Update ComfyUI_Pops.py
add
This commit is contained in:
smthemex
+101
-186
@@ -418,170 +418,8 @@ class Pops_Prior_Embedding:
|
||||
return (decoder,prior,prior_pipeline,positive, negative,input_hidden_state,img_emb_file)
|
||||
|
||||
|
||||
class Pops_Unet_Sampler:
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"model": ("MODEL",),
|
||||
"positive": ("CONDITIONING",),
|
||||
"negative": ("CONDITIONING",),
|
||||
"seed": ("INT", {"default": 2, "min": 1, "max": MAX_SEED}),
|
||||
"steps": ("INT", {"default": 25, "min": 1, "max": 4096}),
|
||||
"guidance_scale": (
|
||||
"FLOAT", {"default": 1.0, "min": 0.1, "max": 24.0, "step": 0.1, "round": False}),
|
||||
"height": ("INT", {"default": 1024, "min": 256, "max": 4096, "step": 64}),
|
||||
"width": ("INT", {"default": 1024, "min": 256, "max": 4096, "step": 64})
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("IMAGE",)
|
||||
RETURN_NAMES = ("image",)
|
||||
FUNCTION = "pops_unet_sampler"
|
||||
CATEGORY = "Pops"
|
||||
|
||||
def pops_unet_sampler(self,model, positive,negative, seed, steps,guidance_scale,height,width):
|
||||
device="cuda"
|
||||
images = model(image_embeds=positive, negative_image_embeds=negative,
|
||||
num_inference_steps=steps, height=height,
|
||||
width=width, guidance_scale=guidance_scale,
|
||||
generator=torch.Generator(device=device).manual_seed(seed)).images
|
||||
images=phi2narry(images[0])
|
||||
return (images,)
|
||||
|
||||
|
||||
class Pops_Ipadapter_Sampler:
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"positive": ("CONDITIONING",),
|
||||
"checkpoints": (folder_paths.get_filename_list("checkpoints"),),
|
||||
"seed": ("INT", {"default": 2, "min": 1, "max": MAX_SEED}),
|
||||
"steps": ("INT", {"default": 25, "min": 1, "max": 4096}),
|
||||
"guidance_scale": (
|
||||
"FLOAT", {"default": 1.0, "min": 0.1, "max": 24.0, "step": 0.1, "round": False}),
|
||||
"height": ("INT", {"default": 1024, "min": 256, "max": 4096, "step": 64}),
|
||||
"width": ("INT", {"default": 1024, "min": 256, "max": 4096, "step": 64})
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("IMAGE",)
|
||||
RETURN_NAMES = ("image",)
|
||||
FUNCTION = "pops_adapter_sampler"
|
||||
CATEGORY = "Pops"
|
||||
|
||||
def pops_adapter_sampler(self,positive,checkpoints,seed,steps,guidance_scale,height,width):
|
||||
base_diffuser = get_instance_path(folder_paths.get_full_path("checkpoints", checkpoints))
|
||||
device="cuda"
|
||||
ipadapter_model = get_instance_path(os.path.join(dir_path, "weights","ip-adapter_sdxl.bin"))
|
||||
original_config_file = get_instance_path(os.path.join(dir_path, "weights", "config", "sd_xl_base.yaml"))
|
||||
ip_pipeline = StableDiffusionXLPipeline.from_single_file(base_diffuser,
|
||||
original_config_file=original_config_file,
|
||||
torch_dtype=torch.float16)
|
||||
if os.path.exists(ipadapter_model):
|
||||
ip_pipeline.load_ip_adapter(ipadapter_model,
|
||||
subfolder="",
|
||||
weight_name="ip-adapter_sdxl.bin", image_encoder_folder=None)
|
||||
else:
|
||||
ip_pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="sdxl_models", weight_name="ip-adapter_sdxl.bin",
|
||||
image_encoder_folder=None)
|
||||
ip_pipeline.to(device)
|
||||
ip_pipeline.set_ip_adapter_scale(guidance_scale)
|
||||
images = ip_pipeline(prompt="", ip_adapter_image_embeds=[
|
||||
torch.stack([torch.zeros_like(positive), positive])],
|
||||
negative_prompt="deformed, ugly, wrong proportion, low res, bad anatomy, worst quality, low quality",
|
||||
num_inference_steps=steps,
|
||||
height=height,
|
||||
width=width,
|
||||
generator=torch.Generator(device="cuda").manual_seed(seed),
|
||||
).images
|
||||
images=phi2narry(images[0])
|
