diff --git a/cli/model-keys.py b/cli/model-keys.py
index 45b900bd7..abab842f4 100755
--- a/cli/model-keys.py
+++ b/cli/model-keys.py
@@ -60,6 +60,8 @@ def guess_dct(dct: dict):
     if has(dct, 'model.diffusion_model.joint_blocks') and len(list(has(dct, 'model.diffusion_model.joint_blocks'))) == 38:
         return 'sd35-large'
     if has(dct, 'model.diffusion_model.double_blocks') and len(list(has(dct, 'model.diffusion_model.double_blocks'))) == 19:
+        if has(dct, 'model.diffusion_model.distilled_guidance_layer'):
+            return 'chroma'
         return 'flux-dev'
     return None
 
diff --git a/configs/chroma/model_index.json b/configs/chroma/model_index.json
new file mode 100644
index 000000000..edab1fdd6
--- /dev/null
+++ b/configs/chroma/model_index.json
@@ -0,0 +1,24 @@
+{
+  "_class_name": "ChromaPipeline",
+  "_diffusers_version": "0.34.0.dev0",
+  "scheduler": [
+    "diffusers",
+    "FlowMatchEulerDiscreteScheduler"
+  ],
+  "text_encoder": [
+    "transformers",
+    "T5EncoderModel"
+  ],
+  "tokenizer": [
+    "transformers",
+    "T5Tokenizer"
+  ],
+  "transformer": [
+    "diffusers",
+    "ChromaTransformer2DModel"
+  ],
+  "vae": [
+    "diffusers",
+    "AutoencoderKL"
+  ]
+}
diff --git a/configs/chroma/scheduler/scheduler_config.json b/configs/chroma/scheduler/scheduler_config.json
new file mode 100644
index 000000000..c9ef07f78
--- /dev/null
+++ b/configs/chroma/scheduler/scheduler_config.json
@@ -0,0 +1,18 @@
+{
+  "_class_name": "FlowMatchEulerDiscreteScheduler",
+  "_diffusers_version": "0.34.0.dev0",
+  "base_image_seq_len": 256,
+  "base_shift": 0.5,
+  "invert_sigmas": false,
+  "max_image_seq_len": 4096,
+  "max_shift": 1.15,
+  "num_train_timesteps": 1000,
+  "shift": 3.0,
+  "shift_terminal": null,
+  "stochastic_sampling": false,
+  "time_shift_type": "exponential",
+  "use_beta_sigmas": false,
+  "use_dynamic_shifting": true,
+  "use_exponential_sigmas": false,
+  "use_karras_sigmas": false
+}
diff --git a/configs/chroma/text_encoder/config.json b/configs/chroma/text_encoder/config.json
new file mode 100644
index 000000000..483b38488
--- /dev/null
+++ b/configs/chroma/text_encoder/config.json
@@ -0,0 +1,32 @@
+{
+  "_name_or_path": "google/t5-v1_1-xxl",
+    "architectures": [
+    "T5EncoderModel"
+  ],
+  "classifier_dropout": 0.0,
+  "d_ff": 10240,
+  "d_kv": 64,
+  "d_model": 4096,
+  "decoder_start_token_id": 0,
+  "dense_act_fn": "gelu_new",
+  "dropout_rate": 0.1,
+  "eos_token_id": 1,
+  "feed_forward_proj": "gated-gelu",
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "is_gated_act": true,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "t5",
+  "num_decoder_layers": 24,
+  "num_heads": 64,
+  "num_layers": 24,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_max_distance": 128,
+  "relative_attention_num_buckets": 32,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.52.4",
+  "use_cache": true,
+  "vocab_size": 32128
+}
diff --git a/configs/chroma/tokenizer/added_tokens.json b/configs/chroma/tokenizer/added_tokens.json
new file mode 100644
index 000000000..3f5132007
--- /dev/null
+++ b/configs/chroma/tokenizer/added_tokens.json
@@ -0,0 +1,102 @@
+{
+  "<extra_id_0>": 32099,
+  "<extra_id_10>": 32089,
+  "<extra_id_11>": 32088,
+  "<extra_id_12>": 32087,
+  "<extra_id_13>": 32086,
+  "<extra_id_14>": 32085,
+  "<extra_id_15>": 32084,
+  "<extra_id_16>": 32083,
+  "<extra_id_17>": 32082,
+  "<extra_id_18>": 32081,
+  "<extra_id_19>": 32080,
+  "<extra_id_1>": 32098,
+  "<extra_id_20>": 32079,
+  "<extra_id_21>": 32078,
+  "<extra_id_22>": 32077,
+  "<extra_id_23>": 32076,
+  "<extra_id_24>": 32075,
+  "<extra_id_25>": 32074,
+  "<extra_id_26>": 32073,
+  "<extra_id_27>": 32072,
+  "<extra_id_28>": 32071,
+  "<extra_id_29>": 32070,
+  "<extra_id_2>": 32097,
+  "<extra_id_30>": 32069,
+  "<extra_id_31>": 32068,
+  "<extra_id_32>": 32067,
+  "<extra_id_33>": 32066,
+  "<extra_id_34>": 32065,
+  "<extra_id_35>": 32064,
+  "<extra_id_36>": 32063,
+  "<extra_id_37>": 32062,
+  "<extra_id_38>": 32061,
+  "<extra_id_39>": 32060,
+  "<extra_id_3>": 32096,
+  "<extra_id_40>": 32059,
+  "<extra_id_41>": 32058,
+  "<extra_id_42>": 32057,
+  "<extra_id_43>": 32056,
+  "<extra_id_44>": 32055,
+  "<extra_id_45>": 32054,
+  "<extra_id_46>": 32053,
+  "<extra_id_47>": 32052,
+  "<extra_id_48>": 32051,
+  "<extra_id_49>": 32050,
+  "<extra_id_4>": 32095,
+  "<extra_id_50>": 32049,
+  "<extra_id_51>": 32048,
+  "<extra_id_52>": 32047,
+  "<extra_id_53>": 32046,
+  "<extra_id_54>": 32045,
+  "<extra_id_55>": 32044,
+  "<extra_id_56>": 32043,
+  "<extra_id_57>": 32042,
+  "<extra_id_58>": 32041,
+  "<extra_id_59>": 32040,
+  "<extra_id_5>": 32094,
+  "<extra_id_60>": 32039,
+  "<extra_id_61>": 32038,
+  "<extra_id_62>": 32037,
+  "<extra_id_63>": 32036,
+  "<extra_id_64>": 32035,
+  "<extra_id_65>": 32034,
+  "<extra_id_66>": 32033,
+  "<extra_id_67>": 32032,
+  "<extra_id_68>": 32031,
+  "<extra_id_69>": 32030,
+  "<extra_id_6>": 32093,
+  "<extra_id_70>": 32029,
+  "<extra_id_71>": 32028,
+  "<extra_id_72>": 32027,
+  "<extra_id_73>": 32026,
+  "<extra_id_74>": 32025,
+  "<extra_id_75>": 32024,
+  "<extra_id_76>": 32023,
+  "<extra_id_77>": 32022,
+  "<extra_id_78>": 32021,
+  "<extra_id_79>": 32020,
+  "<extra_id_7>": 32092,
+  "<extra_id_80>": 32019,
+  "<extra_id_81>": 32018,
+  "<extra_id_82>": 32017,
+  "<extra_id_83>": 32016,
+  "<extra_id_84>": 32015,
+  "<extra_id_85>": 32014,
+  "<extra_id_86>": 32013,
+  "<extra_id_87>": 32012,
+  "<extra_id_88>": 32011,
+  "<extra_id_89>": 32010,
+  "<extra_id_8>": 32091,
+  "<extra_id_90>": 32009,
+  "<extra_id_91>": 32008,
+  "<extra_id_92>": 32007,
+  "<extra_id_93>": 32006,
+  "<extra_id_94>": 32005,
+  "<extra_id_95>": 32004,
+  "<extra_id_96>": 32003,
+  "<extra_id_97>": 32002,
+  "<extra_id_98>": 32001,
+  "<extra_id_99>": 32000,
+  "<extra_id_9>": 32090
+}
diff --git a/configs/chroma/tokenizer/special_tokens_map.json b/configs/chroma/tokenizer/special_tokens_map.json
new file mode 100644
index 000000000..17ade346a
--- /dev/null
+++ b/configs/chroma/tokenizer/special_tokens_map.json
@@ -0,0 +1,125 @@
+{
+  "additional_special_tokens": [
+    "<extra_id_0>",
+    "<extra_id_1>",
+    "<extra_id_2>",
+    "<extra_id_3>",
+    "<extra_id_4>",
+    "<extra_id_5>",
+    "<extra_id_6>",
+    "<extra_id_7>",
+    "<extra_id_8>",
+    "<extra_id_9>",
+    "<extra_id_10>",
+    "<extra_id_11>",
+    "<extra_id_12>",
+    "<extra_id_13>",
+    "<extra_id_14>",
+    "<extra_id_15>",
+    "<extra_id_16>",
+    "<extra_id_17>",
+    "<extra_id_18>",
+    "<extra_id_19>",
+    "<extra_id_20>",
+    "<extra_id_21>",
+    "<extra_id_22>",
+    "<extra_id_23>",
+    "<extra_id_24>",
+    "<extra_id_25>",
+    "<extra_id_26>",
+    "<extra_id_27>",
+    "<extra_id_28>",
+    "<extra_id_29>",
+    "<extra_id_30>",
+    "<extra_id_31>",
+    "<extra_id_32>",
+    "<extra_id_33>",
+    "<extra_id_34>",
+    "<extra_id_35>",
+    "<extra_id_36>",
+    "<extra_id_37>",
+    "<extra_id_38>",
+    "<extra_id_39>",
+    "<extra_id_40>",
+    "<extra_id_41>",
+    "<extra_id_42>",
+    "<extra_id_43>",
+    "<extra_id_44>",
+    "<extra_id_45>",
+    "<extra_id_46>",
+    "<extra_id_47>",
+    "<extra_id_48>",
+    "<extra_id_49>",
+    "<extra_id_50>",
+    "<extra_id_51>",
+    "<extra_id_52>",
+    "<extra_id_53>",
+    "<extra_id_54>",
+    "<extra_id_55>",
+    "<extra_id_56>",
+    "<extra_id_57>",
+    "<extra_id_58>",
+    "<extra_id_59>",
+    "<extra_id_60>",
+    "<extra_id_61>",
+    "<extra_id_62>",
+    "<extra_id_63>",
+    "<extra_id_64>",
+    "<extra_id_65>",
+    "<extra_id_66>",
+    "<extra_id_67>",
+    "<extra_id_68>",
+    "<extra_id_69>",
+    "<extra_id_70>",
+    "<extra_id_71>",
+    "<extra_id_72>",
+    "<extra_id_73>",
+    "<extra_id_74>",
+    "<extra_id_75>",
+    "<extra_id_76>",
+    "<extra_id_77>",
+    "<extra_id_78>",
+    "<extra_id_79>",
+    "<extra_id_80>",
+    "<extra_id_81>",
+    "<extra_id_82>",
+    "<extra_id_83>",
+    "<extra_id_84>",
+    "<extra_id_85>",
+    "<extra_id_86>",
+    "<extra_id_87>",
+    "<extra_id_88>",
+    "<extra_id_89>",
+    "<extra_id_90>",
+    "<extra_id_91>",
+    "<extra_id_92>",
+    "<extra_id_93>",
+    "<extra_id_94>",
+    "<extra_id_95>",
+    "<extra_id_96>",
+    "<extra_id_97>",
+    "<extra_id_98>",
+    "<extra_id_99>"
+  ],
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}
diff --git a/configs/chroma/tokenizer/spiece.model b/configs/chroma/tokenizer/spiece.model
new file mode 100644
index 000000000..4e28ff6eb
Binary files /dev/null and b/configs/chroma/tokenizer/spiece.model differ
diff --git a/configs/chroma/tokenizer/tokenizer_config.json b/configs/chroma/tokenizer/tokenizer_config.json
new file mode 100644
index 000000000..b336fa236
--- /dev/null
+++ b/configs/chroma/tokenizer/tokenizer_config.json
@@ -0,0 +1,940 @@
+{
+  "add_prefix_space": true,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32000": {
+      "content": "<extra_id_99>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32001": {
+      "content": "<extra_id_98>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32002": {
+      "content": "<extra_id_97>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32003": {
+      "content": "<extra_id_96>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32004": {
+      "content": "<extra_id_95>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32005": {
+      "content": "<extra_id_94>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32006": {
+      "content": "<extra_id_93>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32007": {
+      "content": "<extra_id_92>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32008": {
+      "content": "<extra_id_91>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32009": {
+      "content": "<extra_id_90>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32010": {
+      "content": "<extra_id_89>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32011": {
+      "content": "<extra_id_88>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32012": {
+      "content": "<extra_id_87>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32013": {
+      "content": "<extra_id_86>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32014": {
+      "content": "<extra_id_85>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32015": {
+      "content": "<extra_id_84>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32016": {
+      "content": "<extra_id_83>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32017": {
+      "content": "<extra_id_82>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32018": {
+      "content": "<extra_id_81>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32019": {
+      "content": "<extra_id_80>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32020": {
+      "content": "<extra_id_79>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32021": {
+      "content": "<extra_id_78>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32022": {
+      "content": "<extra_id_77>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32023": {
+      "content": "<extra_id_76>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32024": {
+      "content": "<extra_id_75>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32025": {
+      "content": "<extra_id_74>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32026": {
+      "content": "<extra_id_73>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32027": {
+      "content": "<extra_id_72>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32028": {
+      "content": "<extra_id_71>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32029": {
+      "content": "<extra_id_70>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32030": {
+      "content": "<extra_id_69>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32031": {
+      "content": "<extra_id_68>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32032": {
+      "content": "<extra_id_67>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32033": {
+      "content": "<extra_id_66>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32034": {
+      "content": "<extra_id_65>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32035": {
+      "content": "<extra_id_64>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32036": {
+      "content": "<extra_id_63>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32037": {
+      "content": "<extra_id_62>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32038": {
+      "content": "<extra_id_61>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32039": {
+      "content": "<extra_id_60>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32040": {
+      "content": "<extra_id_59>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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diff --git a/configs/chroma/transformer/config.json b/configs/chroma/transformer/config.json
new file mode 100644
index 000000000..86d6daa85
--- /dev/null
+++ b/configs/chroma/transformer/config.json
@@ -0,0 +1,20 @@
+{
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diff --git a/configs/chroma/transformer/diffusion_pytorch_model.safetensors.index.json b/configs/chroma/transformer/diffusion_pytorch_model.safetensors.index.json
new file mode 100644
index 000000000..8a52f5e7b
--- /dev/null
+++ b/configs/chroma/transformer/diffusion_pytorch_model.safetensors.index.json
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+    "transformer_blocks.7.attn.to_add_out.weight": "diffusion_pytorch_model-00001-of-00002.safetensors",
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+    "transformer_blocks.7.attn.to_out.0.bias": "diffusion_pytorch_model-00001-of-00002.safetensors",
+    "transformer_blocks.7.attn.to_out.0.weight": "diffusion_pytorch_model-00001-of-00002.safetensors",
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+    "transformer_blocks.7.attn.to_v.bias": "diffusion_pytorch_model-00001-of-00002.safetensors",
+    "transformer_blocks.7.attn.to_v.weight": "diffusion_pytorch_model-00001-of-00002.safetensors",
+    "transformer_blocks.7.ff.net.0.proj.bias": "diffusion_pytorch_model-00001-of-00002.safetensors",
+    "transformer_blocks.7.ff.net.0.proj.weight": "diffusion_pytorch_model-00001-of-00002.safetensors",
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+    "transformer_blocks.7.ff.net.2.weight": "diffusion_pytorch_model-00001-of-00002.safetensors",
+    "transformer_blocks.7.ff_context.net.0.proj.bias": "diffusion_pytorch_model-00001-of-00002.safetensors",
+    "transformer_blocks.7.ff_context.net.0.proj.weight": "diffusion_pytorch_model-00001-of-00002.safetensors",
+    "transformer_blocks.7.ff_context.net.2.bias": "diffusion_pytorch_model-00001-of-00002.safetensors",
+    "transformer_blocks.7.ff_context.net.2.weight": "diffusion_pytorch_model-00001-of-00002.safetensors",
+    "transformer_blocks.8.attn.add_k_proj.bias": "diffusion_pytorch_model-00001-of-00002.safetensors",
+    "transformer_blocks.8.attn.add_k_proj.weight": "diffusion_pytorch_model-00001-of-00002.safetensors",
+    "transformer_blocks.8.attn.add_q_proj.bias": "diffusion_pytorch_model-00001-of-00002.safetensors",
+    "transformer_blocks.8.attn.add_q_proj.weight": "diffusion_pytorch_model-00001-of-00002.safetensors",
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+    "transformer_blocks.8.attn.norm_q.weight": "diffusion_pytorch_model-00001-of-00002.safetensors",
+    "transformer_blocks.8.attn.to_add_out.bias": "diffusion_pytorch_model-00001-of-00002.safetensors",
+    "transformer_blocks.8.attn.to_add_out.weight": "diffusion_pytorch_model-00001-of-00002.safetensors",
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+    "transformer_blocks.8.attn.to_k.weight": "diffusion_pytorch_model-00001-of-00002.safetensors",
+    "transformer_blocks.8.attn.to_out.0.bias": "diffusion_pytorch_model-00001-of-00002.safetensors",
+    "transformer_blocks.8.attn.to_out.0.weight": "diffusion_pytorch_model-00001-of-00002.safetensors",
+    "transformer_blocks.8.attn.to_q.bias": "diffusion_pytorch_model-00001-of-00002.safetensors",
+    "transformer_blocks.8.attn.to_q.weight": "diffusion_pytorch_model-00001-of-00002.safetensors",
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+    "transformer_blocks.8.ff_context.net.2.weight": "diffusion_pytorch_model-00001-of-00002.safetensors",
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+    "transformer_blocks.9.attn.add_v_proj.weight": "diffusion_pytorch_model-00001-of-00002.safetensors",
+    "transformer_blocks.9.attn.norm_added_k.weight": "diffusion_pytorch_model-00001-of-00002.safetensors",
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+    "transformer_blocks.9.attn.to_add_out.bias": "diffusion_pytorch_model-00001-of-00002.safetensors",
+    "transformer_blocks.9.attn.to_add_out.weight": "diffusion_pytorch_model-00001-of-00002.safetensors",
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+    "transformer_blocks.9.attn.to_out.0.bias": "diffusion_pytorch_model-00001-of-00002.safetensors",
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+    "transformer_blocks.9.attn.to_v.weight": "diffusion_pytorch_model-00001-of-00002.safetensors",
+    "transformer_blocks.9.ff.net.0.proj.bias": "diffusion_pytorch_model-00001-of-00002.safetensors",
+    "transformer_blocks.9.ff.net.0.proj.weight": "diffusion_pytorch_model-00001-of-00002.safetensors",
+    "transformer_blocks.9.ff.net.2.bias": "diffusion_pytorch_model-00001-of-00002.safetensors",
+    "transformer_blocks.9.ff.net.2.weight": "diffusion_pytorch_model-00001-of-00002.safetensors",
+    "transformer_blocks.9.ff_context.net.0.proj.bias": "diffusion_pytorch_model-00001-of-00002.safetensors",
+    "transformer_blocks.9.ff_context.net.0.proj.weight": "diffusion_pytorch_model-00001-of-00002.safetensors",
+    "transformer_blocks.9.ff_context.net.2.bias": "diffusion_pytorch_model-00001-of-00002.safetensors",
+    "transformer_blocks.9.ff_context.net.2.weight": "diffusion_pytorch_model-00001-of-00002.safetensors",
+    "x_embedder.bias": "diffusion_pytorch_model-00001-of-00002.safetensors",
+    "x_embedder.weight": "diffusion_pytorch_model-00001-of-00002.safetensors"
+  }
+}
diff --git a/configs/chroma/vae/config.json b/configs/chroma/vae/config.json
new file mode 100644
index 000000000..188c62140
--- /dev/null
+++ b/configs/chroma/vae/config.json
@@ -0,0 +1,37 @@
+{
+  "_class_name": "AutoencoderKL",
+  "_diffusers_version": "0.34.0.dev0",
+  "act_fn": "silu",
+  "block_out_channels": [
+    128,
+    256,
+    512,
+    512
+  ],
+  "down_block_types": [
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D"
+  ],
+  "force_upcast": true,
+  "in_channels": 3,
+  "latent_channels": 16,
+  "latents_mean": null,
+  "latents_std": null,
+  "layers_per_block": 2,
+  "mid_block_add_attention": true,
+  "norm_num_groups": 32,
+  "out_channels": 3,
+  "sample_size": 1024,
+  "scaling_factor": 0.3611,
+  "shift_factor": 0.1159,
+  "up_block_types": [
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D"
+  ],
+  "use_post_quant_conv": false,
+  "use_quant_conv": false
+}
diff --git a/extensions-builtin/Lora/lora_extract.py b/extensions-builtin/Lora/lora_extract.py
index 85bcc2c95..c6b65fca7 100644
--- a/extensions-builtin/Lora/lora_extract.py
+++ b/extensions-builtin/Lora/lora_extract.py
@@ -107,6 +107,9 @@ def make_meta(fn, maxrank, rank_ratio):
     elif shared.sd_model_type == "f1":
         meta["model_spec.architecture"] = "flux-1-dev/lora"
         meta["ss_base_model_version"] = "flux1"
+    elif shared.sd_model_type == "chroma":
+        meta["model_spec.architecture"] = "chroma/lora"
+        meta["ss_base_model_version"] = "chroma"
     elif shared.sd_model_type == "sc":
         meta["model_spec.architecture"] = "stable-cascade-v1-prior/lora"
     return meta
diff --git a/extensions-builtin/Lora/networks.py b/extensions-builtin/Lora/networks.py
index e59555993..0a4657383 100644
--- a/extensions-builtin/Lora/networks.py
+++ b/extensions-builtin/Lora/networks.py
@@ -139,7 +139,7 @@ def load_network(name, network_on_disk) -> network.Network:
     net = network.Network(name, network_on_disk)
     net.mtime = os.path.getmtime(network_on_disk.filename)
     sd = sd_models.read_state_dict(network_on_disk.filename, what='network')
-    if shared.sd_model_type == 'f1':  # if kohya flux lora, convert state_dict
+    if shared.sd_model_type in ['f1', 'chroma']:  # if kohya flux lora, convert state_dict
         sd = lora_convert._convert_kohya_flux_lora_to_diffusers(sd) or sd  # pylint: disable=protected-access
     assign_network_names_to_compvis_modules(shared.sd_model)
     keys_failed_to_match = {}
diff --git a/installer.py b/installer.py
index bf4d0b408..a634febf3 100644
--- a/installer.py
+++ b/installer.py
@@ -546,7 +546,7 @@ def check_diffusers():
     t_start = time.time()
     if args.skip_all or args.skip_git or args.experimental:
         return
-    sha = '8adc6003ba4dbf5b61bb4f1ce571e9e55e145a99' # diffusers commit hash
+    sha = '900653c814cfa431877ae46548fe496506bda2ad' # diffusers commit hash
     pkg = pkg_resources.working_set.by_key.get('diffusers', None)
     minor = int(pkg.version.split('.')[1] if pkg is not None else 0)
     cur = opts.get('diffusers_version', '') if minor > 0 else ''
diff --git a/modules/interposer.py b/modules/interposer.py
index 2bf272706..c248c4f00 100644
--- a/modules/interposer.py
+++ b/modules/interposer.py
@@ -101,7 +101,7 @@ def map_model_name(name: str):
         return 'xl'
     if name == 'sd3':
         return 'v3'
-    if name == 'f1':
+    if name in ['f1', 'chroma']:
         return 'fx'
     return name
 
