Upload Qwen-Image-Lightning-8steps-V1.1-bf16.safetensors with ModelScope SDK

Qwen-Image-Lightning-4steps-V1.0-bf16.safetensors

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+size 134

Qwen-Image-Lightning-4steps-V1.0.safetensors

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+oid sha256:af76d68526a238c860e4524e67be8e8ca8524c76b0159ede0bb7241604e2749d
+size 135

Qwen-Image-Lightning-8steps-V1.1-bf16.safetensors

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+size 134

Qwen-Image-Lightning-8steps-V1.1.safetensors

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README.md

@@ -9,6 +9,55 @@ pipeline_tag: text-to-image
 tags:
 - Qwen-Image;
 - distillation;
+- LoRA
+library_name: diffusers
 ---
 
 Please refer to [Qwen-Image-Lightning github](https://github.com/ModelTC/Qwen-Image-Lightning/) to learn how to use the models.
+
+use with diffusers 🧨:
+
+make sure to install diffusers from `main` (`pip install git+https://github.com/huggingface/diffusers.git`)
+```
+from diffusers import DiffusionPipeline, FlowMatchEulerDiscreteScheduler
+import torch 
+import math
+
+# From https://github.com/ModelTC/Qwen-Image-Lightning/blob/342260e8f5468d2f24d084ce04f55e101007118b/generate_with_diffusers.py#L82C9-L97C10
+scheduler_config = {
+    "base_image_seq_len": 256,
+    "base_shift": math.log(3),  # We use shift=3 in distillation
+    "invert_sigmas": False,
+    "max_image_seq_len": 8192,
+    "max_shift": math.log(3),  # We use shift=3 in distillation
+    "num_train_timesteps": 1000,
+    "shift": 1.0,
+    "shift_terminal": None,  # set shift_terminal to None
+    "stochastic_sampling": False,
+    "time_shift_type": "exponential",
+    "use_beta_sigmas": False,
+    "use_dynamic_shifting": True,
+    "use_exponential_sigmas": False,
+    "use_karras_sigmas": False,
+}
+scheduler = FlowMatchEulerDiscreteScheduler.from_config(scheduler_config)
+pipe = DiffusionPipeline.from_pretrained(
+    "Qwen/Qwen-Image", scheduler=scheduler, torch_dtype=torch.bfloat16
+).to("cuda")
+pipe.load_lora_weights(
+    "lightx2v/Qwen-Image-Lightning", weight_name="Qwen-Image-Lightning-8steps-V1.0.safetensors"
+)
+
+prompt = "a tiny astronaut hatching from an egg on the moon, Ultra HD, 4K, cinematic composition."
+negative_prompt = " "
+image = pipe(
+    prompt=prompt,
+    negative_prompt=negative_prompt,
+    width=1024,
+    height=1024,
+    num_inference_steps=8,
+    true_cfg_scale=1.0,
+    generator=torch.manual_seed(0),
+).images[0]
+image.save("qwen_fewsteps.png")
+```