Update app.py

app.py

@@ -131,92 +131,92 @@ def infer(ref_style_file, style_description, caption, progress):
     if low_vram:
         models_to(models_rbm, device=device)
     try:
-        with progress:
-            caption = f"{caption} in {style_description}"
-            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
+        
+        caption = f"{caption} in {style_description}"
+        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)
 
-            progress(0.1, "Loading style reference image")
-            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
+        extras.sampling_configs['cfg'] = 4
+        extras.sampling_configs['shift'] = 2
+        extras.sampling_configs['timesteps'] = 20
+        extras.sampling_configs['t_start'] = 1.0
 
-            progress(0.2, "Processing style reference image")
-            x0_style_forward = models_rbm.effnet(extras.effnet_preprocess(ref_style.to(device)))
+        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
 
-            progress(0.3, "Generating conditions")
-            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:
-                # The sampling process uses more vram, so we offload everything except two modules to the cpu.
-                models_to(models_rbm, device="cpu", excepts=["generator", "previewer"])
-
-            progress(0.4, "Starting Stage C reverse process")
-            # Stage C reverse process.
-            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,
+        progress(0.1, "Loading style reference image")
+        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
+
+        progress(0.2, "Processing style reference image")
+        x0_style_forward = models_rbm.effnet(extras.effnet_preprocess(ref_style.to(device)))
+
+        progress(0.3, "Generating conditions")
+        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:
+            # The sampling process uses more vram, so we offload everything except two modules to the cpu.
+            models_to(models_rbm, device="cpu", excepts=["generator", "previewer"])
+
+        progress(0.4, "Starting Stage C reverse process")
+        # Stage C reverse process.
+        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 progress.track(tqdm(sampling_c, total=extras.sampling_configs['timesteps']), description="Stage C reverse process"):
+            sampled_c = sampled_c
+
+        progress(0.7, "Starting Stage B reverse process")
+        # 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_c, _, _) in progress.track(tqdm(sampling_c, total=extras.sampling_configs['timesteps']), description="Stage C reverse process"):
-                sampled_c = sampled_c
-
-            progress(0.7, "Starting Stage B reverse process")
-            # 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 progress.track(tqdm(sampling_b, total=extras_b.sampling_configs['timesteps']), description="Stage B reverse process"):
-                    sampled_b = sampled_b
-                sampled = models_b.stage_a.decode(sampled_b).float()
-
-            progress(0.9, "Finalizing the output 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}")
-
-            progress(1.0, "Inference complete")
-            return output_file  # Return the path to the saved image
+            for (sampled_b, _, _) in progress.track(tqdm(sampling_b, total=extras_b.sampling_configs['timesteps']), description="Stage B reverse process"):
+                sampled_b = sampled_b
+            sampled = models_b.stage_a.decode(sampled_b).float()
+
+        progress(0.9, "Finalizing the output 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}")
+
+        progress(1.0, "Inference complete")
+        return output_file  # Return the path to the saved image
 
     finally:
         # Clear CUDA cache