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stable-video-diffusion-img2vid-xt-1-1

controlnet-sd-xl-1.0-softedge-dexined

--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-xl-base-1.0 tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - controlnet inference: true --- These are controlnet weights trained on stabilityai/stable-diffusion-xl-base-1.0 with dexined soft edge preprocessing. prompt: a dog sitting in the driver's seat of a car prompt: a man throwing a frisbee in a park prompt: a herd of elephants standing next to each other prompt: a large body of water with a large clock tower prompt: a man standing on a tennis court holding a racquet prompt: a bathroom with a toilet, sink, and trash can prompt: a cupcake sitting on top of a white plate prompt: a young boy blowing out candles on a birthday cake

MOSS-Image-SFT-GGUF

This is a GGUF quantized version of MOSS-Image-SFT by OpenMOSS, the 5.1B text-to-image transformer. unsloth/MOSS-Image-SFT-GGUF uses Unsloth Dynamic 2.0 methodology: the `UD-` files are mixed precision, with each layer's type chosen from a measured sensitivity scan and an importance matrix collected while generating images. - Runs in Unsloth Studio and in stable-diffusion.cpp builds that include MOSS-Image (master-975 or newer), on CPU, Mac and GPU. - Only the transformer is in these files. The VAE and tokenizer are unchanged; take them from unsloth/MOSS-Image-SFT-FP8, which carries the upstream diffusers folders byte for byte. The text encoder runs from the stock unsloth/Qwen3.5-9B-GGUF `Qwen3.5-9B-Q8_0.gguf`. **Recommended:** `MOSS-Image-SFT-UD-Q4_K_XL.gguf` (3.42 GB) for most machines: LPIPS 0.213 against 0.294 for `Q4_K_M`. For output closer to bf16 use `UD-Q5_K_XL` (0.121) or `UD-Q6_K_XL` (0.106); `Q8_0` (0.054) is the closest. On a CUDA GPU, Unsloth Studio defaults to the INT8-ConvRot file in unsloth/MOSS-Image-SFT-FP8 (LPIPS 0.058) instead. Pick **MOSS-Image-SFT** in the Images tab and choose a GGUF file from this repo. Unsloth Studio runs MOSS-Image GGUFs with its bundled stable-diffusion.cpp engine and downloads the VAE, tokenizer and Qwen3.5-9B text encoder for you. Defaults: 1024x1024, 50 steps, CFG 3, shift 5.

SDXL-Lightning

SDXL-Lightning is a lightning-fast text-to-image generation model. It can generate high-quality 1024px images in a few steps. For more information, please refer to our research paper: SDXL-Lightning: Progressive Adversarial Diffusion Distillation. We open-source the model as part of the research. Our models are distilled from stabilityai/stable-diffusion-xl-base-1.0. This repository contains checkpoints for 1-step, 2-step, 4-step, and 8-step distilled models. The generation quality of our 2-step, 4-step, and 8-step model is amazing. Our 1-step model is more experimental. We provide both full UNet and LoRA checkpoints. The full UNet models have the best quality while the LoRA models can be applied to other base models.

AlbedoBase-XL_v31-Large

About this version • Merged over 50 selected latest versions of SDXL models using the recursive script employed in V3. GOAL Stable Diffusion XL has 3.5 billion parameters (excluding the Refiner), which is about 3.6 times more than the SD v1.5 version. I believe that this is not just a number, but a number that can lead to a significant improvement in performance. It has been a while since we realized that the overall performance of SD v1.5 has improved beyond imagination thanks to the explosive contributions of our community. Therefore, I am working on completing this AlbedoBase XL model in order to optimally reproduce the performance improvement that occurred in v1.5 in this XL version as well.

dreamshaper8-lcm-gguf

Quantized GGUF weights of DreamShaper 8 LCM, optimized for fast local CPU and low-memory GPU image generation using `stable-diffusion.cpp`. These models are designed specifically to run inside the **Haven AI Companion** local portrait generation engine.

anythingelse-v4

payload = json.dumps({ "key": "", "model_id": "anythingelse-v4", "prompt": "actual 8K portrait photo of gareth person, portrait, happy colors, bright eyes, clear eyes, warm smile, smooth soft skin, big dreamy eyes, beautiful intricate colored hair, symmetrical, anime wide eyes, soft lighting, detailed face, by makoto shinkai, stanley artgerm lau, wlop, rossdraws, concept art, digital painting, looking into camera", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "embeddings_model_id", "lora": "lora_model_id", "webhook": None, "track_id": None })

tiny-stable-diffusion-with-textual-inversion

dreamshaper-7

`lykon/dreamshaper-7` is a Stable Diffusion model that has been fine-tuned on runwayml/stable-diffusion-v1-5. Please consider supporting me: - on Patreon - or buy me a coffee For more general information on how to run text-to-image models with 🧨 Diffusers, see the docs.

japanese-stable-diffusion-xl