jaidaken
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Includes 30 custom nodes committed directly, 7 Civitai-exclusive loras stored via Git LFS, and a setup script that installs all dependencies and downloads HuggingFace-hosted models on vast.ai. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
66 lines
2.4 KiB
Python
66 lines
2.4 KiB
Python
import comfy.samplers
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import torch
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import comfy.sample
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import latent_preview
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FLUX_SAMPLER_NAMES = [
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"euler", "heun", "heunpp2", "dpm_2", "lms", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_2m",
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"ipndm", "ipndm_v", "deis", "ddim", "uni_pc", "uni_pc_bh2"
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]
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FLUX_SCHEDULER_NAMES = ["simple", "normal", "sgm_uniform", "ddim_uniform", "beta"]
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class FluxSampler:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"model": ("MODEL",),
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"conditioning": ("CONDITIONING",),
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"latent_image": ("LATENT",),
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"sampler_name": (FLUX_SAMPLER_NAMES, {"default": "euler"}),
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"scheduler": (FLUX_SCHEDULER_NAMES, {"default": "beta"}),
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"steps": ("INT", {"default": 30, "min": 1, "max": 10000}),
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"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
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"noise_seed": ("INT", {"default": 143220275975594, "min": 0, "max": 0xffffffffffffffff}),
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}
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}
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RETURN_TYPES = ("LATENT",)
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RETURN_NAMES = ("latent",)
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FUNCTION = "sample"
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CATEGORY = "ControlAltAI Nodes/Flux"
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def sample(self, model, conditioning, latent_image, sampler_name, scheduler, steps, denoise, noise_seed):
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device = comfy.model_management.get_torch_device()
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sampler = comfy.samplers.KSampler(model, steps=steps, device=device, sampler=sampler_name, scheduler=scheduler, denoise=denoise)
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latent = latent_image.copy()
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latent_image = latent["samples"]
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# Handle noise_mask if present
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noise_mask = latent.get("noise_mask", None)
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noise = comfy.sample.prepare_noise(latent_image, noise_seed)
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positive = conditioning
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negative = [] # Empty list for negative conditioning
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callback = latent_preview.prepare_callback(model, steps)
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disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED
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samples = sampler.sample(noise, positive, negative, cfg=1.0, latent_image=latent_image,
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force_full_denoise=True, denoise_mask=noise_mask, callback=callback,
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disable_pbar=disable_pbar, seed=noise_seed)
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out = latent.copy()
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out["samples"] = samples
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return (out,)
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NODE_CLASS_MAPPINGS = {
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"FluxSampler": FluxSampler
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"FluxSampler": "Flux Sampler"
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} |