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--- |
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license: other |
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license_name: flux-1-dev-non-commercial-license |
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license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md |
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language: |
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- en |
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tags: |
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- flux |
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- diffusers |
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- lora |
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- replicate |
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base_model: black-forest-labs/FLUX.1-dev |
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pipeline_tag: text-to-image |
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instance_prompt: ANM |
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widget: |
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- text: >- |
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A ANM style photorealistic street scene during a vibrant sunset, with power |
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lines and utility poles silhouetted against the sky. The sky is painted in |
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rich hues of orange, pink, and purple, with clouds spread across it in |
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intricate patterns. Residential buildings line the street, illuminated by |
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warm, golden light from small shop windows. Cars and pedestrians add life to |
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the urban landscape. |
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output: |
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url: images/example_5asi7pzht.png |
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- text: >- |
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A ANM style cyberpunk cityscape at sunset with towering utility poles and |
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dense power lines silhouetted against a neon-hued sky of oranges, purples, |
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and pinks. The scene features low-rise buildings with illuminated signs and |
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neon lights, creating a moody, futuristic urban atmosphere. A few sleek cars |
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and pedestrians in futuristic attire add life to the scene. |
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output: |
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url: images/example_15byrppzj.png |
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--- |
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# Animewallpaper |
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<Gallery /> |
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Trained on Replicate using: |
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https://replicate.com/ostris/flux-dev-lora-trainer/train |
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## Trigger words |
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You should use `ANM` to trigger the image generation. |
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## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) |
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```py |
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from diffusers import AutoPipelineForText2Image |
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import torch |
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pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') |
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pipeline.load_lora_weights('FounderFeed/animewallpaper', weight_name='lora.safetensors') |
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image = pipeline('your prompt').images[0] |
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``` |
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For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) |
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