diff --git a/modules/lora/lora_extract.py b/modules/lora/lora_extract.py
index 4bd3a3adb..27bfb5c20 100644
--- a/modules/lora/lora_extract.py
+++ b/modules/lora/lora_extract.py
@@ -107,6 +107,9 @@ def make_meta(fn, maxrank, rank_ratio):
     elif shared.sd_model_type == "f1":
         meta["model_spec.architecture"] = "flux-1-dev/lora"
         meta["ss_base_model_version"] = "flux1"
+    elif shared.sd_model_type == "chroma":
+        meta["model_spec.architecture"] = "chroma/lora"
+        meta["ss_base_model_version"] = "chroma"
     elif shared.sd_model_type == "sc":
         meta["model_spec.architecture"] = "stable-cascade-v1-prior/lora"
     return meta
diff --git a/modules/lora/lora_load.py b/modules/lora/lora_load.py
index f02e4a274..100235bd1 100644
--- a/modules/lora/lora_load.py
+++ b/modules/lora/lora_load.py
@@ -85,7 +85,7 @@ def load_safetensors(name, network_on_disk) -> Union[network.Network, None]:
     net = network.Network(name, network_on_disk)
     net.mtime = os.path.getmtime(network_on_disk.filename)
     state_dict = sd_models.read_state_dict(network_on_disk.filename, what='network')
-    if shared.sd_model_type == 'f1': # if kohya flux lora, convert state_dict
+    if shared.sd_model_type in ['f1', 'chroma']: # if kohya flux lora, convert state_dict
         state_dict = lora_convert._convert_kohya_flux_lora_to_diffusers(state_dict) or state_dict # pylint: disable=protected-access
     if shared.sd_model_type == 'sd3': # if kohya flux lora, convert state_dict
         try:
diff --git a/modules/lora/network.py b/modules/lora/network.py
index f6d93009c..41d580e37 100644
--- a/modules/lora/network.py
+++ b/modules/lora/network.py
@@ -18,6 +18,7 @@ class SdVersion(enum.Enum):
     SC = 5
     F1 = 6
     HV = 7
+    CHROMA = 8
 
 
 class NetworkOnDisk:
@@ -59,6 +60,8 @@ class NetworkOnDisk:
             return 'f1'
         if base.startswith("hunyuan_video"):
             return 'hv'
+        if base.startswith("chroma"):
+            return 'chroma'
 
         if arch.startswith("stable-diffusion-v1"):
             return 'sd1'
@@ -70,6 +73,8 @@ class NetworkOnDisk:
             return 'f1'
         if arch.startswith("hunyuan-video"):
             return 'hv'
+        if arch.startswith("chroma"):
+            return 'chroma'
 
         if "v1-5" in str(self.metadata.get('ss_sd_model_name', "")):
             return 'sd1'
@@ -79,6 +84,8 @@ class NetworkOnDisk:
             return 'f1'
         if 'xl' in self.name.lower():
             return 'xl'
+        if 'chroma' in self.name.lower():
+            return 'chroma'
 
         return ''
 
