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Improve FLUX image-to-image (Trajectory Guidance) (#6900)
## Summary
This PR makes some improvements to the FLUX image-to-image and
inpainting behaviours.
Changes:
- Expand inpainting region at a cutoff timestep. This improves seam
coherence around inpainting regions.
- Add Trajectory Guidance to improve the ability to control how much an
image gets modified during image-to-image/inpainting (see the code for a
more technical explanation - it's well-documented).
## `trajectory_guidance_strength` Usage
- The `trajectory_guidance_strength` param has been added to the `FLUX
Denoise` invocation.
- `trajectory_guidance_strength` defaults to `0.0` and should be in the
range [0, 1].
- `trajectory_guidance_strength = 0.0` has no effect on the denoising
process.
- `trajectory_guidance_strength = 1.0` will guide strongly towards the
original image.
## FLUX image-to-image usage tips
- As always, prompt matters a lot.
- If you are trying to making minor perturbations to an image, use
vanilla image-to-image by setting the `denoising_start` param.
- If you are trying to make significant changes to an image, using
trajectory guidance will give more control than using vanilla
image-to-image. Set `denoising_start=0.0` and adjust
`trajectory_guidance_strength` to control the amount of change in the
image.
- The 'transition point' where the image changes the most as you adjust
`trajectory_guidance_strength` or `denoise_start` varies depending on
the noise. So, set a fixed noise seed, then tune those params.
## QA Instructions
- [x] Vanilla image-to-image - No change in output
- [x] Vanilla inpainting - No change in output
- [x] Vanilla outpainting - No change in output
- Trajectory Guidance image-to-image
- [x] TGS = 0.0 is identical to Vanilla case
- [x] TGS = 1.0 guides close to the original image
- Not as close as I'd like, but it's not broken.
- [x] Smooth transition as TGS varies
- [x] Smoke test: TGS with denoise_start > 0.0
- TG inpainting
- [x] TGS = 0.0 is identical to Vanilla case
- [x] TGS = 1.0 guides close to the original image
- Not as close as I'd like, but it's not broken
- [x] Smooth transition as TGS varies
- [x] Smoke test: TGS with denoise_start > 0.0
- TG outpainting
- [x] TGS = 0.0 is identical to Vanilla case
- [x] Smoke test TGS outpainting
- [x] Smoke test FLUX text-to-image
- [x] Preview images look ok for all of above.
## Known issues (will be addressed in follow-up PRs)
- The current TGS scale biases towards creating more change than desired
in the image. More tuning of the TG change schedule is required.
- TGS does not work very well for outpainting right now. This _might_ be
solvable, but more likely we'll just want to discourage it in the Linear
UI.
## Merge Plan
No special instructions.
## Checklist
- [x] _The PR has a short but descriptive title, suitable for a
changelog_
- [x] _Tests added / updated (if applicable)_
- [x] _Documentation added / updated (if applicable)_
This commit is contained in:
Ryan Dick
@@ -20,7 +20,6 @@ from invokeai.app.invocations.model import TransformerField
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from invokeai.app.invocations.primitives import LatentsOutput
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from invokeai.app.services.shared.invocation_context import InvocationContext
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from invokeai.backend.flux.denoise import denoise
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from invokeai.backend.flux.inpaint_extension import InpaintExtension
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from invokeai.backend.flux.model import Flux
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from invokeai.backend.flux.sampling_utils import (
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clip_timestep_schedule,
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@@ -30,6 +29,7 @@ from invokeai.backend.flux.sampling_utils import (
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pack,
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unpack,
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)
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from invokeai.backend.flux.trajectory_guidance_extension import TrajectoryGuidanceExtension
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from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
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from invokeai.backend.lora.lora_patcher import LoRAPatcher
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from invokeai.backend.model_manager.config import ModelFormat
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@@ -43,7 +43,7 @@ from invokeai.backend.util.devices import TorchDevice
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title="FLUX Denoise",
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tags=["image", "flux"],
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category="image",
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version="2.0.0",
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version="2.1.0",
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classification=Classification.Prototype,
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)
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class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
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@@ -68,6 +68,12 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
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description=FieldDescriptions.denoising_start,
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)
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denoising_end: float = InputField(default=1.0, ge=0, le=1, description=FieldDescriptions.denoising_end)
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trajectory_guidance_strength: float = InputField(
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default=0.0,
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ge=0.0,
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le=1.0,
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description="Value indicating how strongly to guide the denoising process towards the initial latents (during image-to-image). Range [0, 1]. A value of 0.0 is equivalent to vanilla image-to-image. A value of 1.0 will guide the denoising process very close to the original latents.",
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)
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transformer: TransformerField = InputField(
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description=FieldDescriptions.flux_model,
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input=Input.Connection,
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@@ -181,14 +187,13 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
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# Now that we have 'packed' the latent tensors, verify that we calculated the image_seq_len correctly.
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assert image_seq_len == x.shape[1]
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# Prepare inpaint extension.
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inpaint_extension: InpaintExtension | None = None
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if inpaint_mask is not None:
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assert init_latents is not None
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inpaint_extension = InpaintExtension(
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# Prepare trajectory guidance extension.
