github地址:GitHub - city96/ComfyUI-GGUF: GGUF Quantization support for native ComfyUI models 量化模型地址:https://huggingface.co/city96/FLUX.1-dev-gguf 量化后flux.1 dev模型大小大约为7G 关于gguf量化模型更多讨论参考:https://www.reddit.com/r/StableDiffusion/comments/1eslcg0/excuse_me_gguf_...
一次输入正向提示（Positive Prompt），另一次输入负向提示（Negative Prompt）。模型会根据这两种提示得到...
use:# bfl_repo = "black-forest-labs/FLUX.1-dev"# revision = "refs/pr/3"## The undistilled model that uses CFG ("pro") which can use negative prompts# was not released.bfl_repo ="black-forest-labs/FLUX.1-schnell"revision ="refs/pr/1"# bfl_repo = "black-forest-labs/FLUX...
# bfl_repo = "black-forest-labs/FLUX.1-dev" # revision = "refs/pr/3" # # The undistilled model that uses CFG ("pro") which can use negative prompts # was not released. bfl_repo = "black-forest-labs/FLUX.1-schnell" revision = "refs/pr/1" # bfl_repo = "black-forest-labs/F...
FLUX.1由前Stability AI核心成员开发,其质量远超SD 3,甚至可以与闭源的Midjourney v6.1模型相媲美。这使得FLUX.1成为AI生成艺术的新标杆,并为开源AI艺术的发展注入了新的动力。 FLUX.1 的背后公司是 Black Forest Labs,由 Stable Diffusion 的原团队以及 Stability AI 的几位前研究人员共同创立。与 Stability AI...
, Negative prompt: blurry, low quality, boring, Steps: 14, Sampler: DDPM, CFG scale: 1.4, Seed: 1679778144, Size: 720×1280, Model hash: 6f228e128a, Model: ForRealXL-V0.5, VAE hash: 235745af8d, VAE: sdxl_vae.safetensors, Variation seed: 1552724213, Variation seed strength: 0.86,...
FLUX.1) torch.manual_seed(9) image = pipe( prompt=prompt, num_inference_steps=50, embedded_guidance=3.5 ) image.save("image_1024.jpg") # Enable classifier-free guidance torch.manual_seed(9) image = pipe( prompt=prompt, negative_prompt=negative_prompt, num_inference_steps=50, c...
"neg_prompt": { "type": "string", "description": "Negative Prompt", "widget": "textarea" }, "seed": { "type": "integer", "description": "Seed (if 0 random)" }, "num_steps": { "type": "integer", "widget": "select", "description": "Steps", "oneOf": [ { "descriptio...
model_id ='black-forest-labs/FLUX.1-dev'adapter_id =f'output/{lora_name}/{lora_name}.safetensors'pipeline = DiffusionPipeline.from_pretrained(model_id) pipeline.load_lora_weights(adapter_id) prompt ="ethnographic photography of man at a picnic"negative_prompt ="blurry, cropped, ugly"pipelin...
sample_prompts_te_outputs = {} # key: prompt, value: text encoder outputs with accelerator.autocast(), torch.no_grad(): for prompt_dict in prompts: for p in [prompt_dict.get("prompt", ""), prompt_dict.get("negative_prompt", "")]: if p not in sample_prompts_te_outputs:...