support qwen-image-edit-2511

diffsynth/models/qwen_image_dit.py

@@ -352,9 +352,38 @@ class QwenImageTransformerBlock(nn.Module):
         self.txt_norm2 = nn.LayerNorm(dim, elementwise_affine=False, eps=eps)
         self.txt_mlp = QwenFeedForward(dim=dim, dim_out=dim)
     
-    def _modulate(self, x, mod_params):
+    def _modulate(self, x, mod_params, index=None):
         shift, scale, gate = mod_params.chunk(3, dim=-1)
-        return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1), gate.unsqueeze(1)    
+        if index is not None:
+            # Assuming mod_params batch dim is 2*actual_batch (chunked into 2 parts)
+            # So shift, scale, gate have shape [2*actual_batch, d]
+            actual_batch = shift.size(0) // 2
+            shift_0, shift_1 = shift[:actual_batch], shift[actual_batch:]  # each: [actual_batch, d]
+            scale_0, scale_1 = scale[:actual_batch], scale[actual_batch:]
+            gate_0, gate_1 = gate[:actual_batch], gate[actual_batch:]
+
+            # index: [b, l] where b is actual batch size
+            # Expand to [b, l, 1] to match feature dimension
+            index_expanded = index.unsqueeze(-1)  # [b, l, 1]
+
+            # Expand chunks to [b, 1, d] then broadcast to [b, l, d]
+            shift_0_exp = shift_0.unsqueeze(1)  # [b, 1, d]
+            shift_1_exp = shift_1.unsqueeze(1)  # [b, 1, d]
+            scale_0_exp = scale_0.unsqueeze(1)
+            scale_1_exp = scale_1.unsqueeze(1)
+            gate_0_exp = gate_0.unsqueeze(1)
+            gate_1_exp = gate_1.unsqueeze(1)
+
+            # Use torch.where to select based on index
+            shift_result = torch.where(index_expanded == 0, shift_0_exp, shift_1_exp)
+            scale_result = torch.where(index_expanded == 0, scale_0_exp, scale_1_exp)
+            gate_result = torch.where(index_expanded == 0, gate_0_exp, gate_1_exp)
+        else:
+            shift_result = shift.unsqueeze(1)
+            scale_result = scale.unsqueeze(1)
+            gate_result = gate.unsqueeze(1)
+
+        return x * (1 + scale_result) + shift_result, gate_result
 
     def forward(
         self,
@@ -364,13 +393,16 @@ class QwenImageTransformerBlock(nn.Module):
         image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
         attention_mask: Optional[torch.Tensor] = None,
         enable_fp8_attention = False,
+        modulate_index: Optional[List[int]] = None,
     ) -> Tuple[torch.Tensor, torch.Tensor]:
 
         img_mod_attn, img_mod_mlp = self.img_mod(temb).chunk(2, dim=-1)  # [B, 3*dim] each
+        if modulate_index is not None:
+            temb = torch.chunk(temb, 2, dim=0)[0]
         txt_mod_attn, txt_mod_mlp = self.txt_mod(temb).chunk(2, dim=-1)  # [B, 3*dim] each
 
         img_normed = self.img_norm1(image)
-        img_modulated, img_gate = self._modulate(img_normed, img_mod_attn)
+        img_modulated, img_gate = self._modulate(img_normed, img_mod_attn, index=modulate_index)
 
         txt_normed = self.txt_norm1(text)
         txt_modulated, txt_gate = self._modulate(txt_normed, txt_mod_attn)
@@ -387,7 +419,7 @@ class QwenImageTransformerBlock(nn.Module):
         text = text + txt_gate * txt_attn_out
 
         img_normed_2 = self.img_norm2(image)
-        img_modulated_2, img_gate_2 = self._modulate(img_normed_2, img_mod_mlp)
+        img_modulated_2, img_gate_2 = self._modulate(img_normed_2, img_mod_mlp, index=modulate_index)
 
         txt_normed_2 = self.txt_norm2(text)
         txt_modulated_2, txt_gate_2 = self._modulate(txt_normed_2, txt_mod_mlp)