diff --git a/modules/model_chroma.py b/modules/model_chroma.py
new file mode 100644
index 000000000..b722837fc
--- /dev/null
+++ b/modules/model_chroma.py
@@ -0,0 +1,339 @@
+import os
+import json
+import torch
+import diffusers
+import transformers
+from safetensors.torch import load_file
+from huggingface_hub import hf_hub_download, auth_check
+from modules import shared, errors, devices, modelloader, sd_models, sd_unet, model_te, model_quant, sd_hijack_te
+
+
+debug = shared.log.trace if os.environ.get('SD_LOAD_DEBUG', None) is not None else lambda *args, **kwargs: None
+
+
+def load_chroma_quanto(checkpoint_info):
+    transformer, text_encoder = None, None
+    quanto = model_quant.load_quanto('Load model: type=Chroma')
+
+    if isinstance(checkpoint_info, str):
+        repo_path = checkpoint_info
+    else:
+        repo_path = checkpoint_info.path
+
+    try:
+        quantization_map = os.path.join(repo_path, "transformer", "quantization_map.json")
+        debug(f'Load model: type=Chroma quantization map="{quantization_map}" repo="{checkpoint_info.name}" component="transformer"')
+        if not os.path.exists(quantization_map):
+            repo_id = sd_models.path_to_repo(checkpoint_info.name)
+            quantization_map = hf_hub_download(repo_id, subfolder='transformer', filename='quantization_map.json', cache_dir=shared.opts.diffusers_dir)
+        with open(quantization_map, "r", encoding='utf8') as f:
+            quantization_map = json.load(f)
+        state_dict = load_file(os.path.join(repo_path, "transformer", "diffusion_pytorch_model.safetensors"))
+        dtype = state_dict['context_embedder.bias'].dtype
+        with torch.device("meta"):
+            transformer = diffusers.ChromaTransformer2DModel.from_config(os.path.join(repo_path, "transformer", "config.json")).to(dtype=dtype)
+        quanto.requantize(transformer, state_dict, quantization_map, device=torch.device("cpu"))
+        if shared.opts.diffusers_eval:
+            transformer.eval()
+        transformer_dtype = transformer.dtype
+        if transformer_dtype != devices.dtype:
+            try:
+                transformer = transformer.to(dtype=devices.dtype)
+            except Exception:
+                shared.log.error(f"Load model: type=Chroma Failed to cast transformer to {devices.dtype}, set dtype to {transformer_dtype}")
+    except Exception as e:
+        shared.log.error(f"Load model: type=Chroma failed to load Quanto transformer: {e}")
+        if debug:
+            errors.display(e, 'Chroma Quanto:')
+
+    try:
+        quantization_map = os.path.join(repo_path, "text_encoder", "quantization_map.json")
+        debug(f'Load model: type=Chroma quantization map="{quantization_map}" repo="{checkpoint_info.name}" component="text_encoder"')
+        if not os.path.exists(quantization_map):
+            repo_id = sd_models.path_to_repo(checkpoint_info.name)
+            quantization_map = hf_hub_download(repo_id, subfolder='text_encoder', filename='quantization_map.json', cache_dir=shared.opts.diffusers_dir)
+        with open(quantization_map, "r", encoding='utf8') as f:
+            quantization_map = json.load(f)
+        with open(os.path.join(repo_path, "text_encoder", "config.json"), encoding='utf8') as f:
+            t5_config = transformers.T5Config(**json.load(f))
+        state_dict = load_file(os.path.join(repo_path, "text_encoder", "model.safetensors"))
+        dtype = state_dict['encoder.block.0.layer.0.SelfAttention.relative_attention_bias.weight'].dtype
+        with torch.device("meta"):
+            text_encoder = transformers.T5EncoderModel(t5_config).to(dtype=dtype)
+        quanto.requantize(text_encoder, state_dict, quantization_map, device=torch.device("cpu"))
+        if shared.opts.diffusers_eval:
+            text_encoder.eval()
+        text_encoder_dtype = text_encoder.dtype
+        if text_encoder_dtype != devices.dtype:
+            try:
+                text_encoder = text_encoder.to(dtype=devices.dtype)
+            except Exception:
+                shared.log.error(f"Load model: type=Chroma Failed to cast text encoder to {devices.dtype}, set dtype to {text_encoder_dtype}")
+    except Exception as e:
+        shared.log.error(f"Load model: type=Chroma failed to load Quanto text encoder: {e}")
+        if debug:
+            errors.display(e, 'Chroma Quanto:')
+
+    return transformer, text_encoder
+
+
+def load_chroma_bnb(checkpoint_info, diffusers_load_config): # pylint: disable=unused-argument
+    transformer, text_encoder = None, None
+    if isinstance(checkpoint_info, str):
+        repo_path = checkpoint_info
+    else:
+        repo_path = checkpoint_info.path
+    model_quant.load_bnb('Load model: type=Chroma')
+    quant = model_quant.get_quant(repo_path)
+    try:
+        # we ignore the distilled guidance layer because it degrades quality too much
+        # see: https://github.com/huggingface/diffusers/pull/11698#issuecomment-2969717180 for more details
+        if quant == 'fp8':
+            quantization_config = transformers.BitsAndBytesConfig(load_in_8bit=True, llm_int8_skip_modules=["distilled_guidance_layer"], bnb_4bit_compute_dtype=devices.dtype)
+            debug(f'Quantization: {quantization_config}')
+            transformer = diffusers.ChromaTransformer2DModel.from_single_file(repo_path, **diffusers_load_config, quantization_config=quantization_config)
+        elif quant == 'fp4':
+            quantization_config = transformers.BitsAndBytesConfig(load_in_4bit=True, llm_int8_skip_modules=["distilled_guidance_layer"], bnb_4bit_compute_dtype=devices.dtype, bnb_4bit_quant_type= 'fp4')
+            debug(f'Quantization: {quantization_config}')
+            transformer = diffusers.ChromaTransformer2DModel.from_single_file(repo_path, **diffusers_load_config, quantization_config=quantization_config)
+        elif quant == 'nf4':
+            quantization_config = transformers.BitsAndBytesConfig(load_in_4bit=True, llm_int8_skip_modules=["distilled_guidance_layer"], bnb_4bit_compute_dtype=devices.dtype, bnb_4bit_quant_type= 'nf4')
+            debug(f'Quantization: {quantization_config}')
+            transformer = diffusers.ChromaTransformer2DModel.from_single_file(repo_path, **diffusers_load_config, quantization_config=quantization_config)
+        else:
+            transformer = diffusers.ChromaTransformer2DModel.from_single_file(repo_path, **diffusers_load_config)
+    except Exception as e:
+        shared.log.error(f"Load model: type=Chroma failed to load BnB transformer: {e}")
+        transformer, text_encoder = None, None
+        if debug:
+            errors.display(e, 'Chroma:')
+    return transformer, text_encoder
+
+
+def load_quants(kwargs, pretrained_model_name_or_path, cache_dir, allow_quant):
+    try:
+        if 'transformer' not in kwargs and model_quant.check_nunchaku('Transformer'):
+            raise NotImplementedError('Nunchaku does not support Chroma Model yet. See https://github.com/mit-han-lab/nunchaku/issues/167')
+        elif 'transformer' not in kwargs and model_quant.check_quant('Transformer'):
+            quant_args = model_quant.create_config(allow=allow_quant, module='Transformer')
+            if quant_args:
+                if os.path.isfile(pretrained_model_name_or_path):
+                    kwargs['transformer'] = diffusers.ChromaTransformer2DModel.from_single_file(pretrained_model_name_or_path, cache_dir=cache_dir, torch_dtype=devices.dtype, **quant_args)                    
+                    pass
+                else:
+                    kwargs['transformer'] = diffusers.ChromaTransformer2DModel.from_pretrained(pretrained_model_name_or_path, subfolder="transformer", cache_dir=cache_dir, torch_dtype=devices.dtype, **quant_args)
+        if 'text_encoder' not in kwargs and model_quant.check_nunchaku('TE'):
+            import nunchaku
+            nunchaku_precision = nunchaku.utils.get_precision()
+            nunchaku_repo = 'mit-han-lab/nunchaku-t5/awq-int4-flux.1-t5xxl.safetensors'
+            shared.log.debug(f'Load module: quant=Nunchaku module=t5 repo="{nunchaku_repo}" precision={nunchaku_precision}')
+            kwargs['text_encoder'] = nunchaku.NunchakuT5EncoderModel.from_pretrained(nunchaku_repo, torch_dtype=devices.dtype)
+        elif 'text_encoder' not in kwargs and model_quant.check_quant('TE'):
+            quant_args = model_quant.create_config(allow=allow_quant, module='TE')
+            if quant_args:
+                if os.path.isfile(pretrained_model_name_or_path):
+                    kwargs['text_encoder'] = transformers.T5EncoderModel.from_single_file(pretrained_model_name_or_path, cache_dir=cache_dir, torch_dtype=devices.dtype, **quant_args)
+                else:
+                    kwargs['text_encoder'] = transformers.T5EncoderModel.from_pretrained(pretrained_model_name_or_path, subfolder="text_encoder", cache_dir=cache_dir, torch_dtype=devices.dtype, **quant_args)
+    except Exception as e:
+        shared.log.error(f'Quantization: {e}')
+        errors.display(e, 'Quantization:')
+    return kwargs
+
+
+def load_transformer(file_path): # triggered by opts.sd_unet change
+    if file_path is None or not os.path.exists(file_path):
+        return None
+    transformer = None
+    quant = model_quant.get_quant(file_path)
+    diffusers_load_config = {
+        "low_cpu_mem_usage": True,
+        "torch_dtype": devices.dtype,
+        "cache_dir": shared.opts.hfcache_dir,
+    }
+    if quant is not None and quant != 'none':
+        shared.log.info(f'Load module: type=UNet/Transformer file="{file_path}" offload={shared.opts.diffusers_offload_mode} prequant={quant} dtype={devices.dtype}')
+    if 'gguf' in file_path.lower():
+        from modules import ggml
+        _transformer = ggml.load_gguf(file_path, cls=diffusers.ChromaTransformer2DModel, compute_dtype=devices.dtype)
+        if _transformer is not None:
+            transformer = _transformer
+    elif quant == 'qint8' or quant == 'qint4':
+        _transformer, _text_encoder = load_chroma_quanto(file_path)
+        if _transformer is not None:
+            transformer = _transformer
+    elif quant == 'fp8' or quant == 'fp4' or quant == 'nf4':
+        _transformer, _text_encoder = load_chroma_bnb(file_path, diffusers_load_config)
+        if _transformer is not None:
+            transformer = _transformer
+    elif 'nf4' in quant: # TODO chroma: loader for civitai nf4 models
+        from modules.model_chroma_nf4 import load_chroma_nf4
+        _transformer, _text_encoder = load_chroma_nf4(file_path, prequantized=True)
+        if _transformer is not None:
+            transformer = _transformer
+    else:
+        quant_args = model_quant.create_bnb_config({})
+        if quant_args:
+            shared.log.info(f'Load module: type=UNet/Transformer file="{file_path}" offload={shared.opts.diffusers_offload_mode} quant=bnb dtype={devices.dtype}')
+            from modules.model_chroma_nf4 import load_chroma_nf4
+            transformer, _text_encoder = load_chroma_nf4(file_path, prequantized=False)
+            if transformer is not None:
+                return transformer
+        quant_args = model_quant.create_config(module='Transformer')
+        if quant_args:
+            shared.log.info(f'Load module: type=UNet/Transformer file="{file_path}" offload={shared.opts.diffusers_offload_mode} quant=torchao dtype={devices.dtype}')
+            transformer = diffusers.ChromaTransformer2DModel.from_single_file(file_path, **diffusers_load_config, **quant_args)
+            if transformer is not None:
+                return transformer
+        shared.log.info(f'Load module: type=UNet/Transformer file="{file_path}" offload={shared.opts.diffusers_offload_mode} quant=none dtype={devices.dtype}')
+        # TODO chroma transformer from-single-file with quant
+        # shared.log.warning('Load module: type=UNet/Transformer does not support load-time quantization')
