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Merge branch 'comfyanonymous:master' into master

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patientx 2025-04-24 14:50:59 +03:00 committed by GitHub
commit 1d9338b4b9
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2 changed files with 28 additions and 19 deletions
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1
comfy/clip_vision.py
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@ -18,6 +18,7 @@ class Output:
setattr(self, key, item)
def clip_preprocess(image, size=224, mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711], crop=True):
image = image[:, :, :, :3] if image.shape[3] > 3 else image
mean = torch.tensor(mean, device=image.device, dtype=image.dtype)
std = torch.tensor(std, device=image.device, dtype=image.dtype)
image = image.movedim(-1, 1)

46
comfy_api_nodes/nodes_api.py
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@ -31,35 +31,43 @@ def downscale_input(image):
s = s.movedim(1,-1)
return s
def validate_and_cast_response (response):
def validate_and_cast_response(response):
# validate raw JSON response
data = response.data
if not data or len(data) == 0:
raise Exception("No images returned from API endpoint")
# Get base64 image data
image_url = data[0].url
b64_data = data[0].b64_json
if not image_url and not b64_data:
raise Exception("No image was generated in the response")
# Initialize list to store image tensors
image_tensors = []
if b64_data:
img_data = base64.b64decode(b64_data)
img = Image.open(io.BytesIO(img_data))
# Process each image in the data array
for image_data in data:
image_url = image_data.url
b64_data = image_data.b64_json
elif image_url:
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))
if not image_url and not b64_data:
raise Exception("No image was generated in the response")
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
img_array = np.array(img).astype(np.float32) / 255.0
elif image_url:
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
return torch.from_numpy(img_array)[None,]
img = img.convert("RGBA")
# 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):
"""