613 lines
22 KiB
Python
613 lines
22 KiB
Python
import copy
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import os
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import gc
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import torch
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import traceback
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from safetensors.torch import load_file
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from .utils import SCHEDULERS, token_auto_concat_embeds, vae_pt_to_vae_diffuser, convert_images_to_tensors, convert_tensors_to_images, resize_images
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from comfy.model_management import get_torch_device
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import folder_paths
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from streamdiffusion import StreamDiffusion
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from streamdiffusion.image_utils import postprocess_image
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from diffusers import StableDiffusionPipeline, AutoencoderKL, AutoencoderTiny
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from pathlib import Path
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class DiffusersPipelineLoader:
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def __init__(self):
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self.tmp_dir = folder_paths.get_temp_directory()
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self.dtype = torch.float32
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "ckpt_name": (folder_paths.get_filename_list("checkpoints"), ), }}
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RETURN_TYPES = ("PIPELINE", "AUTOENCODER", "SCHEDULER",)
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FUNCTION = "create_pipeline"
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CATEGORY = "Diffusers"
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def create_pipeline(self, ckpt_name):
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ckpt_cache_path = os.path.join(self.tmp_dir, ckpt_name)
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StableDiffusionPipeline.from_single_file(
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pretrained_model_link_or_path=folder_paths.get_full_path("checkpoints", ckpt_name),
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torch_dtype=self.dtype,
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cache_dir=self.tmp_dir,
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).save_pretrained(ckpt_cache_path, safe_serialization=True)
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pipe = StableDiffusionPipeline.from_pretrained(
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pretrained_model_name_or_path=ckpt_cache_path,
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torch_dtype=self.dtype,
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cache_dir=self.tmp_dir,
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)
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return ((pipe, ckpt_cache_path), pipe.vae, pipe.scheduler)
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class DiffusersVaeLoader:
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def __init__(self):
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self.tmp_dir = folder_paths.get_temp_directory()
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self.dtype = torch.float32
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "vae_name": (folder_paths.get_filename_list("vae"), ), }}
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RETURN_TYPES = ("AUTOENCODER",)
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FUNCTION = "create_pipeline"
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CATEGORY = "Diffusers"
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def create_pipeline(self, vae_name):
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ckpt_cache_path = os.path.join(self.tmp_dir, vae_name)
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vae_pt_to_vae_diffuser(folder_paths.get_full_path("vae", vae_name), ckpt_cache_path)
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vae = AutoencoderKL.from_pretrained(
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pretrained_model_name_or_path=ckpt_cache_path,
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torch_dtype=self.dtype,
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cache_dir=self.tmp_dir,
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)
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return (vae,)
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class DiffusersSchedulerLoader:
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def __init__(self):
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self.tmp_dir = folder_paths.get_temp_directory()
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self.dtype = torch.float32
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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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"pipeline": ("PIPELINE", ),
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"scheduler_name": (list(SCHEDULERS.keys()), ),
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}
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}
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RETURN_TYPES = ("SCHEDULER",)
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FUNCTION = "load_scheduler"
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CATEGORY = "Diffusers"
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def load_scheduler(self, pipeline, scheduler_name):
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scheduler = SCHEDULERS[scheduler_name].from_pretrained(
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pretrained_model_name_or_path=pipeline[1],
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torch_dtype=self.dtype,
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cache_dir=self.tmp_dir,
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subfolder='scheduler'
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)
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return (scheduler,)
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class DiffusersModelMakeup:
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def __init__(self):
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self.torch_device = get_torch_device()
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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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"pipeline": ("PIPELINE", ),
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"scheduler": ("SCHEDULER", ),
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"autoencoder": ("AUTOENCODER", ),
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},
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}
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RETURN_TYPES = ("MAKED_PIPELINE",)
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FUNCTION = "makeup_pipeline"
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CATEGORY = "Diffusers"
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def makeup_pipeline(self, pipeline, scheduler, autoencoder):
