nodes/comrender-ComfyUI-NanoSeed
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comrender
2025-11-13 17:07:07 +01:00
committed by GitHub
parent d4d85cabcf
commit efb8a7278b
2 changed files with 44 additions and 38 deletions
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__init__.py
+1 -1
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@@ -5,7 +5,7 @@ NODE_CLASS_MAPPINGS = {
}
NODE_DISPLAY_NAME_MAPPINGS = {
"NanoSeedEdit": "Edit image NanoBanana + Seededit by ComRender"
"NanoSeedEdit": "NanoSeed"
}
__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']
nano_seed.py
+43 -37
View File
@@ -7,18 +7,18 @@ from PIL import Image
# Helper function to convert ComfyUI tensor (B=1, H, W, C) to PIL Image (RGB)
def tensor2pil(image_tensor):
# image_tensor is always (1, H, W, C) from the loop
if image_tensor is None or image_tensor.shape[0] == 0:
return None
i = 255. * image_tensor[0].cpu().numpy() # (H, W, C)
image = np.clip(i, 0, 255).astype(np.uint8)
# Handle channels for PIL (always output RGB)
c = image.shape[-1]
if c == 1:
image = np.repeat(image, 3, axis=-1) # (H, W, 3)
image = np.repeat(image, 3, axis=-1)
elif c == 3:
pass # Already good
pass
elif c == 4:
image = image[..., :3] # Drop alpha
image = image[..., :3]
else:
raise ValueError(f"Unsupported channels: {c}. Expected 1, 3, or 4.")
@@ -26,8 +26,10 @@ def tensor2pil(image_tensor):
# Helper function to convert PIL Image (RGB) back to ComfyUI tensor (B=1, H, W, C)
def pil2tensor(pil_image):
arr = np.array(pil_image).astype(np.float32) / 255.0 # (H, W, 3)
arr = arr[np.newaxis, ...] # (1, H, W, 3) - No transpose needed for BHWC
if pil_image is None:
return None
arr = np.array(pil_image).astype(np.float32) / 255.0
arr = arr[np.newaxis, ...]
return torch.from_numpy(arr)
# Main node class
@@ -36,12 +38,16 @@ class NanoSeedEdit:
def INPUT_TYPES(cls):
return {
"required": {
"image": ("IMAGE",), # Supports batch/multi-image input
"prompt": ("STRING", {"default": "Edit the image according to this prompt.", "multiline": True}),
"model": (["nano_banana", "seedream", "flux_kontext_pro", "qwen_edit_plus"],),
"fal_key": ("STRING", {"default": "your_fal_key_here"}),
},
"optional": {
"image1": ("IMAGE",),
"image2": ("IMAGE",),
"image3": ("IMAGE",),
"image4": ("IMAGE",),
"image5": ("IMAGE",),
"width": ("INT", {"default": 0, "min": 0, "max": 4096, "display": "number"}),
"height": ("INT", {"default": 0, "min": 0, "max": 4096, "display": "number"}),
"num_images": ("INT", {"default": 1, "min": 1, "max": 4}),
@@ -55,40 +61,43 @@ class NanoSeedEdit:
CATEGORY = "image/edit"
OUTPUT_NODE = True
def edit_image(self, image, prompt, model, fal_key, width=0, height=0, num_images=1, seed=0):
def edit_image(self, prompt, model, fal_key, image1=None, image2=None, image3=None, image4=None, image5=None,
width=0, height=0, num_images=1, seed=0):
if fal_key == "your_fal_key_here":
raise ValueError("Please set your fal.ai API key in the node.")
batch_size = image.shape[0] if len(image.shape) == 4 else 1
# Limit to 5 images max for multi-support models
batch_size = min(batch_size, 5)
# Collect all non-None images
images = [img for img in [image1, image2, image3, image4, image5] if img is not None]
if not images:
raise ValueError("At least one image input must be connected.")