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traj_guidance_extension: TrajectoryGuidanceExtension | None = None
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if init_latents is not None:
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traj_guidance_extension = TrajectoryGuidanceExtension(
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init_latents=init_latents,
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inpaint_mask=inpaint_mask,
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noise=noise,
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trajectory_guidance_strength=self.trajectory_guidance_strength,
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)
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with (
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@@ -236,7 +241,7 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
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timesteps=timesteps,
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step_callback=self._build_step_callback(context),
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guidance=self.guidance,
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inpaint_extension=inpaint_extension,
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traj_guidance_extension=traj_guidance_extension,
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)
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x = unpack(x.float(), self.height, self.width)
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@@ -2,7 +2,7 @@
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"name": "FLUX Image to Image",
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"author": "InvokeAI",
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"description": "A simple image-to-image workflow using a FLUX dev model. ",
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"version": "1.0.4",
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"version": "1.1.0",
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"contact": "",
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"tags": "image2image, flux, image-to-image",
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"notes": "Prerequisite model downloads: T5 Encoder, CLIP-L Encoder, and FLUX VAE. Quantized and un-quantized versions can be found in the starter models tab within your Model Manager. We recommend using FLUX dev models for image-to-image workflows. The image-to-image performance with FLUX schnell models is poor.",
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@@ -23,17 +23,13 @@
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"nodeId": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
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"fieldName": "vae_model"
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},
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{
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"nodeId": "ace0258f-67d7-4eee-a218-6fff27065214",
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"fieldName": "denoising_start"
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},
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{
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"nodeId": "01f674f8-b3d1-4df1-acac-6cb8e0bfb63c",
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"fieldName": "prompt"
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},
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{
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"nodeId": "ace0258f-67d7-4eee-a218-6fff27065214",
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"fieldName": "num_steps"
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"nodeId": "2981a67c-480f-4237-9384-26b68dbf912b",
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"fieldName": "image"
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}
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],
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"meta": {
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@@ -42,48 +38,18 @@
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},
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"nodes": [
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{
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"id": "2981a67c-480f-4237-9384-26b68dbf912b",
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"id": "eebd7252-0bd8-401a-bb26-2b8bc64892fa",
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"type": "invocation",
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"data": {
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"id": "2981a67c-480f-4237-9384-26b68dbf912b",
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"type": "flux_vae_encode",
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"version": "1.0.0",
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"label": "",
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"notes": "",
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"isOpen": true,
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"isIntermediate": true,
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"useCache": true,
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"inputs": {
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"image": {
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"name": "image",
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"label": "",
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"value": {
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"image_name": "8a5c62aa-9335-45d2-9c71-89af9fc1f8d4.png"
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}
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},
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"vae": {
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"name": "vae",
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"label": ""
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}
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}
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},
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"position": {
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"x": 732.7680166609682,
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"y": -24.37398171806909
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}
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},
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{
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"id": "ace0258f-67d7-4eee-a218-6fff27065214",
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"type": "invocation",
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"data": {
|
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"id": "ace0258f-67d7-4eee-a218-6fff27065214",
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"id": "eebd7252-0bd8-401a-bb26-2b8bc64892fa",
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"type": "flux_denoise",
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"version": "1.0.0",
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"version": "2.1.0",
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"label": "",
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"notes": "",
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"isOpen": true,
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"isIntermediate": true,
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"useCache": true,
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"nodePack": "invokeai",
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"inputs": {
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"board": {
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"name": "board",
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@@ -111,6 +77,11 @@
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"label": "",
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"value": 1
