forked from cgvr/DeltaVR
port InvokeAI API client to Unity, use it in ImageGenerationBox
This commit is contained in:
75
Assets/_PROJECT/Scripts/ModeGeneration/ImageGenerationBox.cs
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75
Assets/_PROJECT/Scripts/ModeGeneration/ImageGenerationBox.cs
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@@ -0,0 +1,75 @@
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using System;
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using Unity.XR.CoreUtils;
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using UnityEngine;
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using UnityEngine.UI;
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public class ImageGenerationBox : MonoBehaviour
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{
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public Material inactiveMaterial;
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public Material loadingMaterial;
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public VoiceTranscriptionBox voiceTranscriptionTestBox;
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public Image UIImage;
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public string promptSuffix = ", single object, front and side fully visible, realistic style, plain neutral background, clear details, soft studio lighting, true-to-scale";
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private MeshRenderer meshRenderer;
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private bool isLoading;
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// Start is called before the first frame update
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void Start()
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{
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meshRenderer = GetComponent<MeshRenderer>();
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}
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// Update is called once per frame
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void Update()
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{
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}
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async void OnTriggerEnter(Collider other)
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{
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if (isLoading) return;
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KbmController controller = other.GetComponent<KbmController>();
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XROrigin playerOrigin = other.GetComponent<XROrigin>();
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if (controller != null || playerOrigin != null)
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{
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string inputPrompt = voiceTranscriptionTestBox.LastTextOutput;
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string refinedPrompt = inputPrompt + promptSuffix;
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isLoading = true;
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meshRenderer.material = loadingMaterial;
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byte[] imageBytes = await InvokeAiClient.Instance.GenerateImage(refinedPrompt);
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Sprite sprite = CreateSprite(imageBytes);
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UIImage.sprite = sprite;
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isLoading = false;
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meshRenderer.material = inactiveMaterial;
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}
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}
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private Sprite CreateSprite(byte[] imageBytes)
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{
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var tex = new Texture2D(2, 2, TextureFormat.RGBA32, false);
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// ImageConversion.LoadImage returns bool (true = success)
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if (!ImageConversion.LoadImage(tex, imageBytes, markNonReadable: false))
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{
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Destroy(tex);
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throw new InvalidOperationException("Failed to decode image bytes into Texture2D.");
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}
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tex.filterMode = FilterMode.Bilinear;
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tex.wrapMode = TextureWrapMode.Clamp;
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var sprite = Sprite.Create(
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tex,
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new Rect(0, 0, tex.width, tex.height),
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new Vector2(0.5f, 0.5f),
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pixelsPerUnit: 100f
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);
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return sprite;
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}
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}
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@@ -0,0 +1,11 @@
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fileFormatVersion: 2
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guid: ea7eedaa608bac7449ba7c5a36697607
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MonoImporter:
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externalObjects: {}
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serializedVersion: 2
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defaultReferences: []
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executionOrder: 0
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icon: {instanceID: 0}
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userData:
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assetBundleName:
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assetBundleVariant:
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542
Assets/_PROJECT/Scripts/ModeGeneration/InvokeAiClient.cs
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542
Assets/_PROJECT/Scripts/ModeGeneration/InvokeAiClient.cs
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@@ -0,0 +1,542 @@
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using System;
