nodes/filliptm-ComfyUI_Fill-Nodes
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Fill
2025-05-19 14:19:41 +09:00
parent e01b0ee208
commit 32c06fae5a
3 changed files with 29 additions and 29 deletions
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nodes/FL_GPT_Image1_ADV.py
+1 -1
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@@ -20,7 +20,7 @@ class FL_GPT_Image1_ADV:
def INPUT_TYPES(cls):
return {
"required": {
"inputcount": ("INT", {"default": 1, "min": 1, "max": 10, "step": 1}), # Max 10 concurrent calls
"inputcount": ("INT", {"default": 1, "min": 1, "max": 100, "step": 1}), # Max 10 concurrent calls
"api_key": ("STRING", {"default": os.getenv("OPENAI_API_KEY", ""), "multiline": False}),
# Global settings from FL_GPT_Image1.py, with _setting suffix
"size_setting": (["1024x1024", "1536x1024", "1024x1536"], {"default": "1024x1024"}), # Adjusted for gpt-image-1 supported sizes
nodes/FL_GeminiImageGenADV.py
+27 -27
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@@ -17,6 +17,8 @@ import concurrent.futures
import random
from typing import List, Tuple, Optional
from comfy.utils import ProgressBar
# Assuming ImageBatch is still needed if we are batching results, or can be removed if Gemini returns a batch
# from nodes import ImageBatch
@@ -34,7 +36,8 @@ class FL_GeminiImageGenADV:
},
"optional": {
"image_1": ("IMAGE", {}), # Moved image_1 to optional. Default will be None if not connected.
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffff}), # Restored full seed range
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffff}), # Restored full seed range
"retry_indefinitely": ("BOOLEAN", {"default": False}),
# Subsequent image_i and prompt_i will be handled by **kwargs based on inputcount
}
}
@@ -148,9 +151,9 @@ Each pair triggers an asynchronous API call. Results are batched.
return pil_images if pil_images else None
def _call_gemini_api(self, client_instance, model_name_full, contents, gen_config_obj, retry_count=0, max_retries=3, call_id="0"):
def _call_gemini_api(self, client_instance, model_name_full, contents, gen_config_obj, retry_indefinitely, retry_count=0, max_retries=3, call_id="0"):
try:
self._log(f"[Call {call_id}] API call attempt #{retry_count + 1} to {model_name_full}")
self._log(f"[Call {call_id}] API call attempt #{retry_count + 1} to {model_name_full}{' (retrying indefinitely)' if retry_indefinitely else ''}")
# Using client.models.generate_content like in FL_GeminiImageEditor
response = client_instance.models.generate_content(
@@ -162,30 +165,30 @@ Each pair triggers an asynchronous API call. Results are batched.
# Validate response structure (adapted from FL_GeminiImageEditor)
if not hasattr(response, 'candidates') or not response.candidates:
self._log(f"[Call {call_id}] Empty response: No candidates found")
if retry_count < max_retries - 1:
self._log(f"[Call {call_id}] Retrying in 2 seconds... (Attempt {retry_count + 2}/{max_retries})")
if retry_indefinitely or retry_count < max_retries - 1:
self._log(f"[Call {call_id}] Retrying in 2 seconds... (Attempt {retry_count + 2 if not retry_indefinitely else 'N/A'}/{max_retries if not retry_indefinitely else 'inf'})")
time.sleep(2)
return self._call_gemini_api(client_instance, model_name_full, contents, gen_config_obj, retry_count + 1, max_retries, call_id)
return self._call_gemini_api(client_instance, model_name_full, contents, gen_config_obj, retry_indefinitely, retry_count + 1, max_retries, call_id)
else:
self._log(f"[Call {call_id}] Maximum retries ({max_retries}) reached. Returning empty response.")
