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add time generation duration output to api nodes

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nofunstudio
2026-01-25 15:02:32 -07:00
parent 68a6a5f914
commit e24b9abe27
11 changed files with 156 additions and 45 deletions
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fal_flux2_klein_edit.py
+14 -4
View File
@@ -94,8 +94,8 @@ class FalAPI_Flux2KleinEdit:
}
}
RETURN_TYPES = ("IMAGE", "STRING", "STRING",)
RETURN_NAMES = ("image", "image_path", "generation_info",)
RETURN_TYPES = ("IMAGE", "STRING", "STRING", "STRING",)
RETURN_NAMES = ("image", "image_path", "generation_info", "generation_time",)
FUNCTION = "generate_image"
CATEGORY = "FAL"
@@ -165,12 +165,19 @@ class FalAPI_Flux2KleinEdit:
mean_val = tensor.mean().item()
return mean_val < 0.01
def format_generation_time(self, elapsed_seconds):
"""Format elapsed time as 'seconds-centiseconds' (e.g., '14-50' for 14.50 seconds)"""
seconds = int(elapsed_seconds)
centiseconds = int((elapsed_seconds - seconds) * 100)
return f"{seconds}-{centiseconds:02d}"
async def generate_image(self, api_key, model_variant, prompt, image1, width, height,
image2=None, image3=None, negative_prompt="", num_inference_steps=28,
guidance_scale=5.0, lora_url="", lora_scale=1.0, seed=-1,
output_format="png", enable_safety_checker=True, acceleration="none"):
print(f"[Flux2KleinEdit] Starting image generation process with {model_variant} model...")
start_time = time.time()
if not api_key:
raise ValueError("A FAL API key is required.")
@@ -283,7 +290,9 @@ class FalAPI_Flux2KleinEdit:
else:
batch_tensor = final_tensors[0]
return (batch_tensor, ";".join(saved_paths), json.dumps(result, indent=2))
generation_time = self.format_generation_time(time.time() - start_time)
print(f"[Flux2KleinEdit] Generation completed in {generation_time} seconds")
return (batch_tensor, ";".join(saved_paths), json.dumps(result, indent=2), generation_time)
except Exception as e:
print(f"[Flux2KleinEdit] Error: {str(e)}")
@@ -296,7 +305,8 @@ class FalAPI_Flux2KleinEdit:
"error": f"Flux2KleinEdit generation failed: {str(e)}",
"timestamp": time.strftime("%Y-%m-%d %H:%M:%S"),
}
return (torch.zeros((1, 64, 64, 3)), "", json.dumps(error_info, indent=2))
generation_time = self.format_generation_time(time.time() - start_time)
return (torch.zeros((1, 64, 64, 3)), "", json.dumps(error_info, indent=2), generation_time)
finally:
# Cleanup temp inputs
fal_flux2_lora_edit.py
+14 -4
View File
@@ -89,8 +89,8 @@ class FalAPI_Flux2LoraEdit:
}
}
RETURN_TYPES = ("IMAGE", "STRING", "STRING",)
RETURN_NAMES = ("image", "image_path", "generation_info",)
RETURN_TYPES = ("IMAGE", "STRING", "STRING", "STRING",)
RETURN_NAMES = ("image", "image_path", "generation_info", "generation_time",)
FUNCTION = "generate_image"
CATEGORY = "FAL"
@@ -153,12 +153,19 @@ class FalAPI_Flux2LoraEdit:
img_array = np.array(pil_img).astype(np.float32) / 255.0
return torch.from_numpy(img_array)[None,]
def format_generation_time(self, elapsed_seconds):
"""Format elapsed time as 'seconds-centiseconds' (e.g., '14-50' for 14.50 seconds)"""
seconds = int(elapsed_seconds)
centiseconds = int((elapsed_seconds - seconds) * 100)
return f"{seconds}-{centiseconds:02d}"
async def generate_image(self, api_key, prompt, image1, width, height,
image2=None, image3=None, num_inference_steps=28, guidance_scale=2.5,
lora_url="", lora_scale=1.0, seed=-1, output_format="png",
enable_safety_checker=True, enable_prompt_expansion=False, acceleration="regular"):
print("[Flux2LoraEdit] Starting image generation process...")
start_time = time.time()
if not api_key:
raise ValueError("A FAL API key is required.")