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using System.Diagnostics;
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using System.Net.Http;
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using System.Security.Cryptography;
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using System.Text;
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using System.Threading.Tasks;
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using UnityEngine;
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using Valve.Newtonsoft.Json;
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using Valve.Newtonsoft.Json.Linq;
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public class InvokeAiClient : MonoBehaviour
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{
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public static InvokeAiClient Instance { get; private set; }
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public string INVOKEAI_BASE_URL;
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public string DEFAULT_QUEUE_ID = "default";
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public string MODEL_KEY;
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private HttpClient httpClient;
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private void Awake()
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{
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httpClient = new HttpClient
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{
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Timeout = TimeSpan.FromSeconds(120)
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};
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httpClient.BaseAddress = new Uri(INVOKEAI_BASE_URL);
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Instance = this;
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}
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// Start is called before the first frame update
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void Start()
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{
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}
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// Update is called once per frame
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void Update()
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{
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}
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private async Task<JArray> ListModels(string modelType = "main")
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{
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var requestUri = $"/api/v2/models/?model_type={Uri.EscapeDataString(modelType)}";
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using var resp = await httpClient.GetAsync(requestUri).ConfigureAwait(false);
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resp.EnsureSuccessStatusCode();
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var json = await resp.Content.ReadAsStringAsync().ConfigureAwait(false);
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var root = JObject.Parse(json);
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return (JArray) root["models"];
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}
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private async Task<JObject> GetModelInfo(string modelKey)
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{
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var requestUri = $"/api/v2/models/i/{Uri.EscapeDataString(modelKey)}";
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using var resp = await httpClient.GetAsync(requestUri).ConfigureAwait(false);
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resp.EnsureSuccessStatusCode();
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var json = await resp.Content.ReadAsStringAsync().ConfigureAwait(false);
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return JObject.Parse(json);
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}
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private async Task<string> GetImageUrl(string imageName)
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{
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var requestUri = $"/api/v1/images/i/{Uri.EscapeDataString(imageName)}/urls";
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UnityEngine.Debug.Log("Get image URL: " + requestUri);
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using var resp = await httpClient.GetAsync(requestUri).ConfigureAwait(false);
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resp.EnsureSuccessStatusCode();
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var json = await resp.Content.ReadAsStringAsync().ConfigureAwait(false);
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var root = JObject.Parse(json);
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return root.Value<string>("image_url");
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}
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private async Task<JObject> WaitForCompletion(string batchId, int timeoutSeconds = 300)
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{
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var sw = Stopwatch.StartNew();
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string queueId = DEFAULT_QUEUE_ID;
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while (true)
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{
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if (sw.Elapsed.TotalSeconds > timeoutSeconds)
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throw new TimeoutException($"Image generation timed out after {timeoutSeconds} seconds");
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// Get batch status
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var statusUrl = $"/api/v1/queue/{Uri.EscapeDataString(queueId)}/b/{Uri.EscapeDataString(batchId)}/status";
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using var statusResp = await httpClient.GetAsync(statusUrl).ConfigureAwait(false);
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statusResp.EnsureSuccessStatusCode();
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var statusJson = await statusResp.Content.ReadAsStringAsync().ConfigureAwait(false);
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var statusData = JObject.Parse(statusJson);
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// Check for failures
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int failedCount = statusData.Value<int?>("failed") ?? 0;