diffsynth/pipelines/qwen_image.py

@@ -4,6 +4,7 @@ from typing import Union
 from tqdm import tqdm
 from einops import rearrange
 import numpy as np
+from math import prod
 
 from ..diffusion import FlowMatchScheduler
 from ..core import ModelConfig, gradient_checkpoint_forward
@@ -125,6 +126,8 @@ class QwenImagePipeline(BasePipeline):
         edit_image: Image.Image = None,
         edit_image_auto_resize: bool = True,
         edit_rope_interpolation: bool = False,
+        # Qwen-Image-Edit-2511
+        zero_cond_t: bool = False,
         # In-context control
         context_image: Image.Image = None,
         # Tile
@@ -156,6 +159,7 @@ class QwenImagePipeline(BasePipeline):
             "eligen_entity_prompts": eligen_entity_prompts, "eligen_entity_masks": eligen_entity_masks, "eligen_enable_on_negative": eligen_enable_on_negative,
             "edit_image": edit_image, "edit_image_auto_resize": edit_image_auto_resize, "edit_rope_interpolation": edit_rope_interpolation, 
             "context_image": context_image,
+            "zero_cond_t": zero_cond_t,
         }
         for unit in self.units:
             inputs_shared, inputs_posi, inputs_nega = self.unit_runner(unit, self, inputs_shared, inputs_posi, inputs_nega)
@@ -678,6 +682,7 @@ def model_fn_qwen_image(
     use_gradient_checkpointing=False,
     use_gradient_checkpointing_offload=False,
     edit_rope_interpolation=False,
+    zero_cond_t=False,
     **kwargs
 ):
     img_shapes = [(latents.shape[0], latents.shape[2]//2, latents.shape[3]//2)]
@@ -698,6 +703,15 @@ def model_fn_qwen_image(
         image = torch.cat([image] + edit_image, dim=1)
 
     image = dit.img_in(image)
+    if zero_cond_t:
+        timestep = torch.cat([timestep, timestep * 0], dim=0)
+        modulate_index = torch.tensor(
+            [[0] * prod(sample[0]) + [1] * sum([prod(s) for s in sample[1:]]) for sample in [img_shapes]],
+            device=timestep.device,
+            dtype=torch.int,
+        )
+    else:
+        modulate_index = None
     conditioning = dit.time_text_embed(timestep, image.dtype)
 
     if entity_prompt_emb is not None:
@@ -728,6 +742,7 @@ def model_fn_qwen_image(
             image_rotary_emb=image_rotary_emb,
             attention_mask=attention_mask,
             enable_fp8_attention=enable_fp8_attention,
+            modulate_index=modulate_index,
         )
         if blockwise_controlnet_conditioning is not None:
             image_slice = image[:, :image_seq_len].clone()
@@ -738,6 +753,8 @@ def model_fn_qwen_image(
             )
             image[:, :image_seq_len] = image_slice + controlnet_output
     
+    if zero_cond_t:
+        conditioning = conditioning.chunk(2, dim=0)[0]
     image = dit.norm_out(image, conditioning)
     image = dit.proj_out(image)
     image = image[:, :image_seq_len]

examples/qwen_image/model_inference/Qwen-Image-Edit-2511.py

@@ -0,0 +1,44 @@
+from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
+from modelscope import dataset_snapshot_download
+from PIL import Image
+import torch
+
+pipe = QwenImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Qwen/Qwen-Image-Edit-2511", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"),
+        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"),
+        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+    processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"),
+)
+
+dataset_snapshot_download(
+    "DiffSynth-Studio/example_image_dataset",
+    allow_file_pattern="qwen_image_edit/*",
+    local_dir="data/example_image_dataset",
+)
+
+prompt = "生成这两个人的合影"
+edit_image = [
+    Image.open("data/example_image_dataset/qwen_image_edit/image1.jpg"),
+    Image.open("data/example_image_dataset/qwen_image_edit/image2.jpg"),
+]
+image = pipe(
+    prompt,
+    edit_image=edit_image,
+    seed=1,
+    num_inference_steps=40,
+    height=1152,
+    width=896,
+    edit_image_auto_resize=True,
+    zero_cond_t=True, # This is a special parameter introduced by Qwen-Image-Edit-2511
+)
+image.save("image.jpg")
+
+# Qwen-Image-Edit-2511 is a multi-image editing model.
+# Please use a list to input `edit_image`, even if the input contains only one image.
+# edit_image = [Image.open("image.jpg")]
+# Please do not input the image directly.
+# edit_image = Image.open("image.jpg")

examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-2511.py

@@ -0,0 +1,54 @@
+from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
+from modelscope import dataset_snapshot_download
+from PIL import Image
+import torch
+
+vram_config = {
+    "offload_dtype": "disk",
+    "offload_device": "disk",
+    "onload_dtype": torch.float8_e4m3fn,
+    "onload_device": "cpu",
+    "preparing_dtype": torch.float8_e4m3fn,
+    "preparing_device": "cuda",
+    "computation_dtype": torch.bfloat16,
+    "computation_device": "cuda",
+}
+pipe = QwenImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Qwen/Qwen-Image-Edit-2511", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", **vram_config),
+        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", **vram_config),
+        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config),
+    ],
+    processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"),
+)
+
+dataset_snapshot_download(
+    "DiffSynth-Studio/example_image_dataset",
+    allow_file_pattern="qwen_image_edit/*",
+    local_dir="data/example_image_dataset",
+)
+
+prompt = "生成这两个人的合影"
+edit_image = [
+    Image.open("data/example_image_dataset/qwen_image_edit/image1.jpg"),
+    Image.open("data/example_image_dataset/qwen_image_edit/image2.jpg"),
+]
+image = pipe(
+    prompt,
+    edit_image=edit_image,
+    seed=1,
+    num_inference_steps=40,
+    height=1152,
+    width=896,
+    edit_image_auto_resize=True,
+    zero_cond_t=True, # This is a special parameter introduced by Qwen-Image-Edit-2511
+)
+image.save("image.jpg")
+
+# Qwen-Image-Edit-2511 is a multi-image editing model.
+# Please use a list to input `edit_image`, even if the input contains only one image.
+# edit_image = [Image.open("image.jpg")]
+# Please do not input the image directly.
+# edit_image = Image.open("image.jpg")

examples/qwen_image/model_training/full/Qwen-Image-Edit-2511.sh

@@ -0,0 +1,16 @@
+accelerate launch --config_file examples/qwen_image/model_training/full/accelerate_config_zero2offload.yaml examples/qwen_image/model_training/train.py \
+  --dataset_base_path data/example_image_dataset \
+  --dataset_metadata_path data/example_image_dataset/metadata_qwen_imgae_edit_multi.json \
+  --data_file_keys "image,edit_image" \
+  --extra_inputs "edit_image" \
+  --max_pixels 1048576 \
+  --dataset_repeat 50 \
+  --model_id_with_origin_paths "Qwen/Qwen-Image-Edit-2511:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors" \
+  --learning_rate 1e-5 \
+  --num_epochs 2 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/Qwen-Image-Edit-2511_full" \
+  --trainable_models "dit" \
+  --use_gradient_checkpointing \
+  --find_unused_parameters \
+  --zero_cond_t # This is a special parameter introduced by Qwen-Image-Edit-2511. Please enable it for this model.

examples/qwen_image/model_training/lora/Qwen-Image-Edit-2511.sh

@@ -0,0 +1,19 @@
+accelerate launch examples/qwen_image/model_training/train.py \
+  --dataset_base_path data/example_image_dataset \
+  --dataset_metadata_path data/example_image_dataset/metadata_qwen_imgae_edit_multi.json \
+  --data_file_keys "image,edit_image" \
+  --extra_inputs "edit_image" \
+  --max_pixels 1048576 \
+  --dataset_repeat 50 \
+  --model_id_with_origin_paths "Qwen/Qwen-Image-Edit-2511:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors" \
+  --learning_rate 1e-4 \
+  --num_epochs 5 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/Qwen-Image-Edit-2511_lora" \
+  --lora_base_model "dit" \
+  --lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" \
+  --lora_rank 32 \
+  --use_gradient_checkpointing \
+  --dataset_num_workers 8 \
+  --find_unused_parameters \
+  --zero_cond_t # This is a special parameter introduced by Qwen-Image-Edit-2511. Please enable it for this model.