@@ -265,7 +272,9 @@ class FalAPI_Flux2LoraEdit:
else:
batch_tensor = final_tensors[0]
return (batch_tensor, ";".join(saved_paths), json.dumps(result, indent=2))
generation_time = self.format_generation_time(time.time() - start_time)
print(f"[Flux2LoraEdit] Generation completed in {generation_time} seconds")
return (batch_tensor, ";".join(saved_paths), json.dumps(result, indent=2), generation_time)
except Exception as e:
print(f"[Flux2LoraEdit] Error: {str(e)}")
@@ -278,7 +287,8 @@ class FalAPI_Flux2LoraEdit:
"error": f"Flux2LoraEdit generation failed: {str(e)}",
"timestamp": time.strftime("%Y-%m-%d %H:%M:%S"),
}
return (torch.zeros((1, 64, 64, 3)), "", json.dumps(error_info, indent=2))
generation_time = self.format_generation_time(time.time() - start_time)
return (torch.zeros((1, 64, 64, 3)), "", json.dumps(error_info, indent=2), generation_time)
finally:
# Cleanup temp inputs
fal_flux2_turbo_edit.py
+14 -4
View File
@@ -62,8 +62,8 @@ class FalAPI_Flux2TurboEdit:
}
}
RETURN_TYPES = ("IMAGE", "STRING", "STRING",)
RETURN_NAMES = ("image", "image_path", "generation_info",)
RETURN_TYPES = ("IMAGE", "STRING", "STRING", "STRING",)
RETURN_NAMES = ("image", "image_path", "generation_info", "generation_time",)
FUNCTION = "generate_image"
CATEGORY = "FAL"
@@ -126,11 +126,18 @@ class FalAPI_Flux2TurboEdit:
img_array = np.array(pil_img).astype(np.float32) / 255.0
return torch.from_numpy(img_array)[None,]
def format_generation_time(self, elapsed_seconds):
"""Format elapsed time as 'seconds-centiseconds' (e.g., '14-50' for 14.50 seconds)"""
seconds = int(elapsed_seconds)
centiseconds = int((elapsed_seconds - seconds) * 100)
return f"{seconds}-{centiseconds:02d}"
async def generate_image(self, api_key, prompt, image1, width, height,
image2=None, image3=None, guidance_scale=2.5,
seed=-1, output_format="png", enable_safety_checker=True):
print("[Flux2TurboEdit] Starting image generation process...")
start_time = time.time()
if not api_key:
raise ValueError("A FAL API key is required.")
@@ -229,7 +236,9 @@ class FalAPI_Flux2TurboEdit:
else:
batch_tensor = final_tensors[0]
return (batch_tensor, ";".join(saved_paths), json.dumps(result, indent=2))
generation_time = self.format_generation_time(time.time() - start_time)
print(f"[Flux2TurboEdit] Generation completed in {generation_time} seconds")
return (batch_tensor, ";".join(saved_paths), json.dumps(result, indent=2), generation_time)
except Exception as e:
print(f"[Flux2TurboEdit] Error: {str(e)}")
@@ -242,7 +251,8 @@ class FalAPI_Flux2TurboEdit:
"error": f"Flux2TurboEdit generation failed: {str(e)}",
"timestamp": time.strftime("%Y-%m-%d %H:%M:%S"),
}
return (torch.zeros((1, 64, 64, 3)), "", json.dumps(error_info, indent=2))
generation_time = self.format_generation_time(time.time() - start_time)
return (torch.zeros((1, 64, 64, 3)), "", json.dumps(error_info, indent=2), generation_time)
finally:
# Cleanup temp inputs
fal_kling_video.py
+14 -4
View File
@@ -34,8 +34,8 @@ class FalAPI_kling_video:
}
}
RETURN_TYPES = ("STRING", "STRING",)
RETURN_NAMES = ("video_path", "generation_info",)
RETURN_TYPES = ("STRING", "STRING", "STRING",)
RETURN_NAMES = ("video_path", "generation_info", "generation_time",)
FUNCTION = "generate_video"
CATEGORY = "FAL"
@@ -107,11 +107,18 @@ class FalAPI_kling_video:
except Exception as e:
print(f"[Kling Video] Error in queue update: {e}")
def format_generation_time(self, elapsed_seconds):
"""Format elapsed time as 'seconds-centiseconds' (e.g., '14-50' for 14.50 seconds)"""
seconds = int(elapsed_seconds)
centiseconds = int((elapsed_seconds - seconds) * 100)
return f"{seconds}-{centiseconds:02d}"
def generate_video(self, api_token, image, prompt):
"""
Generate video using fal-ai/kling-video/v2.1/standard/image-to-video
"""
print(f"[Kling Video] Starting video generation process...")
