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@@ -24,17 +24,29 @@ This repository contains an unified ControlNet for FLUX.1-dev model released by
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  # Keynotes
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  In comparison with [Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro](https://huggingface.co/Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro),
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- - Remove mode embedding. Smaller model size (6.6GB -> 4.0GB).
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  - Improve on canny and pose, better control and aesthetics.
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  - Add support for soft edge. Remove support for tile.
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-
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  # Model Cards
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  - This ControlNet consists of 6 double blocks and 0 single block as the same as [Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro](https://huggingface.co/Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro). Mode embedding is removed.
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  - We train the model from scratch for 300k steps using a dataset of 20M high-quality general and human images. We train at 512x512 resolution in BFloat16, batch size = 128, learning rate = 2e-5, the guidance is uniformly sampled from [1, 7]. We set the text drop ratio to 0.20.
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- - This model supports multiple control modes, including canny, soft edge, depth, pose, gray.
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  - This model can be jointly used with other ControlNets.
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  # Inference
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  ```python
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  import torch
@@ -48,7 +60,11 @@ controlnet = FluxControlNetModel.from_pretrained(controlnet_model_union, torch_d
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  pipe = FluxControlNetPipeline.from_pretrained(base_model, controlnet=controlnet, torch_dtype=torch.bfloat16)
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  pipe.to("cuda")
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- width, height = control_image_depth.size
 
 
 
 
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  image = pipe(
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  prompt,
@@ -57,9 +73,9 @@ image = pipe(
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  height=height,
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  controlnet_conditioning_scale=0.7,
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  control_guidance_end=0.8,
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- num_inference_steps=24,
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  guidance_scale=3.5,
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- generator=torch.manual_seed(42),
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  ).images[0]
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  ```
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  # Keynotes
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  In comparison with [Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro](https://huggingface.co/Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro),
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+ - Remove mode embedding, has smaller model size.
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  - Improve on canny and pose, better control and aesthetics.
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  - Add support for soft edge. Remove support for tile.
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  # Model Cards
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  - This ControlNet consists of 6 double blocks and 0 single block as the same as [Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro](https://huggingface.co/Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro). Mode embedding is removed.
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  - We train the model from scratch for 300k steps using a dataset of 20M high-quality general and human images. We train at 512x512 resolution in BFloat16, batch size = 128, learning rate = 2e-5, the guidance is uniformly sampled from [1, 7]. We set the text drop ratio to 0.20.
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+ - This model supports multiple control modes, including canny, soft edge, depth, pose, gray. You can use it just as a normal ControlNet.
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  - This model can be jointly used with other ControlNets.
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+ # Showcases
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+
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+ <table>
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+ <tr>
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+ <td><img src="./images/canny.png" alt="canny" style="width:100%"></td>
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+ <td><img src="./images/softedge.png" alt="softedge" style="width:100%"></td>
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+ <td><img src="./images/pose.png" alt="pose" style="width:100%"></td>
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+ <td><img src="./images/depth.png" alt="depth" style="width:100%"></td>
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+ <td><img src="./images/gray.png" alt="gray" style="width:100%"></td>
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+ </tr>
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+ </table>
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+
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+
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  # Inference
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  ```python
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  import torch
 
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  pipe = FluxControlNetPipeline.from_pretrained(base_model, controlnet=controlnet, torch_dtype=torch.bfloat16)
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  pipe.to("cuda")
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+ # replace with other conds
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+ control_image = load_image("./conds/canny.png")
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+ width, height = control_image.size
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+
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+ prompt = "A young girl stands gracefully at the edge of a serene beach, her long, flowing hair gently tousled by the sea breeze. She wears a soft, pastel-colored dress that complements the tranquil blues and greens of the coastal scenery. The golden hues of the setting sun cast a warm glow on her face, highlighting her serene expression. The background features a vast, azure ocean with gentle waves lapping at the shore, surrounded by distant cliffs and a clear, cloudless sky. The composition emphasizes the girl's serene presence amidst the natural beauty, with a balanced blend of warm and cool tones."
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  image = pipe(
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  prompt,
 
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  height=height,
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  controlnet_conditioning_scale=0.7,
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  control_guidance_end=0.8,
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+ num_inference_steps=30,
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  guidance_scale=3.5,
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+ generator=torch.Generator(device="cuda").manual_seed(42),
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  ).images[0]
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  ```
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