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support z-image controlnet

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Artiprocher
2026-01-07 15:56:53 +08:00
parent 32449a6aa0
commit bac39b1cd2
28 changed files with 868 additions and 11 deletions
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12
examples/z_image/model_training/validate_lora/Z-Image-Omni-Base.py
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@@ -1,4 +1,5 @@
from diffsynth.pipelines.z_image import ZImagePipeline, ModelConfig
from PIL import Image
import torch
@@ -13,7 +14,18 @@ pipe = ZImagePipeline.from_pretrained(
],
tokenizer_config=ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="tokenizer/"),
)
pipe.load_lora(pipe.dit, "./models/train/Z-Image-Omni-Base_lora/epoch-4.safetensors")
prompt = "a dog"
image = pipe(prompt=prompt, seed=42, rand_device="cuda", num_inference_steps=40, cfg_scale=4)
image.save("image.jpg")
# Edit
# pipe.load_lora(pipe.dit, "./models/train/Z-Image-Omni-Base_lora_edit/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=prompt, seed=42, rand_device="cuda", num_inference_steps=40, cfg_scale=4, edit_image=images)
# image.save("image.jpg")

23
examples/z_image/model_training/validate_lora/Z-Image-Turbo-Fun-Controlnet-Tile-2.1-8steps.py Normal file
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@@ -0,0 +1,23 @@
from diffsynth.pipelines.z_image import ZImagePipeline, ModelConfig, ControlNetInput
from diffsynth import load_state_dict
from PIL import Image
import torch
pipe = ZImagePipeline.from_pretrained(
torch_dtype=torch.bfloat16,
device="cuda",
model_configs=[
ModelConfig(model_id="PAI/Z-Image-Turbo-Fun-Controlnet-Union-2.1", origin_file_pattern="Z-Image-Turbo-Fun-Controlnet-Tile-2.1-8steps.safetensors"),
ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="transformer/*.safetensors"),
ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="text_encoder/*.safetensors"),
ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
],
tokenizer_config=ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="tokenizer/"),
)
pipe.load_lora(pipe.dit, "./models/train/Z-Image-Turbo-Fun-Controlnet-Tile-2.1-8steps_lora/epoch-4.safetensors")
controlnet_image = Image.open("data/example_image_dataset/upscale/image_1.jpg").resize((1024, 1024))
prompt = "a dog"
image = pipe(prompt=prompt, seed=0, height=1024, width=1024, controlnet_inputs=[ControlNetInput(image=controlnet_image, scale=1)])
image.save("image_tile.jpg")

23
examples/z_image/model_training/validate_lora/Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps.py Normal file
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@@ -0,0 +1,23 @@
from diffsynth.pipelines.z_image import ZImagePipeline, ModelConfig, ControlNetInput
from diffsynth import load_state_dict
from PIL import Image
import torch
pipe = ZImagePipeline.from_pretrained(
torch_dtype=torch.bfloat16,
device="cuda",
model_configs=[
ModelConfig(model_id="PAI/Z-Image-Turbo-Fun-Controlnet-Union-2.1", origin_file_pattern="Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps.safetensors"),
ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="transformer/*.safetensors"),
ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="text_encoder/*.safetensors"),
ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
],
tokenizer_config=ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="tokenizer/"),
)
pipe.load_lora(pipe.dit, "./models/train/Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps_lora/epoch-4.safetensors")
controlnet_image = Image.open("data/example_image_dataset/canny/image_1.jpg").resize((1024, 1024))
prompt = "a dog"
image = pipe(prompt=prompt, seed=0, height=1024, width=1024, controlnet_inputs=[ControlNetInput(image=controlnet_image, scale=0.7)])
image.save("image_control.jpg")

23
examples/z_image/model_training/validate_lora/Z-Image-Turbo-Fun-Controlnet-Union-2.1.py Normal file
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@@ -0,0 +1,23 @@
from diffsynth.pipelines.z_image import ZImagePipeline, ModelConfig, ControlNetInput
from diffsynth import load_state_dict
from PIL import Image
import torch
pipe = ZImagePipeline.from_pretrained(
torch_dtype=torch.bfloat16,
device="cuda",
model_configs=[
ModelConfig(model_id="PAI/Z-Image-Turbo-Fun-Controlnet-Union-2.1", origin_file_pattern="Z-Image-Turbo-Fun-Controlnet-Union-2.1.safetensors"),
ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="transformer/*.safetensors"),
ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="text_encoder/*.safetensors"),
ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
],
tokenizer_config=ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="tokenizer/"),
)
pipe.load_lora(pipe.dit, "./models/train/Z-Image-Turbo-Fun-Controlnet-Union-2.1_lora/epoch-4.safetensors")
controlnet_image = Image.open("data/example_image_dataset/canny/image_1.jpg").resize((1024, 1024))
prompt = "a dog"
image = pipe(prompt=prompt, seed=0, height=1024, width=1024, controlnet_inputs=[ControlNetInput(image=controlnet_image, scale=0.7)])
image.save("image_control.jpg")