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Use when generating images from text prompts, performing image-to-image translation, inpainting, or building custom diffusion pipelines.","ibragimov-oasis\u002Fvibe-coder","ibragimov-oasis","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F240665573?v=4","01M6DF78Y45R8228FHAZ7G6JXM","IBRAGIMOV MUKHAMMADJON","01M6K5ZYR8TDPAFG4R6693E2A6",3,"MIT","https:\u002F\u002Fgithub.com\u002Fibragimov-oasis\u002Fvibe-coder\u002Fblob\u002Fmain\u002Fvc-kit\u002Fcontent\u002FCOMBINED\u002FCOMBINED\u002FREPO_DOCS\u002F02-hermes-agent\u002Fskills\u002Fmlops\u002Fmodels\u002Fstable-diffusion\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"Image Generation","Stable Diffusion","diffusion","generation","image","stable diffusion image generation","images","custom","orchestra","with","Accelerate","translation","Art","vision","use","computer","text","Transformers","multimodal","state","2026-11-10 10:12:30",false,"---\nname: stable-diffusion-image-generation\ndescription: State-of-the-art text-to-image generation with Stable Diffusion models via HuggingFace Diffusers. Use when generating images from text prompts, performing image-to-image translation, inpainting, or building custom diffusion pipelines.\nversion: 1.0.0\nauthor: Orchestra Research\nlicense: MIT\ndependencies: [diffusers>=0.30.0, transformers>=4.41.0, accelerate>=0.31.0, torch>=2.0.0]\nmetadata:\n  hermes:\n    tags: [Image Generation, Stable Diffusion, Diffusers, Text-to-Image, Multimodal, Computer Vision]\n\n---\n\n# Stable Diffusion Image Generation\n\nComprehensive guide to generating images with Stable Diffusion using the HuggingFace Diffusers library.\n\n## When to use Stable Diffusion\n\n**Use Stable Diffusion when:**\n- Generating images from text descriptions\n- Performing image-to-image translation (style transfer, enhancement)\n- Inpainting (filling in masked regions)\n- Outpainting (extending images beyond boundaries)\n- Creating variations of existing images\n- Building custom image generation workflows\n\n**Key features:**\n- **Text-to-Image**: Generate images from natural language prompts\n- **Image-to-Image**: Transform existing images with text guidance\n- **Inpainting**: Fill masked regions with context-aware content\n- **ControlNet**: Add spatial conditioning (edges, poses, depth)\n- **LoRA Support**: Efficient fine-tuning and style adaptation\n- **Multiple Models**: SD 1.5, SDXL, SD 3.0, Flux support\n\n**Use alternatives instead:**\n- **DALL-E 3**: For API-based generation without GPU\n- **Midjourney**: For artistic, stylized outputs\n- **Imagen**: For Google Cloud integration\n- **Leonardo.ai**: For web-based creative workflows\n\n## Quick start\n\n### Installation\n\n```bash\npip install diffusers transformers accelerate torch\npip install xformers  # Optional: memory-efficient attention\n```\n\n### Basic text-to-image\n\n```python\nfrom diffusers import DiffusionPipeline\nimport torch\n\n# Load pipeline (auto-detects model type)\npipe = DiffusionPipeline.from_pretrained(\n    \"stable-diffusion-v1-5\u002Fstable-diffusion-v1-5\",\n    torch_dtype=torch.float16\n)\npipe.to(\"cuda\")\n\n# Generate image\nimage = pipe(\n    \"A serene mountain landscape at sunset, highly detailed\",\n    num_inference_steps=50,\n    guidance_scale=7.5\n).images[0]\n\nimage.save(\"output.png\")\n```\n\n### Using SDXL (higher quality)\n\n```python\nfrom diffusers import AutoPipelineForText2Image\nimport torch\n\npipe = AutoPipelineForText2Image.from_pretrained(\n    \"stabilityai\u002Fstable-diffusion-xl-base-1.0\",\n    torch_dtype=torch.float16,\n    variant=\"fp16\"\n)\npipe.to(\"cuda\")\n\n# Enable memory optimization\npipe.enable_model_cpu_offload()\n\nimage = pipe(\n    prompt=\"A futuristic city with flying cars, cinematic lighting\",\n    height=1024,\n    width=1024,\n    num_inference_steps=30\n).images[0]\n```\n\n## Architecture overview\n\n### Three-pillar design\n\nDiffusers is built around three core components:\n\n```\nPipeline (orchestration)\n├── Model (neural networks)\n│   ├── UNet \u002F Transformer (noise prediction)\n│   ├── VAE (latent encoding\u002Fdecoding)\n│   └── Text Encoder (CLIP\u002FT5)\n└── Scheduler (denoising algorithm)\n```\n\n### Pipeline inference flow\n\n```\nText Prompt → Text Encoder → Text Embeddings\n                                    ↓\nRandom Noise → [Denoising Loop] ← Scheduler\n                      ↓\n               Predicted Noise\n                      ↓\n              VAE Decoder → Final Image\n```\n\n## Core concepts\n\n### Pipelines\n\nPipelines orchestrate complete workflows:\n\n| Pipeline | Purpose |\n|----------|---------|\n| `StableDiffusionPipeline` | Text-to-image (SD 1.x\u002F2.x) |\n| `StableDiffusionXLPipeline` | Text-to-image (SDXL) |\n| `StableDiffusion3Pipeline` | Text-to-image (SD 3.0) |\n| `FluxPipeline` | Text-to-image (Flux models) |\n| `StableDiffusionImg2ImgPipeline` | Image-to-image |\n| `StableDiffusionInpaintPipeline` | Inpainting |\n\n### Schedulers\n\nSchedulers control the denoising process:\n\n| Scheduler | Steps | Quality | Use Case |\n|-----------|-------|---------|----------|\n| `EulerDiscreteScheduler` | 20-50 | Good | Default choice |\n| `EulerAncestralDiscreteScheduler` | 20-50 | Good | More variation |\n| `DPMSolverMultistepScheduler` | 15-25 | Excellent | Fast, high quality |\n| `DDIMScheduler` | 50-100 | Good | Deterministic |\n| `LCMScheduler` | 4-8 | Good | Very fast |\n| `UniPCMultistepScheduler` | 15-25 | Excellent | Fast convergence |\n\n### Swapping schedulers\n\n```python\nfrom diffusers import DPMSolverMultistepScheduler\n\n# Swap for faster generation\npipe.scheduler = DPMSolverMultistepScheduler.from_config(\n    pipe.scheduler.config\n)\n\n# Now generate with fewer steps\nimage = pipe(prompt, num_inference_steps=20).images[0]\n```\n\n## Generation parameters\n\n### Key parameters\n\n| Parameter | Default | Description |\n|-----------|---------|-------------|\n| `prompt` | Required | Text description of desired image |\n| `negative_prompt` | None | What to avoid in the image |\n| `num_inference_steps` | 50 | Denoising steps (more = better quality) |\n| `guidance_scale` | 7.5 | Prompt adherence (7-12 typical) |\n| `height`, `width` | 512\u002F1024 | Output dimensions (multiples of 8) |\n| `generator` | None | Torch generator for reproducibility |\n| `num_images_per_prompt` | 1 | Batch size |\n\n### Reproducible generation\n\n```python\nimport torch\n\ngenerator = torch.Generator(device=\"cuda\").manual_seed(42)\n\nimage = pipe(\n    prompt=\"A cat wearing a top hat\",\n    generator=generator,\n    num_inference_steps=50\n).images[0]\n```\n\n### Negative prompts\n\n```python\nimage = pipe(\n    prompt=\"Professional photo of a dog in a garden\",\n    negative_prompt=\"blurry, low quality, distorted, ugly, bad anatomy\",\n    guidance_scale=7.5\n).images[0]\n```\n\n## Image-to-image\n\nTransform existing images with text guidance:\n\n```python\nfrom diffusers import AutoPipelineForImage2Image\nfrom PIL import Image\n\npipe = AutoPipelineForImage2Image.from_pretrained(\n    \"stable-diffusion-v1-5\u002Fstable-diffusion-v1-5\",\n    torch_dtype=torch.float16\n).to(\"cuda\")\n\ninit_image = Image.open(\"input.jpg\").resize((512, 512))\n\nimage = pipe(\n    prompt=\"A watercolor painting of the scene\",\n    image=init_image,\n    strength=0.75,  # How much to transform (0-1)\n    num_inference_steps=50\n).images[0]\n```\n\n## Inpainting\n\nFill masked regions:\n\n```python\nfrom diffusers import AutoPipelineForInpainting\nfrom PIL import Image\n\npipe = AutoPipelineForInpainting.from_pretrained(\n    \"runwayml\u002Fstable-diffusion-inpainting\",\n    torch_dtype=torch.float16\n).to(\"cuda\")\n\nimage = Image.open(\"photo.jpg\")\nmask = Image.open(\"mask.png\")  # White = inpaint region\n\nresult = pipe(\n    prompt=\"A red car parked on the street\",\n    image=image,\n    mask_image=mask,\n    num_inference_steps=50\n).images[0]\n```\n\n## ControlNet\n\nAdd spatial conditioning for precise control:\n\n```python\nfrom diffusers import StableDiffusionControlNetPipeline, ControlNetModel\nimport torch\n\n# Load ControlNet for edge conditioning\ncontrolnet = ControlNetModel.from_pretrained(\n    \"lllyasviel\u002Fcontrol_v11p_sd15_canny\",\n    torch_dtype=torch.float16\n)\n\npipe = StableDiffusionControlNetPipeline.from_pretrained(\n    \"stable-diffusion-v1-5\u002Fstable-diffusion-v1-5\",\n    controlnet=controlnet,\n    torch_dtype=torch.float16\n).to(\"cuda\")\n\n# Use Canny edge image as control\ncontrol_image = get_canny_image(input_image)\n\nimage = pipe(\n    prompt=\"A beautiful house in the style of Van Gogh\",\n    image=control_image,\n    num_inference_steps=30\n).images[0]\n```\n\n### Available ControlNets\n\n| ControlNet | Input Type | Use Case |\n|------------|------------|----------|\n| `canny` | Edge maps | Preserve structure |\n| `openpose` | Pose skeletons | Human poses |\n| `depth` | Depth maps | 3D-aware generation |\n| `normal` | Normal maps | Surface details |\n| `mlsd` | Line segments | Architectural lines |\n| `scribble` | Rough sketches | Sketch-to-image |\n\n## LoRA adapters\n\nLoad fine-tuned style adapters:\n\n```python\nfrom diffusers import DiffusionPipeline\n\npipe = DiffusionPipeline.from_pretrained(\n    \"stable-diffusion-v1-5\u002Fstable-diffusion-v1-5\",\n    torch_dtype=torch.float16\n).to(\"cuda\")\n\n# Load LoRA weights\npipe.load_lora_weights(\"path\u002Fto\u002Flora\", weight_name=\"style.safetensors\")\n\n# Generate