# Convert each to PIL and encode
img_data_uris = []
custom_size = (width > 0 and height > 0)
for b in range(batch_size):
single_image = image[b:b + 1] if batch_size > 1 else image
pil_image = tensor2pil(single_image)
for img_tensor in images:
pil_image = tensor2pil(img_tensor)
if pil_image is None:
continue
if custom_size and model == "nano_banana":
# Resize each input for NanoBanana
pil_image = pil_image.resize((width, height), Image.LANCZOS)
# Encode to base64 data URI
buffer = BytesIO()
pil_image.save(buffer, format="PNG")
img_str = base64.b64encode(buffer.getvalue()).decode()
img_data_uris.append(f"data:image/png;base64,{img_str}")
# Enforce single image for Flux Kontext Pro
if model == "flux_kontext_pro" and len(img_data_uris) > 1:
raise ValueError("Flux Kontext Pro supports only a single input image. Use one image or select another model.")
raise ValueError("Flux Kontext Pro supports only a single input image. Use only image1.")
# Prepare API endpoint and payload
# Prepare payload based on model
if model == "nano_banana":
url = "https://fal.run/fal-ai/nano-banana/edit"
payload = {
"prompt": prompt,
"image_urls": img_data_uris, # Multiple supported
"image_urls": img_data_uris,
"num_images": num_images,
"output_format": "png",
"sync_mode": True,
@@ -97,9 +106,9 @@ class NanoSeedEdit:
url = "https://fal.run/fal-ai/bytedance/seedream/v4/edit"
payload = {
"prompt": prompt,
"image_urls": img_data_uris, # Multiple supported (up to 10)
"num_images": 1, # One generation
"max_images": num_images, # Variations
"image_urls": img_data_uris,
"num_images": 1,
"max_images": num_images,
"seed": seed,
"enable_safety_checker": True,
"sync_mode": True,
@@ -110,13 +119,13 @@ class NanoSeedEdit:
url = "https://fal.run/fal-ai/flux-pro/kontext"
payload = {
"prompt": prompt,
"image_url": img_data_uris[0], # Single
"image_url": img_data_uris[0],
"num_images": num_images,
"seed": seed,
"output_format": "png",
"sync_mode": True,
"guidance_scale": 3.5, # Default
"safety_tolerance": "2", # Default
"guidance_scale": 3.5,
"safety_tolerance": "2",
}
if custom_size:
payload["image_size"] = {"width": width, "height": height}
@@ -124,7 +133,7 @@ class NanoSeedEdit:
url = "https://fal.run/fal-ai/qwen-image-edit-plus"
payload = {
"prompt": prompt,
"image_urls": img_data_uris, # Multiple supported
"image_urls": img_data_uris,
"num_images": num_images,
"seed": seed,
"guidance_scale": 4.0,
@@ -137,7 +146,7 @@ class NanoSeedEdit:
if custom_size:
payload["image_size"] = {"width": width, "height": height}
# Make API call
# API call
headers = {
"Authorization": f"Key {fal_key}",
"Content-Type": "application/json",
@@ -152,32 +161,29 @@ class NanoSeedEdit:
all_edited_tensors = []
# Process each generated image (limit to num_images)
# Process up to num_images
for img_info in api_result["images"][:num_images]:
# Handle data_uri (sync=True) or url
img_data = img_info.get("data_uri") or img_info.get("url")
if not img_data:
continue
if img_data.startswith("data:"):
# Parse base64 data URI
header, encoded = img_data.split(",", 1)
_, encoded = img_data.split(",", 1)
pil_edited = Image.open(BytesIO(base64.b64decode(encoded)))
else:
# Download from URL
img_resp = requests.get(img_data)
if img_resp.status_code != 200:
raise ValueError("Failed to download generated image")
pil_edited = Image.open(BytesIO(img_resp.content))
tensor_edited = pil2tensor(pil_edited)
all_edited_tensors.append(tensor_edited)
if tensor_edited is not None:
all_edited_tensors.append(tensor_edited)
# Stack all edited tensors into a batch
# Stack output
if all_edited_tensors:
batched_output = torch.cat(all_edited_tensors, dim=0)
else:
# Fallback: empty batch of 1 dummy image
batched_output = torch.zeros((1, 512, 512, 3))
return (batched_output,)
return (batched_output,)