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},
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"trajectory_guidance_strength": {
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"name": "trajectory_guidance_strength",
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"label": "",
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"value": 0.0
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},
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"transformer": {
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"name": "transformer",
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"label": ""
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@@ -131,7 +102,7 @@
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},
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"num_steps": {
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"name": "num_steps",
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"label": "Steps (Recommend 30 for Dev, 4 for Schnell)",
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"label": "",
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"value": 30
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},
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"guidance": {
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@@ -147,8 +118,36 @@
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}
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},
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"position": {
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"x": 1182.8836633018684,
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"y": -251.38882958913183
|
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"x": 1159.584057771928,
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"y": -175.90561201366845
|
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}
|
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},
|
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{
|
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"id": "2981a67c-480f-4237-9384-26b68dbf912b",
|
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"type": "invocation",
|
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"data": {
|
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"id": "2981a67c-480f-4237-9384-26b68dbf912b",
|
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"type": "flux_vae_encode",
|
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"version": "1.0.0",
|
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"label": "",
|
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"notes": "",
|
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"isOpen": true,
|
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"isIntermediate": true,
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"useCache": true,
|
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"inputs": {
|
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"image": {
|
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"name": "image",
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"label": ""
|
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},
|
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"vae": {
|
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"name": "vae",
|
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"label": ""
|
||||
}
|
||||
}
|
||||
},
|
||||
"position": {
|
||||
"x": 732.7680166609682,
|
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"y": -24.37398171806909
|
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}
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},
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{
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@@ -202,18 +201,32 @@
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"inputs": {
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"model": {
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"name": "model",
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"label": "Model (dev variant recommended for Image-to-Image)"
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"label": "Model (dev variant recommended for Image-to-Image)",
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"value": {
|
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"key": "b4990a6c-0899-48e9-969b-d6f3801acc6a",
|
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"hash": "random:aad8f7bc19ce76541dfb394b62a30f77722542b66e48064a9f25453263b45fba",
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"name": "FLUX Dev (Quantized)_2",
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"base": "flux",
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"type": "main"
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}
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},
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"t5_encoder_model": {
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"name": "t5_encoder_model",
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"label": ""
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"label": "",
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"value": {
|
||||
"key": "d18d5575-96b6-4da3-b3d8-eb58308d6705",
|
||||
"hash": "random:f2f9ed74acdfb4bf6fec200e780f6c25f8dd8764a35e65d425d606912fdf573a",
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"name": "t5_bnb_int8_quantized_encoder",
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"base": "any",
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"type": "t5_encoder"
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}
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},
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"clip_embed_model": {
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"name": "clip_embed_model",
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"label": "",
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"value": {
|
||||
"key": "fa23a584-b623-415d-832a-21b5098ff1a1",
|
||||
"hash": "blake3:17c19f0ef941c3b7609a9c94a659ca5364de0be364a91d4179f0e39ba17c3b70",
|
||||
"key": "5a19d7e5-8d98-43cd-8a81-87515e4b3b4e",
|
||||
"hash": "random:4bd08514c08fb6ff04088db9aeb45def3c488e8b5fd09a35f2cc4f2dc346f99f",
|
||||
"name": "clip-vit-large-patch14",
|
||||
"base": "any",
|
||||
"type": "clip_embed"
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@@ -223,8 +236,8 @@
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"name": "vae_model",
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||||
"label": "",
|
||||
"value": {
|
||||
"key": "74fc82ba-c0a8-479d-a890-2126f82da758",
|
||||
"hash": "blake3:ce21cb76364aa6e2421311cf4a4b5eb052a76c4f1cd207b50703d8978198a068",
|
||||
"key": "9172beab-5c1d-43f0-b2f0-6e0b956710d9",
|
||||
"hash": "random:c54dde288e5fa2e6137f1c92e9d611f598049e6f16e360207b6d96c9f5a67ba0",
|
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"name": "FLUX.1-schnell_ae",
|
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"base": "flux",
|
||||
"type": "vae"
|
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@@ -308,28 +321,60 @@
|
||||
],
|
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"edges": [
|
||||
{
|
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"id": "reactflow__edge-2981a67c-480f-4237-9384-26b68dbf912bheight-ace0258f-67d7-4eee-a218-6fff27065214height",
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"id": "reactflow__edge-eebd7252-0bd8-401a-bb26-2b8bc64892falatents-7e5172eb-48c1-44db-a770-8fd83e1435d1latents",
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"sourceHandle": "height",
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"targetHandle": "height"
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"source": "eebd7252-0bd8-401a-bb26-2b8bc64892fa",
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"target": "7e5172eb-48c1-44db-a770-8fd83e1435d1",
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"sourceHandle": "latents",
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"targetHandle": "latents"
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},
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{
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"id": "reactflow__edge-2981a67c-480f-4237-9384-26b68dbf912bwidth-ace0258f-67d7-4eee-a218-6fff27065214width",
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"id": "reactflow__edge-f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90transformer-eebd7252-0bd8-401a-bb26-2b8bc64892fatransformer",
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"type": "default",
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"target": "eebd7252-0bd8-401a-bb26-2b8bc64892fa",
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"sourceHandle": "transformer",
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"targetHandle": "transformer"
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},
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{
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"id": "reactflow__edge-01f674f8-b3d1-4df1-acac-6cb8e0bfb63cconditioning-eebd7252-0bd8-401a-bb26-2b8bc64892fapositive_text_conditioning",
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"target": "eebd7252-0bd8-401a-bb26-2b8bc64892fa",
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"sourceHandle": "conditioning",
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{
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"id": "reactflow__edge-2981a67c-480f-4237-9384-26b68dbf912blatents-eebd7252-0bd8-401a-bb26-2b8bc64892falatents",
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"type": "default",
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"target": "eebd7252-0bd8-401a-bb26-2b8bc64892fa",
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"sourceHandle": "width",
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"targetHandle": "width"
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{
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"id": "reactflow__edge-2981a67c-480f-4237-9384-26b68dbf912blatents-ace0258f-67d7-4eee-a218-6fff27065214latents",
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||||
"id": "reactflow__edge-2981a67c-480f-4237-9384-26b68dbf912bheight-eebd7252-0bd8-401a-bb26-2b8bc64892faheight",
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"target": "ace0258f-67d7-4eee-a218-6fff27065214",
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"sourceHandle": "latents",
|
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"targetHandle": "latents"
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"target": "eebd7252-0bd8-401a-bb26-2b8bc64892fa",
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"sourceHandle": "height",
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"targetHandle": "height"
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},
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{
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"id": "reactflow__edge-4754c534-a5f3-4ad0-9382-7887985e668cvalue-eebd7252-0bd8-401a-bb26-2b8bc64892faseed",
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"type": "default",
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"source": "4754c534-a5f3-4ad0-9382-7887985e668c",
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"target": "eebd7252-0bd8-401a-bb26-2b8bc64892fa",
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"sourceHandle": "value",
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"targetHandle": "seed"
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{
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"id": "reactflow__edge-f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90vae-2981a67c-480f-4237-9384-26b68dbf912bvae",
|
||||
@@ -339,38 +384,6 @@
|
||||
"sourceHandle": "vae",
|
||||
"targetHandle": "vae"
|
||||
},
|
||||
{
|
||||
"id": "reactflow__edge-ace0258f-67d7-4eee-a218-6fff27065214latents-7e5172eb-48c1-44db-a770-8fd83e1435d1latents",
|
||||
"type": "default",
|
||||
"source": "ace0258f-67d7-4eee-a218-6fff27065214",
|
||||
"target": "7e5172eb-48c1-44db-a770-8fd83e1435d1",
|
||||
"sourceHandle": "latents",
|
||||
"targetHandle": "latents"
|
||||
},
|
||||
{
|
||||
"id": "reactflow__edge-4754c534-a5f3-4ad0-9382-7887985e668cvalue-ace0258f-67d7-4eee-a218-6fff27065214seed",
|
||||
"type": "default",
|
||||
"source": "4754c534-a5f3-4ad0-9382-7887985e668c",
|
||||
"target": "ace0258f-67d7-4eee-a218-6fff27065214",
|
||||
"sourceHandle": "value",
|
||||
"targetHandle": "seed"
|
||||
},
|
||||
{
|
||||
"id": "reactflow__edge-f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90transformer-ace0258f-67d7-4eee-a218-6fff27065214transformer",
|
||||
"type": "default",
|
||||
"source": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
|
||||
"target": "ace0258f-67d7-4eee-a218-6fff27065214",
|
||||
"sourceHandle": "transformer",
|
||||
"targetHandle": "transformer"
|
||||
},
|
||||
{
|
||||
"id": "reactflow__edge-01f674f8-b3d1-4df1-acac-6cb8e0bfb63cconditioning-ace0258f-67d7-4eee-a218-6fff27065214positive_text_conditioning",
|
||||
"type": "default",
|
||||
"source": "01f674f8-b3d1-4df1-acac-6cb8e0bfb63c",
|
||||
"target": "ace0258f-67d7-4eee-a218-6fff27065214",
|
||||
"sourceHandle": "conditioning",
|
||||
"targetHandle": "positive_text_conditioning"
|
||||
},
|
||||
{
|
||||
"id": "reactflow__edge-f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90vae-7e5172eb-48c1-44db-a770-8fd83e1435d1vae",
|
||||
"type": "default",
|
||||
|
||||
@@ -2,7 +2,7 @@
|
||||
"name": "FLUX Text to Image",
|
||||
"author": "InvokeAI",
|
||||
"description": "A simple text-to-image workflow using FLUX dev or schnell models.",
|
||||
"version": "1.0.4",
|
||||
"version": "1.1.0",
|
||||
"contact": "",
|
||||
"tags": "text2image, flux",
|
||||
"notes": "Prerequisite model downloads: T5 Encoder, CLIP-L Encoder, and FLUX VAE. Quantized and un-quantized versions can be found in the starter models tab within your Model Manager. We recommend 4 steps for FLUX schnell models and 30 steps for FLUX dev models.",
|
||||
@@ -26,10 +26,6 @@
|
||||
{
|
||||
"nodeId": "01f674f8-b3d1-4df1-acac-6cb8e0bfb63c",
|
||||
"fieldName": "prompt"
|
||||
},
|
||||
{
|
||||
"nodeId": "4fe24f07-f906-4f55-ab2c-9beee56ef5bd",
|
||||
"fieldName": "num_steps"
|
||||
}
|
||||
],
|
||||
"meta": {
|
||||
@@ -38,17 +34,18 @@
|
||||
},
|
||||
"nodes": [
|
||||
{
|
||||
"id": "4fe24f07-f906-4f55-ab2c-9beee56ef5bd",
|
||||
"id": "4ecda92d-ee0e-45ca-aa35-6e9410ac39b9",
|
||||
"type": "invocation",
|
||||
"data": {
|
||||
"id": "4fe24f07-f906-4f55-ab2c-9beee56ef5bd",
|
||||
"id": "4ecda92d-ee0e-45ca-aa35-6e9410ac39b9",
|
||||
"type": "flux_denoise",
|
||||
"version": "1.0.0",
|
||||
"version": "2.1.0",
|
||||
"label": "",
|
||||
"notes": "",
|
||||
"isOpen": true,
|
||||
"isIntermediate": true,
|
||||
"useCache": true,
|
||||
"nodePack": "invokeai",
|
||||
"inputs": {
|
||||
"board": {
|
||||
"name": "board",
|
||||
@@ -76,6 +73,11 @@
|
||||
"label": "",
|
||||
"value": 1
|
||||
},
|
||||
"trajectory_guidance_strength": {
|
||||
"name": "trajectory_guidance_strength",
|
||||
"label": "",
|
||||
"value": 0
|
||||
},
|
||||
"transformer": {
|
||||
"name": "transformer",
|
||||
"label": ""
|
||||
@@ -96,8 +98,8 @@
|
||||
},
|
||||
"num_steps": {
|
||||
"name": "num_steps",
|
||||
"label": "Steps (Recommend 30 for Dev, 4 for Schnell)",
|
||||
"value": 30
|
||||
"label": "",
|
||||
"value": 4
|
||||
},
|
||||
"guidance": {
|
||||
"name": "guidance",
|
||||
@@ -112,8 +114,8 @@
|
||||
}
|
||||
},
|
||||
"position": {
|
||||
"x": 1186.1868226120378,
|
||||
"y": -214.9459927686657
|
||||
"x": 1161.0101524413685,
|
||||
"y": -223.33548695623742
|
||||
}
|
||||
},
|
||||
{
|
||||
@@ -167,19 +169,47 @@
|
||||
"inputs": {
|
||||
"model": {
|
||||
"name": "model",
|
||||
"label": ""
|
||||
"label": "",
|
||||
"value": {
|
||||
"key": "b4990a6c-0899-48e9-969b-d6f3801acc6a",
|
||||
"hash": "random:aad8f7bc19ce76541dfb394b62a30f77722542b66e48064a9f25453263b45fba",
|
||||
"name": "FLUX Dev (Quantized)_2",
|
||||
"base": "flux",
|
||||
"type": "main"
|
||||
}
|
||||
},
|
||||
"t5_encoder_model": {
|
||||
"name": "t5_encoder_model",
|
||||
"label": ""
|
||||
"label": "",
|
||||
"value": {
|
||||
"key": "d18d5575-96b6-4da3-b3d8-eb58308d6705",
|
||||
"hash": "random:f2f9ed74acdfb4bf6fec200e780f6c25f8dd8764a35e65d425d606912fdf573a",
|
||||
"name": "t5_bnb_int8_quantized_encoder",
|
||||
"base": "any",
|
||||
"type": "t5_encoder"
|
||||
}
|
||||
},
|
||||
"clip_embed_model": {
|
||||
"name": "clip_embed_model",
|
||||
"label": ""
|
||||
"label": "",
|
||||
"value": {
|
||||
"key": "5a19d7e5-8d98-43cd-8a81-87515e4b3b4e",
|
||||
"hash": "random:4bd08514c08fb6ff04088db9aeb45def3c488e8b5fd09a35f2cc4f2dc346f99f",
|
||||
"name": "clip-vit-large-patch14",
|
||||
"base": "any",
|
||||
"type": "clip_embed"
|
||||
}
|
||||
},
|
||||
"vae_model": {
|
||||
"name": "vae_model",
|
||||
"label": ""
|
||||
"label": "",
|
||||
"value": {
|
||||
"key": "9172beab-5c1d-43f0-b2f0-6e0b956710d9",
|
||||
"hash": "random:c54dde288e5fa2e6137f1c92e9d611f598049e6f16e360207b6d96c9f5a67ba0",
|
||||
"name": "FLUX.1-schnell_ae",
|
||||
"base": "flux",
|
||||
"type": "vae"
|
||||
}
|
||||
}
|
||||
}
|
||||
},
|
||||
@@ -259,33 +289,33 @@
|
||||
],
|
||||
"edges": [
|
||||
{
|
||||
"id": "reactflow__edge-f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90transformer-4fe24f07-f906-4f55-ab2c-9beee56ef5bdtransformer",
|
||||
"id": "reactflow__edge-f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90transformer-4ecda92d-ee0e-45ca-aa35-6e9410ac39b9transformer",
|
||||
"type": "default",
|
||||
"source": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
|
||||
"target": "4fe24f07-f906-4f55-ab2c-9beee56ef5bd",
|
||||
"target": "4ecda92d-ee0e-45ca-aa35-6e9410ac39b9",
|
||||
"sourceHandle": "transformer",
|
||||
"targetHandle": "transformer"
|
||||
},
|
||||
{
|
||||
"id": "reactflow__edge-01f674f8-b3d1-4df1-acac-6cb8e0bfb63cconditioning-4fe24f07-f906-4f55-ab2c-9beee56ef5bdpositive_text_conditioning",
|
||||
"id": "reactflow__edge-01f674f8-b3d1-4df1-acac-6cb8e0bfb63cconditioning-4ecda92d-ee0e-45ca-aa35-6e9410ac39b9positive_text_conditioning",
|
||||
"type": "default",
|
||||
"source": "01f674f8-b3d1-4df1-acac-6cb8e0bfb63c",
|
||||
"target": "4fe24f07-f906-4f55-ab2c-9beee56ef5bd",
|
||||
"target": "4ecda92d-ee0e-45ca-aa35-6e9410ac39b9",
|
||||
"sourceHandle": "conditioning",
|
||||
"targetHandle": "positive_text_conditioning"
|
||||
},
|
||||
{
|
||||
"id": "reactflow__edge-4754c534-a5f3-4ad0-9382-7887985e668cvalue-4fe24f07-f906-4f55-ab2c-9beee56ef5bdseed",
|
||||
"id": "reactflow__edge-4754c534-a5f3-4ad0-9382-7887985e668cvalue-4ecda92d-ee0e-45ca-aa35-6e9410ac39b9seed",
|
||||
"type": "default",
|
||||
"source": "4754c534-a5f3-4ad0-9382-7887985e668c",
|
||||
"target": "4fe24f07-f906-4f55-ab2c-9beee56ef5bd",
|
||||
"target": "4ecda92d-ee0e-45ca-aa35-6e9410ac39b9",
|
||||
"sourceHandle": "value",
|
||||
"targetHandle": "seed"
|
||||
},
|
||||
{
|
||||
"id": "reactflow__edge-4fe24f07-f906-4f55-ab2c-9beee56ef5bdlatents-7e5172eb-48c1-44db-a770-8fd83e1435d1latents",
|
||||
"id": "reactflow__edge-4ecda92d-ee0e-45ca-aa35-6e9410ac39b9latents-7e5172eb-48c1-44db-a770-8fd83e1435d1latents",
|
||||
"type": "default",
|
||||
"source": "4fe24f07-f906-4f55-ab2c-9beee56ef5bd",
|
||||
"source": "4ecda92d-ee0e-45ca-aa35-6e9410ac39b9",
|
||||
"target": "7e5172eb-48c1-44db-a770-8fd83e1435d1",
|
||||
"sourceHandle": "latents",
|
||||
"targetHandle": "latents"
|
||||
|
||||
@@ -3,8 +3,8 @@ from typing import Callable
|
||||
import torch
|
||||
from tqdm import tqdm
|
||||
|
||||
from invokeai.backend.flux.inpaint_extension import InpaintExtension
|
||||
from invokeai.backend.flux.model import Flux
|
||||
from invokeai.backend.flux.trajectory_guidance_extension import TrajectoryGuidanceExtension
|
||||
from invokeai.backend.stable_diffusion.diffusers_pipeline import PipelineIntermediateState
|
||||
|
||||
|
||||
@@ -20,7 +20,7 @@ def denoise(
|
||||
timesteps: list[float],
|
||||
step_callback: Callable[[PipelineIntermediateState], None],
|
||||
guidance: float,
|
||||
inpaint_extension: InpaintExtension | None,
|
||||
traj_guidance_extension: TrajectoryGuidanceExtension | None, # noqa: F821
|
||||
):
|
||||
step = 0
|
||||
# guidance_vec is ignored for schnell.
|
||||
@@ -36,13 +36,15 @@ def denoise(
|
||||
timesteps=t_vec,
|
||||
guidance=guidance_vec,
|
||||
)
|
||||
|
||||
if traj_guidance_extension is not None:
|
||||
pred = traj_guidance_extension.update_noise(
|
||||
t_curr_latents=img, pred_noise=pred, t_curr=t_curr, t_prev=t_prev
|
||||
)
|
||||
|
||||
preview_img = img - t_curr * pred
|
||||
img = img + (t_prev - t_curr) * pred
|
||||
|
||||
if inpaint_extension is not None:
|
||||
img = inpaint_extension.merge_intermediate_latents_with_init_latents(img, t_prev)
|
||||
preview_img = inpaint_extension.merge_intermediate_latents_with_init_latents(preview_img, 0.0)
|
||||
|
||||
step_callback(
|
||||
PipelineIntermediateState(
|
||||
step=step,
|
||||
|
||||
134
invokeai/backend/flux/trajectory_guidance_extension.py
Normal file
134
invokeai/backend/flux/trajectory_guidance_extension.py
Normal file
@@ -0,0 +1,134 @@
|
||||
import torch
|
||||
|
||||
from invokeai.backend.util.build_line import build_line
|
||||
|
||||
|
||||
class TrajectoryGuidanceExtension:
|
||||
"""An implementation of trajectory guidance for FLUX.
|
||||
|
||||
What is trajectory guidance?
|
||||
----------------------------
|
||||
With SD 1 and SDXL, the amount of change in image-to-image denoising is largely controlled by the denoising_start
|
||||
parameter. Doing the same thing with the FLUX model does not work as well, because the FLUX model converges very
|
||||
quickly (roughly time 1.0 to 0.9) to the structure of the final image. The result of this model characteristic is
|
||||
that you typically get one of two outcomes:
|
||||
1) a result that is very similar to the original image
|
||||
2) a result that is very different from the original image, as though it was generated from the text prompt with
|
||||
pure noise.
|
||||
|
||||
To address this issue with image-to-image workflows with FLUX, we employ the concept of trajectory guidance. The
|
||||
idea is that in addition to controlling the denoising_start parameter (i.e. the amount of noise added to the
|
||||
original image), we can also guide the denoising process to stay close to the trajectory that would reproduce the
|
||||
original. By controlling the strength of the trajectory guidance throughout the denoising process, we can achieve
|
||||
FLUX image-to-image behavior with the same level of control offered by SD1 and SDXL.
|
||||
|
||||
What is the trajectory_guidance_strength?
|
||||
-----------------------------------------
|
||||
In the limit, we could apply a different trajectory guidance 'strength' for every latent value in every timestep.
|
||||
This would be impractical for a user, so instead we have engineered a strength schedule that is more convenient to
|
||||
use. The `trajectory_guidance_strength` parameter is a single scalar value that maps to a schedule. The engineered
|
||||
schedule is defined as:
|
||||
1) An initial change_ratio at t=1.0.
|
||||
2) A linear ramp up to change_ratio=1.0 at t = t_cutoff.
|
||||
3) A constant change_ratio=1.0 after t = t_cutoff.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self, init_latents: torch.Tensor, inpaint_mask: torch.Tensor | None, trajectory_guidance_strength: float
|
||||
):
|
||||
"""Initialize TrajectoryGuidanceExtension.
|
||||
|
||||
Args:
|
||||
init_latents (torch.Tensor): The initial latents (i.e. un-noised at timestep 0). In 'packed' format.
|
||||
inpaint_mask (torch.Tensor | None): A mask specifying which elements to inpaint. Range [0, 1]. Values of 1
|
||||
will be re-generated. Values of 0 will remain unchanged. Values between 0 and 1 can be used to blend the
|
||||
inpainted region with the background. In 'packed' format. If None, will be treated as a mask of all 1s.
|
||||
trajectory_guidance_strength (float): A value in [0, 1] specifying the strength of the trajectory guidance.
|
||||
A value of 0.0 is equivalent to vanilla image-to-image. A value of 1.0 will guide the denoising process
|
||||
very close to the original latents.
|
||||
"""
|
||||
assert 0.0 <= trajectory_guidance_strength <= 1.0
|
||||
self._init_latents = init_latents
|
||||
if inpaint_mask is None:
|
||||
# The inpaing mask is None, so we initialize a mask with a single value of 1.0.
|
||||
# This value will be broadcasted and treated as a mask of all 1s.
|
||||
self._inpaint_mask = torch.ones(1, device=init_latents.device, dtype=init_latents.dtype)
|
||||
else:
|
||||
self._inpaint_mask = inpaint_mask
|
||||
|
||||
# Calculate the params that define the trajectory guidance schedule.
|
||||
# These mappings from trajectory_guidance_strength have no theoretical basis - they were tuned manually.
|
||||
self._trajectory_guidance_strength = trajectory_guidance_strength
|
||||
self._change_ratio_at_t_1 = build_line(x1=0.0, y1=1.0, x2=1.0, y2=0.0)(self._trajectory_guidance_strength)
|
||||
self._change_ratio_at_cutoff = 1.0
|
||||
self._t_cutoff = build_line(x1=0.0, y1=1.0, x2=1.0, y2=0.5)(self._trajectory_guidance_strength)
|
||||
|
||||
def _apply_mask_gradient_adjustment(self, t_prev: float) -> torch.Tensor:
|
||||
"""Applies inpaint mask gradient adjustment and returns the inpaint mask to be used at the current timestep."""
|
||||
# As we progress through the denoising process, we promote gradient regions of the mask to have a full weight of
|
||||
# 1.0. This helps to produce more coherent seams around the inpainted region. We experimented with a (small)
|
||||
# number of promotion strategies (e.g. gradual promotion based on timestep), but found that a simple cutoff
|
||||
# threshold worked well.
|
||||
# We use a small epsilon to avoid any potential issues with floating point precision.
|
||||
eps = 1e-4
|
||||
mask_gradient_t_cutoff = 0.5
|
||||
if t_prev > mask_gradient_t_cutoff:
|
||||
# Early in the denoising process, use the inpaint mask as-is.
|
||||
return self._inpaint_mask
|
||||
else:
|
||||
# After the cut-off, promote all non-zero mask values to 1.0.
|
||||
mask = self._inpaint_mask.where(self._inpaint_mask <= (0.0 + eps), 1.0)
|
||||
|
||||
return mask
|
||||
|
||||
def _get_change_ratio(self, t_prev: float) -> float:
|
||||
"""Get the change_ratio for t_prev based on the change schedule."""
|
||||
change_ratio = 1.0
|
||||
if t_prev > self._t_cutoff:
|
||||
# If we are before the cutoff, linearly interpolate between the change_ratio at t=1.0 and the change_ratio
|
||||
# at the cutoff.
|
||||
change_ratio = build_line(
|
||||
x1=1.0, y1=self._change_ratio_at_t_1, x2=self._t_cutoff, y2=self._change_ratio_at_cutoff
|
||||
)(t_prev)
|
||||
|
||||
# The change_ratio should be in the range [0, 1]. Assert that we didn't make any mistakes.
|
||||
eps = 1e-5
|
||||
assert 0.0 - eps <= change_ratio <= 1.0 + eps
|
||||
return change_ratio
|
||||
|
||||
def update_noise(
|
||||
self, t_curr_latents: torch.Tensor, pred_noise: torch.Tensor, t_curr: float, t_prev: float
|
||||
) -> torch.Tensor:
|
||||
# Handle gradient cutoff.
|
||||
mask = self._apply_mask_gradient_adjustment(t_prev)
|
||||
|
||||
mask = mask * self._get_change_ratio(t_prev)
|
||||
|
||||
# NOTE(ryand): During inpainting, it is common to guide the denoising process by noising the initial latents for
|
||||
# the current timestep and then blending the predicted intermediate latents with the noised initial latents.
|
||||
# For example:
|
||||
# ```
|
||||
# noised_init_latents = self._noise * t_prev + (1.0 - t_prev) * self._init_latents
|
||||
# return t_prev_latents * self._inpaint_mask + noised_init_latents * (1.0 - self._inpaint_mask)
|
||||
# ```
|
||||
# Instead of guiding based on the noised initial latents, we have decided to guide based on the noise prediction
|
||||
# that points towards the initial latents. The difference between these guidance strategies is minor, but
|
||||
# qualitatively we found the latter to produce slightly better results. When change_ratio is 0.0 or 1.0 there is
|
||||
# no difference between the two strategies.
|
||||
#
|
||||
# We experimented with a number of related guidance strategies, but not exhaustively. It's entirely possible
|
||||
# that there's a much better way to do this.
|
||||
|
||||
# Calculate noise guidance
|
||||
# What noise should the model have predicted at this timestep to step towards self._init_latents?
|
||||
# Derivation:
|
||||
# > t_prev_latents = t_curr_latents + (t_prev - t_curr) * pred_noise
|
||||
# > t_0_latents = t_curr_latents + (0 - t_curr) * init_traj_noise
|
||||
# > t_0_latents = t_curr_latents - t_curr * init_traj_noise
|
||||
# > init_traj_noise = (t_curr_latents - t_0_latents) / t_curr)
|
||||
init_traj_noise = (t_curr_latents - self._init_latents) / t_curr
|
||||
|
||||
# Blend the init_traj_noise with the pred_noise according to the inpaint mask and the trajectory guidance.
|
||||
noise = pred_noise * mask + init_traj_noise * (1.0 - mask)
|
||||
|
||||
return noise
|
||||
6
invokeai/backend/util/build_line.py
Normal file
6
invokeai/backend/util/build_line.py
Normal file
@@ -0,0 +1,6 @@
|
||||
from typing import Callable
|
||||
|
||||
|
||||
def build_line(x1: float, y1: float, x2: float, y2: float) -> Callable[[float], float]:
|
||||
"""Build a linear function given two points on the line (x1, y1) and (x2, y2)."""
|
||||
return lambda x: (y2 - y1) / (x2 - x1) * (x - x1) + y1
|
||||
@@ -1042,6 +1042,7 @@
|
||||
"strength": "Strength",
|
||||
"symmetry": "Symmetry",
|
||||
"tileSize": "Tile Size",
|
||||
"optimizedInpainting": "Optimized Inpainting",
|
||||
"type": "Type",
|
||||
"postProcessing": "Post-Processing (Shift + U)",
|
||||
"processImage": "Process Image",
|
||||
@@ -1547,6 +1548,12 @@
|
||||
"paragraphs": [
|
||||
"FLUX.1 [dev] models are licensed under the FLUX [dev] non-commercial license. To use this model type for commercial purposes in Invoke, visit our website to learn more."
|
||||
]
|
||||
},
|
||||
"optimizedDenoising": {
|
||||
"heading": "Optimized Inpainting",
|
||||
"paragraphs": [
|
||||
"Enable optimized denoising for enhanced inpainting transformations with Flux models. This setting improves detail and clarity during generation, but may be turned off to preserve more of your original image."
|
||||
]
|
||||
}
|
||||
},
|
||||
"unifiedCanvas": {
|
||||
|
||||
@@ -60,6 +60,7 @@ export type Feature =
|
||||
| 'scale'
|
||||
| 'creativity'
|
||||
| 'structure'
|
||||
| 'optimizedDenoising'
|
||||
| 'fluxDevLicense';
|
||||
|
||||
export type PopoverData = PopoverProps & {
|
||||
|
||||
@@ -40,6 +40,7 @@ export type ParamsState = {
|
||||
cfgRescaleMultiplier: ParameterCFGRescaleMultiplier;
|
||||
guidance: ParameterGuidance;
|
||||
img2imgStrength: ParameterStrength;
|
||||
optimizedDenoisingEnabled: boolean;
|
||||
iterations: number;
|
||||
scheduler: ParameterScheduler;
|
||||
seed: ParameterSeed;
|
||||
@@ -83,6 +84,7 @@ const initialState: ParamsState = {
|
||||
cfgRescaleMultiplier: 0,
|
||||
guidance: 4,
|
||||
img2imgStrength: 0.75,
|
||||
optimizedDenoisingEnabled: true,
|
||||
iterations: 1,
|
||||
scheduler: 'euler',
|
||||
seed: 0,
|
||||
@@ -141,6 +143,9 @@ export const paramsSlice = createSlice({
|
||||
setImg2imgStrength: (state, action: PayloadAction<number>) => {
|
||||
state.img2imgStrength = action.payload;
|
||||
},
|
||||
setOptimizedDenoisingEnabled: (state, action: PayloadAction<boolean>) => {
|
||||
state.optimizedDenoisingEnabled = action.payload;
|
||||
},
|
||||
setSeamlessXAxis: (state, action: PayloadAction<boolean>) => {
|
||||
state.seamlessXAxis = action.payload;
|
||||
},
|
||||
@@ -273,6 +278,7 @@ export const {
|
||||
setScheduler,
|
||||
setSeed,
|
||||
setImg2imgStrength,
|
||||
setOptimizedDenoisingEnabled,
|
||||
setSeamlessXAxis,
|
||||
setSeamlessYAxis,
|
||||
setShouldRandomizeSeed,
|
||||
@@ -341,6 +347,7 @@ export const selectInfillPatchmatchDownscaleSize = createParamsSelector(
|
||||
);
|
||||
export const selectInfillColorValue = createParamsSelector((params) => params.infillColorValue);
|
||||
export const selectImg2imgStrength = createParamsSelector((params) => params.img2imgStrength);
|
||||
export const selectOptimizedDenoisingEnabled = createParamsSelector((params) => params.optimizedDenoisingEnabled);
|
||||
export const selectPositivePrompt = createParamsSelector((params) => params.positivePrompt);
|
||||
export const selectNegativePrompt = createParamsSelector((params) => params.negativePrompt);
|
||||
export const selectPositivePrompt2 = createParamsSelector((params) => params.positivePrompt2);
|
||||
|
||||
@@ -37,7 +37,17 @@ export const buildFLUXGraph = async (
|
||||
|
||||
const { originalSize, scaledSize } = getSizes(bbox);
|
||||
|
||||
const { model, guidance, seed, steps, fluxVAE, t5EncoderModel, clipEmbedModel, img2imgStrength } = params;
|
||||
const {
|
||||
model,
|
||||
guidance,
|
||||
seed,
|
||||
steps,
|
||||
fluxVAE,
|
||||
t5EncoderModel,
|
||||
clipEmbedModel,
|
||||
img2imgStrength,
|
||||
optimizedDenoisingEnabled,
|
||||
} = params;
|
||||
|
||||
assert(model, 'No model found in state');
|
||||
assert(t5EncoderModel, 'No T5 Encoder model found in state');
|
||||
@@ -68,7 +78,8 @@ export const buildFLUXGraph = async (
|
||||
guidance,
|
||||
num_steps: steps,
|
||||
seed,
|
||||
denoising_start: 0, // denoising_start should be 0 when latents are not provided
|
||||
trajectory_guidance_strength: 0,
|
||||
denoising_start: 0,
|
||||
denoising_end: 1,
|
||||
width: scaledSize.width,
|
||||
height: scaledSize.height,
|
||||
@@ -113,6 +124,8 @@ export const buildFLUXGraph = async (
|
||||
clip_embed_model: clipEmbedModel,
|
||||
});
|
||||
|
||||
const denoisingValue = 1 - img2imgStrength;
|
||||
|
||||
if (generationMode === 'txt2img') {
|
||||
canvasOutput = addTextToImage(g, l2i, originalSize, scaledSize);
|
||||
} else if (generationMode === 'img2img') {
|
||||
@@ -125,7 +138,7 @@ export const buildFLUXGraph = async (
|
||||
originalSize,
|
||||
scaledSize,
|
||||
bbox,
|
||||
1 - img2imgStrength,
|
||||
denoisingValue,
|
||||
false
|
||||
);
|
||||
} else if (generationMode === 'inpaint') {
|
||||
@@ -139,9 +152,15 @@ export const buildFLUXGraph = async (
|
||||
modelLoader,
|
||||
originalSize,
|
||||
scaledSize,
|
||||
1 - img2imgStrength,
|
||||
denoisingValue,
|
||||
false
|
||||
);
|
||||
if (optimizedDenoisingEnabled) {
|
||||
g.updateNode(noise, {
|
||||
denoising_start: 0,
|
||||
trajectory_guidance_strength: denoisingValue,
|
||||
});
|
||||
}
|
||||
} else if (generationMode === 'outpaint') {
|
||||
canvasOutput = await addOutpaint(
|
||||
state,
|
||||
@@ -153,7 +172,7 @@ export const buildFLUXGraph = async (
|
||||
modelLoader,
|
||||
originalSize,
|
||||
scaledSize,
|
||||
1 - img2imgStrength,
|
||||
denoisingValue,
|
||||
false
|
||||
);
|
||||
}
|
||||
|
||||
@@ -0,0 +1,35 @@
|
||||
import { FormControl, FormLabel, Switch } from '@invoke-ai/ui-library';
|
||||
import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
|
||||
import { InformationalPopover } from 'common/components/InformationalPopover/InformationalPopover';
|
||||
import {
|
||||
selectOptimizedDenoisingEnabled,
|
||||
setOptimizedDenoisingEnabled,
|
||||
} from 'features/controlLayers/store/paramsSlice';
|
||||
import type { ChangeEvent } from 'react';
|
||||
import { memo, useCallback } from 'react';
|
||||
import { useTranslation } from 'react-i18next';
|
||||
|
||||
export const ParamOptimizedDenoisingToggle = memo(() => {
|
||||
const optimizedDenoisingEnabled = useAppSelector(selectOptimizedDenoisingEnabled);
|
||||
const dispatch = useAppDispatch();
|
||||
|
||||
const onChange = useCallback(
|
||||
(event: ChangeEvent<HTMLInputElement>) => {
|
||||
dispatch(setOptimizedDenoisingEnabled(event.target.checked));
|
||||
},
|
||||
[dispatch]
|
||||
);
|
||||
|
||||
const { t } = useTranslation();
|
||||
|
||||
return (
|
||||
<FormControl w="min-content">
|
||||
<InformationalPopover feature="optimizedDenoising">
|
||||
<FormLabel m={0}>{t('parameters.optimizedInpainting')}</FormLabel>
|
||||
</InformationalPopover>
|
||||
<Switch isChecked={optimizedDenoisingEnabled} onChange={onChange} />
|
||||
</FormControl>
|
||||
);
|
||||
});
|
||||
|
||||
ParamOptimizedDenoisingToggle.displayName = 'ParamOptimizedDenoisingToggle';
|
||||
@@ -3,8 +3,9 @@ import { Expander, Flex, FormControlGroup, StandaloneAccordion } from '@invoke-a
|
||||
import { EMPTY_ARRAY } from 'app/store/constants';
|
||||
import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
|
||||
import { useAppSelector } from 'app/store/storeHooks';
|
||||
import { selectParamsSlice } from 'features/controlLayers/store/paramsSlice';
|
||||
import { selectIsFLUX, selectParamsSlice } from 'features/controlLayers/store/paramsSlice';
|
||||
import { selectCanvasSlice, selectScaleMethod } from 'features/controlLayers/store/selectors';
|
||||
import { ParamOptimizedDenoisingToggle } from 'features/parameters/components/Advanced/ParamOptimizedDenoisingToggle';
|
||||
import BboxScaledHeight from 'features/parameters/components/Bbox/BboxScaledHeight';
|
||||
import BboxScaledWidth from 'features/parameters/components/Bbox/BboxScaledWidth';
|
||||
import BboxScaleMethod from 'features/parameters/components/Bbox/BboxScaleMethod';
|
||||
@@ -59,6 +60,7 @@ export const ImageSettingsAccordion = memo(() => {
|
||||
id: 'image-settings-advanced',
|
||||
defaultIsOpen: false,
|
||||
});
|
||||
const isFLUX = useAppSelector(selectIsFLUX);
|
||||
|
||||
return (
|
||||
<StandaloneAccordion
|
||||
@@ -77,6 +79,7 @@ export const ImageSettingsAccordion = memo(() => {
|
||||
<ParamDenoisingStrength />
|
||||
<Expander label={t('accordions.advanced.options')} isOpen={isOpenExpander} onToggle={onToggleExpander}>
|
||||
<Flex gap={4} pb={4} flexDir="column">
|
||||
{isFLUX && <ParamOptimizedDenoisingToggle />}
|
||||
<BboxScaleMethod />
|
||||
{scaleMethod !== 'none' && (
|
||||
<FormControlGroup formLabelProps={scalingLabelProps}>
|
||||
|
||||
@@ -6340,6 +6340,12 @@ export type components = {
|
||||
* @default 1
|
||||
*/
|
||||
denoising_end?: number;
|
||||
/**
|
||||
* Trajectory Guidance Strength
|
||||
* @description Value indicating how strongly to guide the denoising process towards the initial latents (during image-to-image). Range [0, 1]. A value of 0.0 is equivalent to vanilla image-to-image. A value of 1.0 will guide the denoising process very close to the original latents.
|
||||
* @default 0
|
||||
*/
|
||||
trajectory_guidance_strength?: number;
|
||||
/**
|
||||
* Transformer
|
||||
* @description Flux model (Transformer) to load
|
||||
|
||||
19
tests/backend/util/test_build_line.py
Normal file
19
tests/backend/util/test_build_line.py
Normal file
@@ -0,0 +1,19 @@
|
||||
import math
|
||||
|
||||
import pytest
|
||||
|
||||
from invokeai.backend.util.build_line import build_line
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
["x1", "y1", "x2", "y2", "x3", "y3"],
|
||||
[
|
||||
(0, 0, 1, 1, 2, 2), # y = x
|
||||
(0, 1, 1, 2, 2, 3), # y = x + 1
|
||||
(0, 0, 1, 2, 2, 4), # y = 2x
|
||||
(0, 1, 1, 0, 2, -1), # y = -x + 1
|
||||
(0, 5, 1, 5, 2, 5), # y = 0
|
||||
],
|
||||
)
|
||||
def test_build_line(x1: float, y1: float, x2: float, y2: float, x3: float, y3: float):
|
||||
assert math.isclose(build_line(x1, y1, x2, y2)(x3), y3, rel_tol=1e-9)
|
||||
Reference in New Issue
Block a user