with LoRA style\nimage = pipe(\"A portrait in the trained style\").images[0]\n\n# Adjust LoRA strength\npipe.fuse_lora(lora_scale=0.8)\n\n# Unload LoRA\npipe.unload_lora_weights()\n```\n\n### Multiple LoRAs\n\n```python\n# Load multiple LoRAs\npipe.load_lora_weights(\"lora1\", adapter_name=\"style\")\npipe.load_lora_weights(\"lora2\", adapter_name=\"character\")\n\n# Set weights for each\npipe.set_adapters([\"style\", \"character\"], adapter_weights=[0.7, 0.5])\n\nimage = pipe(\"A portrait\").images[0]\n```\n\n## Memory optimization\n\n### Enable CPU offloading\n\n```python\n# Model CPU offload - moves models to CPU when not in use\npipe.enable_model_cpu_offload()\n\n# Sequential CPU offload - more aggressive, slower\npipe.enable_sequential_cpu_offload()\n```\n\n### Attention slicing\n\n```python\n# Reduce memory by computing attention in chunks\npipe.enable_attention_slicing()\n\n# Or specific chunk size\npipe.enable_attention_slicing(\"max\")\n```\n\n### xFormers memory-efficient attention\n\n```python\n# Requires xformers package\npipe.enable_xformers_memory_efficient_attention()\n```\n\n### VAE slicing for large images\n\n```python\n# Decode latents in tiles for large images\npipe.enable_vae_slicing()\npipe.enable_vae_tiling()\n```\n\n## Model variants\n\n### Loading different precisions\n\n```python\n# FP16 (recommended for GPU)\npipe = DiffusionPipeline.from_pretrained(\n    \"model-id\",\n    torch_dtype=torch.float16,\n    variant=\"fp16\"\n)\n\n# BF16 (better precision, requires Ampere+ GPU)\npipe = DiffusionPipeline.from_pretrained(\n    \"model-id\",\n    torch_dtype=torch.bfloat16\n)\n```\n\n### Loading specific components\n\n```python\nfrom diffusers import UNet2DConditionModel, AutoencoderKL\n\n# Load custom VAE\nvae = AutoencoderKL.from_pretrained(\"stabilityai\u002Fsd-vae-ft-mse\")\n\n# Use with pipeline\npipe = DiffusionPipeline.from_pretrained(\n    \"stable-diffusion-v1-5\u002Fstable-diffusion-v1-5\",\n    vae=vae,\n    torch_dtype=torch.float16\n)\n```\n\n## Batch generation\n\nGenerate multiple images efficiently:\n\n```python\n# Multiple prompts\nprompts = [\n    \"A cat playing piano\",\n    \"A dog reading a book\",\n    \"A bird painting a picture\"\n]\n\nimages = pipe(prompts, num_inference_steps=30).images\n\n# Multiple images per prompt\nimages = pipe(\n    \"A beautiful sunset\",\n    num_images_per_prompt=4,\n    num_inference_steps=30\n).images\n```\n\n## Common workflows\n\n### Workflow 1: High-quality generation\n\n```python\nfrom diffusers import StableDiffusionXLPipeline, DPMSolverMultistepScheduler\nimport torch\n\n# 1. Load SDXL with optimizations\npipe = StableDiffusionXLPipeline.from_pretrained(\n    \"stabilityai\u002Fstable-diffusion-xl-base-1.0\",\n    torch_dtype=torch.float16,\n    variant=\"fp16\"\n)\npipe.to(\"cuda\")\npipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)\npipe.enable_model_cpu_offload()\n\n# 2. Generate with quality settings\nimage = pipe(\n    prompt=\"A majestic lion in the savanna, golden hour lighting, 8k, detailed fur\",\n    negative_prompt=\"blurry, low quality, cartoon, anime, sketch\",\n    num_inference_steps=30,\n    guidance_scale=7.5,\n    height=1024,\n    width=1024\n).images[0]\n```\n\n### Workflow 2: Fast prototyping\n\n```python\nfrom diffusers import AutoPipelineForText2Image, LCMScheduler\nimport torch\n\n# Use LCM for 4-8 step generation\npipe = AutoPipelineForText2Image.from_pretrained(\n    \"stabilityai\u002Fstable-diffusion-xl-base-1.0\",\n    torch_dtype=torch.float16\n).to(\"cuda\")\n\n# Load LCM LoRA for fast generation\npipe.load_lora_weights(\"latent-consistency\u002Flcm-lora-sdxl\")\npipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)\npipe.fuse_lora()\n\n# Generate in ~1 second\nimage = pipe(\n    \"A beautiful landscape\",\n    num_inference_steps=4,\n    guidance_scale=1.0\n).images[0]\n```\n\n## Common issues\n\n**CUDA out of memory:**\n```python\n# Enable memory optimizations\npipe.enable_model_cpu_offload()\npipe.enable_attention_slicing()\npipe.enable_vae_slicing()\n\n# Or use lower precision\npipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)\n```\n\n**Black\u002Fnoise images:**\n```python\n# Check VAE configuration\n# Use safety checker bypass if needed\npipe.safety_checker = None\n\n# Ensure proper dtype consistency\npipe = pipe.to(dtype=torch.float16)\n```\n\n**Slow generation:**\n```python\n# Use faster scheduler\nfrom diffusers import DPMSolverMultistepScheduler\npipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)\n\n# Reduce steps\nimage = pipe(prompt, num_inference_steps=20).images[0]\n```\n\n## References\n\n- **[Advanced Usage](references\u002Fadvanced-usage.md)** - Custom pipelines, fine-tuning, deployment\n- **[Troubleshooting](references\u002Ftroubleshooting.md)** - Common issues and solutions\n\n## Resources\n\n- **Documentation**: https:\u002F\u002Fhuggingface.co\u002Fdocs\u002Fdiffusers\n- **Repository**: https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Fdiffusers\n- **Model Hub**: https:\u002F\u002Fhuggingface.co\u002Fmodels?library=diffusers\n- **Discord**: https:\u002F\u002Fdiscord.gg\u002Fdiffusers\n\n## ? Связи\n\n- [[000 - Map of Maps]] — REPO_DOCS\n- [[000 - Map of Maps]] — Map of Maps\n\n","# Stable Diffusion Image Generation\n\nComprehensive guide to generating images with Stable Diffusion using the HuggingFace Diffusers library.