Update app.py

app.py

@@ -149,96 +149,99 @@ models_rbm = core.Models(
 models_rbm.generator.eval().requires_grad_(False)
 
 def infer(style_description, ref_style_file, caption):
-    # Ensure all models are moved back to the correct device
-    core_b.generator.to(device)
-    models_rbm.generator.to(device)
-    
-    clear_gpu_cache()  # Clear cache before inference
+    try:
+        # Ensure all models are moved back to the correct device
+        models_rbm.generator.to(device)
+        models_b.generator.to(device)
+        
+        clear_gpu_cache()  # Clear cache before inference
 
-    height=1024
-    width=1024
-    batch_size=1
-    output_file='output.png'
-    
-    stage_c_latent_shape, stage_b_latent_shape = calculate_latent_sizes(height, width, batch_size=batch_size)
-
-    extras.sampling_configs['cfg'] = 4
-    extras.sampling_configs['shift'] = 2
-    extras.sampling_configs['timesteps'] = 20
-    extras.sampling_configs['t_start'] = 1.0
-
-    extras_b.sampling_configs['cfg'] = 1.1
-    extras_b.sampling_configs['shift'] = 1
-    extras_b.sampling_configs['timesteps'] = 10
-    extras_b.sampling_configs['t_start'] = 1.0
-
-    ref_style = resize_image(PIL.Image.open(ref_style_file).convert("RGB")).unsqueeze(0).expand(batch_size, -1, -1, -1).to(device)
-
-    batch = {'captions': [caption] * batch_size}
-    batch['style'] = ref_style
-
-    x0_style_forward = models_rbm.effnet(extras.effnet_preprocess(ref_style.to(device)))
-
-    conditions = core.get_conditions(batch, models_rbm, extras, is_eval=True, is_unconditional=False, eval_image_embeds=True, eval_style=True, eval_csd=False) 
-    unconditions = core.get_conditions(batch, models_rbm, extras, is_eval=True, is_unconditional=True, eval_image_embeds=False)    
-    conditions_b = core_b.get_conditions(batch, models_b, extras_b, is_eval=True, is_unconditional=False)
-    unconditions_b = core_b.get_conditions(batch, models_b, extras_b, is_eval=True, is_unconditional=True)
-
-    if low_vram:
-        # Offload non-essential models to CPU for memory savings
-        models_to(models_rbm, device="cpu", excepts=["generator", "previewer"])
-
-    # Stage C reverse process
-    with torch.cuda.amp.autocast():  # Use mixed precision
-        sampling_c = extras.gdf.sample(
-            models_rbm.generator, conditions, stage_c_latent_shape,
-            unconditions, device=device,
-            **extras.sampling_configs,
-            x0_style_forward=x0_style_forward,
-            apply_pushforward=False, tau_pushforward=8,
-            num_iter=3, eta=0.1, tau=20, eval_csd=True,
-            extras=extras, models=models_rbm,
-            lam_style=1, lam_txt_alignment=1.0,
-            use_ddim_sampler=True,
-        )
-        for (sampled_c, _, _) in tqdm(sampling_c, total=extras.sampling_configs['timesteps']):
-            sampled_c = sampled_c
-
-    clear_gpu_cache()  # Clear cache between stages
-
-    # Ensure all models are on the right device again
-    models_b.generator.to(device)
-    
-    # Stage B reverse process
-    with torch.no_grad(), torch.cuda.amp.autocast(dtype=torch.bfloat16):                
-        conditions_b['effnet'] = sampled_c
-        unconditions_b['effnet'] = torch.zeros_like(sampled_c)
+        height = 1024
+        width = 1024
+        batch_size = 1
+        output_file = 'output.png'
+        
+        stage_c_latent_shape, stage_b_latent_shape = calculate_latent_sizes(height, width, batch_size=batch_size)
+
+        extras.sampling_configs['cfg'] = 4
+        extras.sampling_configs['shift'] = 2
+        extras.sampling_configs['timesteps'] = 20
+        extras.sampling_configs['t_start'] = 1.0
+
+        extras_b.sampling_configs['cfg'] = 1.1
+        extras_b.sampling_configs['shift'] = 1
+        extras_b.sampling_configs['timesteps'] = 10
+        extras_b.sampling_configs['t_start'] = 1.0
+
+        ref_style = resize_image(PIL.Image.open(ref_style_file).convert("RGB")).unsqueeze(0).expand(batch_size, -1, -1, -1).to(device)
+
+        batch = {'captions': [caption] * batch_size}
+        batch['style'] = ref_style
+
+        x0_style_forward = models_rbm.effnet(extras.effnet_preprocess(ref_style.to(device)))
+
+        conditions = core.get_conditions(batch, models_rbm, extras, is_eval=True, is_unconditional=False, eval_image_embeds=True, eval_style=True, eval_csd=False) 
+        unconditions = core.get_conditions(batch, models_rbm, extras, is_eval=True, is_unconditional=True, eval_image_embeds=False)    
+        conditions_b = core_b.get_conditions(batch, models_b, extras_b, is_eval=True, is_unconditional=False)
+        unconditions_b = core_b.get_conditions(batch, models_b, extras_b, is_eval=True, is_unconditional=True)
+
+        if low_vram:
+            # Offload non-essential models to CPU for memory savings
+            models_to(models_rbm, device="cpu", excepts=["generator", "previewer"])
+
+        # Stage C reverse process
+        with torch.cuda.amp.autocast():  # Use mixed precision
+            sampling_c = extras.gdf.sample(
+                models_rbm.generator, conditions, stage_c_latent_shape,
+                unconditions, device=device,
+                **extras.sampling_configs,
+                x0_style_forward=x0_style_forward,
+                apply_pushforward=False, tau_pushforward=8,
+                num_iter=3, eta=0.1, tau=20, eval_csd=True,
+                extras=extras, models=models_rbm,
+                lam_style=1, lam_txt_alignment=1.0,
+                use_ddim_sampler=True,
+            )
+            for (sampled_c, _, _) in tqdm(sampling_c, total=extras.sampling_configs['timesteps']):
+                sampled_c = sampled_c
+
+        clear_gpu_cache()  # Clear cache between stages
+
+        # Ensure all models are on the right device again
+        models_b.generator.to(device)
+        
+        # Stage B reverse process
+        with torch.no_grad(), torch.cuda.amp.autocast(dtype=torch.bfloat16):                
+            conditions_b['effnet'] = sampled_c
+            unconditions_b['effnet'] = torch.zeros_like(sampled_c)
+            
+            sampling_b = extras_b.gdf.sample(
+                models_b.generator, conditions_b, stage_b_latent_shape,
+                unconditions_b, device=device, **extras_b.sampling_configs,
+            )
+            for (sampled_b, _, _) in tqdm(sampling_b, total=extras_b.sampling_configs['timesteps']):
+                sampled_b = sampled_b
+            sampled = models_b.stage_a.decode(sampled_b).float()
+
+        # Post-process and save the image
+        sampled = sampled.cpu()  # Move to CPU before processing
+
+        # Ensure the tensor is in [C, H, W] format
+        if sampled.dim() == 4 and sampled.size(0) == 1:
+            sampled = sampled.squeeze(0)
         
