Update app.py

app.py

@@ -33,6 +33,7 @@ torch.backends.cudnn.benchmark = False
 
 torch.set_float32_matmul_precision("highest")
 os.putenv("HF_HUB_ENABLE_HF_TRANSFER","1")
+os.environ["SAFETENSORS_FAST_GPU"] = "1"
 
 FTP_HOST = "1ink.us"
 FTP_USER = "ford442"
@@ -120,46 +121,23 @@ def load_and_prepare_model():
         text_encoder_2=None,
         vae=None,
     )
-    
-    '''
-    scaling_factor (`float`, *optional*, defaults to 0.18215):
-            The component-wise standard deviation of the trained latent space computed using the first batch of the
-            training set. This is used to scale the latent space to have unit variance when training the diffusion
-            model. The latents are scaled with the formula `z = z * scaling_factor` before being passed to the
-            diffusion model. When decoding, the latents are scaled back to the original scale with the formula: `z = 1
-            / scaling_factor * z`. For more details, refer to sections 4.3.2 and D.1 of the [High-Resolution Image
-            Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752) paper.
-        force_upcast (`bool`, *optional*, default to `True`):
-            If enabled it will force the VAE to run in float32 for high image resolution pipelines, such as SD-XL. VAE
-            can be fine-tuned / trained to a lower range without loosing too much precision in which case
-            `force_upcast` can be set to `False` - see: https://huggingface.co/madebyollin/sdxl-vae-fp16-fix
-        
-    '''
+
     pipe.vae=vaeX
     pipe.to(device=device, dtype=torch.bfloat16)
-    #pipe.vae.to(device=device, dtype=torch.bfloat16)
-    #pipe.vae.do_resize=False
-    #pipe.vae.do_rescale=False
-    #pipe.vae.do_convert_rgb=True
-    #pipe.vae.vae_scale_factor=8    #pipe.unet.set_default_attn_processor()
     pipe.vae.set_default_attn_processor()
     print(f'Pipeline: ')
-    #print(f'_optional_components: {pipe._optional_components}')
-    #print(f'watermark: {pipe.watermark}')
     print(f'image_processor: {pipe.image_processor}')
-    #print(f'feature_extractor: {pipe.feature_extractor}')
     print(f'init noise scale: {pipe.scheduler.init_noise_sigma}')
-    #print(f'UNET: {pipe.unet}')
     pipe.watermark=None
-    pipe.safety_checker=None  
+    pipe.safety_checker=None
     return pipe
-    
+
 # Preload and compile both models
 pipe = load_and_prepare_model()
 ip_model = IPAdapterXL(pipe, local_folder, ip_ckpt, device)
 text_encoder=CLIPTextModel.from_pretrained('ford442/RealVisXL_V5.0_BF16', subfolder='text_encoder',token=True).to(device=device, dtype=torch.bfloat16)
 text_encoder_2=CLIPTextModelWithProjection.from_pretrained('ford442/RealVisXL_V5.0_BF16', subfolder='text_encoder_2',token=True).to(device=device, dtype=torch.bfloat16)
-   
+
 MAX_SEED = np.iinfo(np.int32).max
 