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pipe = pipeline[0]
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ckpt_cache_path = pipeline[1]
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pipe.vae = autoencoder
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pipe.scheduler = scheduler
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pipe.safety_checker = None if pipe.safety_checker is None else lambda images, **kwargs: (images, [False])
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pipe.enable_attention_slicing()
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pipe = pipe.to(self.torch_device)
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return ((pipe, ckpt_cache_path),)
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class DiffusersClipTextEncode:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"maked_pipeline": ("MAKED_PIPELINE", ),
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"positive": ("STRING", {"multiline": True}),
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"negative": ("STRING", {"multiline": True}),
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}}
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RETURN_TYPES = ("EMBEDS", "EMBEDS", "STRING", "STRING", )
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RETURN_NAMES = ("positive_embeds", "negative_embeds", "positive", "negative", )
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FUNCTION = "concat_embeds"
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CATEGORY = "Diffusers"
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def concat_embeds(self, maked_pipeline, positive, negative):
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positive_embeds, negative_embeds = token_auto_concat_embeds(maked_pipeline[0], positive,negative)
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return (positive_embeds, negative_embeds, positive, negative, )
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class DiffusersSampler:
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def __init__(self):
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self.torch_device = get_torch_device()
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"maked_pipeline": ("MAKED_PIPELINE", ),
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"positive_embeds": ("EMBEDS", ),
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"negative_embeds": ("EMBEDS", ),
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"width": ("INT", {"default": 512, "min": 1, "max": 8192, "step": 1}),
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"height": ("INT", {"default": 512, "min": 1, "max": 8192, "step": 1}),
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"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
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"cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}),
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"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
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}}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "sample"
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CATEGORY = "Diffusers"
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def sample(self, maked_pipeline, positive_embeds, negative_embeds, height, width, steps, cfg, seed):
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images = maked_pipeline[0](
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prompt_embeds=positive_embeds,
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height=height,
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width=width,
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num_inference_steps=steps,
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guidance_scale=cfg,
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negative_prompt_embeds=negative_embeds,
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generator=torch.Generator(self.torch_device).manual_seed(seed)
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).images
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return (convert_images_to_tensors(images),)
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# - Stream Diffusion -
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class CreateIntListNode:
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@classmethod
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def INPUT_TYPES(s):
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max_element = 10
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return {
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"required": {
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"elements_count" : ("INT", {"default": 2, "min": 1, "max": max_element, "step": 1}),
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},
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"optional": {
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f"element_{i}": ("INT", {"default": 0}) for i in range(1, max_element)
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}
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}
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RETURN_TYPES = ("LIST",)
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FUNCTION = "create_list"
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CATEGORY = "Diffusers/StreamDiffusion"
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def create_list(self, elements_count, **kwargs):
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return ([value for key, value in kwargs.items()][:elements_count], )
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class LcmLoraLoader:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "lora_name": (folder_paths.get_filename_list("loras"), ), }}
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RETURN_TYPES = ("LCM_LORA",)
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FUNCTION = "load_lora"
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CATEGORY = "Diffusers/StreamDiffusion"
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def load_lora(self, lora_name):
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return (load_file(folder_paths.get_full_path("loras", lora_name)), )
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class StreamDiffusionCreateStream:
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def __init__(self):
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self.dtype = torch.float32
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self.torch_device = get_torch_device()
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self.tmp_dir = folder_paths.get_temp_directory()
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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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"maked_pipeline": ("MAKED_PIPELINE", ),
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"t_index_list": ("LIST", ),
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"width": ("INT", {"default": 512, "min": 1, "max": 8192, "step": 1}),
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"height": ("INT", {"default": 512, "min": 1, "max": 8192, "step": 1}),
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"do_add_noise": ("BOOLEAN", {"default": True}),
