Collaborate with Your ViewersĬollaborate with your audience and compete against other streamers in Stream Wars! Viewers can choose words and write solutions! The integration allows you to interact with your audience in a completely new way. But more sophisticated tools are just one click away! Our tool bar offers the most important tools and makes them accessible in an intuitive way. In both cases, we have something for you. But sometimes more advanced tools are needed to create true masterpieces.
In most cases, a brush and some colors are enough. New word packs, created by our fantastic community, are appearing every day in the world of Scribble It! and are immediately available to you!
The model was trained for 150 GPU-hours with Nvidia A100 80G using the canny model as a base model.Have you already played through all of our official word packs? Don't worry, there's still plenty more to explore. The scribble images were generated with HED boundary detection and a set of data augmentations - thresholds, masking, morphological transformations, and non-maximum suppression. The scribble model was trained on 500k scribble-image, caption pairs. Image.save( 'images/bag_scribble_out.png') With several templates to choose from, you can even create lists or. Image = pipe( "bag", image, num_inference_steps= 20).images Use the Scribble app to get artistic and combine photos, text, and illustrations in a note. Pipe.enable_xformers_memory_efficient_attention() # Remove if you do not have xformers installed # see # for installation instructions "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker= None, torch_dtype=torch.float16 "fusing/stable-diffusion-v1-5-controlnet-scribble", torch_dtype=torch.float16 Hed = om_pretrained( 'lllyasviel/ControlNet')Ĭontrolnet = om_pretrained( $ pip install git+įrom diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler Install the additional controlnet models package. We extracted theseĪdditional models from the original controlnet repo into a separate package that can be found on github. Some of the additional conditionings can be extracted from images via additional models. If you want to process an image to create the auxiliary conditioning, external dependencies are required. The auxiliary conditioning is passed directly to the diffusers pipeline. Experimentally, the auxiliary models can be used with other diffusion models such as dreamboothed stable diffusion. Metadata license: openrail base_model: runwayml/stable-diffusion-v1-5 tags: - art - controlnet - stable-diffusionĬontrolnet is an auxiliary model which augments pre-trained diffusion models with an additional conditioning.Ĭontrolnet comes with multiple auxiliary models, each which allows a different type of conditioningĬontrolnet's auxiliary models are trained with stable diffusion 1.5.
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