 neg_prompt_2 = " 'non-photorealistic':1.5, 'unrealistic skin','unattractive face':1.3, 'low quality':1.1, ('dull color scheme', 'dull colors', 'digital noise':1.2),'amateurish', 'poorly drawn face':1.3, 'poorly drawn', 'distorted face', 'low resolution', 'simplistic' "
@@ -176,7 +154,7 @@ def upload_to_ftp(filename):
         print(f"Uploaded {filename} to FTP server")
     except Exception as e:
         print(f"FTP upload error: {e}")
-        
+
 def save_image(img):
     unique_name = str(uuid.uuid4()) + ".png"
     img.save(unique_name,optimize=False,compress_level=0)
@@ -195,8 +173,14 @@ def uploadNote(prompt,num_inference_steps,guidance_scale,timestamp):
         f.write(f"Model Scheduler: Euler_a all_custom before cuda \n")
         f.write(f"Model VAE: sdxl-vae to bfloat safetensor=false before cuda then attn_proc / scale factor 8 \n")
         f.write(f"Model UNET: ford442/RealVisXL_V5.0_BF16 \n")
-    upload_to_ftp(filename) 
-    
+    upload_to_ftp(filename)
+
+def display_image(file):
+    if file is not None:
+        return Image.open(file.name)
+    else:
+        return None
+
 @spaces.GPU(duration=40)
 def generate_30(
     prompt: str = "",
@@ -282,8 +266,8 @@ def generate_30(
         downscale1 = upscale.resize((upscale.width // 4, upscale.height // 4), Image.LANCZOS)
         downscale_path = f"rvIP_upscale_{timestamp}.png"
         downscale1.save(downscale_path,optimize=False,compress_level=0)
-        upload_to_ftp(downscale_path) 
-        image_paths = [save_image(downscale1)]     
+        upload_to_ftp(downscale_path)
+        image_paths = [save_image(downscale1)]
     else:
         print('-- IMAGE REQUIRED --')
     return image_paths
@@ -372,8 +356,8 @@ def generate_60(
         downscale1 = upscale.resize((upscale.width // 4, upscale.height // 4), Image.LANCZOS)
         downscale_path = f"rvIP_upscale_{timestamp}.png"
         downscale1.save(downscale_path,optimize=False,compress_level=0)
-        upload_to_ftp(downscale_path) 
-        image_paths = [save_image(downscale1)]     
+        upload_to_ftp(downscale_path)
+        image_paths = [save_image(downscale1)]
     else:
         print('-- IMAGE REQUIRED --')
     return image_paths
@@ -463,8 +447,8 @@ def generate_90(
         downscale1 = upscale.resize((upscale.width // 4, upscale.height // 4), Image.LANCZOS)
         downscale_path = f"rvIP_upscale_{timestamp}.png"
         downscale1.save(downscale_path,optimize=False,compress_level=0)
-        upload_to_ftp(downscale_path) 
-        image_paths = [save_image(downscale1)]     
+        upload_to_ftp(downscale_path)
+        image_paths = [save_image(downscale1)]
     else:
         print('-- IMAGE REQUIRED --')
     return image_paths
@@ -517,7 +501,7 @@ with gr.Blocks(theme=gr.themes.Origin(),css=css) as demo:
         run_button_30 = gr.Button("Run 30 Seconds", scale=0)
         run_button_60 = gr.Button("Run 60 Seconds", scale=0)
         run_button_90 = gr.Button("Run 90 Seconds", scale=0)
-    result = gr.Gallery(label="Result", columns=1, show_label=False) 
+    result = gr.Gallery(label="Result", columns=1, show_label=False)
     ip_strength =  gr.Slider(
             label="Image Strength",
             minimum=0.0,
@@ -526,7 +510,9 @@ with gr.Blocks(theme=gr.themes.Origin(),css=css) as demo:
             value=1.0,
     )
     with gr.Row():
-        latent_file = gr.File(label="Image Prompt (Required)")
+        with gr.Column():
+            latent_file = gr.File(label="Image Prompt (Required)", file_types=["image"])
+            latent_file_preview = gr.Image(label="Image Prompt Preview", interactive=False)
         file_1_strength =  gr.Slider(
             label="Img 1 %",
             minimum=0.0,
@@ -534,7 +520,9 @@ with gr.Blocks(theme=gr.themes.Origin(),css=css) as demo:
             step=0.01,
             value=1.0,
         )
-        latent_file_2 = gr.File(label="Image Prompt 2 (Optional)")
+        with gr.Column():
+            latent_file_2 = gr.File(label="Image Prompt 2 (Optional)", file_types=["image"])
+            latent_file_2_preview = gr.Image(label="Image Prompt 2 Preview", interactive=False)
         file_2_strength =  gr.Slider(
