Add support for Qwen Image 2 Turbo with training adapter

README.md

@@ -18,6 +18,7 @@ AI Toolkit is an easy to use all in one training suite for diffusion models. I t
 - [Qwen/Qwen-Image](https://huggingface.co/Qwen/Qwen-Image) (Qwen-Image)
 - [Qwen/Qwen-Image-2512](https://huggingface.co/Qwen/Qwen-Image-2512) (Qwen-Image-2512)
 - [Qwen/Qwen-Image-2.1](https://huggingface.co/Qwen/Qwen-Image-2.1) (Qwen-Image-2.1)
+- [Qwen/Qwen-Image-2.1-Turbo](https://huggingface.co/Qwen/Qwen-Image-2.1-Turbo) (Qwen-Image-2.1 Turbo)
 - [HiDream-ai/HiDream-I1-Full](https://huggingface.co/HiDream-ai/HiDream-I1-Full) (HiDream I1)
 - [OmniGen2/OmniGen2](https://huggingface.co/OmniGen2/OmniGen2) (OmniGen2)
 - [Tongyi-MAI/Z-Image-Turbo](https://huggingface.co/Tongyi-MAI/Z-Image-Turbo) (Z-Image Turbo)
@@ -45,6 +46,7 @@ AI Toolkit is an easy to use all in one training suite for diffusion models. I t
 - [Qwen/Qwen-Image-Edit-2509](https://huggingface.co/Qwen/Qwen-Image-Edit-2509) (Qwen-Image-Edit-2509)
 - [Qwen/Qwen-Image-Edit-2511](https://huggingface.co/Qwen/Qwen-Image-Edit-2511) (Qwen-Image-Edit-2511)
 - [Qwen/Qwen-Image-2.1](https://huggingface.co/Qwen/Qwen-Image-2.1) (Qwen-Image-2.1) - one model for both; it edits when your dataset has control images
+- [Qwen/Qwen-Image-2.1-Turbo](https://huggingface.co/Qwen/Qwen-Image-2.1-Turbo) (Qwen-Image-2.1 Turbo) - same, with the 8-step turbo weights
 - [HiDream-ai/HiDream-E1-1](https://huggingface.co/HiDream-ai/HiDream-E1-1) (HiDream E1)
 - [Boogu/Boogu-Image-0.1-Edit](https://huggingface.co/Boogu/Boogu-Image-0.1-Edit) (Boogu Image Edit)
 - [krea/Krea-2-Raw](https://huggingface.co/krea/Krea-2-Raw) (Krea 2 Edit Training)

extensions_built_in/diffusion_models/qwen_image_2/qwen_image_2.py

@@ -19,19 +19,30 @@ see the sampled timestep.
 
 Flow-matching convention matches ai-toolkit (t=1 noise -> t=0 clean, target =
 noise - clean), so `get_noise_prediction` does no time flip or negation.
+
+Qwen-Image-2.1-Turbo is the same architecture distilled to 8 steps at CFG 1.
+Point `name_or_path` at its weight file in the repack
+(`Comfy-Org/Qwen-Image-2.1/diffusion_models/qwen_image_2.1_turbo_int8_convrot.safetensors`);
+`model_kwargs.turbo` samples with the schedule the checkpoint ships. Training
+it goes through a training adapter
+(`assistant_lora_path`): a LoRA that is live on the DiT while training and
+off while sampling, so samples show the turbo model plus what was trained.
 """
 
 import os
 from typing import TYPE_CHECKING, List, Optional
 
+import huggingface_hub
 import numpy as np
 import torch
 from PIL import Image
+from safetensors.torch import load_file
 
 from toolkit.accelerator import unwrap_model
 from toolkit.advanced_prompt_embeds import AdvancedPromptEmbeds
 from toolkit.basic import flush
-from toolkit.config_modules import GenerateImageConfig, ModelConfig
+from toolkit.config_modules import GenerateImageConfig, ModelConfig, NetworkConfig
+from toolkit.lora_special import LoRASpecialNetwork
 from toolkit.metadata import get_meta_for_safetensors
 from toolkit.models.base_model import BaseModel
 from toolkit.samplers.custom_flowmatch_sampler import (
@@ -80,6 +91,16 @@ scheduler_config = {
 COMFY_REPO = "Comfy-Org/Qwen-Image-2.1"
 BASE_REPO = "Qwen/Qwen-Image-2.1"
 
+# the turbo checkpoint is sampled with the schedule it ships
+# (TURBO_SAMPLE_SIGMAS in src/pipeline.py) as-is: no resolution shift, no
+# terminal stretch; the step count is fixed
+turbo_scheduler_config = {
+    **scheduler_config,
+    "shift": 1.0,
+    "shift_terminal": None,
+    "use_dynamic_shifting": False,
+}
+
 # decode above this many output pixels goes through the VAE's tiled path
 TILE_DECODE_ABOVE_PIXELS = 1024 * 1024
 