+        transformer = diffusers.ChromaTransformer2DModel.from_single_file(file_path, **diffusers_load_config)
+    if transformer is None:
+        shared.log.error('Failed to load UNet model')
+        shared.opts.sd_unet = 'Default'
+    return transformer
+
+
+def load_chroma(checkpoint_info, diffusers_load_config): # triggered by opts.sd_checkpoint change
+    fn = checkpoint_info.path
+    repo_id = sd_models.path_to_repo(checkpoint_info.name)
+    login = modelloader.hf_login()
+    try:
+        auth_check(repo_id)
+    except Exception as e:
+        repo_id = None
+        if not os.path.exists(fn):
+            shared.log.error(f'Load model: repo="{repo_id}" login={login} {e}')
+            return None
+
+    prequantized = model_quant.get_quant(checkpoint_info.path)
+    shared.log.debug(f'Load model: type=Chroma model="{checkpoint_info.name}" repo={repo_id or "none"} unet="{shared.opts.sd_unet}" te="{shared.opts.sd_text_encoder}" vae="{shared.opts.sd_vae}" quant={prequantized} offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype}')
+    debug(f'Load model: type=Chroma config={diffusers_load_config}')
+
+    transformer = None
+    text_encoder = None
+    vae = None
+
+    # unload current model
+    sd_models.unload_model_weights()
+    shared.sd_model = None
+    devices.torch_gc(force=True)
+
+    if shared.opts.teacache_enabled:
+        from modules import teacache
+        shared.log.debug(f'Transformers cache: type=teacache patch=forward cls={diffusers.ChromaTransformer2DModel.__name__}')
+        diffusers.ChromaTransformer2DModel.forward = teacache.teacache_chroma_forward # patch must be done before transformer is loaded
+
+    # load overrides if any
+    if shared.opts.sd_unet != 'Default':
+        try:
+            debug(f'Load model: type=Chroma unet="{shared.opts.sd_unet}"')
+            transformer = load_transformer(sd_unet.unet_dict[shared.opts.sd_unet])
+            if transformer is None:
+                shared.opts.sd_unet = 'Default'
+                sd_unet.failed_unet.append(shared.opts.sd_unet)
+        except Exception as e:
+            shared.log.error(f"Load model: type=Chroma failed to load UNet: {e}")
+            shared.opts.sd_unet = 'Default'
+            if debug:
+                errors.display(e, 'Chroma UNet:')
+    if shared.opts.sd_text_encoder != 'Default':
+        try:
+            debug(f'Load model: type=Chroma te="{shared.opts.sd_text_encoder}"')
+            from modules.model_te import load_t5
+            text_encoder = load_t5(name=shared.opts.sd_text_encoder, cache_dir=shared.opts.diffusers_dir)
+        except Exception as e:
+            shared.log.error(f"Load model: type=Chroma failed to load T5: {e}")
+            shared.opts.sd_text_encoder = 'Default'
+            if debug:
+                errors.display(e, 'Chroma T5:')
+    if shared.opts.sd_vae != 'Default' and shared.opts.sd_vae != 'Automatic':
+        try:
+            debug(f'Load model: type=Chroma vae="{shared.opts.sd_vae}"')
+            from modules import sd_vae
+            # vae = sd_vae.load_vae_diffusers(None, sd_vae.vae_dict[shared.opts.sd_vae], 'override')
+            vae_file = sd_vae.vae_dict[shared.opts.sd_vae]
+            if os.path.exists(vae_file):
+                vae_config = os.path.join('configs', 'chroma', 'vae', 'config.json')
+                vae = diffusers.AutoencoderKL.from_single_file(vae_file, config=vae_config, **diffusers_load_config)
+        except Exception as e:
+            shared.log.error(f"Load model: type=Chroma failed to load VAE: {e}")
+            shared.opts.sd_vae = 'Default'
+            if debug:
+                errors.display(e, 'Chroma VAE:')
+
+    # load quantized components if any
+    if prequantized == 'nf4':
+        try:
+            from modules.model_chroma_nf4 import load_chroma_nf4
+            _transformer, _text_encoder = load_chroma_nf4(checkpoint_info)
+            if _transformer is not None:
+                transformer = _transformer
+            if _text_encoder is not None:
+                text_encoder = _text_encoder
+        except Exception as e:
+            shared.log.error(f"Load model: type=Chroma failed to load NF4 components: {e}")
+            if debug:
+                errors.display(e, 'Chroma NF4:')
+    if prequantized == 'qint8' or prequantized == 'qint4':
+        try:
+            _transformer, _text_encoder = load_chroma_quanto(checkpoint_info)
+            if _transformer is not None:
+                transformer = _transformer
+            if _text_encoder is not None:
+                text_encoder = _text_encoder
+        except Exception as e:
+            shared.log.error(f"Load model: type=Chroma failed to load Quanto components: {e}")
+            if debug:
+                errors.display(e, 'Chroma Quanto:')
+
+    # initialize pipeline with pre-loaded components
+    kwargs = {}
+    if transformer is not None:
+        kwargs['transformer'] = transformer
+        sd_unet.loaded_unet = shared.opts.sd_unet
+    if text_encoder is not None:
+        kwargs['text_encoder'] = text_encoder
+        model_te.loaded_te = shared.opts.sd_text_encoder
+    if vae is not None:
+        kwargs['vae'] = vae
+
+    # Todo: atm only ChromaPipeline is implemented in diffusers.
+    # Need to add ChromaFillPipeline, ChromaControlPipeline, ChromaImg2ImgPipeline etc when available.
+    # Chroma will support inpainting *after* its training has finished:
+    # https://huggingface.co/lodestones/Chroma/discussions/28#6826dd2ed86f53ff983add5c
+    cls = diffusers.ChromaPipeline
+    shared.log.debug(f'Load model: type=Chroma cls={cls.__name__} preloaded={list(kwargs)} revision={diffusers_load_config.get("revision", None)}')
+    for c in kwargs:
+        if getattr(kwargs[c], 'quantization_method', None) is not None or getattr(kwargs[c], 'gguf', None) is not None:
+            shared.log.debug(f'Load model: type=Chroma component={c} dtype={kwargs[c].dtype} quant={getattr(kwargs[c], "quantization_method", None) or getattr(kwargs[c], "gguf", None)}')
+        if kwargs[c].dtype == torch.float32 and devices.dtype != torch.float32:
+            try:
+                kwargs[c] = kwargs[c].to(dtype=devices.dtype)
+                shared.log.warning(f'Load model: type=Chroma component={c} dtype={kwargs[c].dtype} cast dtype={devices.dtype} recast')
+            except Exception:
+                pass
+
+    allow_quant = 'gguf' not in (sd_unet.loaded_unet or '') and (prequantized is None or prequantized == 'none')
+    if (fn is None) or (not os.path.exists(fn) or os.path.isdir(fn)):
+        kwargs = load_quants(kwargs, repo_id or fn, cache_dir=shared.opts.diffusers_dir, allow_quant=allow_quant)
+    # kwargs = model_quant.create_config(kwargs, allow_quant)
+    if fn.endswith('.safetensors') and os.path.isfile(fn):
+        pipe = diffusers.ChromaPipeline.from_single_file(fn, cache_dir=shared.opts.diffusers_dir, **kwargs, **diffusers_load_config)
+    else:
+        pipe = cls.from_pretrained(repo_id or fn, cache_dir=shared.opts.diffusers_dir, **kwargs, **diffusers_load_config)
+
+    if shared.opts.teacache_enabled and model_quant.check_nunchaku('Transformer'):
+        from nunchaku.caching.diffusers_adapters import apply_cache_on_pipe
+        apply_cache_on_pipe(pipe, residual_diff_threshold=0.12)
+
+    # release memory
+    transformer = None
+    text_encoder = None
+    vae = None
+    for k in kwargs.keys():
+        kwargs[k] = None
+    sd_hijack_te.init_hijack(pipe)
+    devices.torch_gc(force=True)
+    return pipe
diff --git a/modules/model_chroma_nf4.py b/modules/model_chroma_nf4.py
new file mode 100644
index 000000000..c72d624ab
--- /dev/null
+++ b/modules/model_chroma_nf4.py
@@ -0,0 +1,202 @@
+"""
+Copied from: https://github.com/huggingface/diffusers/issues/9165 
++ adjusted for Chroma by skipping distilled guidance layer
+"""
+
+import os
+import torch
+import torch.nn as nn
+from transformers.quantizers.quantizers_utils import get_module_from_name
+from huggingface_hub import hf_hub_download
+from accelerate import init_empty_weights
+from accelerate.utils import set_module_tensor_to_device
+from diffusers.loaders.single_file_utils import convert_chroma_transformer_checkpoint_to_diffusers
+import safetensors.torch
+from modules import shared, devices, model_quant
+
+
+debug = os.environ.get('SD_LOAD_DEBUG', None) is not None
+
+
+def _replace_with_bnb_linear(
+    model,
+    method="nf4",
+    has_been_replaced=False,
+):
+    """
+    Private method that wraps the recursion for module replacement.
+    Returns the converted model and a boolean that indicates if the conversion has been successfull or not.
+    """
+    bnb = model_quant.load_bnb('Load model: type=Chroma')
+    for name, module in model.named_children():
+        # we ignore the distilled guidance layer because it degrades quality too much
+        # see: https://github.com/huggingface/diffusers/pull/11698#issuecomment-2969717180 for more details
+        if "distilled_guidance_layer" in name:
+            continue
+        if isinstance(module, nn.Linear):
+            with init_empty_weights():
+                in_features = module.in_features
+                out_features = module.out_features
+
+                if method == "llm_int8":
+                    model._modules[name] = bnb.nn.Linear8bitLt( # pylint: disable=protected-access
+                        in_features,
+                        out_features,
+                        module.bias is not None,
+                        has_fp16_weights=False,
+                        threshold=6.0,
+                    )
+                    has_been_replaced = True
+                else:
+                    model._modules[name] = bnb.nn.Linear4bit( # pylint: disable=protected-access
+                        in_features,
+                        out_features,
+                        module.bias is not None,
+                        compute_dtype=devices.dtype,
+                        compress_statistics=False,
+                        quant_type="nf4",
+                    )
+                    has_been_replaced = True
+                # Store the module class in case we need to transpose the weight later
+                model._modules[name].source_cls = type(module) # pylint: disable=protected-access
+                # Force requires grad to False to avoid unexpected errors
+                model._modules[name].requires_grad_(False) # pylint: disable=protected-access
+
+        if len(list(module.children())) > 0:
+            _, has_been_replaced = _replace_with_bnb_linear(
+                module,
+                has_been_replaced=has_been_replaced,
+            )
+        # Remove the last key for recursion
+    return model, has_been_replaced
+
+
+def check_quantized_param(
+    model,
+    param_name: str,
+) -> bool:
+    bnb = model_quant.load_bnb('Load model: type=Chroma')
+    module, tensor_name = get_module_from_name(model, param_name)
+    if isinstance(module._parameters.get(tensor_name, None), bnb.nn.Params4bit): # pylint: disable=protected-access
+        # Add here check for loaded components' dtypes once serialization is implemented
+        return True
+    elif isinstance(module, bnb.nn.Linear4bit) and tensor_name == "bias":
+        # bias could be loaded by regular set_module_tensor_to_device() from accelerate,
+        # but it would wrongly use uninitialized weight there.
+        return True
+    else:
+        return False
+
+
+def create_quantized_param(
+    model,
+    param_value: "torch.Tensor",
+    param_name: str,
+    target_device: "torch.device",
+    state_dict=None,
+    unexpected_keys=None,
+    pre_quantized=False
+):
+    bnb = model_quant.load_bnb('Load model: type=Chroma')
+    module, tensor_name = get_module_from_name(model, param_name)
+
+    if tensor_name not in module._parameters: # pylint: disable=protected-access
