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Add optional custom sigmas input #6

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Add optional custom sigmas input #6

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16 changes: 10 additions & 6 deletions blockpatcher.py
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Original file line number Diff line number Diff line change
Expand Up @@ -14,31 +14,34 @@
FONTS_DIR = os.path.join(os.path.dirname(os.path.realpath(__file__)), "fonts")


class FluxBlockPatcherSampler:
class FluxBlockPatcherSampler:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"model": ("MODEL", ),
"conditioning": ("CONDITIONING", ),
"latent_image": ("LATENT", ),

"noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"steps": ("INT", {"default": 24, "min": 1, "max": 10000}),
"sampler": (comfy.samplers.KSampler.SAMPLERS, ),
"scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
"guidance": ("FLOAT", {"default": 3.5, "min": -10.0, "max": 10.0, "step": 0.1}),
"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.05}),

"blocks": ("STRING", {"multiline": True, "dynamicPrompts": True, "default": "double_blocks\.([0-9]+)\.(img|txt)_(mod|attn|mlp\.[02])\.(lin|qkv|proj)\.(weight|bias)=1.1\nsingle_blocks\.([0-9]+)\.(linear[12]|modulation\.lin)\.(weight|bias)=1.1"}),
},
"optional": {
"sigmas": ("SIGMAS",),
}
}

RETURN_TYPES = ("LATENT", "SAMPLER_PARAMS", "STRING",)
RETURN_NAMES = ("latent", "sampler_params", "patched_blocks",)
FUNCTION = "apply_style"

def apply_style(self, model, conditioning, latent_image, noise_seed, steps, sampler, scheduler, guidance, denoise, blocks):
def apply_style(self, model, conditioning, latent_image, noise_seed, steps, sampler, scheduler, guidance, denoise, blocks, sigmas=None):
# is_schnell = model.model.model_type == comfy.model_base.ModelType.FLOW

sd = model.model_state_dict()
Expand All @@ -49,9 +52,10 @@ def apply_style(self, model, conditioning, latent_image, noise_seed, steps, samp
patched_blocks = []
fbi_params = []
out_latent = None

noise = Noise_RandomNoise(noise_seed)
sigmas = BasicScheduler().get_sigmas(model, scheduler, steps, denoise)[0]
if sigmas is None:
sigmas = BasicScheduler().get_sigmas(model, scheduler, steps, denoise)[0]
cond = conditioning_set_values(conditioning, {"guidance": guidance})
sca = SamplerCustomAdvanced()
latentbatch = LatentBatch()
Expand Down