@@ -141,6 +162,15 @@ class QwenImage2Model(BaseModel):
     def get_train_scheduler():
         return CustomFlowMatchEulerDiscreteScheduler(**scheduler_config)
 
+    @property
+    def is_turbo(self) -> bool:
+        return bool(self.model_config.model_kwargs.get("turbo", False))
+
+    def get_sample_scheduler(self):
+        if self.is_turbo:
+            return CustomFlowMatchEulerDiscreteScheduler(**turbo_scheduler_config)
+        return self.get_train_scheduler()
+
     def get_bucket_divisibility(self):
         # 16 for the VAE, 2 more because the DiT groups target latent tokens
         # into 2x2 blocks, one per vision slot
@@ -165,11 +195,34 @@ class QwenImage2Model(BaseModel):
             # a local full checkpoint supplies its own text encoder / vae
             base_model_path = model_path
 
+        load_kwargs = self.component_load_kwargs("transformer")
         self.print_and_status_update("Loading transformer")
+        if self.model_config.assistant_lora_path is None:
             transformer = QwenImage21Transformer2DModel.load(
+                model_path, config_path=base_model_path, **load_kwargs
+            )
+        else:
+            # the adapter stays a live LoRA on the quantized linears, attached
+            # after quantization (it must wrap the quantized forward) and
+            # before layer offloading (the offloader routes through it)
+            offload = load_kwargs.pop("offload", 0.0)
+            transformer = QwenImage21Transformer2DModel.load_model(
                 model_path,
+                dtype=load_kwargs["dtype"],
                 config_path=base_model_path,
-            **self.component_load_kwargs("transformer"),
+                qtype=(load_kwargs.get("qtype") or "").split("|", 1)[0] or None,
+                quantize_on_load=False,
+            )
+            transformer.aitk_post_load(offload=0.0, **load_kwargs)
+            self.load_training_adapter(transformer)
+            if offload and offload > 0:
+                from toolkit.memory_management import MemoryManager
+
+                MemoryManager.attach(
+                    transformer,
+                    torch.device(load_kwargs["quantize_device"]),
+                    offload_percent=offload,
+                    ignore_modules=list(transformer.get_offload_ignore_modules() or []),
                 )
         flush()
 
@@ -204,6 +257,66 @@ class QwenImage2Model(BaseModel):
         self.pipeline = QwenImage21Pipeline(self)
         self.print_and_status_update("Model Loaded")
 
+    def load_training_adapter(self, transformer: QwenImage21Transformer2DModel):
+        """Attach the training adapter (a LoRA on the DiT) as a live assistant:
+        on at 1.0 while training, off while sampling so samples show the turbo
+        model. It is never merged: the weights are int8, whose grid swallows a
+        delta this small on a requantize."""
+        self.print_and_status_update("Loading assistant LoRA")
+        lora_path = self.model_config.assistant_lora_path
+        if not os.path.exists(lora_path):
+            # assume it is a hub path
+            lora_splits = lora_path.split("/")
+            if len(lora_splits) != 3:
+                raise ValueError(
+                    f"Assistant LoRA path {lora_path} is not a valid local path or hub path."
+                )
+            repo_id = "/".join(lora_splits[:2])
+            filename = lora_splits[2]
+            try:
+                lora_path = huggingface_hub.hf_hub_download(repo_id=repo_id, filename=filename)
+                self.model_config.assistant_lora_path = lora_path
+            except Exception as e:
+                raise ValueError(f"Failed to download assistant LoRA from {lora_path}: {e}")
+        lora_state_dict = load_file(lora_path)
+        dim_key = next(k for k in lora_state_dict if k.endswith("lora_A.weight"))
+        dim = int(lora_state_dict[dim_key].shape[0])
+        lora_state_dict = {
+            key.replace("diffusion_model.", "transformer."): value
+            for key, value in lora_state_dict.items()
+        }
+
+        network_config = NetworkConfig(type="lora", linear=dim, linear_alpha=dim, transformer_only=True)
+        LoRASpecialNetwork.LORA_PREFIX_UNET = "lora_transformer"
+        network = LoRASpecialNetwork(
+            text_encoder=None,
+            unet=transformer,
+            lora_dim=network_config.linear,
+            multiplier=1.0,
+            alpha=network_config.linear_alpha,
+            train_unet=True,
+            train_text_encoder=False,
+            network_config=network_config,
+            network_type=network_config.type,
+            transformer_only=network_config.transformer_only,
+            is_transformer=True,
+            target_lin_modules=self.target_lora_modules,
+            is_assistant_adapter=True,
+            is_ara=True,
+            # transformer_only filters to the block list only through the holder
+            base_model=self,
+        )
+        network.apply_to(None, transformer, apply_text_encoder=False, apply_unet=True)
+        network.force_to(self.device_torch, dtype=self.torch_dtype)
+        network.eval()
+        network.requires_grad_(False)
+        network._update_torch_multiplier()
+        network.load_weights(lora_state_dict)
+        self.assistant_lora: LoRASpecialNetwork = network
+        self.assistant_lora.multiplier = 1.0
+        self.assistant_lora.is_active = True
+        self.invert_assistant_lora = False
+
     # ------------------------------------------------------------------
     # VAE. The latents are RGBA. Images without alpha get an opaque one on
     # encode, and decode drops it again unless RGBA output is on.