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"use_denoising_batch": ("BOOLEAN", {"default": True}),
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"frame_buffer_size": ("INT", {"default": 1, "min": 1, "max": 10000}),
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"cfg_type": (["none", "full", "self", "initialize"], {"default": "none"}),
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"acceleration": (["none", "xformers", "tensorrt"], {"default": "tensorrt"}),
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"lcm_lora" : ("LCM_LORA", ),
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"tiny_vae" : ("STRING", {"default": "madebyollin/taesd"}),
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"use_lcm_lora" : ("BOOLEAN", {"default": True}),
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"use_tiny_vae" : ("BOOLEAN", {"default": True}),
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},
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}
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RETURN_TYPES = ("STREAM",)
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FUNCTION = "load_stream"
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CATEGORY = "Diffusers/StreamDiffusion"
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def load_stream(self, maked_pipeline, t_index_list, width, height, do_add_noise, use_denoising_batch, frame_buffer_size, cfg_type, acceleration, lcm_lora, tiny_vae, use_lcm_lora, use_tiny_vae):
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model_id_or_path = maked_pipeline[1]
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maked_pipeline: StableDiffusionPipeline = copy.deepcopy(maked_pipeline[0])
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lcm_lora = copy.deepcopy(lcm_lora)
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stream = StreamDiffusion(
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pipe = maked_pipeline,
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t_index_list = t_index_list,
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torch_dtype = self.dtype,
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width = width,
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height = height,
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do_add_noise = do_add_noise,
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use_denoising_batch = use_denoising_batch,
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frame_buffer_size = frame_buffer_size,
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cfg_type = cfg_type,
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)
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stream.load_lcm_lora(lcm_lora)
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stream.fuse_lora()
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stream.vae = AutoencoderTiny.from_pretrained(
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pretrained_model_name_or_path=tiny_vae,
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torch_dtype=self.dtype,
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cache_dir=self.tmp_dir,
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).to(
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device=maked_pipeline.device,
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dtype=maked_pipeline.dtype
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)
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batch_size = (
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len(t_index_list) * frame_buffer_size
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if use_denoising_batch
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else frame_buffer_size
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)
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try:
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if acceleration == "xformers":
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stream.pipe.enable_xformers_memory_efficient_attention()
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if acceleration == "tensorrt":
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from polygraphy import cuda
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from streamdiffusion.acceleration.tensorrt import (
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TorchVAEEncoder,
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compile_unet,
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compile_vae_decoder,
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compile_vae_encoder,
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)
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from streamdiffusion.acceleration.tensorrt.engine import (
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AutoencoderKLEngine,
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UNet2DConditionModelEngine,
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)
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from streamdiffusion.acceleration.tensorrt.models import (
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VAE,
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UNet,
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VAEEncoder,
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)
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def create_prefix(
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model_id_or_path: str,
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max_batch_size: int,
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min_batch_size: int,
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):
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maybe_path = Path(model_id_or_path)
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if maybe_path.exists():
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return f"{maybe_path.stem}--lcm_lora-{use_lcm_lora}--tiny_vae-{use_tiny_vae}--max_batch-{max_batch_size}--min_batch-{min_batch_size}--mode-{self.mode}"
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else:
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return f"{model_id_or_path}--lcm_lora-{use_lcm_lora}--tiny_vae-{use_tiny_vae}--max_batch-{max_batch_size}--min_batch-{min_batch_size}--mode-{self.mode}"
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engine_dir = Path(engine_dir)
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unet_path = os.path.join(
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engine_dir,
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create_prefix(
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model_id_or_path=model_id_or_path,
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max_batch_size=stream.trt_unet_batch_size,
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min_batch_size=stream.trt_unet_batch_size,
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),
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"unet.engine",
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)
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vae_encoder_path = os.path.join(
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engine_dir,
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create_prefix(
