Diffusion models learn to remove noise from images step by step. During training, clean images are progressively noised. The model learns the reverse process: starting from noise and denoising toward a coherent image.

At inference, a sampler applies the learned denoising in discrete steps, guided by your prompt and any conditioning (e.g., pose, sketches, references). Fewer steps are faster but may miss detail; more steps add fidelity but increase time.

Core mechanics:

• Noise schedule: defines how much noise is removed per step; schedules pair with samplers for stability.
• Samplers: algorithms that traverse the noise-to-image path (e.g., DPM++ 2M Karras, DDIM, Euler a). Some are deterministic; others are stochastic.
• Classifier-free guidance (CFG): blends guided vs. unguided predictions to follow the prompt. Higher CFG = stronger adherence, but can cause artifacts.
• Seed: initializes the noise tensor for reproducibility.
• Latent diffusion: many modern models denoise in latent space for speed and memory efficiency.

• Stable Diffusion 1.x: fast, lightweight, huge community ecosystem; good for stylized/anime with specialized checkpoints.
• Stable Diffusion 2.x: improved depth/photography in some cases; different VAE/NSFW filters; fewer community styles.
• SDXL (1.0+): higher fidelity, better composition and detail; heavier VRAM requirements; strong general-purpose quality.
• SD3 (announced 2024): larger text understanding and improved generation; check license/runtime specifics.

Pick the smallest model that meets your visual bar; use SDXL/SD3 for higher realism or complex scenes, and community anime-focused checkpoints for stylized work.

• Steps: 20–35 is a good starting band. Increase for intricate scenes; decrease for speed.
• Sampler: DPM++ 2M Karras is a robust default. Try Euler a for stylized/anime or fast previews; DDIM for smooth transitions in img2img.
• CFG scale: 4.5–8.0 typical. Too low = off-prompt; too high = overbaked, oversaturated, or crunchy details.
• Resolution: Generate near final aspect ratio. SDXL often shines at ~832–1024 on the long edge; upscale after.
• Seed: Fix for reproducibility; vary for exploration.
• Denoising strength (img2img/inpaint): 0.35–0.65 for edits; higher values drift more from the source.

• ControlNet: structural control (pose, depth, lineart, soft edge). Great for consistency, pose-matching, and panels.
• LoRA: lightweight adapters to add styles, characters, or skills without swapping the base model.
• IP-Adapter / T2I-Adapter: style or subject guidance from reference images.
• Textual Inversion/Embeddings: custom tokens for niche styles or subjects.

Combine lightly: one or two controls often beat stacking many. Balance CFG and steps when adding strong controls.

• Text-to-Image: craft prompt + negative prompt, pick sampler/steps/CFG, generate multiple seeds, curate.
• Image-to-Image: start with a rough sketch, 3D render, or photo; set denoise 0.4–0.6; preserve composition while changing style.
• Inpainting: mask regions to fix faces, hands, or add objects; use slightly more steps in masked areas.
• Outpainting: extend canvases for full scenes or comic panels; guide with ControlNet lineart or depth.

• Use anime-trained checkpoints or LoRAs for clean linework and flat shading.
• Sampler: Euler a or DPM++ SDE Karras often yields crisp lines.
• Control: Lineart/SoftEdge ControlNet to align outlines; IP-Adapter for style reference.
• Prompts: emphasize line quality, cel shading, color palette, character design tags.
• Post: light denoise, color-grade, optional halftone for print.

• Structure first: rough panel layout → generate panels → refine per-panel with consistent prompts and seeds.
• Consistency: character LoRA + reference images; pose via ControlNet OpenPose or Depth.
• Inking: generate clean lineart, then flat colors; try lineart-specific ControlNet.
• Text/lettering: add in vector tools for clarity; diffusion struggles with text fidelity.

• Steps vs. resolution: prefer moderate steps and correct aspect ratio, then upscale.
• Batch strategy: generate small previews (lower steps), pick winners, upscale/refine selectively.
• Precision/VRAM: use half-precision where supported; enable memory optimizations.
• Sampler tuning: switch to faster samplers for ideation; slower for finals.

• Hands/anatomy: add pose/depth ControlNet; reduce CFG; use targeted LoRA; inpaint hands separately.
• Mushy details: increase steps slightly; try DPM++ 2M Karras; lower denoise in img2img; sharpen post.
• Overbaked contrast/saturation: lower CFG; try a non-ancestral sampler; soften negative prompts.
• Off-prompt drift: raise CFG slightly; increase steps; simplify prompt; ensure correct base model.
• Text on image: generate blank sign/area and add text in a design tool.

Anime character close-up:

• Prompt: "(anime, cel shading), clean lineart, expressive eyes, medium shot, soft rim light, detailed hair, [color palette]"
• Negative: "blurry, extra fingers, lowres, artifacts, text, watermark"

Comic panel action:

• Prompt: "comic ink style, dynamic pose, motion lines, dramatic lighting, high contrast, halftone shading, [setting], [character tokens]"
• Negative: "muddy shading, low detail, incorrect anatomy, text"

Stylized portrait (SDXL):

• Prompt: "high-detail portrait, cinematic lighting, shallow depth of field, intricate textures, [style reference]"
• Negative: "overexposed, harsh contrast, extra limbs, artifacts"