wip, cant test
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pythongosssss
@@ -1,53 +1,15 @@
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import base64
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from PIL import Image
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from io import BytesIO
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import requests
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import numpy as np
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import torch
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# TODO: where does this port come from? Should it be an input or can we use some API to get it?
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invoke_url = "http://localhost:8003/v1/infer"
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# payload = {
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# "text_prompts": [
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# {
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# "text": "realistic futuristic city-downtown with short buildings, sunset",
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# "weight": 1
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# },
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# {
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# "text": "" ,
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# "weight": -1
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# }
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# ],
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# "cfg_scale": 5,
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# "sampler": "K_DPM_2_ANCESTRAL",
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# "seed": 0,
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# "steps": 25
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# }
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# # Size is fixed for now
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# payload_two = { "cfg_scale": 5, "clip_guidance_preset": "NONE",
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# "disable_safety_checker": 'false', # Remain false for now. NIM_ALLOW_UNCHECKED_GENERATION=true
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# "height": 1024,
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# "sampler": "K_DPM_2_ANCESTRAL",
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# "samples": 1, "seed": 0, "steps": 25,
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# "style_preset": "none",
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# "text_prompts": [ {
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# "text": "A photo of a Shiba Inu dog with a backpack riding a bike", "weight": 1
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# } ],
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# "use_refiner": 'false',
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# "width": 1024
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# }
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# response = requests.post(invoke_url, json=payload)
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# response.raise_for_status()
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# data = response.json()
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# img_base64 = data['artifacts'][0]["base64"]
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# img_bytes = base64.b64decode(img_base64)
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class SDXLNIMNode:
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class NIMSDXLNode:
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def __init__(self):
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pass
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@@ -55,30 +17,50 @@ class SDXLNIMNode:
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def INPUT_TYPES(s):
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return {
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"required": {
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"text": ("STRING", {
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"width": (["768", "832", "896", "960", "1024", "1088", "1152", "1216", "1280", "1344"], {
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"default": "1024",
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"tooltip": "Width of the image to generate, in pixels."
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}),
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"height": (["768", "832", "896", "960", "1024", "1088", "1152", "1216", "1280", "1344"], {
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"default": "1024",
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"tooltip": "Height of the image to generate, in pixels."
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}),
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"positive": ("STRING", {
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"multiline": True,
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"default": "realistic futuristic city-downtown with short buildings, sunset"
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"default": "beautiful scenery nature glass bottle landscape, purple galaxy bottle",
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"tooltip": "The attributes you want to include in the image."
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}),
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"negative": ("STRING", {
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"multiline": True,
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"default": "text, watermark",
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"tooltip": "The attributes you want to exclude from the image."
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}),
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"cfg_scale": ("FLOAT", {
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"default": 5.0,
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"min": 1.0,
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"max": 20.0,
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"step": 0.5,
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"display": "slider"
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"display": "slider",
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"tooltip": "How strictly the diffusion process adheres to the prompt text (higher values keep your image closer to your prompt)"
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}),
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"sampler": (["DDIM", "K_EULER_ANCESTRAL", "K_DPM_2_ANCESTRAL"], {
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"default": "K_DPM_2_ANCESTRAL",
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"tooltip": "The sampler to use for generation. Varying diffusion samplers will vary outputs significantly."
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}),
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"sampler": (["K_DPM_2_ANCESTRAL"],),
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"seed": ("INT", {
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"default": 0,
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"min": 0,
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"max": 0xffffffffffffffff, # Max 64-bit unsigned int
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"display": "number"
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"display": "number",
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"tooltip": "The seed which governs generation. Use 0 for a random seed"
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}),
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"steps": ("INT", {
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"default": 25,
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"min": 1,
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"max": 100,
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"step": 1,
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"display": "slider"
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"display": "slider",
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"tooltip": "Number of diffusion steps to run"
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}),
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},
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}
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@@ -87,11 +69,17 @@ class SDXLNIMNode:
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FUNCTION = "generate"
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CATEGORY = "image generation"
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def generate(self, text, cfg_scale, sampler, seed, steps):
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def generate(self, width, height, positive, negative, cfg_scale, sampler, seed, steps):
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payload = {
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"width": width,
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"height": height,
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"text_prompts": [
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{
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"text": text,
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"text": positive,
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"weight": 1
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},
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{
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"text": negative,
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"weight": 1
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}
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],
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@@ -101,23 +89,25 @@ class SDXLNIMNode:
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"steps": steps
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}
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# Your existing API call code here
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response = requests.post(invoke_url, json=payload)
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response.raise_for_status()
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data = response.json()
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img_base64 = data['artifacts'][0]["base64"]
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img_bytes = base64.b64decode(img_base64)
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# Convert to the expected format for ComfyUI nodes
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# Note: You'll need to convert img_bytes to the proper tensor format
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# This is a placeholder - you'll need to implement the actual image conversion
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return (img_bytes,)
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image = Image.open(BytesIO(img_bytes))
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image = image.convert("RGB")
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image = np.array(image).astype(np.float32) / 255.0
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image = torch.from_numpy(image)[None,]
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return (image,)
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# Update the mappings
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NODE_CLASS_MAPPINGS = {
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"SDXLNIMNode": SDXLNIMNode
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"NIMSDXLNode": NIMSDXLNode
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"SDXLNIMNode": "SDXL NIM Node"
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}
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"NIMSDXLNode": "NIM SDXL Node"
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}
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+1
-1
@@ -1,7 +1,7 @@
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[project]
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name = "nimnodes"
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version = "0.1.0"
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description = "Add your description here"
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description = "Provides nodes to utilise NVIDIA NIM, a set of accelerated inference microservices that allow you to run AI models on NVIDIA GPUs anywhere."
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readme = "README.md"
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requires-python = ">=3.12"
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dependencies = ["requests>=2.32.3", "ruff>=0.8.2"]
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