start_time = time.time()
# For errors, return empty strings
empty_path = ""
@@ -231,7 +238,9 @@ class FalAPI_kling_video:
print(f"[Kling Video] Saved metadata -> {metadata_path}")
# 10) Return the video path & metadata JSON string
return (video_path, json.dumps(generation_info, indent=2))
generation_time = self.format_generation_time(time.time() - start_time)
print(f"[Kling Video] Generation completed in {generation_time} seconds")
return (video_path, json.dumps(generation_info, indent=2), generation_time)
except Exception as e:
# Return empty path and an error message in JSON
@@ -241,7 +250,8 @@ class FalAPI_kling_video:
"model": "fal-ai/kling-video/v2.1/standard/image-to-video"
}
print(f"[Kling Video] Error: {str(e)}")
return (empty_path, json.dumps(error_info, indent=2))
generation_time = self.format_generation_time(time.time() - start_time)
return (empty_path, json.dumps(error_info, indent=2), generation_time)
def tensor_to_tempfile(self, tensor):
"""
fal_ltx2_video.py
+14 -4
View File
@@ -160,8 +160,8 @@ class FalAPI_ltx2_video:
}
}
RETURN_TYPES = ("VIDEO", "STRING", "STRING",)
RETURN_NAMES = ("video", "video_path", "generation_info",)
RETURN_TYPES = ("VIDEO", "STRING", "STRING", "STRING",)
RETURN_NAMES = ("video", "video_path", "generation_info", "generation_time",)
FUNCTION = "generate_video"
CATEGORY = "FAL"
@@ -230,6 +230,12 @@ class FalAPI_ltx2_video:
arr = (arr * 255).clip(0, 255).astype("uint8")
return Image.fromarray(arr)
def format_generation_time(self, elapsed_seconds):
"""Format elapsed time as 'seconds-centiseconds' (e.g., '14-50' for 14.50 seconds)"""
seconds = int(elapsed_seconds)
centiseconds = int((elapsed_seconds - seconds) * 100)
return f"{seconds}-{centiseconds:02d}"
async def generate_video(self, api_token, model_variant, image, prompt, num_frames, width, height, fps, image_strength,
negative_prompt="", guidance_scale=3.0, num_inference_steps=40, use_multiscale=True,
generate_audio=False, enable_safety_checker=True, acceleration="none",
@@ -239,6 +245,7 @@ class FalAPI_ltx2_video:
"""Generate video using fal-ai/ltx-2-19b/image-to-video/lora (Async execution)"""
print(f"[LTX-2 Video] Starting video generation with {model_variant} model...")