extensions_built_in/diffusion_models/qwen_image_2/src/pipeline.py

@@ -303,6 +303,21 @@ def run_transformer(
     return unpack_latents(output[:, -target_tokens:], height, width)
 
 
+# Qwen-Image-2.1-Turbo's 8-step schedule, from `sample_sigmas` in the
+# checkpoint's model_index.json (Qwen/Qwen-Image-2.1-Turbo); the final step
+# runs from the last sigma to 0
+TURBO_SAMPLE_SIGMAS = [
+    1.0,
+    0.978453,
+    0.95418,
+    0.926626,
+    0.89508,
+    0.845148,
+    0.704534,
+    0.414568,
+]
+
+
 def calculate_shift(
     image_seq_len: int,
     base_seq_len: int = 256,
@@ -367,7 +382,13 @@ class QwenImage21Pipeline:
             )
         latents = latents.to(device, dtype=dtype)
 
-        scheduler = model.get_train_scheduler()
+        scheduler = model.get_sample_scheduler()
+        if model.is_turbo:
+            # the turbo checkpoint's own 8-step schedule; num_inference_steps
+            # does not override it (other schedules are unevaluated)
+            sigmas = np.array(TURBO_SAMPLE_SIGMAS, dtype=np.float32)
+            scheduler.set_timesteps(sigmas=sigmas, device=device)
+        else:
             sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps)
             mu = calculate_shift(
                 latent_height * latent_width,

extensions_built_in/diffusion_models/ui.tsx

@@ -2,7 +2,7 @@
 // registers in AI_TOOLKIT_MODELS. Loaded at runtime by the UI, not bundled:
 // see ui/src/extensions/README.md for the convention and the allowed imports.
 import Link from "next/link";
-import type { ModelArch } from "@/app/jobs/new/options";
+import type { CustomModelOption, ModelArch } from "@/app/jobs/new/options";
 import type { JobConfig } from "@/types";
 import {
   defaultSampleConfig,
@@ -12,6 +12,55 @@ import {
 const defaultNameOrPath = "";
 const defaultLinearRank = 32;
 