||||
return (images,)
|
||||
|
||||
|
||||
class Pops_Controlnet_Sampler:
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"image": ("IMAGE", ),
|
||||
"vae": (folder_paths.get_filename_list("vae"),),
|
||||
"positive": ("CONDITIONING",),
|
||||
"control_net": ("CONTROL_NET",),
|
||||
"checkpoints": (folder_paths.get_filename_list("checkpoints"),),
|
||||
"seed": ("INT", {"default": 2, "min": 1, "max": MAX_SEED}),
|
||||
"steps": ("INT", {"default": 25, "min": 1, "max": 4096}),
|
||||
"guidance_scale": (
|
||||
"FLOAT", {"default": 1.0, "min": 0.1, "max": 24.0, "step": 0.1, "round": False}),
|
||||
"controlnet_scale": (
|
||||
"FLOAT", {"default": 0.5, "min": 0.1, "max": 24.0, "step": 0.1, "round": False}),
|
||||
"height": ("INT", {"default": 1024, "min": 256, "max": 4096, "step": 64}),
|
||||
"width": ("INT", {"default": 1024, "min": 256, "max": 4096, "step": 64})
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("IMAGE",)
|
||||
RETURN_NAMES = ("image",)
|
||||
FUNCTION = "pops_controlnet_sampler"
|
||||
CATEGORY = "Pops"
|
||||
|
||||
def pops_controlnet_sampler(self,image,vae,positive,control_net,checkpoints,seed,steps,guidance_scale,controlnet_scale,height,width):
|
||||
ss = ImageScale()
|
||||
image = ss.upscale(image, "lanczos", height, width, "center")[0]
|
||||
base_diffuser = get_instance_path(folder_paths.get_full_path("checkpoints", checkpoints))
|
||||
vae=get_instance_path(folder_paths.get_full_path("vae", vae))
|
||||
device = "cuda"
|
||||
ipadapter_model = get_instance_path(os.path.join(dir_path, "weights","ip-adapter_sdxl.bin"))
|
||||
original_config_file = get_instance_path(os.path.join(dir_path, "weights", "config", "sd_xl_base.yaml"))
|
||||
config_file=get_instance_path(os.path.join(file_path,"models","configs","v1-inference.yaml"))
|
||||
vae = AutoencoderKL.from_single_file(vae, config_file=config_file,torch_dtype=torch.float16).to("cuda")
|
||||
pipeline = StableDiffusionXLPipeline.from_single_file(base_diffuser,
|
||||
original_config_file=original_config_file,
|
||||
controlnet=control_net,
|
||||
vae=vae,
|
||||
variant="fp16",
|
||||
use_safetensors=True,
|
||||
torch_dtype=torch.float16)
|
||||
pipeline.enable_xformers_memory_efficient_attention()
|
||||
pipeline.enable_vae_tiling()
|
||||
|
||||
# 加载ip
|
||||
if os.path.exists(ipadapter_model):
|
||||
pipeline.load_ip_adapter(ipadapter_model,
|
||||
subfolder="",
|
||||
weight_name="ip-adapter_sdxl.bin", image_encoder_folder=None)
|
||||
else:
|
||||
pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="sdxl_models", weight_name="ip-adapter_sdxl.bin",
|
||||
image_encoder_folder=None)
|
||||
pipeline.to(device)
|
||||
pipeline.set_ip_adapter_scale(guidance_scale)
|
||||
pipeline.enable_model_cpu_offload()
|
||||
|
||||
images = pipeline(
|
||||
prompt="",
|
||||
image=image,
|
||||
ip_adapter_image_embeds=[
|
||||
torch.stack([torch.zeros_like(positive), positive])],
|
||||
negative_prompt="",
|
||||
num_inference_steps=steps,
|
||||
controlnet_conditioning_scale=controlnet_scale,
|
||||
generator=torch.Generator(device="cuda").manual_seed(seed),
|
||||
).images
|
||||
images=phi2narry(images[0])
|
||||
return (images,)
|
||||
|
||||
|
||||
class Pops_Mean_Sampler:
|
||||
class Pops_Sampler:
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
@@ -592,33 +430,116 @@ class Pops_Mean_Sampler:
|
||||
"model": ("MODEL",),
|
||||
"prior": ("MODEL",),
|
||||
"prior_pipeline": ("MODEL",),
|
||||
"positive": ("CONDITIONING",),
|
||||
"negative": ("CONDITIONING",),
|
||||
"input_hidden_state": ("CONDITIONING",),
|
||||
"checkpoints": (folder_paths.get_filename_list("checkpoints"),),
|
||||
"vae": (folder_paths.get_filename_list("vae"),),
