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README.md

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+---
+license: openrail++
+base_model: stabilityai/stable-diffusion-xl-base-1.0
+tags:
+  - stable-diffusion-xl
+  - controlnet
+  - temporal
+  - video
+  - diffusers
+inference: true
+---
+
+# TemporalNet2 ControlNet for SDXL
+
+This is a TemporalNet2 ControlNet model trained on SDXL (Stable Diffusion XL base 1.0).
+
+## Model Description
+
+TemporalNet2 is a ControlNet variant designed for temporal coherence in video generation. It takes two conditioning inputs:
+- **Previous Frame**: The previous frame in the video sequence (3 channels)
+- **Optical Flow**: The optical flow between the previous and current frame (3 channels)
+
+Total conditioning channels: **6 channels**
+
+This model was trained to generate temporally coherent frames by learning from both the visual content of the previous frame and the motion information encoded in optical flow.
+
+## Usage
+
+```python
+from diffusers import StableDiffusionXLControlNetPipeline, ControlNetModel, EulerDiscreteScheduler
+from PIL import Image
+import torch
+
+# Load the ControlNet model
+controlnet = ControlNetModel.from_pretrained(
+    "YOUR_USERNAME/temporalnet2-sdxl-controlnet",
+    torch_dtype=torch.float16
+)
+
+# Create the pipeline
+pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
+    "stabilityai/stable-diffusion-xl-base-1.0",
+    controlnet=controlnet,
+    torch_dtype=torch.float16
+)
+pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
+pipe.to("cuda")
+
+# Load your conditioning images
+prev_frame = Image.open("previous_frame.jpg")
+optical_flow = Image.open("optical_flow.jpg")
+
+# Concatenate conditioning images (they will be concatenated in the pipeline)
+# Note: You'll need to prepare the 6-channel input by concatenating prev_frame and optical_flow
+prompt = "your prompt describing the scene"
+
+# Generate
+image = pipe(
+    prompt=prompt,
+    image=[prev_frame, optical_flow],  # The pipeline will handle concatenation
+    num_inference_steps=20,
+    guidance_scale=7.5
+).images[0]
+
+image.save("output.jpg")
+```
+
+## Training Details
+
+- **Base Model**: stabilityai/stable-diffusion-xl-base-1.0
+- **Training Resolution**: Multi-resolution (512, 640, 768, 896, 1024px)
+- **Conditioning Channels**: 6 (3 for previous frame + 3 for optical flow)
+- **Training Steps**: 25,000
+- **Mixed Precision**: bfloat16
+
+## Limitations
+
+This model requires specific conditioning inputs:
+1. The previous frame from your video sequence
+2. The optical flow computed between frames
+
+For best results, ensure your optical flow visualization uses a consistent color scheme and magnitude representation.
+
+## License
+
+This model is released under the same license as SDXL (OpenRAIL++).

config.json

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+{
+  "_class_name": "ControlNetModel",
+  "_diffusers_version": "0.35.2",
+  "act_fn": "silu",
+  "addition_embed_type": "text_time",
+  "addition_embed_type_num_heads": 64,
+  "addition_time_embed_dim": 256,
+  "attention_head_dim": [
+    5,
+    10,
+    20
+  ],
+  "block_out_channels": [
+    320,
+    640,
+    1280
+  ],
+  "class_embed_type": null,
+  "conditioning_channels": 6,
+  "conditioning_embedding_out_channels": [
+    16,
+    32,
+    96,
+    256
+  ],
+  "controlnet_conditioning_channel_order": "rgb",
+  "cross_attention_dim": 2048,
+  "down_block_types": [
+    "DownBlock2D",
+    "CrossAttnDownBlock2D",
+    "CrossAttnDownBlock2D"
+  ],
+  "downsample_padding": 1,
+  "encoder_hid_dim": null,
+  "encoder_hid_dim_type": null,
+  "flip_sin_to_cos": true,
+  "freq_shift": 0,
+  "global_pool_conditions": false,
+  "in_channels": 4,
+  "layers_per_block": 2,
+  "mid_block_scale_factor": 1,
+  "mid_block_type": "UNetMidBlock2DCrossAttn",
+  "norm_eps": 1e-05,
+  "norm_num_groups": 32,
+  "num_attention_heads": null,
+  "num_class_embeds": null,
+  "only_cross_attention": false,
+  "projection_class_embeddings_input_dim": 2816,
+  "resnet_time_scale_shift": "default",
+  "transformer_layers_per_block": [
+    1,
+    2,
+    10
+  ],
+  "upcast_attention": null,
+  "use_linear_projection": true
+}

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