improve

app.py

@@ -1,5 +1,4 @@
 import torch
-import os
 import gradio as gr
 from PIL import Image
 import random
@@ -15,7 +14,7 @@ from diffusers import (
     EulerDiscreteScheduler  # <-- Added import
 )
 from share_btn import community_icon_html, loading_icon_html, share_js
-from gallery_history import fetch_gallery_history, show_gallery_history
+# from gallery_history import fetch_gallery_history, show_gallery_history
 from illusion_style import css
 
 BASE_MODEL = "SG161222/Realistic_Vision_V5.1_noVAE"
@@ -112,6 +111,8 @@ def inference(
 
     # Rest of your existing code
     control_image_small = center_crop_resize(control_image)
+    control_image_large = center_crop_resize(control_image, (1024, 1024))
+
     main_pipe.scheduler = SAMPLER_MAP[sampler](main_pipe.scheduler.config)
     my_seed = random.randint(0, 2**32 - 1) if seed == -1 else seed
     generator = torch.Generator(device="cuda").manual_seed(seed)
@@ -128,7 +129,6 @@ def inference(
         num_inference_steps=15,
         output_type="latent"
     )
-    control_image_large = center_crop_resize(control_image, (1024, 1024))
     upscaled_latents = upscale(out, "nearest-exact", 2)
     out_image = image_pipe(
         prompt=prompt,
@@ -184,23 +184,25 @@ with gr.Blocks(css=css) as app:
                 loading_icon = gr.HTML(loading_icon_html)
                 share_button = gr.Button("Share to community", elem_id="share-btn")
 
-    history = show_gallery_history()
+    # history = show_gallery_history()
     prompt.submit(
         inference,
         inputs=[control_image, prompt, negative_prompt, guidance_scale, controlnet_conditioning_scale, control_start, control_end, strength, seed, sampler],
         outputs=[result_image, share_group, used_seed]
-    ).then(
-        fn=fetch_gallery_history, inputs=[prompt, result_image], outputs=history, queue=False
     )
+    # ).then(
+    #    fn=fetch_gallery_history, inputs=[prompt, result_image], outputs=history, queue=False
+    # )
     run_btn.click(
         inference,
         inputs=[control_image, prompt, negative_prompt, guidance_scale, controlnet_conditioning_scale, control_start, control_end, strength, seed, sampler],
         outputs=[result_image, share_group, used_seed]
-    ).then(
-        fn=fetch_gallery_history, inputs=[prompt, result_image], outputs=history, queue=False
     )
+    # ).then(
+    #     fn=fetch_gallery_history, inputs=[prompt, result_image], outputs=history, queue=False
+    # )
     share_button.click(None, [], [], _js=share_js)
 app.queue(max_size=20)
 
 if __name__ == "__main__":
-    app.launch()
+    app.launch(share=True)

requirements.txt

@@ -1,9 +1,11 @@
 diffusers
 transformers
 accelerate
-torch
 xformers
 gradio
 Pillow
 qrcode
-filelock
+filelock
+
+--extra-index-url https://download.pytorch.org/whl/cu118
+torch