return None
if not hasattr(response.candidates[0], 'content') or response.candidates[0].content is None:
self._log(f"[Call {call_id}] Invalid response: candidates[0].content is missing")
if retry_count < max_retries - 1:
self._log(f"[Call {call_id}] Retrying in 2 seconds... (Attempt {retry_count + 2}/{max_retries})")
if retry_indefinitely or retry_count < max_retries - 1:
self._log(f"[Call {call_id}] Retrying in 2 seconds... (Attempt {retry_count + 2 if not retry_indefinitely else 'N/A'}/{max_retries if not retry_indefinitely else 'inf'})")
time.sleep(2)
return self._call_gemini_api(client_instance, model_name_full, contents, gen_config_obj, retry_count + 1, max_retries, call_id)
return self._call_gemini_api(client_instance, model_name_full, contents, gen_config_obj, retry_indefinitely, retry_count + 1, max_retries, call_id)
else:
self._log(f"[Call {call_id}] Maximum retries ({max_retries}) reached. Returning empty response.")
return None
if not hasattr(response.candidates[0].content, 'parts') or response.candidates[0].content.parts is None:
self._log(f"[Call {call_id}] Invalid response: candidates[0].content.parts is missing")
if retry_count < max_retries - 1:
self._log(f"[Call {call_id}] Retrying in 2 seconds... (Attempt {retry_count + 2}/{max_retries})")
if retry_indefinitely or retry_count < max_retries - 1:
self._log(f"[Call {call_id}] Retrying in 2 seconds... (Attempt {retry_count + 2 if not retry_indefinitely else 'N/A'}/{max_retries if not retry_indefinitely else 'inf'})")
time.sleep(2)
return self._call_gemini_api(client_instance, model_name_full, contents, gen_config_obj, retry_count + 1, max_retries, call_id)
return self._call_gemini_api(client_instance, model_name_full, contents, gen_config_obj, retry_indefinitely, retry_count + 1, max_retries, call_id)
else:
self._log(f"[Call {call_id}] Maximum retries ({max_retries}) reached. Returning empty response.")
return None
@@ -195,11 +198,11 @@ Each pair triggers an asynchronous API call. Results are batched.
except Exception as e:
self._log(f"[Call {call_id}] API call error: {str(e)}")
if retry_count < max_retries - 1:
if retry_indefinitely or retry_count < max_retries - 1:
wait_time = 2 * (retry_count + 1) # Progressive backoff
self._log(f"[Call {call_id}] Retrying in {wait_time}s... (Attempt {retry_count + 2}/{max_retries})")
self._log(f"[Call {call_id}] Retrying in {wait_time}s... (Attempt {retry_count + 2 if not retry_indefinitely else 'N/A'}/{max_retries if not retry_indefinitely else 'inf'})")
time.sleep(wait_time)
return self._call_gemini_api(client_instance, model_name_full, contents, gen_config_obj, retry_count + 1, max_retries, call_id)
return self._call_gemini_api(client_instance, model_name_full, contents, gen_config_obj, retry_indefinitely, retry_count + 1, max_retries, call_id)
else:
self._log(f"[Call {call_id}] Max retries ({max_retries}) reached. Giving up.")
return None
@@ -297,7 +300,7 @@ Each pair triggers an asynchronous API call. Results are batched.
return image_tensor, final_response_text
async def _generate_single_image_async(self, api_key, model_name_full, prompt_text, input_pil_images: Optional[List[Image.Image]], temperature, max_retries, seed_val, call_id): # Parameter changed to input_pil_images
async def _generate_single_image_async(self, api_key, model_name_full, prompt_text, input_pil_images: Optional[List[Image.Image]], temperature, max_retries, retry_indefinitely, seed_val, call_id):
try:
try:
client_instance = genai.Client(api_key=api_key)
@@ -335,7 +338,7 @@ Each pair triggers an asynchronous API call. Results are batched.
loop = asyncio.get_event_loop()
response = await loop.run_in_executor(
None,
lambda: self._call_gemini_api(client_instance, model_name_full, contents, gen_config_obj, 0, max_retries, call_id)
lambda: self._call_gemini_api(client_instance, model_name_full, contents, gen_config_obj, retry_indefinitely, 0, max_retries, call_id)
)
img_tensor, response_text = self._process_api_response(response, call_id)
@@ -346,7 +349,7 @@ Each pair triggers an asynchronous API call. Results are batched.
error_msg = f"Call {call_id} Error: {str(e)}"
return self._create_error_image(error_msg), error_msg, call_id
def generate_images_advanced(self, inputcount, api_key, model, temperature, max_retries, prompt_1, image_1=None, seed=0, **kwargs):
def generate_images_advanced(self, inputcount, api_key, model, temperature, max_retries, prompt_1, image_1=None, seed=0, retry_indefinitely=False, **kwargs):
self.log_messages = []
if not api_key:
error_msg = "API key not provided."