start_time = time.time()
empty_path = ""
@@ -397,7 +404,9 @@ class FalAPI_ltx2_video:
# Create VideoFromFile object for ComfyUI's VIDEO type
video_output = VideoFromFile(video_path)
return (video_output, video_path, json.dumps(generation_info, indent=2))
generation_time = self.format_generation_time(time.time() - start_time)
print(f"[LTX-2 Video] Generation completed in {generation_time} seconds")
return (video_output, video_path, json.dumps(generation_info, indent=2), generation_time)
except Exception as e:
# Clean up temp file on error
@@ -413,7 +422,8 @@ class FalAPI_ltx2_video:
"model": f"fal-ai/ltx-2-19b/{model_variant}/image-to-video/lora"
}
print(f"[LTX-2 Video] Error: {str(e)}")
return (None, empty_path, json.dumps(error_info, indent=2))
generation_time = self.format_generation_time(time.time() - start_time)
return (None, empty_path, json.dumps(error_info, indent=2), generation_time)
@classmethod
def IS_CHANGED(cls, **kwargs):
fal_qwen_edit.py
+14 -4
View File
@@ -85,8 +85,8 @@ class FalAPI_QwenEditPlus:
}
}
RETURN_TYPES = ("IMAGE", "STRING", "STRING",)
RETURN_NAMES = ("image", "image_path", "generation_info",)
RETURN_TYPES = ("IMAGE", "STRING", "STRING", "STRING",)
RETURN_NAMES = ("image", "image_path", "generation_info", "generation_time",)
FUNCTION = "generate_image"
CATEGORY = "FAL"
@@ -149,12 +149,19 @@ class FalAPI_QwenEditPlus:
img_array = np.array(pil_img).astype(np.float32) / 255.0
return torch.from_numpy(img_array)[None,]
def format_generation_time(self, elapsed_seconds):
"""Format elapsed time as 'seconds-centiseconds' (e.g., '14-50' for 14.50 seconds)"""
seconds = int(elapsed_seconds)
centiseconds = int((elapsed_seconds - seconds) * 100)
return f"{seconds}-{centiseconds:02d}"
async def generate_image(self, api_key, prompt, image1, width, height,
image2=None, image3=None, num_inference_steps=28, guidance_scale=4.0,
lora_url="", lora_scale=0.84, seed=-1, output_format="png",
enable_safety_checker=True, acceleration="regular"):
print("[QwenEditPlus] Starting image generation process...")
start_time = time.time()
if not api_key:
raise ValueError("A FAL API key is required.")
@@ -260,7 +267,9 @@ class FalAPI_QwenEditPlus:
else:
batch_tensor = final_tensors[0]
return (batch_tensor, ";".join(saved_paths), json.dumps(result, indent=2))
generation_time = self.format_generation_time(time.time() - start_time)
print(f"[QwenEditPlus] Generation completed in {generation_time} seconds")
return (batch_tensor, ";".join(saved_paths), json.dumps(result, indent=2), generation_time)
except Exception as e:
print(f"[QwenEditPlus] Error: {str(e)}")
@@ -273,7 +282,8 @@ class FalAPI_QwenEditPlus:
"error": f"QwenEditPlus generation failed: {str(e)}",
"timestamp": time.strftime("%Y-%m-%d %H:%M:%S"),
}
return (torch.zeros((1, 64, 64, 3)), "", json.dumps(error_info, indent=2))
generation_time = self.format_generation_time(time.time() - start_time)
return (torch.zeros((1, 64, 64, 3)), "", json.dumps(error_info, indent=2), generation_time)
finally:
# Cleanup temp inputs
fal_recraft_upscale.py
+14 -4
View File
@@ -33,8 +33,8 @@ class FalAPI_recraft_upscale:
}
}
RETURN_TYPES = ("IMAGE", "STRING",)
RETURN_NAMES = ("upscaled_image", "generation_info",)
RETURN_TYPES = ("IMAGE", "STRING", "STRING",)
RETURN_NAMES = ("upscaled_image", "generation_info", "generation_time",)
FUNCTION = "upscale"
CATEGORY = "FAL"
@@ -105,11 +105,18 @@ class FalAPI_recraft_upscale:
except Exception as e:
print(f"[Recraft Upscale] Error in queue update: {e}")
def format_generation_time(self, elapsed_seconds):
"""Format elapsed time as 'seconds-centiseconds' (e.g., '14-50' for 14.50 seconds)"""
seconds = int(elapsed_seconds)
centiseconds = int((elapsed_seconds - seconds) * 100)
return f"{seconds}-{centiseconds:02d}"
def upscale(self, api_token, image, enable_safety_checker):
"""
Upscale image using fal-ai/recraft/upscale/crisp
"""
print(f"[Recraft Upscale] Starting upscale process...")