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if (failedCount > 0)
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{
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var queueStatusUrl = $"/api/v1/queue/{Uri.EscapeDataString(queueId)}/status";
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using var queueResp = await httpClient.GetAsync(queueStatusUrl).ConfigureAwait(false);
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queueResp.EnsureSuccessStatusCode();
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var queueJson = await queueResp.Content.ReadAsStringAsync().ConfigureAwait(false);
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var queueData = JObject.Parse(queueJson);
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throw new InvalidOperationException(
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$"Image generation failed. Batch {batchId} has {failedCount} failed item(s). " +
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$"Queue status: {queueData.ToString(Formatting.Indented)}"
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);
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}
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// Check completion
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int completed = statusData.Value<int?>("completed") ?? 0;
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int total = statusData.Value<int?>("total") ?? 0;
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if (completed == total && total > 0)
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{
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// Get most recent non-intermediate image
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const string imagesPath = "/api/v1/images/?is_intermediate=false&limit=10";
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using var imagesResp = await httpClient.GetAsync(imagesPath).ConfigureAwait(false);
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imagesResp.EnsureSuccessStatusCode();
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var imagesJson = await imagesResp.Content.ReadAsStringAsync().ConfigureAwait(false);
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var imagesData = JObject.Parse(imagesJson);
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var items = imagesData["items"] as JArray;
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if (items != null && items.Count > 0)
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{
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var imageName = items[0].Value<string>("image_name");
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// Return result object mirroring your Python structure
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var result = new JObject
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{
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["batch_id"] = batchId,
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["status"] = "completed",
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["result"] = new JObject
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{
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["outputs"] = new JObject
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{
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["save_image"] = new JObject
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{
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["type"] = "image_output",
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["image"] = new JObject
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{
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["image_name"] = imageName
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}
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}
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}
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}
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};
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return result;
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}
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// If no images found, return the status object
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return statusData;
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}
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// Wait before checking again
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await Task.Delay(1000).ConfigureAwait(false);
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}
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}
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private async Task<JObject> EnqueueGraph(JToken graph)
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{
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string queueId = DEFAULT_QUEUE_ID;
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// Build request JSON dynamically
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var payload = new JObject
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{
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["batch"] = new JObject
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{
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["graph"] = graph, // graph can be any JSON structure
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["runs"] = 1,
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["data"] = JValue.CreateNull()
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}
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};
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var url = $"/api/v1/queue/{Uri.EscapeDataString(queueId)}/enqueue_batch";
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using var content = new StringContent(payload.ToString(Formatting.None), Encoding.UTF8, "application/json");
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using var resp = await httpClient.PostAsync(url, content).ConfigureAwait(false);
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resp.EnsureSuccessStatusCode();
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var json = await resp.Content.ReadAsStringAsync().ConfigureAwait(false);
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return JObject.Parse(json);
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}
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private static void RequireFields(JObject info, string nameOrKey, params string[] fields)
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{
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foreach (var f in fields)
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{
