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https://github.com/modelscope/DiffSynth-Studio.git
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support flux any training
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Artiprocher
20
examples/flux/model_training/validate_full/FLEX.2-preview.py
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20
examples/flux/model_training/validate_full/FLEX.2-preview.py
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import torch
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from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig
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from diffsynth import load_state_dict
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pipe = FluxImagePipeline.from_pretrained(
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torch_dtype=torch.bfloat16,
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device="cuda",
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model_configs=[
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ModelConfig(model_id="ostris/Flex.2-preview", origin_file_pattern="Flex.2-preview.safetensors"),
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ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"),
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ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"),
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ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"),
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],
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)
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state_dict = load_state_dict("models/train/FLEX.2-preview_full/epoch-0.safetensors")
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pipe.dit.load_state_dict(state_dict)
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image = pipe(prompt="dog,white and brown dog, sitting on wall, under pink flowers", seed=0)
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image.save("image_FLEX.2-preview_full.jpg")
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import torch
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from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig, ControlNetInput
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from diffsynth import load_state_dict
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from PIL import Image
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pipe = FluxImagePipeline.from_pretrained(
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torch_dtype=torch.bfloat16,
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device="cuda",
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model_configs=[
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ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"),
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ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"),
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ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"),
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ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"),
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ModelConfig(model_id="alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta", origin_file_pattern="diffusion_pytorch_model.safetensors"),
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],
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)
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state_dict = load_state_dict("models/train/FLUX.1-dev-Controlnet-Inpainting-Beta_full/epoch-0.safetensors")
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pipe.controlnet.models[0].load_state_dict(state_dict)
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image = pipe(
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prompt="a cat sitting on a chair, wearing sunglasses",
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controlnet_inputs=[ControlNetInput(
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image=Image.open("data/example_image_dataset/inpaint/image_1.jpg"),
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inpaint_mask=Image.open("data/example_image_dataset/inpaint/mask.jpg"),
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scale=0.9
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)],
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height=1024, width=1024,
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seed=0, rand_device="cuda",
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)
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image.save("image_FLUX.1-dev-Controlnet-Inpainting-Beta_full.jpg")
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import torch
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from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig, ControlNetInput
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from diffsynth import load_state_dict
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from PIL import Image
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pipe = FluxImagePipeline.from_pretrained(
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torch_dtype=torch.bfloat16,
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device="cuda",
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model_configs=[
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ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"),
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ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"),
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ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"),
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ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"),
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ModelConfig(model_id="InstantX/FLUX.1-dev-Controlnet-Union-alpha", origin_file_pattern="diffusion_pytorch_model.safetensors"),
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],
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)
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state_dict = load_state_dict("models/train/FLUX.1-dev-Controlnet-Union-alpha_full/epoch-0.safetensors")
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pipe.controlnet.models[0].load_state_dict(state_dict)
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image = pipe(
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prompt="a dog",
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controlnet_inputs=[ControlNetInput(
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image=Image.open("data/example_image_dataset/canny/image_1.jpg"),
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scale=0.9,
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processor_id="canny",
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)],
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height=768, width=768,
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seed=0, rand_device="cuda",
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)
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image.save("image_FLUX.1-dev-Controlnet-Union-alpha_full.jpg")
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import torch
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from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig, ControlNetInput
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from diffsynth import load_state_dict
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from PIL import Image
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pipe = FluxImagePipeline.from_pretrained(
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torch_dtype=torch.bfloat16,
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device="cuda",
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model_configs=[
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ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"),
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ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"),
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ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"),
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ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"),
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ModelConfig(model_id="jasperai/Flux.1-dev-Controlnet-Upscaler", origin_file_pattern="diffusion_pytorch_model.safetensors"),
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],
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)
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state_dict = load_state_dict("models/train/FLUX.1-dev-Controlnet-Upscaler_full/epoch-0.safetensors")
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pipe.controlnet.models[0].load_state_dict(state_dict)
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image = pipe(
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prompt="a dog",
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controlnet_inputs=[ControlNetInput(
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image=Image.open("data/example_image_dataset/upscale/image_1.jpg"),
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scale=0.9
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)],
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height=768, width=768,
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seed=0, rand_device="cuda",
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)
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image.save("image_FLUX.1-dev-Controlnet-Upscaler_full.jpg")
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import torch
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from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig, ControlNetInput
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from diffsynth import load_state_dict
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from PIL import Image
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pipe = FluxImagePipeline.from_pretrained(
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torch_dtype=torch.bfloat16,
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device="cuda",
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model_configs=[
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ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"),
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ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"),
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ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"),
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ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"),
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ModelConfig(model_id="ByteDance/InfiniteYou", origin_file_pattern="infu_flux_v1.0/aes_stage2/image_proj_model.bin"),
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ModelConfig(model_id="ByteDance/InfiniteYou", origin_file_pattern="infu_flux_v1.0/aes_stage2/InfuseNetModel/*.safetensors"),
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],
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)
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state_dict = load_state_dict("models/train/FLUX.1-dev-InfiniteYou_full/epoch-0.safetensors")
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state_dict_projector = {i.replace("image_proj_model.", ""): state_dict[i] for i in state_dict if i.startswith("image_proj_model.")}
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pipe.image_proj_model.load_state_dict(state_dict_projector)
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state_dict_controlnet = {i.replace("controlnet.models.0.", ""): state_dict[i] for i in state_dict if i.startswith("controlnet.models.0.")}
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pipe.controlnet.models[0].load_state_dict(state_dict_controlnet)
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image = pipe(
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prompt="a man with a red hat",
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controlnet_inputs=[ControlNetInput(
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image=Image.open("data/example_image_dataset/infiniteyou/image_1.jpg"),
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)],
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height=1024, width=1024,
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seed=0, rand_device="cuda",
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)
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image.save("image_FLUX.1-dev-InfiniteYou_full.jpg")
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25
examples/flux/model_training/validate_full/Step1X-Edit.py
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examples/flux/model_training/validate_full/Step1X-Edit.py
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import torch
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from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig
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from diffsynth import load_state_dict
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from PIL import Image
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pipe = FluxImagePipeline.from_pretrained(
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torch_dtype=torch.bfloat16,
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device="cuda",
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model_configs=[
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ModelConfig(model_id="Qwen/Qwen2.5-VL-7B-Instruct"),
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ModelConfig(model_id="stepfun-ai/Step1X-Edit", origin_file_pattern="step1x-edit-i1258.safetensors"),
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ModelConfig(model_id="stepfun-ai/Step1X-Edit", origin_file_pattern="vae.safetensors"),
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],
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)
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state_dict = load_state_dict("models/train/Step1X-Edit_full/epoch-0.safetensors")
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pipe.dit.load_state_dict(state_dict)
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image = pipe(
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prompt="Make the dog turn its head around.",
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step1x_reference_image=Image.open("data/example_image_dataset/2.jpg").resize((768, 768)),
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height=768, width=768, cfg_scale=6,
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seed=0
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)
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image.save("image_Step1X-Edit_full.jpg")
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