+        raise ValueError(f"{module} does not have a parameter or a buffer named {tensor_name}.")
+
+    old_value = getattr(module, tensor_name)
+
+    if tensor_name == "bias":
+        if param_value is None:
+            new_value = old_value.to(target_device)
+        else:
+            new_value = param_value.to(target_device)
+        new_value = torch.nn.Parameter(new_value, requires_grad=old_value.requires_grad)
+        module._parameters[tensor_name] = new_value # pylint: disable=protected-access
+        return
+
+    if not isinstance(module._parameters[tensor_name], bnb.nn.Params4bit): # pylint: disable=protected-access
+        raise ValueError("this function only loads `Linear4bit components`")
+    if (
+        old_value.device == torch.device("meta")
+        and target_device not in ["meta", torch.device("meta")]
+        and param_value is None
+    ):
+        raise ValueError(f"{tensor_name} is on the meta device, we need a `value` to put in on {target_device}.")
+
+    if pre_quantized:
+        if (param_name + ".quant_state.bitsandbytes__fp4" not in state_dict) and (param_name + ".quant_state.bitsandbytes__nf4" not in state_dict):
+            raise ValueError(f"Supplied state dict for {param_name} does not contain `bitsandbytes__*` and possibly other `quantized_stats` components.")
+        quantized_stats = {}
+        for k, v in state_dict.items():
+            # `startswith` to counter for edge cases where `param_name`
+            # substring can be present in multiple places in the `state_dict`
+            if param_name + "." in k and k.startswith(param_name):
+                quantized_stats[k] = v
+                if unexpected_keys is not None and k in unexpected_keys:
+                    unexpected_keys.remove(k)
+        new_value = bnb.nn.Params4bit.from_prequantized(
+            data=param_value,
+            quantized_stats=quantized_stats,
+            requires_grad=False,
+            device=target_device,
+        )
+    else:
+        new_value = param_value.to("cpu")
+        kwargs = old_value.__dict__
+        new_value = bnb.nn.Params4bit(new_value, requires_grad=False, **kwargs).to(target_device)
+    module._parameters[tensor_name] = new_value # pylint: disable=protected-access
+
+
+def load_chroma_nf4(checkpoint_info, prequantized: bool = True):
+    transformer = None
+    text_encoder = None
+    if isinstance(checkpoint_info, str):
+        repo_path = checkpoint_info
+    else:
+        repo_path = checkpoint_info.path
+    if os.path.exists(repo_path) and os.path.isfile(repo_path):
+        ckpt_path = repo_path
+    elif os.path.exists(repo_path) and os.path.isdir(repo_path) and os.path.exists(os.path.join(repo_path, "diffusion_pytorch_model.safetensors")):
+        ckpt_path = os.path.join(repo_path, "diffusion_pytorch_model.safetensors")
+    else:
+        ckpt_path = hf_hub_download(repo_path, filename="diffusion_pytorch_model.safetensors", cache_dir=shared.opts.diffusers_dir)
+    original_state_dict = safetensors.torch.load_file(ckpt_path)
+
+    try:
+        converted_state_dict = convert_chroma_transformer_checkpoint_to_diffusers(original_state_dict)
+    except Exception as e:
+        shared.log.error(f"Load model: type=Chroma Failed to convert UNET: {e}")
+        if debug:
+            from modules import errors
+            errors.display(e, 'Chroma convert:')
+        converted_state_dict = original_state_dict
+
+    with init_empty_weights():
+        from diffusers import ChromaTransformer2DModel
+        config = ChromaTransformer2DModel.load_config(os.path.join('configs', 'chroma'), subfolder="transformer")
+        transformer = ChromaTransformer2DModel.from_config(config).to(devices.dtype)
+        expected_state_dict_keys = list(transformer.state_dict().keys())
+
+    _replace_with_bnb_linear(transformer, "nf4")
+
+    try:
+        for param_name, param in converted_state_dict.items():
+            if param_name not in expected_state_dict_keys:
+                continue
+            is_param_float8_e4m3fn = hasattr(torch, "float8_e4m3fn") and param.dtype == torch.float8_e4m3fn
+            if torch.is_floating_point(param) and not is_param_float8_e4m3fn:
+                param = param.to(devices.dtype)
+            if not check_quantized_param(transformer, param_name):
+                set_module_tensor_to_device(transformer, param_name, device=0, value=param)
+            else:
+                create_quantized_param(transformer, param, param_name, target_device=0, state_dict=original_state_dict, pre_quantized=prequantized)
+    except Exception as e:
+        transformer, text_encoder = None
+        shared.log.error(f"Load model: type=Chroma failed to load UNET: {e}")
+        if debug:
+            from modules import errors
+            errors.display(e, 'Chroma:')
+
+    del original_state_dict
+    devices.torch_gc(force=True)
+    return transformer, text_encoder
diff --git a/modules/model_quant.py b/modules/model_quant.py
index 3c57ac67c..74bd5f822 100644
--- a/modules/model_quant.py
+++ b/modules/model_quant.py
@@ -427,8 +427,12 @@ def optimum_quanto_model(model, op=None, sd_model=None, weights=None, activation
     from modules import devices, shared
     quanto = load_quanto('Quantize model: type=Optimum Quanto')
     global quant_last_model_name, quant_last_model_device # pylint: disable=global-statement
-    if sd_model is not None and "Flux" in sd_model.__class__.__name__: # LayerNorm is not supported
+    if sd_model is not None and "Flux" in sd_model.__class__.__name__ or "Chroma" in sd_model.__class__.__name__: # LayerNorm is not supported
         exclude_list = ["transformer_blocks.*.norm1.norm", "transformer_blocks.*.norm2", "transformer_blocks.*.norm1_context.norm", "transformer_blocks.*.norm2_context", "single_transformer_blocks.*.norm.norm", "norm_out.norm"]
+        if "Chroma" in sd_model.__class__.__name__:
+            # we ignore the distilled guidance layer because it degrades quality too much
+            # see: https://github.com/huggingface/diffusers/pull/11698#issuecomment-2969717180 for more details
+            exclude_list.append("distilled_guidance_layer.*")
     else:
         exclude_list = None
     weights = getattr(quanto, weights) if weights is not None else getattr(quanto, shared.opts.optimum_quanto_weights_type)
diff --git a/modules/modeldata.py b/modules/modeldata.py
index 688cff23d..0aaaaeb63 100644
--- a/modules/modeldata.py
+++ b/modules/modeldata.py
@@ -27,6 +27,8 @@ def get_model_type(pipe):
         model_type = 'sc'
     elif "AuraFlow" in name:
         model_type = 'auraflow'
+    elif 'Chroma' in name:
+        model_type = 'chroma'
     elif "Flux" in name or "Flex1" in name or "Flex2" in name:
         model_type = 'f1'
     elif "Lumina2" in name:
diff --git a/modules/processing_args.py b/modules/processing_args.py
index 6ef43667b..fca0115de 100644
--- a/modules/processing_args.py
+++ b/modules/processing_args.py
@@ -150,6 +150,7 @@ def set_pipeline_args(p, model, prompts:list, negative_prompts:list, prompts_2:t
         'StableDiffusion' in model.__class__.__name__ or
         'StableCascade' in model.__class__.__name__ or
         'Flux' in model.__class__.__name__ or
+        'Chroma' in model.__class__.__name__ or
         'HiDreamImagePipeline' in model.__class__.__name__ # hidream-e1 has different embeds
     ):
         try:
@@ -174,15 +175,14 @@ def set_pipeline_args(p, model, prompts:list, negative_prompts:list, prompts_2:t
             args['prompt_embeds_llama3'] = prompt_embeds[1]
         elif hasattr(model, 'text_encoder') and hasattr(model, 'tokenizer') and 'prompt_embeds' in possible and prompt_parser_diffusers.embedder is not None:
             args['prompt_embeds'] = prompt_parser_diffusers.embedder('prompt_embeds')
-            if prompt_parser_diffusers.embedder is not None:
-                if 'StableCascade' in model.__class__.__name__:
-                    args['prompt_embeds_pooled'] = prompt_parser_diffusers.embedder('positive_pooleds').unsqueeze(0)
-                elif 'XL' in model.__class__.__name__:
-                    args['pooled_prompt_embeds'] = prompt_parser_diffusers.embedder('positive_pooleds')
-                elif 'StableDiffusion3' in model.__class__.__name__:
-                    args['pooled_prompt_embeds'] = prompt_parser_diffusers.embedder('positive_pooleds')
-                elif 'Flux' in model.__class__.__name__:
-                    args['pooled_prompt_embeds'] = prompt_parser_diffusers.embedder('positive_pooleds')
+            if 'StableCascade' in model.__class__.__name__:
+                args['prompt_embeds_pooled'] = prompt_parser_diffusers.embedder('positive_pooleds').unsqueeze(0)
+            elif 'XL' in model.__class__.__name__:
+                args['pooled_prompt_embeds'] = prompt_parser_diffusers.embedder('positive_pooleds')
+            elif 'StableDiffusion3' in model.__class__.__name__:
+                args['pooled_prompt_embeds'] = prompt_parser_diffusers.embedder('positive_pooleds')
+            elif 'Flux' in model.__class__.__name__:
+                args['pooled_prompt_embeds'] = prompt_parser_diffusers.embedder('positive_pooleds')
         else:
             args['prompt'] = prompts
     if 'negative_prompt' in possible:
@@ -193,13 +193,12 @@ def set_pipeline_args(p, model, prompts:list, negative_prompts:list, prompts_2:t
             args['negative_prompt_embeds_llama3'] = negative_prompt_embeds[1]
         elif hasattr(model, 'text_encoder') and hasattr(model, 'tokenizer') and 'negative_prompt_embeds' in possible and prompt_parser_diffusers.embedder is not None:
             args['negative_prompt_embeds'] = prompt_parser_diffusers.embedder('negative_prompt_embeds')
-            if prompt_parser_diffusers.embedder is not None:
-                if 'StableCascade' in model.__class__.__name__:
-                    args['negative_prompt_embeds_pooled'] = prompt_parser_diffusers.embedder('negative_pooleds').unsqueeze(0)
-                elif 'XL' in model.__class__.__name__:
-                    args['negative_pooled_prompt_embeds'] = prompt_parser_diffusers.embedder('negative_pooleds')
-                elif 'StableDiffusion3' in model.__class__.__name__:
-                    args['negative_pooled_prompt_embeds'] = prompt_parser_diffusers.embedder('negative_pooleds')
+            if 'StableCascade' in model.__class__.__name__:
+                args['negative_prompt_embeds_pooled'] = prompt_parser_diffusers.embedder('negative_pooleds').unsqueeze(0)
+            elif 'XL' in model.__class__.__name__:
+                args['negative_pooled_prompt_embeds'] = prompt_parser_diffusers.embedder('negative_pooleds')
+            elif 'StableDiffusion3' in model.__class__.__name__:
+                args['negative_pooled_prompt_embeds'] = prompt_parser_diffusers.embedder('negative_pooleds')
         else:
             if 'PixArtSigmaPipeline' in model.__class__.__name__: # pixart-sigma pipeline throws list-of-list for negative prompt
                 args['negative_prompt'] = negative_prompts[0]
diff --git a/modules/prompt_parser_diffusers.py b/modules/prompt_parser_diffusers.py
index ad80e3af0..1e4f5246a 100644
--- a/modules/prompt_parser_diffusers.py
+++ b/modules/prompt_parser_diffusers.py
@@ -7,7 +7,7 @@ import torch
 from compel.embeddings_provider import BaseTextualInversionManager, EmbeddingsProvider
 from transformers import PreTrainedTokenizer
 from modules import shared, prompt_parser, devices, sd_models
-from modules.prompt_parser_xhinker import get_weighted_text_embeddings_sd15, get_weighted_text_embeddings_sdxl_2p, get_weighted_text_embeddings_sd3, get_weighted_text_embeddings_flux1
+from modules.prompt_parser_xhinker import get_weighted_text_embeddings_sd15, get_weighted_text_embeddings_sdxl_2p, get_weighted_text_embeddings_sd3, get_weighted_text_embeddings_flux1, get_weighted_text_embeddings_chroma
 
