Add files using upload-large-folder tool
Browse files- README.md +10 -42
- demo_images/readme_text2img.jpeg +0 -0
- scheduler/scheduler_config.json +1 -1
README.md
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@@ -7,8 +7,15 @@ tags:
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- controlnet
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- diffusers
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- text-to-image
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---
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# DiffusionSat Custom Pipelines
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Custom community pipelines for loading DiffusionSat checkpoints directly with `diffusers.DiffusionPipeline.from_pretrained()`.
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image = pipe(
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"satellite image of farmland",
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metadata=None, # Optional
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num_inference_steps=30,
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).images[0]
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```
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### 2. ControlNet Pipeline
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Use `pipeline_diffusionsat_controlnet.py` for ControlNet generation.
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```python
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import torch
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from diffusers import DiffusionPipeline, ControlNetModel
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from diffusers.utils import load_image
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# 1. Load ControlNet
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controlnet = ControlNetModel.from_pretrained(
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"path/to/ckpt/diffusionsat/controlnet",
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torch_dtype=torch.float16
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)
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# 2. Load Pipeline with ControlNet
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pipe = DiffusionPipeline.from_pretrained(
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"path/to/ckpt/diffusionsat",
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controlnet=controlnet,
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custom_pipeline="./pipeline_diffusionsat_controlnet.py", # Path to this file
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torch_dtype=torch.float16,
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trust_remote_code=True,
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)
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pipe = pipe.to("cuda")
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# 3. Prepare Control Image
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control_image = load_image("path/to/conditioning_image.png")
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# 4. Generate
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# metadata: Target image metadata (optional)
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# cond_metadata: Conditioning image metadata (optional)
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image = pipe(
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"satellite image of farmland",
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image=control_image,
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metadata=None,
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cond_metadata=None,
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num_inference_steps=30,
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).images[0]
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```
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- controlnet
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- diffusers
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- text-to-image
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widget:
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- prompt: satellite image of farmland
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output:
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url: demo_images/readme_text2img.jpeg
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---
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> [!NOTE]
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> If you encounter pipeline loading failure or unexpected output, please contact bili_sakura@zju.edu.cn.
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# DiffusionSat Custom Pipelines
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Custom community pipelines for loading DiffusionSat checkpoints directly with `diffusers.DiffusionPipeline.from_pretrained()`.
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image = pipe(
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"satellite image of farmland",
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metadata=None, # Optional
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height=256,
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width=256,
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num_inference_steps=30,
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).images[0]
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```
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demo_images/readme_text2img.jpeg
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scheduler/scheduler_config.json
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@@ -6,7 +6,7 @@
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"beta_start": 0.00085,
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"clip_sample": false,
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"num_train_timesteps": 1000,
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"prediction_type": "
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"set_alpha_to_one": false,
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"skip_prk_steps": true,
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"steps_offset": 1,
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"beta_start": 0.00085,
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"clip_sample": false,
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"num_train_timesteps": 1000,
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"prediction_type": "v_prediction",
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"set_alpha_to_one": false,
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"skip_prk_steps": true,
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"steps_offset": 1,
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