|
||||
"sampler_type":(["Unet","IP_adapter","Controlnet","Mean"],),
|
||||
"seed": ("INT", {"default": 2, "min": 1, "max": MAX_SEED}),
|
||||
"steps": ("INT", {"default": 25, "min": 1, "max": 4096}),
|
||||
"guidance_scale": (
|
||||
"FLOAT", {"default": 4.0, "min": 0.1, "max": 50.0, "step": 0.1, "round": False}),
|
||||
"FLOAT", {"default": 1.0, "min": 0.1, "max": 24.0, "step": 0.1, "round": False}),
|
||||
"height": ("INT", {"default": 1024, "min": 256, "max": 4096, "step": 64}),
|
||||
"width": ("INT", {"default": 1024, "min": 256, "max": 4096, "step": 64})
|
||||
}
|
||||
"width": ("INT", {"default": 1024, "min": 256, "max": 4096, "step": 64}),
|
||||
"controlnet_scale": (
|
||||
"FLOAT", {"default": 0.5, "min": 0.1, "max": 24.0, "step": 0.1, "round": False})
|
||||
},
|
||||
"optional": {"image": ("IMAGE",),
|
||||
"control_net": ("CONTROL_NET",),}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("IMAGE",)
|
||||
RETURN_NAMES = ("image",)
|
||||
FUNCTION = "pops_mean_sampler"
|
||||
FUNCTION = "pops_sampler"
|
||||
CATEGORY = "Pops"
|
||||
|
||||
def pops_mean_sampler(self,model,prior,prior_pipeline,input_hidden_state,seed,steps,guidance_scale,height,width):
|
||||
device = "cuda"
|
||||
mean_emb = 0.5 * input_hidden_state[:, 0] + 0.5 * input_hidden_state[:, 1]
|
||||
mean_emb = (mean_emb * prior.clip_std) + prior.clip_mean
|
||||
del prior
|
||||
zero_embeds = prior_pipeline.get_zero_embed(mean_emb.shape[0], device=mean_emb.device)
|
||||
del prior_pipeline
|
||||
images = model(image_embeds=mean_emb, negative_image_embeds=zero_embeds,
|
||||
num_inference_steps=steps, height=height,
|
||||
width=width, guidance_scale=guidance_scale,
|
||||
generator=torch.Generator(device=device).manual_seed(seed)).images
|
||||
def pops_sampler(self,model,prior,prior_pipeline, positive,negative,input_hidden_state, checkpoints,vae,sampler_type,seed, steps,guidance_scale,height,width,controlnet_scale,**kwargs):
|
||||
device="cuda"
|
||||
if sampler_type=="Unet":
|
||||
del prior, prior_pipeline
|
||||
images = model(image_embeds=positive, negative_image_embeds=negative,
|
||||
num_inference_steps=steps, height=height,
|
||||
width=width, guidance_scale=guidance_scale,
|
||||
generator=torch.Generator(device=device).manual_seed(seed)).images
|
||||
elif sampler_type=="IP_adapter":
|
||||
base_diffuser = get_instance_path(folder_paths.get_full_path("checkpoints", checkpoints))
|
||||
ipadapter_model = get_instance_path(os.path.join(dir_path, "weights", "ip-adapter_sdxl.bin"))
|
||||
original_config_file = get_instance_path(os.path.join(dir_path, "weights", "config", "sd_xl_base.yaml"))
|
||||
ip_pipeline = StableDiffusionXLPipeline.from_single_file(base_diffuser,
|
||||
original_config_file=original_config_file,
|
||||
torch_dtype=torch.float16)
|
||||
if os.path.exists(ipadapter_model):
|
||||
ip_pipeline.load_ip_adapter(ipadapter_model,
|
||||
subfolder="",
|
||||
weight_name="ip-adapter_sdxl.bin", image_encoder_folder=None)
|
||||
else:
|
||||
ip_pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="sdxl_models",
|
||||
weight_name="ip-adapter_sdxl.bin",
|
||||
image_encoder_folder=None)
|
||||
ip_pipeline.to(device)
|
||||
ip_pipeline.set_ip_adapter_scale(guidance_scale)
|
||||
images = ip_pipeline(prompt="", ip_adapter_image_embeds=[
|
||||
torch.stack([torch.zeros_like(positive), positive])],
|
||||
negative_prompt="deformed, ugly, wrong proportion, low res, bad anatomy, worst quality, low quality",
|
||||
num_inference_steps=steps,
|
||||
height=height,
|
||||
width=width,
|
||||
generator=torch.Generator(device="cuda").manual_seed(seed),
|
||||
).images
|
||||
elif sampler_type=="Controlnet":