\n\n## When to use Stable Diffusion\n\n**Use Stable Diffusion when:**\n- Generating images from text descriptions\n- Performing image-to-image translation (style transfer, enhancement)\n- Inpainting (filling in masked regions)\n- Outpainting (extending images beyond boundaries)\n- Creating variations of existing images\n- Building custom image generation workflows\n\n**Key features:**\n- **Text-to-Image**: Generate images from natural language prompts\n- **Image-to-Image**: Transform existing images with text guidance\n- **Inpainting**: Fill masked regions with context-aware content\n- **ControlNet**: Add spatial conditioning (edges, poses, depth)\n- **LoRA Support**: Efficient fine-tuning and style adaptation\n- **Multiple Models**: SD 1.5, SDXL, SD 3.0, Flux support\n\n**Use alternatives instead:**\n- **DALL-E 3**: For API-based generation without GPU\n- **Midjourney**: For artistic, stylized outputs\n- **Imagen**: For Google Cloud integration\n- **Leonardo.ai**: For web-based creative workflows\n\n## Quick start\n\n### Installation\n\n```bash\npip install diffusers transformers accelerate torch\npip install xformers  # Optional: memory-efficient attention\n```\n\n### Basic text-to-image\n\n```python\nfrom diffusers import DiffusionPipeline\nimport torch\n\n# Load pipeline (auto-detects model type)\npipe = DiffusionPipeline.from_pretrained(\n    \"stable-diffusion-v1-5\u002Fstable-diffusion-v1-5\",\n    torch_dtype=torch.float16\n)\npipe.to(\"cuda\")\n\n# Generate image\nimage = pipe(\n    \"A serene mountain landscape at sunset, highly detailed\",\n    num_inference_steps=50,\n    guidance_scale=7.5\n).images[0]\n\nimage.save(\"output.png\")\n```\n\n### Using SDXL (higher quality)\n\n```python\nfrom diffusers import AutoPipelineForText2Image\nimport torch\n\npipe = AutoPipelineForText2Image.from_pretrained(\n    \"stabilityai\u002Fstable-diffusion-xl-base-1.0\",\n    torch_dtype=torch.float16,\n    variant=\"fp16\"\n)\npipe.to(\"cuda\")\n\n# Enable memory optimization\npipe.enable_model_cpu_offload()\n\nimage = pipe(\n    prompt=\"A futuristic city with flying cars, cinematic lighting\",\n    height=1024,\n    width=1024,\n    num_inference_steps=30\n).images[0]\n```\n\n## Architecture overview\n\n### Three-pillar design\n\nDiffusers is built around three core components:\n\n```\nPipeline (orchestration)\n├── Model (neural networks)\n│   ├── UNet \u002F Transformer (noise prediction)\n│   ├── VAE (latent encoding\u002Fdecoding)\n│   └── Text Encoder (CLIP\u002FT5)\n└── Scheduler (denoising algorithm)\n```\n\n### Pipeline inference flow\n\n```\nText Prompt → Text Encoder → Text Embeddings\n                                    ↓\nRandom Noise → [Denoising Loop] ← Scheduler\n                      ↓\n               Predicted Noise\n                      ↓\n              VAE Decoder → Final Image\n```\n\n## Core concepts\n\n### Pipelines\n\nPipelines orchestrate complete workflows:\n\n| Pipeline | Purpose |\n|----------|---------|\n| `StableDiffusionPipeline` | Text-to-image (SD 1.x\u002F2.x) |\n| `StableDiffusionXLPipeline` | Text-to-image (SDXL) |\n| `StableDiffusion3Pipeline` | Text-to-image (SD 3.0) |\n| `FluxPipeline` | Text-to-image (Flux models) |\n| `StableDiffusionImg2ImgPipeline` | Image-to-image |\n| `StableDiffusionInpaintPipeline` | Inpainting |\n\n### Schedulers\n\nSchedulers control the denoising process:\n\n| Scheduler | Steps | Quality | Use Case |\n|-----------|-------|---------|----------|\n| `EulerDiscreteScheduler` | 20-50 | Good | Default choice |\n| `EulerAncestralDiscreteScheduler` | 20-50 | Good | More variation |\n| `DPMSolverMultistepScheduler` | 15-25 | Excellent | Fast, high quality |\n| `DDIMScheduler` | 50-100 | Good | Deterministic |\n| `LCMScheduler` | 4-8 | Good | Very fast |\n| `UniPCMultistepScheduler` | 15-25 | Excellent | Fast convergence |\n\n### Swapping schedulers\n\n```python\nfrom diffusers import DPMSolverMultistepScheduler\n\n# Swap for faster generation\npipe.scheduler = DPMSolverMultistepScheduler.from_config(\n    pipe.scheduler.config\n)\n\n# Now generate with fewer steps\nimage = pipe(prompt, num_inference_steps=20).images[0]\n```\n\n## Generation