 debug_enabled = os.environ.get('SD_PROMPT_DEBUG', None)
 debug = shared.log.trace if debug_enabled else lambda *args, **kwargs: None
@@ -27,6 +27,7 @@ def prompt_compatible(pipe = None):
         'DemoFusion' not in pipe.__class__.__name__ and
         'StableCascade' not in pipe.__class__.__name__ and
         'Flux' not in pipe.__class__.__name__ and
+        'Chroma' not in pipe.__class__.__name__ and
         'HiDreamImage' not in pipe.__class__.__name__
     ):
         shared.log.warning(f"Prompt parser not supported: {pipe.__class__.__name__}")
@@ -510,6 +511,10 @@ def get_weighted_text_embeddings(pipe, prompt: str = "", neg_prompt: str = "", c
         prompt_embeds, pooled_prompt_embeds, _ = pipe.encode_prompt(prompt=prompt, prompt_2=prompt_2, device=device, num_images_per_prompt=1)
         return prompt_embeds, pooled_prompt_embeds, None, None # no negative support
 
+    if "Chroma" in pipe.__class__.__name__: # does not use clip and has no pooled embeds
+        prompt_embeds, _, _, negative_prompt_embeds, _, _ = pipe.encode_prompt(prompt=prompt, negative_prompt=neg_prompt, device=device, num_images_per_prompt=1)
+        return prompt_embeds, None, negative_prompt_embeds, None
+
     if "HiDreamImage" in pipe.__class__.__name__: # clip is only used for the pooled embeds
         prompt_embeds_t5, negative_prompt_embeds_t5, prompt_embeds_llama3, negative_prompt_embeds_llama3, pooled_prompt_embeds, negative_pooled_prompt_embeds = pipe.encode_prompt(
             prompt=prompt, prompt_2=prompt_2, prompt_3=prompt_3, prompt_4=prompt_4,
@@ -662,6 +667,8 @@ def get_xhinker_text_embeddings(pipe, prompt: str = "", neg_prompt: str = "", cl
         prompt_embed, negative_embed, positive_pooled, negative_pooled = get_weighted_text_embeddings_sd3(pipe=pipe, prompt=prompt, neg_prompt=neg_prompt, use_t5_encoder=bool(pipe.text_encoder_3))
     elif 'Flux' in pipe.__class__.__name__:
         prompt_embed, positive_pooled = get_weighted_text_embeddings_flux1(pipe=pipe, prompt=prompt, prompt2=prompt_2, device=devices.device)
+    elif 'Chroma' in pipe.__class__.__name__:
+        prompt_embed, negative_embed = get_weighted_text_embeddings_chroma(pipe=pipe, prompt=prompt, neg_prompt=neg_prompt, device=devices.device)
     elif 'XL' in pipe.__class__.__name__:
         prompt_embed, negative_embed, positive_pooled, negative_pooled = get_weighted_text_embeddings_sdxl_2p(pipe=pipe, prompt=prompt, prompt_2=prompt_2, neg_prompt=neg_prompt, neg_prompt_2=neg_prompt_2)
     else:
diff --git a/modules/prompt_parser_xhinker.py b/modules/prompt_parser_xhinker.py
index c0ddc9bc7..d2cc1971d 100644
--- a/modules/prompt_parser_xhinker.py
+++ b/modules/prompt_parser_xhinker.py
@@ -17,11 +17,13 @@
 ## -----------------------------------------------------------------------------
 
 import torch
+import torch.nn.functional as F
 from transformers import CLIPTokenizer, T5Tokenizer
 from diffusers import StableDiffusionPipeline
 from diffusers import StableDiffusionXLPipeline
 from diffusers import StableDiffusion3Pipeline
 from diffusers import FluxPipeline
+from diffusers import ChromaPipeline
 from modules.prompt_parser import parse_prompt_attention  # use built-in A1111 parser
 
 
@@ -1424,3 +1426,75 @@ def get_weighted_text_embeddings_flux1(
     t5_prompt_embeds = t5_prompt_embeds.to(dtype=pipe.text_encoder_2.dtype, device=device)
 