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model_id_or_path=model_id_or_path,
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max_batch_size=batch_size
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if self.mode == "txt2img"
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else stream.frame_bff_size,
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min_batch_size=batch_size
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if self.mode == "txt2img"
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else stream.frame_bff_size,
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),
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"vae_encoder.engine",
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)
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vae_decoder_path = os.path.join(
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engine_dir,
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create_prefix(
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model_id_or_path=model_id_or_path,
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max_batch_size=batch_size
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if self.mode == "txt2img"
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else stream.frame_bff_size,
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min_batch_size=batch_size
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if self.mode == "txt2img"
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else stream.frame_bff_size,
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),
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"vae_decoder.engine",
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)
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if not os.path.exists(unet_path):
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os.makedirs(os.path.dirname(unet_path), exist_ok=True)
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unet_model = UNet(
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fp16=True,
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device=stream.device,
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max_batch_size=stream.trt_unet_batch_size,
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min_batch_size=stream.trt_unet_batch_size,
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embedding_dim=stream.text_encoder.config.hidden_size,
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unet_dim=stream.unet.config.in_channels,
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)
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compile_unet(
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stream.unet,
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unet_model,
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unet_path + ".onnx",
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unet_path + ".opt.onnx",
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unet_path,
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opt_batch_size=stream.trt_unet_batch_size,
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)
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if not os.path.exists(vae_decoder_path):
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os.makedirs(os.path.dirname(vae_decoder_path), exist_ok=True)
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stream.vae.forward = stream.vae.decode
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vae_decoder_model = VAE(
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device=stream.device,
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max_batch_size=batch_size
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if self.mode == "txt2img"
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else stream.frame_bff_size,
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min_batch_size=batch_size
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if self.mode == "txt2img"
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else stream.frame_bff_size,
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)
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compile_vae_decoder(
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stream.vae,
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vae_decoder_model,
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vae_decoder_path + ".onnx",
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vae_decoder_path + ".opt.onnx",
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vae_decoder_path,
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opt_batch_size=batch_size
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if self.mode == "txt2img"
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else stream.frame_bff_size,
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)
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delattr(stream.vae, "forward")
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if not os.path.exists(vae_encoder_path):
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os.makedirs(os.path.dirname(vae_encoder_path), exist_ok=True)
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vae_encoder = TorchVAEEncoder(stream.vae).to(torch.device("cuda"))
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vae_encoder_model = VAEEncoder(
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device=stream.device,
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max_batch_size=batch_size
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if self.mode == "txt2img"
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else stream.frame_bff_size,
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min_batch_size=batch_size
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if self.mode == "txt2img"
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else stream.frame_bff_size,
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)
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compile_vae_encoder(
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vae_encoder,
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vae_encoder_model,
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vae_encoder_path + ".onnx",
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vae_encoder_path + ".opt.onnx",
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vae_encoder_path,
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opt_batch_size=batch_size
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if self.mode == "txt2img"
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else stream.frame_bff_size,
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)
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cuda_steram = cuda.Stream()
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vae_config = stream.vae.config
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vae_dtype = stream.vae.dtype
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stream.unet = UNet2DConditionModelEngine(
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unet_path, cuda_steram, use_cuda_graph=False
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)
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stream.vae = AutoencoderKLEngine(
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vae_encoder_path,