parameters\n\n### Key parameters\n\n| Parameter | Default | Description |\n|-----------|---------|-------------|\n| `prompt` | Required | Text description of desired image |\n| `negative_prompt` | None | What to avoid in the image |\n| `num_inference_steps` | 50 | Denoising steps (more = better quality) |\n| `guidance_scale` | 7.5 | Prompt adherence (7-12 typical) |\n| `height`, `width` | 512\u002F1024 | Output dimensions (multiples of 8) |\n| `generator` | None | Torch generator for reproducibility |\n| `num_images_per_prompt` | 1 | Batch size |\n\n### Reproducible generation\n\n```python\nimport torch\n\ngenerator = torch.Generator(device=\"cuda\").manual_seed(42)\n\nimage = pipe(\n    prompt=\"A cat wearing a top hat\",\n    generator=generator,\n    num_inference_steps=50\n).images[0]\n```\n\n### Negative prompts\n\n```python\nimage = pipe(\n    prompt=\"Professional photo of a dog in a garden\",\n    negative_prompt=\"blurry, low quality, distorted, ugly, bad anatomy\",\n    guidance_scale=7.5\n).images[0]\n```\n\n## Image-to-image\n\nTransform existing images with text guidance:\n\n```python\nfrom diffusers import AutoPipelineForImage2Image\nfrom PIL import Image\n\npipe = AutoPipelineForImage2Image.from_pretrained(\n    \"stable-diffusion-v1-5\u002Fstable-diffusion-v1-5\",\n    torch_dtype=torch.float16\n).to(\"cuda\")\n\ninit_image = Image.open(\"input.jpg\").resize((512, 512))\n\nimage = pipe(\n    prompt=\"A watercolor painting of the scene\",\n    image=init_image,\n    strength=0.75,  # How much to transform (0-1)\n    num_inference_steps=50\n).images[0]\n```\n\n## Inpainting\n\nFill masked regions:\n\n```python\nfrom diffusers import AutoPipelineForInpainting\nfrom PIL import Image\n\npipe = AutoPipelineForInpainting.from_pretrained(\n    \"runwayml\u002Fstable-diffusion-inpainting\",\n    torch_dtype=torch.float16\n).to(\"cuda\")\n\nimage = Image.open(\"photo.jpg\")\nmask = Image.open(\"mask.png\")  # White = inpaint region\n\nresult = pipe(\n    prompt=\"A red car parked on the street\",\n    image=image,\n    mask_image=mask,\n    num_inference_steps=50\n).images[0]\n```\n\n## ControlNet\n\nAdd spatial conditioning for precise control:\n\n```python\nfrom diffusers import StableDiffusionControlNetPipeline, ControlNetModel\nimport torch\n\n# Load ControlNet for edge conditioning\ncontrolnet = ControlNetModel.from_pretrained(\n    \"lllyasviel\u002Fcontrol_v11p_sd15_canny\",\n    torch_dtype=torch.float16\n)\n\npipe = StableDiffusionControlNetPipeline.from_pretrained(\n    \"stable-diffusion-v1-5\u002Fstable-diffusion-v1-5\",\n    controlnet=controlnet,\n    torch_dtype=torch.float16\n).to(\"cuda\")\n\n# Use Canny edge image as control\ncontrol_image = get_canny_image(input_image)\n\nimage = pipe(\n    prompt=\"A beautiful house in the style of Van Gogh\",\n    image=control_image,\n    num_inference_steps=30\n).images[0]\n```\n\n### Available ControlNets\n\n| ControlNet | Input Type | Use Case |\n|------------|------------|----------|\n| `canny` | Edge maps | Preserve structure |\n| `openpose` | Pose skeletons | Human poses |\n| `depth` | Depth maps | 3D-aware generation |\n| `normal` | Normal maps | Surface details |\n| `mlsd` | Line segments | Architectural lines |\n| `scribble` | Rough sketches | Sketch-to-image |\n\n## LoRA adapters\n\nLoad fine-tuned style adapters:\n\n```python\nfrom diffusers import DiffusionPipeline\n\npipe = DiffusionPipeline.from_pretrained(\n    \"stable-diffusion-v1-5\u002Fstable-diffusion-v1-5\",\n    torch_dtype=torch.float16\n).to(\"cuda\")\n\n# Load LoRA weights\npipe.load_lora_weights(\"path\u002Fto\u002Flora\", weight_name=\"style.safetensors\")\n\n# Generate with LoRA style\nimage = pipe(\"A portrait in the trained style\").images[0]\n\n# Adjust LoRA strength\npipe.fuse_lora(lora_scale=0.8)\n\n# Unload LoRA\npipe.unload_lora_weights()\n```\n\n### Multiple LoRAs\n\n```python\n# Load multiple LoRAs\npipe.load_lora_weights(\"lora1\", adapter_name=\"style\")\npipe.load_lora_weights(\"lora2\", adapter_name=\"character\")\n\n# Set weights for each\npipe.set_adapters([\"style\", \"character\"], adapter_weights=[0.7, 0.5])\n\nimage = pipe(\"A portrait\").images[0]\n```\n\n## Memory optimization\n\n### Enable CPU offloading\n\n```python\n# Model CPU offload - moves models to CPU when not in use\npipe.enable_model_cpu_offload()\n\n# Sequential CPU offload - more aggressive, slower\npipe.enable_sequential_cpu_offload()\n```\n\n### Attention slicing\n\n```python\n# Reduce memory by computing attention in chunks\npipe.enable_attention_slicing()\n\n# Or specific chunk size\npipe.enable_attention_slicing(\"max\")\n```\n\n### xFormers memory-efficient attention\n\n```python\n# Requires xformers