+// Qwen-Image-2.1 and its turbo distill share the RGBA VAE, so both entries
+// carry this option
+const qwenImage21TransparencyOption: CustomModelOption[] = [
+  {
+    type: "checkbox",
+    label: "Transparency (RGBA)",
+    getValue: (config: JobConfig) =>
+      config?.config?.process?.[0]?.model?.model_kwargs?.rgba ?? false,
+    onChange: (
+      value: boolean,
+      config: JobConfig,
+      setJobConfig: (value: any, key: string) => void,
+    ) => {
+      const kwargs = {
+        ...(config?.config?.process?.[0]?.model?.model_kwargs ?? {}),
+      };
+      if (value) {
+        kwargs.rgba = true;
+      } else {
+        delete kwargs.rgba;
+      }
+      setJobConfig(kwargs, "config.process[0].model.model_kwargs");
+    },
+    doc: {
+      title: "Transparency (RGBA)",
+      description: (
+        <div className="space-y-2">
+          <p>
+            This model&apos;s VAE is natively RGBA, so alpha can be carried
+            end to end. When on, dataset images and reference images load
+            with their alpha channel, the VAE encodes all four channels, and
+            samples are saved as PNGs with their transparency intact.
+          </p>
+          <p>
+            Images with no alpha of their own get a fully opaque one, so a
+            mixed dataset is fine. Turn this off to train and sample flat
+            RGB: alpha is dropped on the way in and added back as opaque.
+          </p>
+          <p>
+            Changing this re-caches latents, and it cannot be combined with
+            a dataset using <code>alpha_mask</code>, which consumes the
+            alpha channel as a loss mask instead.
+          </p>
+        </div>
+      ),
+    },
+  },
+];
+
 export const AI_TOOLKIT_UI_MODELS: ModelArch[] = [
   {
     name: "anima",
@@ -573,52 +622,56 @@ export const AI_TOOLKIT_UI_MODELS: ModelArch[] = [
       "model.low_vram",
       "model.layer_offloading",
     ],
-    customModelSelectOptions: [
+    customModelSelectOptions: qwenImage21TransparencyOption,
+  },
   {
-        type: "checkbox",
-        label: "Transparency (RGBA)",
-        getValue: (config: JobConfig) =>
-          config?.config?.process?.[0]?.model?.model_kwargs?.rgba ?? false,
-        onChange: (
-          value: boolean,
-          config: JobConfig,
-          setJobConfig: (value: any, key: string) => void,
-        ) => {
-          const kwargs = {
-            ...(config?.config?.process?.[0]?.model?.model_kwargs ?? {}),
-          };
-          if (value) {
-            kwargs.rgba = true;
-          } else {
-            delete kwargs.rgba;
-          }
-          setJobConfig(kwargs, "config.process[0].model.model_kwargs");
-        },
-        doc: {
-          title: "Transparency (RGBA)",
-          description: (
-            <div className="space-y-2">
-              <p>
-                This model&apos;s VAE is natively RGBA, so alpha can be carried
-                end to end. When on, dataset images and reference images load
-                with their alpha channel, the VAE encodes all four channels, and
-                samples are saved as PNGs with their transparency intact.
-              </p>
-              <p>
-                Images with no alpha of their own get a fully opaque one, so a
-                mixed dataset is fine. Turn this off to train and sample flat
-                RGB: alpha is dropped on the way in and added back as opaque.
-              </p>
-              <p>
-                Changing this re-caches latents, and it cannot be combined with
-                a dataset using <code>alpha_mask</code>, which consumes the
-                alpha channel as a loss mask instead.
-              </p>
-            </div>
-          ),
-        },
-      },
+    name: "qwen_image_2:turbo",
+    label: "Qwen-Image-2.1 Turbo (w/ Training Adapter)",
+    generateNameOverride: "Qwen-Image-2.1 Turbo",
+    group: "image",
+    defaults: {
+      // default updates when [selected, unselected] in the UI
+      // the turbo weight file in the repack (text encoder and VAE come from
+      // the same repack); model_kwargs.turbo selects the turbo schedule
+      "config.process[0].model.name_or_path": [
+        "Comfy-Org/Qwen-Image-2.1/diffusion_models/qwen_image_2.1_turbo_int8_convrot.safetensors",
+        defaultNameOrPath,
       ],
+      "config.process[0].model.quantize": [true, false],
+      "config.process[0].model.quantize_te": [true, false],
+      "config.process[0].model.low_vram": [true, false],
+      "config.process[0].train.unload_text_encoder": [false, false],
+      "config.process[0].sample.sampler": ["flowmatch", "flowmatch"],
+      "config.process[0].train.noise_scheduler": ["flowmatch", "flowmatch"],
+      "config.process[0].train.timestep_type": ["shift", "sigmoid"],
+      "config.process[0].model.qtype": ["convrot8", "qfloat8"],
+      "config.process[0].model.qtype_te": ["convrot8", "qfloat8"],
+      // training adapter: live on the DiT while training, off while sampling
+      "config.process[0].model.assistant_lora_path": [
+        'ostris/qwen_image_2_turbo_training_adapter/qwen_image_2_turbo_training_adapter_v1.safetensors',
+        undefined
+      ],
+      // the turbo checkpoint samples with its own fixed 8-step schedule at
+      // CFG 1; the step count here is informational
+      "config.process[0].sample.guidance_scale": [1.0, 4.0],
+      "config.process[0].sample.sample_steps": [8, 25],
+      "config.process[0].model.model_kwargs": [
+        {
+          turbo: true,
+          rgba: false,
+        },
+        {},
+      ],
+    },
+    disableSections: ["network.conv", "train.unload_text_encoder"],
+    additionalSections: [
+      "datasets.multi_control_paths",
+      "sample.multi_ctrl_imgs",
+      "model.low_vram",
+      "model.layer_offloading",
+      "model.assistant_lora_path",
+    ],
+    customModelSelectOptions: qwenImage21TransparencyOption,
   },
   {
     name: "ming_image",

testing/test_model_loading.py

@@ -189,6 +189,18 @@ MODEL_TESTS = {
         "sample": {**IMG, "num_inference_steps": 20, "guidance_scale": 1.0},
         "needs_control_image": True,
     },
+    "qwen_image_2:turbo": {
+        # model_kwargs.turbo samples with the checkpoint's fixed 8-step
+        # schedule at CFG 1
+        "model": {
+            "name_or_path": "Comfy-Org/Qwen-Image-2.1/diffusion_models/qwen_image_2.1_turbo_int8_convrot.safetensors",
+            "quantize": True,
+            "quantize_te": True,
+            "model_kwargs": {"turbo": True},
+        },
+        "sample": {**IMG, "num_inference_steps": 8, "guidance_scale": 1.0},
+        "needs_control_image": True,
+    },
     "ming_image": {
         "model": {
             "name_or_path": "Comfy-Org/Ming-Image",