             label="Img 2 %",
             minimum=0.0,
@@ -542,7 +530,9 @@ with gr.Blocks(theme=gr.themes.Origin(),css=css) as demo:
             step=0.01,
             value=1.0,
         )
-        latent_file_3 = gr.File(label="Image Prompt 3 (Optional)")
+        with gr.Column():
+            latent_file_3 = gr.File(label="Image Prompt 3 (Optional)", file_types=["image"])
+            latent_file_3_preview = gr.Image(label="Image Prompt 3 Preview", interactive=False)
         file_3_strength =  gr.Slider(
             label="Img 3 %",
             minimum=0.0,
@@ -550,7 +540,9 @@ with gr.Blocks(theme=gr.themes.Origin(),css=css) as demo:
             step=0.01,
             value=1.0,
         )
-        latent_file_4 = gr.File(label="Image Prompt 4 (Optional)")
+        with gr.Column():
+            latent_file_4 = gr.File(label="Image Prompt 4 (Optional)", file_types=["image"])
+            latent_file_4_preview = gr.Image(label="Image Prompt 4 Preview", interactive=False)
         file_4_strength =  gr.Slider(
             label="Img 4 %",
             minimum=0.0,
@@ -558,7 +550,9 @@ with gr.Blocks(theme=gr.themes.Origin(),css=css) as demo:
             step=0.01,
             value=1.0,
         )
-        latent_file_5 = gr.File(label="Image Prompt 5 (Optional)")
+        with gr.Column():
+            latent_file_5 = gr.File(label="Image Prompt 5 (Optional)", file_types=["image"])
+            latent_file_5_preview = gr.Image(label="Image Prompt 5 Preview", interactive=False)
         file_5_strength =  gr.Slider(
             label="Img 5 %",
             minimum=0.0,
@@ -636,7 +630,33 @@ with gr.Blocks(theme=gr.themes.Origin(),css=css) as demo:
         outputs=negative_prompt,
         api_name=False,
     )
-    
+
+    latent_file.change(
+        display_image,
+        inputs=[latent_file],
+        outputs=[latent_file_preview]
+    )
+    latent_file_2.change(
+        display_image,
+        inputs=[latent_file_2],
+        outputs=[latent_file_2_preview]
+    )
+    latent_file_3.change(
+        display_image,
+        inputs=[latent_file_3],
+        outputs=[latent_file_3_preview]
+    )
+    latent_file_4.change(
+        display_image,
+        inputs=[latent_file_4],
+        outputs=[latent_file_4_preview]
+    )
+    latent_file_5.change(
+        display_image,
+        inputs=[latent_file_5],
+        outputs=[latent_file_5_preview]
+    )
+
     gr.on(
         triggers=[
             run_button_30.click,
@@ -668,7 +688,7 @@ with gr.Blocks(theme=gr.themes.Origin(),css=css) as demo:
         ],
         outputs=[result],
     )
-    
+
     gr.on(
         triggers=[
             run_button_60.click,
@@ -700,7 +720,7 @@ with gr.Blocks(theme=gr.themes.Origin(),css=css) as demo:
         ],
         outputs=[result],
     )
-    
+
     gr.on(
         triggers=[
             run_button_90.click,
@@ -749,7 +769,7 @@ with gr.Blocks(theme=gr.themes.Origin(),css=css) as demo:
     gr.Markdown(
     """
     <div style="text-align: justify;">
-    ⚡This is the demo space for generating images using Stable Diffusion XL with quality styles, different models, and types. Try the sample prompts to generate higher quality images. Try the sample prompts for generating higher quality images. 
+    ⚡This is the demo space for generating images using Stable Diffusion XL with quality styles, different models, and types. Try the sample prompts to generate higher quality images. Try the sample prompts for generating higher quality images.
     <a href='https://huggingface.co/spaces/prithivMLmods/Top-Prompt-Collection' target='_blank'>Try prompts</a>.
     </div>
     """)
@@ -759,12 +779,12 @@ with gr.Blocks(theme=gr.themes.Origin(),css=css) as demo:
     <div style="text-align: justify;">
     ⚠️ Users are accountable for the content they generate and are responsible for ensuring it meets appropriate ethical standards.
     </div>
-    """) 
+    """)
 
 def text_generation(input_text, seed):
     full_prompt = "Text Generator Application by ecarbo"
     return full_prompt
-    
+
 title = "Text Generator Demo GPT-Neo"
 description = "Text Generator Application by ecarbo"