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var v = info[f];
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if (v == null || v.Type == JTokenType.Null)
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throw new ArgumentException($"Model {nameOrKey} is missing required field: {f}");
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}
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}
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private static long GenerateUInt32Seed()
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{
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Span<byte> bytes = stackalloc byte[4];
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RandomNumberGenerator.Fill(bytes);
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uint u = BitConverter.ToUInt32(bytes);
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return (long)u; // preserve full 0..4294967295 range
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}
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private static JObject Edge(string srcNode, string srcField, string dstNode, string dstField) => new JObject
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{
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["source"] = new JObject { ["node_id"] = srcNode, ["field"] = srcField },
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["destination"] = new JObject { ["node_id"] = dstNode, ["field"] = dstField }
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};
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private async Task<JObject> CreateText2ImgGraph(
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string prompt,
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string negativePrompt = "",
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string modelKey = null,
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string loraKey = null,
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double loraWeight = 1.0,
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string vaeKey = null,
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int width = 512,
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int height = 512,
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int steps = 30,
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double cfgScale = 7.5,
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string scheduler = "euler",
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long? seed = null)
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{
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// 1) Use default model if not specified: pick first "sd-1" from main list
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if (string.IsNullOrEmpty(modelKey))
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{
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var models = await ListModels("main");
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foreach (var token in models)
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{
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if (token is JObject m && string.Equals(m.Value<string>("base"), "sd-1", StringComparison.OrdinalIgnoreCase))
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{
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modelKey = m.Value<string>("key");
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break;
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}
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}
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if (string.IsNullOrEmpty(modelKey))
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throw new ArgumentException("No suitable model found (sd-1)", nameof(modelKey));
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}
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// 2) Get model information
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var modelInfo = await GetModelInfo(modelKey);
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if (modelInfo == null)
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throw new ArgumentException($"Model {modelKey} not found", nameof(modelKey));
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if (modelInfo.Type != JTokenType.Object)
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throw new ArgumentException($"Model {modelKey} returned invalid data type: {modelInfo.Type}", nameof(modelKey));
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// 3) Validate required fields
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RequireFields(modelInfo, modelKey, "key", "hash", "name", "base", "type");
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// 4) Generate random 32-bit seed if not provided (0..2^32-1)
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if (seed == null)
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seed = GenerateUInt32Seed();
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// 5) Detect SDXL
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bool isSdxl = string.Equals(modelInfo.Value<string>("base"), "sdxl", StringComparison.OrdinalIgnoreCase);
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// 6) Build nodes
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var nodes = new JObject
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{
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// Main model loader
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["model_loader"] = new JObject
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{
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["type"] = isSdxl ? "sdxl_model_loader" : "main_model_loader",
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["id"] = "model_loader",
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["model"] = new JObject
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{
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["key"] = modelInfo.Value<string>("key"),
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["hash"] = modelInfo.Value<string>("hash"),
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["name"] = modelInfo.Value<string>("name"),
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["base"] = modelInfo.Value<string>("base"),
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["type"] = modelInfo.Value<string>("type")
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}
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},
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// Positive prompt
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["positive_prompt"] = new JObject
