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Support new flux model variants #541

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yolain
2024-11-22 17:35:35 +08:00
parent acfdd7713c
commit 7fb85eb987
1 changed files with 82 additions and 15 deletions
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py/easyNodes.py
+82 -15
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@@ -2634,6 +2634,7 @@ class applyPowerPaint:
del pipe
return (new_pipe,)
from node_helpers import conditioning_set_values
class applyInpaint:
@classmethod
def INPUT_TYPES(s):
@@ -2652,6 +2653,9 @@ class applyInpaint:
"start_at": ("INT", {"default": 0, "min": 0, "max": 10000}),
"end_at": ("INT", {"default": 10000, "min": 0, "max": 10000}),
},
"optional":{
"noise_mask": ("BOOLEAN", {"default": True})
}
}
RETURN_TYPES = ("PIPE_LINE",)
@@ -2659,14 +2663,48 @@ class applyInpaint:
CATEGORY = "EasyUse/Inpaint"
FUNCTION = "apply"
def inpaint_model_conditioning(self, pipe, image, vae, mask, grow_mask_by):
def inpaint_model_conditioning(self, pipe, image, vae, mask, grow_mask_by, noise_mask=True):
if grow_mask_by >0:
mask, = GrowMask().expand_mask(mask, grow_mask_by, False)
positive, negative, latent = InpaintModelConditioning().encode(pipe['positive'], pipe['negative'], image,
vae, mask)
pipe['positive'] = positive
pipe['negative'] = negative
pipe['samples'] = latent
positive, negative, = pipe['positive'], pipe['negative']
pixels = image
x = (pixels.shape[1] // 8) * 8
y = (pixels.shape[2] // 8) * 8
mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])),
size=(pixels.shape[1], pixels.shape[2]), mode="bilinear")
orig_pixels = pixels
pixels = orig_pixels.clone()
if pixels.shape[1] != x or pixels.shape[2] != y:
x_offset = (pixels.shape[1] % 8) // 2
y_offset = (pixels.shape[2] % 8) // 2
pixels = pixels[:, x_offset:x + x_offset, y_offset:y + y_offset, :]
mask = mask[:, :, x_offset:x + x_offset, y_offset:y + y_offset]
m = (1.0 - mask.round()).squeeze(1)
for i in range(3):
pixels[:, :, :, i] -= 0.5
pixels[:, :, :, i] *= m
pixels[:, :, :, i] += 0.5
concat_latent = vae.encode(pixels)
orig_latent = vae.encode(orig_pixels)
out_latent = {}
out_latent["samples"] = orig_latent
if noise_mask:
out_latent["noise_mask"] = mask
out = []
for conditioning in [positive, negative]:
c = conditioning_set_values(conditioning, {"concat_latent_image": concat_latent,
"concat_mask": mask})
out.append(c)
pipe['positive'] = out[0]
pipe['negative'] = out[1]
pipe['samples'] = out_latent
return pipe
@@ -2711,7 +2749,7 @@ class applyInpaint:
clip_name = os.path.join("powerpaint",os.path.basename(clip_parsed_url.path))
return model_name, clip_name
def apply(self, pipe, image, mask, inpaint_mode, encode, grow_mask_by, dtype, fitting, function, scale, start_at, end_at):
def apply(self, pipe, image, mask, inpaint_mode, encode, grow_mask_by, dtype, fitting, function, scale, start_at, end_at, noise_mask=True):
new_pipe = {
**pipe,
}
@@ -2746,9 +2784,9 @@ class applyInpaint:
list(FOOOCUS_INPAINT_HEAD.keys())[0],
list(FOOOCUS_INPAINT_PATCH.keys())[0])
new_pipe['model'] = model
new_pipe = self.inpaint_model_conditioning(new_pipe, image, vae, mask, 0)
new_pipe = self.inpaint_model_conditioning(new_pipe, image, vae, mask, 0, noise_mask=noise_mask)
else:
new_pipe = self.inpaint_model_conditioning(new_pipe, image, vae, mask, grow_mask_by)