start_time = time.time()
# For errors, return an empty 1024x1024 image
empty_image = torch.zeros((1, 1024, 1024, 3))
@@ -241,7 +248,9 @@ class FalAPI_recraft_upscale:
print(f"[Recraft Upscale] Final tensor shape: {img_tensor.shape}")
# 11) Return the image tensor & metadata JSON string
return (img_tensor, json.dumps(generation_info, indent=2))
generation_time = self.format_generation_time(time.time() - start_time)
print(f"[Recraft Upscale] Upscale completed in {generation_time} seconds")
return (img_tensor, json.dumps(generation_info, indent=2), generation_time)
except Exception as e:
# Return an empty image and an error message in JSON
@@ -251,7 +260,8 @@ class FalAPI_recraft_upscale:
"model": "fal-ai/recraft/upscale/crisp"
}
print(f"[Recraft Upscale] Error: {str(e)}")
return (empty_image, json.dumps(error_info, indent=2))
generation_time = self.format_generation_time(time.time() - start_time)
return (empty_image, json.dumps(error_info, indent=2), generation_time)
def tensor_to_tempfile(self, tensor):
"""
fal_seedance_video.py
+14 -4
View File
@@ -92,8 +92,8 @@ class FalAPI_seedance_video:
}
}
RETURN_TYPES = ("VIDEO", "STRING", "STRING",)
RETURN_NAMES = ("video", "video_path", "generation_info",)
RETURN_TYPES = ("VIDEO", "STRING", "STRING", "STRING",)
RETURN_NAMES = ("video", "video_path", "generation_info", "generation_time",)
FUNCTION = "generate_video"
CATEGORY = "FAL"
@@ -140,6 +140,12 @@ class FalAPI_seedance_video:
except Exception as e:
print(f"[Seedance Video] Error in queue update: {e}")
def format_generation_time(self, elapsed_seconds):
"""Format elapsed time as 'seconds-centiseconds' (e.g., '14-50' for 14.50 seconds)"""
seconds = int(elapsed_seconds)
centiseconds = int((elapsed_seconds - seconds) * 100)
return f"{seconds}-{centiseconds:02d}"
async def generate_video(self, api_token, image, prompt, aspect_ratio, resolution,
duration, camera_fixed, generate_audio, enable_safety_checker, end_image=None, seed=-1):
"""
@@ -147,6 +153,7 @@ class FalAPI_seedance_video:
(Async execution for parallel processing)
"""
print(f"[Seedance Video] Starting video generation process...")