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{
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["type"] = isSdxl ? "sdxl_compel_prompt" : "compel",
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["id"] = "positive_prompt",
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["prompt"] = prompt
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},
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// Negative prompt
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["negative_prompt"] = new JObject
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{
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["type"] = isSdxl ? "sdxl_compel_prompt" : "compel",
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["id"] = "negative_prompt",
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["prompt"] = negativePrompt
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},
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// Noise generation
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["noise"] = new JObject
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{
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["type"] = "noise",
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["id"] = "noise",
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["seed"] = seed,
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["width"] = width,
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["height"] = height,
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["use_cpu"] = false
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},
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// Denoise latents
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["denoise"] = new JObject
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{
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["type"] = "denoise_latents",
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["id"] = "denoise",
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["steps"] = steps,
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["cfg_scale"] = cfgScale,
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["scheduler"] = scheduler,
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["denoising_start"] = 0,
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["denoising_end"] = 1
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},
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// Latents to image
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["latents_to_image"] = new JObject
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{
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["type"] = "l2i",
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["id"] = "latents_to_image"
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},
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// Save image
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["save_image"] = new JObject
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{
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["type"] = "save_image",
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["id"] = "save_image",
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["is_intermediate"] = false
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}
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};
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// SDXL: add style fields (matches your Python **kwargs expansions)
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if (isSdxl)
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{
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(nodes["positive_prompt"] as JObject)["style"] = prompt;
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(nodes["negative_prompt"] as JObject)["style"] = "";
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}
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// 7) Optional: LoRA
|
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if (!string.IsNullOrEmpty(loraKey))
|
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{
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var loraInfo = await GetModelInfo(loraKey);
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if (loraInfo == null)
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throw new ArgumentException($"LoRA model {loraKey} not found", nameof(loraKey));
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RequireFields(loraInfo, loraKey, "key", "hash", "name", "base", "type");
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nodes["lora_loader"] = new JObject
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{
|
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["type"] = "lora_loader",
|
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["id"] = "lora_loader",
|
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["lora"] = new JObject
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{
|
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["key"] = loraInfo.Value<string>("key"),
|
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["hash"] = loraInfo.Value<string>("hash"),
|
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["name"] = loraInfo.Value<string>("name"),
|
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["base"] = loraInfo.Value<string>("base"),
|
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["type"] = loraInfo.Value<string>("type")
|
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},
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["weight"] = loraWeight
|
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};
|
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}
|
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|
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// 8) Optional: VAE override
|
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if (!string.IsNullOrEmpty(vaeKey))
|
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{
|
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var vaeInfo = await GetModelInfo(vaeKey);
|
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if (vaeInfo == null)
|
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throw new ArgumentException($"VAE model {vaeKey} not found", nameof(vaeKey));
|
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RequireFields(vaeInfo, vaeKey, "key", "hash", "name", "base", "type");
|
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|
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nodes["vae_loader"] = new JObject
|
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{
