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fix multiple image return from api nodes (#7772)

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thot experiment 2025-04-24 00:29:05 -07:00 committed by GitHub
parent e2eed9eb9b
commit 5c80da31db
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1 changed files with 27 additions and 19 deletions
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46
comfy_api_nodes/nodes_api.py
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@ -31,35 +31,43 @@ def downscale_input(image):
s = s.movedim(1,-1) s = s.movedim(1,-1)
return s return s
def validate_and_cast_response (response): def validate_and_cast_response(response):
# validate raw JSON response # validate raw JSON response
data = response.data data = response.data
if not data or len(data) == 0: if not data or len(data) == 0:
raise Exception("No images returned from API endpoint") raise Exception("No images returned from API endpoint")
# Get base64 image data # Initialize list to store image tensors
image_url = data[0].url image_tensors = []
b64_data = data[0].b64_json
if not image_url and not b64_data:
raise Exception("No image was generated in the response")
if b64_data: # Process each image in the data array
img_data = base64.b64decode(b64_data) for image_data in data:
img = Image.open(io.BytesIO(img_data)) image_url = image_data.url
b64_data = image_data.b64_json
elif image_url: if not image_url and not b64_data:
img_response = requests.get(image_url) raise Exception("No image was generated in the response")
if img_response.status_code != 200:
raise Exception("Failed to download the image")
img = Image.open(io.BytesIO(img_response.content))
img = img.convert("RGBA") if b64_data:
img_data = base64.b64decode(b64_data)
img = Image.open(io.BytesIO(img_data))
# Convert to numpy array, normalize to float32 between 0 and 1 elif image_url:
img_array = np.array(img).astype(np.float32) / 255.0 img_response = requests.get(image_url)
if img_response.status_code != 200:
raise Exception("Failed to download the image")
img = Image.open(io.BytesIO(img_response.content))
# Convert to torch tensor and add batch dimension img = img.convert("RGBA")
return torch.from_numpy(img_array)[None,]
# Convert to numpy array, normalize to float32 between 0 and 1
img_array = np.array(img).astype(np.float32) / 255.0
img_tensor = torch.from_numpy(img_array)
# Add to list of tensors
image_tensors.append(img_tensor)
return torch.stack(image_tensors, dim=0)
class OpenAIDalle2(ComfyNodeABC): class OpenAIDalle2(ComfyNodeABC):
""" """