examples/qwen_image/model_training/train.py

@@ -20,6 +20,7 @@ class QwenImageTrainingModule(DiffusionTrainingModule):
         offload_models=None,
         device="cpu",
         task="sft",
+        zero_cond_t=False,
     ):
         super().__init__()
         # Load models
@@ -43,6 +44,7 @@ class QwenImageTrainingModule(DiffusionTrainingModule):
         self.extra_inputs = extra_inputs.split(",") if extra_inputs is not None else []
         self.fp8_models = fp8_models
         self.task = task
+        self.zero_cond_t = zero_cond_t
         self.task_to_loss = {
             "sft:data_process": lambda pipe, *args: args,
             "direct_distill:data_process": lambda pipe, *args: args,
@@ -68,6 +70,7 @@ class QwenImageTrainingModule(DiffusionTrainingModule):
             "use_gradient_checkpointing": self.use_gradient_checkpointing,
             "use_gradient_checkpointing_offload": self.use_gradient_checkpointing_offload,
             "edit_image_auto_resize": True,
+            "zero_cond_t": self.zero_cond_t,
         }
         inputs_shared = self.parse_extra_inputs(data, self.extra_inputs, inputs_shared)
         return inputs_shared, inputs_posi, inputs_nega
@@ -87,6 +90,7 @@ def qwen_image_parser():
     parser = add_image_size_config(parser)
     parser.add_argument("--tokenizer_path", type=str, default=None, help="Path to tokenizer.")
     parser.add_argument("--processor_path", type=str, default=None, help="Path to the processor. If provided, the processor will be used for image editing.")
+    parser.add_argument("--zero_cond_t", default=False, action="store_true", help="A special parameter introduced by Qwen-Image-Edit-2511. Please enable it for this model.")
     return parser
 
 
@@ -130,6 +134,7 @@ if __name__ == "__main__":
         offload_models=args.offload_models,
         task=args.task,
         device=accelerator.device,
+        zero_cond_t=args.zero_cond_t,
     )
     model_logger = ModelLogger(
         args.output_path,

examples/qwen_image/model_training/validate_full/Qwen-Image-Edit-2511.py

@@ -0,0 +1,26 @@
+import torch
+from PIL import Image
+from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
+from diffsynth import load_state_dict
+
+pipe = QwenImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Qwen/Qwen-Image-Edit-2511", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"),
+        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"),
+        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+    tokenizer_config=None,
+    processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"),
+)
+state_dict = load_state_dict("models/train/Qwen-Image-Edit-2511_full/epoch-1.safetensors")
+pipe.dit.load_state_dict(state_dict)
+
+prompt = "Change the color of the dress in Figure 1 to the color shown in Figure 2."
+images = [
+    Image.open("data/example_image_dataset/edit/image1.jpg").resize((1024, 1024)),
+    Image.open("data/example_image_dataset/edit/image_color.jpg").resize((1024, 1024)),
+]
+image = pipe(prompt, edit_image=images, seed=123, num_inference_steps=40, height=1024, width=1024, zero_cond_t=True)
+image.save("image.jpg")

examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit-2511.py

@@ -0,0 +1,24 @@
+import torch
+from PIL import Image
+from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
+
+pipe = QwenImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Qwen/Qwen-Image-Edit-2511", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"),
+        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"),
+        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+    tokenizer_config=None,
+    processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"),
+)
+pipe.load_lora(pipe.dit, "models/train/Qwen-Image-Edit-2511_lora/epoch-4.safetensors")
+
+prompt = "Change the color of the dress in Figure 1 to the color shown in Figure 2."
+images = [
+    Image.open("data/example_image_dataset/edit/image1.jpg").resize((1024, 1024)),
+    Image.open("data/example_image_dataset/edit/image_color.jpg").resize((1024, 1024)),
+]
+image = pipe(prompt, edit_image=images, seed=123, num_inference_steps=40, height=1024, width=1024, zero_cond_t=True)
+image.save("image.jpg")