forked from cgvr/DeltaVR
123 lines
3.9 KiB
Plaintext
123 lines
3.9 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "50e24baa",
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"metadata": {},
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"outputs": [],
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"source": [
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"from invokeai_mcp_server import create_text2img_graph, enqueue_graph, wait_for_completion, get_image_url\n",
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"from urllib.parse import urljoin\n",
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"\n",
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"import asyncio"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "0407cd9a",
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"metadata": {},
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"outputs": [],
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"source": [
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"INVOKEAI_BASE_URL = \"http://127.0.0.1:9090\"\n",
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"\n",
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"\n",
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"async def generate_image(arguments: dict):\n",
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"\n",
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" # Extract parameters\n",
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" prompt = arguments[\"prompt\"]\n",
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" negative_prompt = arguments.get(\"negative_prompt\", \"\")\n",
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" width = arguments.get(\"width\", 512)\n",
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" height = arguments.get(\"height\", 512)\n",
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" steps = arguments.get(\"steps\", 30)\n",
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" cfg_scale = arguments.get(\"cfg_scale\", 7.5)\n",
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" scheduler = arguments.get(\"scheduler\", \"euler\")\n",
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" seed = arguments.get(\"seed\")\n",
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" model_key = arguments.get(\"model_key\")\n",
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" lora_key = arguments.get(\"lora_key\")\n",
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" lora_weight = arguments.get(\"lora_weight\", 1.0)\n",
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" vae_key = arguments.get(\"vae_key\")\n",
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"\n",
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" #logger.info(f\"Generating image with prompt: {prompt[:50]}...\")\n",
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"\n",
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" # Create graph\n",
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" graph = await create_text2img_graph(\n",
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" prompt=prompt,\n",
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" negative_prompt=negative_prompt,\n",
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" model_key=model_key,\n",
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" lora_key=lora_key,\n",
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" lora_weight=lora_weight,\n",
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" vae_key=vae_key,\n",
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" width=width,\n",
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" height=height,\n",
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" steps=steps,\n",
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" cfg_scale=cfg_scale,\n",
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" scheduler=scheduler,\n",
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" seed=seed\n",
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" )\n",
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"\n",
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" # Enqueue and wait for completion\n",
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" result = await enqueue_graph(graph)\n",
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" batch_id = result[\"batch\"][\"batch_id\"]\n",
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"\n",
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" #logger.info(f\"Enqueued batch {batch_id}, waiting for completion...\")\n",
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"\n",
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" completed = await wait_for_completion(batch_id)\n",
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"\n",
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" # Extract image name from result\n",
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" if \"result\" in completed and \"outputs\" in completed[\"result\"]:\n",
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" outputs = completed[\"result\"][\"outputs\"]\n",
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" # Find the image output\n",
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" for node_id, output in outputs.items():\n",
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" if output.get(\"type\") == \"image_output\":\n",
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" image_name = output[\"image\"][\"image_name\"]\n",
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" image_url = await get_image_url(image_name)\n",
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"\n",
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" text=f\"Image generated successfully!\\n\\nImage Name: {image_name}\\nImage URL: {image_url}\\n\\nYou can view the image at: {urljoin(INVOKEAI_BASE_URL, f'/api/v1/images/i/{image_name}/full')}\"\n",
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" print(text)\n",
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"\n",
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" # Fallback if we couldn't find image output\n",
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" #text=f\"Image generation completed but output format was unexpected. Batch ID: {batch_id}\\n\\nResult: {json.dumps(completed, indent=2)}\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6cf9d879",
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"metadata": {},
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"outputs": [],
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"source": [
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"async def main():\n",
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" args = {\n",
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" \"prompt\": \"a golden katana with a fancy pommel\"\n",
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" }\n",
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" await generate_image(args)\n",
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"\n",
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"asyncio.run(main())"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": ".venv",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.11"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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