nodes/HelloVision-ComfyUI_HelloMeme
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add seed parameters

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songkey-win
2024-11-05 19:09:35 +08:00
parent 258f1e69ce
commit 7d853e5b48
1 changed files with 22 additions and 4 deletions
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meme.py
+22 -4
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@@ -1,5 +1,7 @@
import os
import os.path as osp
import random
import folder_paths
import torch
import numpy as np
@@ -243,7 +245,8 @@ class HMPipelineImage:
"drive_image_params": ("DRIVE_IMAGE_PARAMS",),
"prompt": ("STRING", {"default": '(best quality), highly detailed, ultra-detailed, headshot, person, well-placed five sense organs, looking at the viewer, centered composition, sharp focus, realistic skin texture'}),
"negative_prompt": ("STRING", {"default": ''}),
"steps": ("INT", {"default": 25, "min": 1, "max": 10000}),
"steps": ("INT", {"default": 25, "min": 1, "max": 1000}),
"seed": ("INT", {"default": -1, "min": -1, "max": 100000}),
"guidance_scale": ("FLOAT", {"default": 2.0, "min": 0.0, "max": 100.0, "step": 0.01}),
}
}
@@ -252,10 +255,16 @@ class HMPipelineImage:
FUNCTION = "sample"
CATEGORY = "hellomeme"
def sample(self, pipeline, image, drive_image_params, prompt, negative_prompt, steps=25, guidance_scale=2.0):
def sample(self, pipeline, image, drive_image_params, prompt, negative_prompt, steps=25, seed=-1, guidance_scale=2.0):
image_np = (image[0] * 255).cpu().numpy().astype(np.uint8)
image_np = cv2.resize(image_np, (512, 512))
image_pil = Image.fromarray(image_np)
if seed < 0:
generator = torch.Generator(device=pipeline.device).manual_seed(random.randint(0, 100000))
else:
generator = torch.Generator(device=pipeline.device).manual_seed(seed)
result_img = pipeline(
prompt=[prompt],
strength=1.0,
@@ -264,6 +273,7 @@ class HMPipelineImage:
num_inference_steps=steps,
negative_prompt=[negative_prompt],
guidance_scale=guidance_scale,
generator=generator,
output_type='np'
)
return (torch.from_numpy(np.clip(result_img[0], 0, 1)), )
@@ -319,7 +329,8 @@ class HMPipelineVideo:
"drive_video_params": ("DRIVE_VIDEO_PARAMS",),
"prompt": ("STRING", {"default": '(best quality), highly detailed, ultra-detailed, headshot, person, well-placed five sense organs, looking at the viewer, centered composition, sharp focus, realistic skin texture'}),
"negative_prompt": ("STRING", {"default": ''}),
"steps": ("INT", {"default": 25, "min": 1, "max": 10000}),
"steps": ("INT", {"default": 25, "min": 1, "max": 1000}),
"seed": ("INT", {"default": -1, "min": -1, "max": 100000}),
"guidance_scale": ("FLOAT", {"default": 2.0, "min": 0.0, "max": 100.0, "step": 0.01}),
}
}
@@ -328,10 +339,16 @@ class HMPipelineVideo:
FUNCTION = "sample"
CATEGORY = "hellomeme"
def sample(self, pipeline, image, drive_video_params, prompt, negative_prompt, steps=25, guidance_scale=2.0):
def sample(self, pipeline, image, drive_video_params, prompt, negative_prompt, steps=25, seed=-1, guidance_scale=2.0):
image_np = (image[0] * 255).cpu().numpy().astype(np.uint8)
image_np = cv2.resize(image_np, (512, 512))
image_pil = Image.fromarray(image_np)
if seed < 0:
generator = torch.Generator(device=pipeline.device).manual_seed(random.randint(0, 100000))
else:
generator = torch.Generator(device=pipeline.device).manual_seed(seed)
res_frames = pipeline(
prompt=[prompt],
strength=1.0,
@@ -340,6 +357,7 @@ class HMPipelineVideo:
num_inference_steps=steps,
negative_prompt=[negative_prompt],
guidance_scale=guidance_scale,
generator=generator,
output_type='np'
)
res_frames = [np.clip(x[0], 0, 1) for x in res_frames]