|
||||
del model,prior,prior_pipeline
|
||||
image = kwargs["image"]
|
||||
control_net=kwargs["control_net"]
|
||||
ss = ImageScale()
|
||||
image = ss.upscale(image, "lanczos", height, width, "center")[0]
|
||||
base_diffuser = get_instance_path(folder_paths.get_full_path("checkpoints", checkpoints))
|
||||
vae = get_instance_path(folder_paths.get_full_path("vae", vae))
|
||||
ipadapter_model = get_instance_path(os.path.join(dir_path, "weights", "ip-adapter_sdxl.bin"))
|
||||
original_config_file = get_instance_path(os.path.join(dir_path, "weights", "config", "sd_xl_base.yaml"))
|
||||
config_file = get_instance_path(os.path.join(file_path, "models", "configs", "v1-inference.yaml"))
|
||||
vae = AutoencoderKL.from_single_file(vae, config_file=config_file, torch_dtype=torch.float16).to("cuda")
|
||||
pipeline = StableDiffusionXLPipeline.from_single_file(base_diffuser,
|
||||
original_config_file=original_config_file,
|
||||
controlnet=control_net,
|
||||
vae=vae,
|
||||
variant="fp16",
|
||||
use_safetensors=True,
|
||||
torch_dtype=torch.float16)
|
||||
pipeline.enable_xformers_memory_efficient_attention()
|
||||
pipeline.enable_vae_tiling()
|
||||
# 加载ip
|
||||
if os.path.exists(ipadapter_model):
|
||||
pipeline.load_ip_adapter(ipadapter_model,
|
||||
subfolder="",
|
||||
weight_name="ip-adapter_sdxl.bin", image_encoder_folder=None)
|
||||
else:
|
||||
pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="sdxl_models", weight_name="ip-adapter_sdxl.bin",
|
||||
image_encoder_folder=None)
|
||||
pipeline.to(device)
|
||||
pipeline.set_ip_adapter_scale(guidance_scale)
|
||||
pipeline.enable_model_cpu_offload()
|
||||
|
||||
images = pipeline(
|
||||
prompt="",
|
||||
image=image,
|
||||
ip_adapter_image_embeds=[
|
||||
torch.stack([torch.zeros_like(positive), positive])],
|
||||
negative_prompt="",
|
||||
num_inference_steps=steps,
|
||||
controlnet_conditioning_scale=controlnet_scale,
|
||||
generator=torch.Generator(device="cuda").manual_seed(seed),
|
||||
).images
|
||||
else:
|
||||
mean_emb = 0.5 * input_hidden_state[:, 0] + 0.5 * input_hidden_state[:, 1]
|
||||
mean_emb = (mean_emb * prior.clip_std) + prior.clip_mean
|
||||
del prior
|
||||
zero_embeds = prior_pipeline.get_zero_embed(mean_emb.shape[0], device=mean_emb.device)
|
||||
del prior_pipeline
|
||||
images = model(image_embeds=mean_emb, negative_image_embeds=zero_embeds,
|
||||
num_inference_steps=steps, height=height,
|
||||
width=width, guidance_scale=guidance_scale,
|
||||
generator=torch.Generator(device=device).manual_seed(seed)).images
|
||||
images=phi2narry(images[0])
|
||||
return (images,)
|
||||
|
||||
@@ -665,10 +586,7 @@ class Imgae_To_Path:
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"Pops_Repo_Choice": Pops_Repo_Choice,
|
||||
"Pops_Prior_Embedding":Pops_Prior_Embedding,
|
||||
"Pops_Unet_Sampler":Pops_Unet_Sampler,
|
||||
"Pops_Ipadapter_Sampler":Pops_Ipadapter_Sampler,
|
||||
"Pops_Controlnet_Sampler":Pops_Controlnet_Sampler,
|
||||
"Pops_Mean_Sampler":Pops_Mean_Sampler,
|
||||
"Pops_Sampler":Pops_Sampler,
|
||||
"Imgae_To_Path": Imgae_To_Path,
|
||||
|
||||
}
|
||||
@@ -676,9 +594,6 @@ NODE_CLASS_MAPPINGS = {
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"Pops_Repo_Choice": "Pops_Repo_Choice",
|
||||
"Pops_Prior_Embedding":"Pops_Prior_Embedding",
|
||||
"Pops_Unet_Sampler":"Pops_Unet_Sampler",
|
||||
"Pops_Ipadapter_Sampler":"Pops_Ipadapter_Sampler",
|
||||
"Pops_Controlnet_Sampler":"Pops_Controlnet_Sampler",
|
||||
"Pops_Mean_Sampler":"Pops_Mean_Sampler",
|
||||
"Pops_Sampler":"Pops_Sampler",
|
||||
"Imgae_To_Path": "Imgae_To_Path",
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user