149 lines
6.5 KiB
Python
149 lines
6.5 KiB
Python
import torch
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from diffusers import LuminaText2ImgPipeline, FlowMatchEulerDiscreteScheduler
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import comfy.model_management as mm
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import os
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import numpy as np
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import traceback
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import math
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class LuminaDiffusersNode:
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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_path": ("STRING", {"default": "Lumina-Next-SFT-diffusers"}),
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"prompt": ("STRING", {"multiline": True}),
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"negative_prompt": ("STRING", {"multiline": True}),
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"num_inference_steps": ("INT", {"default": 30, "min": 1, "max": 200}),
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"guidance_scale": ("FLOAT", {"default": 4.0, "min": 0.1, "max": 20.0}),
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"width": ("INT", {"default": 1024, "min": 512, "max": 2048, "step": 64}),
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"height": ("INT", {"default": 1024, "min": 512, "max": 2048, "step": 64}),
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"seed": ("INT", {"default": -1}),
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"batch_size": ("INT", {"default": 1, "min": 1, "max": 4}),
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"scaling_watershed": ("FLOAT", {"default": 0.3, "min": 0.0, "max": 1.0}),
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"time_aware_scaling": ("FLOAT", {"default": 1.0, "min": 0.1, "max": 2.0}),
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"context_drop_ratio": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 0.5}),
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}
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}
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RETURN_TYPES = ("IMAGE", "LATENT")
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FUNCTION = "generate"
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CATEGORY = "LuminaWrapper"
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def __init__(self):
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self.pipe = None
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def load_model(self, model_path):
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try:
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device = mm.get_torch_device()
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dtype = torch.bfloat16 if torch.cuda.is_available() and torch.cuda.is_bf16_supported() else torch.float32
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print(f"Attempting to load Lumina model from: {model_path}")
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print(f"Device: {device}, Dtype: {dtype}")
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full_path = os.path.join(os.path.dirname(__file__), model_path)
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if not os.path.exists(full_path):
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raise ValueError(f"Model path does not exist: {full_path}")
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print(f"Loading Lumina model from: {full_path}")
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self.pipe = LuminaText2ImgPipeline.from_pretrained(full_path, torch_dtype=dtype)
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self.pipe.scheduler = FlowMatchEulerDiscreteScheduler.from_config(self.pipe.scheduler.config)
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self.pipe.to(device)
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print("Pipeline successfully loaded and moved to device.")
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except Exception as e:
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print(f"Error in load_model: {str(e)}")
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traceback.print_exc()
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def apply_time_aware_scaling(self, transformer, scale_factor):
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if hasattr(transformer, 'text_encoder'):
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transformer.text_encoder.config.time_aware_scaling = scale_factor
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if hasattr(transformer, 'unet'):
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transformer.unet.config.time_aware_scaling = scale_factor
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def apply_context_drop(self, transformer, drop_ratio):
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if hasattr(transformer, 'text_encoder'):
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transformer.text_encoder.config.context_drop_ratio = drop_ratio
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if hasattr(transformer, 'unet'):
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transformer.unet.config.context_drop_ratio = drop_ratio
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def generate(self, model_path, prompt, negative_prompt, num_inference_steps, guidance_scale, width, height, seed, batch_size, scaling_watershed, time_aware_scaling, context_drop_ratio):
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try:
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if self.pipe is None:
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print("Pipeline not loaded. Attempting to load model.")
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self.load_model(model_path)
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if self.pipe is None:
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raise ValueError("Failed to load the pipeline.")
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device = mm.get_torch_device()
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print(f"Generation device: {device}")
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if seed == -1:
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seed = int.from_bytes(os.urandom(4), "big")
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generator = torch.Generator(device=device).manual_seed(seed)
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# Prepare Lumina-specific kwargs
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scale_factor = math.sqrt(width * height / 1024**2)
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# Modify the pipe's transformer to include Lumina-specific features
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if hasattr(self.pipe, 'transformer'):
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self.pipe.transformer.scale_factor = scale_factor
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self.pipe.transformer.scale_watershed = scaling_watershed
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self.apply_time_aware_scaling(self.pipe.transformer, time_aware_scaling)
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self.apply_context_drop(self.pipe.transformer, context_drop_ratio)
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print(f"Starting generation with seed: {seed}")
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output = self.pipe(
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prompt=[prompt] * batch_size,
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negative_prompt=[negative_prompt] * batch_size,
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num_inference_steps=num_inference_steps,
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guidance_scale=guidance_scale,
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width=width,
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height=height,
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generator=generator,
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num_images_per_prompt=1,
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output_type="pt",
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)
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print(f"Raw output shape: {output.images.shape}")
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print(f"Raw output min: {output.images.min()}, max: {output.images.max()}")
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images = output.images
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images = images.permute(0, 2, 3, 1).cpu()
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print(f"Permuted images shape: {images.shape}")
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print(f"Images min: {images.min()}, max: {images.max()}")
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# Apply normalization
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images = (images + 1) / 2 # Assuming the output is in the range [-1, 1]
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images = (images * 255).round().clamp(0, 255).to(torch.uint8)
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print(f"Final images shape: {images.shape}")
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print(f"Final images min: {images.min()}, max: {images.max()}")
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# Generate latents
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with torch.no_grad():
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latents = self.pipe.vae.encode(output.images.to(self.pipe.vae.dtype)).latent_dist.sample()
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latents = latents * self.pipe.vae.config.scaling_factor
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print(f"Latents shape: {latents.shape}")
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print(f"Latents min: {latents.min()}, max: {latents.max()}")
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latents_for_comfy = {"samples": latents.cpu()}
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return (images, latents_for_comfy)
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except Exception as e:
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print(f"Error in generate: {str(e)}")
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traceback.print_exc()
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return (torch.zeros((batch_size, height, width, 3), dtype=torch.uint8),
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{"samples": torch.zeros((batch_size, 4, height // 8, width // 8), dtype=torch.float32)})
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NODE_CLASS_MAPPINGS = {
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"LuminaDiffusersNode": LuminaDiffusersNode
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"LuminaDiffusersNode": "Lumina-Next-SFT Diffusers"
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}
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