start_time = time.time()
# For errors, return empty strings
empty_path = ""
@@ -316,7 +323,9 @@ class FalAPI_seedance_video:
video_output = VideoFromFile(video_path)
# 11) Return the video object, video path string & metadata JSON string
return (video_output, video_path, json.dumps(generation_info, indent=2))
generation_time = self.format_generation_time(time.time() - start_time)
print(f"[Seedance Video] Generation completed in {generation_time} seconds")
return (video_output, video_path, json.dumps(generation_info, indent=2), generation_time)
except Exception as e:
# Return None for video, empty path and an error message in JSON
@@ -326,7 +335,8 @@ class FalAPI_seedance_video:
"model": "fal-ai/bytedance/seedance/v1.5/pro/image-to-video"
}
print(f"[Seedance Video] Error: {str(e)}")
return (None, empty_path, json.dumps(error_info, indent=2))
generation_time = self.format_generation_time(time.time() - start_time)
return (None, empty_path, json.dumps(error_info, indent=2), generation_time)
def tensor_to_tempfile(self, tensor):
"""
nano_banana_pro.py
+16 -5
View File
@@ -72,8 +72,8 @@ class FalAPI_NanoBananaPro:
}
}
RETURN_TYPES = ("IMAGE", "STRING", "STRING",)
RETURN_NAMES = ("images", "image_paths", "generation_info",)
RETURN_TYPES = ("IMAGE", "STRING", "STRING", "STRING",)
RETURN_NAMES = ("images", "image_paths", "generation_info", "generation_time",)
FUNCTION = "generate_image"
CATEGORY = "FAL"
@@ -132,9 +132,16 @@ class FalAPI_NanoBananaPro:
arr = (arr * 255).clip(0, 255).astype("uint8")
return Image.fromarray(arr)
def format_generation_time(self, elapsed_seconds):
"""Format elapsed time as 'seconds-centiseconds' (e.g., '14-50' for 14.50 seconds)"""
seconds = int(elapsed_seconds)
centiseconds = int((elapsed_seconds - seconds) * 100)
return f"{seconds}-{centiseconds:02d}"
async def generate_image(self, api_key, prompt, num_images, image1, aspect_ratio, resolution,
image2=None, image3=None, image4=None, seed=-1):
print("[NanoBananaPro] Starting generation process...")
start_time = time.time()
if not api_key:
raise ValueError("A FAL API Key is required.")
@@ -238,7 +245,8 @@ class FalAPI_NanoBananaPro:
path, _ = await asyncio.to_thread(self.save_image_and_metadata, black_pil, timeout_info, number)
saved_paths.append(path)
return (black_tensor, ";".join(saved_paths), json.dumps(timeout_info, indent=2))
generation_time = self.format_generation_time(time.time() - start_time)
return (black_tensor, ";".join(saved_paths), json.dumps(timeout_info, indent=2), generation_time)
# Check status
try:
@@ -326,7 +334,9 @@ class FalAPI_NanoBananaPro:
else:
batch_tensor = final_tensors[0]
return (batch_tensor, ";".join(saved_paths), json.dumps(result, indent=2))
generation_time = self.format_generation_time(time.time() - start_time)
print(f"[NanoBananaPro] Generation completed in {generation_time} seconds")
return (batch_tensor, ";".join(saved_paths), json.dumps(result, indent=2), generation_time)
except Exception as e:
print(f"[NanoBananaPro] Error: {e}")
@@ -337,7 +347,8 @@ class FalAPI_NanoBananaPro:
# Return empty/error
empty = torch.zeros((1, 64, 64, 3))
return (empty, "", str(e))
generation_time = self.format_generation_time(time.time() - start_time)
return (empty, "", str(e), generation_time)
finally:
# Cleanup temp inputs
wavespeed_ai_image.py
+14 -4
View File
@@ -63,8 +63,8 @@ class WaveSpeedAI_Image:
}
}
RETURN_TYPES = ("IMAGE", "STRING", "STRING",)
RETURN_NAMES = ("image", "image_path", "generation_info",)
RETURN_TYPES = ("IMAGE", "STRING", "STRING", "STRING",)
RETURN_NAMES = ("image", "image_path", "generation_info", "generation_time",)
FUNCTION = "generate_image"
CATEGORY = "WaveSpeedAI"
@@ -93,6 +93,7 @@ class WaveSpeedAI_Image:
image2=None, image3=None, lora_url="", lora_scale=0.84,
seed=-1, output_format="jpeg"):
print("[WaveSpeedAI] Starting image generation process...")
start_time = time.time()
if not api_key:
raise ValueError("A WaveSpeedAI API key is required.")
