flux_ipadapter_refactor

diffsynth/pipelines/flux_image_new.py

@@ -43,6 +43,7 @@ class FluxImagePipeline(BasePipeline):
             FluxImageUnit_InputImageEmbedder(),
             FluxImageUnit_ImageIDs(),
             FluxImageUnit_EmbeddedGuidanceEmbedder(),
+            FluxImageUnit_IPAdapter(),
         ]
         self.model_fn = model_fn_flux_image
         
@@ -98,6 +99,8 @@ class FluxImagePipeline(BasePipeline):
         pipe.vae_decoder = model_manager.fetch_model("flux_vae_decoder")
         pipe.vae_encoder = model_manager.fetch_model("flux_vae_encoder")
         pipe.prompter.fetch_models(pipe.text_encoder_1, pipe.text_encoder_2)
+        pipe.ipadapter = model_manager.fetch_model("flux_ipadapter")
+        pipe.ipadapter_image_encoder = model_manager.fetch_model("siglip_vision_model")
 
         return pipe
     
@@ -294,6 +297,29 @@ class FluxImageUnit_EmbeddedGuidanceEmbedder(PipelineUnit):
         return {"guidance": guidance}
 
 
+class FluxImageUnit_IPAdapter(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            take_over=True,
+            onload_model_names=("ipadapter_image_encoder", "ipadapter")
+        )
+
+    def process(self, pipe: FluxImagePipeline, inputs_shared, inputs_posi, inputs_nega):
+        ipadapter_images, ipadapter_scale = inputs_shared.get("ipadapter_images", None), inputs_shared.get("ipadapter_scale", 1.0)
+        if ipadapter_images is None:
+            return inputs_shared, inputs_posi, inputs_nega
+
+        pipe.load_models_to_device(self.onload_model_names)
+        images = [image.convert("RGB").resize((384, 384), resample=3) for image in ipadapter_images]
+        images = [pipe.preprocess_image(image).to(device=pipe.device, dtype=pipe.torch_dtype) for image in images]
+        ipadapter_images = torch.cat(images, dim=0)
+        ipadapter_image_encoding = pipe.ipadapter_image_encoder(ipadapter_images).pooler_output
+
+        inputs_posi.update({"ipadapter_kwargs_list": pipe.ipadapter(ipadapter_image_encoding, scale=ipadapter_scale)})
+        if inputs_shared.get("cfg_scale", 1.0) != 1.0:
+            inputs_nega.update({"ipadapter_kwargs_list": pipe.ipadapter(torch.zeros_like(ipadapter_image_encoding))})
+        return inputs_shared, inputs_posi, inputs_nega
+
 
 class TeaCache:
     def __init__(self, num_inference_steps, rel_l1_thresh):

examples/flux/flux_ipadapter.py

@@ -0,0 +1,29 @@
+import torch
+from PIL import Image
+from diffsynth import save_video, VideoData, download_models
+from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig
+from modelscope import dataset_snapshot_download
+
+#TODO: repalce the local path with model_id
+pipe = FluxImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"),
+        ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"),
+        ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"),
+        ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"),
+        ModelConfig(model_id="InstantX/FLUX.1-dev-IP-Adapter", origin_file_pattern="ip-adapter.bin"),
+        ModelConfig(path="models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/image_encoder")
+    ],
+)
+
+seed = 42
+origin_prompt = "a rabbit in a garden, colorful flowers"
+image = pipe(prompt=origin_prompt, height=1280, width=960, seed=seed)
+image.save("style image.jpg")
+
+torch.manual_seed(seed)
+image = pipe(prompt="A piggy", height=1280, width=960, seed=seed,
+    ipadapter_images=[image], ipadapter_scale=0.7)
+image.save("A piggy.jpg")