zimagei2l

test.py

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+from diffsynth.pipelines.z_image import (
+    ZImagePipeline, ModelConfig,
+    ZImageUnit_Image2LoRAEncode, ZImageUnit_Image2LoRADecode
+)
+from modelscope import snapshot_download
+from safetensors.torch import save_file
+import torch
+from PIL import Image
+
+vram_config = {
+    "offload_dtype": torch.bfloat16,
+    "offload_device": "cuda",
+    "onload_dtype": torch.bfloat16,
+    "onload_device": "cuda",
+    "preparing_dtype": torch.bfloat16,
+    "preparing_device": "cuda",
+    "computation_dtype": torch.bfloat16,
+    "computation_device": "cuda",
+}
+
+# Load models
+pipe = ZImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Tongyi-MAI/Z-Image-Base-1211_Temp", origin_file_pattern="transformer/*.safetensors", **vram_config),
+        ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="text_encoder/*.safetensors", **vram_config),
+        ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config),
+        ModelConfig(model_id="DiffSynth-Studio/General-Image-Encoders", origin_file_pattern="SigLIP2-G384/model.safetensors", **vram_config),
+        ModelConfig(model_id="DiffSynth-Studio/General-Image-Encoders", origin_file_pattern="DINOv3-7B/model.safetensors", **vram_config),
+        ModelConfig("models/train/Z-Image-i2L_v13/step-58000.safetensors", **vram_config),
+    ],
+    tokenizer_config=ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="tokenizer/"),
+    vram_limit=80,
+)
+
+# Load images
+snapshot_download(
+    model_id="DiffSynth-Studio/Qwen-Image-i2L",
+    allow_file_pattern="assets/style/*",
+    local_dir="data/examples"
+)
+for style_id in range(1, 5):
+    images = [Image.open(f"data/examples/assets/style/{style_id}/{i}.jpg") for i in range(4)]
+
+    with torch.no_grad():
+        embs = ZImageUnit_Image2LoRAEncode().process(pipe, image2lora_images=images)
+        lora = ZImageUnit_Image2LoRADecode().process(pipe, **embs)["lora"]
+
+    prompt = "a cat"
+    pipe.clear_lora()
+    pipe.load_lora(pipe.dit, state_dict=lora, alpha=1)
+    image = pipe(prompt=prompt, seed=123, cfg_scale=4, num_inference_steps=50)
+    image.save(f"image_lora_{style_id}.jpg")
+
+pipe.clear_lora()
+image = pipe(prompt=prompt, seed=123, cfg_scale=4, num_inference_steps=50)
+image.save("image_base.jpg")