@@ -183,7 +184,9 @@ class WaveSpeedAI_Image:
pil_image = Image.open(BytesIO(resp.content))
output_tensor = self.pil_to_tensor(pil_image)
return (output_tensor, image_path, json.dumps(generation_info, indent=2))
generation_time = self.format_generation_time(time.time() - start_time)
print(f"[WaveSpeedAI] Generation completed in {generation_time} seconds")
return (output_tensor, image_path, json.dumps(generation_info, indent=2), generation_time)
except Exception as e:
error_info = {
@@ -191,7 +194,8 @@ class WaveSpeedAI_Image:
"timestamp": time.strftime("%Y-%m-%d %H:%M:%S"),
}
print(f"[WaveSpeedAI] Error: {str(e)}")
return (None, "", json.dumps(error_info, indent=2))
generation_time = self.format_generation_time(time.time() - start_time)
return (None, "", json.dumps(error_info, indent=2), generation_time)
def tensor_to_data_uri(self, tensor, image_format="PNG"):
pil_img = self.tensor_to_pil(tensor)
@@ -210,6 +214,12 @@ class WaveSpeedAI_Image:
img_array = np.array(pil_img).astype(np.float32) / 255.0
return torch.from_numpy(img_array)[None,]
def format_generation_time(self, elapsed_seconds):
"""Format elapsed time as 'seconds-centiseconds' (e.g., '14-50' for 14.50 seconds)"""
seconds = int(elapsed_seconds)
centiseconds = int((elapsed_seconds - seconds) * 100)
return f"{seconds}-{centiseconds:02d}"
@classmethod
def IS_CHANGED(cls, **kwargs):
return float("NaN")
wavespeed_flux2_lora_edit.py
+14 -4
View File
@@ -57,8 +57,8 @@ class WaveSpeedAI_Flux2LoraEdit:
}
}
RETURN_TYPES = ("IMAGE", "STRING", "STRING",)
RETURN_NAMES = ("image", "image_path", "generation_info",)
RETURN_TYPES = ("IMAGE", "STRING", "STRING", "STRING",)
RETURN_NAMES = ("image", "image_path", "generation_info", "generation_time",)
FUNCTION = "generate_image"
CATEGORY = "WaveSpeedAI"
@@ -100,10 +100,17 @@ class WaveSpeedAI_Flux2LoraEdit:
img_array = np.array(pil_img).astype(np.float32) / 255.0
return torch.from_numpy(img_array)[None,]
def format_generation_time(self, elapsed_seconds):
"""Format elapsed time as 'seconds-centiseconds' (e.g., '14-50' for 14.50 seconds)"""
seconds = int(elapsed_seconds)
centiseconds = int((elapsed_seconds - seconds) * 100)
return f"{seconds}-{centiseconds:02d}"
async def generate_image(self, api_key, prompt, image1, width, height, image2=None, image3=None,
lora_url="", lora_scale=1.0, seed=-1):
print("[WaveSpeedAI Flux2] Starting image generation process...")
start_time = time.time()
if not api_key:
raise ValueError("A WaveSpeedAI API key is required.")
@@ -215,7 +222,9 @@ class WaveSpeedAI_Flux2LoraEdit:
pil_image = Image.open(BytesIO(resp.content)).convert("RGB")
output_tensor = self.pil_to_tensor(pil_image)
return (output_tensor, image_path, json.dumps(generation_info, indent=2))
generation_time = self.format_generation_time(time.time() - start_time)
print(f"[WaveSpeedAI Flux2] Generation completed in {generation_time} seconds")
return (output_tensor, image_path, json.dumps(generation_info, indent=2), generation_time)
except Exception as e:
print(f"[WaveSpeedAI Flux2] Error: {str(e)}")
@@ -223,7 +232,8 @@ class WaveSpeedAI_Flux2LoraEdit:
"error": f"WaveSpeedAI Flux2 generation failed: {str(e)}",
"timestamp": time.strftime("%Y-%m-%d %H:%M:%S"),
}
return (torch.zeros((1, 64, 64, 3)), "", json.dumps(error_info, indent=2))
generation_time = self.format_generation_time(time.time() - start_time)
return (torch.zeros((1, 64, 64, 3)), "", json.dumps(error_info, indent=2), generation_time)
@classmethod
def IS_CHANGED(cls, **kwargs):