package\npipe.enable_xformers_memory_efficient_attention()\n```\n\n### VAE slicing for large images\n\n```python\n# Decode latents in tiles for large images\npipe.enable_vae_slicing()\npipe.enable_vae_tiling()\n```\n\n## Model variants\n\n### Loading different precisions\n\n```python\n# FP16 (recommended for GPU)\npipe = DiffusionPipeline.from_pretrained(\n    \"model-id\",\n    torch_dtype=torch.float16,\n    variant=\"fp16\"\n)\n\n# BF16 (better precision, requires Ampere+ GPU)\npipe = DiffusionPipeline.from_pretrained(\n    \"model-id\",\n    torch_dtype=torch.bfloat16\n)\n```\n\n### Loading specific components\n\n```python\nfrom diffusers import UNet2DConditionModel, AutoencoderKL\n\n# Load custom VAE\nvae = AutoencoderKL.from_pretrained(\"stabilityai\u002Fsd-vae-ft-mse\")\n\n# Use with pipeline\npipe = DiffusionPipeline.from_pretrained(\n    \"stable-diffusion-v1-5\u002Fstable-diffusion-v1-5\",\n    vae=vae,\n    torch_dtype=torch.float16\n)\n```\n\n## Batch generation\n\nGenerate multiple images efficiently:\n\n```python\n# Multiple prompts\nprompts = [\n    \"A cat playing piano\",\n    \"A dog reading a book\",\n    \"A bird painting a picture\"\n]\n\nimages = pipe(prompts, num_inference_steps=30).images\n\n# Multiple images per prompt\nimages = pipe(\n    \"A beautiful sunset\",\n    num_images_per_prompt=4,\n    num_inference_steps=30\n).images\n```\n\n## Common workflows\n\n### Workflow 1: High-quality generation\n\n```python\nfrom diffusers import StableDiffusionXLPipeline, DPMSolverMultistepScheduler\nimport torch\n\n# 1. Load SDXL with optimizations\npipe = StableDiffusionXLPipeline.from_pretrained(\n    \"stabilityai\u002Fstable-diffusion-xl-base-1.0\",\n    torch_dtype=torch.float16,\n    variant=\"fp16\"\n)\npipe.to(\"cuda\")\npipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)\npipe.enable_model_cpu_offload()\n\n# 2. Generate with quality settings\nimage = pipe(\n    prompt=\"A majestic lion in the savanna, golden hour lighting, 8k, detailed fur\",\n    negative_prompt=\"blurry, low quality, cartoon, anime, sketch\",\n    num_inference_steps=30,\n    guidance_scale=7.5,\n    height=1024,\n    width=1024\n).images[0]\n```\n\n### Workflow 2: Fast prototyping\n\n```python\nfrom diffusers import AutoPipelineForText2Image, LCMScheduler\nimport torch\n\n# Use LCM for 4-8 step generation\npipe = AutoPipelineForText2Image.from_pretrained(\n    \"stabilityai\u002Fstable-diffusion-xl-base-1.0\",\n    torch_dtype=torch.float16\n).to(\"cuda\")\n\n# Load LCM LoRA for fast generation\npipe.load_lora_weights(\"latent-consistency\u002Flcm-lora-sdxl\")\npipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)\npipe.fuse_lora()\n\n# Generate in ~1 second\nimage = pipe(\n    \"A beautiful landscape\",\n    num_inference_steps=4,\n    guidance_scale=1.0\n).images[0]\n```\n\n## Common issues\n\n**CUDA out of memory:**\n```python\n# Enable memory optimizations\npipe.enable_model_cpu_offload()\npipe.enable_attention_slicing()\npipe.enable_vae_slicing()\n\n# Or use lower precision\npipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)\n```\n\n**Black\u002Fnoise images:**\n```python\n# Check VAE configuration\n# Use safety checker bypass if needed\npipe.safety_checker = None\n\n# Ensure proper dtype consistency\npipe = pipe.to(dtype=torch.float16)\n```\n\n**Slow generation:**\n```python\n# Use faster scheduler\nfrom diffusers import DPMSolverMultistepScheduler\npipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)\n\n# Reduce steps\nimage = pipe(prompt, num_inference_steps=20).images[0]\n```\n\n## References\n\n- **[Advanced Usage](references\u002Fadvanced-usage.md)** - Custom pipelines, fine-tuning, deployment\n- **[Troubleshooting](references\u002Ftroubleshooting.md)** - Common issues and solutions\n\n## Resources\n\n- **Documentation**: https:\u002F\u002Fhuggingface.co\u002Fdocs\u002Fdiffusers\n- **Repository**: https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Fdiffusers\n- **Model Hub**: https:\u002F\u002Fhuggingface.co\u002Fmodels?library=diffusers\n- **Discord**: https:\u002F\u002Fdiscord.gg\u002Fdiffusers\n\n## ? Связи\n\n- [[000 - Map of Maps]] — REPO_DOCS\n- [[000 - Map of Maps]] — Map of Maps\n\n",{"name":5,"author":49,"license":15,"version":50,"metadata":51,"description":7,"dependencies":58},"Orchestra Research","1.0.0",{"hermes":52},{"tags":53},[24,25,54,55,56,57],"Diffusers","Text-to-Image","Multimodal","Computer Vision",[59,60,61,62],"diffusers>=0.30.0","transformers>=4.41.0","accelerate>=0.31.0","torch>=2.0.0","main","bdd9f958d7bb1a0f19b8ad8da020b0bab0f53eb1","vc-kit\u002Fcontent\u002FCOMBINED\u002FCOMBINED\u002FREPO_DOCS\u002F02-hermes-agent\u002Fskills\u002Fmlops\u002Fmodels\u002Fstable-diffusion",1,"https:\u002F\u002Fgithub.com\u002Fibragimov-oasis\u002Fvibe-coder","https:\u002F\u002Fgithub.com\u002Fibragimov-oasis","2026-06-24 