-        sampling_b = extras_b.gdf.sample(
-            models_b.generator, conditions_b, stage_b_latent_shape,
-            unconditions_b, device=device, **extras_b.sampling_configs,
-        )
-        for (sampled_b, _, _) in tqdm(sampling_b, total=extras_b.sampling_configs['timesteps']):
-            sampled_b = sampled_b
-        sampled = models_b.stage_a.decode(sampled_b).float()
-
-    # Post-process and save the image
-    sampled = torch.cat([
-        torch.nn.functional.interpolate(ref_style.cpu(), size=(height, width)),
-        sampled.cpu(),
-    ], dim=0)
-
-    # Remove the batch dimension and keep only the generated image
-    sampled = sampled[1]  # This selects the generated image, discarding the reference style image
-
-    # Ensure the tensor is in [C, H, W] format
-    if sampled.dim() == 3 and sampled.shape[0] == 3:
-        sampled_image = T.ToPILImage()(sampled)  # Convert tensor to PIL image
-        sampled_image.save(output_file)  # Save the image as a PNG
-    else:
-        raise ValueError(f"Expected tensor of shape [3, H, W] but got {sampled.shape}")
-
-    clear_gpu_cache()  # Clear cache after inference
+        if sampled.dim() == 3 and sampled.shape[0] == 3:
+            sampled_image = T.ToPILImage()(sampled)  # Convert tensor to PIL image
+            sampled_image.save(output_file)  # Save the image as a PNG
+        else:
+            raise ValueError(f"Expected tensor of shape [3, H, W] but got {sampled.shape}")
+
+    except Exception as e:
+        print(f"An error occurred during inference: {str(e)}")
+        return None
+
+    finally:
+        clear_gpu_cache()  # Always clear cache after inference
 
     return output_file  # Return the path to the saved image