nodes/maepopi-Diffusers-in-ComfyUI
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new inference operational

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This commit is contained in:
Maëlys Jusseaux
2024-07-25 17:17:32 +02:00
parent eac39100e0
commit 8e7bea863d
1 changed files with 27 additions and 25 deletions
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diffusers_in_comfy.py
+27 -25
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@@ -123,43 +123,45 @@ class ImageInference:
"width": ("INT", {"default": 512, "min": 1, "max": 8192, "step": 1}),
"height": ("INT", {"default": 512, "min": 1, "max": 8192, "step": 1}),
"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
"cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}),
}}
"cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}),
},
"optional": {
"controlnet_image" : ("IMAGE",),
"controlnet_scale": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step":0.1, "round": 0.01}),
}
}
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("Result",)
FUNCTION = "generate_image"
FUNCTION = "infer_image"
CATEGORY = "Diffusers-in-Comfy"
def convert_images_to_tensors(self, images):
return torch.stack([np.transpose(ToTensor()(image), (1, 2, 0)) for image in images])
def generate_image(self, pipeline, seed, steps, cfg, positive, negative, width, height):
def infer_image(self, pipeline, seed, steps, cfg, positive, negative, width, height, controlnet_image, controlnet_scale):
generator = torch.Generator(device='cuda').manual_seed(seed)
if negative == '':
images = pipeline(
prompt=positive,
generator=generator,
num_inference_steps = steps,
guidance_scale = cfg,
width=width,
height=height,
).images
else:
images = pipeline(
prompt=positive,
negative_prompt=negative,
generator=generator,
num_inference_steps = steps,
guidance_scale = cfg,
width=width,
height=height,
).images
args = {
"prompt": positive,
"generator": generator,
"num_inference_steps" : steps,
"guidance_scale" : cfg,
"width" : width,
"height" : height,
}
if negative != '':
args['negative'] = negative
if controlnet_image:
args['image'] = controlnet_image
args['controlnet_scale'] = controlnet_scale
images = pipeline(**args).images
return (self.convert_images_to_tensors(images),)