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sync with repo 28.08

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netadmin
2024-08-28 19:33:34 +03:00
parent 727693318c
commit ad1e3ecbcb
134 changed files with 112534 additions and 12635 deletions
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128
nodes.py
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@@ -47,11 +47,18 @@ MAX_RESOLUTION=16384
class CLIPTextEncode:
@classmethod
def INPUT_TYPES(s):
return {"required": {"text": ("STRING", {"multiline": True, "dynamicPrompts": True}), "clip": ("CLIP", )}}
return {
"required": {
"text": ("STRING", {"multiline": True, "dynamicPrompts": True, "tooltip": "The text to be encoded."}),
"clip": ("CLIP", {"tooltip": "The CLIP model used for encoding the text."})
}
}
RETURN_TYPES = ("CONDITIONING",)
OUTPUT_TOOLTIPS = ("A conditioning containing the embedded text used to guide the diffusion model.",)
FUNCTION = "encode"
CATEGORY = "conditioning"
DESCRIPTION = "Encodes a text prompt using a CLIP model into an embedding that can be used to guide the diffusion model towards generating specific images."
def encode(self, clip, text):
tokens = clip.tokenize(text)
@@ -260,11 +267,18 @@ class ConditioningSetTimestepRange:
class VAEDecode:
@classmethod
def INPUT_TYPES(s):
return {"required": { "samples": ("LATENT", ), "vae": ("VAE", )}}
return {
"required": {
"samples": ("LATENT", {"tooltip": "The latent to be decoded."}),
"vae": ("VAE", {"tooltip": "The VAE model used for decoding the latent."})
}
}
RETURN_TYPES = ("IMAGE",)
OUTPUT_TOOLTIPS = ("The decoded image.",)
FUNCTION = "decode"
CATEGORY = "latent"
DESCRIPTION = "Decodes latent images back into pixel space images."
def decode(self, vae, samples):
return (vae.decode(samples["samples"]), )
@@ -506,12 +520,19 @@ class CheckpointLoader:
class CheckpointLoaderSimple:
@classmethod
def INPUT_TYPES(s):
return {"required": { "ckpt_name": (folder_paths.get_filename_list("checkpoints"), ),
}}
return {
"required": {
"ckpt_name": (folder_paths.get_filename_list("checkpoints"), {"tooltip": "The name of the checkpoint (model) to load."}),
}
}
RETURN_TYPES = ("MODEL", "CLIP", "VAE")
OUTPUT_TOOLTIPS = ("The model used for denoising latents.",
"The CLIP model used for encoding text prompts.",
"The VAE model used for encoding and decoding images to and from latent space.")
FUNCTION = "load_checkpoint"
CATEGORY = "loaders"
DESCRIPTION = "Loads a diffusion model checkpoint, diffusion models are used to denoise latents."
def load_checkpoint(self, ckpt_name):
ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
@@ -582,16 +603,22 @@ class LoraLoader:
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"clip": ("CLIP", ),
"lora_name": (folder_paths.get_filename_list("loras"), ),
"strength_model": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01}),
"strength_clip": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01}),
}}
return {
"required": {
"model": ("MODEL", {"tooltip": "The diffusion model the LoRA will be applied to."}),
"clip": ("CLIP", {"tooltip": "The CLIP model the LoRA will be applied to."}),
"lora_name": (folder_paths.get_filename_list("loras"), {"tooltip": "The name of the LoRA."}),
"strength_model": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01, "tooltip": "How strongly to modify the diffusion model. This value can be negative."}),
"strength_clip": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01, "tooltip": "How strongly to modify the CLIP model. This value can be negative."}),
}
}
RETURN_TYPES = ("MODEL", "CLIP")
OUTPUT_TOOLTIPS = ("The modified diffusion model.", "The modified CLIP model.")
FUNCTION = "load_lora"
CATEGORY = "loaders"
DESCRIPTION = "LoRAs are used to modify diffusion and CLIP models, altering the way in which latents are denoised such as applying styles. Multiple LoRA nodes can be linked together."
def load_lora(self, model, clip, lora_name, strength_model, strength_clip):
if strength_model == 0 and strength_clip == 0:
@@ -638,6 +665,8 @@ class VAELoader:
sd1_taesd_dec = False
sd3_taesd_enc = False
sd3_taesd_dec = False
f1_taesd_enc = False
f1_taesd_dec = False
for v in approx_vaes:
if v.startswith("taesd_decoder."):
@@ -652,12 +681,18 @@ class VAELoader:
sd3_taesd_dec = True
elif v.startswith("taesd3_encoder."):
sd3_taesd_enc = True
elif v.startswith("taef1_encoder."):
f1_taesd_dec = True
elif v.startswith("taef1_decoder."):
f1_taesd_enc = True
if sd1_taesd_dec and sd1_taesd_enc:
vaes.append("taesd")
if sdxl_taesd_dec and sdxl_taesd_enc:
vaes.append("taesdxl")
if sd3_taesd_dec and sd3_taesd_enc:
vaes.append("taesd3")
if f1_taesd_dec and f1_taesd_enc:
vaes.append("taef1")
return vaes
@staticmethod
@@ -685,6 +720,9 @@ class VAELoader:
elif name == "taesd3":
sd["vae_scale"] = torch.tensor(1.5305)
sd["vae_shift"] = torch.tensor(0.0609)
elif name == "taef1":
sd["vae_scale"] = torch.tensor(0.3611)
sd["vae_shift"] = torch.tensor(0.1159)
return sd
@classmethod
@@ -697,7 +735,7 @@ class VAELoader:
#TODO: scale factor?