16:31:18",{"source":8,"skillName":5,"commands":71,"prompt":81,"zipAvailable":82},[72,75,78],{"tool":73,"command":74},"npx","npx skills add ibragimov-oasis\u002Fvibe-coder --skill stable-diffusion-image-generation",{"tool":76,"command":77},"bunx","bunx skills add ibragimov-oasis\u002Fvibe-coder --skill stable-diffusion-image-generation",{"tool":79,"command":80},"pnpm","pnpm dlx skills add ibragimov-oasis\u002Fvibe-coder --skill stable-diffusion-image-generation","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fibragimov-oasis\u002Fvibe-coder\u002Fblob\u002Fmain\u002Fvc-kit\u002Fcontent\u002FCOMBINED\u002FCOMBINED\u002FREPO_DOCS\u002F02-hermes-agent\u002Fskills\u002Fmlops\u002Fmodels\u002Fstable-diffusion\u002FSKILL.md",true,[],[85,120,147,167,192,222],{"skillNo":86,"name":87,"nameZh":6,"description":88,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":89,"categories":90,"keywords":99,"publishedAt":44},"01M6Q4Q22KKKVMJJYA411DQ4ER","ckm:ui-styling","Create beautiful, accessible user interfaces with shadcn\u002Fui components (built on Radix UI + Tailwind), Tailwind CSS utility-first styling, and canvas-based visual designs. Use when building user interfaces, implementing design systems, creating responsive layouts, adding accessible components (dialogs, dropdowns, forms, tables), customizing themes and colors, implementing dark mode, generating visual designs and posters, or establishing consistent styling patterns across applications.","https:\u002F\u002Fgithub.com\u002Fibragimov-oasis\u002Fvibe-coder\u002Fblob\u002Fmain\u002Fvc-kit\u002Fcontent\u002FCOMBINED\u002FCOMBINED\u002Fui-design\u002Fui-rules\u002Fui-ux-pro-max\u002Fclaude\u002Fskills\u002Fui-styling\u002FSKILL.md",[91,94],{"categoryNo":92,"categoryName":93,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000015","视觉设计",{"categoryNo":95,"categoryName":96,"taxonomyType":97,"taxonomyTypeDesc":98},"01JBSEEDCAT000000000000032","设计师","PROFESSION","适用职业",[100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119],"ui styling","ui","styling","ckm:ui styling","ckm","design","css","canvas","components","component","create","design systems","Tailwind","across","themes","utility","mode","implementing","artifact\u002Fui-component","source\u002Fui-design",{"skillNo":121,"name":122,"nameZh":6,"description":123,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":124,"categories":125,"keywords":134,"publishedAt":44},"01M6Q4PQ2NX4NDVWTEHA3MQ2R0","ckm:design","Comprehensive design skill: brand identity, design tokens, UI styling, logo generation (55 styles, Gemini AI), corporate identity program (50 deliverables, CIP mockups), HTML presentations (Chart.js), banner design (22 styles, social\u002Fads\u002Fweb\u002Fprint), icon design (15 styles, SVG, Gemini 3.1 Pro), social photos (HTML→screenshot, multi-platform). Actions: design logo, create CIP, generate mockups, build slides, design banner, generate icon, create social photos, social media images, brand identity, design system. Platforms: Facebook, Twitter, LinkedIn, YouTube, Instagram, Pinterest, TikTok, Threads, Google Ads.","https:\u002F\u002Fgithub.com\u002Fibragimov-oasis\u002Fvibe-coder\u002Fblob\u002Fmain\u002Fvc-kit\u002Fcontent\u002FCOMBINED\u002FCOMBINED\u002Fui-design\u002Fui-rules\u002Fui-ux-pro-max\u002Fclaude\u002Fskills\u002Fdesign\u002FSKILL.md",[126,129,130,133],{"categoryNo":127,"categoryName":128,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000003","演示文稿",{"categoryNo":92,"categoryName":93,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":131,"categoryName":132,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":95,"categoryName":96,"taxonomyType":97,"taxonomyTypeDesc":98},[105,122,104,135,109,100,136,110,137,138,139,140,141,142,143,144,145,146,118,119],"google","presentations","generate","screenshot","actions","Media","logo","ads","youtube","tiktok","artifact","chart",{"skillNo":148,"name":149,"nameZh":6,"description":150,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":151,"categories":152,"keywords":155,"publishedAt":44},"01M6Q4PJE27GEA3M0CECEBCZKS","ckm:design-system","Token architecture, component specifications, and slide generation. Three-layer tokens (primitive→semantic→component), CSS variables, spacing\u002Ftypography scales, component specs, strategic slide creation. Use for design tokens, systematic design, brand-compliant presentations.","https:\u002F\u002Fgithub.com\u002Fibragimov-oasis\u002Fvibe-coder\u002Fblob\u002Fmain\u002Fvc-kit\u002Fcontent\u002FCOMBINED\u002FCOMBINED\u002Fui-design\u002Fui-rules\u002Fui-ux-pro-max\u002Fclaude\u002Fskills\u002Fdesign-system\u002FSKILL.md",[153,154],{"categoryNo":92,"categoryName":93,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":95,"categoryName":96,"taxonomyType":97,"taxonomyTypeDesc":98},[105,156,157,158,122,104,106,109,159,136,145,160,161,162,163,164,165,166,118,119],"design