block wise controlnet

examples/qwen_image/model_training/validate_full/Qwen-Image-BlockWise-Controlnet.py

@@ -0,0 +1,38 @@
+from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
+from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig, ControlNetInput
+from diffsynth import load_state_dict
+import torch
+from PIL import Image
+from diffsynth.controlnets.processors import Annotator
+import os
+
+
+pipe = QwenImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Qwen/Qwen-Image", 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"),
+        ModelConfig(path="models/DiffSynth-Studio/BlockWiseControlnet/model_init.safetensors"),
+    ],
+    tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
+)
+
+state_dict = load_state_dict("models/train/Qwen-Image-BlockWiseControlNet_full_lr1e-3_wd1e-6/step-26000.safetensors")
+pipe.blockwise_controlnet.load_state_dict(state_dict)
+
+prompt = "精致肖像，水下少女，蓝裙飘逸，发丝轻扬，光影透澈，气泡环绕，面容恬静，细节精致，梦幻唯美。"
+image = Image.open("test_image.jpg").convert("RGB").resize((1024, 1024))
+canny_image = Annotator("canny")(image)
+canny_image.save("canny_image_test.jpg")
+
+controlnet_input = ControlNetInput(
+    image=canny_image,
+    scale=1.0,
+    processor_id="canny",
+)
+
+for seed in range(100, 200):
+    image = pipe(prompt, seed=seed, height=1024, width=1024, controlnet_inputs=[controlnet_input], num_inference_steps=30, cfg_scale=4.0)
+    image.save(f"test_image_controlnet_step2k_1_{seed}.jpg")