nodes/ltdrdata-ComfyUI-Impact-Pack
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Dr.Lt.Data
2024-03-03 02:05:27 +09:00
parent 64c834609f
commit 860fead58e
3 changed files with 35 additions and 22 deletions
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modules/impact/core.py
+12 -5
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@@ -18,6 +18,7 @@ from comfy import model_management
from impact import utils
from impact import impact_sampling
from concurrent.futures import ThreadPoolExecutor
from comfy.ldm.cascade.stage_c_coder import StageC_coder
SEG = namedtuple("SEG",
@@ -214,6 +215,9 @@ def enhance_detail(image, model, clip, vae, guide_size, guide_size_for_bbox, max
new_w = w
new_h = h
new_w += new_w % 2
new_h += new_h % 2
if detailer_hook is not None:
new_w, new_h = detailer_hook.touch_scaled_size(new_w, new_h)
@@ -232,11 +236,14 @@ def enhance_detail(image, model, clip, vae, guide_size, guide_size_for_bbox, max
if noise_mask is not None and inpaint_model:
positive, negative, latent_image = nodes.InpaintModelConditioning().encode(positive, negative, upscaled_image, vae, noise_mask)
else:
if isinstance(vae, cascade.StageC_coder):
nodes_stable_cascade.StableCascade_StageC_VAEEncode().generate(pixels, vae, compression=compression)
return vae_encode.encode(vae, pixels)[0]
if isinstance(vae.first_stage_model, StageC_coder):
latent_image = detailer_hook.stable_cascade_vae_encode(vae, upscaled_image)
if latent_image is None:
print(f"[Impact Pack] When using the StableCascade model, it is necessary to connect the StableCascade_DetailerHook.")
raise Exception("StableCascade_DetailerHook is not provided.")
else:
latent_image = to_latent_image(upscaled_image, vae)
latent_image = to_latent_image(upscaled_image, vae)
if noise_mask is not None:
latent_image['noise_mask'] = noise_mask
@@ -265,7 +272,7 @@ def enhance_detail(image, model, clip, vae, guide_size, guide_size_for_bbox, max
# non-latent downscale - latent downscale cause bad quality
if detailer_hook is not None:
refined_latent = detailer_hook.pre_decode(refined_latent)
stage_b = detailer_hook.stable_cascade_stage_b(vae, image, positive, negative, refined_latent)
stage_b = detailer_hook.stable_cascade_stage_b(image, positive, negative, refined_latent)
else:
stage_b = None
modules/impact/hook_nodes.py
+3 -4
View File
@@ -96,8 +96,7 @@ class StableCascade_DetailerHookProvider:
"b_cfg": ("FLOAT", {"default": 1.1, "min": 0.0, "max": 100.0, "step": 0.1, "round": 0.01}),
"b_sampler_name": (comfy.samplers.KSampler.SAMPLERS,),
"b_scheduler": (comfy.samplers.KSampler.SCHEDULERS,),
"b_denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"c_compression": ("INT", {"default": 4, "min": 4, "max": 128, "step": 1}),
"c_compression": ("INT", {"default": 42, "min": 4, "max": 128, "step": 1}),
},
}
@@ -106,7 +105,7 @@ class StableCascade_DetailerHookProvider:
CATEGORY = "ImpactPack/Util"
def doit(self, b_model, b_vae, b_seed, b_steps, b_cfg, b_sampler_name, b_scheduler, b_denoise, c_compression):
hook = hooks.StableCascade_DetailerHook(b_model, b_vae, b_seed, b_steps, b_cfg, b_sampler_name, b_scheduler, b_denoise, c_compression)
def doit(self, b_model, b_vae, b_seed, b_steps, b_cfg, b_sampler_name, b_scheduler, c_compression):
hook = hooks.StableCascade_DetailerHook(b_model, b_vae, b_seed, b_steps, b_cfg, b_sampler_name, b_scheduler, c_compression)
return (hook, )
modules/impact/hooks.py
+20 -13
View File
@@ -105,6 +105,13 @@ class DetailerHookCombine(PixelKSampleHookCombine):
image = self.hook2.post_paste(image)
return image
def stable_cascade_vae_encode(self, vae, pixels):
latent = self.hook1.stable_cascade_vae_encode(vae, pixels)
if latent is not None:
return latent
return self.hook2.stable_cascade_vae_encode(vae, pixels)
def stable_cascade_stage_b(self, image, positive, negative, latent):
image = self.hook1.stable_cascade_stage_b(image, positive, negative, latent)
if image is not None:
@@ -173,12 +180,15 @@ class DetailerHook(PixelKSampleHook):
def post_paste(self, image):
return image
def stable_cascade_vae_encode(self, vae, pixels):
return None
def stable_cascade_stage_b(self, image, positive, negative, latent):
return None
class StableCascade_DetailerHook(DetailerHook):
def __init__(self, b_model, b_vae, b_seed, b_steps, b_cfg, b_sampler_name, b_scheduler, b_denoise, c_compression):
def __init__(self, b_model, b_vae, b_seed, b_steps, b_cfg, b_sampler_name, b_scheduler, c_compression):
super().__init__()
self.b_model = b_model
self.b_vae = b_vae
@@ -187,28 +197,25 @@ class StableCascade_DetailerHook(DetailerHook):
self.b_cfg = b_cfg
self.b_sampler_name = b_sampler_name
self.b_scheduler = b_scheduler
self.b_denoise = b_denoise
self.c_compression = c_compression
self.orig_size = None
self.b_latent = None
def touch_scaled_size(self, w, h):
compression = min(self.c_compression, w, h)
self.orig_size = w, h
return w // compression, h // compression
def stable_cascade_vae_encode(self, vae, pixels):
obj = comfy_extras.nodes_stable_cascade.StableCascade_StageC_VAEEncode()
stage_c, stage_b = obj.generate(pixels, vae, compression=self.c_compression)
self.b_latent = stage_b
return stage_c
def stable_cascade_stage_b(self, image, positive, negative, latent):
w, h = self.orig_size
# prepare stage_b
upscaled_image = utils.tensor_resize(image, w, h)
b_latent = utils.to_latent_image(upscaled_image, self.b_vae)
b_latent['noise_mask'] = latent['noise_mask']
# self.b_latent['noise_mask'] = latent['noise_mask']
b_positive = comfy_extras.nodes_stable_cascade.StableCascade_StageB_Conditioning().set_prior(positive, latent)[0]
# stage_b sampling
b_latent = impact_sampling.ksampler_wrapper(self.b_model, self.b_seed, self.b_steps, self.b_cfg, self.b_sampler_name, self.b_scheduler, b_positive, negative, b_latent, self.b_denoise)
b_latent = impact_sampling.ksampler_wrapper(self.b_model, self.b_seed, self.b_steps, self.b_cfg, self.b_sampler_name, self.b_scheduler, b_positive, negative, self.b_latent, 1.0)
# stage_b decoding
self.b_latent = None
return self.b_vae.decode(b_latent['samples'])