torch_dtype=torch.bfloat16).to(device)
+        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)
+        pipe.enable_xformers_memory_efficient_attention()
+        pipe = pipe.to(device)
+        torch.cuda.empty_cache()
 
-def genie (Prompt, negative_prompt, height, width, scale, steps, seed):
-    generator = np.random.seed(0) if seed == 0 else torch.manual_seed(seed)
-    #generator=np.random.seed(0)
-    int_image = pipe(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale, output_type="latent").images
-    image = refiner(Prompt, negative_prompt=negative_prompt, image=int_image, denoising_start=.99).images[0]
-    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=.99).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'),
@@ -41,6 +54,6 @@ gr.Interface(fn=genie, inputs=[gr.Textbox(label='What you want the AI to generat
                                gr.Slider(10, maximum=50, value=25, step=5, label='Number of Prior Iterations'),
                                gr.Slider(minimum=0, step=1, maximum=9999999999999999, randomize=True, label='Seed: 0 is Random')],
              outputs=gr.Image(label='Generated Image'), 
-             title="Manju Dream Booth V2.5 with Fusion XL - GPU", 
+             title="Manju Dream Booth V2.4 with Stable Diffusion 3 & Fusion XL - GPU", 
              description="<br><br><b/>Warning: This Demo is capable of producing NSFW content.", 
              article = "If You Enjoyed this Demo and would like to Donate, you can send any amount to any of these Wallets. <br><br>SHIB (BEP20): 0xbE8f2f3B71DFEB84E5F7E3aae1909d60658aB891 <br>PayPal: https://www.paypal.me/ManjushriBodhisattva <br>ETH: 0xbE8f2f3B71DFEB84E5F7E3aae1909d60658aB891 <br>DOGE: D9QdVPtcU1EFH8jDC8jhU9uBcSTqUiA8h6<br><br>Code Monkey: <a href=\"https://huggingface.co/Manjushri\">Manjushri</a>").launch(debug=True)