def load_vae(self, vae_name):
if vae_name in ["taesd", "taesdxl", "taesd3"]:
if vae_name in ["taesd", "taesdxl", "taesd3", "taef1"]:
sd = self.load_taesd(vae_name)
else:
vae_path = folder_paths.get_full_path("vae", vae_name)
@@ -817,7 +855,7 @@ class ControlNetApplyAdvanced:
class UNETLoader:
@classmethod
def INPUT_TYPES(s):
return {"required": { "unet_name": (folder_paths.get_filename_list("unet"), ),
return {"required": { "unet_name": (folder_paths.get_filename_list("diffusion_models"), ),
"weight_dtype": (["default", "fp8_e4m3fn", "fp8_e5m2"],)
}}
RETURN_TYPES = ("MODEL",)
@@ -826,14 +864,14 @@ class UNETLoader:
CATEGORY = "advanced/loaders"
def load_unet(self, unet_name, weight_dtype):
dtype = None
model_options = {}
if weight_dtype == "fp8_e4m3fn":
dtype = torch.float8_e4m3fn
model_options["dtype"] = torch.float8_e4m3fn
elif weight_dtype == "fp8_e5m2":
dtype = torch.float8_e5m2
model_options["dtype"] = torch.float8_e5m2
unet_path = folder_paths.get_full_path("unet", unet_name)
model = comfy.sd.load_unet(unet_path, dtype=dtype)
unet_path = folder_paths.get_full_path("diffusion_models", unet_name)
model = comfy.sd.load_diffusion_model(unet_path, model_options=model_options)
return (model,)
class CLIPLoader:
@@ -1033,13 +1071,19 @@ class EmptyLatentImage:
@classmethod
def INPUT_TYPES(s):
return {"required": { "width": ("INT", {"default": 512, "min": 16, "max": MAX_RESOLUTION, "step": 8}),
"height": ("INT", {"default": 512, "min": 16, "max": MAX_RESOLUTION, "step": 8}),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096})}}
return {
"required": {
"width": ("INT", {"default": 512, "min": 16, "max": MAX_RESOLUTION, "step": 8, "tooltip": "The width of the latent images in pixels."}),
"height": ("INT", {"default": 512, "min": 16, "max": MAX_RESOLUTION, "step": 8, "tooltip": "The height of the latent images in pixels."}),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096, "tooltip": "The number of latent images in the batch."})
}
}
RETURN_TYPES = ("LATENT",)
OUTPUT_TOOLTIPS = ("The empty latent image batch.",)
FUNCTION = "generate"
CATEGORY = "latent"
DESCRIPTION = "Create a new batch of empty latent images to be denoised via sampling."
def generate(self, width, height, batch_size=1):
latent = torch.zeros([batch_size, 4, height // 8, width // 8], device=self.device)
@@ -1359,24 +1403,27 @@ def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive,
class KSampler:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"model": ("MODEL",),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"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}),
"sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
"scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
"positive": ("CONDITIONING", ),
"negative": ("CONDITIONING", ),
"latent_image": ("LATENT", ),
"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
}
}
return {
"required": {
"model": ("MODEL", {"tooltip": "The model used for denoising the input latent."}),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "The random seed used for creating the noise."}),
"steps": ("INT", {"default": 20, "min": 1, "max": 10000, "tooltip": "The number of steps used in the denoising process."}),
"cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01, "tooltip": "The Classifier-Free Guidance scale balances creativity and adherence to the prompt. Higher values result in images more closely matching the prompt however too high values will negatively impact quality."}),
"sampler_name": (comfy.samplers.KSampler.SAMPLERS, {"tooltip": "The algorithm used when sampling, this can affect the quality, speed, and style of the generated output."}),
"scheduler": (comfy.samplers.KSampler.SCHEDULERS, {"tooltip": "The scheduler controls how noise is gradually removed to form the image."}),
"positive": ("CONDITIONING", {"tooltip": "The conditioning describing the attributes you want to include in the image."}),
"negative": ("CONDITIONING", {"tooltip": "The conditioning describing the attributes you want to exclude from the image."}),
"latent_image": ("LATENT", {"tooltip": "The latent image to denoise."}),
"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "The amount of denoising applied, lower values will maintain the structure of the initial image allowing for image to image sampling."}),
}
}
RETURN_TYPES = ("LATENT",)
OUTPUT_TOOLTIPS = ("The denoised latent.",)
FUNCTION = "sample"
CATEGORY = "sampling"
DESCRIPTION = "Uses the provided model, positive and negative conditioning to denoise the latent image."
def sample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0):
return common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise)
@@ -1424,11 +1471,15 @@ class SaveImage:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"images": ("IMAGE", ),
"filename_prefix": ("STRING", {"default": "ComfyUI"})},
"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
}
return {
"required": {
"images": ("IMAGE", {"tooltip": "The images to save."}),
"filename_prefix": ("STRING", {"default": "ComfyUI", "tooltip": "The prefix for the file to save. This may include formatting information such as %date:yyyy-MM-dd% or %Empty Latent Image.width% to include values from nodes."})
},
"hidden": {
"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"
},
}
RETURN_TYPES = ()
FUNCTION = "save_images"
@@ -1436,6 +1487,7 @@ class SaveImage:
OUTPUT_NODE = True
CATEGORY = "image"
DESCRIPTION = "Saves the input images to your ComfyUI output directory."
def save_images(self, images, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None):
filename_prefix += self.prefix_append