     return t5_prompt_embeds, prompt_embeds
+
+
+def get_weighted_text_embeddings_chroma(
+    pipe: ChromaPipeline,
+    prompt: str = "",
+    neg_prompt: str = "",
+    device=None
+):
+    """
+    This function can process long prompt with weights for Chroma model
+
+    Args:
+        pipe (ChromaPipeline)
+        prompt (str)
+        neg_prompt (str)
+        device (torch.device, optional): Device to run the embeddings on.
+    Returns:
+        prompt_embeds (T5 prompt embeds as torch.Tensor)
+        neg_prompt_embeds (T5 prompt embeds as torch.Tensor)
+    """
+    if device is None:
+        device = pipe.text_encoder.device
+
+    # prompt
+    prompt_tokens, prompt_weights = get_prompts_tokens_with_weights_t5(
+        pipe.tokenizer, prompt
+    )
+
+    prompt_tokens = torch.tensor([prompt_tokens], dtype=torch.long)
+
+    t5_prompt_embeds = pipe.text_encoder(prompt_tokens.to(device))[0].squeeze(0)
+    t5_prompt_embeds = t5_prompt_embeds.to(device=device)
+
+    # add weight to t5 prompt
+    for z in range(len(prompt_weights)):
+        if prompt_weights[z] != 1.0:
+            t5_prompt_embeds[z] = t5_prompt_embeds[z] * prompt_weights[z]
+    t5_prompt_embeds = t5_prompt_embeds.unsqueeze(0)
+    t5_prompt_embeds = t5_prompt_embeds.to(dtype=pipe.text_encoder.dtype, device=device)
+
+    # negative prompt
+    neg_prompt_tokens, neg_prompt_weights = get_prompts_tokens_with_weights_t5(
+        pipe.tokenizer, neg_prompt
+    )
+
+    neg_prompt_tokens = torch.tensor([neg_prompt_tokens], dtype=torch.long)
+
+    t5_neg_prompt_embeds = pipe.text_encoder(neg_prompt_tokens.to(device))[0].squeeze(0)
+    t5_neg_prompt_embeds = t5_neg_prompt_embeds.to(device=device)
+
+    # add weight to neg t5 embeddings
+    for z in range(len(neg_prompt_weights)):
+        if neg_prompt_weights[z] != 1.0:
+            t5_neg_prompt_embeds[z] = t5_neg_prompt_embeds[z] * neg_prompt_weights[z]
+    t5_neg_prompt_embeds = t5_neg_prompt_embeds.unsqueeze(0)
+    t5_neg_prompt_embeds = t5_neg_prompt_embeds.to(dtype=pipe.text_encoder.dtype, device=device)
+
+    def pad_prompt_embeds_to_same_size(prompt_embeds_a, prompt_embeds_b):
+        size_a = prompt_embeds_a.size(1)
+        size_b = prompt_embeds_b.size(1)
+
+        if size_a < size_b:
+            pad_size = size_b - size_a
+            prompt_embeds_a = F.pad(prompt_embeds_a, (0, 0, 0, pad_size))  # Pad dim=1
+        elif size_b < size_a:
+            pad_size = size_a - size_b
+            prompt_embeds_b = F.pad(prompt_embeds_b, (0, 0, 0, pad_size))  # Pad dim=1
+
+        return prompt_embeds_a, prompt_embeds_b
+
+    # chroma needs positive and negative prompt embeddings to have the same length (for now)
+    return pad_prompt_embeds_to_same_size(t5_prompt_embeds, t5_neg_prompt_embeds)
diff --git a/modules/sd_detect.py b/modules/sd_detect.py
index 3c2c1be72..9a360e922 100644
--- a/modules/sd_detect.py
+++ b/modules/sd_detect.py
@@ -92,6 +92,8 @@ def detect_pipeline(f: str, op: str = 'model', warning=True, quiet=False):
                 guess = 'Stable Diffusion 3'
             if 'hidream' in f.lower():
                 guess = 'HiDream'
+            if 'chroma' in f.lower():
+                guess = 'Chroma'
             if 'flux' in f.lower() or 'flex.1' in f.lower() or 'lodestones' in f.lower():
                 guess = 'FLUX'
                 if size > 11000 and size < 16000:
diff --git a/modules/sd_models.py b/modules/sd_models.py
index d8aad13a3..2c908e5ba 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -329,6 +329,9 @@ def load_diffuser_force(model_type, checkpoint_info, diffusers_load_config, op='
         elif model_type in ['FLEX']:
             from modules.model_flex import load_flex
             sd_model = load_flex(checkpoint_info, diffusers_load_config)
+        elif model_type in ['Chroma']:
+            from modules.model_chroma import load_chroma
+            sd_model = load_chroma(checkpoint_info, diffusers_load_config)
         elif model_type in ['Lumina 2']:
             from modules.model_lumina import load_lumina2
             sd_model = load_lumina2(checkpoint_info, diffusers_load_config)
diff --git a/modules/sd_offload.py b/modules/sd_offload.py
index c57165fb5..d2a56c349 100644
--- a/modules/sd_offload.py
+++ b/modules/sd_offload.py
@@ -11,7 +11,7 @@ from modules.timer import process as process_timer
 
 debug = os.environ.get('SD_MOVE_DEBUG', None) is not None
 debug_move = log.trace if debug else lambda *args, **kwargs: None
-offload_warn = ['sc', 'sd3', 'f1', 'h1', 'hunyuandit', 'auraflow', 'omnigen', 'cogview4']
+offload_warn = ['sc', 'sd3', 'f1', 'h1', 'hunyuandit', 'auraflow', 'omnigen', 'cogview4', 'chroma']
 offload_post = ['h1']
 offload_hook_instance = None
 balanced_offload_exclude = ['OmniGenPipeline', 'CogView4Pipeline']
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index f73520027..aa3e4d21a 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -12,7 +12,7 @@ samplers = all_samplers
 samplers_for_img2img = all_samplers
 samplers_map = {}
 loaded_config = None
-flow_models =  ['Flux', 'StableDiffusion3', 'Lumina', 'AuraFlow', 'Sana', 'CogView4', 'HiDream']
+flow_models = ['Flux', 'StableDiffusion3', 'Lumina', 'AuraFlow', 'Sana', 'CogView4', 'HiDream', 'Chroma']
 flow_models += ['Hunyuan', 'LTX', 'Mochi']
 
 
diff --git a/modules/sd_samplers_common.py b/modules/sd_samplers_common.py
index 52fd6313e..21e01a45b 100644
--- a/modules/sd_samplers_common.py
+++ b/modules/sd_samplers_common.py
@@ -9,7 +9,7 @@ from modules import shared, devices, processing, images, sd_vae_approx, sd_vae_t
 
 SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options'])
 approximation_indexes = { "Simple": 0, "Approximate": 1, "TAESD": 2, "Full VAE": 3 }
-flow_models = ['f1', 'sd3', 'lumina', 'auraflow', 'sana', 'lumina2', 'cogview4', 'h1']
+flow_models = ['f1', 'sd3', 'lumina', 'auraflow', 'sana', 'lumina2', 'cogview4', 'h1', 'chroma']
 warned = False
 queue_lock = threading.Lock()
 
diff --git a/modules/sd_vae_remote.py b/modules/sd_vae_remote.py
index 741d349bc..5ee5522d4 100644
--- a/modules/sd_vae_remote.py
+++ b/modules/sd_vae_remote.py
@@ -12,6 +12,7 @@ hf_decode_endpoints = {
     'sd': 'https://q1bj3bpq6kzilnsu.us-east-1.aws.endpoints.huggingface.cloud',
     'sdxl': 'https://x2dmsqunjd6k9prw.us-east-1.aws.endpoints.huggingface.cloud',
     'f1': 'https://whhx50ex1aryqvw6.us-east-1.aws.endpoints.huggingface.cloud',
+    'chroma': 'https://whhx50ex1aryqvw6.us-east-1.aws.endpoints.huggingface.cloud',
     'h1': 'https://whhx50ex1aryqvw6.us-east-1.aws.endpoints.huggingface.cloud',
     'hunyuanvideo': 'https://o7ywnmrahorts457.us-east-1.aws.endpoints.huggingface.cloud',
 }
@@ -19,6 +20,7 @@ hf_encode_endpoints = {
     'sd': 'https://qc6479g0aac6qwy9.us-east-1.aws.endpoints.huggingface.cloud',
     'sdxl': 'https://xjqqhmyn62rog84g.us-east-1.aws.endpoints.huggingface.cloud',
     'f1': 'https://ptccx55jz97f9zgo.us-east-1.aws.endpoints.huggingface.cloud',
+    'chroma': 'https://ptccx55jz97f9zgo.us-east-1.aws.endpoints.huggingface.cloud',
 }
 dtypes = {
     "float16": torch.float16,
@@ -48,7 +50,7 @@ def remote_decode(latents: torch.Tensor, width: int = 0, height: int = 0, model_
         params = {}
         try:
             latent = latent_copy[i]
-            if model_type != 'f1':
+            if model_type not in ['f1', 'chroma']:
                 latent = latent.unsqueeze(0)
             params = {
                 "input_tensor_type": "binary",
@@ -74,7 +76,7 @@ def remote_decode(latents: torch.Tensor, width: int = 0, height: int = 0, model_
                 params["output_type"] = "pt"
                 params["output_tensor_type"] = "binary"
                 headers["Accept"] = "tensor/binary"
-            if (model_type == 'f1' or model_type == 'h1') and (width > 0) and (height > 0):
+            if (model_type in ['f1', 'h1', 'chroma']) and (width > 0) and (height > 0):
                 params['width'] = width
                 params['height'] = height
             if shared.sd_model.vae is not None and shared.sd_model.vae.config is not None:
diff --git a/modules/sd_vae_taesd.py b/modules/sd_vae_taesd.py
index 3bcd1b322..1eca9ae1f 100644
--- a/modules/sd_vae_taesd.py
+++ b/modules/sd_vae_taesd.py
@@ -55,7 +55,7 @@ def get_model(model_type = 'decoder', variant = None):
     cls = shared.sd_model_type
     if cls in {'ldm', 'pixartalpha'}:
         cls = 'sd'
-    elif cls in {'h1', 'lumina2'}:
+    elif cls in {'h1', 'lumina2', 'chroma'}:
         cls = 'f1'
     elif cls == 'pixartsigma':
         cls = 'sdxl'
diff --git a/modules/shared_items.py b/modules/shared_items.py
index 9a09b8c7f..8d64f947b 100644
--- a/modules/shared_items.py
+++ b/modules/shared_items.py
@@ -27,6 +27,7 @@ pipelines = {
     'DeepFloyd IF': getattr(diffusers, 'IFPipeline', None),
     'FLUX': getattr(diffusers, 'FluxPipeline', None),
     'FLEX': getattr(diffusers, 'AutoPipelineForText2Image', None),
+    'Chroma': getattr(diffusers, 'ChromaPipeline', None),
     'Sana': getattr(diffusers, 'SanaPipeline', None),
     'Lumina-Next': getattr(diffusers, 'LuminaText2ImgPipeline', None),
     'Lumina 2': getattr(diffusers, 'Lumina2Pipeline', None),
diff --git a/modules/teacache/__init__.py b/modules/teacache/__init__.py
index 2fb81b7c9..56a261241 100644
--- a/modules/teacache/__init__.py
+++ b/modules/teacache/__init__.py
@@ -4,9 +4,10 @@ from .teacache_lumina2 import teacache_lumina2_forward
 from .teacache_ltx import teacache_ltx_forward
 from .teacache_mochi import teacache_mochi_forward
 from .teacache_cogvideox import teacache_cog_forward
+from .teacache_chroma import teacache_chroma_forward
 
