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Improve batch images and mask handling

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Fixed batch image processing to prevent duplicates and layer deletion while ensuring proper mask loading from input_mask. Images are now added as new layers without removing existing ones, and masks are always checked from backend regardless of image state.
This commit is contained in:
Dariusz L
2025-08-09 00:49:58 +02:00
parent 949ffa0143
commit 285ad035b2
4 changed files with 388 additions and 116 deletions
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76
canvas_node.py
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@@ -263,24 +263,48 @@ class LayerForgeNode:
input_data = {}
if input_image is not None:
# Convert image tensor to base64
# Convert image tensor(s) to base64 - handle batch
if isinstance(input_image, torch.Tensor):
# Ensure correct shape [B, H, W, C]
if input_image.dim() == 3:
input_image = input_image.unsqueeze(0)
# Convert to numpy and then to PIL
img_np = (input_image.squeeze(0).cpu().numpy() * 255).astype(np.uint8)
pil_img = Image.fromarray(img_np, 'RGB')
batch_size = input_image.shape[0]
log_info(f"Processing batch of {batch_size} image(s)")
# Convert to base64
buffered = io.BytesIO()
pil_img.save(buffered, format="PNG")
img_str = base64.b64encode(buffered.getvalue()).decode()
input_data['input_image'] = f"data:image/png;base64,{img_str}"
input_data['input_image_width'] = pil_img.width
input_data['input_image_height'] = pil_img.height
log_debug(f"Stored input image: {pil_img.width}x{pil_img.height}")
if batch_size == 1:
# Single image - keep backward compatibility
img_np = (input_image.squeeze(0).cpu().numpy() * 255).astype(np.uint8)
pil_img = Image.fromarray(img_np, 'RGB')
# Convert to base64
buffered = io.BytesIO()
pil_img.save(buffered, format="PNG")
img_str = base64.b64encode(buffered.getvalue()).decode()
input_data['input_image'] = f"data:image/png;base64,{img_str}"
input_data['input_image_width'] = pil_img.width
input_data['input_image_height'] = pil_img.height
log_debug(f"Stored single input image: {pil_img.width}x{pil_img.height}")
else:
# Multiple images - store as array
images_array = []
for i in range(batch_size):
img_np = (input_image[i].cpu().numpy() * 255).astype(np.uint8)
pil_img = Image.fromarray(img_np, 'RGB')
# Convert to base64
buffered = io.BytesIO()
pil_img.save(buffered, format="PNG")
img_str = base64.b64encode(buffered.getvalue()).decode()
images_array.append({
'data': f"data:image/png;base64,{img_str}",
'width': pil_img.width,
'height': pil_img.height
})
log_debug(f"Stored batch image {i+1}/{batch_size}: {pil_img.width}x{pil_img.height}")
input_data['input_images_batch'] = images_array
log_info(f"Stored batch of {batch_size} images")
if input_mask is not None:
# Convert mask tensor to base64
@@ -498,7 +522,7 @@ class LayerForgeNode:
with cls._storage_lock:
input_key = f"{node_id}_input"
input_data = cls._canvas_data_storage.pop(input_key, None)
input_data = cls._canvas_data_storage.get(input_key, None)
if input_data:
log_info(f"Input data found for node {node_id}, sending to frontend")
@@ -521,6 +545,32 @@ class LayerForgeNode:
'error': str(e)
}, status=500)
@PromptServer.instance.routes.post("/layerforge/clear_input_data/{node_id}")
async def clear_input_data(request):
try:
node_id = request.match_info["node_id"]
log_info(f"Clearing input data for node: {node_id}")
with cls._storage_lock:
input_key = f"{node_id}_input"
if input_key in cls._canvas_data_storage:
del cls._canvas_data_storage[input_key]
log_info(f"Input data cleared for node {node_id}")
else:
log_debug(f"No input data to clear for node {node_id}")
return web.json_response({
'success': True,
'message': f'Input data cleared for node {node_id}'
})
except Exception as e:
log_error(f"Error in clear_input_data: {str(e)}")
return web.json_response({
'success': False,
'error': str(e)
}, status=500)
@PromptServer.instance.routes.get("/ycnode/get_canvas_data/{node_id}")
async def get_canvas_data(request):
try: