FireRed Image Edit 1.0 looks like a Qwen 2511 derivative with a nudge, and the author's testing lands exactly there: not a class above 2511, but more reliable on single-image edits, and worse once you feed it multiple images. This episode runs five edit tasks across two graph variants to get to that answer.

Evidence note: this episode ships three workflow JSON files (28, 29 and 28 nodes). All graph values below are read from them; the comparison results are the author's on-camera tests, reported as such.

When this is useful

  • You edit one image at a time and need the untouched regions to stay untouched.
  • You rely on an 8-step Lightning accelerator and want to know whether an edit model still holds up with it.
  • You are deciding between FireRed and 2511 for a multi-image task and would rather not find out the hard way.

The two graphs, and when each one applies

  1. Anti-drift (防偏移), with a reference latent. This is the safe variant. When you edit one region, the reference latent keeps the rest of the frame stable. Use it whenever "do not touch anything else" is the requirement.
  2. Divergent (发散), looser. This allows larger changes. The trade-off is explicit in the recording: face similarity drops.
  3. Dual-image (双图). A third graph for two input images. The author tested it and recommends sticking with 2511 for this case instead.

The three graphs are structurally close to the 2511 counterparts. In the author's words, you mostly swap the backbone.

What the graphs actually load

  • Backbone: FireRed-Image-Edit-1.0_fp8_e4m3fn.safetensors
  • Text encoder: qwen_2.5_vl_7b_fp8_scaled.safetensors, CLIPLoader type qwen_image
  • VAE: qwen_image_vae.safetensors
  • Accelerator: Qwen-Image-Lightning-8steps-V2.0 bf16.safetensors, strength 1.0 on the two single-image graphs and 0.66 on the dual-image graph
  • Sampler: 8 steps, CFG 1, euler, simple, denoise 1
  • Empty latent: 928x1664
  • Front-end scale: ImageScaleToTotalPixels at 1.0; two intermediate scalers at 3.0 are bypassed

That 8-step Lightning pairing is the practical core of the release: the model was not retrained for speed, the accelerator was bolted on, and the author says a 4-step accelerator made no visible difference on the anti-drift graph.

The five single-image tests

Test Task Result reported
1 Place a person into a city scene FireRed integrated the subject with the scene lighting better; the 2511 result looked pasted
2 Night scene to dusk FireRed again
3 Hand holding a cup FireRed, with more natural hands
4 Remove a woman from the frame FireRed clean; the 2511 result showed grid-like artifacts on the floor
5 Change a garment's colour FireRed, with higher saturation and a stronger response to the prompt
Bonus Glass cup to ceramic cup FireRed, with a more convincing glossy surface

The author's read on why: FireRed seems to accept more of the prompt and act on it, which reads as a small win within single-image editing rather than a different tier of model.

Then the counter-example: for a four-way multi-image comparison the best result came from 2511, which is why he recommends 2511 there and says FireRed shows no clear advantage.

Step by step

  1. Pick the graph by intent: anti-drift for surgical edits, divergent for larger changes, dual-image only if you must.
  2. Load the source image. If you are doing a targeted edit, mask the region first.
  3. Keep the front-end scaler at 1.0; the 3.0 scalers are bypassed in the shipped files and are not needed for these tests.
  4. Leave the Lightning LoRA at 1.0 (0.66 on the dual-image graph) and the sampler at 8 steps / CFG 1 / euler / simple.
  5. Write a short, direct instruction. The author notes his own prompts were very simple, and that FireRed still responded well.
  6. Run the anti-drift graph first when fidelity to the original matters, then re-run in the divergent graph only if the edit is too timid.
  7. For multi-image input, use the divergent graph and expect less face similarity; the author still prefers 2511 there.

Parameter basis

Read from the three JSON files in 工作流及封面(视频184):

Node Anti-drift Divergent Dual-image
Nodes 29 28 28
UNETLoader FireRed fp8_e4m3fn same same
LoraLoaderModelOnly Lightning 8-steps, 1.0 1.0 0.66
KSampler 8 / 1 / euler / simple same same
EmptySD3LatentImage 928x1664 928x1664 928x1664
ImageScaleToTotalPixels 1.0 active, 3.0 x2 bypassed same 1.0 x2 active, one 3.0 bypassed

Limits

  • Not a new tier. The author says outright that FireRed is close to 2511 and that scores do not separate them much.
  • Multi-image is the weak spot. Four-image comparison went to 2511.
  • Accelerator risk. Every test used the Lightning accelerator, and the author flags that acceleration may itself introduce instability in results.
  • Single-image only. All five comparisons are single-image edits; nothing here validates batch or sequential edits.
  • Visual judgement. Five tasks, no metric, and the author reads them himself.
  • Implementation caveat. He notes the multi-image gap might be a ComfyUI implementation detail rather than a model limitation.

Episode resources