@@ -361,6 +364,8 @@ Each pair triggers an asynchronous API call. Results are batched.
asyncio.set_event_loop(loop)
tasks = []
pbar = ProgressBar(inputcount) # Initialize progress bar
for slot_idx in range(1, inputcount + 1):
current_prompt = prompt_1 if slot_idx == 1 else kwargs.get(f"prompt_{slot_idx}", f"Default prompt for image {slot_idx}")
@@ -370,19 +375,16 @@ Each pair triggers an asynchronous API call. Results are batched.
else:
current_image_tensor_for_slot = kwargs.get(f"image_{slot_idx}")
# This will return a list of PIL images if current_image_tensor_for_slot is a batch or single image,
# or None if it's None or invalid.
pil_images_for_this_slot = self._process_tensor_to_pil_list(current_image_tensor_for_slot, f"InputSlot{slot_idx}")
# Each slot_idx corresponds to one API call.
# The seed is incremented per slot_idx (per API call).
current_task_seed = seed + (slot_idx - 1) if seed != 0 else 0
task_call_id = str(slot_idx) # Simplified call_id
task_call_id = str(slot_idx)
tasks.append(self._generate_single_image_async(
api_key, model, current_prompt, pil_images_for_this_slot, # Pass the list of PIL images
temperature, max_retries, current_task_seed, task_call_id
api_key, model, current_prompt, pil_images_for_this_slot,
temperature, max_retries, retry_indefinitely, current_task_seed, task_call_id
))
pbar.update_absolute(slot_idx) # Update progress bar after task is added
if not tasks:
self._log("No tasks were created. This might indicate an issue with inputcount or logic.")
@@ -395,7 +397,6 @@ Each pair triggers an asynchronous API call. Results are batched.
if loop and not loop.is_closed():
loop.close()
# Sort results by call_id (which is now just the string of slot_idx)
results_with_id.sort(key=lambda x: int(x[2]))
output_images = []
@@ -408,7 +409,6 @@ Each pair triggers an asynchronous API call. Results are batched.
batched_images = torch.cat(output_images, dim=0) if output_images else self._create_error_image("No images generated")
combined_responses = "\n\n".join(output_texts)
# Prepend logs
final_log_output = "Processing Logs:\n" + "\n".join(self.log_messages) + "\n\n" + combined_responses
return (batched_images, final_log_output)
pyproject.toml
+1 -1
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@@ -1,7 +1,7 @@
[project]
name = "comfyui_fill-nodes"
description = "Fill-Nodes is a versatile collection of custom nodes for ComfyUI that extends functionality across multiple domains. Features include advanced image processing (pixelation, slicing, masking), visual effects generation (glitch, halftone, pixel art), comprehensive file handling (PDF creation/extraction, Google Drive integration), AI model interfaces (GPT, DALL-E, Hugging Face), utility nodes for workflow enhancement, and specialized tools for video processing, captioning, and batch operations. The pack provides both practical workflow solutions and creative tools within a unified node collection."
version = "1.5.2"
version = "1.5.3"
license = "LICENSE"
dependencies = ["diffusers", "librosa", "sounddevice", "glitch_this", "PyOpenGL", "glfw", "scipy>=1.13.1", "requests", "aiohttp", "moviepy", "matplotlib", "reportlab", "openai", "PyPDF2", "pdf2image", "PyMuPDF", "reportlab", "PyPDF2", "ollama", "kornia", "opencv-python", "gdown", "open_clip_torch", "google-genai"]