new_pipe = self.inpaint_model_conditioning(new_pipe, image, vae, mask, grow_mask_by, noise_mask=noise_mask)
elif encode == 'different_diffusion':
if inpaint_mode == 'fooocus_inpaint':
latent, = VAEEncodeForInpaint().encode(vae, image, mask, grow_mask_by)
@@ -2757,9 +2795,9 @@ class applyInpaint:
list(FOOOCUS_INPAINT_HEAD.keys())[0],
list(FOOOCUS_INPAINT_PATCH.keys())[0])
new_pipe['model'] = model
new_pipe = self.inpaint_model_conditioning(new_pipe, image, vae, mask, 0)
new_pipe = self.inpaint_model_conditioning(new_pipe, image, vae, mask, 0, noise_mask=noise_mask)
else:
new_pipe = self.inpaint_model_conditioning(new_pipe, image, vae, mask, grow_mask_by)
new_pipe = self.inpaint_model_conditioning(new_pipe, image, vae, mask, grow_mask_by, noise_mask=noise_mask)
cls = ALL_NODE_CLASS_MAPPINGS['DifferentialDiffusion']
if cls is not None:
model, = cls().apply(new_pipe['model'])
@@ -4319,7 +4357,7 @@ class samplerCustomSettings:
def INPUT_TYPES(cls):
return {"required": {
"pipe": ("PIPE_LINE",),
"guider": (['CFG','DualCFG','IP2P+DualCFG','Basic'],{"default":"Basic"}),
"guider": (['CFG','DualCFG','Basic', 'IP2P+CFG', 'IP2P+DualCFG','IP2P+Basic'],{"default":"Basic"}),
"cfg": ("FLOAT", {"default": 3.5, "min": 0.0, "max": 100.0}),
"cfg_negative": ("FLOAT", {"default": 1.5, "min": 0.0, "max": 100.0}),
"sampler_name": (comfy.samplers.KSampler.SAMPLERS + ['inversed_euler'],),
@@ -4353,6 +4391,35 @@ class samplerCustomSettings:
FUNCTION = "settings"
CATEGORY = "EasyUse/PreSampling"
def ip2p(self, positive, negative, vae, pixels, latent=None):
if latent is not None:
concat_latent = latent
else:
x = (pixels.shape[1] // 8) * 8
y = (pixels.shape[2] // 8) * 8
if pixels.shape[1] != x or pixels.shape[2] != y:
x_offset = (pixels.shape[1] % 8) // 2
y_offset = (pixels.shape[2] % 8) // 2
pixels = pixels[:,x_offset:x + x_offset, y_offset:y + y_offset,:]
concat_latent = vae.encode(pixels)
out_latent = {}
out_latent["samples"] = torch.zeros_like(concat_latent)
out = []
for conditioning in [positive, negative]:
c = []
for t in conditioning:
d = t[1].copy()
d["concat_latent_image"] = concat_latent
n = [t[0], d]
c.append(n)
out.append(c)
return (out[0], out[1], out_latent)
def settings(self, pipe, guider, cfg, cfg_negative, sampler_name, scheduler, coeff, steps, sigma_max, sigma_min, rho, beta_d, beta_min, eps_s, flip_sigmas, denoise, add_noise, seed, image_to_latent=None, latent=None, optional_sampler=None, optional_sigmas=None, prompt=None, extra_pnginfo=None, my_unique_id=None):
# 图生图转换
@@ -4368,7 +4435,7 @@ class samplerCustomSettings:
samples = pipe["samples"]
images = pipe["images"]
else:
if guider == "IP2P+DualCFG":
if "IP2P" in guider:
positive, negative, latent = self.ip2p(pipe['positive'], pipe['negative'], vae, image_to_latent)
samples = latent
else:
@@ -4376,7 +4443,7 @@ class samplerCustomSettings:
samples = RepeatLatentBatch().repeat(samples, batch_size)[0]
images = image_to_latent
elif latent is not None:
if guider == "IP2P+DualCFG":
if "IP2P" in guider:
positive, negative, latent = self.ip2p(pipe['positive'], pipe['negative'], latent=latent)
samples = latent
else:
@@ -5127,7 +5194,7 @@ class samplerFull:
c.append(n)
positive = c
if guider == 'CFG':
if guider in ['CFG', 'IP2P+CFG']:
_guider, = self.get_custom_cls('CFGGuider').get_guider(model, positive, negative, cfg)
elif guider in ['DualCFG', 'IP2P+DualCFG']:
_guider, = self.get_custom_cls('DualCFGGuider').get_guider(model, positive, middle,