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vae_decoder_path,
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cuda_steram,
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stream.pipe.vae_scale_factor,
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use_cuda_graph=False,
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)
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setattr(stream.vae, "config", vae_config)
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setattr(stream.vae, "dtype", vae_dtype)
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gc.collect()
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torch.cuda.empty_cache()
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print("TensorRT acceleration enabled.")
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if acceleration == "sfast":
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from streamdiffusion.acceleration.sfast import (
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accelerate_with_stable_fast,
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)
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stream = accelerate_with_stable_fast(stream)
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print("StableFast acceleration enabled.")
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except Exception:
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traceback.print_exc()
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print("Acceleration has failed. Falling back to normal mode.")
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return (stream, )
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class StreamDiffusionSampler:
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def __init__(self):
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self.torch_device = get_torch_device()
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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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"stream": ("STREAM", ),
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"positive": ("STRING", {"multiline": True}),
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"negative": ("STRING", {"multiline": True}),
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"steps": ("INT", {"default": 50, "min": 1, "max": 10000}),
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"cfg": ("FLOAT", {"default": 1.2, "min": 0.0, "max": 100.0}),
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"delta": ("FLOAT", {"default": 1, "min": 0.0, "max": 1.0}),
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"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
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"num": ("INT", {"default": 1, "min": 1, "max": 10000}),
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"warmup": ("INT", {"default": 1, "min": 0, "max": 10000}),
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},
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"optional" : {
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"image" : ("IMAGE", )
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}
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "sample"
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CATEGORY = "Diffusers/StreamDiffusion"
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def sample(self, stream: StreamDiffusion, positive, negative, steps, cfg, delta, seed, num, warmup, image = None):
|
|
stream.prepare(
|
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prompt = positive,
|
|
negative_prompt = negative,
|
|
num_inference_steps = steps,
|
|
guidance_scale = cfg,
|
|
delta = delta,
|
|
seed = seed
|
|
)
|
|
|
|
if image != None:
|
|
image = convert_tensors_to_images(image)
|
|
image = resize_images(image, (stream.width, stream.height))
|
|
|
|
for _ in range(warmup):
|
|
stream()
|
|
|
|
result = []
|
|
for _ in range(num):
|
|
x_outputs = []
|
|
if image is None:
|
|
x_outputs.append(stream.txt2img())
|
|
else:
|
|
stream(image[0])
|
|
for i in image[1:] + image[-1:]:
|
|
x_outputs.append(stream(i))
|
|
for x_output in x_outputs:
|
|
result.append(postprocess_image(x_output, output_type="pil")[0])
|
|
|
|
return (convert_images_to_tensors(result),)
|
|
|
|
class StreamDiffusionWarmup:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"stream": ("STREAM", ),
|
|
"negative": ("STRING", {"multiline": True}),
|
|
"steps": ("INT", {"default": 50, "min": 1, "max": 10000}),
|
|
"cfg": ("FLOAT", {"default": 1.2, "min": 0.0, "max": 100.0}),
|
|
"delta": ("FLOAT", {"default": 1, "min": 0.0, "max": 1.0}),
|
|
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
|
|
"warmup": ("INT", {"default": 1, "min": 0, "max": 10000}),
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ("WARMUP_STREAM",)
|
|
|
|
FUNCTION = "stream_warmup"
|
|
|
|
CATEGORY = "Diffusers/StreamDiffusion"
|
|
|
|
def stream_warmup(self, stream: StreamDiffusion, negative, steps, cfg, delta, seed, warmup):
|
|
stream.prepare(
|
|
prompt="",
|
|
negative_prompt=negative,
|
|
num_inference_steps = steps,
|
|
guidance_scale = cfg,
|
|
delta = delta,
|
|
seed = seed
|
|
)
|
|
|
|
for _ in range(warmup):
|
|
stream()
|
|
|
|
return (stream, )
|
|
|
|
|
|
class StreamDiffusionFastSampler:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"warmup_stream": ("WARMUP_STREAM", ),
|
|
"positive": ("STRING", {"multiline": True}),
|
|
"num": ("INT", {"default": 1, "min": 1, "max": 10000}),
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ("IMAGE",)
|
|
|
|
FUNCTION = "sample"
|
|
|
|
CATEGORY = "Diffusers/StreamDiffusion"
|
|
|
|
def sample(self, warmup_stream, positive, num):
|
|
stream: StreamDiffusion = warmup_stream
|
|
|
|
stream.update_prompt(positive)
|
|
|
|
result = []
|
|
for _ in range(num):
|
|
x_output = stream.txt2img()
|
|
result.append(postprocess_image(x_output, output_type="pil")[0])
|
|
return (convert_images_to_tensors(result),)
|
|
|
|
# - - - - - - - - - - - - - - - - - -
|
|
|
|
|
|
NODE_CLASS_MAPPINGS = {
|
|
"DiffusersPipelineLoader": DiffusersPipelineLoader,
|
|
"DiffusersVaeLoader": DiffusersVaeLoader,
|
|
"DiffusersSchedulerLoader": DiffusersSchedulerLoader,
|
|
"DiffusersModelMakeup": DiffusersModelMakeup,
|
|
"DiffusersClipTextEncode": DiffusersClipTextEncode,
|
|
"DiffusersSampler": DiffusersSampler,
|
|
"CreateIntListNode": CreateIntListNode,
|
|
"LcmLoraLoader": LcmLoraLoader,
|
|
"StreamDiffusionCreateStream": StreamDiffusionCreateStream,
|
|
"StreamDiffusionSampler": StreamDiffusionSampler,
|
|
"StreamDiffusionWarmup": StreamDiffusionWarmup,
|
|
"StreamDiffusionFastSampler": StreamDiffusionFastSampler,
|
|
}
|
|
|
|
NODE_DISPLAY_NAME_MAPPINGS = {
|
|
"DiffusersPipelineLoader": "Diffusers Pipeline Loader",
|
|
"DiffusersVaeLoader": "Diffusers Vae Loader",
|
|
"DiffusersSchedulerLoader": "Diffusers Scheduler Loader",
|
|
"DiffusersModelMakeup": "Diffusers Model Makeup",
|
|
"DiffusersClipTextEncode": "Diffusers Clip Text Encode",
|
|
"DiffusersSampler": "Diffusers Sampler",
|
|
"CreateIntListNode": "Create Int List",
|
|
"LcmLoraLoader": "LCM Lora Loader",
|
|
"StreamDiffusionCreateStream": "StreamDiffusion Create Stream",
|
|
"StreamDiffusionSampler": "StreamDiffusion Sampler",
|
|
"StreamDiffusionWarmup": "StreamDiffusion Warmup",
|
|
"StreamDiffusionFastSampler": "StreamDiffusion Fast Sampler",
|
|
}
|