system","system","ckm:design system","architecture","token","creation","systematic","css variables","Variables","spacing","layer",{"skillNo":168,"name":169,"nameZh":6,"description":170,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":171,"categories":172,"keywords":178,"publishedAt":44},"01M6Q4P90JXFYMH2S634QM6SQJ","ckm:banner-design","Design banners for social media, ads, website heroes, creative assets, and print. Multiple art direction options with AI-generated visuals. Actions: design, create, generate banner. Platforms: Facebook, Twitter\u002FX, LinkedIn, YouTube, Instagram, Google Display, website hero, print. Styles: minimalist, gradient, bold typography, photo-based, illustrated, geometric, retro, glassmorphism, 3D, neon, duotone, editorial, collage. Uses ui-ux-pro-max, frontend-design, ai-artist, ai-multimodal skills.","https:\u002F\u002Fgithub.com\u002Fibragimov-oasis\u002Fvibe-coder\u002Fblob\u002Fmain\u002Fvc-kit\u002Fcontent\u002FCOMBINED\u002FCOMBINED\u002Fui-design\u002Fui-rules\u002Fui-ux-pro-max\u002Fclaude\u002Fskills\u002Fbanner-design\u002FSKILL.md",[173,176,177],{"categoryNo":174,"categoryName":175,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",{"categoryNo":92,"categoryName":93,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":95,"categoryName":96,"taxonomyType":97,"taxonomyTypeDesc":98},[105,179,180,181,104,182,139,140,183,184,36,185,186,187,188,189,118,119,190,191],"banner design","ckm:banner design","banner","frontend","3D","generated","social","dimensions","neon","direction","display","frontend-design","hero",{"skillNo":193,"name":194,"nameZh":6,"description":195,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":196,"categories":197,"keywords":204,"publishedAt":44},"01M6Q2RZXBJ9TBE413GXCYQNDB","seo","Comprehensive SEO analysis for any website or business type. Performs full site audits, single-page deep analysis, technical SEO checks (crawlability, indexability, Core Web Vitals with INP), schema markup detection\u002Fvalidation\u002Fgeneration, content quality assessment (E-E-A-T framework per Dec 2025 update extending to all competitive queries), image optimization, sitemap analysis, and Generative Engine Optimization (GEO) for AI Overviews, ChatGPT, and Perplexity citations. Analyzes AI crawler accessibility (GPTBot, ClaudeBot, PerplexityBot), llms.txt compliance, brand mention signals, and passage-level citability. Industry detection for SaaS, e-commerce, local business, publishers, agencies. Triggers on: \"SEO\", \"audit\", \"schema\", \"Core Web Vitals\", \"sitemap\", \"E-E-A-T\", \"AI Overviews\", \"GEO\", \"technical SEO\", \"content quality\", \"page speed\", \"structured data\".","https:\u002F\u002Fgithub.com\u002Fibragimov-oasis\u002Fvibe-coder\u002Fblob\u002Fmain\u002Fvc-kit\u002Fcontent\u002FCOMBINED\u002FCOMBINED\u002Fskills\u002Fskills-seo\u002Fseo\u002Fclaude-seo\u002Fskills\u002Fseo\u002FSKILL.md",[198,201],{"categoryNo":199,"categoryName":200,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000019","营销推广",{"categoryNo":202,"categoryName":203,"taxonomyType":97,"taxonomyTypeDesc":98},"01JBSEEDCAT000000000000035","市场营销",[194,28,205,206,207,208,209,145,210,211,212,213,214,215,216,217,218,219,220,221],"llms","assessment","deep analysis","Citations","analysis","local","deep","crawlability","seo analysis","2025","commerce","URL","domain\u002Fseo","source\u002Fskills-seo","vitals","generative","publishers",{"skillNo":223,"name":224,"nameZh":6,"description":225,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":226,"categories":227,"keywords":230,"publishedAt":44},"01M6Q2RVE7VGFCFQKBK468NHVY","seo-technical","Technical SEO audit across 9 categories: crawlability, indexability, security, URL structure, mobile, Core Web Vitals, structured data, JavaScript rendering, and IndexNow protocol. Use when user says \"technical SEO\", \"crawl issues\", \"robots.txt\", \"Core Web Vitals\", \"site speed\", or \"security headers\".","https:\u002F\u002Fgithub.com\u002Fibragimov-oasis\u002Fvibe-coder\u002Fblob\u002Fmain\u002Fvc-kit\u002Fcontent\u002FCOMBINED\u002FCOMBINED\u002Fskills\u002Fskills-seo\u002Fseo\u002Fclaude-seo\u002Fskills\u002Fseo-technical\u002FSKILL.md",[228,229],{"categoryNo":199,"categoryName":200,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":202,"categoryName":203,"taxonomyType":97,"taxonomyTypeDesc":98},[194,231,232,233,234,235,236,237,238,145,113,212,239,240,216,241,242,217,218,219],"technical","seo technical","issues","audit","data","mobile","crawl","structure","rendering","technical seo audit","headers","categories"]