 
-supported_models = ['Flux', 'CogVideoX', 'Mochi', 'LTX', 'HiDream', 'Lumina2']
+supported_models = ['Flux', 'Chroma', 'CogVideoX', 'Mochi', 'LTX', 'HiDream', 'Lumina2']
 
 
 def apply_teacache(p):
diff --git a/modules/teacache/teacache_chroma.py b/modules/teacache/teacache_chroma.py
new file mode 100644
index 000000000..999b3095b
--- /dev/null
+++ b/modules/teacache/teacache_chroma.py
@@ -0,0 +1,325 @@
+from typing import Any, Dict, Optional, Union
+import torch
+import numpy as np
+from diffusers.models.modeling_outputs import Transformer2DModelOutput
+from diffusers.utils import USE_PEFT_BACKEND, is_torch_version, logging, scale_lora_layers, unscale_lora_layers
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+def teacache_chroma_forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor = None,
+        timestep: torch.LongTensor = None,
+        img_ids: torch.Tensor = None,
+        txt_ids: torch.Tensor = None,
+        joint_attention_kwargs: Optional[Dict[str, Any]] = None,
+        controlnet_block_samples=None,
+        controlnet_single_block_samples=None,
+        return_dict: bool = True,
+        controlnet_blocks_repeat: bool = False,
+    ) -> Union[torch.Tensor, Transformer2DModelOutput]:
+    """
+    The [`ChromaTransformer2DModel`] forward method.
+    Args:
+        hidden_states (`torch.Tensor` of shape `(batch_size, image_sequence_length, in_channels)`):
+            Input `hidden_states`.
+        encoder_hidden_states (`torch.Tensor` of shape `(batch_size, text_sequence_length, joint_attention_dim)`):
+            Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.
+        timestep ( `torch.LongTensor`):
+            Used to indicate denoising step.
+        block_controlnet_hidden_states: (`list` of `torch.Tensor`):
+            A list of tensors that if specified are added to the residuals of transformer blocks.
+        joint_attention_kwargs (`dict`, *optional*):
+            A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+            `self.processor` in
+            [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+        return_dict (`bool`, *optional*, defaults to `True`):
+            Whether or not to return a [`~models.transformer_2d.Transformer2DModelOutput`] instead of a plain
+            tuple.
+    Returns:
+        If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
+        `tuple` where the first element is the sample tensor.
+    """
+    if joint_attention_kwargs is not None:
+        joint_attention_kwargs = joint_attention_kwargs.copy()
+        lora_scale = joint_attention_kwargs.pop("scale", 1.0)
+    else:
+        lora_scale = 1.0
+
+    if USE_PEFT_BACKEND:
+        # weight the lora layers by setting `lora_scale` for each PEFT layer
+        scale_lora_layers(self, lora_scale)
+    else:
+        if joint_attention_kwargs is not None and joint_attention_kwargs.get("scale", None) is not None:
+            logger.warning(
+                "Passing `scale` via `joint_attention_kwargs` when not using the PEFT backend is ineffective."
+            )
+
+    hidden_states = self.x_embedder(hidden_states)
+
+    timestep = timestep.to(hidden_states.dtype) * 1000
+
+    input_vec = self.time_text_embed(timestep)
+    pooled_temb = self.distilled_guidance_layer(input_vec)
+
+    encoder_hidden_states = self.context_embedder(encoder_hidden_states)
+
+    if txt_ids.ndim == 3:
+        logger.warning(
+            "Passing `txt_ids` 3d torch.Tensor is deprecated."
+            "Please remove the batch dimension and pass it as a 2d torch Tensor"
+        )
+        txt_ids = txt_ids[0]
+    if img_ids.ndim == 3:
+        logger.warning(
+            "Passing `img_ids` 3d torch.Tensor is deprecated."
+            "Please remove the batch dimension and pass it as a 2d torch Tensor"
+        )
+        img_ids = img_ids[0]
+
+    ids = torch.cat((txt_ids, img_ids), dim=0)
+    image_rotary_emb = self.pos_embed(ids)
+
+    if joint_attention_kwargs is not None and "ip_adapter_image_embeds" in joint_attention_kwargs:
+        ip_adapter_image_embeds = joint_attention_kwargs.pop("ip_adapter_image_embeds")
+        ip_hidden_states = self.encoder_hid_proj(ip_adapter_image_embeds)
+        joint_attention_kwargs.update({"ip_hidden_states": ip_hidden_states})
+
+    if self.enable_teacache:
+        inp = hidden_states.clone()
+        input_vec_ = input_vec.clone()
+        modulated_inp, _gate_msa, _shift_mlp, _scale_mlp, _gate_mlp = self.transformer_blocks[0].norm1(inp, emb=input_vec_)
+        if self.cnt == 0 or self.cnt == self.num_steps-1:
+            should_calc = True
+            self.accumulated_rel_l1_distance = 0
+        else:
+            coefficients = [4.98651651e+02, -2.83781631e+02,  5.58554382e+01, -3.82021401e+00, 2.64230861e-01]
+            rescale_func = np.poly1d(coefficients)
+            self.accumulated_rel_l1_distance += rescale_func(((modulated_inp-self.previous_modulated_input).abs().mean() / self.previous_modulated_input.abs().mean()).cpu().item())
+            if self.accumulated_rel_l1_distance < self.rel_l1_thresh:
+                should_calc = False
+            else:
+                should_calc = True
+                self.accumulated_rel_l1_distance = 0
+        self.previous_modulated_input = modulated_inp
+        self.cnt += 1
+        if self.cnt == self.num_steps:
+            self.cnt = 0
+
+    if self.enable_teacache:
+        if not should_calc:
+            hidden_states += self.previous_residual
+        else:
+            ori_hidden_states = hidden_states.clone()
+            for index_block, block in enumerate(self.transformer_blocks):
+                img_offset = 3 * len(self.single_transformer_blocks)
+                txt_offset = img_offset + 6 * len(self.transformer_blocks)
+                img_modulation = img_offset + 6 * index_block
+                text_modulation = txt_offset + 6 * index_block
+                temb = torch.cat(
+                    (
+                        pooled_temb[:, img_modulation : img_modulation + 6],
+                        pooled_temb[:, text_modulation : text_modulation + 6],
+                    ),
+                    dim=1,
+                )
+                if torch.is_grad_enabled() and self.gradient_checkpointing:
+
+                    def create_custom_forward4(module, return_dict=None):
+                        def custom_forward(*inputs):
+                            if return_dict is not None:
+                                return module(*inputs, return_dict=return_dict)
+                            else:
+                                return module(*inputs)
+
+                        return custom_forward
+
+                    ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
+                    encoder_hidden_states, hidden_states = torch.utils.checkpoint.checkpoint(
+                        create_custom_forward4(block),
+                        hidden_states,
+                        encoder_hidden_states,
+                        temb,
+                        image_rotary_emb,
+                        **ckpt_kwargs,
+                    )
+
+                else:
+                    encoder_hidden_states, hidden_states = block(
+                        hidden_states=hidden_states,
+                        encoder_hidden_states=encoder_hidden_states,
+                        temb=temb,
+                        image_rotary_emb=image_rotary_emb,
+                        joint_attention_kwargs=joint_attention_kwargs,
+                    )
+
+                # controlnet residual
+                if controlnet_block_samples is not None:
+                    interval_control = len(self.transformer_blocks) / len(controlnet_block_samples)
+                    interval_control = int(np.ceil(interval_control))
+                    # For Xlabs ControlNet.
+                    if controlnet_blocks_repeat:
+                        hidden_states = (
+                            hidden_states + controlnet_block_samples[index_block % len(controlnet_block_samples)]
+                        )
+                    else:
+                        hidden_states = hidden_states + controlnet_block_samples[index_block // interval_control]
+            hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
+
+            for index_block, block in enumerate(self.single_transformer_blocks):
+                start_idx = 3 * index_block
+                temb = pooled_temb[:, start_idx : start_idx + 3]
+
+                if torch.is_grad_enabled() and self.gradient_checkpointing:
+
+                    def create_custom_forward2(module, return_dict=None):
+                        def custom_forward(*inputs):
+                            if return_dict is not None:
+                                return module(*inputs, return_dict=return_dict)
+                            else:
+                                return module(*inputs)
+
+                        return custom_forward
+
+                    ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
+                    hidden_states = torch.utils.checkpoint.checkpoint(
+                        create_custom_forward2(block),
+                        hidden_states,
+                        temb,
+                        image_rotary_emb,
+                        **ckpt_kwargs,
+                    )
+
+                else:
+                    hidden_states = block(
+                        hidden_states=hidden_states,
+                        temb=temb,
+                        image_rotary_emb=image_rotary_emb,
+                        joint_attention_kwargs=joint_attention_kwargs,
+                    )
+
+                # controlnet residual
+                if controlnet_single_block_samples is not None:
+                    interval_control = len(self.single_transformer_blocks) / len(controlnet_single_block_samples)
+                    interval_control = int(np.ceil(interval_control))
+                    hidden_states[:, encoder_hidden_states.shape[1] :, ...] = (
+                        hidden_states[:, encoder_hidden_states.shape[1] :, ...]
+                        + controlnet_single_block_samples[index_block // interval_control]
+                    )
+
+            hidden_states = hidden_states[:, encoder_hidden_states.shape[1] :, ...]
+            self.previous_residual = hidden_states - ori_hidden_states
+    else:
+        for index_block, block in enumerate(self.transformer_blocks):
+            img_offset = 3 * len(self.single_transformer_blocks)
+            txt_offset = img_offset + 6 * len(self.transformer_blocks)
+            img_modulation = img_offset + 6 * index_block
+            text_modulation = txt_offset + 6 * index_block
+            temb = torch.cat(
+                (
+                    pooled_temb[:, img_modulation : img_modulation + 6],
+                    pooled_temb[:, text_modulation : text_modulation + 6],
+                ),
+                dim=1,
+            )
+
+            if torch.is_grad_enabled() and self.gradient_checkpointing:
+
+                def create_custom_forward1(module, return_dict=None):
+                    def custom_forward(*inputs):
+                        if return_dict is not None:
+                            return module(*inputs, return_dict=return_dict)
+                        else:
+                            return module(*inputs)
+
+                    return custom_forward
+
+                ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
+                encoder_hidden_states, hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward1(block),
+                    hidden_states,
+                    encoder_hidden_states,
+                    temb,
+                    image_rotary_emb,
+                    **ckpt_kwargs,
+                )
+
+            else:
+                encoder_hidden_states, hidden_states = block(
+                    hidden_states=hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                    temb=temb,
+                    image_rotary_emb=image_rotary_emb,
+                    joint_attention_kwargs=joint_attention_kwargs,
+                )
+
+            # controlnet residual
+            if controlnet_block_samples is not None:
+                interval_control = len(self.transformer_blocks) / len(controlnet_block_samples)
+                interval_control = int(np.ceil(interval_control))
+                # For Xlabs ControlNet.
+                if controlnet_blocks_repeat:
+                    hidden_states = (
+                        hidden_states + controlnet_block_samples[index_block % len(controlnet_block_samples)]
+                    )
+                else:
+                    hidden_states = hidden_states + controlnet_block_samples[index_block // interval_control]
+        hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
+
+        for index_block, block in enumerate(self.single_transformer_blocks):
+            start_idx = 3 * index_block
+            temb = pooled_temb[:, start_idx : start_idx + 3]
+
+            if torch.is_grad_enabled() and self.gradient_checkpointing:
+
+                def create_custom_forward3(module, return_dict=None):
+                    def custom_forward(*inputs):
+                        if return_dict is not None:
+                            return module(*inputs, return_dict=return_dict)
+                        else:
+                            return module(*inputs)
+
+                    return custom_forward
+
+                ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
+                hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward3(block),
+                    hidden_states,
+                    temb,
+                    image_rotary_emb,
+                    **ckpt_kwargs,
+                )
+
+            else:
+                hidden_states = block(
+                    hidden_states=hidden_states,
+                    temb=temb,
+                    image_rotary_emb=image_rotary_emb,
+                    joint_attention_kwargs=joint_attention_kwargs,
+                )
+
+            # controlnet residual
+            if controlnet_single_block_samples is not None:
+                interval_control = len(self.single_transformer_blocks) / len(controlnet_single_block_samples)
+                interval_control = int(np.ceil(interval_control))
+                hidden_states[:, encoder_hidden_states.shape[1] :, ...] = (
+                    hidden_states[:, encoder_hidden_states.shape[1] :, ...]
+                    + controlnet_single_block_samples[index_block // interval_control]
+                )
+        hidden_states = hidden_states[:, encoder_hidden_states.shape[1] :, ...]
+
+    temb = pooled_temb[:, -2:]
+    hidden_states = self.norm_out(hidden_states, temb)
+    output = self.proj_out(hidden_states)
+
+    if USE_PEFT_BACKEND:
+        # remove `lora_scale` from each PEFT layer
+        unscale_lora_layers(self, lora_scale)
+
+    if not return_dict:
+        return (output,)
+
+    return Transformer2DModelOutput(sample=output)
\ No newline at end of file