Model card auto-generated by SimpleTuner

README.md

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+---
+license: openrail++
+base_model: "terminusresearch/pixart-900m-1024-ft-v0.6"
+tags:
+  - pixart_sigma
+  - pixart_sigma-diffusers
+  - text-to-image
+  - image-to-image
+  - diffusers
+  - simpletuner
+  - not-for-all-audiences
+  - lora
+  - controlnet
+  - template:sd-lora
+  - standard
+pipeline_tag: text-to-image
+inference: true
+widget:
+- text: 'A photo-realistic image of a cat'
+  parameters:
+    negative_prompt: 'ugly, cropped, blurry, low-quality, mediocre average'
+  output:
+    url: ./assets/image_0_0.png
+---
+
+# pixart-controlnet-lora-test
+
+This is a ControlNet PEFT LoRA derived from [terminusresearch/pixart-900m-1024-ft-v0.6](https://huggingface.co/terminusresearch/pixart-900m-1024-ft-v0.6).
+
+The main validation prompt used during training was:
+```
+A photo-realistic image of a cat
+```
+
+
+## Validation settings
+- CFG: `4.0`
+- CFG Rescale: `0.0`
+- Steps: `16`
+- Sampler: `ddim`
+- Seed: `42`
+- Resolution: `1024x1024`
+
+
+Note: The validation settings are not necessarily the same as the [training settings](#training-settings).
+
+You can find some example images in the following gallery:
+
+
+<Gallery />
+
+The text encoder **was not** trained.
+You may reuse the base model text encoder for inference.
+
+
+## Training settings
+
+- Training epochs: 224
+- Training steps: 450
+- Learning rate: 0.0001
+  - Learning rate schedule: constant
+  - Warmup steps: 500
+- Max grad value: 0.01
+- Effective batch size: 3
+  - Micro-batch size: 1
+  - Gradient accumulation steps: 1
+  - Number of GPUs: 3
+- Gradient checkpointing: False
+- Prediction type: epsilon (extra parameters=['training_scheduler_timestep_spacing=trailing', 'inference_scheduler_timestep_spacing=trailing', 'controlnet_enabled'])
+- Optimizer: adamw_bf16
+- Trainable parameter precision: Pure BF16
+- Base model precision: `no_change`
+- Caption dropout probability: 0.0%
+
+
+- LoRA Rank: 64
+- LoRA Alpha: 64.0
+- LoRA Dropout: 0.1
+- LoRA initialisation style: default
+    
+
+## Datasets
+
+### antelope-data-1024
+- Repeats: 0
+- Total number of images: ~6
+- Total number of aspect buckets: 1
+- Resolution: 1.048576 megapixels
+- Cropped: True
+- Crop style: center
+- Crop aspect: square
+- Used for regularisation data: No
+
+
+## Inference
+
+
+```python
+import torch
+from diffusers import PixArtSigmaPipeline, PixArtSigmaControlNetPipeline
+# if you're not in the SimpleTuner environment, this import will fail.
+from helpers.models.pixart.controlnet import PixArtSigmaControlNetAdapterModel
+
+# Load base model
+base_model_id = "terminusresearch/pixart-900m-1024-ft-v0.6"
+controlnet_id = "bghira/pixart-controlnet-lora-test"
+
+# Load ControlNet adapter
+controlnet = PixArtSigmaControlNetAdapterModel.from_pretrained(
+    f"{controlnet_id}/controlnet"
+)
+
+# Create pipeline
+pipeline = PixArtSigmaControlNetPipeline.from_pretrained(
+    base_model_id,
+    controlnet=controlnet,
+    torch_dtype=torch.bfloat16
+)
+pipeline.to('cuda' if torch.cuda.is_available() else 'cpu')
+
+# Load your control image
+from PIL import Image
+control_image = Image.open("path/to/control/image.png")
+
+# Generate
+prompt = "A photo-realistic image of a cat"
+image = pipeline(
+    prompt=prompt,
+    image=control_image,
+    num_inference_steps=16,
+    guidance_scale=4.0,
+    generator=torch.Generator(device='cuda').manual_seed(42),
+    controlnet_conditioning_scale=1.0,
+).images[0]
+
+image.save("output.png")
+
+
+