|
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["type"] = "vae_loader",
|
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["id"] = "vae_loader",
|
||||
["vae_model"] = new JObject
|
||||
{
|
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["key"] = vaeInfo.Value<string>("key"),
|
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["hash"] = vaeInfo.Value<string>("hash"),
|
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["name"] = vaeInfo.Value<string>("name"),
|
||||
["base"] = vaeInfo.Value<string>("base"),
|
||||
["type"] = vaeInfo.Value<string>("type")
|
||||
}
|
||||
};
|
||||
}
|
||||
|
||||
|
||||
var edges = new JArray();
|
||||
|
||||
// Determine sources
|
||||
bool hasLora = !string.IsNullOrEmpty(loraKey);
|
||||
string unetSource = hasLora ? "lora_loader" : "model_loader";
|
||||
string clipSource = hasLora ? "lora_loader" : "model_loader";
|
||||
string vaeSource = !string.IsNullOrEmpty(vaeKey) ? "vae_loader" : "model_loader";
|
||||
|
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// If using LoRA, connect model_loader -> lora_loader (unet & clip)
|
||||
if (hasLora)
|
||||
{
|
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edges.Add(Edge("model_loader", "unet", "lora_loader", "unet"));
|
||||
edges.Add(Edge("model_loader", "clip", "lora_loader", "clip"));
|
||||
// Note: lora_loader doesn't have clip2; SDXL clip2 comes from model_loader directly (handled below)
|
||||
}
|
||||
|
||||
// Connect UNet to denoise
|
||||
edges.Add(Edge(unetSource, "unet", "denoise", "unet"));
|
||||
|
||||
// Connect CLIP to prompts
|
||||
edges.Add(Edge(clipSource, "clip", "positive_prompt", "clip"));
|
||||
edges.Add(Edge(clipSource, "clip", "negative_prompt", "clip"));
|
||||
|
||||
// SDXL: connect clip2 from model_loader to both prompts
|
||||
if (isSdxl)
|
||||
{
|
||||
edges.Add(Edge("model_loader", "clip2", "positive_prompt", "clip2"));
|
||||
edges.Add(Edge("model_loader", "clip2", "negative_prompt", "clip2"));
|
||||
}
|
||||
|
||||
// Prompts -> denoise conditioning
|
||||
edges.Add(Edge("positive_prompt", "conditioning", "denoise", "positive_conditioning"));
|
||||
edges.Add(Edge("negative_prompt", "conditioning", "denoise", "negative_conditioning"));
|
||||
|
||||
// Noise -> denoise
|
||||
edges.Add(Edge("noise", "noise", "denoise", "noise"));
|
||||
|
||||
// Denoise -> l2i, and VAE -> l2i
|
||||
edges.Add(Edge("denoise", "latents", "latents_to_image", "latents"));
|
||||
edges.Add(Edge(vaeSource, "vae", "latents_to_image", "vae"));
|
||||
|
||||
// l2i -> save_image
|
||||
edges.Add(Edge("latents_to_image", "image", "save_image", "image"));
|
||||
|
||||
// 7) Return final graph object
|
||||
var graph = new JObject
|
||||
{
|
||||
["id"] = "text2img_graph",
|
||||
["nodes"] = nodes,
|
||||
["edges"] = edges
|
||||
};
|
||||
|
||||
return graph;
|
||||
}
|
||||
|
||||
|
||||
private async Task<string> GenerateImageUrl(JObject arguments)
|
||||
{
|
||||
if (arguments == null) throw new ArgumentNullException(nameof(arguments));
|
||||
|
||||
// --- Extract parameters (with defaults) ---
|
||||
string prompt = arguments.Value<string>("prompt")
|
||||
?? throw new ArgumentException("Argument 'prompt' is required.", nameof(arguments));
|
||||
string negativePrompt = arguments.Value<string>("negative_prompt") ?? "";
|
||||
int width = arguments.Value<int?>("width") ?? 512;
|
||||
int height = arguments.Value<int?>("height") ?? 512;
|
||||
int steps = arguments.Value<int?>("steps") ?? 30;
|
||||
double cfgScale = arguments.Value<double?>("cfg_scale") ?? 7.5;
|
||||
string scheduler = arguments.Value<string>("scheduler") ?? "euler";
|
||||
long? seed = arguments.Value<long?>("seed");
|
||||
string modelKey = arguments.Value<string>("model_key");
|
||||
string loraKey = arguments.Value<string>("lora_key");
|
||||
double loraWeight = arguments.Value<double?>("lora_weight") ?? 1.0;
|
||||
string vaeKey = arguments.Value<string>("vae_key");
|
||||
|
||||
|
||||
// --- Create graph ---
|
||||
JObject graph = await CreateText2ImgGraph(
|
||||
prompt: prompt,
|
||||
negativePrompt: negativePrompt,
|
||||
modelKey: modelKey,
|
||||
loraKey: loraKey,
|
||||
loraWeight: loraWeight,
|
||||
vaeKey: vaeKey,
|
||||
width: width,
|
||||
height: height,
|
||||
steps: steps,
|
||||
cfgScale: cfgScale,
|
||||
scheduler: scheduler,
|
||||
seed: seed
|
||||
);
|
||||
|
||||
// --- Enqueue ---
|
||||
JObject enqueueResult = await EnqueueGraph(graph);
|
||||
string batchId = enqueueResult.SelectToken("batch.batch_id")?.Value<string>();
|
||||
if (string.IsNullOrEmpty(batchId))
|
||||
throw new InvalidOperationException("Enqueue response did not contain 'batch.batch_id'.");
|
||||
|
||||
UnityEngine.Debug.Log($"Enqueued batch {batchId}, waiting for completion...");
|
||||
|
||||
// --- Wait for completion ---
|
||||
JObject completed = await WaitForCompletion(batchId);
|
||||
|
||||
// --- Extract image output ---
|
||||
var outputs = completed.SelectToken("result.outputs") as JObject;
|
||||
if (outputs != null)
|
||||
{
|
||||
foreach (var prop in outputs.Properties())
|
||||
{
|
||||
var output = prop.Value as JObject;
|
||||
if (output?.Value<string>("type") == "image_output")
|
||||
{
|
||||
string imageName = output.SelectToken("image.image_name")?.Value<string>();
|
||||
if (string.IsNullOrEmpty(imageName))
|
||||
continue;
|
||||
|
||||
// Resolve relative URL for the image (API-dependent)
|
||||
string imageRelativeUrl = await GetImageUrl(imageName);
|
||||
return imageRelativeUrl;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
throw new InvalidOperationException("Failed to generate image: no image_output found in result.");
|
||||
}
|
||||
|
||||
public async Task<byte[]> GenerateImage(string prompt)
|
||||
{
|
||||
JObject args = new JObject()
|
||||
{
|
||||
["prompt"] = prompt,
|
||||
["width"] = 512,
|
||||
["height"] = 512,
|
||||
["model_key"] = MODEL_KEY,
|
||||
};
|
||||
|
||||
string imageUrl = await GenerateImageUrl(args);
|
||||
|
||||
|
||||
var req = new HttpRequestMessage(HttpMethod.Get, imageUrl);
|
||||
using var resp = await httpClient.SendAsync(req, HttpCompletionOption.ResponseHeadersRead);
|
||||
resp.EnsureSuccessStatusCode();
|
||||
|
||||
return await resp.Content.ReadAsByteArrayAsync();
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,11 @@
|
||||
fileFormatVersion: 2
|
||||
guid: 4591f6805db240a4ca28e515091ca909
|
||||
MonoImporter:
|
||||
externalObjects: {}
|
||||
serializedVersion: 2
|
||||
defaultReferences: []
|
||||
executionOrder: 0
|
||||
icon: {instanceID: 0}
|
||||
userData:
|
||||
assetBundleName:
|
||||
assetBundleVariant:
|
||||
Reference in New Issue
Block a user